R&D Management in industrial companies: Tools and organizational changes. A case study

R&D Management in industrial companies: Tools and organizational changes. A case study. TESIS DOCTORAL POR COMPENDIO Mención Doctorado Industrial Programa de Doctorado “Gestión Estratégica y Negocios Internacionales” Directores de Tesis Prof.a Dra. Joaquina Laffarga Prof. Dr. José Luis Galan Gonzalez Prof. Dr. Juan Luis Ramos Martin Tutor Industrial D. José López Dominguez Doctorando Miguel Valdivia Borrero Sevilla, 2020 Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Mención Doctorado Industrial Mención Doctorado Industrial Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Mención Doctorado Industrial Esta tesis se ha desarrollado en el marco del Programa de Doctorado Gestión Estratégica y Negocios Internacionales de la Universidad de Sevilla y, por su ámbito de investigación, en la línea de la gestión y dirección de empresas. Además, opta a la Mención de Doctorado Industrial al haber sido realizada en colaboración con una empresa con la que el doctorando mantiene una relación laboral desde hace más de 9 años; habiendo estado el doctorando involucrado en los temas de análisis e investigación que se presentan en esta tesis. Como se desarrolla a lo largo de los capítulos de la tesis, el estudio analiza y profundiza sobre el sistema de gestión integral de proyectos de I+D dentro de una compañía multinacional, desde el análisis técnico a su enfoque a mercado. Esta cuestión de investigación constituye un reto y problema para la empresa, en la medida que se trata de una compañía que ha experimentado una profunda transformación, pasando de una firma industrial tradicional a una basada en la tecnología y la innovación. En consecuencia, el estudio debe permitir comprender los puntos críticos del proceso de cambio y desarrollar instrumentos que permitan una eficaz gestión de la I+D. Como parte de las herramientas para la gestión de la I+D, la tesis analiza en mayor detalle el modelo desarrollado para la valoración de proyectos de I+D e innovación, que pretende ser un avance respecto a los modelos actuales, al incorporar mayor capacidad analítica y amplitud de aplicación. Igualmente, se analiza la transformación de la compañía, desde una empresa típicamente de base industrial a una compañía tecnológica, analizando cuáles han sido sus fortalezas y sus inconvenientes. En consecuencia, esta investigación comparte y satisface, en cierta medida, el interés de la compañía para analizar, comprender y mejorar los cambios y modelos de gestión implementados en la empresa. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Introducción Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Introducción La I+D y la innovación son elementos claves, necesarios y relacionados para que las empresas puedan ser competitivas en todo tipo de mercados y sectores (Shavinina, 2003; Cefis and Orietta, 2006; Tohidi and Jabbari, 2012). A la hora de afrontar cómo innovar, las empresas deben tomar decisiones acerca de la estrategia de implementación de la I+D (Kodama, 2003; Zhang et al., 2007; Johnson et al., 2008), la gestión de esta función o recurso y el objetivo donde enfocar la innovación. No es lo mismo gestionar y organizar la I+D en la búsqueda de innovaciones disruptivas que incrementales; innovar en producto en un mismo mercado o en la búsqueda de nuevos mercados; o innovar en procesos organizativos en lugar de productos. Las aproximaciones que las organizaciones pueden utilizar varían en función de su objetivo e incluso se pueden emplear varios enfoques a la vez (Green et al., 1994; Nieto, 2003). Sobre la gestión de la I+D e innovación existe una amplia literatura (Coombs, 1996; Argyres and Silverman, 2004; Adams et al. , 2006; Albors-Garrigos et al. , 2018; Tidd, 2018), debido a su importancia teórica y práctica, ya que la inmensa mayoría de organizaciones se han dado cuenta de la importancia de innovar para alcanzar sus objetivos. Existen sectores en los que las empresas históricamente han tenido que ser innovadoras para mantenerse en la vanguardia de sus competidores, como, por ejemplo, la industria aeronáutica o farmacéutica, entre otras; por otro lado, hay sectores tradicionales o industriales, asentados, donde las innovaciones o las inversiones en I+D eran limitadas o casi inexistentes, como sucedía en el sector textil o agroalimentario, pero que cuyas empresas están sintiendo la importancia de la innovación para ser competitivas. Si nos fijamos en los sectores de la energía y medioambiente, que constituyen el contexto industrial en el que esta tesis está enfocada, la necesidad de innovación es más acuciante, ya que en las últimas dos décadas la conversión hacía fuentes de energías renovables y sostenibles ha empujado la aparición de nuevas tecnologías y productos, por la creciente preocupación con el cambio climático (la OCDE ya califica estas industrias como de tecnología alta y media-alta) (Bointner, 2014). Estos cambios tecnológicos han impulsado a muchas empresas del sector, de distinto tamaño, mercado o servicios, a aprovechar las oportunidades que han surgido, y están surgiendo, en el negocio de las energías renovables y medioambiente, para realizar cambios en sus estrategias que permitan que las compañías tengan acceso a estos nuevos productos. En España, los casos de Gamesa, en la producción de aerogeneradores, o de Iberdrola, en la generación de energía eléctrica a partir de fuentes renovables, son ejemplos ilustrativos, pero representan sólo la punta del iceberg de una miríada de nuevas empresas y de corporate venturing que están modificando sustancialmente el sector. En muchos casos, son empresas con una potente base industrial, con productos maduros donde la innovación es limitada y la inversión en I+D reducida o casi inexistente. ¿Cómo conseguir que estas empresas tengan acceso a nuevos mercados emergentes donde la base tecnológica es crítica? En el nuevo contexto tecnológico, estas compañías requieren una profunda transformación tanto de sus operaciones como de sus estrategias, organización y cultura (Kodama, 2003). En esta tesis se describe y analiza, mediante la metodología del caso longitudinal, la transformación Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Introducción de una empresa con profunda base industrial en una empresa tecnológica, explorando diferentes aspectos que este cambio radical conlleva. Estos aspectos se sitúan en niveles diferentes de la organización y abarcan cuestiones diversas. En este estudio se abordan tres de esas cuestiones, que definen los objetivos generales que se persiguen con la investigación. En primer lugar, y al nivel superior de la organización, estas profundas trasformaciones se inician por decisiones de los órganos de gobierno de la compañía, en especial su Consejo de Administración, dando instrucciones u orientaciones al poder ejecutivo (Consejo de Dirección, Comité de Estrategia, Comité Operativo) para implantar dicho cambio estratégico (Guerras y Navas, 2015). Las primeras decisiones deben marcar el nivel o grado de cambio y los pasos o fases del proceso a desarrollar ((Johnson et al., 2006). También es preciso establecer cuál va a ser la importancia de la I+D e innovación para la Compañía, no solo para el departamento o unidad de I+D en cuestión, sino también su función como vector de crecimiento y base de la ventaja competitiva de la compañía. En este caso, el nivel jerárquico y ejecutivo del máximo dirigente de la I+D en la compañía señala claramente la orientación de la empresa en el ámbito de la innovación. Por tanto, la relevancia del generalmente denominado CTO (Chief Technology Officer) define internamente, y también externamente, cual es la importancia que la compañía quiere darle a la investigación y la innovación (Smith and Smith, 2003; Borden and Weig, 2018) Como parte fundamental de la transformación de la empresa, y desde el punto de vista organizativo, es preciso considerar la propia estructura de la I+D, si se define de forma centralizada versus descentralizada. Esta decisión determinará en gran medida los resultados y procesos operativos de la compañía y vendrá condicionada por el propio objetivo que la empresa fije para la investigación (Zhang et al., 2007; Tirpak et al., 2015). El primero de los objetivos que persigue esta tesis es comprender el proceso del cambio de una empresa industrial a una tecnológica, analizando y mostrando las ventajas e inconvenientes de esa transformación. El estudio de caso longitudinal se centra en una empresa que, además de un aumento notable de sus inversiones en I+D, incorporó el puesto de CTO a su comité de dirección y consejo de administración, y que tomó decisiones relevantes sobre la estructura organizativa de la I+D de la compañía. A partir del estudio de esta compañía se alcanzan conclusiones que consideramos aplicables a otras empresas que desarrollan, o están pensando iniciar un proceso similar. En segundo lugar, más allá de querer ser una empresa de I+D, que representaría la intención o propósito estratégico (Hamel and Prahalad, 1991), está la propia gestión de la I+D dentro la organización. En una empresa privada esta gestión persigue objetivos de retorno distintos a los que tienen las universidades o centros públicos de investigación. La I+D, como cualquier otra función, proceso o proyecto dentro de una compañía, necesita un método de gestión para alcanzar sus objetivos (Argyres and Silverman, 2004; Zhang et al., 2007). La gestión de proyectos de I+D se puede hacer por Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Introducción Blanchet-Scalliet, C., El, N., Martellini, L. (2005) Dynamic asset pricing theory with uncertain time-horizon. Journal of Economic Dynamics & Control , 29, 1737–1764. Boer, F.P. (1998) Traps, pitfalls and snares in the valuation of technology. Research Technology Management , 41, 45. _____. (2003) Risk-adjusted valuation of R&D projects. R&D Management , 46, 50. Bointner, R. (2014) Innovation in the energy sector : Lessons learnt from R & D expenditures and patents in selected IEA countries. Energy Policy , 73, 733–747. Borden, K., Weig, F. (2018) Why you need a CTO — and how to make her successful structured role for a chief technology officer is a first line. McKinsey & Company Operations , 2018. Cefis, E., Orietta, M. (2006) Survivor: The role of innovation in firms’ survival. Research policy , 35, 626–641. Celiktas, M. (2016) Real options in defense R & D : a decision tree analysis approach for options to defer, abandon, and expand. Monterey, California: Naval Postgraduate School , 2016. Cetindamar, D., Pala, O. (2011) Chief technology officer roles and performance. Technology Analysis and Strategic Management , 23, 1031–1046. Chandler, A.D. (1962). Strategy and structure: chapters in the history of American industrial enterprises. Chen, J., Yin, X., Mei, L. (2018) Holistic Innovation : An Emerging Innovation Paradigm. International Journal of Innovation Studies , 2, 1–13. Chiesa, V., Researcher, S., Milano, P. (1996) Separating Research from Development : Evidence from the Pharmaceutical Industry. European Management Journal , 14, 638–647. Cohen, W.M., Klepper, S. (1992) The Anatomy of Industry R&D Intensity Distributions. The American Economic Review , 82, 773–799. Cooper, D.J., Morgan, W. (2008) Case Study in Research Accounting. Accounting Horizons , 22, 159–178. Cooper, R.G. (1990) Stage-Gate Systems : A New Tool for Managing New Products. Business Horizons , 33, 44–54. _____. (2008) Perspective: The Stage-Gate Idea-to-Lunch Process- Update, What’s New, and NexGen Systems. Journal of Product Innovation , 25, 213–232. Crama, P., De Reyck, B., Degraeve, Z., Chong, W. (2007) Research and development project valuation and licensing negotiations at Phytopharm Plc. Interfaces , 37, 472–487. Datar, V., Mathews, S. (2004) European real options: An intuitive algorithm for the Black- Scholes formula. Journal of Applied Finance , 14, 7–13. Davis, G.A., Owens, B. (2003) Optimizing the level of renewable electric R&D expenditures using real options analysis. Energy Policy , 31, 1589–1608. Detert, N., Kotani, K. (2013) Real options approach to renewable energy investments in Mongolia. Energy Policy , 56, 136–150. Farrukh, C., Dissel, M., Jackson, K., Phaal, R., Probert, D.R. (2009) Valuing technology along a timeline of technological maturity. International Journal of Technology Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Introducción Management , 48, 42–55. Fernandes, G., Perobelli, F.F.C., Brandaõ, L.E.T. (2016) A model for valuing new technologies under a pull incentives environment. Renewable and Sustainable Energy Reviews , 59, 482–493. Fernández Sánchez, E. (2005). Estrategia de innovación Fertig, E. (2018) Rare breakthroughs vs . incremental development in R & D strategy for an early-stage energy technology. Energy Policy , 123, 711–721. Gassmann, O., Zedtwitz, M. Von. (1998) Organization of industrial R & D on a global scale. R&D Management , 28, 147–161. _____. (1999) New concepts and trends in international R & D organization. Research Policy , 28, 231–250. Green, K., Mcmeekin, A., Irwin, A. (1994) Technological trajectories and R&D for environmental innovation in UK firms. Futures , 26, 1047–1059. Grimaldi, M., Cricelli, L., Rogo, F. (2017) Valuating and analyzing the patent portfolio : the patent portfolio value index portfolio. European Journal of Innovation Management , 2017. Guarascio, D., Pianta, M., Bogliacino, F. (2017) Export, R&D and New Products: A Model and a Test on European Industries In Foundations of Economic Change. s.l.: s.e.: pp. 393– 432. Guerras, L.A., Navas, J.E. (2015). Casos de Dirección Estratégica de la Empresa Halkos, G., Bousinakis, D. 2012. Importance and influence of organizational changes on companies and their employees. MPRA Paper, s.l. Hall, B.H.., Lerner, J. (2010) The Financing of R&D and Innovation In Handbook of the Economics of Innovation s.l.: s.e.: v.1: pp. 609–639. Hamel, G., Prahalad, C.K. (1991) Corporate imagination and expeditionary marketing. Harvard business review , 69, 81—92. Hartmann, M., Hassan, A. (2006) Application of real options analysis for pharmaceutical R&D project valuation-Empirical results from a survey. Research Policy , 35, 343–354. Hassanzadeh, F., Collan, M., Modarres, M. (2012) A practical R&D selection model using fuzzy pay-off method. International Journal of Advanced Manufacturing Technology , 58, 227–236. Hoisl, K., Gruber, M., Conti, A. (2017) R&D team diversity and performance in hypercompetitive environments. Strategic Management Journal , 36, 1455–1477. Holtbrügge, D., Schuster, T., & Wilbs, A. (2017) Restructuring and Organizational Change at Siemens. A Case Study In The Internationalization of Firms: Case Studies from the Nürnberg Metropolitan Region s.l.: s.e.: p. 115. Honoré, F., Munari, F., Van Pottelsberghe De La Potterie, B. (2015) Corporate governance practices and companies’ R&D intensity: Evidence from European countries. Research Policy , 44, 533–543. Hunger, J.D., Wheelen, T.L. (2003). Essentials of strategic management Valuation of Technology: Exploring a Practical Hybrid Model (2003, s.l.). 2003. Ed. Hunt, Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Introducción F.H., Probert, D.R., Wong, J.C., Phaal, R. s.l., 47–53 p. Energy Technology Perspectives 2014 (2014, s.l.). 2014. Ed. IEA International Energy Agency. s.l., Jeon, C., Shin, J. (2014) Long-term renewable energy technology valuation using system dynamics and Monte Carlo simulation: Photovoltaic technology case. Energy , 66, 447– 457. Johnson, G., Scholes, K., Whittington., R. (2008). Exploring corporate strategy: text & cases. Johnson, G., Scholes, K., Whittington, R. (2006). Dirección estratégica Kahneman, D., Tversky, A. (2013) Handbook of the Fundamentals of financial decision making In Chapter 6: Prospect theory: an analysis of decision under risk. s.l.: s.e.: pp. 99– 127. Kjaerland, F. (2007) A real option analysis of investments in hydropower-The case of Norway. Energy Policy , 35, 5901–5908. Kodama, M. (2003) Strategic Innovation in Traditional Big Business : Case Studies of Two Japanese. Organization Studies , 24, 235–268. Lambert, M., Moreno, M., Platania, F. (2015) Real options valuation under uncertainty. SSRN 2647293 , 2015. Lee, S.-C. (2011) Using real option analysis for highly uncertain technology investments: The case of wind energy technology. Renewable and Sustainable Energy Reviews , 15, 4443–4450. Lerner, J., Wulf, J. (2007) Innovation and Incentives: Evidence from Corporate R&D (En no seguir está basada con como se paga a los ejecutivos). Review of Economics and Statistics , 89, 634–644. Lint, O., Pennings, E. (1998) R&D as an option on market introduction. R&D Management , 28, 279–287. Linton, J.D., Walsh, S.T., Morabito, J. (2002) Analysis, ranking and selection of R & D projects in a portfolio. R and D Management , 32, 139–148. Liu, Y. (2011) Valuation of R & D Projects. Asia Pacific Management Review , 16, 289–304. Loch, C.H., Bode-Greuel, K. (2001) Evaluating growth options as sources of value for pharmaceutical research projects. R & D Management , 31, 231–248. Luo, L., Sheu, H., Hu, Y. (2008) Evaluating R&D projects with hedging behavior. Research- Technology Management , 51, 51–57. Managi, S., Zhang, Z., Horie, S. (2016) A real options approach to environmental R&D project evaluation. Environmental Economics and Policy Studies , 18, 359–394. Marafon, A.D., Leonardo, E., Lacerda, R.T. de O., Ensslin, S.R. (2015) The effectiveness of multi-criteria decision aid methodology A case study of R&D management. European Journal of Innovation Management , 18, 86–109. Martín-Barrera, G., Zamora-Ramírez, C., González-González, J.M. (2016) Application of real options valuation for analysing the impact of public R & D fi nancing on renewable energy projects : A company s perspective (En paper de valoraciones que me puede servir Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Introducción como modelo para escribir mi paper). Renewable and Sustainable Energy Reviews , 62, 1– 10. Martinez, M.G., Zouaghi, F., Marco, T.G. (2017) Diversity is strategy : the effect of R & D team diversity on innovative performance. R&D Management , 47, 311–329. Mascareñas, J. (2007) Opciones reales en la valoración de proyectos de inversión. Monografías de Juan Mascareñas sobre Finanzas Corporativas. Universidad Computlense de Madrid. España. , 2007. Medcof, J.W., Lee, T. (2017) The effects of the chief technology officer and firm and industry R & D intensity on organizational performance. R & D Management , 47, 767– 781. Meer-Kooistra, J. van der;, Scapens, R.W. (2008) The governance of lateral relations between and within organisations. Management Accounting Research , 19, 365–384. Menegaki, A. (2008) Valuation for renewable energy: A comparative review. Renewable and Sustainable Energy Reviews , 12, 2422–2437. Moehrle, M.G., Walter, L. (2008) Risk and uncertainty in R & D management. R&D Management , 38, 449–451. Myers, S.C. (1984) Finance Theory and Financial Strategy. Interfaces , 14, 126137. Nieto, M. (2003) From R & D management to knowledge management An overview of studies of innovation management. Technological Forecasting & Social Change , 70, 135– 161. Nobel, R., Birkinshaw, J. (1998) Innovation in multinational corporations: control and communication patterns in international R&D operations. Strategic Management Journal , 19, 479–496. Oreskovich, A.-M., Gittins, J. (2016) The evaluation of risk in pharmaceutical research: a study of current models and techniques. R&D Management , 46, 900–913. Oriani, R., Sobrero, M. (2008) Uncertainty and the market valuation of R&D within a real options logic. Strategic Management Journal , 29, 343–361. Otley, D.., Beery, J. (1994) Case Study Research in management accounting and Control. Management Accounting Reserach , 5, 45–65. Perdue, R.K., Mcallister, W.J., King, P. V, Berkey, B.G., King, P. V, Berkey, B.G. (1999) Valuation of R and D Projects Using Options Pricing and Decision Analysis Models. Interfaces , 29, 57–74. Perlitz, M., Peske, T., Schrank, R. (1999) Real options valuation: The new frontier in R&D project evaluation? R&D Management , 29, 255–269. Pettigrew, A.M. (1992) The character and significance of strategy process research. Strategic Management Journal , 13, 5–16. Pettigrew, A.M., Woodman, R.W., Cameron, K.S. (2001) Studying organizational change and development: Challenges for future research. Academy of Management Journal , 44, 697–713. Poh, K. l., Ang, B. w., Bai, F. (2001) A comparative analysis of R&D project evaluation methods. R&D Management , 31, 63–75. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Introducción Raisch, S., Birkinshaw, J., Probst, G., Tushman, M.L. (2009) Organizational Ambidexterity : Balancing Exploitation and Exploration for Sustained Performance. Organization Science , 20, 685–695. Raynor, M.E., Leroux, X. (2004) Strategic Flexibility in R & D. Research Technology Management , 47, 27–32. Reyck, B. De, Degraeve, Z., Vandenborre, R. (2008) Project options valuation with net present value and decision tree analysis. European Journal of Operational Research , 184, 341–355. Rod Coombs. (1996) Core competencies and the strategic management of R&D. R&D Management , 26, 345–355. Romanelli, E., Tushman, M.L. (1994) Organizational Transformation as Punctuated Equilibrium: An Empirical Test. Academy of Management Journal , 37, 1141–1166. Overview of Evaluation Methods for R & D Programs (2007, s.l.). 2007. Ed. Ruegg, R. s.l., Salimi, N., Rezaei, J. (2018) Evaluating firms ’ R & D performance using best worst method. Evaluation and Programm Planning , 66, 147–155. Santos, L., Soares, I., Mendes, C., Ferreira, P. (2014) Real Options versus Traditional Methods to assess Renewable Energy Projects. Renewable Energy , 2014. Scarlat, N., Motola, V., Dallemand, J.F., Monforti-Ferrario, F., Mofor, L. (2015) Evaluation of energy potential of Municipal Solid Waste from African urban areas. Renewable and Sustainable Energy Reviews , 50, 1269–1286. Schachter, J.A., Mancarella, P. (2016) A critical review of Real Options thinking for valuing investment flexibility in Smart Grids and low carbon energy systems. Renewable and Sustainable Energy Reviews , 56, 261–271. Shavinina, L. V. (2003). The International Handbook on Innovation Siddiqui, A.S., Marnay, C., Wiser, R.H. (2007) Real options valuation of US federal renewable energy research, development, demonstration, and deployment. Energy Policy , 35, 265–279. Simons, R. (1994) How new top managers use control systems as levers of strategic renewal. Strategic Management Journal , 15, 169–189. Smith, R.D. (2003) The chief technology officer: strategic responsibilities and relationships. Research Technology Management , 46, 28–36. Smith, R.D., Smith, R.D. (2003) The chief technology officer: strategic responsibilities and relationships. Research Technology Management , 46, 28–36. Souder, W.E. (1972) Comparative Analysis of R&D Investment Models. A I I E Transactions , 4, 57–64. Stawasz, E., Stos, D. (2016) " A method of simplified assessment of the economic potential of R&D projects ". In s.l.: s.e.: pp. 29–46., Págs. 29–46. Tidd, J. (2018) Innovation management challenges: from fads to fundamentals. International Journal of Innovation Management , 22, . Tirpak, T.M., Miller, R., Schwartz, L., Kashdan, D. (2015) R&D Structure in a changin world. Research-Technology Management , 45, 19–26. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Introducción Tohidi, H., Jabbari, M.M. (2012) Innovation as a Success Key for Organizations. Procedia Technology , 1, 560–564. Tompkins, R.G. (2002) Static versus Dynamic Hedging of Exotic Options: An Evaluation of Hedge Performance via Simulation. The Journal of Risk Finance , 3, 6–34. Trigeorgis, L. (1997) Real Options: Managerial Flexibility and Strategy in Resource Allocation. Journal of Banking & Finance , 21, 285–288. Tushman, M.L., Romanelli, E. (1985) Organizational evolution: A metamorphosis model of convergence and reorientation. Research in Organizational Behavior , 7, 171–222. Valdivia, M., Galan, J.L., Laffarga, J., Ramos, J.L. (2016) Biofuels 2020: Biorefineries based on lignocellulosic materials. Microbial Biotechnology , 9, 585–594. Vega-Gonzalez, L.R., Rivera-Velasco, G. (2016) Methodology for Rapid Technology Valuation with Restricted Incomes. Business and Economic Research , 6, 175–191. Van de Ven, A.H. (1992) Suggestions for studying strategy process: A research note. Strategic Management Journal , 13, 169–188. Van de Ven, A.H., Poole, M.S. (2005) Alternative approaches for studying organizational change. Organization Studies , 26, 1377–1404. Virany, B., Tushman, M.L., Romanelli, E. (1992) Executive Succession and Organization Outcomes in Turbulent Environments: An Organization Learning Approach. Organization Science , 3, 72–91. Wang, A., Halal, W. (2010) Comparison of Real Asset Valuation Models: A Literature Review. International Journal of Business and Management , 5, 14–24. Whittington, R., Johnson, G., Scholes, K., Angwin, D., Regnér, P. (2017). Exploring Strategy Willigers, B.J.A., Hansen, T.L. (2008) Project valuation in the pharmaceutical industry : a comparison of least-squares Monte Carlo real option valuation and conventional approaches. R & D Management , 38, 520–537. Wolff, M.F. (2007) Forget R&D Spending – think innovation (En tengo que buscar artículo orignal). Research Technology Management , 50, 7–9. Wouters, M., Roorda, B., Gal, R. (2011) Managing Uncertainty During R&d Projects: A Case Study. Research-Technology Management , 54, 37–46. Case study research: Design and methods (2nd ed.) (1994, Beverly Hills, CA). 1994. Ed. Yin, R. Beverly Hills, CA, Yongtae, P., Seonwoo, K. (2005) Linkage between knowledge management and R&D management. Journal of Knowledge Management , 9, 34–44. Zainal, Z. (2007) Case study as a research method. Jurnal Kemanusiaan , 5, . Zhang, J., Baden-fuller, C., Mangematin, V. (2007) Technological knowledge base , R&D organization structure and alliance formation : Evidence from the biopharmaceutical industry. Research Policy , 36, 515–528. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 23 Chapter 1: When industrial companies become technology-driven: Abengoa case study. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 24 Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 1: When industrial companies become technology-driven: Abengoa case study. 25 Abstract Companies are forced to review their strategies maintained its competency in the market. There are different paths to follow in order to be reinvented, one of them and most often is bet for R&D and innovation. In this paper it is presented a real case in which an industrial company shift its strategy to become a technology-driven company. It is analyzed the changes made in company strategy, organizational structure, and management and governance. The roles made by the R&D and innovation leaders within the organization, the R&D structure and the financial ratios evolution are also analyzed with the longitudinal analysis of the case study methodology. The aim of this study is to serve as guidance and inspiration for other companied that are involved or are considering to be involved in this kind of strategy changes. Keywords Strategy shift, Longitudinal Case Study, Innovation, Structure Organization. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 1: When industrial companies become technology-driven: Abengoa case study. 32 networks, and industrial engineering and construction. The 2001 Annual Report highlights Abengoa as a technological company which defined itself as “It is now an industrial and technological company…” Emphasizing the technology as a main driver of its description (Abengoa, 2001). During this period, R&D and technology increased its importance in the company strategy. It started getting higher visibility as it was the first time that R&D investment was reported separately as a financial statement, by this time Abengoa investment in R&D was 17.5 million Euros. Furthermore, at this time, Abengoa was able to obtain a subsidy from the Department of Energy in the USA (DoE) for a total amount of 32.4 million USD for R&D in second generation ethanol (Abengoa, 2003). These were the firsts stages of the gamble that the company was doing on R&D to become more competitive in technology markets. 4.2 Abengoa takes a bet on increased R&D investments: 2004-2009 In 2004, Abengoa was divided into five different units: Solar, Bioenergy, Environmental Services, Information Technologies, and Industrial Engineering and Construction. At that time, the latter covered most of Abengoa’s activities—and made up almost half of the company’s revenues. As mentioned, the construction industry is a conservative sector that mainly relies on mature technologies. Thus, innovation was very limited and mainly comprised the application of lessons learned from one project to another. In Abengoa’s 2005 Annual Report (Abengoa, 2005) a strategy was announced to diversify the business by intensifying its focus on tech-heavy sectors such as solar energy and biofuels (see Figure 3) Figure 3. Abengoa Business Units in 2005 (Abengoa, 2005). Figure adapted from Abengoa Annual Report. Within each of the above-mentioned Business Units (Figure 2) were individual R&D departments. The R&D departments sought out financing and oversaw project progress. The director within each Business Unit defined the R&D strategy. Exceptions to this were in the solar and bioenergy units, which both created an independent R&D company inside their units—an organizational change that reflected the greater need for innovation in these areas. At the corporate level however, R&D lacked representation and activity was simply presented to corporate leaders through financial and administrative summary reports, such as a follow-up activity report that was updated every six months. Thus, there were no toplevel management positions to guide research innovation and development strategies. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 1: When industrial companies become technology-driven: Abengoa case study. 33 In addition, the R&D managers at this time were most often personnel that lacked science backgrounds or previous R&D experience outside of the company. Instead, they typically had experience in engineering and construction. In 2009, the number of PhDs working for Abengoa was less than 15. Despite having a relatively limited number of academic staff, Abengoa took major strides forward by beginning construction of a large thermoelectric solar energy tower with a power capacity of 11 MW in 2005, and a demo facility to produce ethanol from biomass in 2006. Although most of Abengoa’s strategy was based on the natural growth of the company, agreements were established with third-party companies to buy technology or acquire “know-how”. An example of this strategy was the agreement reached with Dyadic International to develop an in-house Abengoa enzyme cocktail—a technology that is essential for many critical steps in the development of second-generation biofuel technologies (Valdivia et al., 2016). While Abengoa had much R&D success over this period, some organizational and strategic decisions were made that were not in line with the company’s policies. For example, while R&D activities were well developed in the solar and bioenergy units, the remaining units were lagging behind. As well, each Business Unit had developed its own independent management procedure for project selection and development, and these procedures were at times not fully aligned with corporate strategies. Another weakness was that certain departments and leadership saw R&D departments as more of a cost to the company than an opportunity to create new products and gain market dominance. The culture of the company was standing in the way of progress, and while there was a lot of work to do on this front, it would soon change. 4.3 R&D gains representation at top management levels 2009-2010 A key readout of whether a company places importance on technology and R&D is whether the activity is represented by top-level management boards (Cetindamar and Pala, 2011). Abengoa made a strong commitment in this realm when it created the position of CTO in 2009. The CTO held a seat on the Board of Directors and the company’s strategic planning team. At this time, R&D investments were around 60M Euros and involved over 400 personnel devoted to innovation, technology and R&D. The CTO received clear instructions from the Board of Directors: i) to ensure that the technological strategy was in line with the business strategy; ii) to harmonize the management structure among the different units, and iii) to assure that the market saw Abengoa as a technological company, participating in international research and innovation forums. These responsibilities are well-aligned with the roles that are expected from the position as described in the literature (Adler and Ferdows, 1990); Smith, 2003; Cetindamar and Pala, 2011). Under the leadership of the CTO, the different R&D and innovation departments were reorganized. An R&D and innovation department was established in all Business Units— even in those in the more conservative sectors with mature technologies. The CTO was also responsible for converting the R&D units within the most innovative business departments Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 1: When industrial companies become technology-driven: Abengoa case study. 34 (i.e., solar and bioenergy) into independent companies that eventually built a strong presence in Spain and the United States. The new departments and companies had independent finances, and these were reported directly to the Executive Chairman of Abengoa. At this point, hydrogen and emission reduction companies were created. These Business Units were born with a clear objective: to consolidate themselves as economically viable businesses within a period of 4-5 years. To further promote innovation in the organization, the Board of Directors established a set of overarching R&D objectives. As stated by Lerner and Wulf (2007) and Honoré et al. (2015), this approach is an effective way to advance R&D in organizations. Deeper organizational measures were taken by top management to ensure that there was a clear strategy for innovation. The following three key management tools were implemented within all departments: - Stage-gate methodology: This project management tool, based on the methodology published by Cooper et al. (1990), was integrated into Abengoa’s ‘Internal Mandatory Rules’ (abbreviated NOC in Spanish) such that every company project was required to follow it. - R&D Key Performance Indicators (KPIs) were defined to measure progress. These included: o Personnel devoted to R&D&I (research, development and innovation) o PhDs working for the company o Filed Patents o R&D intensity (i.e., investment in R&D divided by total of sales) The KPIs were presented by the CTO to the Board of Directors each six months. By the end of 2010 the values for these KPIs were (Abengoa, 2010): o Personnel devoted to R&D&I: 441 o PhDs working for the company: 22 o Filed Patents:113 o R&D Intensity: 1.7% - R&D valuation: a techno-economic analysis tool was developed in-house to assess the potential return of every R&D project. This tool was implemented to ensure that all projects had a clear market target. Furthermore, it measured whether the resources allocated to each project were being used effectively to reach objectives. Thanks to the company’s investment in innovation, it began to gain a technological edge. For example, Abengoa’s second commercial solar tower power plant, which was completed in 2010, was the biggest tower in the world with a production capacity of 20 MW. This tower was built next to Abengoa’s 10 MW solar tower—combined; these towers comprised the largest solar complex in the world. Also, in 2010 Abengoa began its first international project: the construction of a secondgeneration biofuel plant located in Hugoton, Kansas (United States). Although this project was built on experience gained from the construction of an existing 5 ML facility in Salamanca (Spain), the technological challenge was enormous, particularly because it required scaling up to almost 95 ML. The success of this project was another clear Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 1: When industrial companies become technology-driven: Abengoa case study. 35 demonstration of the competitive advantage gained by investing in innovation and technology. By the end of this period, investments in R&D were 92.6M Euros—almost double the amount made in 2005. Financially it was a big challenge for Abengoa to support this level of investment, although around one quarter of these investments were paid for through European R&D grant and project funds. 4.4 The creation of Abengoa Research 2011-2012 An important milestone in the transformation of the company was the creation of Abengoa Research (AR). AR was born to lead and manage basic research and complex technical problems. Since its inception, AR was led by a renowned university professor, who was in charge of establishing the organizational structure and strategic goals of AR, which had two main objectives: - Identify breakthroughs and new technologies with market potential that may provide competitive advantage. - Solve complex technical problems that require deeper scientific investigation. AR was organized into various scientific knowledge areas and each area was led by one senior scientist with a strong background in academic science. With the creation of AR, the company took one step further into the realm of basic research. The number of PhDs in the company reflected this shift; before the creation of AR there were 22 PhDs and by the end of 2013 the number had increased to 67. Another aim of AR was to consolidate and improve the company’s patent portfolio. For this, a Patent Office was created, which took over existing patent resources from the Business Units. Up until this time, the Business Units had done an excellent job of capturing intellectual property (IP), having applied for over 100 patents. The mission of the new Patent Office was to optimize the IP strategy for all of Abengoa’s technologies and to improve the quality of the patents, rather than increasing the total number. AR, as a new entity, had to define its relationship with the other Business Units. AR was created to become a sort of technological research center within the company—one that could provide key technological know-how. The collaboration framework between AR and other Business Units is illustrated in Figure 4: Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 1: When industrial companies become technology-driven: Abengoa case study. 36 Figure 4. Abengoa R&D and Innovation. This structural scheme of Abengoa Research and its relationship with the Business units is adapted from the 2013 Abengoa Annual Report. AR focused on foundational research, while the Business Units focused on the final development steps. For this arrangement to work, both needed to have tightly aligned aims and well-defined mechanisms for knowledge transfer. One hurdle was that AR staff—who had backgrounds in academia—brought with them a culture that was very different from that of other Abengoa R&D employees. Although AR had a budget to spend on developing future technologies and breakthroughs that would benefit Abengoa in the medium-long term (~10 years), all other AR projects were financed by the Business Units. This scheme was established to ensure that AR activities were tightly aligned with company needs. Despite the expected friction between AR and the Business Units, Abengoa’s top management had a strong vision and a plan for AR, which ensured that its integration was supported, and this helped to ensure that the initiative moved forward. During this time, Abengoa continued to use its established management tools for all R&D projects and continued to create new Business Units in sectors where technology played a critical role. This time was also marked by Abengoa’s establishment of two new initiatives: i) Seapower, to develop marine and tidal energy, and ii) Energy Crops, to develop sustainable energy from agriculture. Furthermore, Abengoa was also concentrating its business in two main sectors, energy and environment, and reducing investment in those that were no longer considered core business, such as information technology and valorization of residues—businesses that were sold. During this time of expansion, Abengoa initiated a number of projects, such as the construction of solar power plants in Abu Dhabi l; the Waste to Biofuel project, which aimed to convert municipal solid wastes into biofuel; and a project that used molten salts to store solar energy. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 1: When industrial companies become technology-driven: Abengoa case study. 37 4.5 The centralization of R&D 2013-2015 In 2013, the company started to shift its strategy and overall organizational structure. Abengoa switched from a model in which the different Business Units acted as independent organizations with common corporate management, to one in which all units were integrated into one unified organization. Within this new structure, products progressed from R&D to the final product through different Abengoa branches within the company. AR was made a big part of this reorganization. The Abengoa Board of Directors and Chairman made AR’s responsibility to develop technologies from early development stages all the way to a market-ready product. Thus, between 2013 and 2014, all personnel involved in R&D were moved under the AR umbrella. To support this development, AR changed its organization by specializing into teams that were aligned with specific Abengoa business areas: bioenergy, concentrated solar energy, photovoltaics, power systems, chemical processes and complementary areas of engineering services. These areas were each led by a Technology Area Director, who had a degree of independence from the AR general manager but worked with him to define targets. A key issue at this point was that AR failed to seek out new leaders with the expertise required to fill these new positions; rather, the existing scientific and business personnel were folded into this new structure. This saw AR, which was based at Abengoa headquarters in Seville, grow from 66 personnel in 2013 to more than 400 by the end of 2014. In this new role, AR was in charge of maintaining and reinforcing technology under development and defining a plan for the next ten years of Abengoa R&D. In order to achieve these aims, AR faced a number of sizable risks and challenges: - Collaboration: the need for Business Units to recognize and work with AR as a key player in technology development - Commercialization: the need to maintain the commercial focus of the research, while at the same time identifying disruptive ideas - Work Culture: To consolidate and foster cohesion between the scientific and Business Unit personnel so that they share the same objective In order to face these challenges, steering meetings were held between AR leaders and Business Unit leaders at different levels. At the upper most level was the Technological Strategy Meeting, a monthly meeting between top level AR and Business Unit personnel. These personnel included the AR General Manager, the Technology and Area Directors, the Business Unit General Manager and the Technology Development leaders. The objectives of this meeting were to i) define the core R&D strategy; ii) review and assess the value of technologies; and iii) approve new R&D projects and cancel existing projects that were deemed unnecessary. In addition, weekly Technology Management Meetings were held between AR and Business Units to monitor project progress. These meetings were attended by the Technology Area Director, the Project leads and managers in AR and the Business Units. The general objectives of the meetings were as follows: - To assess project progress based on Stage-Gate methodology (budget and schedule) Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 1: When industrial companies become technology-driven: Abengoa case study. 38 - To solve issues relating to technologies and research - To define the market potential of the projects and related technologies - To analyze technological and market risks - To identify alternative strategies - To define and propose new projects Further to this organizational model, management tools were also updated. As part of this, the Technology and R&D Competitiveness Value Assessment (TechValue) tool was developed and launched. TechValue served to measure the direct impact of the R&D on costs. Complementary to R&D valuation, TechValue measured the cost reductions on commercial products that directly resulted from R&D. The main goal of this tool was not to evaluate the viability of technology or its market value, but to measure the competitiveness of the technology in the market. It also enabled the company to determine whether the competitiveness of a product could be improved through R&D or innovation, or whether it was already a mature technology. TechValue results enabled Abengoa to decide if a product had matured enough for R&D activities to be halted so that design and engineering improvements could be made. The R&D value, KPI’s and Stage-Gate methodology also continued to be part of the management tool-kit. Regarding the R&D strategy and project management, Abengoa was organized according to the Horizons defined by McKinsey&Company (Baghai, M., Coley, S., & White, 2000). In the case of AR, these horizons were adapted to its activity: H1 were very advanced projects with first commercial actions already performed; H2 represented mature projects that required further research and/or development before stepping up to H1; H3 were exploratory projects that were aligned with Abengoa’s general interests. As previously mentioned, AR projects were financed by the Business Units in full, except for the subsidies obtained by AR. Therefore, AR needed to work closely with Business Units to define project scope and budget. Thanks to this requirement, continuous communication between AR and Business Units was in place. Regarding IP, because the Business Units financed technology development and led commercialization, they also filed patents. As the importance of technology activities grew, in October 2014 the Board of Directors created a Technology and Strategy Commission, formed by 2 independent directors and 1 director that represented the original bunch of investors, with some, but not a vast, experience in technology and R&D. This commission had the following objectives: i) to analyze and evaluate the potential development of marketable technology; ii) to oversee R&D strategy; and iii) to supervise the main R&D activities, such as the patent portfolio and the implementation of AR within the company. Under these leadership and management conditions, Abengoa was forced to make difficult decisions; for example, its Seapower technology projects were frozen due to unfavourable market conditions. This type of measure was understood and supported as part of the company’s dual focus on technology development and sound business decisions. R&D figures by the end of 2014 demonstrated that Abengoa’s R&D success continued: it had filed over 312 patents, employed 92 full time PhDs, and its investment in R&D reached nearly $600M USD. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 1: When industrial companies become technology-driven: Abengoa case study. 39 4.6 Changes in Governance and Control System Above we have described the most significant events and stages during Abengoa’s transformation from an industrial company to a technology and innovation company. The description has been mainly focused on the strategy and organizational changes of the firm. Nevertheless, the changes should have also been in other aspects of the organization, such as the governance, and the control systems (Virany et al., 1992; Romanelli and Tushman, 1994). Regarding the governance, we briefly analyze the Board of Directors, its composition and the capital structure during the period of change. For the control system, our analysis will focus on the financial ratios used to measure the company performance and its solvency. Power Distribution/Governance Capital structure The Company became public in 1996, but since then the family owners have always retained more than 50% of the shares, in fact by 2003, they retained more than 60% of the total shares and until 2014 they were never below 54% of the political rights of the company. Hence, there was not a significant change in the company shareholders, with the political control remaining under the traditional shareholders. Board of Directors and Management In 2001, Abengoa Board of Directors (BoD) was formed by 4 persons, all of them belonging to the original founders’ family, it meant that none of them were independent. It was in 2003 when the number BoD members increased to 7, to include three independent members on the BoD, with relevant expertise in management and economics. The representation of independent directors increased in the years 2005 and 2006 to reach 56%. However, this percentage decreased in 2007, when the number of BoD members increased to 13, with only one of the new additions being an independent member; meaning only 31% were now independent members. The new directors were all external and represented the different original share-holder families in the BoD. In 2008 the number of BoD increased again to with the addition of one more executive and one more independent. However, throughout this period none of the Independent External Directors had a background in the field of science and technology or innovation. The next relevant change in the BoD and the Executive Management of the Company, was the appointment of a Chief Executive Officer (CEO) who was different to the Executive Chairman, this appointment occurred on 2010. It is significant as it was the first time in the history of the company that the Chairman put part of his responsibilities on the CEO. The BoD structure was stably maintained until 2014, which is the end of the analysis period of this study. To increase the international presence of the company and to have better visibility of the worldwide political, economic and technology situation, Abengoa created in June 2010 an Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 1: When industrial companies become technology-driven: Abengoa case study. 40 International Advisory Board formed by international prestigious professionals to advise the company on its international strategy. The profiles of the different members of this Advisory Board were from different fields, economists, managers, and also scientists. It should be pointed out that the role of this board was just advisory and they had no legal responsibility. On the topic of Executive Management, Abengoa had a Steering Committee formed by the top managers of the company, which were: Chairman, CEO, head of corporate departments and head of the Business Units. This committee managed the daily organization of the company and any instruction made by the BoD, in short, it was the first operative committee completely formed by executives. Control System-Financial ratios In addition to the above description of the BoD evolution, the control system of the company, should have shown the strategic change of the company. Here we analyze the control system through profitability and solvency ratios. Beforehand, it must be clarified that data used to calculate the ratios are based on the company’s annual accounts. Also, it has to be noted that the accounting regulation rules modification (2008), from which all European public companies shall follow the International Financial Reporting Standards (IFRS); this implies that the accounts previous to 2008 were done following different rules. In addition, the group of firms, that formed Abengoa Group, under study have a complex consolidation that also changes each year of our time horizon. In Figure 5 below we show (i) Earnings Before Interest, Taxes, Depreciation and Amortization (EBITDA), which measure the profitability of the company without considering financial and tax aspects; (ii) Return on Assets (ROA), which measure the capacity of the company assets to generate incomes; and (iii) Return on Equity (ROE), which measures the ability of the company to give returns to its shareholders. Figure 5 Evolution in time of different Financial ratios. Source Abengoa Annual Reports 0,0% 5,0% 10,0% 15,0% 20,0% 25,0% 30,0% - 200 400 600 800 1.000 1.200 1.400 1.600 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 % Millions € Título del eje Financial Ratios EBITDA (M€) ROE ROA Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 1: When industrial companies become technology-driven: Abengoa case study. 41 EBITDA The company’s EBITDA consistently increased every year at high ratios during the 2004 to 2014 period. It should be noted an increase of around 10% with respect to the EBDITA of the previous year, except for 2014 in which the increase was just 3%. In the period 2004 to 2014 EBITDA grew at a compound annual rate of 22.8%. This increase demonstrates the company growth based on the new technology business generated thanks the new strategy. The EBITDA increase comes from the growth of assets, both tangibles and intangibles, which includes the asset called “investment in projects”. Under the view of the R&D and innovation strategy, it can be said that part of the EBITDA increase came from those intangibles assets that are achieved thanks to the investment in R&D, mainly the income coming from patents and technology fees. It must be noted, that the accounting of these projects is subject to IAS 12, which allows the activation of expenses and the counterpart as incomes; the application of this accounting standard allowed the company to obtain certain incomes. Return on Assets (ROA) and Return on Equity (ROE ) Both profitability ratios have had complete divergent performance. In the case of the ROA, which measure the assets profitability through the ratio EBIT (earnings before interests and taxes) and the total assets, the evolution of this ratio does not show the impact of the new strategy. As is shown in Figure 5 EBITDA increase was not translated into a sustained increase in the ROA, probably due to the long return period of the executed projects. Regarding ROE, in the period 2004 to 2010, the ROE was maintained in the range of 20% to 30% depending on the year; these are high values compared with its peers. From 2010 to 2014 the ROE suffers a large deterioration, decreasing more than 70% in three years. The progress in the transformation towards a technology-intensive company was made with a loss of profitability for shareholders, motivated by the decrease in the net income. In addition to the performance financial ratios, it is critical to focus on the solvency ratios, to analyze if the strategy shift influenced the company’s solvency. Table 1 Historical evolution of financial ratios 2004-2014 In Table 1, some common solvency ratios are calculated. Short-term solvency was measured with the traditional ratio CA/CL which relates current assets to current liabilities. It shows the company’s capacity to cover its immediate liabilities. It’s a short-term solvency measure. Long-term solvency was measured with two indicators, the relationship between total assets and total liabilities (TA/TL), which indicates the company's ability to be able to pay its debts, basically the tendency that the company may incur bankruptcy. The other ratio is the relationship between cash flow (profits + amortization) and the total liabilities (CF/TL) which indicates the ability to pay its debts with the generated funds. Ratios 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 CA/CL 1.03 0.93 0.94 1.2 1.0 0.83 1 1 0.83 0.84 0.88 TA/TL 1.2 1.2 1.1 0.96 1.13 1.1 1.1 1.09 1.09 1.09 1.1 CF/TL 0.036 0.04 0.05 0.03 0.039 0.046 0.034 0.07 0.07 0.069 0.065 TL/NL 6.44 6.66 11.8 9.13 13.4 9.56 9.41 9.86 10.21 10.17 8.5 EBIT/FE 2.07 1.85 0.7 1.73 1.04 2.33 1.49 1.19 1.42 1.44 1.33 Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 1: When industrial companies become technology-driven: Abengoa case study. 48 technologies under a pull incentives environment. Renewable and Sustainable Energy Reviews , 59, 482–493. Fernández Sánchez, E. (2005). Estrategia de innovación Fertig, E. (2018) Rare breakthroughs vs . incremental development in R & D strategy for an early-stage energy technology. Energy Policy , 123, 711–721. Gassmann, O., Zedtwitz, M. Von. (1998) Organization of industrial R & D on a global scale. R&D Management , 28, 147–161. _____. (1999) New concepts and trends in international R & D organization. Research Policy , 28, 231–250. Green, K., Mcmeekin, A., Irwin, A. (1994) Technological trajectories and R&D for environmental innovation in UK firms. Futures , 26, 1047–1059. Grimaldi, M., Cricelli, L., Rogo, F. (2017) Valuating and analyzing the patent portfolio : the patent portfolio value index portfolio. European Journal of Innovation Management , 2017. Guarascio, D., Pianta, M., Bogliacino, F. (2017) Export, R&D and New Products: A Model and a Test on European Industries In Foundations of Economic Change. s.l.: s.e.: pp. 393– 432. Guerras, L.A., Navas, J.E. (2015). Casos de Dirección Estratégica de la Empresa Halkos, G., Bousinakis, D. 2012. Importance and influence of organizational changes on companies and their employees. MPRA Paper, s.l. Hall, B.H.., Lerner, J. (2010) The Financing of R&D and Innovation In Handbook of the Economics of Innovation s.l.: s.e.: v.1: pp. 609–639. Hamel, G., Prahalad, C.K. (1991) Corporate imagination and expeditionary marketing. Harvard business review , 69, 81—92. Hartmann, M., Hassan, A. (2006) Application of real options analysis for pharmaceutical R&D project valuation-Empirical results from a survey. Research Policy , 35, 343–354. Hassanzadeh, F., Collan, M., Modarres, M. (2012) A practical R&D selection model using fuzzy pay-off method. International Journal of Advanced Manufacturing Technology , 58, 227–236. Hoisl, K., Gruber, M., Conti, A. (2017) R&D team diversity and performance in hypercompetitive environments. Strategic Management Journal , 36, 1455–1477. Holtbrügge, D., Schuster, T., & Wilbs, A. (2017) Restructuring and Organizational Change at Siemens. A Case Study In The Internationalization of Firms: Case Studies from the Nürnberg Metropolitan Region s.l.: s.e.: p. 115. Honoré, F., Munari, F., Van Pottelsberghe De La Potterie, B. (2015) Corporate governance practices and companies’ R&D intensity: Evidence from European countries. Research Policy , 44, 533–543. Hunger, J.D., Wheelen, T.L. (2003). Essentials of strategic management Valuation of Technology: Exploring a Practical Hybrid Model (2003, s.l.). 2003. Ed. Hunt, F.H., Probert, D.R., Wong, J.C., Phaal, R. s.l., 47–53 p. Energy Technology Perspectives 2014 (2014, s.l.). 2014. Ed. IEA International Energy Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 1: When industrial companies become technology-driven: Abengoa case study. 49 Agency. s.l., Jeon, C., Shin, J. (2014) Long-term renewable energy technology valuation using system dynamics and Monte Carlo simulation: Photovoltaic technology case. Energy , 66, 447– 457. Johnson, G., Scholes, K., Whittington., R. (2008). Exploring corporate strategy: text & cases. Johnson, G., Scholes, K., Whittington, R. (2006). Dirección estratégica Kahneman, D., Tversky, A. (2013) Handbook of the Fundamentals of financial decision making In Chapter 6: Prospect theory: an analysis of decision under risk. s.l.: s.e.: pp. 99– 127. Kjaerland, F. (2007) A real option analysis of investments in hydropower-The case of Norway. Energy Policy , 35, 5901–5908. Kodama, M. (2003) Strategic Innovation in Traditional Big Business : Case Studies of Two Japanese. Organization Studies , 24, 235–268. Lambert, M., Moreno, M., Platania, F. (2015) Real options valuation under uncertainty. SSRN 2647293 , 2015. Lee, S.-C. (2011) Using real option analysis for highly uncertain technology investments: The case of wind energy technology. Renewable and Sustainable Energy Reviews , 15, 4443–4450. Lerner, J., Wulf, J. (2007) Innovation and Incentives: Evidence from Corporate R&D (En no seguir está basada con como se paga a los ejecutivos). Review of Economics and Statistics , 89, 634–644. Lint, O., Pennings, E. (1998) R&D as an option on market introduction. R&D Management , 28, 279–287. Linton, J.D., Walsh, S.T., Morabito, J. (2002) Analysis, ranking and selection of R & D projects in a portfolio. R and D Management , 32, 139–148. Liu, Y. (2011) Valuation of R & D Projects. Asia Pacific Management Review , 16, 289–304. Loch, C.H., Bode-Greuel, K. (2001) Evaluating growth options as sources of value for pharmaceutical research projects. R & D Management , 31, 231–248. Luo, L., Sheu, H., Hu, Y. (2008) Evaluating R&D projects with hedging behavior. Research- Technology Management , 51, 51–57. Managi, S., Zhang, Z., Horie, S. (2016) A real options approach to environmental R&D project evaluation. Environmental Economics and Policy Studies , 18, 359–394. Marafon, A.D., Leonardo, E., Lacerda, R.T. de O., Ensslin, S.R. (2015) The effectiveness of multi-criteria decision aid methodology A case study of R&D management. European Journal of Innovation Management , 18, 86–109. Martín-Barrera, G., Zamora-Ramírez, C., González-González, J.M. (2016) Application of real options valuation for analysing the impact of public R & D fi nancing on renewable energy projects : A company s perspective (En paper de valoraciones que me puede servir como modelo para escribir mi paper). Renewable and Sustainable Energy Reviews , 62, 1– 10. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 1: When industrial companies become technology-driven: Abengoa case study. 50 Martinez, M.G., Zouaghi, F., Marco, T.G. (2017) Diversity is strategy : the effect of R & D team diversity on innovative performance. R&D Management , 47, 311–329. Mascareñas, J. (2007) Opciones reales en la valoración de proyectos de inversión. Monografías de Juan Mascareñas sobre Finanzas Corporativas. Universidad Computlense de Madrid. España. , 2007. Medcof, J.W., Lee, T. (2017) The effects of the chief technology officer and firm and industry R & D intensity on organizational performance. R & D Management , 47, 767– 781. Meer-Kooistra, J. van der;, Scapens, R.W. (2008) The governance of lateral relations between and within organisations. Management Accounting Research , 19, 365–384. Menegaki, A. (2008) Valuation for renewable energy: A comparative review. Renewable and Sustainable Energy Reviews , 12, 2422–2437. Moehrle, M.G., Walter, L. (2008) Risk and uncertainty in R & D management. R&D Management , 38, 449–451. Myers, S.C. (1984) Finance Theory and Financial Strategy. Interfaces , 14, 126137. Nieto, M. (2003) From R & D management to knowledge management An overview of studies of innovation management. Technological Forecasting & Social Change , 70, 135– 161. Nobel, R., Birkinshaw, J. (1998) Innovation in multinational corporations: control and communication patterns in international R&D operations. Strategic Management Journal , 19, 479–496. Oreskovich, A.-M., Gittins, J. (2016) The evaluation of risk in pharmaceutical research: a study of current models and techniques. R&D Management , 46, 900–913. Oriani, R., Sobrero, M. (2008) Uncertainty and the market valuation of R&D within a real options logic. Strategic Management Journal , 29, 343–361. Otley, D.., Beery, J. (1994) Case Study Research in management accounting and Control. Management Accounting Reserach , 5, 45–65. Perdue, R.K., Mcallister, W.J., King, P. V, Berkey, B.G., King, P. V, Berkey, B.G. (1999) Valuation of R and D Projects Using Options Pricing and Decision Analysis Models. Interfaces , 29, 57–74. Perlitz, M., Peske, T., Schrank, R. (1999) Real options valuation: The new frontier in R&D project evaluation? R&D Management , 29, 255–269. Pettigrew, A.M. (1992) The character and significance of strategy process research. Strategic Management Journal , 13, 5–16. Pettigrew, A.M., Woodman, R.W., Cameron, K.S. (2001) Studying organizational change and development: Challenges for future research. Academy of Management Journal , 44, 697–713. Poh, K. l., Ang, B. w., Bai, F. (2001) A comparative analysis of R&D project evaluation methods. R&D Management , 31, 63–75. Raisch, S., Birkinshaw, J., Probst, G., Tushman, M.L. (2009) Organizational Ambidexterity : Balancing Exploitation and Exploration for Sustained Performance. Organization Science , 20, 685–695. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 1: When industrial companies become technology-driven: Abengoa case study. 51 Raynor, M.E., Leroux, X. (2004) Strategic Flexibility in R & D. Research Technology Management , 47, 27–32. Reyck, B. De, Degraeve, Z., Vandenborre, R. (2008) Project options valuation with net present value and decision tree analysis. European Journal of Operational Research , 184, 341–355. Rod Coombs. (1996) Core competencies and the strategic management of R&D. R&D Management , 26, 345–355. Romanelli, E., Tushman, M.L. (1994) Organizational Transformation as Punctuated Equilibrium: An Empirical Test. Academy of Management Journal , 37, 1141–1166. Overview of Evaluation Methods for R & D Programs (2007, s.l.). 2007. Ed. Ruegg, R. s.l., Salimi, N., Rezaei, J. (2018) Evaluating firms ’ R & D performance using best worst method. Evaluation and Programm Planning , 66, 147–155. Santos, L., Soares, I., Mendes, C., Ferreira, P. (2014) Real Options versus Traditional Methods to assess Renewable Energy Projects. Renewable Energy , 2014. Scarlat, N., Motola, V., Dallemand, J.F., Monforti-Ferrario, F., Mofor, L. (2015) Evaluation of energy potential of Municipal Solid Waste from African urban areas. Renewable and Sustainable Energy Reviews , 50, 1269–1286. Schachter, J.A., Mancarella, P. (2016) A critical review of Real Options thinking for valuing investment flexibility in Smart Grids and low carbon energy systems. Renewable and Sustainable Energy Reviews , 56, 261–271. Shavinina, L. V. (2003). The International Handbook on Innovation Siddiqui, A.S., Marnay, C., Wiser, R.H. (2007) Real options valuation of US federal renewable energy research, development, demonstration, and deployment. Energy Policy , 35, 265–279. Simons, R. (1994) How new top managers use control systems as levers of strategic renewal. Strategic Management Journal , 15, 169–189. Smith, R.D. (2003) The chief technology officer: strategic responsibilities and relationships. Research Technology Management , 46, 28–36. Smith, R.D., Smith, R.D. (2003) The chief technology officer: strategic responsibilities and relationships. Research Technology Management , 46, 28–36. Souder, W.E. (1972) Comparative Analysis of R&D Investment Models. A I I E Transactions , 4, 57–64. Stawasz, E., Stos, D. (2016) " A method of simplified assessment of the economic potential of R&D projects ". In s.l.: s.e.: pp. 29–46., Págs. 29–46. Tidd, J. (2018) Innovation management challenges: from fads to fundamentals. International Journal of Innovation Management , 22, . Tirpak, T.M., Miller, R., Schwartz, L., Kashdan, D. (2015) R&D Structure in a changin world. Research-Technology Management , 45, 19–26. Tohidi, H., Jabbari, M.M. (2012) Innovation as a Success Key for Organizations. Procedia Technology , 1, 560–564. Tompkins, R.G. (2002) Static versus Dynamic Hedging of Exotic Options: An Evaluation of Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 1: When industrial companies become technology-driven: Abengoa case study. 52 Hedge Performance via Simulation. The Journal of Risk Finance , 3, 6–34. Trigeorgis, L. (1997) Real Options: Managerial Flexibility and Strategy in Resource Allocation. Journal of Banking & Finance , 21, 285–288. Tushman, M.L., Romanelli, E. (1985) Organizational evolution: A metamorphosis model of convergence and reorientation. Research in Organizational Behavior , 7, 171–222. Valdivia, M., Galan, J.L., Laffarga, J., Ramos, J.L. (2016) Biofuels 2020: Biorefineries based on lignocellulosic materials. Microbial Biotechnology , 9, 585–594. Vega-Gonzalez, L.R., Rivera-Velasco, G. (2016) Methodology for Rapid Technology Valuation with Restricted Incomes. Business and Economic Research , 6, 175–191. Van de Ven, A.H. (1992) Suggestions for studying strategy process: A research note. Strategic Management Journal , 13, 169–188. Van de Ven, A.H., Poole, M.S. (2005) Alternative approaches for studying organizational change. Organization Studies , 26, 1377–1404. Virany, B., Tushman, M.L., Romanelli, E. (1992) Executive Succession and Organization Outcomes in Turbulent Environments: An Organization Learning Approach. Organization Science , 3, 72–91. Wang, A., Halal, W. (2010) Comparison of Real Asset Valuation Models: A Literature Review. International Journal of Business and Management , 5, 14–24. Whittington, R., Johnson, G., Scholes, K., Angwin, D., Regnér, P. (2017). Exploring Strategy Willigers, B.J.A., Hansen, T.L. (2008) Project valuation in the pharmaceutical industry : a comparison of least-squares Monte Carlo real option valuation and conventional approaches. R & D Management , 38, 520–537. Wolff, M.F. (2007) Forget R&D Spending – think innovation (En tengo que buscar artículo orignal). Research Technology Management , 50, 7–9. Wouters, M., Roorda, B., Gal, R. (2011) Managing Uncertainty During R&d Projects: A Case Study. Research-Technology Management , 54, 37–46. Case study research: Design and methods (2nd ed.) (1994, Beverly Hills, CA). 1994. Ed. Yin, R. Beverly Hills, CA, Yongtae, P., Seonwoo, K. (2005) Linkage between knowledge management and R&D management. Journal of Knowledge Management , 9, 34–44. Zainal, Z. (2007) Case study as a research method. Jurnal Kemanusiaan , 5, . Zhang, J., Baden-fuller, C., Mangematin, V. (2007) Technological knowledge base , R&D organization structure and alliance formation : Evidence from the biopharmaceutical industry. Research Policy , 36, 515–528. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 53 Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 54 Chapter 2: A research and technology valuation model for decision analysis in the environmental and renewable energy sectors. 2 2 Valdivia, M., Galan, J. L., Laffarga, J., & Ramos, J. L. (2020). A research and technology valuation model for decision analysis in the environmental and renewable energy sectors. Renewable and Sustainable Energy Reviews, 122, 109726. Impact factor JCR: 10,556 Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 55 Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 2: A research and technology valuation model for decision analysis in the environmental and renewable energy sectors 56 Abstract Annually, companies invest vast amounts of resources into research and development (R&D) in order to remain competitive in today’s markets. This investment intensive effort needs to be carefully managed and monitored in order to make the most of these investments. Making informed decisions regarding resource allocation is vital for maximizing return on investment (ROI). One important way to achieve this is use of a valuation model that is capable of estimating the potential return of R&D investment by assessing among other parameters, the level of uncertainty and risk associated with this kind of project. This paper presents a valuation decision model that has been applied to the renewable and environmental sector. Here we present a flexible, user-friendly and understandable model that reflects the intrinsic risks associated with R&D projects. The model can be customized for use across a variety of capital intensive and medium-long term R&D projects in the private and public realms. A real-life case study is used to illustrate the utility of the model within the biofuel sector; specifically, for a project focused on the production of biofuel from municipal solid waste. Use of valuation models, such as the one described here, when used from the very initial phases of the project till project completion can serve a vital role in R&D management—one that enables decision-makers to define realistic and productive targets throughout the full R&D project period. Key Words R&D Management, R&D valuation, Real Options, NPV Valuation, R&D uncertainty, R&D risk, Project Management. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 2: A research and technology valuation model for decision analysis in the environmental and renewable energy sectors 57 1. Introduction Investment in research, development and innovation is critical to for companies to stay competitive in medium and long term. This is particularly true for those in technology-driven sectors, such as pharmaceutical, environmental or renewable energy industries. For these sectors, the risks associated with expending resources to develop new and successful products are significant. These risks are linked to the requirement for extensive time and resources necessary to convert a good idea into an innovative product (Moehrle and Walter, 2008). Furthermore, the resources that companies manage, both in human and economic terms, are dwarfed by the extensive resources that could be expended on potential R&D projects (Hassanzadeh et al., 2012). Thus, it is critical that project managers and decisionmakers in industry have the appropriate tools required to decide where to devote these limited resources (Raynor and Leroux, 2004). Decision analysis and valuation methods represent important tools that help managers to maximize financial returns on investments and guide the selection of critical variables during the different development phases of R&D projects. R&D projects within a company can be monitored through different qualitative and quantitative methods as described by Ruegg (Ruegg, 2007) and Souder (Souder, 1972). While R&D management through valuation models are well described in a number of journals, techno-economic valuation approaches for R&D are applied mostly in large companies and not so often in small enterprises or large academic projects. Most models, regardless of the underlying methodology used, are focused on measuring the economic return on investments. However, as stated by Davis and Owens (Davis and Owens, 2003), these models are most useful for providing insight into a given process, rather than by providing a final overall value for returns. Currently, the three most widely used models are: 1) discounted cash flows (DCF), which enables the measurement of a net present value of the investment (NPV); 2) the tree decision model; and 3) real options valuations. There are other methodologies, such as system dynamics or data envelopment analyses, that are no longer widely used in industry. Companies within the pharmaceutical sector have used the above-named valuation methodologies extensively, particularly due to the historical importance of R&D in this sector. Other sectors, such as infrastructure or energy, where R&D has not been historically so important, are now developing appropriate tools to guide sustainable business models. Not surprisingly, these sectors are adopting models developed originally by the pharmaceutical sector. Nonetheless, these methodologies are not yet, in general, used on a regular basis due to their lack of transparency or suitability. Hartamann and Hasaan (Hartmann and Hassan, 2006) stated that a number of companies rely on the traditional deterministic NPV model despite its lack of flexibility, because managers feel more comfortable with the logic behind the model. To improve the evaluation of R&D projects within the energy and environmental sectors, the ARDV model (applicable R&D valuation model) a flexible valuation methodology based on decision analysis, NPV analysis, Decision Tree Analysis and Monte Carlo simulation is presented here. This novel evaluation approach is intended for use by project managers and Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 2: A research and technology valuation model for decision analysis in the environmental and renewable energy sectors 64 Figure 1 Valuation decision model. This scheme shows the interrelationships between the different types of variables and the intrinsic characteristics of the project 4. A case study and justification Below is described a detailed example in which the ARDV model was used to valuate an R&D project. The model has been validated through its use in a multinational company that is focused on the engineering and construction of renewable energy projects with proprietary technology. The company, which has its headquarters in the South of Spain, had (in 2015) a presence in more than 70 countries and more than 20,000 employees worldwide. The company has a strong innovative culture and is a pioneer in commercializing new technologies in biofuels, solar or hydrogen energies. It owned the first commercial solar tower in Africa and the biggest thermal solar complex in Europe. The company understood the food versus fuel dilemma and promoted research in the area of bioethanol from agricultural residues and municipal solid wastes. The company invested nearly €100 million in R&D in 2014. This R&D was organized according to the different sectors in which the company had a presence: bioenergy, solar, water, hydrogen and power electronics. Each of these sectors run their own projects under a project director and a number of project managers that handle the allocated funds. The case presented in this paper was set in the bioenergy area, although the project includes a combination of energy and environmental activities and involved second generation (2G) biofuel production. One of the key goals of 2G technology is to produce biofuels from agricultural residues, which is currently in early stages of commercialization (Valdivia et al., 2016). In this case, the raw material that is used to produce fuel is municipal solid waste Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 2: A research and technology valuation model for decision analysis in the environmental and renewable energy sectors 65 (MSW). The impetus for this project was based on the fact that almost all of the ethanol produced for blending with gasoline is derived from cereal grains or sugarcane. The project addresses an emerging issue regarding MSW—the sheer volume of it is becoming a problem for municipalities worldwide and one that is expected to worsen as the world population increases (IEA International Energy Agency, 2014; Scarlat et al., 2015). In summary, the R&D project under evaluation in this example involves the production of bioethanol from MSW in order to aid the treatment of MSW and to generate a biofuel. Figure 2, below, provides a high-level overview of the process of producing ethanol using MSW as raw starting material. Figure 2. High-level process configuration for ethanol production using Municipal Solid Waste (MSW) as raw material. The figure shows the main steps in the conversion of MSW into ethanol. The first step is pre-sorting the MSW; in the second step, the organic fraction is sent for pre-treatment and enzymatic hydrolysis, during which the enzyme cocktail is added. The sugar produced during hydrolysis is fermented to produce the beer that is distilled to obtain ethanol. The obvious first step required to build the model is data collection. This step can be divided into three main areas: technical inputs, market inputs and financial hypothesis. The second step is to select the main variables that influence the technical development of the R&D project. The critical technical variables are selected by the project director and should include all variables that impact commercial viability, should not be more than five in order to ensure that analysis of the data is feasible. Namely, for this project, these variables are; i) ethanol yield per ton of raw material, ii) natural gas consumption, iii) enzyme dose, iv) and capital expenditure (Capex). Ethanol yield per ton of raw material serves as readout of improvements made to the pretreatment of the starting material; it measures the amount of product (ethanol) that can be obtained per unit of raw material (ton of MSW). The natural gas consumption is the main expense in the operation of the plant, or the main variable cost; thus, reducing this value will directly benefit the profitability of the process. The performance of 2G ethanol technology depends heavily on enzyme performance (Valdivia et al., 2016), which is the enabling additive that transforms the raw material into a viable product. The Capex, which measures the funds needed to be financed per project, is a critical determinant of financial viability. As described in the previous section, each of the variables are defined by a range and a trend that models how they change as the project progresses. These values are defined by considering acceptable parameters for current state-of-the-art processes and the technological objectives of the project. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 2: A research and technology valuation model for decision analysis in the environmental and renewable energy sectors 66 Process improvements arise from two main factors: the R&D activities, and the learning curve, which defines the incremental improvements made as projects are successfully commercialized and scaled up. However, both sources of improvement are uncertain; thus, in order to include this in the model for each of the selected variables, a value range is defined. Table 2 shows, on the one hand, how the variable ranges change based on the number of plants built, termed the learning curve (see the various columns in Table 2). On the other hand, Table 2 also shows the ranges of each variable, which depend on success of the R&D project. The project director is the person in charge of defining these ranges, in accordance with the project targets. Table 3 Technical variables to be included in the model. The first column indicates the model’s technical variable. The rest of the columns are variable ranges and probabilities depending on the number of plants that are built. For each of the above variables, the most accurate probabilistic curve is defined, thanks to a software model known as @Risk. As an example, a curve for natural gas consumption in the first facility, as shown in Table 2 above. On the market side, the methodology is similar. In this case, three variables that are most likely to influence commercial viability were selected. These variables are selected by the project director, who has most in-depth knowledge of the project, and by the corporate strategy department of the company, which is responsible for providing market know-how and defining prices (i.e., a market analysis expert). The variables that were chosen were: i) the price of ethanol; ii) the fee that the company would charge municipalities per ton of MSW treated; and iii) number of industrial plants that will ultimately use the commercialized technology. Regarding ethanol price, four scenarios are defined in the model. These scenarios are based on projected legislation changes, the company’s past experiences and the existence of subsidies for advanced biofuels. The scenarios shown in Figure 3 correspond to the future price ranges provided to the model. Number of Facilities Constructed Critical Technical Variables Probability Value Probability Value Probability Value Probability Value 15% 18 25% 18 25% 15 40% 15 60% 21 60% 21 60% 18 50% 18 25% 24 15% 24 15% 21 10% 21 20% 65 15% 65 10% 65 10% 65 60% 75 70% 75 80% 75 80% 75 20% 85 15% 85 10% 85 10% 85 10% 12 10% 10 5% 915% 9 30% 15 40% 12 15% 10 60% 10 60% 18 50% 15 80% 12 25% 12 10% 260 20% 260 50% 260 80% 260 55% 311 60% 311 40% 311 15% 311 35% 350 20% 330 10% 330 5% 330 Enzyme Dose (mg/g) CapEx (M€) 1st Facility 2nd and 3rd Facility 4th to 15th Facility Facility >15 Natural Gas Use (MW) EtOH per raw material (gal/Ton MSW) Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 2: A research and technology valuation model for decision analysis in the environmental and renewable energy sectors 67 Figure 3 Ethanol prices. All scenarios are internal predictions made by the company. Scenario 1: High oil prices and fiscal credit until 2022 (blue lines); Scenario 2: Internal company strategic plan (orange lines); Scenario 3: high oil prices and fiscal credit extended beyond 2022 (grey lines); Scenario 4: No fiscal credit since 2014 (yellow lines) The second variable, which is critical to the success of the project, is the fee that the company would charge municipalities per ton of solid waste treated. This is a competitive market where companies develop new technologies to fulfill the prerequisites set by municipal administration, in terms of prices and environmental requirements. It is expected that the fee paid by the municipalities will decrease in coming years, mainly because incoming technologies will further increase the competitiveness of the market. As with the other variables, probabilistic curves were inputted into the model based on internal company projections. Table 3 shows the MSW fee range; also, note that as the number of facilities increase, competitiveness is expected to increase , and the fee paid by the municipality thus decreases. See for instance the probabilistic scenario for the first facility and the probability scenarios when the technology is fully mature with more than 15 plants built. Table 4 MSW fee. The table shows the evolution of the MSW fee variable. The columns show the ranges of the variables and probabilities depending on the number of plants that are built. A third key variable is the number of industrial plants that will ultimately use the technology. The potential market readout slowly grows from the start of the commercialization process up until the end of the 20 years period (ie, the obsolescence period) (Figure 4). The project pipeline is based on internal company projections. Each opportunity is defined for the Number of Facilities Constructed Critical Marlet Variables Probability Value Probability Value Probability Value Probability Value 0% 30 0% 30 2% 30 35% 30 0% 40 8% 40 17% 40 29% 40 12% 50 11% 50 21% 50 22% 50 20% 60 18% 60 23% 60 9% 60 50% 70 45% 70 24% 70 5% 70 18% 80 18% 80 13% 80 0% 80 2nd and 3rd Facility 4th to 15th Facility Facility >15 MSW fee (€/ton) 1st Facility Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 2: A research and technology valuation model for decision analysis in the environmental and renewable energy sectors 68 municipalities worldwide that have shown interest in building a facility with these characteristics. The projection is that with a 90% of the total probability the number of plants will be in the range between 14 and 23 facilities with a minimum of 9 and a maximum of 29 plants being built. Figure 4 Number of commercial opportunities in the next 20 years. The diagram shows the probabilities of plants of being built in the valuation period. Each project in the pipeline has a particular probability of occurrence. The sum up of each number of plants corresponds to 100% probability for the analysed variable To the above project variables, we shall ad the financial parameters to measure the project, which will be influenced by the maturity of the project. As explained in the previous section, the maturity of the technology is the factor that most influences the financial parameters used. The maturity of this project is considered to be ‘H2’ (as per McKinsey Methodology (Baghai, M., Coley, S., & White, 2000)) in terms of a R&D project, due to the fact that the technology has already been demonstrated at bench scale, but not at the commercial plant scale. The return rate required (IRR) for this kind of project ranges between 8% and 10%. For this specific project, a value of 8% has been chosen for the first two plants and a value of 10% has been chosen for subsequent plants. This was done to take into account the greater risks associated with the first plants to be developed, while ensuring that investor margins were not overvalued at early stages and to enable the technology to become commercial though requesting lower returns. Once all the variables were inputted and defined, the model provided a cash flow model per year from 2016 to 2029 for each project, including the option to consolidate all of the Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 2: A research and technology valuation model for decision analysis in the environmental and renewable energy sectors 69 projects. This last readout is critical, as it enables the user to identify the total amount of resources required and the cash recovery. Results The first set of results presented below is for the evaluation of a stand-alone plant. Subsequently, the evaluation of all R&D programs is shown, including the calculation of overall development cost. Construction of the first stand-alone facility Figure 5: IRR for the initial facility. The area covered by the figure corresponds to 100% probability for the analysed variable. IRR distribution for the first commercial plant built. With a probability of 43.3% the IRR of the plant will be above 8% and only in 4.7% of cases the IRR will be above 10%. The first analysis reveals that in approximately 45% of the simulated cases the IRR will be greater than 8%, which provides the go ahead for construction of the plant. While the model is useful for calculating the probability of positive returns, it can also be used to inform the project team about how to influence these forecasted results. To gain this insight, the user must identify which variables exert the greatest influence on the final IRR of the project. This can be achieved by carrying out correlation analysis, which reveals the relationship between IRR and key influencers. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 2: A research and technology valuation model for decision analysis in the environmental and renewable energy sectors 70 Figure 6: Sensibility analysis. The analysis of variable shows that the variable that influence the model the most is the MSW fee and CAPEX; whereas enzyme dosage, the ethanol prices and the raw materials have much lower effect. The first variable is a market input—specifically, it is the fee that is charged per ton of MSW treated. While the leadership overseeing R&D has limited ability to influence this variable, the risks associated with it must be carefully considered and be used to determine which markets are targeted. The second variable is CaPex (Figure 6). Typically, every R&D project that enters into the last phase of development must concentrate its efforts on improving process design to reduce the required investments since this variable has a clear negative effect (Figure 6) Yet another market variable is ethanol price. This must be carefully considered as it will greatly impact whether the project achieves commercial success. The fourth variable is the ‘potential to ethanol’, which is related to two factors: the quality of the starting raw materials, which varies according to geography (note: discuss this factor it is not further discussed because it does not influence model outcomes); and how the raw material is pretreated (a variable that the model shows directly affects economic returns). The last variable that was considered is enzyme dose. The model shows that this variable has a similar impact as the ‘potential to ethanol’ and also that reductions in the dose will positively impact outcomes (Figure 6). After this initial analyses, and by considering how the variables influence each other and the outcomes, the developers can then decide on which variables they want to focus. Construction of subsequent facilities After analyzing how R&D improvements differentially influence the IRR for the first facility, further analysis was conducted for the construction of subsequent facilities. In Figure 7, the change in IRR over time is shown; the graphic shows that the R&D and construction of new Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 2: A research and technology valuation model for decision analysis in the environmental and renewable energy sectors 71 plants improves the returns (i.e., from 7.8% to 8.35%) , until the number of facilities is above 15 when the return drops to 7% (Figure 7) and drops even further if the number of plants is above 15. it Figure 7: The evolution of IRR as the project matures. Red graphic: 1st plant to be built, with an expected mean return of 7.8%; Blue graphic: 2nd and 3rd plants with expected returns around 8.3%. Green graphic: 4th to 15th plants with an expected return of 7% and Purple line plants > 15. The area covered by the figures for each of the scenarios (1st plant, 2nd and 3rd plant, 4th to 15th plants and plants>15) corresponds to 100% probability for the analysed variable. While this seems counterintuitive, it is hypothesized that as the number of plants increase, the market becomes saturated, and consequently the fee per ton of MSW will decrease enough to offset any gains made due to technical improvements. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 2: A research and technology valuation model for decision analysis in the environmental and renewable energy sectors 72 Figure 8: Hypothesized effects of changes to MSW fees. Red graphic: 1st plant built; Blue graphic: 2nd and 3rd plants. Green graphic: 4th to 15th plants and Purple line plants>15. The area covered by the figures for each of the scenarios (1st plant, 2nd and 3rd plant, 4th to 15th plants and plants>15) corresponds to 100% probability for the analysed variable. Bearing this data in mind, it is key to determine the minimum fee per ton of MSW that the project requires to be viable, i.e., to establish the number of plants to be built. The model can quickly determine this by enabling one to run a simulation while varying the value of the MSW fee. In this particular case the minimum fee required for an IRR above 8% is 70.3€/ton. This is much higher than the €41.7 /ton that the model gives for the point at which more than 15 plants are constructed. Figure 8 clearly shows that for an expected IRR of 8% the MSW fee decreases as the number of plants increases. Thus, the model can identify key variables and threshold values that can help to reveal a clear path towards the development of competitive products. By accounting for how the technologies evolve over time, the model also enables companies to forecast key market values to can could inform the drafting of future agreements and strategies to adapt to competitive pressures. Program valuation The next step in the analysis is to determine the total program return, or the total value that will be created by the R&D investment. The model helps to weigh product pipelines and project developments against the total resources needed for product commercialization— key factors that can help managers proactively adjust resource allocations as the project advances. The model considers the fulfillment of the investment criteria for each commercial opportunity, and also the total program value creation. This latter value is determined by Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 2: A research and technology valuation model for decision analysis in the environmental and renewable energy sectors 73 calculating of NPV ranges to be created for investors, while considering the discount rate. As previously mentioned, this is a techno-economic valuation; thus, it is not purely a financial analysis and, along with profitability of the R&D project, the model also aims to identify project viability from a technical perspective. As such, the discount rate is calculated based on the maturity of the project and the number of resources allocated to R&D, similarly to how the methodology is used to calculate IRR. As an H2 project, the discount rate is 10%. For other stages, such as H1, the range is less, due to lower risks (i.e., 8-10%), while values increase for higher risk H3 projects (i.e., 12-15%). The timeline that the model considers is 15 years, which corresponds to the lifespan of the technology—a value that is determined by considering commercialization readiness, obsolescence period and the time span required to fully capture the full potential value created by the technology. The R&D investment period is set to 6 years from the date of valuation, and the intensity of the R&D investment, as expected, decreases from about 24 million € for the first year to about 3 million € for the third year and even lower for the sixth year (Figure 9). Figure 9: R&D Investment for the MSW program. Considering the characteristics chosen above, the value generated by the total program is shown in the Figure 10. The left-hand side of the graphic shows that the project has a 32% chance of causing net economic loss for the company, with potential losses reaching a maximum of €34M. However, the right-hand s of the figure shows that the project has a 25% chance of generating net positive economic returns valued greater than €100M (Figure 10). The spread of the results shown in Figure 10 is a consequence of the uncertainties and the risks involved in the project. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 2: A research and technology valuation model for decision analysis in the environmental and renewable energy sectors 80 Management , 48, 42–55. Fernandes, G., Perobelli, F.F.C., Brandaõ, L.E.T. (2016) A model for valuing new technologies under a pull incentives environment. Renewable and Sustainable Energy Reviews , 59, 482–493. Fernández Sánchez, E. (2005). Estrategia de innovación Fertig, E. (2018) Rare breakthroughs vs . incremental development in R & D strategy for an early-stage energy technology. Energy Policy , 123, 711–721. Gassmann, O., Zedtwitz, M. Von. (1998) Organization of industrial R & D on a global scale. R&D Management , 28, 147–161. _____. (1999) New concepts and trends in international R & D organization. Research Policy , 28, 231–250. Green, K., Mcmeekin, A., Irwin, A. (1994) Technological trajectories and R&D for environmental innovation in UK firms. Futures , 26, 1047–1059. Grimaldi, M., Cricelli, L., Rogo, F. (2017) Valuating and analyzing the patent portfolio : the patent portfolio value index portfolio. European Journal of Innovation Management , 2017. Guarascio, D., Pianta, M., Bogliacino, F. (2017) Export, R&D and New Products: A Model and a Test on European Industries In Foundations of Economic Change. s.l.: s.e.: pp. 393– 432. Guerras, L.A., Navas, J.E. (2015). Casos de Dirección Estratégica de la Empresa Halkos, G., Bousinakis, D. 2012. Importance and influence of organizational changes on companies and their employees. MPRA Paper, s.l. Hall, B.H.., Lerner, J. (2010) The Financing of R&D and Innovation In Handbook of the Economics of Innovation s.l.: s.e.: v.1: pp. 609–639. Hamel, G., Prahalad, C.K. (1991) Corporate imagination and expeditionary marketing. Harvard business review , 69, 81—92. Hartmann, M., Hassan, A. (2006) Application of real options analysis for pharmaceutical R&D project valuation-Empirical results from a survey. Research Policy , 35, 343–354. Hassanzadeh, F., Collan, M., Modarres, M. (2012) A practical R&D selection model using fuzzy pay-off method. International Journal of Advanced Manufacturing Technology , 58, 227–236. Hoisl, K., Gruber, M., Conti, A. (2017) R&D team diversity and performance in hypercompetitive environments. Strategic Management Journal , 36, 1455–1477. Holtbrügge, D., Schuster, T., & Wilbs, A. (2017) Restructuring and Organizational Change at Siemens. A Case Study In The Internationalization of Firms: Case Studies from the Nürnberg Metropolitan Region s.l.: s.e.: p. 115. Honoré, F., Munari, F., Van Pottelsberghe De La Potterie, B. (2015) Corporate governance practices and companies’ R&D intensity: Evidence from European countries. Research Policy , 44, 533–543. Hunger, J.D., Wheelen, T.L. (2003). Essentials of strategic management Valuation of Technology: Exploring a Practical Hybrid Model (2003, s.l.). 2003. Ed. Hunt, Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 2: A research and technology valuation model for decision analysis in the environmental and renewable energy sectors 81 F.H., Probert, D.R., Wong, J.C., Phaal, R. s.l., 47–53 p. Energy Technology Perspectives 2014 (2014, s.l.). 2014. Ed. IEA International Energy Agency. s.l., Jeon, C., Shin, J. (2014) Long-term renewable energy technology valuation using system dynamics and Monte Carlo simulation: Photovoltaic technology case. Energy , 66, 447– 457. Johnson, G., Scholes, K., Whittington., R. (2008). Exploring corporate strategy: text & cases. Johnson, G., Scholes, K., Whittington, R. (2006). Dirección estratégica Kahneman, D., Tversky, A. (2013) Handbook of the Fundamentals of financial decision making In Chapter 6: Prospect theory: an analysis of decision under risk. s.l.: s.e.: pp. 99– 127. Kjaerland, F. (2007) A real option analysis of investments in hydropower-The case of Norway. Energy Policy , 35, 5901–5908. Kodama, M. (2003) Strategic Innovation in Traditional Big Business : Case Studies of Two Japanese. Organization Studies , 24, 235–268. Lambert, M., Moreno, M., Platania, F. (2015) Real options valuation under uncertainty. SSRN 2647293 , 2015. Lee, S.-C. (2011) Using real option analysis for highly uncertain technology investments: The case of wind energy technology. Renewable and Sustainable Energy Reviews , 15, 4443–4450. Lerner, J., Wulf, J. (2007) Innovation and Incentives: Evidence from Corporate R&D (En no seguir está basada con como se paga a los ejecutivos). Review of Economics and Statistics , 89, 634–644. Lint, O., Pennings, E. (1998) R&D as an option on market introduction. R&D Management , 28, 279–287. Linton, J.D., Walsh, S.T., Morabito, J. (2002) Analysis, ranking and selection of R & D projects in a portfolio. R and D Management , 32, 139–148. Liu, Y. (2011) Valuation of R & D Projects. Asia Pacific Management Review , 16, 289–304. Loch, C.H., Bode-Greuel, K. (2001) Evaluating growth options as sources of value for pharmaceutical research projects. R & D Management , 31, 231–248. Luo, L., Sheu, H., Hu, Y. (2008) Evaluating R&D projects with hedging behavior. Research- Technology Management , 51, 51–57. Managi, S., Zhang, Z., Horie, S. (2016) A real options approach to environmental R&D project evaluation. Environmental Economics and Policy Studies , 18, 359–394. Marafon, A.D., Leonardo, E., Lacerda, R.T. de O., Ensslin, S.R. (2015) The effectiveness of multi-criteria decision aid methodology A case study of R&D management. European Journal of Innovation Management , 18, 86–109. Martín-Barrera, G., Zamora-Ramírez, C., González-González, J.M. (2016) Application of real options valuation for analysing the impact of public R & D fi nancing on renewable energy projects : A company s perspective (En paper de valoraciones que me puede servir Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 2: A research and technology valuation model for decision analysis in the environmental and renewable energy sectors 82 como modelo para escribir mi paper). Renewable and Sustainable Energy Reviews , 62, 1– 10. Martinez, M.G., Zouaghi, F., Marco, T.G. (2017) Diversity is strategy : the effect of R & D team diversity on innovative performance. R&D Management , 47, 311–329. Mascareñas, J. (2007) Opciones reales en la valoración de proyectos de inversión. Monografías de Juan Mascareñas sobre Finanzas Corporativas. Universidad Computlense de Madrid. España. , 2007. Medcof, J.W., Lee, T. (2017) The effects of the chief technology officer and firm and industry R & D intensity on organizational performance. R & D Management , 47, 767– 781. Meer-Kooistra, J. van der;, Scapens, R.W. (2008) The governance of lateral relations between and within organisations. Management Accounting Research , 19, 365–384. Menegaki, A. (2008) Valuation for renewable energy: A comparative review. Renewable and Sustainable Energy Reviews , 12, 2422–2437. Moehrle, M.G., Walter, L. (2008) Risk and uncertainty in R & D management. R&D Management , 38, 449–451. Myers, S.C. (1984) Finance Theory and Financial Strategy. Interfaces , 14, 126137. Nieto, M. (2003) From R & D management to knowledge management An overview of studies of innovation management. Technological Forecasting & Social Change , 70, 135– 161. Nobel, R., Birkinshaw, J. (1998) Innovation in multinational corporations: control and communication patterns in international R&D operations. Strategic Management Journal , 19, 479–496. Oreskovich, A.-M., Gittins, J. (2016) The evaluation of risk in pharmaceutical research: a study of current models and techniques. R&D Management , 46, 900–913. Oriani, R., Sobrero, M. (2008) Uncertainty and the market valuation of R&D within a real options logic. Strategic Management Journal , 29, 343–361. Otley, D.., Beery, J. (1994) Case Study Research in management accounting and Control. Management Accounting Reserach , 5, 45–65. Perdue, R.K., Mcallister, W.J., King, P. V, Berkey, B.G., King, P. V, Berkey, B.G. (1999) Valuation of R and D Projects Using Options Pricing and Decision Analysis Models. Interfaces , 29, 57–74. Perlitz, M., Peske, T., Schrank, R. (1999) Real options valuation: The new frontier in R&D project evaluation? R&D Management , 29, 255–269. Pettigrew, A.M. (1992) The character and significance of strategy process research. Strategic Management Journal , 13, 5–16. Pettigrew, A.M., Woodman, R.W., Cameron, K.S. (2001) Studying organizational change and development: Challenges for future research. Academy of Management Journal , 44, 697–713. Poh, K. l., Ang, B. w., Bai, F. (2001) A comparative analysis of R&D project evaluation methods. R&D Management , 31, 63–75. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 2: A research and technology valuation model for decision analysis in the environmental and renewable energy sectors 83 Raisch, S., Birkinshaw, J., Probst, G., Tushman, M.L. (2009) Organizational Ambidexterity : Balancing Exploitation and Exploration for Sustained Performance. Organization Science , 20, 685–695. Raynor, M.E., Leroux, X. (2004) Strategic Flexibility in R & D. Research Technology Management , 47, 27–32. Reyck, B. De, Degraeve, Z., Vandenborre, R. (2008) Project options valuation with net present value and decision tree analysis. European Journal of Operational Research , 184, 341–355. Rod Coombs. (1996) Core competencies and the strategic management of R&D. R&D Management , 26, 345–355. Romanelli, E., Tushman, M.L. (1994) Organizational Transformation as Punctuated Equilibrium: An Empirical Test. Academy of Management Journal , 37, 1141–1166. Overview of Evaluation Methods for R & D Programs (2007, s.l.). 2007. Ed. Ruegg, R. s.l., Salimi, N., Rezaei, J. (2018) Evaluating firms ’ R & D performance using best worst method. Evaluation and Programm Planning , 66, 147–155. Santos, L., Soares, I., Mendes, C., Ferreira, P. (2014) Real Options versus Traditional Methods to assess Renewable Energy Projects. Renewable Energy , 2014. Scarlat, N., Motola, V., Dallemand, J.F., Monforti-Ferrario, F., Mofor, L. (2015) Evaluation of energy potential of Municipal Solid Waste from African urban areas. Renewable and Sustainable Energy Reviews , 50, 1269–1286. Schachter, J.A., Mancarella, P. (2016) A critical review of Real Options thinking for valuing investment flexibility in Smart Grids and low carbon energy systems. Renewable and Sustainable Energy Reviews , 56, 261–271. Shavinina, L. V. (2003). The International Handbook on Innovation Siddiqui, A.S., Marnay, C., Wiser, R.H. (2007) Real options valuation of US federal renewable energy research, development, demonstration, and deployment. Energy Policy , 35, 265–279. Simons, R. (1994) How new top managers use control systems as levers of strategic renewal. Strategic Management Journal , 15, 169–189. Smith, R.D. (2003) The chief technology officer: strategic responsibilities and relationships. Research Technology Management , 46, 28–36. Smith, R.D., Smith, R.D. (2003) The chief technology officer: strategic responsibilities and relationships. Research Technology Management , 46, 28–36. Souder, W.E. (1972) Comparative Analysis of R&D Investment Models. A I I E Transactions , 4, 57–64. Stawasz, E., Stos, D. (2016) " A method of simplified assessment of the economic potential of R&D projects ". In s.l.: s.e.: pp. 29–46., Págs. 29–46. Tidd, J. (2018) Innovation management challenges: from fads to fundamentals. International Journal of Innovation Management , 22, . Tirpak, T.M., Miller, R., Schwartz, L., Kashdan, D. (2015) R&D Structure in a changin world. Research-Technology Management , 45, 19–26. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 2: A research and technology valuation model for decision analysis in the environmental and renewable energy sectors 84 Tohidi, H., Jabbari, M.M. (2012) Innovation as a Success Key for Organizations. Procedia Technology , 1, 560–564. Tompkins, R.G. (2002) Static versus Dynamic Hedging of Exotic Options: An Evaluation of Hedge Performance via Simulation. The Journal of Risk Finance , 3, 6–34. Trigeorgis, L. (1997) Real Options: Managerial Flexibility and Strategy in Resource Allocation. Journal of Banking & Finance , 21, 285–288. Tushman, M.L., Romanelli, E. (1985) Organizational evolution: A metamorphosis model of convergence and reorientation. Research in Organizational Behavior , 7, 171–222. Valdivia, M., Galan, J.L., Laffarga, J., Ramos, J.L. (2016) Biofuels 2020: Biorefineries based on lignocellulosic materials. Microbial Biotechnology , 9, 585–594. Vega-Gonzalez, L.R., Rivera-Velasco, G. (2016) Methodology for Rapid Technology Valuation with Restricted Incomes. Business and Economic Research , 6, 175–191. Van de Ven, A.H. (1992) Suggestions for studying strategy process: A research note. Strategic Management Journal , 13, 169–188. Van de Ven, A.H., Poole, M.S. (2005) Alternative approaches for studying organizational change. Organization Studies , 26, 1377–1404. Virany, B., Tushman, M.L., Romanelli, E. (1992) Executive Succession and Organization Outcomes in Turbulent Environments: An Organization Learning Approach. Organization Science , 3, 72–91. Wang, A., Halal, W. (2010) Comparison of Real Asset Valuation Models: A Literature Review. International Journal of Business and Management , 5, 14–24. Whittington, R., Johnson, G., Scholes, K., Angwin, D., Regnér, P. (2017). Exploring Strategy Willigers, B.J.A., Hansen, T.L. (2008) Project valuation in the pharmaceutical industry : a comparison of least-squares Monte Carlo real option valuation and conventional approaches. R & D Management , 38, 520–537. Wolff, M.F. (2007) Forget R&D Spending – think innovation (En tengo que buscar artículo orignal). Research Technology Management , 50, 7–9. Wouters, M., Roorda, B., Gal, R. (2011) Managing Uncertainty During R&d Projects: A Case Study. Research-Technology Management , 54, 37–46. Case study research: Design and methods (2nd ed.) (1994, Beverly Hills, CA). 1994. Ed. Yin, R. Beverly Hills, CA, Yongtae, P., Seonwoo, K. (2005) Linkage between knowledge management and R&D management. Journal of Knowledge Management , 9, 34–44. Zainal, Z. (2007) Case study as a research method. Jurnal Kemanusiaan , 5, . Zhang, J., Baden-fuller, C., Mangematin, V. (2007) Technological knowledge base , R&D organization structure and alliance formation : Evidence from the biopharmaceutical industry. Research Policy , 36, 515–528. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 85 Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 86 Chapter 3: Biofuels 2020: Biorefineries based on lignocellulosic materials. 3 3 Valdivia, M., Galan, J. L., Laffarga, J., & Ramos, J. L. (2016). Biofuels 2020: biorefineries based on lignocellulosic materials. Microbial biotechnology, 9(5), 585-594. Impact factor JCR: 4,86 Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 87 Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 3: Biofuels 2020: Biorefineries based on lignocellulosic materials 88 Abstract The production of liquid biofuels to blend with gasoline is of worldwide importance to secure the energy supply while reducing the use of fossil fuels, supporting the development of rural technology with knowledge-based jobs and mitigating greenhouse gas emissions. Today, engineering for plant construction is accessible and new processes using agricultural residues and municipal solid wastes have reached a good degree of maturity and high conversion yields (almost 90% of polysaccharides are converted into monosaccharides ready for fermentation). For the complete success of the second generation (2G) technology it is still necessary to overcome a number of limitations that prevent a first-of-a-kind plant from operating at nominal capacity. We also claim that the triumph of 2G technology requires the development of favorable logistics to guarantee biomass supply and make all actors (farmers, investors, industrial entrepreneurs, government, others) aware that success relies on agreement advances. The growth of ethanol production for 2020 seems to be secured with a number of 2G plants, but public/private investments are still necessary to enable 2G technology to move on ahead from its very early stages to a more mature consolidated technology. Key Words R&D Management, 2G Biofuels, Market Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 3: Biofuels 2020: Biorefineries based on lignocellulosic materials 89 1. Introduction Biofuels produced from crops have been the driving force in renewable energies. In the first decade of the 21st century, there was a major focus on the debate of food vs. fuel. Reports made by national and international agencies, such as OCDE (OCDE, 2008), FAO, EU, and others concluded that the food commodity prices were being impacted by consumption for the production of biofuels. Other slightly later reports (Mohr & Raman, 2013) studied the sustainability of 1G ethanol production and the implications of alternative feedstocks. Influenced by the global debate, policies were implemented to promote the production of liquid biofuels from feedstocks not used for human consumption, and give rise to what is called, second generation biofuels (2G). Lignocellulosic material, from herbaceous crops, hardwood and softwood, are the main feedstocks used for the production of liquid biofuels, particularly ethanol. The main drivers behind a push toward both 1G and 2G biofuel production, are (i) energy supply security and reduction in dependency on oil imports, (ii) support for rural areas through technology deployment and creation of knowledge-based jobs and (iii) mitigation of the greenhouse gas emission (GHG), and the reduction in emissions of particulate matter that are toxic for the environment, animals and humans —promoting a low-carbon and sustainable economy. Over the last decade, research in the 2G market has been searching for a significant breakthrough that will lead to it being cost-competitive with first generation biofuels. Unfortunately, the development of the lignocellulosic ethanol market has been slower than expected due to a perception of high technological risk, intensive capital costs and the low oil prices that result in poor economics for the biorefineries (Stephnen et al., 2012). An intensive growth period (2014-2020) has been forecast and production capacity is expected to reach 2220 Million litres by 2020, from a capacity of 750 Million liters in 2014 (United Nations, 2016). Other organizations, are not as optimistic but also estimate growth, for example, the OECD-FAO (Food and Agriculture Organization of the United Nations) estimate a total capacity of 1703 Million liters for second generation ethanol by 2024, mainly in USA and Europe (UNCTAD, 2015). In spite of discrepancies in the forecast figures for 2G ethanol, the general feeling is clear, there will be very significant growth over the next decade. Some studies have estimated the value that can be generated by the lignocellulosic industry, for example, (Hertel et al., 2015) give a value to second generation industry of $64 billion under baseline conditions. Apart from the direct evolution of the lignocellulosic industry, there are other external factors that have a clear impact on the future of 2G production, in particular oil prices; for lignocellulosic biofuels to be cost-competitive an oil price in the range of $70-85/barrel is required (Sims et al.,2009). A focused strategy to battle climate change through regulation will strengthen the stance of alternative technologies and secure the second generation biofuel industry. In conjunction with climate change mitigation strategies, new technologies also require a boost to allow the development of new products. Some steps have been made globally in this regard and the USA is currently the most advanced market owing to a stable legal framework. The EU is a step behind due to social concerns and the lack of a common Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 3: Biofuels 2020: Biorefineries based on lignocellulosic materials 96 The facility cost should take into account both, the capital cost needed per gallon (Capex) and the cost to operate the plant (Opex). Both are influenced by the kind of project. There are three main types of projects: - Greenfield/ standalone: this is a plant by itself. It needs a complete value chain management. Its location influences the Capex and also the logistics of biomass supply and ethanol sales. In terms of Capex, it needs the construction of the core process, biomass handling, pretreatment, enzymatic hydrolysis, fermentation and distillation; and also auxiliary operation units such as cogeneration and a waste water treatment plant; these usually significantly increase the overall cost. - Co-location/ Bolt-on: in this concept some existing infrastructures and operations can be shared thanks to the proximity of other industries. This model requires at a minimum the construction of the process area. The value chain for feedstock procurement and logistic must be completely developed. The construction of auxiliary operations units is limited. - Hybrid /integrated: The whole value chain is completely integrated within a 1G facility, taking advantage of the synergies in feedstock supply and product logistics. The construction of auxiliary operation units is not necessary. The aim of the two last configurations is to decrease the cost of the auxiliary or complementary operation units and focus the efforts on reducing the cost of the technological core areas. For example, for a green field project the cogeneration area may represent up to 30-35% of the total equipment costs, as such choosing a location where the energy instead of being produced on site, could be bought at regular prices from close industries or facilities represents a significant advantage. Some other issues, such as cake and syrup destination need, however, to be solved. After feedstock availability, reducing the investment cost through the reduction of the auxiliary equipment is the second step toward making a commercially viable project. The challenge, to the industry, is to be as flexible as possible to increase the potential locations where to place viable technical and economical projects. There are a range of alternatives, outside improving the logistic model, where the industry is working to reduce operational capital costs. These include enzyme cost reduction by improving activity, valorization of the lignin contained in the raw material, increasing the pretreatment efficiency or improving the yeast production organism. Cost structure The location of a plant will definitively influence the overall facility layout. Investment costs will differ significantly depending on the configuration required. For a green field standalone project, a non-core area such as cogeneration increases the initial investment needed by more than 30% (Figure 4). Absence of synergies with external utilities concomitantly results in an increase in storage equipment and logistical costs. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 3: Biofuels 2020: Biorefineries based on lignocellulosic materials 97 Figure 8 Equipment cost area distribution of a general lignocellulosic greenfield facility. Pretreatment and Boiler are the most cost intensive areas. Source: Abengoa A co-located plant may require less than 50% in capital investment than a stand-alone plant. Within this strategy, Poet-DSM has co-located a 2G facility within a 1G plant. Our estimates are that investment cost ranges between $10 - 14/gal, for a plant with a nominal plate of 25 Mgal according to its location. 5. Biomass handling & Pretreatment As described above, the first step in a lignocellulosic facility is to prepare the biomass for processing. This is critical because it influences all of the downstream processing. We can distinguish two main steps, one where biomass is just pre-processed to obtain the designed PSD and, in the subsequent step, the biomass is pretreated in order to make hemicellulose and cellulose accessible for cellulases. For the biomass handling area, the main objective is an appropriate delivery of milled feedstock to the pretreatment system with a consistent quality that meets the required specifications in terms of particle size and total ash content. As mentioned in the feedstock section, the logistics model is critical to obtain a homogenous stream at the beginning of the process. No special innovations have been made in this area, but some experts are starting to develop different methodologies that will improve the facility performance and also the integrated logistic model. Once the biomass has gone through the handling system, the next step is the pre-treatment that will release the oligomers to be transformed into sugars. This is a critical step in lignocellulosic biofuel production. Each market player has developed their own technology Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 3: Biofuels 2020: Biorefineries based on lignocellulosic materials 98 as the feedstock defines the pretreatment technology and conditions. One of the main hurdles that the industry faces is that the pretreatment needs to be optimized for each raw material, limiting the flexibility of the plant to process different feedstocks. That is why more versatile pre-treatments are required so as the process becomes less raw-material dependent. Pretreatment is the “core” area that is most capital intensive, requiring an investment that may represent between 30% and 50% of the total equipment cost. One of the main challenges is the intrinsic recalcitrance of the lignocellulosic biomass which results in lower biomass to sugar yields and therefore in the higher pretreatment costs (Stephen et al., 2012). A number of technologies are available today for the pretreatment of lignocellulose, including, chemical, physical and biological processes. Some of these technologies have already been commercialized and are well known, while others are still at lab scale. The most relevant commercial technologies are given in Table 2: Table 7 Pretreatment technologies. The 2G commercial technologies are protected by a number of patents that guard the technology while the economic viability of the projects are improved. This is why different pretreatment have been considered. The different strategies result in a series of advantages/disadvantages that are enumerated in Table 2. Steam explosion is a well-known advanced technology which consists of heating the biomass in water under pressure followed by a sudden decompression of the reaction vessel. As a result of the violent decompression, the structure of lignocellulose is disrupted and the fibers are opened up, leaving sugar polymers more accessible to the subsequent enzymatic hydrolysis (Stelte, 2013). As no chemicals (other than water) are used, equipment corrosion is minimal and requirements for the reactor metallurgy are less demanding. Also, the level of release of chemicals that may act as inhibitors in the saccharification or fermentation steps is very low. The main drawbacks of steam explosion are related to the mildness of the process which limits the effectiveness of the pretreatment and demands the use of very high Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 3: Biofuels 2020: Biorefineries based on lignocellulosic materials 99 enzyme loads in the saccharification step. Also, steam explosion is not effective on high lignin content softwood samples, and the effectiveness with hardwood is also limited (Brownell et al, 1986, Yang & Wyman., 2008). Another option is the use of dilute acid in the pretreatment, this involves the use of dilute aqueous solutions of inorganic acid (HCl, H2SO4) combined with temperature. This pretreatment results in good depolymerization and release of hemicellulose and cellulose. Compared to steam explosion, dilute acid pretreatment is more efficient for woody samples. As acidic conditions allow partial depolymerization of hemicellulose and cellulose, the enzyme loading required is lower compared to simple steam explosion. However, this kind of pre-treatment requires high capital investment due to the special reactor metallurgy; operational costs are also higher. It is well known that aqueous ammonia treatment allows biomass delignification without a significant degradation of sugars. However, the effectiveness of this pretreatment with some feedstock, such as woody biomass residues is rather limited (Yang & Wyman., 2008). Commercial pretreatments for corn stover, wheat straw and sugar cane straw are currently being optimized, however, one of the main challenges that the sector must overcome in the coming years is to enhance process versatility to be able to deal with more than one raw material at a time. In terms of operational cost, pretreatment consumes chemicals and steam, this can represent up to 20-25% of the total operational costs. Lignin removal is another key step in the development of the biofuel industry; in the current methodology, lignin is maintained until the distillation phase. However, there are several pretreatment technologies in development that try to separate the components of the biomass in different streams, one of the most promising is the use of Ionic Liquids (ILs) which are able to dissolve lignocellulose under mild conditions, resulting in more accessible cellulose and recovery of lignin in the raw material. Nevertheless, there are still challenges to the industrial deployment of this technology, including high cost and regeneration of ILs (Tadesse & Luque, 2011). Searching for other alternatives may reduce costs and increase the possibility of using lignin in new ways that are not currently used to add value. Further discussion on the lignin issue follows below. In the first commercial plants, one of the main bottlenecks in the commissioning and startup phase was achieving suitable performance in the pretreatment area. Problems such as blockage due to non-defined particle size distribution, not achieving the pretreated biomass specifications, and not being able to reach high enough downstream have all been major hiccups. The main challenges in pretreatment are: development of a versatile technology that can reach appropriated levels of pretreated biomass material independently of the raw material used; generation of combination plants that can more easily process under milder conditions to allow optimization both economically and environmentally; to optimize pre-treatment processes using less corrosive chemicals making construction materials cheaper and, as consequence reduce the initial investment (in particular, steel alloys resistant to acid or base). Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 3: Biofuels 2020: Biorefineries based on lignocellulosic materials 100 6. Enzymatic hydrolysis After the material is conditioned in the pretreatment area, the next step, the enzymatic hydrolysis or saccharification is one of the most critical factors in lignocellulosic biofuel production, and represents one of the main technology development areas. Enzymatic hydrolysis represents the second main operational cost, after the biomass production; in 2G it is ~25-30% of the operational costs while in 1G it is below 3%. Why this difference? The answer is the enzymatic cocktail needed for 2G. These cocktails have a combination of a wide range of activities and properties, such as cellulases, hemicellulases and ß-glucosidases that convert the polysaccharides into C6 and C5 sugars (Alvarez et al. 2015). Most of the enzymes used in the conversion to sugars are of microbial origin, but have been ‘hitched’ to other than their native host, that is, they area engineered. Today only about three companies have commercialized this kind of cocktail: Novozymes, Dupont and Abengoa. In the market there is a consensus that the final enzyme cost contribution should be stabilized around $0.4/gal. Nowadays, this cost contribution is achieved at least at a demo scale by the enzymatic cocktail developed by Abengoa, although the performance on a regular basis at commercial scale is still a pending issue. Reported commercial data indicate that the three commercial cocktails may operate within the same range. Reducing the enzymatic cocktail cost contribution is critical for the viability of the 2G technology because the cost of enzymes can be up to 30%. How can these cocktails be improved? There are several possibilities, (i) reduce the enzyme loading by improving the enzyme activity through genetic engineering using high performance mutagenesis strategies coupled with massive tracking systems, (ii) Reduce the cost of protein through better production methodologies, (iii) Increase the overall hydrolysis yield by adapting the enzyme cocktail performance to the process conditions. For the last one the exploration of extreme environments and the use of metagenomic techniques that allow the identification of new enzymes that are more efficient is a critical focus (Elleuche et al, 2014). The most relevant aspect is the use of thermophilic enzymes because the increase of temperature prevents hydrolysates from being contaminated and, therefore, the loss of raw material; this increases the overall performance of the process. Another factor is that the total solid ratios at which the technology can work is relatively low compared with 1G, this is due to the lower yield per tonne of raw material introduced and the longer residence times needed. In 2G the typical solid ratio is 20% versus 33% for 1G. Although the enzymes would work even better at lower solid concentration, this is the minimum ratio at which ethanol concentrations are reached. One of the main improvements that can be made is to increase the performance of the enzyme cocktail at higher total solid, or at least reaching optimum performance at current levels. As noted above, the 2G enzyme cocktail seems to be a niche market with very few players. A significant increase in the volume of 2G enzyme market is required in order to provide producers with the equity to optimize their cost structure. Enzyme producers need to be involved in the industrial development process if they want 2G facilities to achieve their nominal capacity. If the market is converted into an oligopoly with few competitors gaining most of the value from the 2G technology, the development of the industry globally will Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 3: Biofuels 2020: Biorefineries based on lignocellulosic materials 101 become difficult. Enzyme developers need to be involved in the industry growth, participating in different ways with, not only the industrial developers, but also with other stakeholders. 7. Fermentation After the enzymatic hydrolysis, a sugar stream is obtained that differs from the first generation sugars in that the amount of C5 sugars represents almost 30% of total sugars, C5 sugars are released from the hemicellulose with xylose. The current fermentation technology produces ethanol, although other alternative alcohols or bioproducts can be synthesized e.g., alkanes or long chain alcohols, butanol, jet-fuels, etc. The fermentation of glucose and xylose in the same reactor can be considered a well developed technology, with conversion yields of more than 95%. Very efficient yeasts have been designed and optimized to ferment xylose and glucose simultaneously. While this is true with herbaceous lignocellulosic material, when the raw material comes from wood, a series of inhibitors are generated in the pre-treatment that make the fermentation processes less efficient (Heer & Saner 2008, Tomás-Pejó and Olsson, 2015). Another very important aspect is to shorten the fermentation time; this could lead to a reduction in the number of fermenters per plant with a consequent saving in the initial investment. In addition, it should be possible to work at different pHs or temperatures, to improve the propagation phase. In this area, unlike at the enzyme market, there is no risk of an oligopoly which could cap the market and there is still room for improvement in terms of operational conditions to decrease the overall costs. The processing of C5 is a must. It is not economically viable to process lignocellulosic biomass without getting any value from the hemicellulose or the lignin contained in the raw material. Several production platforms, using bacteria or yeast, are under investigation but none of them have as yet reached high enough productivity. Some experts predict there will one day be a lignocellulosic sugar market, but today’s oil prices make it non-competitive, except for in some niche cases where the upstream product is produced outside of the sugar production facility. This is the reason why so many joint ventures and co-ops have arisen in this market in recent years. The industry must look for win - win agreements. This will include on one hand, the 2G technologists or the companies that have the necessary technology to reach affordable lignocellulosic sugars and on the other hand, the final product companies (chemical companies) that can benefit from green sugars and alcohols. Clearly the final market needs will accelerate the product development based on the specification requirements and continuous collaboration between technologists and final clients. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 3: Biofuels 2020: Biorefineries based on lignocellulosic materials 102 8. Distillation, Solid&Liquid Separation and Evaporation The final step in reaching the product is the distillation of the stream, which comes out from the fermenters. The distillation of ethanol, if the stream is produced on specification, is an area that does not present major issues. It represents between 15-20% of total equipment costs. A critical factor in this step is the use of the stillage produced during the distillation. To have an economically feasible technology, the valorization of this stream product is mandatory. The stillage has a high concentration of lignin, 20-30% depending on raw material source. At present, in commercial plants, the stillage, following separation of the water content, is sent to the cogeneration area to be burned to provide power vapor to the 2G process. This waste is currently burned in cogeneration plants where the value of the generated energy is $5-10 US / ton. To increase the profitability of the plants of lignocellulosic material it is necessary to add value to the stillage. In the configurations where there is not a cogeneration area within the plant or nearby, the lignin stream is used for irrigation in areas close to the facility. The main problem is the large volume produced. For example, a 25 Mgal plant, produces around 300,000 ton/day of stillage. The other option is to send it to a wastewater treatment plant, if the configuration has it, or send it to a disposal agency; both of which represent a significant extra cost for the plant (Ramos et al., 2016). So, to increase the value obtained for this co-product, one of the main challenges of this industry is to valorize the stillage as was done with fiber in the first generation industry. In the process of ethanol generation from corn or other cereal grains, the economy of the process rests not only in the sale of alcohol but also in the grain residue that is left, which is known as DGG and is used as animal feed (Patrik R. Lennartsson, 2014). As has been stated, the stillage contains up to 30% lignin within its composition coming from the lignocellulosic biomass. Lignin is a natural polymer with excellent properties that can be used in the chemical industry. A number of reviews on lignin valorization are available (Rasgauskas et al. 2014), but there are few success cases that have been implemented. A difference that 2G lignin has compared to commercial preparations, principally those which come from the pulp and paper industry, is the absence of sulphur; this is a great advantage for its use in some chemical applications such as resins or composites. Most 2G technologists are struggling with themselves on how to increase lignin value. The current proposal is to extract lignin from this waste to be used in the synthesis of new resins to replace those based on chemical compounds derived from the petrochemical industry. Once the technology has been fully developed and the main challenges resolved the question is: Would the market be ready for this new product? 9. Concluding remarks Production of liquid biofuels from grains in the so-called 1G generation is a mature technology that seems to have plateaus in technology and production capacity. An increase in the production of ethanol is foreseen to be the result of the conversion of agricultural wastes and municipal solid residues into ethanol using the so-called 2G technology. A number of 2G plants have been built, but their operability at full nominal capacity has not Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 3: Biofuels 2020: Biorefineries based on lignocellulosic materials 103 been attained so far due to mechanical hurdles. The identification of these limitations, their potential solutions and combined public/private investment will be crucial to enable 2G technology to move on ahead from its very early stages to a more mature consolidated technology. International expert agencies believe that an estimate of 15 2G plants will be built by 2024. In our view, we also foresee that ethanol will not be the only product to be derived from polysaccharides originated from agricultural residues and wastes, but also another more potent liquid biofuel, such as butanol, is making symbiotic biofuels part of this new market. Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 3: Biofuels 2020: Biorefineries based on lignocellulosic materials 104 References Álvarez C., Reyes-Sosa F. and Diez B. (2016) Enzymatic hydrolysis of biomass from wood. Microbial Biotechnology 9, 149–156 Boland. S. and Unnasch. S. (2015) GHG Reductions from the RFS2. Life Cycle Associates Report LCA.6075.116.2015, Prepared for Renewable Fuels Association. Brownell H.H., Yu E.K.C. and Saddler N. (1986) Steam-explosion pretreatment of wood: Effect of chip size, acid, moisture content and pressure drop. Biotechnology and Bioengineering, 28, 792-801 Elleuche S., Schröeder C., Sahm K. and Antranikian G. (2014) Extremozymes — biocatalysts with unique properties from extremophilic microorganisms. Current Opinion in Biotechnology, 29:116–123 Heer D. and Sauer U. (2008). Identification of furfural as a key toxin in lignocellulosic hydrolysates and evolution of a tolerant yeast strain. Microbial Biotechnology 1: 497–506 Hertel T.W., Steinbuks J. and Tyner W.E. (2015) What Is the Social Value of Second Generation Biofuels? Applied Economic Perspectives and Policy 0: 1–19. Mohr A. and Raman S. (2013) Lessons from first generation biofuels and implications for the sustainability appraisal of second generation biofuels. Energy Policy 63: 114-122 Lamers P., Tan E., Searcy E., Scarlata C., Cafferty K. and Jacobson J., (2015) Strategic supply system design- a holistic evaluation of operational and production cost for a biorefinery supply chain. Biofuel, Bioprod. Bioref. 9:648–660 OECD. (2008), Biofuel Support Policies: An Economic Assessment , OECD Publishing, Paris. DOI: http://dx.doi.org/10.1787/9789264050112-en OECD/Food and Agriculture Organization of the United Nations (2015), OECD-FAO Agricultural Outlook 2015, OECD Publishing, Paris. http://dx.doi.org/10.1787/agr_outlook- 2015-en Ragauskas A., Beckham G., Biddy M., Chandra R., Chen F., Davis M., et al. (2014) Lignin Valorization: Improving Lignin Processing in the Biorefinery. Science 344, 6185 Ramos JL, Valdivia M., Garcia-Lorente F., Segura A. (2016) Benefits and perspectives on the use of biofuels. Microbial Biotechnology 9, 436–440 Sanford K., Chotani G., Danielson N. and Zahn J. (2016) Scaling up of renewable chemicals. Current Opinion in Biotechnology 38:112–122 Sims R.E.H., Mabee W., Saddler J. and Taylor M. (2009) An overview of second generation biofuel technologies 101: 1570-1580 Somerville C., Youngs H., Taylor C., Davis S. and Long S. (2010) Feedstocks for Lignocellulosic Biofuels 329: 790 Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 Chapter 3: Biofuels 2020: Biorefineries based on lignocellulosic materials 105 Stelte, W. (2013). Steam explosion for biomass pre-treatment. Danish Technological Institute. Stephen J., Mabee W.E. and Saddler J.N. (2012) Will second-generation ethanol be able to compete with first-generation ethanol? Opportunities for cost reduction. Biofuels, Bioprod. Bioref. 6:159–176 Tadesse H and Luque R. (2011) Advances on biomass pretreatment using ionic liquids: An overview. Energy Environ. Sci. 4: 3913-3929 Tolonen A., Zuroff T., Ramya M., Boutard M., Cerisy T. and Curtis W. (2015) Physiology, genomics, and pathway engineering of an ethanol-tolerant strain of Clostridium phytofermentans . Appl Environ Microbiol 81:5440 –5448. Tomás-Pejó E. and Olsson L. (2015) Influence of the propagation strategy for obtaining robust Saccharomyces cerevisiae cells that efficiently co-ferment xylose and glucose in lignocellulosic hydrolysates. ) Microbial Biotechnology 8: 999–1005 UNCTAD (2015) Second-Generation Biofuel Markets: State of Play, Trade and Developing Country Perspectives (UNCTAD/DITC/TED/2015/8) U.S. Department of Energy. 2011. U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and Bioproducts Industry. R.D. Perlack and B.J. Stokes (Leads), ORNL/TM-2011/224. Oak Ridge National Laboratory, Oak Ridge, TN. 227p. Yang B. and Wyman C., (2008)Pretreatment: the key to unlocking low-cost cellulosic ethanol. Biofuels, Bioprod. Bioref. 2:26–40 Código seguro de Verificación : GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 | Puede verificar la integridad de este documento en la siguiente dirección : https://sede.administracionespublicas.gob.es/valida ÁMBITOPREFIJO CSV FECHA Y HORA DEL DOCUMENTO GEISER GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14 29/04/2020 10:39:38 Horario peninsular Nº registro DIRECCIÓN DE VALIDACIÓN Validez del documento O00008744e2000018458 https://sede.administracionespublicas.gob.es/valida Copia GEISER-cfcf-4858-20cc-4e22-9f7b-a89e-aaa8-ba14