A roadmap for managing an ageing workforce in the manufacturing sector: An Italian case study

CASE STUDY A roadmap for managing an ageing workforce in the manufacturing sector: An Italian case study [version 1; peer review: 2 approved] Niloofar Katiraee1, Giulia Caprari1, Ajay Das2, Daria Battini1 1Universita degli Studi di Padova Dipartimento di Tecnica e Gestione dei Sistemi Industriali, Vicenza, 36100, Italy 2Baruch College Zicklin School of Business, New York, USA First published: 26 Aug 2025, 5:253 https://doi.org/10.12688/openreseurope.20511.1 Latest published: 26 Aug 2025, 5:253 https://doi.org/10.12688/openreseurope.20511.1 v1 Abstract Background Managing an aging workforce presents increasing challenges across industries, particularly in physically demanding sectors such as manufacturing. Key issues include knowledge transfer, worker retention, and maintaining productivity in operational environments that strain older workers. Northern Italy, a region with a strong manufacturing base, provides a relevant context for examining these challenges. This study applies the "Models and Methods for an Active Ageing Workforce" (MAIA) framework, which addresses six domains critical to supporting older workers: organizational culture, work design, health management, knowledge transfer, intergenerational coexistence and retirement pathway. Methods and Results A case-based methodology was employed within a multinational manufacturing company in Northern Italy. Data were collected through qualitative and quantitative approaches, including stakeholder interviews, workplace observations, and targeted surveys assessing physical and mental workload. Analysis of this data, combined with discussions involving managers, foremen, and HR representatives, informed the development of a comprehensive roadmap for managing the aging workforce. The roadmap identifies key addressed areas and critical gaps and proposes targeted tools and actions to improve work conditions for older employees. This approach demonstrates the practical application of the MAIA framework and the ISO 25550:2022 standard in an industrial aging Open Peer Review Approval Status 1 2 version 1 26 Aug 2025 view view Antonella Petrillo, University of Naples “Parthenope”, Napoli, Italy 1. Lisa Meehan , Auckland University of Technology (AUT), Auckland, New Zealand 2. Any reports and responses or comments on the article can be found at the end of the article. Open Research Europe  Page 1 of 22 Open Research Europe 2025, 5:253 Last updated: 17 SEP 2025 workforce context. Conclusions The study highlights the importance of a structured framework for managing aging workers in physically demanding environments. By identifying specific challenges and proposing actionable solutions, the developed roadmap supports effective workforce planning and management. The results contribute valuable insights into optimizing organizational culture, health management, knowledge transfer, and retirement planning to promote sustainable employment for older workers in manufacturing. Plain language summary As people work longer and populations age, many industries face challenges managing older workers, especially in physically demanding jobs like manufacturing. This study looks at a manufacturing company in Northern Italy to understand how to better support older employees. We used a framework called MAIA that focuses on six important areas, including workplace culture, job design, health, knowledge sharing, how different generations work together, and retirement planning. We collected information by talking to workers and managers, observing the workplace, and using surveys to measure the physical and mental demands of the job. Based on this information, we developed a detailed roadmap that not only outlines ways to improve working conditions and support older workers but also highlights what actions have already been implemented and which key areas have been addressed within the company. This roadmap identifies gaps and suggests further actions and strategies that can be taken to better support the aging workforce. While we are still evaluating its full impact, we believe this roadmap can serve as a valuable guide to help companies plan and implement effective measures for an aging workforce. Keywords Ageing workforce management, knowledge transfer, worker retention, labour shortage, manufacturing system, ergonomic assessment, case study This article is included in the Horizon 2020 gateway. Open Research Europe  Page 2 of 22 Open Research Europe 2025, 5:253 Last updated: 17 SEP 2025 Corresponding author: Niloofar Katiraee ([email protected]) Author roles: Katiraee N: Conceptualization, Data Curation, Investigation, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing; Caprari G: Conceptualization, Data Curation, Investigation, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing; Das A: Conceptualization, Investigation, Supervision, Validation, Writing – Review & Editing; Battini D: Funding Acquisition, Project Administration, Supervision, Validation, Writing – Review & Editing Competing interests: No competing interests were disclosed. Grant information: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 873077 (MAIAH2020-MSCA-RISE2019). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Katiraee N et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite this article: Katiraee N, Caprari G, Das A and Battini D. A roadmap for managing an ageing workforce in the manufacturing sector: An Italian case study [version 1; peer review: 2 approved] Open Research Europe 2025, 5:253 https://doi.org/10.12688/openreseurope.20511.1 First published: 26 Aug 2025, 5:253 https://doi.org/10.12688/openreseurope.20511.1 This article is included in the Marie-SklodowskaCurie Actions (MSCA) gateway. Open Research Europe  Page 3 of 22 Open Research Europe 2025, 5:253 Last updated: 17 SEP 2025 1. Introduction As populations age worldwide, the workforce is undergoing a major demographic shift, which is reshaping how societies, economies, and organizations function (Zhao, 2024). Industrial sectors are experiencing a rise in the proportion of older workers, bringing both challenges and opportunities for companies (Boenzi et al., 2015). This is particularly evident in manufacturing roles, where the physical and cognitive demands are high, necessitating innovative strategies to ensure productivity while safeguarding worker well-being (Goyal et al., 2014). Replacing aging workforces or early retirement is not always a feasible solution, as it can lead to the loss of valuable accumulated knowledge and expertise (Streb et al., 2008). Additionally, a shrinking pool of younger workers is creating labor shortages, leaving companies with little choice but to develop strategic approaches for managing an aging workforce (Leibold & Voelpel, 2007). Thus, organizations must create agefriendly environments that prioritize health, enhance motivation, and ensure the engagement of older employees to maintain productivity and facilitate knowledge transfer to younger generations (Katiraee et al., 2024; Ranasinghe et al., 2024). Intergenerational harmony is critical to the long-term success of companies, especially as they navigate this demographic shift. The core challenge of effectively managing aging workforces lies in a) documenting and transferring their valuable experience and knowledge along with b) finding measures to decelerate and cope with the gradual decline in their physical and cognitive abilities. The ‘how’ of doing so constitutes the subject of the current study. The objective of this study is to examine the complexities of knowledge transfer, worker retention, and productivity within an aging workforce in the manufacturing sector located in northern Italy. An empirical study benefits from a sound conceptual point of departure. Studies have proposed conceptual frameworks for managing the aging workforce (i.e., Katiraee et al., 2024; Rasmussen, 1997; Wilckens et al., 2020). One such framework is the Models and Methods for an Active Ageing Workforce (MAIA) (Figure 2), developed through international collaboration and data collection (Katiraee et al., 2024). While frameworks such as Rasmussen’s focus primarily on occupational safety and major accidents, and Wilckens et al., 2020 address aging workforce challenges without considering multi-level interactions, the MAIA framework provides a more comprehensive approach. It accounts for new and evolving issues of aging workforces, such as ergonomics, health management, knowledge transfer, and retirement pathways, and does so across three levels—international, country, and company. Additionally, the MAIA framework was developed and refined through data collection and analysis from multiple project partners, incorporating insights from diverse regions and organizations. This iterative development process ensures the framework is tailored to address the current and emerging needs of aging workforces. While the framework provides valuable conceptual insights, its practical applicability and utility in real-world settings are still in an exploratory stage. Accordingly, we use the MAIA framework to operationalize and guide our empirical investigation. We applied the MAIA framework in a large multinational manufacturing company that was developing guidelines for creating a more age-inclusive and sustainable work environment. We utilized the MAIA framework to structure and conduct a series of interviews with department heads, manual workers, and HR personnel to better understand and address the complexities of knowledge transfer, worker retention, and productivity in an operational environment with an aging workforce. The following sections provide a theoretical background on the challenges of managing an aging workforce (Section 2) and the methods to address them. Section 3 describes the research methodology, followed by observations and an in-depth analysis of the results in Section 4. Finally, the discussion and conclusion are presented in Section 5 and Section 6, respectively. 2. Theoretical background 2.1 Aging workforce challenges The world is experiencing a significant demographic shift, with the proportion of older workers growing rapidly (Katiraee et al., 2020). This change, driven by low fertility rates and increased life expectancy, is placing pressure on companies, especially in manufacturing systems, where physical demands are high, such as in manual assembly systems or tasks in warehouses. Figure 1 illustrates the upward trend in the proportion of aging employees across various age groups over the last decade. The core challenge of aging workforces lies in balancing their experience and knowledge which have accumulated over time (DeLong, 2004), with the decline in their physical and cognitive abilities (Fisher et al., 2017). These contrasting factors create difficulties for decision-makers in the workplace. Furthermore, integrating new technologies (e.g., cobots and digital tools) presents additional challenges. Older workers may face difficulties in adapting to new technologies, and the process of learning and training can be time-consuming and challenging compared to younger employees (Sanders, 2018). Given these complexities, several important questions arise: • How can managers and organizations effectively address these challenges? A common solution, as proposed in the literature, involves developing tailored strategies and guidelines specific to each company, depending on the organization’s requirements and the nature of the task being performed by employees. • Can these strategies be generalized for all workers? Should workers be treated equally? Previous research, including studies by Katiraee et al. (2019; 2021a) and the recent ISO standard ISO 25550:2022, emphasizes the importance of individualization. Workers may differ significantly in terms of age, gender, skills, and physical or mental capabilities, suggesting that a onesize-fits-all approach may not be effective. Page 4 of 22 Open Research Europe 2025, 5:253 Last updated: 17 SEP 2025 Figure 1. Older persons in employment, by age class, EU-27, 2014–2023 (% of total employment). Source: Eurostat (lfsa_egan). • Should early retirement be considered as a solution? While early retirement might seem like a straightforward solution, it often results in losing valuable knowledge and experience. Additionally, it can lead to significant economic costs related to retirement pensions and policies, which can impact both companies and governments. For this reason, managing an aging workforce presents significant challenges for managers and practitioners. To address these issues, various frameworks and guidelines have been developed, including the European MAIA project (Katiraee et al., 2024). In the next subsection, we will provide a brief introduction to the MAIA framework. 2.2 Introduction to the MAIA framework Managing an aging workforce is crucial for maintaining productivity, reducing health risks, and preventing knowledge loss (DeLong, 2004). Strategies such as ergonomic workplace design, flexible work arrangements, and knowledge management systems are essential for fostering an age-inclusive environment (Katiraee et al., 2024; Ranasinghe et al., 2024). The MAIA framework offers a structured approach to addressing these challenges across six key domains. Figure 2 illustrates the MAIA framework. A brief explanation of this framework is provided in this section, and further details can be found in Katiraee et al. (2024). As shown in Figure 2, the MAIA framework offers a roadmap includes six main action domains and three levels of interventions. The domains are: 1) Organizational culture and leadership, 2) work design and ergonomic, 3) health management, 4) knowledge management, 5) intergenerational coexistence, and 6) retirement pathways. These domains are structured across three levels including international, country, and company level. According to this roadmap, several key issues related to the aging workforce need to be addressed, including organizational culture, work design, worker’s health, knowledge transfer and communication among different groups of employees, and retirement planning. Policies, strategies, and guidelines applied for each domain have been discussed completely across all three levels (international, country, and company) by Katiraee et al., 2024. The roadmap has been refined based on data gathered from worldwide partners involved in this project, ensuring a comprehensive and globally informed perspective. We applied the MAIA roadmap in a real-world setting, a manufacturing company specializing in the production of agricultural machines / electronic devices, to identify the complexities of knowledge transfer, worker retention, and productivity in an aging operational environment. In the following section, we outline the company description and methodology used for data collection and analysis. 3. Methods 3.1 The company case/ case study introduction The company under study is an Italian firm specializing in advanced industrial electronic components with over 1,500 employees globally, operating numerous production sites in Italy and abroad, sales offices, and local representative offices. The studied company is guided by core values, including sustainability, trust, people development, independence, partnership, and ambition. Since 2020, the company has adopted an “adaptive organization” model where Agile Methodologies such as Scrum and Kanban are integral to the company’s approach, promoting collaboration, accountability, and continuous improvement. The company is committed to creating a positive organizational climate, particularly for its aging workforce, with a specific focus on considering the physical limitations of the workers and improving knowledge transfer processes post-retirement. This study focuses on the Italian workforce, particularly employees aged 55 and above, who make up a significant portion of both blue-collar and white-collar roles. To achieve this, the company utilized the MAIA roadmap to gather data for analysis. The Page 5 of 22 Open Research Europe 2025, 5:253 Last updated: 17 SEP 2025 Figure 2. MAIA roadmap for implementing an age-inclusive workforce management practice in worldwide companies (Katiraee et al., 2024). aim was to identify areas of the workplace environment that require further improvement, those that are already wellmanaged, and to outline actions that need to be initiated from scratch. Table 1 provides an analysis of the aging workforce in the case studied across various roles for both blueand white-collar workers, detailing the proportion of workers aged 55 or older, the average workforce age in each role, and early retirement preferences. The data includes the total workforce for both categories, encompassing both permanent and temporary employees. The inclusion of temporary workers reflects the company’s practice of hiring seasonal staff during peak demand periods, highlighting the dynamic nature of its workforce. Notably, physically demanding blue-collar roles, such as assembly line and warehousing, have a significant proportion of aging workers, with approximately 90% indicating interest in early retirement based on a performed survey (detail in Section 4). The table also highlights the implications of these trends. For instance, in the warehousing role, the relatively high average age of 50.4 suggests that, within a decade, a considerable portion of the workforce may retire, assuming all workers retire at the standard age of 66–67, potentially leading to a workforce gap. If pre-retirement trends are factored in, this shortage could become even more severe, presenting challenges related to the loss of workforce knowledge, skills, and experience, and intensifying replacement issues. In contrast, white-collar roles exhibit a lower proportion of aging workers and a younger average age overall. However, roles like R&D and operations still have a considerable number of workers aged 55 or older, underlining the need for strategic workforce planning across all levels of the organization. To ensure operational excellence, the company identifies critical roles within the organization based on three criteria: unique expertise required, limited availability of skills in the labour market, and strategic importance to the company. Table 2 presents these roles, which were identified through interviews. • Roles are currently held by workers aged over 55. • Roles are filled by younger workers. • Roles remain unfilled due to a lack of suitable replacements. Using the information about the company, we selected a representative sample for our survey and distributed the questionnaire, as outlined in Section 3.2. 3.2 The Interview sample and data analysis process In this study, we adopted a case-based research approach (Yin, 2009) and used unstructured and semi-structured interviews for data collection (Podsakoff et al., 2016). This methodology is particularly effective for capturing respondents’ experiences and perceptions of performing tasks within the company. Page 6 of 22 Open Research Europe 2025, 5:253 Last updated: 17 SEP 2025 Table 1. Workforce age distribution in the studied company for each role. Collar Role Total workers (permanent and temporary) Total workers aged 55 and above %of the workforce aged 55 or more Average workforce age The expected request for early retirement Blue Collar Assembly line 170 27 15.88% 46.1 Pick and place 53 13 24.53% 47.7 Warehouse 26 12 46.15% 50.4 Total 249 52 20.88% 48.06 90% White collar R&D 96 18 18.75% 43.7 Operations 139 17 12.23% 41.6 Marketing and sales 77 11 14.29% 42.1 Finance and Legal 30 4 13.33% 38.32 HR 31 3 9.68% 36.5 IT 15 2 13.33% 36.7 Total 388 55 14.18%39.8 Table 2. Critical roles. Function Role Critical tasks R&D Plastic injection molding expert How to design a piece in order to set up a feasible industrialization R&D Electronic engineer Electronic design and testing experience R&D Hardware engineer Hardware testing set up R&D Mechanical designer How to design a piece able to solve specific problems R&D Patents expert Document management about patents releases R&D Product encoding expert BOM knowledge about products R&D Fluid-dynamic simulations engineer Virtual product testing Operations Electronic process engineer Electronic parts production processes Operations Press foreman Sheet metal forming - deep drawing: codesign with the supplier to define the mold specifications and how to do the machines set up Operations Plant manager Management skills to manage workers in the factory Operations Maintenance coordinator (unfilled) Electrical and mechanical maintenance in production lines Marketing & Sales Sales manager Historical knowledge about product and customer (especially the human aspect) Marketing & Sales Sales manager Historical knowledge about product and customer (especially the human aspect) Page 7 of 22 Open Research Europe 2025, 5:253 Last updated: 17 SEP 2025 Our focus was on employees aged 55 and older in a large multinational manufacturing company that was implementing age-inclusive guidelines. The sample comprised 103 interviews with employees from various departments. Data was gathered from two distinct groups of permanent workers: blue-collar and white-collar. Temporary workers were excluded from the survey due to limited access to information and constraints in conducting interviews. Blue-collar workers were involved in manual and physically demanding tasks, such as assembly and warehouse operations, while white-collar workers were engaged in administrative tasks, such as R&D and IT. Table 3 presents a breakdown of the interview sample across both blue-collar and white-collar roles, along with their respective departments, providing a clearer understanding of the sample examined in this study. The interview questions were designed in alignment with the six domains of the MAIA framework, tailored to address the research aim, and customized for both blue-collar and whitecollar workers. For certain domains, such as Domain 2: Work Design and Ergonomics, the questions were developed using the simplified NASA-TLX to assess mental and physical workload (Noyes & Bruneau, 2007). Additionally, to validate the qualitative data for this domain, quantitative ergonomic tools like RULA (Rapid Upper Limb Assessment) (McAtamney & Corlett, 1993) and REBA (Rapid Entire Body Assessment) (Hignett & McAtamney, 2000) were used to measure the physical strain experienced during critical tasks. For other domains, particularly Domain 1 (organizational culture and leadership) and Domain 6 (retirement pathway), the open-ended questions were crafted to reflect the specific characteristics and operational needs of the company. These questions were designed to be flexible, allowing the interview to naturally expand into more detailed discussions, enabling deeper insights into the issues at hand. Domain 5, concerning ‘Intergenerational Coexistence,’ was not considered in the analysis due to the difficulty in obtaining the necessary data to construct a consistent picture of the company’s current situation regarding this topic, as well as its significant overlap with aspects already covered under the Knowledge Management domain. To ensure reliability and validity, each interview was conducted by two researchers. Following the interviews, the researchers engaged in a thorough discussion and analysis to consolidate their findings. For the blue-collar, the obtained information was also reviewed and analyzed by the HR specialist, who managed relations with unions, and the foreman, who provided operational insights. Meanwhile, for the white-collar, the analysis was discussed with HR specialists and managers to ensure that the data reflected the perspectives and needs of each distinct group. These findings were then presented to the project manager and the designated company representative for validation. This collaborative approach ensured that the data were both comprehensive and aligned with the practical realities of the organization. After analysing the responses for each domain, we develop a roadmap that incorporates the suggested actions and tools, as well as those already applied by the company (Figure 5). The suggested action for each domain is derived from the MAIA framework and supported by the new ISO 25550:2022. This ISO standard aims to help organizations develop, implement, maintain and support an age-inclusive workforce (ISO 25550: 2022). These actions are evaluated using the maturity model proposed by Khraiwesh (2020) (depicted in Figure 3), which categorizes the company’s readiness and progress across five levels, from ‘Initial’ to ‘Optimized’. This model helps the company track its evolution, prioritize improvements, and implement continuous enhancements. By aligning each domain’s actions with its maturity level, the company can strategically advance from reactive solutions to proactive, sustainable practices, fostering an inclusive, age-friendly work environment. Table 3. A breakdown of the interview sample across blue-collar and white-collar roles. Collars (Blue & White) Total permanent workers Total workers aged 55 and above Roles #Number of 55+ workers 198 52 • Assembly line (#27) • Pick and place (#13) • Warehouse (#12) 385 55 • R&D (#18) • Operations (#17) • Marketing and sales (#11) • Finance and legal (#4) • HR (#3) • IT(# 2) Page 8 of 22 Open Research Europe 2025, 5:253 Last updated: 17 SEP 2025 Figure 3. The maturity level (Khraiwesh, 2020). All detailed questions and their corresponding results are provided in Section 4. The results of the interviews were analysed within the context of the MAIA framework, existing literature, and ISO, ultimately leading to the development of a tailored roadmap for the company under study (Figure 5). 4. Findings Combining qualitative interviews with ergonomic assessments enabled a comprehensive understanding of the company’s current aging worker initiatives and identified areas that require further development to create a more inclusive work environment for an aging workforce. The MAIA framework was deployed as a structural guide in data collection and analysis. The recommendations were based on the framework as well as related ISO 25550:2022 The company’s readiness to address the challenges posed by an aging workforce was analyzed across five key domains of the MAIA roadmap: Organizational Culture and Leadership, Work Design and Ergonomics, Health Management, Knowledge Management, and Retirement Pathways. This section presents the findings for each domain, outlining the current issues and suggesting actions based on interviews, ergonomic assessments, and other data collection methods. In the following sections, we delve into each domain, discussing the company’s priorities and highlighting areas that are either less considered or overlooked, based on the assessed maturity level. 4.1. Domain 1: Organizational culture and leadership This domain focuses on the company’s culture and leadership practices, particularly their impact on aging workers. Unstructured questions were posed to 24 heads of functions responsible for both blue-collar and white-collar roles. The analysis of interviews with 24 heads of functions highlighted several issues related to aging workers within the company, focusing on job fit, hiring practices, training opportunities, and retirement perspectives (See in Table 4). Current challenges include bias in hiring aging workers, lack of processes for assessing job fit, limited opportunities for continuous learning, insufficient knowledge sharing, reluctance to engage in job rotation, and taboo about retirement. This taboo is more evident among white-collar employees, whose roles are less physically demanding. Unlike blue-collar workers, who often express a willingness to retire, white-collar employees may hesitate to openly discuss their desire for early retirement. Furthermore, workers’ hesitation to participate in job rotation stems from several underlying factors. Some employees lack confidence in their abilities and feel uncomfortable taking on new tasks due to concerns about making mistakes or the effort required for learning new processes and new technologies. These concerns can lead to a sense of stagnation and hinder organizational flexibility. Based on MAIA framework guidelines and our analysis of ground realities, we suggest the company consider a multi-pronged approach: Workshops and Awareness Programs: Organize workshops to reduce age-related biases in hiring practices and to raise awareness among workers about their capabilities and strengths. Job Assessment Plans: Establish processes for job fit assessment using appropriate tools, ensuring worker participation and input. Continuous Learning Opportunities: Design continuous learning paths, including skill development programs tailored to older employees, to foster a growth mindset. Recognition Programs: Implement reward-based systems to acknowledge the contributions of aging workers, thereby boosting motivation and engagement. Retirement Awareness Initiatives: Facilitate open discussions about retirement to break the taboo and create plans that balance workers’ aspirations and organizational needs. 4.2. Domain 2: Work design and ergonomics This domain considers both cognitive and physical ergonomics. Hence, this domain evaluates the physical and mental workload associated with aging workers’ tasks using a semi-structured questionnaire based on the simplified NASATLX framework (Noyes & Bruneau, 2007) (Table 5). The simplified NASA-TLX questionnaire, with scores ranging Page 9 of 22 Open Research Europe 2025, 5:253 Last updated: 17 SEP 2025 Additionally, encouraging physical activity and promoting health through low-cost initiatives, such as fitness programs or stress reduction workshops, could improve workers' physical well-being without requiring substantial financial investment. 5.2.6 Formalizing knowledge transfer and mentoring programs Establishing structured mentoring and knowledge transfer programs is essential for mitigating the risks of knowledge loss. This could involve pairing older workers with younger employees in formal mentorship roles, as well as leveraging digital tools, such as augmented reality (AR), to facilitate knowledge sharing. Additionally, the development of knowledge-mapping tools to identify critical roles and expertise within the company would ensure that vital skills are not lost when older workers retire. 5.2.7 Flexible retirement pathways and succession planning Gradual retirement pathways, including options for phased retirement and flexible work arrangements, would allow older workers to transition out of the workforce at a pace that benefits both them and the company. Developing succession plans in advance, coupled with mentoring programs, would also ensure continuity and smooth transitions for critical roles. 5.4 Broader implications for manufacturing industries The findings of this case study offer broader insights for other companies within the manufacturing sector facing similar challenges. The MAIA framework, while comprehensive, needs to be adapted to each company’s unique context, balancing resource constraints with strategic priorities. Manufacturing industries, in particular, need to consider the high physical demands of many roles and invest in ergonomic solutions and health management programs to safeguard their aging workforce. Additionally, fostering an organizational culture that values intergenerational collaboration and knowledge transfer is critical for sustaining long-term productivity and innovation. 6. Conclusion This research aimed to investigate aging workforce issues in a manufacturing setting, applying the MAIA framework. The new ISO: 25550 standards on aging societies were also consulted. The study began by assessing critical issues across each MAIA framework domain through information gathered from operational practices, tools present in the company, and insights into the company’s working culture. This included informal behaviours and attitudes among employees that influence the workplace but are not formally documented. Once the company’s current challenges were identified, the MAIA framework and ISO: 25550 guidelines helped prioritize urgent actions, and appropriate tools were recommended to foster an active aging workforce. The result was a customized roadmap, designed to guide the company in implementing agefriendly policies across various departments, ensuring that the suggested actions and tools are applied to meet the organization’s needs. One key finding was the central role of the “Organizational Culture and Leadership” domain, which emerged as a foundational pillar for implementing age-friendly strategies. Organizational culture shapes team cohesion, workplace climate, and overall effectiveness, making it a crucial starting point for fostering age-inclusive policies that enable the success of other strategic domains. Two other domains that held particular importance were “Work Design and Ergonomics” and “Knowledge Management”. Ergonomics took center stage for blue-collar workers. The NASA-TLX questionnaire and ergonomic assessments, including RULA and REBA, revealed the need for greater job rotation and individualized job assignments, essential for promoting health and well-being in the workplace. “Knowledge Management” emerged as critical issue for whitecollar employees, particularly in preserving the expertise and competencies of aging workers. Formal knowledge mapping processes and the creation of intergenerational knowledge transfer mechanisms, notably through the introduction of “trainer/mentor” roles were recommended remedies. The final roadmap revealed a synergistic effect across all domains, with certain actions—such as implementing job rotation and creating trainer/mentor roles—proving beneficial across multiple areas of the organization. This highlights not only the interconnection between different domains but also the substantial improvement potential that can arise from even a few targeted actions. The developed roadmap is thus instrumental in helping the company strategically navigate workforce aging, offering practical steps for improving productivity, worker well-being, and intergenerational collaboration. 6.1 Study limitations There were certain limitations encountered in the study. It was not possible to suggest comprehensive actions for the “Intergenerational Coexistence” domain of the MAIA framework due to difficulties in collecting consistent information across the company’s various departments. This topic requires further investigation to gain a complete understanding of the current situation in this dimension of the workplace environment. In the “Work Design and Ergonomics” domain, while ergonomic risks were assessed using RULA and REBA, advanced technologies like motion capture systems could be employed in future research to provide more precise results and analyze more workstations. Additionally, involving a larger sample of workers in ergonomic assessments, considering factors such as gender, age, and body type, would offer a more comprehensive analysis. Considering all domains, the study could be extended to implement the proposed tools and evaluate their effectiveness and adaptability in the workplace. Page 16 of 22 Open Research Europe 2025, 5:253 Last updated: 17 SEP 2025 Overall, the MAIA framework should be applied to a broader range of industrial companies, allowing for the development of roadmaps tailored to specific needs. This would support businesses in addressing the demographic changes affecting today’s workforce. The expansion of these actions would help companies build resilience in response to future workforce shortages. Further research is needed to deeply investigate the implications of this trend and how it will affect the future of organizations. Ethics and consent Formal ethical approval was not obtained from an Institutional Review Board (IRB), as the study was conducted internally with organizational approval from the participating company. The company reviewed and approved the research procedures before the start of the study. At the time of the research, the study was assessed to involve no risk and did not include sensitive or medical personal data requiring IRB oversight. All research procedures complied with the principles of the Declaration of Helsinki. All participants provided written informed consent before taking part in the study. They were informed about the purpose of the research, the voluntary nature of their participation, their right to withdraw at any time, and the confidentiality of their responses Data consent The data generated and/or analyzed during the current study are not publicly available due to confidentiality agreements References Ansari NA, Sheikh MJ: Evaluation of work posture by RULA and REBA: a case study. IOSR J Mech Civil Eng. 2014; 11(4): 18–23. Reference Source Battini D, Berti N, Finco S, et al.: WEM-Platform: a real-time platform for fullbody ergonomic assessment and feedback in manufacturing and logistics systems. Comput Ind Eng. 2022; 164: 107881. Publisher Full Text Boenzi F, Digiesi S, Mossa G, et al.: Modelling workforce aging in job rotation problems. IFAC Pap OnLine. 2015; 48(3): 604–609. Publisher Full Text DeLong DW: Lost knowledge: confronting the threat of an aging workforce. Oxford University Press, 2004. Publisher Full Text  FisherGG,ChaffeeDS,TetrickLE,et al.: Cognitive functioning, aging, and work: a review and recommendations for research and practice. J Occup Health Psychol. 2017; 22(3): 314–336. PubMed Abstract | Publisher Full Text  GoyalM,SinghS,SibingaEMS,et al.: Meditation programs for psychological stress and well-being: a systematic review and meta-analysis. JAMA Intern Med. 2014; 174(3): 357–368. PubMed Abstract | Publisher Full Text | Free Full Text Hignett S, McAtamney L: Rapid Entire Body Assessment (REBA). Appl Ergon. 2000; 31(2): 201–205. PubMed Abstract | Publisher Full Text Katiraee N, Battini D, Battaia O, et al.: Human diversity factors in production system modelling and design: state of the art and future researches. IFAC Pap OnLine. 2019; 52(13): 2544–2549. Publisher Full Text Katiraee N, Berti N, Calzavara M, et al.: The workforce ageing phenomenon: statistics, policies and practices. In: XXV Summer School “Francesco Turco”– Industrial Systems Engineering. 2020. Reference Source Katiraee N, Berti N, Das A, et al.: A new roadmap for an age-inclusive workforce management practice and an international policies comparison [version 2; peer review: 2 approved, 1 approved with reservations]. Open Res Eur. 2024; 4: 85. PubMed Abstract | Publisher Full Text | Free Full Text Katiraee N, Calzavara M, Finco S, et al.: Consideration of workers’ differences in production systems modelling and design: state of the art and directions for future research. Int J Prod Res. 2021a; 59(11): 3237–3268. Publisher Full Text Katiraee N, Finco S, Battaïa O, et al.: Assembly line balancing with inexperienced and trainer workers. In: Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems: IFIP WG 5.7 International Conference, APMS 2021, Nantes, France, September 5–9, 2021, Proceedings, Part II. Springer International Publishing, 2021b; 497–506. Publisher Full Text Khraiwesh M: Measures of organizational training in the Capability Maturity Model Integration (CMMI®). Int J Adv Comput Sci Appl. 2020; 11(2): 584–592. Publisher Full Text Leibold M, Voelpel SC: Managing the aging workforce: challenges and solutions. John Wiley & Sons, 2007. Reference Source  McAtamneyL,CorlettEN:RULA: a survey method for the investigation of work-related upper limb disorders. Appl Ergon. 1993; 24(2): 91–99. PubMed Abstract | Publisher Full Text with the participating company and the difficulty in fully anonymizing the qualitative interview data. However, access to redacted data may be granted on reasonable request. Interested researchers should contact the corresponding author at [niloofar[email protected]], outlining the purpose of their request. Access may be granted at the discretion of the authors and by ethical and legal considerations. Data availability The interview data generated during the current study are not publicly available due to confidentiality agreements with the participating company and the nature of the organizational case study. The data contains context-specific insights that cannot be fully anonymized without compromising participant privacy and organizational confidentiality. However, access to redacted data may be granted upon reasonable request. Interested researchers may contact the corresponding author at niloofar[email protected], outlining the purpose of their request. Access will be considered at the discretion of the authors and subject to ethical and legal requirements. Acknowledgements This article includes content generated with the assistance of ChatGPT (version GPT-4.5, OpenAI), which was used solely to improve the English language and grammar of the manuscript. Page 17 of 22 Open Research Europe 2025, 5:253 Last updated: 17 SEP 2025 Noyes JM, Bruneau DP: A self-analysis of the NASA-TLX workload measure. Ergonomics. 2007; 50(4): 514–519. PubMed Abstract | Publisher Full Text No IS: 25550: 2022; Ageing societies-General requirements and guidelines for an age-Inclusive workforce. International Organization for Standardization. Geneva, Switzerland, 2022. Reference Source  PadulaRS,ComperMLC,SparerEH,et al.: Job rotation designed to prevent musculoskeletal disorders and control risk in manufacturing industries: a systematic review. Appl Ergon. 2017; 58: 386–397. PubMed Abstract | Publisher Full Text | Free Full Text  PodsakoffPM,MacKenzieSB,PodsakoffNP:Recommendations for creating better concept definitions in the organizational, behavioral, and social sciences. Organizational Research Methods. 2016; 19(2): 159–203. Publisher Full Text  RanasingheT,GrosseEH,GlockCH,et al.: Never too late to learn: Unlocking the potential of aging workforce in manufacturing and service industries. International Journal of Production Economics. 2024; 270(1): 109193. Publisher Full Text Rasmussen J: Risk management in a dynamic society: a modelling problem. Safety Science. 1997; 27(2–3): 183–213. Publisher Full Text Sanders MJ: Older manufacturing workers and adaptation to age-related changes. Am J Occup Ther. 2018; 72(3): 7203205060p1–7203205060p11. PubMed Abstract | Publisher Full Text Streb CK, Voelpel SC, Leibold M: Managing the aging workforce: status quo and implications for the advancement of theory and practice. Eur Manag J. 2008; 26(1): 1–10. Publisher Full Text Wilckens MR, Wöhrmann AM, Adams C, et al.: Integrating the German and US perspective on organizational practices for later life work: the Later Life Work Index. Current and Emerging Trends in Aging and Work. 2020; 59–79. Publisher Full Text Yin RK: Case study research: design and methods. Sage, 2009; 5. Reference Source Zhao Y: The impact of population ageing on the economy. International Journal of Social Sciences and Public Administration. 2024; 2(2): 108–121. Publisher Full Text Page 18 of 22 Open Research Europe 2025, 5:253 Last updated: 17 SEP 2025 Open Peer Review Current Peer Review Status: Version 1 Reviewer Report17 September 2025 https://doi.org/10.21956/openreseurope.22195.r59240 © 2025 Meehan L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Lisa Meehan Auckland University of Technology (AUT), Auckland, New Zealand This is a thoughtful and practice-oriented paper on a pressing topic. The authors apply the MAIA framework and ISO 25550:2022 to a real-world manufacturing case in Northern Italy. The study draws on interviews with older blueand white-collar employees, input from managers and HR, and ergonomic assessments (NASA-TLX, RULA, REBA). The resulting roadmap is both timely and relevant, and the mixed-methods approach is a particular strength. Major comments 1. Clarify the main contribution:The paper would benefit from stating more clearly whether its central strength lies in the empirical case study, the conceptual use of MAIA, the practical roadmap, or in the combination of these elements. 2. Intergenerational coexistence domain:Domain 5 was omitted, yet it is central to knowledge transfer. Even if data were limited, the paper could suggest how this might be explored in the future (e.g. through workshops or targeted surveys). 3. Roadmap and maturity model: The roadmap is useful, but the maturity model results are not fully shown. A table or chart indicating the company's current level in each domain could be helpful. 4. Context and transferability:The discussion could more explicitly distinguish between what is specific to Northern Italy (e.g. union structures, demographics) and what is more generally applicable. This would help readers gauge the wider relevance of the roadmap. 5. Succession planning and knowledge transfer: The paper identifies these as issues but if it is feasible, it could point to concrete tools (e.g. digital knowledge repositories or succession plan templates) to give practitioners more practical guidance. Minor suggestions Open Research Europe  Page 19 of 22 Open Research Europe 2025, 5:253 Last updated: 17 SEP 2025 - Use "ageing" or "aging" consistently - Consider simplifying some longer sentences for readability - Consider replacing the termforemenwith a more gender-neutral alternative, such assupervisor or shift supervisor Is the background of the case’s history and progression described in sufficient detail? Yes Is the work clearly and accurately presented and does it cite the current literature? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Is the case presented with sufficient detail to be useful for teaching or other practitioners? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Economics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Reviewer Report09 September 2025 https://doi.org/10.21956/openreseurope.22195.r59555 © 2025 Petrillo A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Antonella Petrillo University of Naples “Parthenope”, Napoli, Italy The article "A roadmap for managing an aging workforce in the manufacturing sector: An Italian case study" presents an analysis of the challenges associated with managing an aging workforce in a multinational manufacturing company in Northern Italy. The authors apply the MAIA (Models and Methods for an Active Aging Workforce) framework, integrated with the ISO 25550:2022 standard, to assess five key domains: organizational culture and leadership, ergonomics and work Open Research Europe  Page 20 of 22 Open Research Europe 2025, 5:253 Last updated: 17 SEP 2025 design, health management, knowledge management, and retirement pathways. Through interviews (103 participants), field observations, and ergonomic assessments (NASA-TLX, RULA, REBA), the authors develop a roadmap of targeted actions that includes tools already implemented and future proposals. The results highlight strengths (use of ergonomic tools, organizational flexibility, skill matrix) and weaknesses (lack of succession planning, cultural resistance, lack of structured mentoring programs). The main contribution is the practical demonstration of how the MAIA framework can guide age-inclusive policies in the manufacturing sector. The paper addresses a real and urgent challenge, with strong practical and policy interest. However, the article presents some critical issues and weaknesses, as detailed below. 1. The "Intergenerational coexistence" domain has been omitted. Add details. Clarify and propose a methodology for the missing domain. 2. It is stated that it was not possible to collect consistent data. However, intergenerationality is central to knowledge transmission. Clarify more systematically why data were not available and propose a future methodology (e.g., mixed young/old focus groups, comparative survey) to fill this gap. 3. The study is based on a single case study, which limits external validity. Include a more detailed section on methodological limitations and indicate how the results could be verified in other contexts (e.g., comparison with manufacturing SMEs or companies in other sectors). 4. Succession planning and knowledge transfer. Although identified as a critical issue, a concrete proposal for digital or process-based tools (beyond AR and mentoring) is lacking. Provide practical examples of already validated systems (e.g., knowledge management platforms, skills databases) and assess their applicability to the company studied. 5. RULA/REBA evaluations were conducted only on a limited subset of activities. Further specify the selection criteria and discuss the methodological implications (possible bias). Recommend future studies with a larger sample size and the use of advanced technologies (e.g., motion capture). 6. The article acknowledges cultural resistance but does not delve into how this influences industrial relations (e.g., the role of unions). Expand the discussion to include union dynamics or Italian regulations, which could impact phasing-out processes or job rotation. 7. Formal IRB approval is stated to be lacking. While not mandatory in a corporate context, it would be helpful to specify what specific privacy protection measures were applied (beyond written consent). 8. Strengthen the discussion on the generalizability of the findings beyond the individual company case. 9. Further detail the sampling and criteria in ergonomic analyses. 10. Specify how cultural and union resistance can hinder/facilitate the implementation of the roadmap. The article provides an important and original contribution to the application of the MAIA framework in a real-world context. With tminor revision the study will become an even more solid and useful reference for both the scientific community and companies dealing with the challenges of an aging workforce. Is the background of the case’s history and progression described in sufficient detail? Yes Open Research Europe  Page 21 of 22 Open Research Europe 2025, 5:253 Last updated: 17 SEP 2025 Is the work clearly and accurately presented and does it cite the current literature? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Is the case presented with sufficient detail to be useful for teaching or other practitioners? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Industrial Plant I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Open Research Europe  Page 22 of 22 Open Research Europe 2025, 5:253 Last updated: 17 SEP 2025