Improvement Model of OEE in the Production Process of Cardboard Boxes Through SMED, TPM, Automation, and IoT

Improvement Model of OEE in the Production Process of Cardboard Boxes Through SMED, TPM, Automation, and IoT Item Type info:eu-repo/semantics/article Authors Gonzales-Vera, Rolando; Rodriguez-Barrientos, Karol; CastroRangel, Percy; Alvarez, José C.; Lepore, Robert DOI 10.14445/23488360/IJME-V12I6P104 Publisher Seventh Sense Research Group Journal Ssrg International Journal of Mechanical Engineering Rights info:eu-repo/semantics/openAccess; Attribution 4.0 International Download date 04/11/2025 11:47:41 Item License http://creativecommons.org/licenses/by/4.0/ Link to Item http://hdl.handle.net/10757/686858 SSRG International Journal of Mechanical Engineering Volume 12 Issue 6, 34-51, June 2025 ISSN: 2348-8360/ https://doi.org/10.14445/23488360/IJME-V12I6P104 © 2025 Seventh Sense Research Group® This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Original Article Improvement Model of OEE in the Production Process of Cardboard Boxes Through SMED, TPM, Automation, and IoT Rolando Gonzales-Vera1, Karol Rodriguez-Barrientos2, Percy Castro-Rangel3, José C. Alvarez4, Robert Lepore5 1,2,3,4Department of Industrial Engineering, Universidad Peruana de Ciencias Aplicadas, Lima, Peru. 5Systems & Industrial Engineering, The University of Arizona, Tucson, USA. 1Corresponding Author : [email protected] Received: 08 April 2025 Revised: 10 May 2025 Accepted: 07 June 2025 Published: 30 June 2025 Abstract - As processes become more complex, measuring them using key KPI indicators, such as Overall Equipment Effectiveness (OEE), is essential. This study analyses the OEE of the cardboard box production process-the product with the highest annual revenue in a Peruvian printing company. The current OEE is 64.27%, which falls short of the graphic industry standard (72.5%). Increasing the OEE would provide several benefits, such as higher availability, improved product quality, reduced setup times, and lower machine wear. Therefore, an innovative solution model is proposed that will use SMED and TPM tools, as well as Automation and IoT technologies. These Lean tools will be enhanced through the mentioned technologies. The solution model will follow the PMBOK approach due to its significant contributions to project management and will consist of three phases: initial, implementation, and closure. Improvements will be projected for key SMART indicators that are identified. Subsequently, these improvements will be validated through simulation in Arena software across three scenarios: moderate, optimistic, and pessimistic. All three scenarios will show improvements over the current OEE, approaching the industry standard. Setup time in die-cutting was reduced to 836.69 minutes, die-cutter wear decreased to 24.58%, and lubricant usage in guillotines and offset machines increased to 255.04 ml and 569.21 ml, respectively. Keywords - OEE, TPM, SMED, Automation, IoT, Graphic Sector. 1. Introduction It is essential at a global level to evaluate the performance of various organizational processes through relevant indicators, such as KPIs. A key KPI indicator that helps determine whether established process objectives are being met is Overall Equipment Effectiveness (OEE). However, many companies worldwide lack the means to monitor productivity and OEE objectives in real time. Graphic printing is also among the affected sectors, and it has shown considerably high production rates in Latin America, averaging over 80%. Nationally, the graphic printing sector’s Gross Domestic Product (GDP) has been declining over the last decade due to the decline in the consumption of physical publications. This study analyzes the OEE level of the cardboard box production process (the product that generated the most annual sales) in a Peruvian graphic printing company. In the diagnostic study conducted in this paper, machine wear, poor lubrication, and setup issues were identified and measured using quality tools such as histograms, scatter plots, Pareto charts, Ishikawa diagrams, and Failure Mode and Effects Analysis (FMEA), among others. This led to the determination that a Peruvian graphic printing company’s production process of cardboard boxes (the product that generated the highest annual sales) achieves an OEE of 64.27%. It is worth mentioning that this OEE level is mainly due to the impact of two factors on this indicator: quality and availability. An adequate OEE level provides several benefits for a company in general. Studies have reduced machining setup time within an oil and gas company, achieving significant improvements such as a 91.6% reduction in setup time and a 44.6% increase in OEE [1]. A conceptual model reflects improvements in equipment availability by 13%, in efficiency, in product quality and an increase in OEE by 62.2% [2]. Implementing an automated system in the therapeutic drug monitoring laboratory reduced the cycle time by 1.5 hours and the cost per sample to 6.60 euros, increasing the OEE by improving availability by 25% and maintaining a quality above 94% [3]. Applying an IoT-based automatic diagnostic system in a mold company reduced downtime by 5% and increased OEE thanks to a detection accuracy of up to 96% in operating states, microstops and configurations [4]. The increase in process OEE offers other great benefits, as Rolando Gonzales-Vera et al. / IJME, 12(6), 34-51, 2025 35 mentioned by the authors. Comparing the current OEE of the process under study (64.27%) with the standard value of the graphic sector (72.50%), a technical gap of 8.23% is found. Furthermore, this current OEE value generates economic losses for the company, representing 2.55% of annual sales. Aiming to optimize the company’s current OEE represents the desire to demonstrate how useful this can be by offering various benefits, such as increased machine availability, reduced setup times and machine wear, increased production volumes, which generate additional revenue, among other benefits. Therefore, this study proposes a solution model based on three stages: initial phase, implementation, and closure. This model suggests using Lean tools such as SMED and TPM (the pillars of Autonomous Maintenance and Planned Maintenance) and technologies such as Automation and the Internet of Things (IoT). The proposed model is embodied in a guide based on the Project Management Body of Knowledge (PMBOK), an internationally recognized standard by the Project Management Institute (PMI), as it allows for a clear and systematic structure of the planning, execution, and control of improvement projects in industrial environments. In the initial phase of the model, the project scope is defined, and SMART objectives focused on improving the root causes of poor OEE are identified. Regarding implementation, the proposed tools and technologies are implemented step by step so that, in the closing phase, improvements to the root causes of the main problem are evaluated and the project is finalized with a closure report. 2. Literature Review It is essential to present a comprehensive analysis of the reviewed literature that pursues the common goal of optimizing OEE through implementing Lean tools, such as SMED and TPM, and technologies such as Automation and IoT. The achievements of various authors are presented chronologically. 2.1. SMED for OEE Improvement The SMED method has demonstrated its significant capacity to reduce changeover times and optimize equipment availability. Integrating SMED with complementary Lean tools was proposed to reduce internal activities and achieve greater process standardization significantly [1]. In a food plant, there were reports of efficiency improvements of up to 36% thanks to the reorganization and outsourcing of tasks during changeovers [2]. These studies conclude that proper implementation of SMED allows for the recovery of productive time, especially in processes with high changeover frequency. SMED was implemented with an operational focus, prioritizing staff training and workstation redesign [5]. It reduced tire calibration times by more than 50% by applying SMED, while emphasizing task sequencing [3]. In the hygiene products industry, changeover times were reduced without compromising process stability [6]. As can be seen, these results reinforce the effectiveness of SMED in various industrial sectors. SMED was integrated with production scheduling, enabling simultaneous improvement in operational efficiency and alignment with demand [4]. Furthermore, it was demonstrated that SMED reduces time losses and serves as a basis for increasing OEE by facilitating more consistent production cycles [7]. They highlighted its importance for small and medium-sized manufacturing companies, where operational flexibility is critical for competitiveness [8]. While the potential of SMED is well documented, it is worth mentioning that most studies focus on its isolated application. Few studies have explored its integration with emerging technologies, such as automation or IoT sensors, which is limited in the short term. This research proposes a solution that integrates SMED with technological tools and, consequently, influences a cumulative improvement of OEE. 2.2. TPM for OEE Improvement Total Productive Maintenance (TPM) maximizes equipment efficiency by eliminating failures, downtime, and micro-stops. It was stated that the pillars of autonomous and planned maintenance have a greater impact on availability, especially in equipment operating at high capacity [9]. It was proposed to adapt TPM to digitalized environments to incorporate sensors for predictive actions [10]. They applied TPM with IoT on a conveyor line, which increased availability by more than 90% through data-driven decisions [11]. Indicators were implemented to evaluate TPM and more accurately monitor its impact on OEE [12].Haga clic o pulse aquí para escribir texto.. A sequential TPM implementation framework that enabled sustained improvements in industrial production plants [13]. Consequently, it is demonstrated that its effectiveness depends on both the technique and organizational commitment. TPM was analyzed in the cork industry and identified the most adaptable tools for addressing critical failures, where the need for trained personnel and well-defined procedures was essential [14]. They showed that a well-implemented TPM strategy can increase productivity in the pharmaceutical industry by more than 20% and improve operational reliability [15]. Despite its great conceptual importance, TPM continues to present challenges in environments with low levels of digitalization. Integration with technologies such as IoT allows for anticipating failures and optimizing maintenance plans, but requires minimal infrastructure. This model addresses this need through progressive integration, where Rolando Gonzales-Vera et al. / IJME, 12(6), 34-51, 2025 36 TPM is supported by sensors for wear and lubrication monitoring, maximizing its effectiveness under controlled conditions. 2.3. Automation for OEE Improvement Industrial automation has been recognized as a key tool for reducing manual intervention, minimizing operational errors, and improving process stability. A productivity framework was developed for the semiconductor industry, demonstrating that structured automation can improve OEE in high-precision settings [16]. They highlighted its contribution to the circular economic approach, as it allows for extending the product lifecycle through more consistent and controlled processes [17]. Automation was studied in a packaging company, achieving effective integration with food safety standards such as BRC certification, facilitating better process control without increasing operational complexity [18]. They proposed an intelligent VSM model, integrating automation with Industry 4.0 technologies, facilitating bottleneck visualization and faster decision-making [19]. These studies agree that automation directly affects performance, especially in highly variable processes. A hybrid model was designed for performance monitoring in injection molding, using digital architecture that allowed for improved monitoring of process efficiency [20]. They presented a methodology for converting graphical models into automatic control languages, facilitating the integration of legacy systems with new technologies [21]. Similarly, an interoperable system was developed to extract control data from old machinery, opening a path to digitizing operations without total replacement [22]. Although the benefits of automation are widely recognized, much of the literature focuses on high-tech industries, ignoring sectors where processes are still partially manual. In these contexts, the key is to identify repetitive and error-prone subprocesses. In this model, automation focused on the setup, cutting, and die-cutting subprocesses, which reduced operational variability, improved accuracy, and generated more predictable conditions to achieve a higher OEE. 2.4. IoT for OEE Improvement The Internet of Things (IoT) has transformed industrial monitoring systems by enabling real-time analysis of variables and facilitating predictive maintenance. An IoT-based automatic diagnostic system was implemented, reducing downtime and anticipated production line failures [23]. They demonstrated that integrating IoT into a conveyor line enabled faster maintenance decisions and increased asset availability by over 90% [24]. IoT dashboards were used to monitor key variables such as pressure, temperature, and speed, which helped keep the process within defined parameters [20]. A multi-theoretical conceptual model was developed to explain IoT adoption in manufacturing, highlighting that success depends on the technology and organizational preparedness [25]. It was identified that IoT improves visibility into the production process, enabling more accurate diagnostics and resource savings [26]. They developed an intelligent monitoring system that enabled automated decision-making based on sensor data at each process stage [27]. TPM, IoT, and Lean Six Sigma were combined in a pharmaceutical plant, improving maintenance planning and reducing unforeseen failures [28]. They reinforced the importance of IoT by pointing out that its implementation, along with SMED and TPM, directly contributes to a sustained increase in OEE [7]. The reviewed studies conclude that the impact of IoT is significant, provided critical operating variables are identified and an organized response is generated to the captured data. This work’s proposal considered sensors to monitor diecutting machine wear and lubricant use in offset and guillotine presses. This integration facilitated maintenance planning, reduced corrective interventions, and contributed to a quantifiable improvement in OEE, validated through simulation. 3. Innovative Proposal 3.1. Rationale The design of this proposal to increase Overall Equipment Effectiveness (OEE) in the carton production process is based on key research studies addressing the use of tools and technologies such as SMED, TPM, automation, and IoT. These studies highlight industrial approaches that optimize operational efficiency and reduce downtime. One-Minute Die Change (SMED) is a technique used to minimize tool changeover times and, therefore, increase equipment availability. This implementation in machining processes achieved a 44.6% increase in OEE, a 30% reduction in changeover times and a 9% increase in OEE was reported [2]. These results emphasize the importance of SMED in optimizing downtime and increasing efficiency. Regarding Total Productive Maintenance (TPM), its perspective on autonomous and planned maintenance is crucial to avoiding unexpected failures and improving machine availability. OEE increased from 60.7% to 65.3% thanks to this implementation [29], while preventive maintenance improved by 88% and repair time was reduced by 53% [10]. These facts confirm that TPM is essential for preventing unplanned downtime and maintaining efficient machine operation. Rolando Gonzales-Vera et al. / IJME, 12(6), 34-51, 2025 37 Automation is another fundamental tool. Robots and PLCs have helped companies to reduce human errors and accelerate production processes [17]. Efficiency was also improved by 15% and costs reduced by 20% through automation [30]. Finally, IoT allows real-time monitoring and anticipation of failures, improving connectivity and enabling faster decision-making, optimizing production processes [25]. Furthermore, it increases the accuracy of OEE measurements by 25% and reduces operating costs by 18% [20]. This demonstrates the value of IoT in improving machine efficiency and connectivity, providing a solid foundation for implementing SMED, TPM, automation, and IoT to improve OEE and operational efficiency, enabling the effective integration of these tools into the carton production process. 3.2. Proposed Model The objective is to increase the OEE of the cardboard box production process by 64.27% through the use of SMED and TPM tools, optimized with automation and IoT, respectively. The solution is developed in a three-phase conceptual model, guided by the PMBOK approach. The first phase includes defining the scope and SMART objectives related to the causes of low OEE. The second phase focuses on implementing SMED, TPM, automation, and IoT. Finally, the third phase involves evaluating improvements and closing the project to achieve an increase in OEE and other additional benefits. This solution model (summarized in Figure 1) is distinguished by its innovative approach in the graphic sector, combining Lean tools such as SMED and TPM with advanced technologies like Automation and the Internet of Things (IoT). Rather than applying these techniques and technologies separately, it proposes a sequential integration: first SMED, then automation, followed by TPM, and finally, its integration with IoT. This combination seeks to maximize the effectiveness of Lean tools and significantly improve OEE, highlighting the value of technologies in process optimization. 3.3. Model Details A flowchart was created to present the implemented procedures and improvement approach to outline the specific steps taken, as shown in Figure 2. Fig. 1 Proposed conceptual solution model 3.3.1. Initial Phase In this first phase, the first step is determining the project scope. This is achieved using a project charter. The project committee will consist of the general manager, operations manager, press supervisor, maintenance supervisor, and the Quality Management System leader. The project scope is also defined by assigning roles to the project members through a RACI matrix (Responsible, Accountable, Consulted, and Informed). The second step in this initial phase involves identifying the SMART objectives closely related to the root causes contributing to the current low OEE. The identified root causes focus on machine wear, lubrication, and setup configuration. The conducted diagnosis reveals that within the overall carton box production process, the cutting, printing, and die-cutting subprocesses are the most critical as they have the lowest OEE compared to other subprocesses. Therefore, the SMART objectives are: Defective products in process Low machine availability Increase the OEE of the cardboard box production process TPM Autonomous and Planned Maintenance SMED Automation IoT Better product quality Higher machine availability Initial Phase Closure Implementation Output Input Rolando Gonzales-Vera et al. / IJME, 12(6), 34-51, 2025 38 Reduce setup time in the die-cutting process by 33.33% within six months by implementing SMED and integrating Automation with SMED. Reduce wear on die-cutting machines due to improper pressure by 30% within six months through implementing TPM and integrating IoT with TPM. Improve machine lubrication in the cutting process by 43.50% within four months by implementing standardized procedures and specific training. Improve machine lubrication in printing by 23.81% within four months through standardized procedures and specific training. 3.3.2. Implementation SMED Implementing SMED (Single-Minute Exchange of Die) in carton box production focuses on reducing setup times for the cutting, printing, and die-cutting subprocesses. The six steps of the implementation process are as follows (summarized in Figure 3): Breakdown of the changeover into operations: Identify each operation involved in the setup process. Separation of internal and external activities: Classify each operation as internal (requires stopping the machine) or external (can be performed while the machine is running). Conversion of internal activities to external: Restructure the process to move as many internal activities as possible to external ones, allowing the machine to operate for longer without interruptions. Reduction of internal activities: Minimize internal activities that cannot be converted to external through the standardization of procedures. Reduction of external activities: Optimize them so they are performed quickly. Standardization of the changeover: Document and formalize the new process to ensure the changes are maintained and consistently applied. Fig. 2 Procedure for the solution model Fig. 3 Flowchart for SMED implementation Rolando Gonzales-Vera et al. / IJME, 12(6), 34-51, 2025 39 The setup verification sheet is fundamental for recording and controlling the activities performed during setup changes. After every setup, each operator must complete it, ensuring all activities are carried out according to the expected standards and times. This instrument allows for detailed monitoring of each task, offering a tangible way to evaluate the process’s efficiency. Centralizing information makes identifying problem areas or unexpected downtime easier, allowing for quick adjustments. It also guarantees operational responsibility since each operator signs and commits to following the steps. SMED with Automation Automation complements the implementation of SMED, focusing on critical tasks such as configuration adjustments and tool changes using cobots (collaborative robots). The key steps for automating SMED are (summarized in Figure 4):  Automation plan for configuration adjustments: Identify the tasks that can be automated, such as roller changes, cutting pressure adjustments, and component alignment.  Use of cobots in the setup process: Cobots carry out setup activities automatically, reducing the time previously taken for manual tasks.  Automatic sequencing and setup optimization: Cobots are programmed to perform configuration adjustments in an optimal sequential order, minimizing downtime. The Configuration Adjustment Automation Plan details the automated tasks, the cobots assigned, current times, and the reductions achieved, allowing for continuous monitoring of the process and ensuring effective time reductions.This instrument acts as a structured guide that allows for the evaluation of automation effectiveness. Documenting the adjustments made by cobots enables an objective comparison of the impact of automation versus manual methods. It also provides a clear basis for continuous improvement since each automated process is recorded and can be reviewed for further optimization. TPM Autonomous Maintenance Autonomous Maintenance within TPM aims to have operators take responsibility for the basic maintenance of machines. This process follows five key steps (summarized in Figure 5):  Initial cleaning: Operators thoroughly clean the machines to remove dust and debris.  Eliminating contaminant sources: Areas prone to contaminant buildup are identified, and preventive measures are taken.  Establish cleaning and lubrication standards: Create clear procedures to perform these tasks efficiently.  Inspection and standardization: Operators check machines to identify any signs of wear.  Implement systematic Autonomous Maintenance: Operators regularly perform established tasks, creating a discipline of continuous and repetitive maintenance. Fig. 4 Flowchart for SMED implementation with automation Rolando Gonzales-Vera et al. / IJME, 12(6), 34-51, 2025 40 Fig. 5 Flowchart for TPM – autonomous maintenance implementation Fig. 6 Flowchart for TPM – planned maintenance implementation Planned Maintenance Planned maintenance is essential for preventing failures and reducing unplanned downtime. It involves the following steps (summarized in Figure 6):  Assessment of the current machine condition: Perform diagnostics to identify signs of wear or potential failures.  Restoration of machine condition: Perform repairs to restore optimal operating conditions.  Creation of a preventive maintenance system: Schedule regular maintenance based on machine usage and condition.  Evaluation of the Planned Maintenance system: Periodic reviews of maintenance results are conducted to ensure effectiveness and adjust if necessary. The One-Point Lesson guides operators through planned maintenance procedures, detailing each step with clear instructions to ensure all tasks are performed consistently and efficiently. This format is a simple yet effective tool that can standardize technical knowledge quickly and clearly. Following this document ensures that all operators follow the same procedure, reducing variability and improving the quality of maintenance work. TPM with Internet of Things (IoT) Integrating IoT sensors enables real-time monitoring of machine status, which facilitates predictive maintenance. Some key steps for this implementation are (summarized in Figure 7):  Implementation of IoT sensors: Installation of sensors at critical points of the machines, monitoring variables such as pressure, vibration, and temperature.  Automatic alert generation and proactive response: When sensors detect values outside normal parameters, alarms are generated, allowing immediate action.  Data analysis for predictive maintenance: Stored data is analyzed to identify repetitive failures and anticipate potential anomalies.  Action plan and monitoring: Based on the data and alarms generated, the necessary operational work is planned to avoid unplanned downtime. Rolando Gonzales-Vera et al. / IJME, 12(6), 34-51, 2025 41 Fig. 7 Flowchart for TPM implementation with IoT The IoT Sensor Implementation Plan shows the installed sensors’ location, monitoring frequency, and the respective installed measurements that will trigger automatic alarms and enable efficient predictive maintenance management. This document is crucial because it provides a structured and detailed overview of the technology used for machine monitoring. With this document, sensors can be managed, and their effectiveness in data collection can be evaluated, ensuring that predictive maintenance works proactively and not reactively. 3.3.3. Closure Phase In this final phase, the improvements are evaluated using quality tools such as histograms (for setup times), scatter plots (for lubrication), and the Process Capability Index (Cpk) (for die-cutting machine wear). Regarding the setup times for die-cutting, the histogram shown in Figure 8 reveals a notable improvement in efficiency and consistency following the implementation of improvements. The reduction in mean and standard deviation reflects an optimized process, resulting in shorter and more stable times. Fig. 8 Histogram for setup delays in die-cutting Rolando Gonzales-Vera et al. / IJME, 12(6), 34-51, 2025 48 Interoperable system simulations were employed to reduce downtime by 18% [22], while IoT-based simulations demonstrated a 20% reduction in failure response time [19]. IoT simulations were used to enhance flexibility in Industry 4.0, with improvements in operational efficiency [34]. In conclusion, simulation is an essential tool for predicting the impact of SMED, TPM, IoT, and Automation improvements, enabling significant increases in operational efficiency and OEE across various industrial sectors. 6. Discussion 6.1. Scenarios Vs Results The analysis of the three proposed scenarios (moderate, optimistic, and pessimistic) demonstrates the model’s capacity to significantly improve OEE in the cardboard box production process by integrating SMED, TPM, Automation, and IoT. The quantitative results reflect the impact of these technologies and methodologies on the efficiency and availability of the production process.  Moderate scenario: This scenario increases OEE from 64.27% to 71.90% (shown in Table 6), which is significant and brings the index closer to the graphic sector standard of 72.5%. This increase is mainly achieved by reducing die-cutting setup time from 1,200 to 950 minutes and printing from 1,875 to 1,450 minutes. Machine availability improves, reducing unexpected stoppages and increasing effective production time. Table 6. Comparison of current OEE Vs. moderate scenario OEE (As–Is) OEE (To-Be Moderate) 64.27% 71.90% Optimistic Scenario: With a more ambitious improvement, OEE reaches 82.70% (shown in Table 7), surpassing industry standards. This is achieved by reducing die-cutting setup time to 836.69 minutes and printing setup time to 1,200 minutes. Additionally, equipment availability increases due to combining TPM and predictive maintenance with IoT, enabling real-time machine status monitoring. This optimization reduces equipment wear, particularly for diecutters, whose wear rate decreases from 36.87% to 24.58%. Table 7. Comparison of current OEE VS. optimistic scenario OEE (As–Is) OEE (To – Be Moderate) 64.27% 82.70%  Pessimistic Scenario: Even under less favorable conditions, the proposed model increases OEE from 64.27% to 66.22% (Table 8). In this case, improvements in setup times and equipment availability are moderate but still show an increase that helps maintain stable production and reduce process losses. This scenario demonstrates that the applied improvement tools and technologies can enhance process efficiency even with resource limitations. Table 8. Comparison of current OEE Vs pessimistic scenario OEE (As–Is) OEE (To – Be Pessimistic) 64.27% 66.22% The results highlight the model’s ability to adapt to different conditions and investment scales, supporting its flexibility and scalability. The improvement in OEE across all scenarios underscores the importance of integrating Lean tools and Industry 4.0 technologies in production processes to achieve competitive efficiency levels. 6.2. Results Analysis The analysis considered economic, operational, and maintenance criteria, providing a comprehensive view of the model’s impact. Table 9. Financial indicators  Economic criterion: The financial results summarized in Table 9 demonstrate that the model is profitable in all evaluated scenarios. In the moderate scenario, the Net Present Value (NPV) is $ 53,567.50, and the Internal Rate of Return (IRR) reaches 36.77%, significantly exceeding the Cost of Capital (COC) of 19.16%. In the optimistic scenario, these values increase due to improvements in OEE and reduced operating costs, optimizing resource usage. Additional revenues from increased production and reduced downtime enable an investment payback period of approximately three years, supporting the economic feasibility of the proposal.  Operational criterion: The improvements in the operational efficiency of the production process are remarkable. In the die-cutting subprocess, reducing setup times from 1,200 to 836.69 minutes in the optimistic scenario allows for greater production flexibility, optimizing workflow and reducing waiting times. In printing, reducing setup times from 1,875 to 1,200 minutes in the same scenario increases production capacity. These optimizations improve equipment availability and enhance responsiveness to fluctuating market demands.  Maintenance criterion: The implementation of TPM, along with predictive maintenance through IoT, provides Scenario Moderate Optimistic Pessimistic COC 19.16% 19.16% 19.16% NPV 53,567.50 177555.74 10023.76 IRR 36.77% 72.60% 32.34% B/C 1.4363 2.4463 1.3266 PBP 3.07 1.70 3.54 Years 3 1 3 Months 1 8 6 Rolando Gonzales-Vera et al. / IJME, 12(6), 34-51, 2025 49 more efficient asset management for the company. In the optimistic scenario, die-cutter wear is reduced from 36.87% to 24.58%, minimizing failure stoppages and extending equipment lifespan. Additionally, increased lubricant usage in guillotines and printing (255.04 ml and 569.21 ml, respectively) helps maintain equipment in optimal operating conditions. These changes reflect decreased corrective maintenance costs and greater equipment reliability, ensuring continuous and uninterrupted operation. 7. Conclusion  The implementation of the model based on SMED, TPM, Automation, and IoT increases the OEE of the cardboard box production process from an initial 64.27% to 82.70% in the optimistic scenario. This improvement allows the company to reach and surpass the industry standard of 72.5%, positioning it at a competitive level in the market. This increase in efficiency optimizes resource usage and reduces operating costs by minimizing downtime, resulting in a key competitive advantage for the company.  The financial results validate the viability of the proposed model in all evaluated scenarios. In the moderate scenario, with an NPV of $ 53,567.50 and an IRR of 36.77%, the project’s profitability significantly exceeds the Cost of Capital (19.16%), indicating a payback period of approximately three years. This demonstrates that the model is financially sustainable and contributes to the company’s long-term economic stability, enabling increased revenue derived from greater operational efficiency.  The reduction in die-cutter wear (from 36.87% to 24.58% in the optimistic scenario) and the optimization of lubricant usage are indicators of efficient maintenance management. TPM implementation, together with realtime monitoring via IoT, reduces unplanned stoppages, ensuring the availability and continuity of the production process. These results confirm that the proposed model increases efficiency and ensures operational sustainability by optimizing the use and maintenance of assets.  The model improves efficiency, reduces costs, and contributes to more sustainable production. Reduced downtime and lower equipment wear decrease the need for frequent repairs and material use, which reduces the company’s carbon footprint and promotes responsible manufacturing practices. This approach, aligned with sustainability trends, provides an additional advantage regarding corporate social responsibility.  The model establishes a solid foundation for digital transformation in the graphic sector by integrating IoT and Automation into production processes. This allows for smarter and more adaptive operations that can efficiently respond to changes in demand and market fluctuations. This model represents a significant step toward smart manufacturing, strengthening the company’s position as an innovative and resilient player in a constantly evolving sector. Acknowledgments The authors would like to acknowledge Dirección de Investigación de la Universidad Peruana de Ciencias Aplicadas, which supported financial and research facilities through UPC-EXPOST-2025-1 and the University of Arizona. 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