Journal of Industrial Engineering and Management

Empowering factory employees through low-cost automation in IOT adoption

Yuji Yamamoto, Alvaro Munoz Aranda, Kristian Sandström — Fri, 08 May 2026 08:05:27 +0000

Purpose: This study aims to understand the potential and challenges of incorporating low-cost automation (LCA) in the adoption of the Internet of Things (IoT) within the context of Industry 4.0 implementation and how it influences factory employee empowerment.

Design/methodology/approach: Multiple case studies were conducted with industrial companies located in Sweden. Relevant empirical evidence was collected from the workshops where factory employees explored and prototyped simple IoT solutions to improve their work environments.

Findings: The study revealed multiple benefits of LCA incorporation, including enhancing factory employees' knowledge, skills, and motivation for improvement. The compatibility of LCA with Lean production was also found to be positive for the employees. However, the study also identified several challenges, mainly concerning preparing organisational preconditions that enable or facilitate the incorporation, such as its strategic alignment and securing managers' understanding and support. The study found a tension between simple IoT solutions and large integrated solutions in terms of empowerment. Managing the tension was found particularly challenging.

Originality/value: This paper provides a more comprehensive understanding of the potential and limitations of incorporating LCA into IoT adoption than previous studies. It highlights the potential of a bottom-up approach to IoT adoption which is currently predominated by top-down, engineering-driven approaches.

Mitigating supply chain risks of concentrate feed for the dairy cows through smart supply chain implementation

Iphov Kumala Sriwana, Muhammad Almaududi Pulungan, Zahrayna Shebina Syabira — Fri, 08 May 2026 08:05:27 +0000

Purpose: This research aims to mitigate risks within the MAKO (concentrate feed for the dairy cows) supply chain by integrating the House of Risk (HOR) method with the application of smart supply chain technologies based on the Internet of Things (IoT). The objective is to enhance operational efficiency, reduce risks throughout the supply chain, and strengthen the company's competitiveness by leveraging technology at every stage of supply chain management.

Design/methodology/approach: This research was conducted using the House of Risk (HOR) method to identify, evaluate, mitigate, and manage risks within the MAKO (concentrate feed for the dairy cows) supply chain. Mitigation actions for prioritized risk agents were implemented through the application of smart supply chain technologies, including e-procurement systems, barcode systems, and a website-based database utilizing the Internet of Things (IoT) to monitor supply chain flows in real time.

Findings: The research identified 28 risk events and 25 risk agents within the supply chain, with 12 risk agents prioritized for mitigation. Based on the results of proactive action design, 13 risk mitigation actions were proposed. The priority mitigation actions included training on the use of smart supply chain technologies, developing website-based databases to integrate internal and external data, implementing barcode technology for warehouse management, and adopting e-procurement technology for procurement processes.

Research limitations/implications: This research is limited to identifying risks in the MAKO (concentrate feed for the dairy cows) supply chain and designing the user interface for smart supply chain technologies based on the Internet of Things (IoT), without full-scale system implementation.

Practical implications: The findings contribute to the development of smart supply chain management systems tailored for businesses operating within the livestock sector.

Social implications: This research encourages improved digital literacy and technological transformation readiness among livestock sector entrepreneurs.

Originality/value: This research integrates the House of Risk (HOR) method for risk mitigation with the application of smart supply chain technologies, including e-procurement systems, barcode systems, and website-based database using Internet of Things (IoT), within the specific context of the livestock sector.

KaizenAI: Methodology for the integration of machine learning in manufacturing processes based on Kaizen principles. Case study: Bottling industry

Fri, 08 May 2026 08:05:27 +0000

Purpose: Digital transformation in manufacturing has placed artificial intelligence (AI) at the center of the debate on efficiency and sustainability. However, its adoption in established plants faces barriers related to cultural resistance, data quality, and the absence of methodologies that guide its progressive implementation. In this context, the article proposes KaizenAI, a hybrid methodology that integrates the principles of continuous improvement with the predictive capabilities of AI.

Methodoloy/approach: This article presents KaizenAI, a hybrid methodology that integrates the principles of the PDCA cycle with user-centered design (UCD) tools, quality function deployment (QFD), Kata routines, and digital 5S principles, combining them with machine learning models to anticipate failures and optimize processes. The proposal was validated through a case study in a bottling plant, applying a predictive model based on 18 months of operational data from the OEE system.

Findings: Preliminary results show that a plant with intermediate digital maturity (2.6/6) can develop effective predictive capabilities by integrating the Kaizen approach with interpretable statistical models. The SARIMA model outperformed Random Forest and XGBoost with a 98.2% reduction in MAE, demonstrating that methodological simplicity can surpass algorithmic complexity in industrial environments with high variability. Moreover, the Kaizen–AI convergence acts as a methodological bridge for introducing digital capabilities without breaking the incremental logic of continuous improvement.

Originality / Value: The main contribution of the study lies in integrating the Kaizen philosophy with AI within a hybrid framework that connects the human with the digital and the incremental with the predictive. This approach facilitates the gradual adoption of AI without disrupting continuous improvement and offers a practical pathway toward Industry 4.0 while minimizing organizational resistance.

A decision support system for faculty performance management: A case report using statistical analysis, text mining, and artificial intelligence

Sergio Rosales-Anzola, Doris Baptista, Christian Guillen-Drija — Tue, 19 May 2026 14:35:27 +0000

Purpose: This study presents a management methodology for comprehensively evaluating teaching performance by integrating statistical analysis of quantitative data, sentiment mining from text, and artificial intelligence tools. The objective is to provide academic managers with a robust and efficient diagnostic system that enables the continuous improvement of educational quality through the systematic identification of faculty strengths and areas for improvement, thereby facilitating the decision-making process in academic management.

Design/methodology/approach: The research adopts an Action Research approach, developing and implementing the EvalúaPro application using MATLAB® App Designer. Student evaluations from the 2425-1 (September-December 2024), 2425-2 (January-April 2025) and 2425-3 (April-July 2025) academic periods were analyzed, which included quantitative (Likert scale questions) and qualitative (open-ended comments) components. For the 2425-1 period, 362 evaluations were analyzed, corresponding to 30 sections of 21 courses taught by 20 faculty members. For the 2425-2 period, 338 evaluations from 33 sections of 24 courses taught by 24 faculty members were processed, and for the 2425-3 period, 447 evaluations were analyzed, corresponding to 31 sections of 24 courses taught by 23 faculty members. All participants belonged to a department within the engineering faculty. Teaching competencies were strategically categorized into Soft Skills (Effective Communication, Interpersonal Skills, Time Management, and Organization) and Technical/Professional Skills (Content Mastery, Teaching Methodology). The qualitative analysis implemented the VADER algorithm for sentiment mining, while descriptive statistics were used for the quantitative analysis. Validation included tests with department heads to assess the application's effectiveness as a management tool.

Findings: The methodology proved highly effective for the managerial diagnosis of teaching performance, facilitating the identification of patterns at both individual and departmental levels. In the validation with department heads, 87.5% "agreed" or "strongly agreed" that the information presented by the prototype facilitates decision-making regarding faculty support, monitoring, and evaluation (37.5% "strongly agree," 50% "agree," 6.3% "neither agree nor disagree," 6.3% "strongly disagree"). Regarding the generated improvement plan, 93.8% of department heads "agreed" or "strongly agreed" that it accelerates feedback to faculty (43.8% "strongly agree," 50% "agree," 6.3% "strongly disagree"). Concerning its utility for diagnosis and decision-making for continuous improvement, 87.5% expressed they "agreed" or "strongly agreed" (62.5% "strongly agree," 25% "agree," 12.5% "neither agree nor disagree"). The system generated personalized improvement plans for faculty with scores below 3.0 and departmental strategies when more than 25% of professors showed similar areas for improvement. Furthermore, the system translates its integrated data analysis into a predictive tool, automatically alerting managers to signs of student dissatisfaction and thereby facilitating preemptive support measures.

Research limitations/implications: The main limitations include adapting the VADER algorithm for the specific academic context and requiring constant feedback to refine the artificial intelligence algorithms. Further research is required to validate the effectiveness of the automatically generated improvement plans in subsequent academic periods and their impact on improving teaching performance.

Practical implications: The methodology significantly reduces academic managers' time analyzing teaching evaluations, enabling faster and more specific feedback. The system facilitates identifying specific training needs that institutional resources, such as the Teaching Center, can address, thereby improving the efficiency of academic human resource management.

Social implications: Implementing this methodology enhances the analysis of educational evaluation, ensuring that student opinions are systematically considered for continuous institutional improvement, which can potentially reduce student attrition and enhance the overall educational experience.

Originality/value: This methodology represents an innovation that improves educational management by integrating advanced data analysis tools with structured managerial processes. The holistic approach, which combines statistical analysis, text mining, and artificial intelligence for faculty evaluation, offers significant value to educational institutions seeking to implement evidence-based continuous improvement systems. The strategic categorization of skills and the automatic generation of personalized improvement plans constitute an original contribution to educational management.

Daily job rotation strategies in Industry 5.0: A literature review on operational and human-centric outcomes

Thu, 21 May 2026 09:58:14 +0000

Purpose: Daily job or task rotation is frequently associated with Industry 5.0 principles, particularly in its potential to enhance worker engagement and motivation on the shop floor. However, existing research in this area has predominantly focused on ergonomic considerations. This study aims to investigate the broader effects of daily job rotation on workers and to gain a deeper understanding of how organizations implement and manage such practices within their operational environments.

Design/methodology/approach: A systematic literature review was conducted using three databases in accordance with PRISMA guidelines. Each retained study was classified to highlight operational differences between rotation policies. Reported effects on human factors were analyzed using the Team and Job Design Model (Humphrey & Morgeson, 2008), while impacts on workers’ performance were examined through the Overall Labor Effectiveness (OLE) framework.

Findings: Many studies omit key operational details, limiting understanding of what workers actually experience during rotations. The review also highlights a limited use of technology to support job rotation decisions. While some authors advocate for data-driven approaches, most observed policies occur at predetermined intervals, often aligned with breaks for convenience. Daily job rotation appears to increase job satisfaction and alleviate monotony, yet its effects on perceived workload and motivation require further investigation. Similarly, evidence regarding performance outcomes is inconclusive. Workers’ subjective appraisals of their performance tend to be more positive than objective measurements.

Originality/value: This study provides managers with a list of characteristics intrinsic to job rotation’s policy design. The results and ensuing discussion outline future research avenues to enhance workers’ engagement and motivation in manufacturing contexts aligned with Industry 5.0 principles.

Optimitzation of aggregate planning and inventory in the sunflower supply chain based on situational analysis using soft system dynamics methodology

Nunung Nurhasanah, Tharra Azzahra Riyana — Tue, 02 Jun 2026 13:59:17 +0000

Purpose: This study seeks to improve the international competitiveness of the Indonesian sunflower industry. The study includes several particular objectives to accomplish this goal: (1) To assess the present condition of the sunflower industry supply chain, and (2) To formulate an optimization model for production and inventory management.

Design/methodology/approach: The Soft System Dynamics Methodology (SSDM) was used for situational analysis, followed by optimization of production planning through demand forecasting using the Artificial Neural Network method and aggregate planning using the Heuristic method. Furthermore, inventory optimization was carried out using the working capital and storage space restriction model with three algorithms: Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing.

Findings: Based on the inventory optimization using the three algorithms, it was found that the Genetic Algorithm (GA) resulted in the lowest total inventory cost calculation, amounting to IDR2,943,675.

Research limitations/implications: This study investigates a sunflower industry located in Bandung Regency, Indonesia. The optimization models applied—namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA)—were specifically adapted to the available operational data. As a result, the findings may not be directly generalizable to other agro-industries operating under different conditions. Furthermore, limitations in resources, such as machinery and storage capacity, posed constraints on the system simulation for inventory planning.

Practical implications: This research offers practical methods and approaches for sunflower agro-industries to enhance operational efficiency, reduce production costs, and optimize storage space utilization. Recommendations, such as the moderate production scenario, provide actionable insights for fulfilling consumer demand optimally while avoiding lost sales.

Social implications: The study has potential socioeconomic benefits, particularly for farmers and local workers involved in the industry. Improved planning systems can contribute to economic stability for these communities while also ensuring high-quality sunflower-derived products become more widely available and affordable to the public.

Originality/value: The integration of diverse methodologies, including the Soft System Dynamics Methodology (SSDM), demand forecasting through Artificial Neural Networks (ANN), aggregate planning via heuristic methods, and inventory optimization using GA, PSO, and SA, distinguishes this study as a unique, comprehensive approach. This holistic framework adds significant value to the academic field of production and inventory planning, especially for small-scale industries like sunflower agro-industries.

Models and strategies for supply chain synchronization: A 2010-2025 review of small business and agro-industrial sector experiences

Tue, 02 Jun 2026 13:59:46 +0000

Purpose: This article presents a systematic review of the knowledge management literature focused on integration strategies and models in supply chain (SC) collaboration. Although previous research has highlighted the benefits of SC linkages, limited attention has been given to the specific strategies and models that underpin these connections, particularly how they operate within SC integration.

Design/methodology/approach: A four-phase systematic review was conducted, analyzing 173 publications from 2010 to 2024 in both Spanish and English. The study examines: SC collaboration improvement, quantitative integration methods, integration strategies, and model development for effective SC linkages.

Findings: The results highlight the crucial role of digital technologies and collaborative approaches for small and medium-sized enterprises (SMEs) in maintaining competitiveness and long-term viability. Significant differences in linkage models and strategies were observed across continents, with most integration strategies developed at the initial stages of the SC. More than 50% of the studies report linkage activities between suppliers, organizations, and customers. Information exchange between SC actors was identified as a key factor in reinforcing these connections.

Research limitations/implications: The study is limited by the scope of the publications reviewed, which are confined to literature published between 2010 and 2024. Future research could explore more diverse regional contexts and expand on the evolving technological challenges faced by SMEs in SC collaboration.

Practical implications: SMEs must adopt digital technologies to overcome resource constraints and enhance their ability to integrate into global SCs. The research identifies region-specific strategies and tools that can guide SMEs in adopting context-appropriate models for effective SC collaboration.

Originality/value: This study provides new insights into the differentiation of SC integration models and strategies across continents, particularly within the agro-industrial sector. It offers a comprehensive analysis of the digital and structural challenges that SMEs face and describes practical solutions to enhance SC integration through tailored regional strategies.

Dynamic game of closed-loop supply chain with remanufacturer participation considering product design for recycling

Jianying Sun — Tue, 02 Jun 2026 14:01:26 +0000

Purpose: The change in recovery rate is dynamic, while the front and back states are related and evolve continuously until reach a steady state. This paper studies the dynamic balancing strategies of members of a closed-loop supply chain system (CLSC) consisting of the original equipment re-manufacturer (OEM) and the Third-party remanufacturer (TPR).

Design/methodology/approach: Using the Itô process, the characteristics of the stochastic evolution of the recovery rate of the supply chain system are described. Based on the behavior of OEM investment in product recyclable design, the profit target function of OEM seeking to maximize profit is constructed. According to the composition of the retailer's profit, the profit target function of the retailer seeking to maximize the profit is constructed. The behavior of recycling and remanufacturing activities of TPR provided a basis for the profit target function of TPR constructed to pursue profit maximization. The evolution of optimal decision-making and profit of a closed-loop supply chain under centralized decision-making, decentralized decision-making, and TPR cost-sharing coordination contract mechanism is also discussed. The evolution process of recovery rate under decentralized decision making, centralized decision making, and coordinated contract mechanism of different TPRs sharing the proportion of product recoverable design investment cost is studied.

Findings: The results show that the recovery rate of a closed-loop supply chain under a coordination contract mechanism is higher than that under decentralized decision-making but lower than that under centralized decision-making. The higher proportion of TPR cost-sharing leads to a higher recovery rate.

Originality/value: This study introduces a dynamic game-theoretic model for closed-loop supply chains, incorporating third-party remanufacturers and product recyclable design. It uses the Itô process to model stochastic recovery rates and explores centralized, decentralized, and cost-sharing mechanisms. Findings highlight that cost-sharing improves recovery rates, offering practical insights for sustainable supply chain coordination.

The path to operational excellence: Implementing Kaizen principles for competitive advantage. A multiple case study

Luis Paipa, Arturo T. De Zan — Tue, 02 Jun 2026 14:01:55 +0000

Purpose: This study aims to examine the practical application of Kaizen philosophy and its relevance to strengthening organizational competitiveness in Colombian companies from different industrial sectors.

Design/methodology/approach: The research adopts a multiple case study approach to evaluate the maturity level of Kaizen principles—creating customer value, flow optimization, Gemba orientation, people empowerment, and scientific transparency—using the Kaizen Maturity Model (KMM).

Findings: The results reveal the levels of implementation and main challenges faced by each analyzed organization. A detailed diagnosis and practical recommendations are provided for improving operational efficiency and fostering a culture of continuous improvement.

Research limitations/implications: This study focuses exclusively on Colombian companies, which may limit the generalizability of the findings to other contexts or regions. Future research could explore the application of the Kaizen Maturity Model in a broader range of industries and geographical areas.

Practical implications: The article offers a benchmarking tool based on the Kaizen Maturity Model (KMM) to help companies identify and address gaps in their operational processes, ultimately facilitating their journey toward operational excellence.

Social implications: By fostering a culture of continuous improvement, the adoption of Kaizen principles can contribute to enhanced organizational efficiency, employee empowerment, and better alignment with societal expectations of sustainable and ethical business practices.

Originality/value: This study provides a novel application of the Kaizen Maturity Model (KMM) in Colombian companies, delivering actionable insights and practical recommendations to emulate the efficiency and adaptability exemplified by Toyota.