International Journal of Production Management and Engineering <p style="text-align: justify; text-justify: inter-ideograph; margin: 0cm 0cm 6.0pt 0cm;"><strong>International Journal of Production Management and Engineering </strong>is an <em>open access scientific journal </em>that publishes theoretical and empirical peer-reviewed articles in English twice a year. Contributions must promote the progress and understanding of phenomena related with all aspects of production engineering and management.</p> Universitat Politècnica de València en-US International Journal of Production Management and Engineering 2340-4876 <p><a href="" rel="license"><img src="" alt="Creative Commons License" /></a></p> <p>This work is licensed under a <a href="" target="_blank" rel="noopener">Creative Commons Attribution-NonCommercial-NoDerivatives- 4.0 International License</a> </p> <p> </p> Rediscovering scientific management. The evolution from industrial engineering to industrial data science Industrial Engineering, through its role as design, planning and organizational body of the industrial production, has been crucial for the success of manufacturing companies for decades. The potential, expected over the course of Industry 4.0 and through the application of Data Analytic tools and methods, requires a coupling to established methods. This creates the necessity to extend the traditional job description of Industrial Engineering by new tools from the field of Data Analytics, namely Industrial Data Science. Originating from the historic pioneers of Industrial Engineering, it is evident that the basic principles will remain valuable. However, further development in view of the data analytic possibilities is already taking place. This paper reviews the origins of Industrial Engineering with reference to four pioneers, draws a connection to current day usage, and considers possibilities for future applications of Industrial Data Science. Jochen Deuse Nikolai West Marius Syberg Copyright (c) 2022 International Journal of Production Management and Engineering 2022-01-31 2022-01-31 10 1 1 12 10.4995/ijpme.2022.16617 Solving stochastic multi-manned U-shaped assembly line balancing problem using differential evolution algorithm The U-shaped assembly lines help to have more flexibility than the straight assembly lines, where the operators can perform tasks in both sides of the line, the entrance and the exit sides. Having more than one operator in any station of the line can reduce the line length and thereby affects the number of produced products. This paper combines the U-shaped assembly line balancing problem with the multi-manned assembly line balancing problem in one problem. In addition, the processing times of the tasks are considered as stochastic, where they are represented as random variables with known means and variances. The problem is formulated as a mixed-integer linear programming and the cycle time constraints are formulated as chance-constraints. The proposed algorithm for solving the problem is a differential evolution algorithm. The parameter of the algorithm is optimized using experimental design and the computational results are done on 71 adapted problems selected from well-known benchmarks. Mohammad Zakaraia Hegazy Zaher Naglaa Ragaa Copyright (c) 2021 International Journal of Production Management and Engineering 2022-01-31 2022-01-31 10 1 13 22 10.4995/ijpme.2021.16084 Conceptual model for assessing the lean manufacturing implementation maturity level in machinery and equipment of small and medium-sized enterprises The adoption of lean manufacturing (LM) in small and medium-sized enterprises (SMEs) is not as vigorous as in large organizations. This purpose of this study is to assess the maturity level of LM implementation in the machinery and equipment (M&amp;E) SMEs. The close-ended survey questionnaire method was adopted in three Malaysian manufacturing M&amp;E SMEs, and data was collected for the descriptive analysis. The findings showed that these case companies are generally at a low-to-moderate level in terms of LM understanding. Meanwhile, the extent of LM implementation and the success level is still moderate. The proposed LM conceptual model provides valuable perspectives and establishes a holistic understanding of the phenomena in LM maturity status for M&amp;E SMEs. The proper synchronization of LM understanding, implementation, and success are vital to building the strong LM maturity foundation for lean organizational transformation. It serves as useful guidance and strategic framework to other companies in dealing with the operational excellence challenges. The significance of this study will help M&amp;E SMEs to identify their current position and promote progress in the lean application journey. This will benefit the management team and lean practitioners in decision-making and enhance tactics to attain a higher level of success. Jia Yuik Chong Puvanasvaran Perumal Copyright (c) 2022 International Journal of Production Management and Engineering 2022-01-31 2022-01-31 10 1 23 32 10.4995/ijpme.2022.15894 Analysis of the project success factor through time, cost, labour, health, safety, environment and quality aspects at PT XYZ <p class="TtuloAbstract">This study aims to analyze the failure factors of PT. XYZ in 2018 – 2020 in terms of time, cost, labor, Health, Safety, and Environment (HSE), and quality based on the Success Project Factor (SPF). It includes 183 projects with the Non-Probability Sampling technique. The researcher uses fishbone and Pareto to identify problems. The results showed Schedule Performance Index (SPI)"‰&lt;"Š1 indicated the project is in the late category, the Cost Performance Index (CPI)"‰&lt;"‰1 indicated cost overrun, Safety Performance Index (SFPI)"‰&gt;"‰0 indicated the K3 target could not be reached, the Client Satisfaction Index (CSI)"‰="‰34.03, indicated that it is in the dissatisfied category, then Productivity Coefficient Plan &lt; Realization, it meant the workforce was less productive. After the analysis of fishbone and Pareto, the data show that the highest cause was 13% due to lack of supervision, project cost aspects were 13% due to delays, HSE project aspect were 13% due to no K3 process before work begins, the quality aspect was 17% due to no training, and the labor aspect was 17% due to poor worker discipline.</p> Moh Komarudin Rosalendro Eddy Nugroho Copyright (c) 2022 International Journal of Production Management and Engineering 2022-01-31 2022-01-31 10 1 33 50 10.4995/ijpme.2022.16021 Cyber-physical production system assessment within the manufacturing industries in the Amazon <p class="TtuloAbstract">Cyber-physical production systems (CPPS) represent a relevant aspect related to industry 4.0 and the advances promoted by the digitization and use of artificial intelligence in the production environment in the search for the development of smart factories. This study aims to assess the maturity level of cyber-physical production system (CPPS) within manufacturing industries in the Amazon. The research uses a quali-quantitative approach to analyze the problem by conducting exploratory case studies (indepth case) and the research framework used aimed to evaluate and measure the CPPS within three manufacturing industries in the Amazon (n"‰="‰3) to measure their maturity. Findings reveal a positive relationship between the type of production system adopted by the company, the level of automation, and the maturity of the CPPS. The proposed methodology can assist other companies in the development of the technological strategy, supporting the digital transformation process in order to obtain competitive advantage. The study contributes by addressing the topic of cyber-physical production systems from the point of view of operations management and strategy.</p> Moises Andrade Coelho Franciel Andrade de Oliveira Lindara Hage Dessimoni Nicole Sales Libório Copyright (c) 2021 International Journal of Production Management and Engineering 2022-01-31 2022-01-31 10 1 51 64 10.4995/ijpme.2022.16130 Mapping the scientific structure of organization and management of enterprises using complex networks Understanding the scientific and social structure of a discipline is a fundamental aspect for scientific evaluation processes, identifying trends and niches, and balancing the trade-off between exploitation and exploration in research. In the present contribution, the production of doctoral theses is used as a proxy to analyze the scientific structure of the knowledge area of business organization in Spain. To that end, a complex networks approach is selected, and two different networks are built: (i) the social network of co-participation in thesis examining committees and thesis supervision, and (ii) a bipartite network of theses and thesis descriptors. The former has a modular structure that is partially explained by thematic specialization in different subdisciplines. The latter serves to assess the interdisciplinary structure of the discipline, as it enables the characterization of affinity levels between fields, research poles and thematic clusters. Our results have implications for the scientific evaluation and formal definition of related fields. Alicia Olivares-Gil Adrián Arnaiz-Rodríguez José Miguel Ramírez-Sanz José Luis Garrido-Labrador Virginia Ahedo César García-Osorio José Ignacio Santos José Manuel Galán Copyright (c) 2022 International Journal of Production Management and Engineering 2022-01-31 2022-01-31 10 1 65 76 10.4995/ijpme.2022.16666 Evolution of Servitization: new business model opportunities The concept of Servitization has been constantly developing since its outset, but in the last decade due to the irruption of Industry 4.0, the complexity of the concept and its typologies of value propositions have evolved considerably, opening up endless opportunities. In this sense, the main objective of this research is to show a summary review of the evolution of Servitization since its beginnings and the new typologies that are emerging due to the digitalization that arises through Industry 4.0. For this purpose, a systematic review of the leading databases in the field of services has been conducted. The results of the literature review show the potential of Servitization and the need to understand each reality in order to adapt to new capabilities that help the companies who become service-oriented benefit from major advantages. Ultimately, it can be concluded that, in the short term, Industry 4.0 and its new business models are the key, however, Servitization will continue to evolve to a point where all organizations will need to adapt to new trends. Aitor Ruiz de la Torre David Sanchez Copyright (c) 2022 International Journal of Production Management and Engineering 2022-01-31 2022-01-31 10 1 77 90 10.4995/ijpme.2022.16719 Comparative study of whale optimization algorithm and flower pollination algorithm to solve workers assignment problem <p class="TtuloAbstract">Many important problems in engineering management can be formulated as Resource Assignment Problem (RAP). The Workers Assignment Problem (WAP) is considered as a sub-class of RAP which aims to find an optimal assignment of workers to a number of tasks in order to optimize certain objectives. WAP is an NP-hard combinatorial optimization problem. Due to its importance, several algorithms have been developed to solve it. In this paper, it is considered that a manager is required to provide a training course to his workers in order to improve their level of skill or experience to have a sustainable competitive advantage in the industry. The training cost of each worker to perform a particular job is different. The WAP is to find the best assignment of workers to training courses such that the total training cost is minimized. Two metaheuristic optimizations named Whale Optimization Algorithm (WOA) and Flower Pollination Algorithm (FPA) are utilized to final the optimal solution that reduces the total cost. MATLAB Software is used to perform the simulation of the two proposed methods into WAP. The computational results for a set of randomly generated problems of various sizes show that the FPA is able to find good quality solutions.</p> Huthaifa Al-Khazraji Copyright (c) 2022 International Journal of Production Management and Engineering 2022-01-31 2022-01-31 10 1 91 98 10.4995/ijpme.2022.16736