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Key factors for operational performance in manufacturing systems: Conceptual model,systematic literature review and implications
Affiliation:1. Department of Production and Systems, Federal University of Santa Maria – UFSM, Av. Roraima 1000, Camobi, Santa Maria, RS, Brazil;2. Postgraduate Program in Production Engineering (PPGEP), Federal University of Rio Grande do Sul, Av. Osvaldo Aranha 99, Porto Alegre, RS, Brazil;3. School of Business and Social Sciences (Aarhus BSS), Department of Business Development and Technology (BTECH), Aarhus University, Birk Centerpark 15, 7400, Herning, Denmark;1. Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China;2. China Acad Machinery Sci & Technol, State Key Lab Adv Forming Technol & Equipment, Beijing, 100044, China;3. Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China;1. School of Industrial Engineering, Carlo Cattaneo – LIUC University, 21053, Castellanza, Italy;2. Dept of Logistics and Operations Management, Cardiff Business School, CF10 3EU, Cardiff, UK;1. Department of Management, Dhurakij Pundit University, Thailand;2. Department of Management, Monash University, Australia;3. Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, PR China
Abstract:PurposeThe performance of discrete items manufacturing systems (MS) is a primary concern of industrial firms. However, the understanding of the interrelations between performance and its key factors requires further advancements. Thus, several questions remain unanswered in the Operations and Production Management (OPM) field to understand and manage the relationship between these key factors. To address these challenges, this paper conceptualizes and examines the relevant antecedents and essential elements for the design and optimization of competitive MS.Design/methodology/approachFirst, drawing on the consolidated OPM literature, a novel conceptual model was developed incorporating the conceptual relationships essential to MS performance. Second, we conducted a systematic literature review based on the PRISMA protocol to analyze and validate the proposed conceptual model and to indicate the field’s current knowledge gaps and future research directions.FindingsFindings validated the proposed conceptual model by establishing the complex causal interrelations among key factors that influence discrete MS performance. Moreover, we found that the operational performance of discrete MS is multidimensional and directly dependent on the variables and mechanisms associated with the production flow. Findings also demonstrated that the degree of importance of the antecedents of MS performance vary and are temporally interrelated. Lastly, the paper advances the understanding of MS by revealing the predominance of quantitative approaches (e.g., discrete events simulation and closed mathematical models) in the literature as well as an emphasis on describing these approaches rather than characterizing MS appropriately.Research and Practical implicationsThis paper extends our knowledge on the operational performance challenges in discrete MS by proposing a visual, direct, and intuitive conceptual model that enables firms to better comprehend these complex challenges. This research also answers ongoing calls for investigations of the antecedents and elements of competitive MS design and optimization. Our findings show that decision-making in discrete MS is established temporally based on strategic, operational, and control definitions, influencing firms’ operational performance. Finally, since it draws on seminal OPM literature specializing in MS, this study informs scholars, industrial managers, and aid decision-making about discrete MS.Originality/valueThe first original aspect of this study lies in bridging the gaps identified in the OPM literature by providing a robust conceptual framework that highlights the key factors of operational performance in discrete MS. Its second original aspect is that it adopts different information sources in an independent and complementary way to achieve greater generalizability and robustness of the contributions.
Keywords:Operational performance  Manufacturing systems  Conceptual model  Literature review  Decision making
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