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Journal articles on the topic 'Production planning and control systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

SUGIMURA, Nobuhiro, and Koji IWAMURA. "2201 DYNAMIC INTEGRATED PROCESS PLANNING AND PRODUCTION CONTROL IN FLEXIBLE MANUFACTURING SYSTEMS WITH AUTONOMOUS AGENTS." Proceedings of Manufacturing Systems Division Conference 2007 (2007): 37–38. http://dx.doi.org/10.1299/jsmemsd.2007.37.

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2

Kubat, Cemalettin, Ercan Öztemel, and Harun Taşkιn. "Decision support systems in production planning and control." Production Planning & Control 18, no. 1 (January 2007): 1–2. http://dx.doi.org/10.1080/09537280600940572.

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3

GUNASEKARAN, A., S. K. GOYAL, I. VIRTANEN, and P. YLI-OLLI. "Production planning and control in automated manufacturing systems." Production Planning & Control 4, no. 1 (January 1993): 2–16. http://dx.doi.org/10.1080/09537289308919414.

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4

Bonney, Maurice. "Reflections on production planning and control (PPC)." Gestão & Produção 7, no. 3 (December 2000): 181–207. http://dx.doi.org/10.1590/s0104-530x2000000300002.

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The paper examines the current state of Production Planning and Control (PPC), identifies some technical and systems changes that have occurred over recent years and links these with the requirements being placed on companies by the market. PPC is being asked to respond effectively to these internal and external changes by being more dynamic and providing better control of resources and delivery performance. Some of the requirements to be satisfied by the new PPC systems are identified. To meet these requirements it is suggested that better understanding is required of how different factors affect PPC systems performance and that administrative systems need improving. The quantitative, administrative and behavioural aspects of PPC are discussed. A framework for developing an agenda for action and research is provided.

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5

Mitropoulos, Costas, Dennis W. McLeavey, and Seetharama L. Narasimhan. "Production Planning and Inventory Control." Journal of the Operational Research Society 36, no. 6 (June 1985): 545. http://dx.doi.org/10.2307/2582831.

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6

Mitropoulos, Costas. "Production Planning and Inventory Control." Journal of the Operational Research Society 36, no. 6 (June 1985): 545. http://dx.doi.org/10.1057/jors.1985.94.

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7

Fortvin, Leonard. "Production planning and inventory control." European Journal of Operational Research 26, no. 2 (August 1986): 314–15. http://dx.doi.org/10.1016/0377-2217(86)90198-0.

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8

Paul, Brian K. "Production planning and inventory control." Journal of Manufacturing Systems 15, no. 4 (January 1996): 294. http://dx.doi.org/10.1016/0278-6125(96)84554-7.

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9

Ward, T. L., and P. A. S. Ralston. "Fuzzy Control of Production Planning in Distributed Manufacturing Systems." IFAC Proceedings Volumes 24, no. 5 (August 1991): 97–101. http://dx.doi.org/10.1016/s1474-6670(17)51230-6.

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10

Manikas, Andrew, Mahesh Gupta, and Lynn Boyd. "Experiential exercises with four production planning and control systems." International Journal of Production Research 53, no. 14 (November 27, 2014): 4206–17. http://dx.doi.org/10.1080/00207543.2014.985393.

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11

Helber, Stefan. "Lot sizing in capacitated production planning and control systems." Operations-Research-Spektrum 17, no. 1 (March 1995): 5–18. http://dx.doi.org/10.1007/bf01719725.

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12

Afraimovich, L. G., and M. Kh Prilutskii. "Multiindex optimal production planning problems." Automation and Remote Control 71, no. 10 (October 2010): 2145–51. http://dx.doi.org/10.1134/s0005117910100139.

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13

Weidig, Christian, Chantal Steimer, and Jan Christian Aurich. "Process Planning and Decentralised Process Control for Cybertronic Production Systems." Applied Mechanics and Materials 794 (October 2015): 486–93. http://dx.doi.org/10.4028/www.scientific.net/amm.794.486.

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Cybertronic systems which augment mechatronic basis systems by means of communication, collaboration and intelligence are proposed to be utilised to develop next generation production systems. A model-based development process is proposed to enable integrated design and development of the product and the production system. This paper will highlight the importance of process planning as collaborative activity within the integrated design and development process. A concept will be introduced how process planning can be conducted for cybertronic production systems, considering decentralised production planning and control already during the design phase.

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14

Shen, Randolph F. C. "Aggregate production planning by stochastic control." European Journal of Operational Research 73, no. 2 (March 1994): 346–59. http://dx.doi.org/10.1016/0377-2217(94)90270-4.

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15

Vollmer, T., B. Zhou, T. Heutmann, R. Kiesel, and R. Schmitt. "Entwicklung der Produktions-IT*/Roadmap for production planning and control." wt Werkstattstechnik online 107, no. 11-12 (2017): 858–62. http://dx.doi.org/10.37544/1436-4980-2017-11-12-82.

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Automatisierung, intelligente Produktionsplanung und Monitoring sind gängige Anwendungen – daher sind Informationstechnologie (IT)-Systeme aus der Produktion nicht mehr wegzudenken. Dieser Beitrag gibt einen Überblick über die aktuellen Entwicklungen und Innovationen im Bereich der Produktions-IT (zum Beispiel Enterprise Resource Planning ERP, Manufacturing Excecution Systems MES und Computer Aided Quality Assurance CAQ), nennt Anwendungsgebiete und beleuchtet deren Potenziale anhand konkreter Fallbeispiele. Es werden darüber hinaus die Herausforderungen diskutiert, welche es im Rahmen von Industrie 4.0 zu meistern gilt.   IT systems nowadays are common in manufacturing environments for applications such as automation, intelligent production planning or monitoring. This article provides an overview about current developments and innovations in production planning and control (e.g. ERP, MES; CAQ), mentions promising areas for application and evaluates their potential referring to case studies. Based on this, challenges coming along with changes within the broad field of Industry 4.0 are discussed.

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16

BERTRAND, J. WILL M., and J. C. WORTMANN. "Information systems for production planning and control: developments in perspective." Production Planning & Control 3, no. 3 (July 1992): 280–89. http://dx.doi.org/10.1080/09537289208919400.

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17

Perkins, James R. "Optimal flow control in manufacturing systems: production planning and scheduling." Automatica 37, no. 9 (September 2001): 1499–500. http://dx.doi.org/10.1016/s0005-1098(01)00107-8.

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18

Caridi, Maria, and Sergio Cavalieri. "Multi-agent systems in production planning and control: an overview." Production Planning & Control 15, no. 2 (March 2004): 106–18. http://dx.doi.org/10.1080/09537280410001662556.

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19

Haley, K. B. "Production planning, scheduling, and inventory control: Concepts, techniques, and systems." European Journal of Operational Research 20, no. 2 (May 1985): 269. http://dx.doi.org/10.1016/0377-2217(85)90069-4.

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20

Kingsman, Brian G. "Modelling input–output workload control for dynamic capacity planning in production planning systems." International Journal of Production Economics 68, no. 1 (October 2000): 73–93. http://dx.doi.org/10.1016/s0925-5273(00)00037-2.

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21

Lima, Rui M. "Integrating Production Planning and Control Business Processes." International Journal of Productivity Management and Assessment Technologies 1, no. 4 (October 2012): 1–21. http://dx.doi.org/10.4018/ijpmat.2012100101.

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Organizations have production planning and control (PPC) processes supported by systems that execute, mainly, repetitive calculations. Based on these calculation results, decisions are taken by production managers. These decision processes make the connection between different levels of aggregation of information and could benefit from the increment of the level of automation. An increased level of application of business process modelling languages is proposed in order to contribute to increment the level of process automation and the detail of business analysis. Thus being, concepts of integration of production management processes, specifically of production planning and control processes are presented. These concepts, the application of business process modelling language (BPML) and some solutions of PPC integration compose the core content of this work. Additionally, criteria for evaluation of these processes of integration are identified and discussed. Finally, the presentation of an industrial case will be supported by BPML model.

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22

Selmaier, A., T. Donhauser, T. Lechler, J. Zeitler, and J. Franke. "Simulationsgestützte Produktionsplanung flexibler Fertigungssysteme*/Simulation-based production planning of flexible manufacturing systems." wt Werkstattstechnik online 109, no. 04 (2019): 242–49. http://dx.doi.org/10.37544/1436-4980-2019-04-40.

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Während sich das Verhalten starr verketteter Systeme relativ einfach mittels Materialflusssimulationen modellieren lässt, sind herkömmliche Simulationsansätze für flexible Fertigungssysteme aufgrund des hohen Datenerhebungs- sowie Parametrisieraufwands nur bedingt geeignet. Jedoch kann durch das automatische Übertragen von Echtzeitdaten in das Simulationsmodell der aktuelle Zustand solcher Systeme deutlich verbessert abgebildet werden. Der Beitrag stellt ein Konzept für die simulationsgestützte Produktionsplanung schnellveränderlicher Systeme vor.   While the behaviour of rigidly linked systems is relatively easy to model by means of material flow simulation, traditional simulation approaches are only suitable to a limited extent for flexible manufacturing systems due to the high data collection and parameterization effort. However, the use of real-time data can significantly improve the simulation of such systems. This paper presents an approach for simulation-based production planning of rapidly changing systems.

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23

Hees, A., K. Zellner, and G. Reinhart. "Produktionsplanung in RMS/Production Planning in RMS - System for Production Process Planning in Reconfigurable Manufacturing Systems (RMS)." wt Werkstattstechnik online 105, no. 04 (2015): 209–14. http://dx.doi.org/10.37544/1436-4980-2015-04-51.

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Zur Sicherung der Wettbewerbsfähigkeit in dynamischen Märkten müssen produzierende Unternehmen ihre Produktionssysteme in häufigen Intervallen anpassen. Ein Ansatz, diesen Herausforderungen zu begegnen, sind rekonfigurierbare Produktionssysteme (englisch: Reconfigurable Manufacturing Systems – RMS). Vorgestellt wird ein neuer Ansatz für die Produktionsplanung und -steuerung (PPS) in RMS – bestehend aus einem Datenmodell, einem Konfigurationsmanagement und einer Planungsmethode.   Manufacturing companies have to adapt their manufacturing systems in frequent and short intervals to secure their competitiveness in dynamic markets. One approach to ensure companies’ success are Reconfigurable Manufacturing Systems (RMS). In this context, a new approach for production planning (PPC) in RMS, consisting of a data model, a configuration management and a planning method, is described in this paper.

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24

May, Marvin Carl, Andreas Kuhnle, and Gisela Lanza. "Digitale Produktion und intelligente Steuerung/Digital Production and Intelligent Production Control – Integrating digital and real-world production for adaptive and automated control." wt Werkstattstechnik online 110, no. 04 (2020): 255–60. http://dx.doi.org/10.37544/1436-4980-2020-04-89.

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Im Rahmen der stufenweisen Umsetzung von Industrie 4.0 erreicht die Vernetzung und Digitalisierung die gesamte Produktion. Den physischen Produktionsprozess nicht nur cyber-physisch zu begleiten, sondern durch eine virtuelle, digitale Kopie zu erfassen und zu optimieren, stellt ein enormes Potenzial für die Produktionssystemplanung und -steuerung dar. Zudem erlauben digitale Modelle die Anwendung intelligenter Produktionssteuerungsverfahren und leisten damit einen Beitrag zur Verbreitung optimierender adaptiver Systeme.   In the wake of implementing Industrie 4.0 both integration and digitalization affect the entire production. Physical production systems offer enormous potential for production planning and control through virtual, digital copies and their optimization, well beyond purely cyber-physical production system extensions. Furthermore, digital models enable the application of intelligent production control and hence contribute to the dissemination of adaptively optimizing systems.

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25

Kumar Agarwal, Ajay, and Rakesh Kumar. "Directions of Production Planning & Production Control System: Mathematical Evolution from the Flexibility Point of View." Ecuadorian Science Journal 4, no. 2 (September 30, 2020): 17–24. http://dx.doi.org/10.46480/esj.4.2.68.

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Production Planning and Production Control systems are some of the keys that determine the development of modern production systems. The article presents the importance of a flexibility factor in the process of manufacturing, planning, and control systems development during the 20th century. Finally, actual problems and trends in the production system design and control over the design were perceived in addition to possible feasible directions of the development of manufacturing systems, probably in the 21st century were specified. Production planning and production control are usually painstaking to be one of the most noteworthy issues in the planning and operation of a manufacturing structure. A better planning system has a significant bang on cost reduction, increased productivity, customer satisfaction, and overall spirited improvement for a product. Also, the current customer demand for prominent diversity products has put into an increase in product complications that further lay emphasis on necessitates for superior planning. Proficient planning leads to the amplification in capability exploitation competence and, therefore, thereby reducing the operational time required to intact jobs and accordingly escalating the profitability of an organization in the current spirited environment. There are different systems of manufacturing, planning, and control for a job-shop together with flow-shop in which the jobs are to be a progression through a series of machines for an optimizing number of required performance measures

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26

Popescu, Liviu. "Applications of Optimal Control to Production Planning." Information Technology And Control 49, no. 1 (March 25, 2020): 89–99. http://dx.doi.org/10.5755/j01.itc.49.1.23891.

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In this paper we solve a problem of optimization and production planning using the optimal control methods and Pontryagin Maximum Principle. We propose an economic model and find an optimal plan of production for n products, to ensure the required quantity at specified delivery data with minimum cost of inventory and production. We prove that the economic system is not controllable, in the sense that we cannot reach any final stock quantity. Finally, we justify this construction with a numerical example.

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27

BAUER, THOMAS, THOMAS BEUTER, MICHAEL WEITNER, and THOMAS BÄR. "INTEGRATION OF ENGINEERING AND PRODUCTION PLANNING WORKFLOWS." Journal of Advanced Manufacturing Systems 04, no. 01 (June 2005): 37–52. http://dx.doi.org/10.1142/s0219686705000564.

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To improve the total product development process, engineering and production planning processes have to be integrated into one overall workflow. These processes are performed in different application systems, partially with workflow support (e.g. Enovia Workflow) and partially without (e.g. Manufacturing Hub). In order to achieve higher process reliability and performance as well as continuous information flow, the overall workflow shall be controlled by a general-purpose workflow management system (WfMS). Thus, the overall workflow may serve as a base for monitoring, reporting, and process visualization with the goal of managing the workflow execution. In this paper, we present a general approach that realizes the control of the overall workflow in conjunction with the execution of local workflow fragments by heterogeneous application systems. This approach is used to integrate the Dassault Systemes products Enovia V5 and Manufacturing Hub into an overall workflow controlled by the IBM WBI Workflow component. The paper describes how such an integration can be realized for these systems.

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28

Lewis, Colin, G. Fandel, Th Gulledge, and A. Jones. "Operations Research in Production Planning and Control." Journal of the Operational Research Society 45, no. 4 (April 1994): 484. http://dx.doi.org/10.2307/2584224.

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29

Krishna Kumar, C., and Bani K. Sinha. "Efficiency based production planning and control models." European Journal of Operational Research 117, no. 3 (September 1999): 450–69. http://dx.doi.org/10.1016/s0377-2217(98)00235-5.

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30

Lewis, Colin. "Operations Research in Production Planning and Control." Journal of the Operational Research Society 45, no. 4 (April 1994): 484. http://dx.doi.org/10.1057/jors.1994.71.

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31

Zipkin, P. "Multi-stage production planning and inventory control." European Journal of Operational Research 29, no. 3 (June 1987): 386–87. http://dx.doi.org/10.1016/0377-2217(87)90253-0.

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32

Dawes, T. B., and N. J. Boughton. "Improved production planning and control via low-cost prototype solutions." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 216, no. 4 (April 1, 2002): 649–53. http://dx.doi.org/10.1243/0954405021520184.

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This paper describes prototype management information systems used to support the transformation of a job-orientated manufacturer into a preferred supplier to the Aerospace Industry. As a jobbing manufacturer the company excelled at reactive management, responding rapidly to short lead-time demands. This approach, however, was inappropriate for the increasingly competitive production of steady state orders (‘runners’) and spare parts orders (‘repeaters’). As a result, the company experienced falling delivery performances and increasing work-in-progress, inventory levels and overtime requirements. Furthermore, there was only a modest understanding of customer requirements due to the poor visibility of the order book. There was also little awareness of internal and supplier capabilities, and the planning and control systems were non-existent, relying heavily on senior management expertise. The company, however, was reluctant to invest in new management information systems, aware of the risk of expensive systems unsuited to the company requirements. Instead, prototype solutions were developed using low-cost and readily available software, which provided the stability and visibility that the company required and formed the basis of a more long-term understanding of planning and control requirements. This paper describes the iterative prototype developments to support the new company infrastructure and quantifies the benefits that were achieved through their introduction.

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33

Hadeli, Paul Valckenaers, Hendrik Van Brussel, Paul Verstraete, Bart Saint Germain, and Jan Van Belle. "PRODUCTION PLANNING AND CONTROL IN BIO-INSPIRED HOLONIC MANUFACTURING EXECUTION SYSTEMS." IFAC Proceedings Volumes 40, no. 3 (2007): 42–49. http://dx.doi.org/10.3182/20070523-3-es-4908.00008.

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34

Boukas, E. K., H. Yang, and Q. Zhang. "Minimax production planning in failure-prone manufacturing systems." Journal of Optimization Theory and Applications 87, no. 2 (November 1995): 269–86. http://dx.doi.org/10.1007/bf02192564.

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35

Fischer, Günther, Tatiana Ermolieva, Yuri Ermoliev, and Harrij van Velthuizen. "Livestock production planning under environmental risks and uncertainties." Journal of Systems Science and Systems Engineering 15, no. 4 (December 2006): 399–418. http://dx.doi.org/10.1007/s11518-006-5018-2.

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36

Волочиенко and Vladimir Volochienko. "Improvement of Automated Control Systems for Production and Production Logistics Development." Administration 3, no. 2 (June 17, 2015): 22–26. http://dx.doi.org/10.12737/11506.

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This paper has been devoted to a retrospective review of production logistics formation in the frames of “Logistics” Scientific School at The State University of Management. The author’s contribution to the resolution of production management automation problems in the real time has been described. This contribution enables to implement scientific and methodological preliminary work on situational management for production logistics systems based on application of recognition methods and systems. Scientific novelty of the author’s contribution in the SUM‘s Logistics Scientific School, consisting of development related to the theory and tools for operational planning and management of engineering production based on application of recognition methods and market influences accounting, has been represented. The practical importance of situational management of production logistics systems has been revealed. Perspective directions for application of recognition methods and systems in conditions of such active complex object as a machinebuilding enterprise and logistics systems have been showed.

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37

Yalaoui, Alice, Khalil Chaabi, and Farouk Yalaoui. "Integrated production planning and preventive maintenance in deteriorating production systems." Information Sciences 278 (September 2014): 841–61. http://dx.doi.org/10.1016/j.ins.2014.03.097.

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38

Aghezzaf, El-Houssaine, and Najib M. Najid. "Integrated production planning and preventive maintenance in deteriorating production systems." Information Sciences 178, no. 17 (September 2008): 3382–92. http://dx.doi.org/10.1016/j.ins.2008.05.007.

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39

Block, Christian, Friedrich Morlock, Thomas Dorka, and Bernd Kuhlenkötter. "A Human Centered Multi-Agent-System for Production Planning and Control." Applied Mechanics and Materials 840 (June 2016): 132–39. http://dx.doi.org/10.4028/www.scientific.net/amm.840.132.

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In consequence of Industrie 4.0, the application of new information and communication technologies is increasing. A central problem of industrial enterprises is the lack of any IT system which would provide required exercises concerning production planning and control (PPC). Therefore, several software systems are applied in manufacturing. Thus the demand arises to intelligently link the available and new IT systems in manufacturing. A multi-agent system offers a modeling concept for a planning support tool of PPC for the intelligent link of several systems and it increases the automation of planning processes. However, the disadvantage of multi-agent systems is that processes of autonomously acting agents are not comprehensible for humans. Nevertheless, in Industrie 4.0 the human shall be involved as the executive body or even as a decision maker. Therefore, this article describes an agent-based approach in which an agent system is used as an interface solution between IT systems and extended by a visualization approach in order to implement a partially autonomous support system for an agent-based PPC.

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40

Qiu-Zhong, Zhou, Ji Fa, and Tao Ze. "Production Planning Based on Multi-Level Process Flow." Open Automation and Control Systems Journal 7, no. 1 (February 18, 2015): 67–73. http://dx.doi.org/10.2174/1874444301507010067.

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41

Kuvykin, V. I. "Optimal Planning and Analysis of Continuous Production Models." Automation and Remote Control 79, no. 2 (February 2018): 384–90. http://dx.doi.org/10.1134/s0005117918020170.

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42

Dumont, Laurence B., Philippe Marier, Nadia Lehoux, Louis Gosselin, and Hugues Fortin. "Integrating Electric Energy Cost in Lumber Production Planning." IFAC-PapersOnLine 52, no. 13 (2019): 2249–54. http://dx.doi.org/10.1016/j.ifacol.2019.11.540.

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43

Abad, Prakash L. "An Optimal control approach to marketing-production planning." Optimal Control Applications and Methods 3, no. 1 (October 29, 2007): 1–14. http://dx.doi.org/10.1002/oca.4660030101.

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44

Bruce, George J., and Kenneth Reay. "Cost-Effective Planning and Control." Journal of Ship Production 7, no. 03 (August 1, 1991): 183–87. http://dx.doi.org/10.5957/jsp.1991.7.3.183.

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Situations have frequently been encountered where It Is necessary to reestablish control of a shipbuilding contract when it has been lost. This has to be done quickly, and requires a combination of an effective planning and control system with a computer for data processing. Expediency dictates the use of readily available PCs and proprietary software. The approach adopted was found to be robust and effective, and has been used as a basis for development of more formal planning and control systems. These are now in use as the means of planning and implementing ship production.

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45

Niderla, Jonrad, Tomasz Rymarczyk, and Jan Sikora. "MANUFACTURING PLANNING AND CONTROL SYSTEM USING TOMOGRAPHIC SENSORS." Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska 8, no. 3 (September 25, 2018): 29–34. http://dx.doi.org/10.5604/01.3001.0012.5280.

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The article presents an idea of a production process control system. Advanced automation and control of production processes play a key role in maintaining competitiveness. The proposed solution consists of sensor networks for measurement process parameters, production resources and equipment state. The system uses wired and wireless communication, which gives possibility to acquisition data from existing in enterprise sensors and systems as well as acquisition data from new systems and sensors used to measure all processes, starting from production preparation to the final product. The solution contains process tomography sensors based on electrical capacitance tomography, electrical impedance tomography and ultrasound tomography. The use of tomographic methods enables to manage the intelligent structure of the companies in terms of processes and products. Industrial tomography enables observation of physical and chemical phenomena without the need to penetrate inside. It will enable the optimization and auto-optimization of design processes and production. Such solutions can operate autonomously, monitor and control measurements. All sensors return to the system continuous data about state of processes in some technologically closed objects like fermenters. Process tomography can also be used to acquisition data about a flow of liquids and loose ingredients in pipeline based on transport systems. Data acquired from sensors are collected in data warehouses in order to future processing and building the knowledge base. The results of the data analysis are showed in user control panels and are used directly in the control of the production process to increase the efficiency and quality of the products. Control methods cover issues related to the processing of data obtained from various sensors located at nodes. Monitoring takes place within the scope of acquired and processed data and parameter automation.

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46

SETHI, SURESH P., HANQIN ZHANG, and QING ZHANG. "MINIMUM AVERAGE COST PRODUCTION PLANNING IN STOCHASTIC MANUFACTURING SYSTEMS." Mathematical Models and Methods in Applied Sciences 08, no. 07 (November 1998): 1251–76. http://dx.doi.org/10.1142/s0218202598000585.

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Recently, the production control problem in stochastic manufacturing systems has generated a great deal of interest. The goal is to obtain production rates to minimize total expected surplus and production cost. This paper reviews the research devoted to minimum average cost production planning problems in stochastic manufacturing systems. Manufacturing systems involve a single or parallel failure-prone machines producing a number of different products, random production capacity, and constant demands.

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47

Maccarthy, Bart L., and Flavio C. F. Fernandes. "A multi-dimensional classification of production systems for the design and selection of production planning and control systems." Production Planning & Control 11, no. 5 (January 2000): 481–96. http://dx.doi.org/10.1080/09537280050051988.

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48

Bettayeb, B., S. J. Bassetto, and M. Sahnoun. "Quality control planning to prevent excessive scrap production." Journal of Manufacturing Systems 33, no. 3 (July 2014): 400–411. http://dx.doi.org/10.1016/j.jmsy.2014.01.001.

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49

Diaz C., Jenny L., and Carlos Ocampo-Martinez. "Optimal production planning for flexible manufacturing systems: an energy-based approach." IFAC-PapersOnLine 53, no. 2 (2020): 10461–67. http://dx.doi.org/10.1016/j.ifacol.2020.12.2789.

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50

Bruch, Jessica, and Monica Bellgran. "Integrated portfolio planning of products and production systems." Journal of Manufacturing Technology Management 25, no. 2 (February 25, 2014): 155–74. http://dx.doi.org/10.1108/jmtm-09-2013-0126.

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Purpose – The purpose of the research presented is to analyse and discuss critical challenges related to the development of a production system portfolio. Design/methodology/approach – The study employs a longitudinal case study of an industrialization project at a global supplier in the automotive industry. Findings – This research makes two clear theoretical contributions. First, it extends the existing research on the manufacturing and R&D interface by proposing an innovative structure for production system development facilitating manufacturing companies in their efforts of being fast and cost-effective when introducing new products to the market. Second, this research identifies challenges related to the adoption of a production system portfolio and the necessary actions of a manufacturing company applying such a portfolio strategy. Research limitations/implications – The findings should be seen as a first attempt at assisting the development of a production system portfolio that matches the product portfolio. However, since the findings are based on only one case, the findings are to some extent context-specific and thus need to be complemented by more research. Practical implications – The research unveils challenges related to production system development and provides managers with a better understanding of some of the implications of the adoption of a portfolio strategy. Originality/value – This empirical study is among the first to explore the implications of a production system portfolio strategy. It advances the understanding towards a fully integrated product and production system development.

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