System model of production inventory control

The primary consequence of just-in-time (JIT) production is reduction of inventory with associated financial benefits. However, existing methods cannot adequately explain the causality that leads to failed (or successful) JIT implementations. This paper describes a fresh theoretical approach to examining the larger organisational factors in which inventory control systems are embedded. A structured descriptive system model for production inventory control is described, including base stock, CONWIP, kanban and hybrid systems. The model was then interrogated to produce lists of tentative critical success factors for implementation of these strategies in JIT and lean production, and propose explanations as to why it succeeds or fails.

[1]  Enver Yücesan,et al.  Modeling just-in-time production systems: a critical review , 1993, WSC '93.

[2]  Waldemar Karwowski,et al.  Human performance in lean production environment: Critical assessment and research framework , 2003 .

[3]  Mohamed Mohamed Naim,et al.  A supply chain diagnostic methodology: determining the vector of change , 2002 .

[4]  Peter T. Ward,et al.  Lean manufacturing: context, practice bundles, and performance , 2003 .

[5]  Luke Collins,et al.  Innovation everywhere - Organisations under pressure to innovate are realising that they need to look beyond the R&D department for help, finds Luke Collins , 2005 .

[6]  Howard H. Bashford,et al.  Production Equations for Unsteady-State Construction Processes , 2007 .

[7]  Haldun Aytug,et al.  Determining the Number of Kanbans: A Simulation Metamodeling Approach , 1996, Simul..

[8]  C. S. P. Rao,et al.  Simulation of Machine Breakdowns in a Pull Production System Operated by Various Control Mechanisms , 2003, Modelling, Simulation, and Optimization.

[9]  Selim Zaim,et al.  The impact of supply chain management practices on performance of SMEs , 2007, Ind. Manag. Data Syst..

[10]  Tony Bendell,et al.  A review and comparison of six sigma and the lean organisations , 2006 .

[11]  Emerging production systems and industrial relations: Confusion, diffusion, and exclusion? , 1998 .

[12]  H.-J. Jo,et al.  The mutation of the Toyota Production System: adapting the TPS at Hyundai Motor Company , 2007 .

[13]  G. Zäpfel Customer-order-driven production: An economical concept for responding to demand uncertainty? , 1998 .

[14]  Jon C. Yingling,et al.  Quantifying benefits of conversion to lean manufacturing with discrete event simulation: A case study , 2000 .

[15]  Jamshed J. Mistry,et al.  Origins of profitability through JIT processes in the supply chain , 2005, Ind. Manag. Data Syst..

[16]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

[17]  Masaru Yoshimori,et al.  Whose company is it? The concept of the corporation in Japan and the west , 1995 .

[18]  Saifallah Benjaafar,et al.  Modeling and Analysis of Congestion in the Design of Facility Layouts , 2002, Manag. Sci..

[19]  Feryal Erhun,et al.  An overview of design and operational issues of kanban systems , 1999 .

[20]  Gerhard Plenert,et al.  MRP,JIT, and OPT: What's b:20Best”? , 1986 .

[21]  H. Van Landeghem,et al.  Analysing the effects of Lean manufacturing using a value stream mapping-based simulation generator , 2007 .

[22]  Paul Forrester,et al.  A model for evaluating the degree of leanness of manufacturing firms , 2002 .

[23]  Andrew Kusiak,et al.  Manufacturing control with a push-pull approach , 1998 .

[24]  Vidhi Mehta,et al.  Characteristics of a Work Organization from a Lean Perspective , 2005 .

[25]  D. Pons,et al.  Design with uncertain qualitative variables under imperfect knowledge , 2004, IEEE Engineering Management Review.

[26]  B. Sarker,et al.  Economic manufacturing quantity in a just-in-time delivery system , 1992 .

[27]  Tadaaki Fukukawa,et al.  The determination of the optimal number of kanbans in a Just-In-Time production system , 1993 .

[28]  Armin Scholl Mixed-Model Assembly Lines , 1995 .

[29]  Dave Alford,et al.  Mass customisation } an automotive perspective , 2000 .

[30]  Maria Caridi,et al.  Multi-agent systems in production planning and control: An application to the scheduling of mixed-model assembly lines , 2000 .

[31]  Bernard J. Schroer Simulation as a Tool in Understanding the Concepts of Lean Manufacturing , 2004, Simul..

[32]  Matthias Holweg,et al.  The genealogy of lean production , 2007 .

[33]  A. S. White Management of inventory using control theory , 1999 .

[34]  Colin Herron,et al.  A methodology for developing sustainable quantifiable productivity improvement in manufacturing companies , 2006 .

[35]  Jeffrey L. Callen,et al.  Just-In-Time: A Cross-sectional Plant Analysis , 2000 .

[36]  Russell L. Ackoff,et al.  The Art and Science of Mess Management , 1981 .

[37]  S. M. Meerkov,et al.  Lean buffering in serial production lines with Bernoulli machines , 2006 .

[38]  Prabhat Hajela,et al.  Optimal Design in the Presence of Modeling Uncertainties , 2006 .

[39]  Stephen J. Childe,et al.  Incorporating links to ISO 9001 into manufacturing process models using IDEF 9000 , 2003 .

[40]  Riikka Kaipia,et al.  VMI: What are you losing if you let your customer place orders? , 2002 .

[41]  Jt Black,et al.  Design rules for implementing the Toyota Production System , 2007 .

[42]  G. Abdou,et al.  A systematic simulation approach for the design of JIT manufacturing systems , 1993 .

[43]  Asbjoern M. Bonvik,et al.  A comparison of production-line control mechanisms , 1997 .

[44]  Mahmoud Houshmand,et al.  An extended model of design process of lean production systems by means of process variables , 2006 .