Service-level performance of MRP, kanban, CONWIP and DBR due to parameter stability and environmental robustness

Decisions regarding production planning and control strategy (PPCS) choices can be classified as strategic, whereas parametrization issues are of a tactical nature. However, readjustment is often skipped either as a result of a lack of planning expertise or because it would require extended planning. For this reason, robustness, which is defined as PPCS behaviour within dynamic environments, is investigated. To achieve a greater understanding of the sensitivity on parameter changes in a production system, PPCS stability is examined. An eM-Plant based simulation model is presented that discusses the service-level performance of material requirement planning (MRP), kanban, constant work in process (CONWIP) and drum–buffer–rope (DBR) in a flow-shop with attention to the work in process (WIP). Although the service-level performance of CONWIP exceeds that of the other systems, CONWIP struggles to maintain its advantage under dynamic conditions. The paper seeks to support industrial practitioneers both in their choice of a specific PPCS and to parametrize the PPCS successfully.

[1]  Chandrasekharan Rajendran,et al.  A comparative study of dispatching rules in dynamic flowshops and jobshops , 1999, Eur. J. Oper. Res..

[2]  Jack P. C. Kleijnen,et al.  Short-term robustness of production management systems: A case study , 2003, Eur. J. Oper. Res..

[3]  Mary K. Vernon,et al.  Re-Examining the Performance of MRP and Kanban Material Control Strategies for Multi-Product Flexible Manufacturing Systems , 2004 .

[4]  Wallace J. Hopp,et al.  Factory physics : foundations of manufacturing management , 1996 .

[5]  Gerhard Plenert,et al.  Focusing material requirements planning (MRP) towards performance , 1999, Eur. J. Oper. Res..

[6]  Norman M. Sadeh,et al.  An empirical study of policies to integrate reactive scheduling and control in just-in-time job shop environments , 2004 .

[7]  Jumpol Vorasayan,et al.  Allocating work in process in a multiple-product CONWIP system with lost sales , 2005 .

[8]  Dingwei Wang,et al.  A simulation and comparative study of the CONWIP, Kanban and MRP production control systems in a cold rolling plant , 1998 .

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

[10]  Hokey Min,et al.  TOC-based performance measures and five focusing steps in a job-shop manufacturing environment , 2002 .

[11]  Shie-Gheun Koh,et al.  Comparison of DBR with CONWIP in an unbalanced production line with three stations , 2004 .

[12]  SARAH M. RYAN,et al.  Determining inventory levels in a CONWIP controlled job shop , 2000 .

[13]  Daisuke Hirotani,et al.  Comparing CONWIP, synchronized CONWIP, and Kanban in complex supply chains , 2005 .

[14]  U. Graf,et al.  Formeln und Tabellen der angewandten mathematischen Statistik , 1987 .

[15]  Mehmet Savsar,et al.  A neural network procedure for kanban allocation in JIT production control systems , 2000 .

[16]  David L. Woodruff,et al.  CONWIP: a pull alternative to kanban , 1990 .