×

Analysis of decentralized multi-product pull systems with lost sales. (English) Zbl 1187.90026

Summary: We study a pull-type, flexible, multi-product, and multi-stage production/inventory system with decentralized two-card kanban control policies. Each stage involves a processor and two buffers with finite target levels. Production stages, arranged in series, can process several product types one at a time. Transportation of semi-finished parts from one stage to another is performed in fixed lot sizes. The exact analysis is mathematically intractable even for smaller systems. We present a robust approximation algorithm to model two-card kanban systems with batch transfers under arbitrary complexity. The algorithm uses phase-type modeling to find effective processing times and busy period analysis to identify delays among product types in resource contention. Our algorithm reduces the effort required for estimating performance measures by a considerable margin and resolves the state-space explosion problem of analytical approaches.
Using this analytical tool, we present new findings for a better understanding of some tactical and operational issues. We show that flow of material in small procurement sizes smoothes flow of information within the system, but also necessitates more frequent shipments between stages, raising the risk of late delivery. Balancing the risk of information delays vis-à-vis shipment delays is critical for the success of two-card kanban systems. Although product variety causes time wasted in setup operations, it also facilitates relatively short production cycles enabling processors to switch from one product type to another more rapidly. The latter point is crucial especially in high-demand environments. Increasing production line size prevents quick response to customer demand, but it may improve system performance if the vendor lead-time is long or subject to high variation. Finally, variability in transportation and processing times causes the most damage if it arises at stages closer to the customer.

MSC:

90B05 Inventory, storage, reservoirs
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Abdou, A systematic simulation approach for the design of JIT manufacturing systems, J Oper Manage 11 pp 225– (1993)
[2] Adbulnour, Effect of maintenance policies on the just-in-time production system, Int J Prod Res 33 pp 565– (1995)
[3] Albino, Approximation approach for the performance analysis of production lines under a kanban discipline, Int J Prod Res 40 pp 197– (1995) · doi:10.1016/0925-5273(94)00044-7
[4] Akturk, An overview of design and operational issues of kanban systems, Int J Prod Res 37 pp 3859– (1999) · Zbl 0948.90542
[5] Altiok, Performance Analysis of Manufacturing Systems (1997) · Zbl 0942.90003 · doi:10.1007/978-1-4612-1924-8
[6] Altiok, Multi-stage, pull-type production/inventory systems, IIE Trans 27 pp 190– (1995) · Zbl 0841.90058
[7] Altiok, Pull-type manufacturing systems with multiple product types, IIE Trans 32 pp 115– (2000)
[8] Baynat, A product-form approximation method for general closed queuing networks with several classes of customers, Perform Eval 24 pp 165– (1996) · Zbl 0875.68061
[9] Baynat, A multi-class approximation technique for the analysis of kanban-like control systems, Int J Prod Res 39 pp 307– (2001) · Zbl 1017.90501
[10] Berkley, Tandem queues and kanban-controlled lines, Int J Prod Res 34 pp 2057– (1991) · Zbl 0729.90576
[11] Berkley, Simulation tests of FCFS and SPT sequencing in kanban systems, Decision Sci 24 pp 218– (1993)
[12] Berkley, A simulation study of container size in two-card kanban systems, Int J Prod Res 34 pp 3417– (1996) · Zbl 0919.90069
[13] Buzacott, Matrix-geometric and recursive algorithm solution of a two-stage unreliable flow line, IIE Trans 19 pp 429– (1987)
[14] Di Mascolo, An analytical method for performance evaluation of kanban controlled production systems, Oper Res 44 pp 50– (1996) · Zbl 0849.90073
[15] Erhun, Interaction of design and operational parameters in periodic review kanban systems, Int J Prod Res 36 pp 3315– (2003)
[16] Fujiwara, Evaluation of performance measures for multi-part, single-product kanban controlled assembly systems with stochastic acquisition and product lead times, Int J Prod Res 36 pp 1427– (1998) · Zbl 0947.90562
[17] Fullerton, Production performance benefits from JIT implementation, J Oper Manage 19 pp 81– (2001)
[18] Groenevelt 4 pp 629– (1993)
[19] Gupta, A system dynamics model for a multi-stage, multi-line dual-card JIT-kanban system, Int J Prod Res 27 pp 309– (1989)
[20] Gurgur, Approximate analysis of a decentralized, multi-stage, pull production/inventory system, Ann Oper Res 125 pp 95– (2004) · Zbl 1048.90096
[21] Herzog, Solution of queuing problems by recursive technique, IBM J Res Dev 19 pp 295– (1975) · Zbl 0307.68043
[22] Karmarkar, Batching policy in kanban systems, J Manufact Syst 8 pp 317– (1989)
[23] Kleinrock, Queuing Systems Volume I: Theory (1975)
[24] Krieg, A decomposition method for multi-product kanban systems with set-up times and lost sales, IIE Trans 34 pp 613– (2002)
[25] Krieg, Analysis of multi-product kanban systems with state-dependent setups and lost sales, Ann Oper Res 125 pp 141– (2004) · Zbl 1048.90098
[26] Lee, Information distortion in a supply chain: The bullwhip effect, Manage Sci 43 pp 546– (1997) · Zbl 0888.90047
[27] Mitra, Analysis of a kanban discipline for cell coordination in production lines I, Manage Sci 36 pp 1548– (1990)
[28] Mitra, Analysis of a kanban discipline for cell coordination in production lines II, Oper Res 39 pp 807– (1991) · Zbl 0800.90551
[29] Moeeni, A robust design methodology for kanban system design, Int J Prod Res 35 pp 2821– (1997) · Zbl 0942.90530
[30] Monden, Toyota Production System (1993) · doi:10.1007/978-1-4615-9714-8
[31] Onvural, On the complexity of the matrix-geometric solution of exponential open queuing networks with blocking pp 3– (1987)
[32] Philipoom, Simultaneously determining the number of kanbans, container sizes, and the final-assembly sequence of products in a just-in-time shop, Int J Prod Res 34 pp 51– (1996) · Zbl 0923.90079
[33] Sarker, Operations planning for a multi-stage kanban system, Eur J Oper Res 112 pp 284– (1999) · Zbl 0948.90055
[34] Sarker, The performance of push and pull systems: A simulation and comparative study, Int J Prod Res 27 pp 1715– (1989)
[35] Sarker, The effect of imbalance in a just-in-time production system: A simulation study, Int J Prod Res 26 pp 1– (1988)
[36] Savsar, Effects of kanban withdrawal policies and other factors on the performance of JIT systems-A simulation study, Int J Prod Res 34 pp 2879– (1996) · Zbl 0929.90026
[37] So, Allocating buffer storage in a pull system, Int J Prod Res 26 pp 1959– (1988)
[38] Stewart, Comparison of numerical techniques in markov modeling, Commun ACM 21 pp 144– (1978) · Zbl 0383.65067
[39] Yavuz, A kanban-based simulation study of a mixed model just-in-time manufacturing line, Int J Prod Res 33 pp 1027– (1995) · Zbl 0916.90141
[40] White, JIT manufacturing: A survey of implementations in small and large U.S. manufacturers, Manage Sci 45 pp 1– (1999) · Zbl 1231.90168
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.