Integrating Kanban Type Pull Systems and MRP Type Push Systems: Insights From a Markovian Model

Deleersnyder, Jean-Luc; Hodgson, Thom J.; King, Robert E.; O’Grady, Peter; Savva, Andreas

doi:10.1080/07408179208964223

Cited by 73 publications

(29 citation statements)

References 8 publications

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“…Production management systems are classified into push production systems (such as Material Requirement Planning, MRP) and pull production systems (such as Lean Production System, LPS) (Deleersnyder et al, 1992;Spearman and Zazanis, 1992;Bonney et al, 1999). The distinction between push and pull system is on how production orders are released to work stations in response to demands (Krishnamurthy et al, 2004).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The advantages and disadvantages of push and pull production systems have been discussed in literatures (Lee, 1989;Deleersnyder et al, 1992;Spearman and Zazanis, 1992). The production lead time and batch sizes affect the inventory undulations under a pull production system, while the degree of forecasting errors are the main concern in the push production system (Kimura and Terada, 1981).…”

mentioning

confidence: 99%

See 1 more Smart Citation

The Influence of Production Management Practices and Systems on Business Performance: From the Perspective of the Push-pull Production Systems

Wee

Peng

Yang

et al. 2014

OSCM: An Int. Journal

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Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

The Influence of Production Management Practices and Systems on Business Performance: From the Perspective of the Push-pull Production Systems

Wee

Peng

Yang

et al. 2014

OSCM: An Int. Journal

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“…The behaviour of the strategies under uncertainties is investigated in order to make realistic approximations in planning, and suggest precautionary actions to account for the uncertainties (Murthy andMa 1991, Miltenburg andWijngaard 1991) . There are several studies which conceptually compare the operating logic of the MPC strategies (Fandel and Francois 1988, Karmarkar 1989, Deleersnyder et al 1992 . In the major model-based comparative studies, simulation is recognized as the most powerful tool in analysing the MPC systems because of their inherent complexities (Krajewski et al 1987, Spearman andZazanis 1992) .…”

Section: Introductionmentioning

confidence: 99%

Product flexibility in selecting manufacturing planning and control strategy

Persentili¹,

Alptekin²

2000

International Journal of Production Research

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The manufacturing systems capable of producing several products simultaneously are frequently subject to changes in product types due to demand¯uctuations. In such systems a product¯exible manufacturing planning and control (MPC) strategy is needed to change from one product type to another with minimum deterioration to system performance levels. The objective of this research is to develop a systematic analysis and evaluation approach in order to compare the MRP-push and JIT-pull strategies quantitatively based on a product¯exibility measure. A new product¯exibility measure is developed based on the sensitivity to change concept and presented together with the implementation in a real manufacturing system. Simulation is used to compare the performance of a JIT-pull with an MRP-push strategy based on performance measures, e.g. manufacturing lead time, work-in-process inventory, backorders, machine utilization and throughput. The performances of the two strategies are evaluated in two scenarios: (i) a single product; (ii) a second product is added (the ® rst product being simple and the second being complex in terms of processing). The impacts of adding the second product on the performance measures for the push and pull strategies are then assessed. A multi-attribute evaluation scheme is used to compare the two strategies where the attribute values are the change in performance measures as the second product is added. The proposed product¯exibility measure is utilized in the interpretation of the results.

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“…Then, in (Hodgson et al 2, 1991), they generalize the scenario to other types of industries. Furthermore, (Deleersnyder et al, 1992) use a markovian model to develop a general N-stage hybrid push/pull system. Lower inventory levels and better response to demand changes were reported.…”

Section: Theory Of Constraintsmentioning

confidence: 99%

Designing a robust production system for erratic demand environments.

El-Khoury¹,

Elias²

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Integrating Kanban Type Pull Systems and MRP Type Push Systems: Insights From a Markovian Model

Cited by 73 publications

References 8 publications

The Influence of Production Management Practices and Systems on Business Performance: From the Perspective of the Push-pull Production Systems

The Influence of Production Management Practices and Systems on Business Performance: From the Perspective of the Push-pull Production Systems

Product flexibility in selecting manufacturing planning and control strategy

Designing a robust production system for erratic demand environments.

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