Delivery time dynamics in an assemble-to-order inventory and order based production control system

Junyi Lin*, Mohamed M. Naim, Virginia L.M. Spiegler

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

System dynamics play a critical role in influencing supply chain performance. However, the dynamic property of the assemble-to-order (ATO) system remain unexplored. Based on control theory, the inventory and order based production control system (IOBPCS) family, can be utilized as a base framework for assessing system dynamics. However, the underlying assumption in traditional IOBPCS-based analytical studies is that the system is linear and the delivery time to end customers is negligible or backlog is used as a surrogate indicator. Our aim is to incorporate customer delivery lead-time variance as the third assessment measure alongside capacity availability and inventory variance as part of the so-called ‘performance triangle’– capacity at the supplier, the customer order decoupling point (CODP) inventory and the delivery lead-time. Using the ‘performance triangle’ and adopting non-linear control engineering techniques, we assess the dynamic behaviour of an ATO system in the electronics sector. We benchmark the ATO system dynamics model against the IOBPCS family. We exploit frequency response analysis to ensure a robust system design by considering three measures of the ‘performance triangle’. The findings suggest delivery LT variance can be minimised by maintaining the ATO system as a true Push-Pull hybrid state with sufficient CODP stock, although increased operational cost driven by bullwhip and CODP variance need to be considered. However, if the hybrid ATO system 'switches' to the pure Push state, the mean and variance of delivery LT can be significantly increased.

Original languageEnglish
Article number107531
JournalInternational Journal of Production Economics
Volume223
DOIs
Publication statusPublished - May 2020

Keywords

  • Minimum reasonable capacity
  • Minimum reasonable lead-time
  • Nonlinear control theory
  • System dynamics
  • Trade-off analysis

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