Advanced planning and scheduling with collaboration processes in agile supply and demand networks

Yohanes Kristianto, Mian M. Ajmal, Petri Helo

Research output: Contribution to journalJournal articleResearchpeer-review

8 Citations (Scopus)

Abstract

Purpose: The general purpose of the paper is to improve supply chain (SC) responsiveness and agility by developing advanced planning and scheduling (APS) with collaboration process into agile supply and demand networks (ASDN). Design/methodology/approach: Some industrial examples are presented to extract the APS requirements, then business models that are supported by analytical models are developed into APS modules to respond to the requirements. At the end, the modules are attached into an ASDN simulator to measure the benefit of the APS with collaboration process. Findings: The results show that the APS with collaboration process is superior to existing APS software in terms of promising lead times to customers at minimum inventory level. Research limitations/implications: Since the APS with collaboration process cannot optimize transportation planning, SCs cannot therefore optimize networks by finding the optimum network configuration. Currently, the simulator needs to be tested in several possible network scenarios to find the optimal network configuration. Practical implications: The APS with collaboration process makes it possible to give guaranteed lead times at minimum inventory level. Furthermore, it is possible to combine the APS with collaboration process with enterprise resources planning or MRP II by considering the criticality of the planning. Originality/value: The attachment of APS with collaboration process business into ASDN represents the original aspect of this paper.

Original languageEnglish
JournalBusiness Process Management Journal
Volume17
Issue number1
Pages (from-to)107-126
Number of pages20
ISSN1463-7154
DOIs
Publication statusPublished - 1 Feb 2011

Keywords

  • Flexible labour
  • Market share
  • Production scheduling
  • Supply chain management
  • Value chain

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