Inventory performance of some supply chain inventory policies under impulse demands

Subhash Wadhwa, Bibhushan, Tung Sun Chan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

27 Citations (Scopus)


This paper attempts to study the impact of impulsive demand disturbances on the inventory-based performance of some inventory control policies. The supply chain is modelled as a network of autonomous supply chain nodes. The customer places a constant demand except for a brief period of sudden and steep change in demand (called demand impulse). Under this setting, the behaviour of each inventory policy is analysed for inventory performance of each node. It is found that the independent decision-making by each node leads to a bullwhip effect in the supply chain whereby demand information is amplified and distorted. However, under a scenario where the retailer places a constant order irrespective of the end customer demand, the inventory variance was actually found to decrease along the supply chain. The variance of the inventory remained constant along the chain when only the actual demands are transmitted by each node. The results also showed that the inventory policy which is best for one supply chain node is generally less efficient from a supply chain perspective. Moreover, the policy which performs poorly for one node can be most efficient for the supply chain. In a way, our results also provide a case for coordinated inventory management in the supply chain where all members prepare a joint inventory management policy that is beneficial for all the supply chain nodes. The results have significant industrial implications.
Original languageEnglish
Pages (from-to)3307-3332
Number of pages26
JournalInternational Journal of Production Research
Issue number12
Publication statusPublished - 1 Jan 2009
Externally publishedYes


  • Impulse demands
  • Inventory control policies
  • Inventory performance
  • Simulation
  • Supply chain

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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