• DocumentCode
    2796261
  • Title

    An integrated excess stock return model with deteriorating items for vendor-buyer

  • Author

    Chung, Shen Lian ; Wee, Hui Ming

  • Author_Institution
    Dept. of Inf. Manage., St. John´´s Univ., Taipei
  • Volume
    7
  • fYear
    2008
  • fDate
    12-15 July 2008
  • Firstpage
    3915
  • Lastpage
    3920
  • Abstract
    Excess stock is a common phenomenon in a supply chain owing to over ordering and/or incorrect demand predictions. Excess stock is a valueless asset to an organization; it consumes working capital, uses valuable storage space, and decreases the return on investment. Therefore, excessive stock must be dealt with in order to derive an optimal inventory level for minimum total relevant costs. Previous researchers had considered the views of buyers to establish excess stock models to determine the inventory retention level, but the views of the vendors were not consulted. This results in local optimality only and unsatisfied vendors. This study to consider the views of the vendor-buyer and deteriorating items, and to develop an analytical model for the integrated inventory decisions in a supply chain. The results of the model can be a guide to decide the optimal quantity of return units to dispose of under various return purchase prices. A numerical example is provided to illustrate the theory.
  • Keywords
    decision making; stock control; supply chain management; deteriorating items; incorrect demand predictions; integrated excess stock return model; integrated inventory decisions; minimum total relevant costing; optimal inventory level; over ordering; return on investment; supply chain; vendor-buyer views; Analytical models; Cost function; Cybernetics; Inventory management; Investments; Machine learning; Mathematical model; Production systems; Supply chain management; Supply chains; Deteriorating item; Excess stock; Return inventory; Supply chain inventory management;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2008 International Conference on
  • Conference_Location
    Kunming
  • Print_ISBN
    978-1-4244-2095-7
  • Electronic_ISBN
    978-1-4244-2096-4
  • Type

    conf

  • DOI
    10.1109/ICMLC.2008.4621087
  • Filename
    4621087