Analyzing the Effectiveness of the Availability Management Process

  • Young M. Lee
Part of the Springer Series in Advanced Manufacturing book series (SSAM)


Availability management is a major factor in successful supply chain management since it influences key supply chain performance metrics such as customer service level and inventory. The availability management process involves generating availability outlook, scheduling customer orders against the availability outlook, and fulfilling the orders. The process is also associated with many uncertainties such as customer demand, customer preference of product configuration, and changes in supply constraints and various supply chain policies, which also affect the supply chain performance. As e-commerce is becoming a major part of business transactions it is much easier for customers to compare availability and services from many different sellers. Therefore, it is important for sellers to process customer orders in real-time, promise ship dates, fulfill the orders as promised, and to have availability of resources to be able to promise customers desirable ship dates. In today’s competitive and dynamic business environment, companies need to continually evaluate the effectiveness of the availability management process and supporting IT system and look for ways to transform the process to achieve better customer service and profitability. To do that, there is a need for an easy-to-use modeling tool that can accurately assess the effectiveness of the existing availability management process, evaluate the impact of potential changes in the process and identify opportunities for improvement. In this chapter, we describe an availability management simulation tool that was developed at IBM to support the continuous effort to improve the availability management process. The simulation model has become a critical tool in making strategic business decisions that impact customer service and profitability at IBM.


Supply Chain Customer Service Customer Order Supply Chain Performance Schedule Delay 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  • Young M. Lee
    • 1
  1. 1.Research CenterIBM Thomas J. WatsonYorktown HeightsUSA

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