A perceptions framework for categorizing inventory policies in single-stage inventory systems
By: Noel Watson
Contributors: Harvard Business School. Division of Research
Series: Working paper / Division of Research, Harvard Business School -- 07-036; Working paper (Harvard Business School. Division of Research) -- 07-036
Publisher: Division of Research, Harvard Business School
Published: 2006
Place of publication: Boston]
Language: eng
Number of pages: 32
Description: In this paper we propose a perceptions framework for categorizing a range of inventory policies, including optimal inventory policies, that can be employed in a single-stage supply chain. The perceptions framework is based on forecasting with Auto-regressive Integrated Moving Average (ARIMA) time series models within the context of a single stage stochastic inventory system with periodic review, constant leadtimes, infinite supply, full backlogging, linear holding and penalty costs and no ordering costs. Forecasting ARIMA time series requires tracking forecast errors (interpolations) and using these forecast errors and past demand realizations to predict future demand (extrapolating). Categorizing deviations from optimal inventory policies is possible if we allow the perception about demand implied by the interpolations or extrapolations to be different from the actual demand process. We do not use perception in its more conventional sense; we are not making any claims about the actual perception of any manager. Rather the perceptions here serve as a device for modeling and categorizing a range of inventory policies.
Notes: "November 30, 2006"--Added t.p.

Includes bibliographical references.