OPTIMAL ORDERING IN A DUAL‐SUPPLIER SYSTEM WITH DEMAND FORECAST UPDATES

Abstract

We study how an updated demand forecast affects a manufacturer's choice in ordering raw materials. With demand forecast updates, we develop a model where raw materials are ordered from two suppliers—one fast but expensive and the other cheap but slow—and further provide an explicit solution to the resulting dynamic optimization problem. Under some mild conditions, we demonstrate that the cost function is convex and twice‐differentiable with respect to order quantity. With this model, we are able to evaluate the benefit of demand information updating which leads to the identification of directions for further improvement. We further demonstrate that the model applies to multiple‐period problems provided that some demand regularity conditions are satisfied. Data collected from a manufacturer support the structure and conclusion of the model. Although the model is described in the context of in‐bound logistics, it can be applied to production and out‐bound logistics decisions as well.

Keywords

SUPPLY CHAIN MANAGEMENT DEMAND INFORMATION REVISIONS FORECASTING

Get full access to this article

View all access options for this article.

References

Cohen

M. A.

and Mallik

(1997), “Global Supply Chains: Research and Applications. Production and Operations Management, 6, 3, 193–210.

Fisher

Hammond

J. H.

Obermeyer

W. R.

, and Raman

(1994), “Making Supply Meet Demand in an Uncertain World,” Harvard Business Review, May‐June, 83–93.

Fisher

and Raman

(1996), “Reducing the Cost of Demand Uncertainty Through Accurate Response to Early Sales. Operations Research, 44, 1, 87–99.

Hausman

W. H.

Lee

H. L.

, and Zhang

V. L.

(1993), “Optimal Ordering for an Inventory System with Dual Lead Times.” Working Paper, Stanford University.

Heyman

D. P.

and Sobel

M. J.

(1984), Stochastic Models in Operations Research, Volume II, McGraw‐Hill, New York.

Iyer

A. V.

and Bergen

M. E.

(1997), “Quick Response in Manufacturing Retail Channels. Management Science, 43, 4, 559–570.

Lee

H. L.

Padmanabhan

, and Whang

(1997), “Information Distortion in a Supply Chain: The Bullwhip Effect. Management Science, 43, 4, 546–558.

Lovejoy

W. S.

(1990), “Myopic Policies for Some Inventory Models with Uncertain Demand Distributions. Management Science, 36, 6, 724–738.

Murray

G. R.

and Silver

E. A.

(1966), “A Bayesian Analysis of the Style Goods Inventory Problem. Management Science, 12, 11, 785–797.

10.

Nahmias

(1997), Production and Operations Analysis, 3rd ed., Irwin, Chicago.

11.

Porteus

E. L.

(1990), “Stochastic Inventory Theory,” In Heyman

D. P.

and Sobel

M. J.

(Eds.) Handbooks in Operations Research and Management Science, Volume 2: Stochastic Models, North‐Holland, Amsterdam.

12.

Samuelson

P. A.

and Nordhaus

W. D.

(1998), Economics, 16th ed., Irwin, Chicago.

13.

Scheller‐Wolf

and Tayur

(1997), “A Markovian Dual‐Source Production‐Inventory Model with Order Bands,” Working Paper, Carnegie Mellon University.

14.

Yan

Liu

, and Hsu

(1998), “Optimal Ordering in a Dual‐Supplier System with Demand Forecast Updates,” Technical Report 98‐03, The Dept. of Systems Engineering and Engineering Management, The Chinese University of Hong Kong.

15.

Zhang

V. L.

(1996), “Ordering Policies for an Inventory System with Three Supply Modes. Naval Research Logistics, 43, 8, 691–708.