Global Dual Sourcing: Tailored Base-Surge Allocation to Near- and Offshore Production
Gad Allon,
Jan A. Van Mieghem
Kellogg School of Management, Northwestern University, Evanston, Illinois 60208
Kellogg School of Management, Northwestern University, Evanston, Illinois 60208
g-allon{at}kellogg.northwestern.edu
vanmieghem{at}kellogg.northwestern.edu
When designing a sourcing strategy in practice, a key task is to determine the average order rates placed to each source because that affects cost and supplier management. We consider a firm that has access to a responsive nearshore source (e.g., Mexico) and a low-cost offshore source (e.g., China). The firm must determine an inventory sourcing policy to satisfy random demand over time. Unfortunately, the optimal policy is too complex to allow a direct answer to our key question. Therefore, we analyze a tailored base-surge (TBS) sourcing policy that is simple, used in practice, and captures the classic trade-off between cost and responsiveness. The TBS policy combines push and pull controls by replenishing at a constant rate from the offshore source and producing at the nearshore plant only when inventory is below a target. The constant base allocation allows the offshore facility to focus on cost efficiency, whereas the nearshore facility's quick response capability is utilized only dynamically to guarantee high service. The research goals are to (i) determine the allocation of random demand into base and surge capacity, (ii) estimate corresponding working capital requirements, and (iii) identify and value the key drivers of dual sourcing. We present performance bounds on the optimal cost and prove that economic optimization brings the system into heavy traffic. We analyze the sourcing policy that is asymptotically optimal for high-volume systems and present a simple "square-root" formula that is insightful to answer our questions and sufficiently accurate for practice, as is demonstrated with a validation study.
Key Words: inventory production; stochastic models; applications; probability; stochastic model applications
History: Received: September 25, 2008;
accepted: August 21, 2009.
Copyright © 2009 by INFORMS.
