Knowledge You Can Act on: Optimal Policies for Assembly Systems with Expediting and Advance Demand Information

Abstract

We consider a nonstationary, stochastic, multistage supply system with a general assembly structure, in which customers can place orders in advance of their future demand requirements. This advance demand information is now recognized in both theory and practice as an important strategy for managing the mismatch between supply and demand. In conjunction, we allow expediting of components and partially completed subassemblies in the system to provide the supply chain with the means to manage the stockout risk and significantly enhance cost savings realized through advance demand information. To solve the resulting assembly system, we develop a new method based on identifying local properties of optimal decisions. This new method allows us to solve assembly systems with multiple product flows. We derive the structure of the optimal policy, which represents a double-tiered echelon basestock policy whose basestock levels depend on the state of advance demand information. This form of the optimal policy allows us to: (i) provide actionable policies for firms to manage large-scale assembly systems with expediting and advance demand information; (ii) prove that advance demand information and expediting of stock both reduce the amount of inventory optimally held in the system; and (iii) numerically solve such assembly systems, and quantify the savings realized. In contrast to the conventional wisdom, we discover that advance demand information and expediting of stock are complementary under short demand information horizons. They are substitutes only under longer information horizons.

A Survey of Recent Progress in the Asymptotic Analysis of Inventory Systems

“…Whittmore and Saunders (1977) show that the optimal policy is no longer a simple base-stock policy when the lead time difference is larger than one. In the subsequent decades, a vast literature has arisen for such models, and we refer the interested reader to the survey of Minner (2003), as well as for example, the more recent works of Feng et al (2006), Fox et al (2006), Chen et al (2013), Huggins and Olsen (2010), Angelus and Ozer (2015), Boute and Van Mieghem (2015), Gong et al (2014), Song and Zipkin (2009), and the references therein. As an exact solution seems out of reach, researchers have instead investigated certain structural properties of the optimal policy (Hua et al 2015), and analyzed various heuristic policies including single index (SI) policies (Scheller-Wolf et al 2008), dual index (DI) policies (Veeraraghavan and Scheller-Wolf 2008), and additional policies Van Mieghem 2015, Sheopuri et al 2010).…”

“…(2006), Chen et al. (2013), Huggins and Olsen (2010), Angelus and Ozer (2015), Boute and Van Mieghem (2015), Gong et al. (2014), Song and Zipkin (2009), and the references therein.…”

The added value of advance demand information for the planning of a repair kit

“…The repair kit problem can be considered as a multi-product problem with each set of spare parts required by a customer defining a product. Multi-product problems that integrate ADI have been studied by Lu et al (2003), Bernstein and DeCroix (2015) and Angelus and Özer (2016). Lu et al (2003) consider an assembly system under continuous review where advance orders for assembled products are triggering the replenishment of component inventories that are managed using basestock policies.…”

“…Lu et al (2003) consider an assembly system under continuous review where advance orders for assembled products are triggering the replenishment of component inventories that are managed using basestock policies. A similar system but with periodic review is studied by Angelus and Özer (2016), who also combine advance demand information with order expediting. In Bernstein and DeCroix (2015) advance demand information is available in aggregated form, either as the total volume of demand across several products or as the demand mix between these products.…”

“…Since Angelus and Özer (2016) only consider one final product to be assembled, we have a more complicated situation because repair jobs can be different for different customers. Similar to the repair kit problem, Lu et al (2003) also consider several final end products.…”