Conference Agenda

We explore the challenge of spares allocation within a complex inventory system encompassing multiple items and locations, all under periodic review. The primary aim is to enhance service efficiency by maximizing the window fill rate, which denotes the likelihood of promptly serving a random customer within a specified time frame. The window fill rate offers a nuanced metric, accounting for customers' willingness to endure some waiting time before being attended to.

Our study delves into the formulation of the window fill rate and reveals its varying behavior concerning the number of spares, manifesting as either a constant, concave, or convex-concave function contingent upon the acceptable wait time. Leveraging these insights, we devise an effective algorithm to optimize spares allocation within predefined budget constraints or target window fill rates. Notably, our analysis suggests that when the acceptable wait time or budget is limited, spares tend to concentrate in select locations or item types, while others may remain unallocated.

Furthermore, we substantiate our findings through numerical simulations using two distinct large-scale synthetic scenarios. These simulations serve to elucidate the comparative costs associated with periodic versus continuous review strategies, illustrating the potential advantages of employing the window fill rate as a criterion for optimality. The numerical analysis underscores both the intricacies inherent in optimizing the window fill rate and the tangible savings achievable through its judicious utilization in spares allocation decisions.

Optimizing retail assortments is essential to maximize revenues, but also can have an impact on inventory ages and therefore product expiration. The limiting condition for assortment decisions is the shelf space of retailers. Assuming a constant available, but in size restricted shelf space, the assortment size also defines how much shelf space each product gets, and therefore how much items of each product can be stored on the shelf. That is why assortment planning is also important in terms of inventory management. Especially for perishable products, the inventory level on the shelf has to be carefully chosen in order to prevent deteriorating overstocks. Current literature considers single-period assortment and shelf space models, despite products can be stored and replenished over multiple sales periods, leading to different expiration dates on the shelf. We contribute with an assortment and shelf space model that considers replenishment and customer withdrawal over several periods, taking into account stochastic demand, space elasticity, substitution effects and customer’s freshness preferences. To solve the model, we use an iterative heuristic approach with a recurring update of demand to account for substitution effects and their impacts on the assortment and shelf space decisions.

Two primary goals of grocery retailers when managing perishable products (e.g., food) are (i) to offer products with a high remaining shelf life to consumers, as those are perceived to be of high quality, and (ii) to minimize waste driven by products that exceed their maximum remaining shelf life. Both goals prompt retailers to integrate a minimum remaining shelf-life requirement, known as “Minimum Life on Receipt” (MLOR), into the contracts with their suppliers. However, little is known about how such an MLOR requirement affects the contractual agreement between retailers and food producers and how it further affects replenishment and production decisions, profitability, and waste of the retailer and the food producer. In this paper, we investigate how an MLOR requirement of the retailer affects the operational performance of a wholesale price contact in a two-echelon perishable-product supply chain. We consider that both the retailer and the food producer follow a basestock policy, where the basestock levels are optimized depending on the wholesale price set by the food producer and the MLOR requirement. Our results challenge widely accepted findings that retailers benefit from organizing their inventory management following a first-in-first-out (FIFO) issuing policy compared to a last-in-first-out (LIFO) policy.

This paper investigates the impact of bilateral relationships on procurement and competition incentives in a supply chain environment from a behavioral perspective. We focus on a two-retailer-two-supplier market where retailers negotiate supply procurement contracts with their potential suppliers. In our model, potential trade partners are determined by a bilateral relationship network, which is a bipartite graph where nodes represents firms and links rep- resents bilateral relationships among firms, and not every downstream retailer has links with every upstream supplier. We first derive the bargaining equilibrium outcome as theoretical benchmark and then conduct a laboratory experiment to confirm the results from theoretical benchmark. Our experimental data suggest systematic deviations from the theoretical benchmark and reveal behavioral regularities on contracting behavior. In particular, we show that firms with more (less) potential partners and/or higher (lower) perceived values tend to earn more (less) than theoretical benchmark. We develop a new behavioral theory, referred to as desperateness model, where a firm’s unfavorable bargaining position inflicts additional distress to the firm. This additional stress amplifies the bargaining incentives of firms in such a way that the firms with favorable bargaining positions can take advantage of the desperateness of firms with unfavorable positions to extract additional profit. We demonstrate that our desperateness theory explains and predicts the firms’ supply contract bargaining behavior well.