Mitigating Inventory Overstocking: Optimal Order‐up‐to Level to Achieve a Target Fill Rate over a Finite Horizon

Abstract

S ervice level agreements (SLAs) are widely adopted performance-based contracts in operations management practice, and fill rate is the most common performance metric among all the measurements in SLAs. Traditional procedures characterizing the order-up-to level satisfying a specified fill rate implicitly assume an infinite performance review horizon. However, in practice, inventory managers are liable to maintain and report fill rates over a finite performance review horizon. This horizon discrepancy leads to deviation between the target fill rate and actual achieved fill rate. In this study, we first examine the behavior of the fill rate distribution over a finite horizon with positive lead time. We analytically prove that the expected fill rate assuming an infinite performance review horizon exceeds the expected fill rate assuming a finite performance review horizon, implying that there exists some inventory "waste" (i.e., overstocking) when the traditional procedure is used. Based on this observation and the complexity of the problem, we propose a simulation-based algorithm to reduce excess inventory while maintaining the contractual target fill rate. When the lead time is significant relative to the length of the contract horizon, we show that the improvement in the inventory system can be over 5%. Further, we extend our basic setting to incorporate the penalty for failing to meet a target, and show how one can solve large-scale problems via stochastic approximation. The primary managerial implication of our study is that ignoring the performance review horizon in an SLA will cause overstocking, especially when the lead time is large.

Mapping the Ethicality of Algorithmic Pricing: A Review of Dynamic and Personalized Pricing

“…Pricing policies can also substantially vary based on the context they are used in. Inventory management is a critical business function for many firms, given that marginal improvements can lead to substantial savings (Tan et al 2017). In short, an inventory-based dynamic pricing policy needs to correspond with ordering and supply decisions, production control and safety stock to hedge potential demand uncertainty (Li et al 2015a).…”

“…Thus, firms are often faced with a trade-off between overstocking and understocking applying markdown programs or low pricing strategies as a way to balance the two sides (Huang et al 2014;Özer and Zheng 2016;Zhang et al 2019). Overstocking, sometimes referred to as 'inventory waste,' creates costs for holding inventory and accounts for lost sales (Huang et al 2014;Tan et al 2017). In this regard, Liu and Van Ryzin (2008) analyze deliberate understocking, which creates rationing risk and is bound to incentivize buyers to purchase earlier and at higher prices.…”

Fill‐rate service level constrained distribution network design

“…The most common product availability measure observed in practical settings is the fill‐rate service level (Bijvank, 2014; Teunter et al., 2017; Tan et al., 2017; Babiloni and Guijarro, 2020), defined as the fraction (β) of demand met directly from inventory on hand in any period (Schneider, 1981). However, the most commonly used product availability measure in JLIP is the $$\alpha _L$$ service level, defined as the probability ($$\alpha _L$$) that stockouts do not occur during a replenishment cycle (Schneider, 1981), where L refers to the lead time.…”

On the shortage control in a continuous review (Q,r) inventory policy usingαLservice-level

“…According to several authors (Babiloni and Guijarro [5], Teunter et al [33], Tan et al [29], Bijvank [7]) the β service-level definition is the most observed in practical settings. Consequently, it can be inferred that the size of the stockouts is the most important shortage dimension for inventory managers.…”