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The Effect of Distribution Processes on Replenishment Lead Time and Inventory

Production and Operations Management 2017
We investigate the interrelationship of distribution center picking policies and supply chain inventory performance. In particular, we show how the picking sequence in the upstream supply chain link affects the inventory levels of items being replenished to a downstream link for a common “ship‐when‐full” trailer dispatching policy. Perturbing the picking sequence affects items’ inventory levels asymmetrically which causes the aggregate system inventory to vary. We separate the items in replenishment into those units in transit and those awaiting shipment from the upstream distribution step: we call the latter the residual replenishment. We show that the process governing aggregate residual replenishment is Markov and has a stationary distribution that is discrete uniform. Computing the item‐level residual distribution is intractable and so we develop analytical models from which we derive hypotheses for the effectiveness of stable vs. random picking sequences, how item residual replenishment varies with stable picking sequences, and how the aggregate inventory level changes with picking sequence. These suggest a heuristic sequencing algorithm for minimizing inventory, which performs well in simulation tests over a large testbed of parameter sets.

Coordination of Autonomous Healthcare Entities: Emergency Response to Multiple Casualty Incidents

Production and Operations Management 2017 open access
In recent years, many urban areas have established healthcare coalitions (HCCs) composed of autonomous (and often competing) hospitals, with the goal of improving emergency preparedness and response. We study the role of such coalitions in the specific context of response to multiple‐casualty incidents in an urban setting, where on‐scene responders must determine how to send casualties to medical facilities. A key function in incident response is multi‐agency coordination. When this coordination is provided by an HCC, responders can use richer information about hospital capacities to decide where to send casualties. Using bed availability data from an urban area and a suburban area in the United States, we analyze the response capability of healthcare infrastructures under different levels of coordination, and we develop a stress test to identify areas of weakness. We find that improved coordination efforts should focus on decision support using information about inpatient resources, especially in urban areas with high inter‐hospital variability in resource availability. We also find that coordination has the largest benefit in small incidents. This benefit is a new value proposition for HCCs, which were originally formed to improve preparedness for large disasters.

Markdown Budgets for Retail Buyers: Help or Hindrance?

Production and Operations Management 2017 open access
For many retailers, markdown decisions are taken by retail buyers whose compensation is based on sales revenue so their objective is to maximize it through the season. This implies that the buyers' objectives are not perfectly aligned with the overall profitability the firm. Many retailers set markdown budgets prior to the season to control margin erosion and increase profitability. Markdown budget constrains the buyers on the amount of discounts that they can apply on a given inventory of merchandise and sets a limit on the dollar value of markdowns for the season. While markdown budgets may be useful in preventing excessive discounts, they can have a detrimental effect on the buyers' ability to respond to poor market and remove distressed inventory. We investigate the effectiveness of this practice in aligning the incentives of buyers with that of the firm, and provide guidance on how these budgets should be established ahead of time. We consider a firm with a fixed inventory of a seasonable item, and a single chance to mark the price down. The retailer knows only the demand distribution at the beginning of the season, but the market information is revealed during the season to the buyer. We first characterize the buyer's markdown policy and understand the circumstances under which this can be different from the retailer's markdown policy. We use our model to determine the optimal markdown budget and quantify its effectiveness considering different factors such as the level of demand uncertainty, initial markup, and market's responsiveness to markdowns.