Matan Shnaiderman’s research while affiliated with Bar Ilan University and other places

Inventory planning in fashion markets is highly challenging, owing to uncertain demand; yet, in making inventory decisions, retailers may be able to capitalise on high substitutability between products. This research develops single-period inventory-management models describing a market with two substitutable products, under stockout-based substitution; i.e. when a customer’s preferred product is out-of-stock, s/he may choose to purchase the substitute. Two settings are considered: centralised (a single retailer who sells both products) and competitive (two retailers, each selling one product). For each setting, we derive closed-form analytical solutions for the inventory levels that maximise expected profit. The model is further enriched with sales data from an online apparel retailer offering substitutable products (a sneaker in different colours), and we analyse the sensitivity of the optimal inventory levels and profits to parameter values. Key findings include the following: (i) Under competitive conditions, both retailers always order positive inventory so as not to lose customers. However, in a single-retailer setting, there are situations in which the retailer orders inventory for only one product. (ii) The optimal inventory levels and corresponding profits are highly sensitive to consumers’ willingness to substitute between products. These findings provide concrete insights that can guide fashion brands’ inventory-management decisions.

Fashion is replaced every season and collections change rapidly, depending on certain events. There are only a few weeks between the fashion shows and the collections reaching their sale points. As the pattern of demand is seasonal, new items must be produced every season. Additionally, colours and patterns change rapidly, creating a need for producers and consumers to continually remain updated. This research study proposes a forecasting model that enhances the accuracy of fashion trend forecasting in the context of multiple variants of colour clothing. The model aims to maximise the firms’ profits, while minimising forecasting errors and reducing costs that result from excess production or, alternatively, from the loss of potential revenues due to low demand. In the proposed model, the expected profit was notably higher when the customers’ readiness to compromise was low or when only one type of product was in stock.

In the apparel market's dynamic environment, fashion firms aim to successfully forecast both the desirability of new collections and the volume of each item to be produced and released in the market under conditions of uncertainty. This research study proposes an inventory-management model developed for substitutable products (sold in several versions or colors) under competitive settings (each retailer sells a single product). The model focuses on stockout based and dynamic based substitutions in inventories. In other words, if there is no inventory of the desired product, the customer will choose to purchase the substitutable product. The model aims to maximize the profits of firms while considering the demand across substitutable products. Under competitive conditions, both retailers will always order positive inventory so as not to lose customers. In addition, an empirical analysis for sales of substitutable items (a product that is offered in different colors) of an online apparel retailer is conducted to empirically estimate the demand distribution.

The research proposes an inventory management model for clothing sold in various colors. The model aims at maximizing the profits of firms, while reducing the costs that result from excess capacity of production or, alternatively, from loss of potential revenues due to low demand while considering the demand dependencies across colors. If the inventory level does not meet the demand, then each customer who does not receive her product in the desired color may choose to buy the other color (if available), or alternatively, waive. We will empirically estimate the demand distribution. We collaborate with an online apparel retailer (one of the largest commerce sites in the world) to assemble the dataset of sales. In addition, we implement a machine learning method to create a forecast based on the social network. The findings of this study can enable fashion brands to manage their inventory and forecast the future purchasing behavior of their customers accurately, incorporate the social networks information, and provide a guide to managing their inventory and forecasting activities as well.

Postponement strategies are becoming increasingly important in light of a global trend in which products’ life-cycles are decreasing, such that even products that are not traditionally considered seasonal become “obsolete” within a short period of time (e.g., electronic devices, new cars). Our work addresses postponed-pricing and ordering decisions for a retailer who sells a newsvendor-type inventoried product, in a selling season that is divided into two sub-periods. The division of the selling season enables the retailer to on-line adjust her decisions when faced with a scenario (one that is highly prevalent in reality) in which potential demand changes (increases or decreases) following consumers’ experiences of the product in early stages of the selling season. We assume that the retailer has two opportunities for receiving shipments: prior to the first sub-period and prior to the second one. The retailer determines each order quantity (base-stock level) on the basis of the demand distribution for the corresponding sub-period. In each sub-period, after observing additional market signals, the retailer determines the price of the product for that sub-period. With the aid of a stochastic programming approach, we develop optimization problems and solution methods in order to obtain pricing and ordering decisions that maximize the expected profit of the retailer. We present an extensive numerical example that compares the suggested strategy to three alternative strategies, and conclude that price postponement and responsiveness to demand changes can each reduce leftovers and lost sales as well as substantially increase expected profit. © 2018 Springer Science+Business Media, LLC, part of Springer Nature

This paper deals with scheduling of appointments between providers and customers (with reservations or walk-in ones) in municipal service centers. In order to improve the service level and reduce the uncertainty of the number of customers’ demand, a free transportation service from the customers’ locations to the service center and back is operated. An optimal transportation service level (TSL) is set in order to minimize the provider’s total idle time and overtime on the one hand, and the transportation service’s operation cost on the other hand. We show how the optimal number of customers to book in advance depends, analogically to inventory management models, on the ratio between the provider’s idle time (“surplus”) and overtime (“shortage”) unit costs. The lower impact of the TSL on the demand, the lower optimal TSL and expected cost, especially if the surplus cost is higher than the shortage cost. Furthermore, we add a safety constraint, according to which, the TSL level must be high enough such that the probability for non-arrival of at-risk customers is small. We numerically find that high percentage of at-risk customers in the population, may significantly increase the TSL, and consequently, lead to meaningful jump of the expected cost (up to 26%).

We consider a manufacturing firm whose production is characterized by polluting emissions, an incorporated pollution abatement process and continuous-time inventory control. Recognizing the stochastic nature of both pollution and inventory dynamics, we study the impact of consumer demand and pollution uncertainty on production-inventory policies under environmental costs/taxes imposed on the manufacturer. We find that the manufacturer, facing environmental uncertainty, reduces both inventory and pollution levels in the long run. The same effect is observed in terms of inventories under proportional and progressively growing environmental taxes but not necessarily in terms of pollution. In particular, emission taxes most impact expected steady state inventories while ambient pollution taxes combat long-run pollution levels.

Prompt response to customer demand has long been a point of major concern in supply chains. “Inventory wars” between suppliers and their customers are common, owing to cases in which one supply chain party attempts to decrease its stock at the expense of the other party. In order to ensure that suppliers meet their commitments to fulfill orders on time, customers must formulate incentives or, alternatively, enforce penalties. This paper deals with a customer organization that has a contract with a supplier, based on Just-In-Time strategy. Initiating a policy of sanctions, the customer becomes the lead player in a Stackelberg game and forces the supplier to hold inventory, which is made available to the customer in real-time. Using a class of sanctioning functions, we show that the customer can force the supplier to hold inventory up to some maximal value, rendering actual enforcement of sanctions unnecessary. However, contrary to expectations, escalation of the enforcement level can in fact reduce the capacity of the supplier to replenish on time. Consequently, the customer must sanction meticulously in order to receive his inventory on time. Having the possibility to devote a few hours each day to sanctioning activity significantly reduces the customer's expected cost. In particular, numerical examples show that the customer's costs under an enforcement level may be only 2 percent higher than his costs in a situation in which all inventory is necessarily replenished on time.