Matan Shnaiderman

The impact of partial information sharing in a two-echelon supply chain

Abstract We consider a simple two-echelon supply chain composed of a manufacturer and a retailer in which the demand process of the retailer is an AR(1) where the random component is a function of both sides’ information. We focus on partial information sharing under which each side informs the other of an interval in which the exact value of its own component of demand lies. These various levels of information sharing can reduce the supply chain costs.

Supply Chain With Inventory Review and Dependent Demand Distributions: Dynamic Inventory Outsourcing

In this paper, we consider inventory outsourcing by a producer to a distributor. The distributor charges a cost for each unit it handles and the manufacturer responds with a production and inventory policy over a finite contract period. As a result, the two parties enter a noncooperative differential game. We address the effect of information asymmetry in such a game under a stochastic demand when the inventory level can only be observed by the manufacturer intermittently.

Continuous-Time Replenishment Under Intermittent Observability

In this technical note we study continuous-time stochastic control of a dynamic production and replenishment system characterized by bounded control and an additive type of uncertainty. The study is motivated by problems arising in supply chains involving periodic exchange of information between a manufacturing system (supplier) and a customer (retailer). As a result, the inventories are only observed periodically while the replenishment is possible at any point of time. We identify replenishment policies for different operational conditions and show that, even for one-product-type system, the consideration of random demand over multiple update periods leads to a non-intuitive, and nontrivial, optimal production control.

A logit–based model for facility placement planning in supply chain management

The facility placement in supply chain management entails suppliers and consumers along with the terminals in between for distributing commodities. This study seeks to find the best terminal placement by taking into consideration the costs for both transportation and terminal construction. We call this a supplier-terminal-consumer (STC) problem and show that the STC is an NP-hard quadratic assignment problem. The NP-hard problems in real-size are proven to be intractable; hence, we develop a two-fold heuristic method for solving the STC problems. First, we identify the commodity flow by using a logit-based mathematical programming (Logit-MP) methodology based on the demand for the commodity and the locations of the candidate-terminals. We apply Logit-MP in an iterative process and specify the maximum utilisation of the candidate-terminals. Second, the best possible locations for the terminals are identified by analysing the utilisation rates in a geographic information system interface and using an interpolation method for converting the point-based utilisation rates into spatial data. We present numerical results of a large-size transportation case study for the city of Chicago where the commodity, terminals and consumers are interpreted as wheat, silos and bakeries, respectively.

On Optimality of a Class of Dynamic Myopic Policies for Continuous-Time Replenishment with Periodic Updates

This paper is motivated by inventory problems arising in supply chains characterized by continuous replenishment programs based on information exchanged (reviewed) only intermittently between a manufacturing system (supplier) and a customer (retailer). When the replenishment is once-per-period, rather than at any point of time, a well-known result is the optimality of the so-called myopic base-stock policy. We generalize the notion of the base-stock policy and study the optimality of the corresponding class of dynamic myopic policies. We identify a myopic policy and prove that although the replenishment rule is dynamic, this policy is optimal when the demands are stationary and the number of review periods tends to infinity.

Control and enforcement in order to increase supplier inventory in a JIT contract

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 customers expected cost. In particular, numerical examples show that the customers 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.

Postponed two-pricing and ordering opportunity for selling a single season inventoried product

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.

The Effect of Uncertainty on Production-Inventory Policies With Environmental Considerations

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.

The Influence of Transportation Service Level on a Municipal Service Center’s Costs: A Numerical Study Based on Supply Chain Management Models

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%).