The impact of exponential smoothing forecasts on the bullwhip effect

An important phenomenon often observed in supply chain management, known as the bullwhip effect, implies that demand variability increases as one moves up the supply chain, i.e., as one moves away from customer demand. In this paper we quantify this effect for simple, two‐stage, supply chains consisting of a single retailer and a single manufacturer. We demonstrate that the use of an exponential smoothing forecast by the retailer can cause the bullwhip effect and contrast these results with the increase in variability due to the use of a moving average forecast. We consider two types of demand processes, a correlated demand process and a demand process with a linear trend. We then discuss several important managerial insights that can be drawn from this research. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 269–286, 2000     

A branch and bound algorithm for computing optimal replacement policies in consecutive k‐out‐of‐n‐systems

This paper presents a branch and bound algorithm for computing optimal replacement policies in a discrete‐time, infinite‐horizon, dynamic programming model of a binary coherent system with n statistically independent components, and then specializes the algorithm to consecutive k‐out‐of‐n systems. The objective is to minimize the long‐run expected average undiscounted cost per period. (Costs arise when the system fails and when failed components are replaced.) An earlier paper established the optimality of following a critical component policy (CCP), i.e., a policy specified by a critical component set and the rule: Replace a component if and only if it is failed and in the critical component set. Computing an optimal CCP is a optimization problem with n binary variables and a nonlinear objective function. Our branch and bound algorithm for solving this problem has memory storage requirement O(n) for consecutive k‐out‐of‐n systems. Extensive computational experiments on such systems involving over 350,000 test problems with n ranging from 10 to 150 find this algorithm to be effective when n ≤ 40 or k is near n. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 288–302, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10017     

A Bayesian analysis on the effect of multiple supply options in a quick response environment

In the apparel industry, vendors often suffer from high mismatches in supply and demand. To cope with this problem, they procure the same style product from different suppliers with different manufacturing costs. Especially in the quick response environment, which allows vendors to monitor trends in customer demand and search for available suppliers through the electronic market, they have additional opportunities to improve their decision‐making. In this paper, we propose an analytical profit maximization model and develop efficient decision tools to help both the middle and lower level managers pursuing this strategy. Furthermore, we have shown how significantly the vendors' potential competitive edge can be improved by exploiting multiple supply options, even at the expense of high premium procurement costs for late orders. The effect is critical, especially in a highly competitive market, and it has important implications for the top managers. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2003     

The cost impact of using simple forecasting techniques in a supply chain

In this paper we consider an inventory model in which the retailer does not know the exact distribution of demand and thus must use some observed demand data to forecast demand. We present an extension of the basic newsvendor model that allows us to quantify the value of the observed demand data and the impact of suboptimal forecasting on the expected costs at the retailer. We demonstrate the approach through an example in which the retailer employs a commonly used forecasting technique, exponential smoothing. The model is also used to quantify the value of information and information sharing for a decoupled supply chain in which both the retailer and the manufacturer must forecast demand. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 388–411, 2003     

The value of information sharing in a two‐stage supply chain with production capacity constraints

We consider a simple two‐stage supply chain with a single retailer facing i.i.d. demand and a single manufacturer with finite production capacity. We analyze the value of information sharing between the retailer and the manufacturer over a finite time horizon. In our model, the manufacturer receives demand information from the retailer even during time periods in which the retailer does not order. To analyze the impact of information sharing, we consider the following three strategies: (1) the retailer does not share demand information with the manufacturer; (2) the retailer does share demand information with the manufacturer and the manufacturer uses the optimal policy to schedule production; (3) the retailer shares demand information with the manufacturer and the manufacturer uses a greedy policy to schedule production. These strategies allow us to study the impact of information sharing on the manufacturer as a function of the production capacity, and the frequency and timing in which demand information is shared. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2003     

A two‐echelon inventory‐location problem with service considerations

We study the problem of designing a two‐echelon spare parts inventory system consisting of a central plant and a number of service centers each serving a set of customers with stochastic demand. Processing and storage capacities at both levels of facilities are limited. The manufacturing process is modeled as a queuing system at the plant. The goal is to optimize the base‐stock levels at both echelons, the location of service centers, and the allocation of customers to centers simultaneously, subject to service constraints. A mixed integer nonlinear programming model (MINLP) is formulated to minimize the total expected cost of the system. The problem is NP‐hard and a Lagrangian heuristic is proposed. We present computational results and discuss the trade‐off between cost and service. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009     

Free riding in a multi‐channel supply chain

Free riding in a multichannel supply chain occurs when one retail channel engages in the customer service activities necessary to sell a product, while another channel benefits from those activities by making the final sale. Although free riding is, in general, considered to have a negative impact on supply chain performance, certain recent industry practices suggest an opposite view: a manufacturer may purposely induce free riding by setting up a high‐cost, customer service‐oriented direct store to allow consumers to experience the product, anticipating their purchase at a retail store. This article examines how the free riding phenomenon affects a manufacturer's supply chain structure decision when there are fixed plus incremental variable costs for operating the direct store. We consider factors such as the effort required to find and buy the product at a retail store after visiting the direct store, the existence of competing products in the market, and the extent of consumer need to obtain direct‐store service. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

Two‐machine flow shop scheduling with common due window to minimize weighted number of early and tardy jobs

This article studies two due window scheduling problems to minimize the weighted number of early and tardy jobs in a two‐machine flow shop, where the window size is externally determined. These new scheduling models have many practical applications in real life. However, results on these problems have rarely appeared in the literature because of a lack of structural and optimality properties for solving them. In this article, we derive several dominance properties and theorems, including elimination rules and sequencing rules based on Johnsos order, lower bounds on the penalty, and upper bounds on the window location, which help to significantly trim the search space for the problems. We further show that the problems are NP‐hard in the ordinary sense only. We finally develop efficient pseudopolynomial dynamic programming algorithms for solving the problems. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

Approximation algorithms for general one‐warehouse multi‐retailer systems

Logistical planning problems are complicated in practice because planners have to deal with the challenges of demand planning and supply replenishment, while taking into account the issues of (i) inventory perishability and storage charges, (ii) management of backlog and/or lost sales, and (iii) cost saving opportunities due to economies of scale in order replenishment and transportation. It is therefore not surprising that many logistical planning problems are computationally difficult, and finding a good solution to these problems necessitates the development of many ad hoc algorithmic procedures to address various features of the planning problems. In this article, we identify simple conditions and structural properties associated with these logistical planning problems in which the warehouse is managed as a cross‐docking facility. Despite the nonlinear cost structures in the problems, we show that a solution that is within ε‐optimality can be obtained by solving a related piece‐wise linear concave cost multi‐commodity network flow problem. An immediate consequence of this result is that certain classes of logistical planning problems can be approximated by a factor of (1 + ε) in polynomial time. This significantly improves upon the results found in literature for these classes of problems. We also show that the piece‐wise linear concave cost network flow problem can be approximated to within a logarithmic factor via a large scale linear programming relaxation. We use polymatroidal constraints to capture the piece‐wise concavity feature of the cost functions. This gives rise to a unified and generic LP‐based approach for a large class of complicated logistical planning problems. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

A characterization of optimal base‐stock levels for a multistage serial supply chain

In this article, we present a multistage model to optimize inventory control decisions under stochastic demand and continuous review. We first formulate the general problem for continuous stages and use a decomposition solution approach: since it is never optimal to let orders cross, the general problem can be broken into a set of single‐unit subproblems that can be solved in a sequential fashion. These subproblems are optimal control problems for which a differential equation must be solved. This can be done easily by recursively identifying coefficients and performing a line search. The methodology is then extended to a discrete number of stages and allows us to compute the optimal solution in an efficient manner, with a competitive complexity. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 32–46, 2016