Kevin H. Shang

Marketing on the internet - who can benefit from an online marketing approach

Abstract The research builds upon the literature in electronic commerce and past research in marketing with the objective of understanding factors that impact a products adaptability to online marketing. A review of marketing channel choice literature reveals a set of factors and channel choice functions that are considered important in making channel decisions. Using this as a basis, four major channel functions, namely, product customization, availability, logistics, and transaction complexity are considered relevant in understanding the implications for Internet marketing. By building upon previous research in the area of channel selection, we provide a means of classifying Internet marketing initiatives based on product characteristics. The classification scheme based on product characteristics can help analyze the significance of each factor on the success of a firms online marketing approach. Further, the classification scheme is used to discuss decision support implications.

Inspection and Replenishment Policies for Systems with Inventory Record Inaccuracy

For many companies, inventory record inaccuracy is a major obstacle to achieving operational excellence. In this paper, we consider an inventory system in which inventory records are inaccurate. The manager makes inventory inspection and replenishment decisions at the beginning of each period. There is a cost associated with each inspection. If an inspection is performed, inventory records are aligned with physical inventory. The objective is to develop a joint inspection and replenishment policy that minimizes total costs in a finite horizon. We prove that an inspection adjusted base-stock (IABS) policy is optimal for the single-period problem. In the finite-horizon problem, we show that the IABS policy is near optimal in a numerical study. Under this policy, the manager performs an inspection if the inventory recorded is less than a threshold level, and orders up to a base-stock level that depends on the number of periods since the last inspection. The prevalent approach to deal with inventory inaccuracy in practice is to implement cycle-count programs. Based on the structure of the IABS policy, we propose a new cycle-count policy with state-dependent base-stock levels (CCABS). We show that CCABS is almost as effective as the IABS policy. In addition, we provide guidelines for practitioners to design effective cycle-count programs by conducting sensitivity analyses on the IABS policy. Finally, by comparing the costs associated with these policies and several benchmark systems, we quantify the true value of accurate inventory information, which may be provided by radio-frequency identification (RFID) systems.

Newsvendor Bounds and Heuristic for Optimal Policies in Serial Supply Chains

We consider the classicN-stage serial supply systems with linear costs and stationary random demands. There are deterministic transportation leadtimes between stages, and unsatisfied demands are backlogged. The optimal inventory policy for this system is known to be an echelon base-stock policy, which can be computed through minimizingN nested convex functions recursively. To identify the key determinants of the optimal policy, we develop a simple and surprisingly good heuristic. This method minimizes 2 N separate newsvendor-type cost functions, each of which uses the original problem data only. These functions are lower and upper bounds for the echelon cost functions; their minimizers form bounds for the optimal echelon base-stock levels. The heuristic is the simple average of the solution bounds. In extensive numerical experiments, the average relative error of the heuristic is 0.24%, with the maximum error less than 1.5%. The bounds and the heuristic, which can be easily obtained by simple spreadsheet calculations, enhance the accessibility and implementability of the multiechelon inventory theory. More importantly, the closed-form expressions provide an analytical tool for us to gain insights into issues such as system bottlenecks, effects of system parameters, and coordination mechanisms in decentralized systems.

Coordination Mechanisms in Decentralized Serial Inventory Systems with Batch Ordering

This paper studies a periodic-review, serial supply chain in which materials are ordered and shipped according to (R,nQ) policies. Three information scenarios are considered, depending on the level of information available: echelon, local, and quasilocal. In the echelon scenario, each stage can access the inventory and cost information within its echelon (comprising the stage itself and all downstream stages); in the local scenario, each stage only accesses its own local information. Finally, in the quasilocal scenario, each stage knows its local information, plus the actual customer demands. We propose coordination schemes that regulate the stages to achieve the supply chains optimal cost under each information setting. All these coordination schemes fit comfortably within an emerging practice called supply chain finance, which includes the organization and technology needed to implement them.

Evaluation of cycle-count policies for supply chains with inventory inaccuracy and implications on RFID investments

Inventory record inaccuracy leads to ineffective replenishment decisions and deteriorates supply chain performance. Conducting cycle counts (i.e., periodic inventory auditing) is a common approach to correcting inventory records. It is not clear, however, how inaccuracy at different locations affects supply chain performance and how an effective cycle-count program for a multi-stage supply chain should be designed. This paper aims to answer these questions by considering a serial supply chain that has inventory record inaccuracy and operates under local base-stock policies. A random error, representing a stock loss, such as shrinkage or spoilage, reduces the physical inventory at each location in each period. The errors are cumulative and are not observed until a location performs a cycle count. We provide a simple recursion to evaluate the system cost and propose a heuristic to obtain effective base-stock levels. For a two-stage system with identical error distributions and counting costs, we prove that it is more effective to conduct more frequent cycle counts at the downstream stage. In a numerical study for more general systems, we find that location (proximity to the customer), error rates, and counting costs are primary factors that determine which stages should get a higher priority when allocating cycle counts. However, it is in general not effective to allocate all cycle counts to the priority stages only. One should balance cycle counts between priority stages and non-priority stages by considering secondary factors such as lead times, holding costs, and the supply chain length. In particular, more cycle counts should be allocated to a stage when the ratio of its lead time to the total system lead time is small and the ratio of its holding cost to the total system holding cost is large. In addition, more cycle counts should be allocated to downstream stages when the number of stages in the supply chain is large. The analysis and insights generated from our study can be used to design guidelines or scorecard systems that help managers design better cycle-count policies. Finally, we discuss implications of our study on RFID investments in a supply chain.

A Simple Heuristic for Serial Inventory Systems with Fixed Order Costs

We propose a heuristic for finding base order quantities for stochastic inventory models. The heuristic includes two steps. The first clusters the stages according to cost parameters. The second solves a single-stage problem for each cluster with the original problem data. In a numerical study, we show that the heuristic is near optimal.

Serial Supply Chains with Economies of Scale: Bounds and Approximations

We consider two models of stochastic serial inventory systems with economies of scale for which the forms of optimal policies are known. In the first model, each stage has a fixed-order quantity, while in the second model, there is a fixed-order cost for external supplies. For each model, we show that the optimal policy parameters can be bounded and approximated by a series of independent, single-stage optimal policy parameters. We further construct closed-form bounds and approximations for the single-stage solutions and apply them to the serial systems. These results provide simple and effective solutions that will help to facilitate implementations in practice. They also allow us to see the connections between the serial and single-stage systems and sharpen our intuition on optimal policy parameters and system behavior.

Improving Supply Chain Performance: Real-Time Demand Information and Flexible Deliveries

In some supply chains, materials are ordered periodically according to local information. This paper investigates how to improve the performance of such a supply chain. Specifically, we consider a serial inventory system in which each stage implements a local reorder interval policy; i.e., each stage orders up to a local base-stock level according to a fixed-interval schedule. A fixed cost is incurred for placing an order. Two improvement strategies are considered: (1) expanding the information flow by acquiring real-time demand information and (2) accelerating the material flow via flexible deliveries. The first strategy leads to a reorder interval policy with full information; the second strategy leads to a reorder point policy with local information. Both policies have been studied in the literature. Thus, to assess the benefit of these strategies, we analyze the local reorder interval policy. We develop a bottom-up recursion to evaluate the system cost and provide a method to obtain the optimal policy. A numerical study shows the following: Increasing the flexibility of deliveries lowers costs more than does expanding information flow; the fixed order costs and the system lead times are key drivers that determine the effectiveness of these improvement strategies. In addition, we find that using optimal batch sizes in the reorder point policy and demand rate to infer reorder intervals may lead to significant cost inefficiency.

A Closed-Form Approximation for Serial Inventory Systems and Its Application to System Design

We analyze a serial base-stock inventory model with Poisson demand and a fill-rate constraint. Our objective is to gain insights into the linkage between the stages to facilitate optimal system design and decentralized system control. To this end, we develop a closed-form approximation for the optimal base-stock levels. The development consists of two key steps: (1) convert the service-constrained model into a backorder cost model by imputing an appropriate backorder cost rate, and then adapt the single-stage approximation developed for the latter, and (2) use a logistic distribution to approximate the lead-time demand distribution in the single-stage approximation obtained in (1) to yield closed-form expressions. We then use the closed-form expressions to conduct sensitivity analyses and establish qualitative properties on system design issues, such as optimal total system stock, stock positioning, and internal fill rates. The closed-form approximation and most of the qualitative properties apply equally to the model with a backorder cost, although some differences do exist. Other results of this study include a bottom-up recursive procedure to evaluate any given echelon base-stock policy and lower bounds on the optimal echelon base-stock levels.

A Simple Heuristic for Joint Inventory and Pricing Models with Lead Time and Backorders

We study a joint inventory and pricing problem in a single-stage system with a positive lead time. We consider both additive and multiplicative demand forms. This problem is, in general, intractable due to its computational complexity. We develop a simple heuristic that resolves this issue. The heuristic involves a myopic pricing policy that generates each period’s price as a function of the initial inventory level and a base-stock policy for inventory replenishment. In each period, the firm monitors its so-called price-deflated inventory position and places an order to reach a target base-stock level. The price-deflated inventory position weights the on-hand and pipeline inventory according to a factor that reflects the sensitivity of price to the net inventory level. To assess the effectiveness of our heuristic, we construct an upper bound to the exact system. The upper bound is based on an information-relaxation approach and involves a penalty function derived from the proposed heuristic. A numerical study suggests that the heuristic is near-optimal. The heuristic approach can be applied to a wide variety of inventory systems, such as systems with fixed ordering costs or fixed batch sizes. The heuristic enables us to explore the use of price as a lever to balance supply and demand. Our findings indicate that a responsive strategy (that effectively reduces the replenishment lead time) leads to a more stable pricing policy and that the value of dynamic pricing increases with lead time. This paper was accepted by Martin Lariviere, operations management .