Teck-Hua Ho

• Christian Terwiesch, Justin Ren, Teck-Hua Ho, Morris A. Cohen (2005), An Empirical Analysis of Forecast Sharing in the Semiconductor Equipment Supply Chain, Management Science, 208–220. Abstract

  We study the demand forecast-sharing process between a buyer of customized production equipment and a set of equipment suppliers. Based on a large data collection we undertook in the semiconductor equipment supply chain, we empirically investigate the relationship between the buyer’s forecasting behavior and the supplier’s delivery performance. The buyer’s forecasting behavior is characterized by the frequency and magnitude of forecast revisions it requests (forecast volatility) as well as by the fraction of orders that were forecasted but never actually purchased (forecast inflation). The supplier’s delivery performance is measured by its ability to meet delivery dates requested by the customers. Based on a duration analysis, we are able to show that suppliers penalize buyers for unreliable forecasts by providing lower service levels. Vice versa, we also show that buyers penalize suppliers that have a history of poor service by providing them with overly inflated forecasts.

• Morris A. Cohen, Teck-Hua Ho, Justin Ren, Christian Terwiesch (2003), Measuring Imputed Cost in the Semiconductor Equipment Supply Chain, Management Science, Volume 49, No.12. Abstract

  We consider the order-fulfillment process of a supplier producing a customized capital good, such as production equipment, commercial aircraft, medical devices, or defense systems. As is common in these industries, prior to receiving a firm purchase order from the customer, the supplier receives a series of shared forecasts, which are called “soft orders.” Facing a stochastic internal manufacturing lead time, the supplier must decide at what time to begin the fulfillment of the order. This decision requires a trade-off between starting too early, leading to potential holding or cancellation costs, and starting too late, leading to potential delay costs. We collect detailed data of shared forecasts, actual purchase orders, production lead times, and delivery dates for a supplier-buyer dyad in the semiconductor equipment supply chain. Under the assumption that the supplier acts rationally, optimally balancing the cancellation, holding, and delay costs, we are able to estimate the corresponding imputed cost parameters based on the observed data. Our estimation results reveal that the supplier perceives the cost of cancellation to be about two times higher and the holding costs to be about three times higher than the delay cost. In other words, the supplier is very conservative when commencing the order fulfillment, which undermines the effectiveness of the overall forecast-sharing mechanism.

• Teck-Hua Ho, Sergei Savin, Christian Terwiesch (2002), Managing Demand and Sales Dynamics in New Product Diffusion, Management Science, 48 (2002), 187-206. Abstract

  The Bass diffusion model is a well-known parametric approach to estimating new product demand trajectory over time. This paper generalizes the Bass model by allowing for a supply constraint. In the presence of a supply constraint, potential customers who are not able to obtain the new product join the waiting queue, generating backorders and potentially reversing their adoption decision, resulting in lost sales. Consequently, they do not generate the positive “word-of-mouth” that is typically assumed in the Bass model, leading to significant changes in the new product diffusion dynamics. We study how a firm should manage its supply processes in a new product diffusion environment with backorders and lost sales. We consider a make-to-stock production environment and use optimal control theory to establish that it is never optimal to delay demand fulfillment. This result is interesting because immediate fulfillment may accelerate the diffusion process and thereby result in a greater loss of customers in the future. Using this result, we derive closed-form expressions for the resulting demand and sales dynamics over the product life cycle. We then use these expressions to investigate how the firm should determine the size of its capacity and the time to market its new product. We show that delaying a product launch to build up an initial inventory may be optimal and can be used as a substitute for capacity. Also, the optimal time to market and capacity increase with the coefficients of innovation and imitation in the adoption population. We compare our optimal capacity and time to market policies with those resulting from exogeneous demand forecasts in order to quantify the value of endogenizing demand.

• Christopher S. Tang, David Bell, Teck-Hua Ho (2001), Store Choice and Shopping Behavior: How Price Format Works, California Management Review, 53 (2), 56-70. Abstract

  This article presents a perceived shopping utility framework for analyzing the impact of retail price format on store choice. This, in turn, determines three key performance metrics: number of shoppers; number of trips; and average spending per trip. When choosing a store, consumers evaluate both the fixed and variable utilities of shopping. The fixed utility does not vary from trip to trip whereas the variable utility depends on the size and composition of the shopping list. This article summarizes prior findings on store choice, analyzes how retailers can improve their performance, and interprets the practices of leading retailers. It presents a framework that can accommodate situations where retailers face multiple segments of buyers who have different sensitivities to fixed and variable utilities.

• Teck-Hua Ho, Christopher S. Tang, David Bell (1998), Rational Shopping Behavior and the Option Value of Variable Pricing, Management Science, 44 (12), 145-160. Abstract

  When a product’s price fluctuates at a store, how should rational, cost-minimizing shoppers shop for it? Specifically, how frequently should they visit the store, and how much of the product should they buy when they get there? Would this rational shopping behavior differ across Every Day Low Price (EDLP) and Promotional Pricing (HILO) stores? If shoppers are rational, which retail price format is more profitable, EDLP or HILO? To answer these questions, we develop a normative model that shows how rational customers should shop when the price of the product is random. We derive a closed-form expression for the optimal purchasing policy and show that the optimal quantity to purchase under a given price scenario is linearly decreasing in the difference between the price under that scenario and the average price. This purchase flexibility due to price variability has a direct impact on shopping frequency. Indeed, the benefit of this purchase flexibility can be captured via an ‘‘option value’’ that implicitly reduces the fixed cost associated with each shopping trip. Consequently, rational shoppers should shop more often and buy fewer units per trip when they face higher price variability. Our results suggest that if two stores charge the same average price for a product, rational shoppers incur a lower level of expenditure at the store with a higher price variability. Since stores with different price variabilities coexist in practice, we expect stores with higher price variability to charge a higher average price. Thus, given two stores, a higher relative mean price for a given item should be indicative of higher price variability, and vice versa. These model implications are tested using multicategory scanner panel data from 513 households and pricing data for three stores (two EDLP stores and one HILO store) and 33 product categories over a two-year period. We find strong empirical support for the model implications.

• David Bell, Teck-Hua Ho, Christopher S Tang (1998), Determining Where to Shop: Fixed and Variable Costs of Shopping, Journal of Marketing Research, 35 (August), 352-369. Abstract

  The authors develop and test a new model of store choice behavior whose basic premise is that each shopper is more likely to visit the store with the lowest total shopping cost. The total shopping cost is composed of fixed and variable costs. The fixed cost is independent of, whereas the variable cost depends on, the shopping list (i.e., the products and their respective quantities to be purchased). Besides travel distance, the fixed cost includes a shopper’s inherent preference for the store and historic store loyalty. The variable cost is a weighted sum of the quantities of items on the shopping list multiplied by their expected prices at the store. The article has three objectives: (1) to model and estimate the relative importance of fixed and variable shopping costs, (2) to investigate customer segmentation in response to shopping costs, and (3) to introduce a new measure (the basket size threshold) that defines competition between stores from a shopping cost perspective. The model controls for two important phenomena: Consumer shopping lists might differ from the collection of goods ultimately bought, and shoppers might develop category-specific store loyalty.

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