Kut C. So

Price, delivery time guarantees and capacity selection

This paper studies the impact of using delivery time guarantees as a competitive strategy in service industries where demands are sensitive to both price and delivery time. We assume that delivery reliability is crucial, and investment in capacity expansion is plausible in order to maintain a high probability of delivering the time guarantee. A mathematical framework is proposed to understand the interrelations among pricing, delivery time guarantee and capacity expansion decisions. Specifically, an optimization model is developed to determine the joint optimal selection of these three important decision variables, with an objective of maximizing the average net profit. We characterize the optimal decisions and study their qualitative behaviors as various parameters change. We further present a numerical example to illustrate how the results of our model can be used to provide useful managerial insights for selecting the best competing strategies for firms with different operating characteristics. Our model and results are also applicable to a make-to-order manufacturing environment.

Price and Time Competition for Service Delivery

Many service firms use delivery time guarantees to compete for customers in the marketplace. In this research we develop a stylized model to analyze the impact of using time guarantees on competition. Demands are assumed to be sensitive to both the price and delivery time guarantees, and the objective of each firm is to select the best price and time guarantee to maximize its operating profit. We first analyze the optimization problem for the individual firms and then study the equilibrium solution in a multiple-firm competition. Using a numerical study, we further illustrate how the different firm and market characteristics would affect the price and delivery time competition in the market. Our results suggest that the equilibrium price and time guarantee decisions in an oligopolistic market with identical firms behave in a similar fashion as the optimal solution in a monopolistic situation from a previous study. However, when there are heterogeneous firms in the market, these firms will exploit their distinctive firm characteristics to differentiate their services. Assuming all other factors being equal, the high capacity firms provide better time guarantees, while firms with lower operating costs offer lower prices, and the differentiation becomes more acute as demands become more time-sensitive. Furthermore, as time-attractiveness of the market increases, firms compete less on price, and the equilibrium prices of the firms increase as a result. Our findings provide important implications about firm behaviour under price and time competition.

The Effect of the Coefficient of Variation of Operation Times on the Allocation of Storage Space in Production Line Systems

Abstract This paper studies the effect of the coefficient of variation of operation times on the optimal allocation of storage space in production line systems. The operation times at each station are modelled by a two-stage Coxian distribution. This work extends the results of our previous study of the storage allocation problem with exponentially distributed operation times. Interpreting Stage 1 of the two-stage Coxian distribution as the normal service for an item at a station and Stage 2 as down time at the station, our model can also be used to study the effect of breakdowns on the allocation of storage space in production line systems. The results show that the “bowl effect” whereby the center stations should be given preferential treatment becomes more pronounced with higher variability in the operation times. Another general conclusion is that the overall optimal storage allocation commonly follows a “storage bowl phenomenon” whereby the allocation of buffer storage space fits an inverted bowl pat...

Impact of supplier's lead time and forecast demand updating on retailer's order quantity variability in a two-level supply chain

Abstract Motivated by the dramatic boom-and-bust cycles in semiconductor industry, we use an analytical model to analyze two important factors that can contribute to the high degree of order quantity variability experienced by semiconductor manufacturers: suppliers lead time and forecast demand updating. Specifically, we use a two-level supply chain model to study how the suppliers variable delivery lead times and the correlation of the external demands can amplify the variability of the order quantities of the downstream member in the supply chain. Based on our analytical and numerical results, some useful insights are used for managing suppliers lead time performance in order to reduce order quantity variability in a supply chain. For instance, our results suggest that in allocating scarce capacity by the supplier, higher priority should be given to those products whose demands are highly variable and correlated for maintaining consistent delivery lead times for these products.

The effect of machine breakdowns and interstage storage on the performance of production line systems

Both machine breakdowns and interstage storage can significantly affect the efficiency of a production line. In this paper we use an exact analytic model to conduct a detailed study of how these two factors affect the throughput of a line. Based on the empirical results, a simple heuristic method is given to estimate the amount of storage space required to offset the negative effect of machine breakdowns. The results provide useful guidelines for designing or analysing production line systems.

Allocating buffer storages in a pull system

This report proposes a method of estimating the amount of safety stock needed in each station of a production line due to variation in processing times, machine breakdowns and demand fluctuation in order to meet a predetermined desired level of performance. The production line is assumed to operate as a pull system and the measure of performance is the average percentage of demand backlogged. Multiple machines and different batch sizes in the stations are included in the model. Dynamic production control is used and is based on the current inventory level in every station of the system. Simulation results are used to test the performance of the system in which the maximum inventory level allowed in each station is based on the estimation given by our method.

On the optimal design of tandem queueing systems with finite buffers

We consider tandem queueing systems that can be formulated as a continuous-time Markov chain, and investigate how to maximize the throughput when the queue capacities are limited. We consider various constrained optimization problems where the decision variables are of one or more of the following types: (1) expected service times, (2) queue capacities, and (3) the number of servers at the respective stations. After surveying our previous studies of this kind, we open up consideration of three new problems by presenting some numerical results that should give some insight into the general form of the optimal design.

Optimal Component Stocking Policy for Assemble-to-Order Systems with Lead-Time-Dependent Component and Product Pricing

Short delivery time and the efficient management of component inventories are two crucial elements that determine the competitiveness of many contract assembly manufacturers, especially in the electronics industry. In this paper, we develop and analyze an optimization model to determine the optimal stocking quantities for components of an assemble-to-order product in an environment where demand is uncertain and the price for the final product and the costs of components depend on their delivery lead times. We provide an efficient solution procedure to solve the problem in which the manufacturer must deliver the full order quantity possibly in multiple shipments. We further extend our model to the situation where the manufacturer has the option of not delivering the full quantity but instead takes the penalty for a delivery shortage. We derive some analytical results that illustrate how different model parameters affect the optimal solution and provide useful insights for managing components in the assemble-to-order environment.

Effect of Supply Reliability in a Retail Setting with Joint Marketing and Inventory Decisions

This paper studies the impact of supply reliability on a retail firms performance under joint marketing and inventory decisions. The firm sells a product in a single selling season and can exert marketing effort to influence consumer demand. We develop a modeling framework to quantify the value of improving supply reliability and investigate how this value depends on different model parameters. Our results provide useful insights into how firms should make investment decisions on adopting new technologies to improve supply reliability. First, we establish a necessary and sufficient condition under which the maximum unit cost a firm is willing to pay to improve supply reliability increases in product price. We further show that this condition would hold in most practical situations. Thus, with some caveats, our result supports the intuition that a firm is willing to pay more to improve supply reliability for products with a higher price. Next, we show that for two products with the same price, a firm is willing to pay more to improve supply reliability for the product with a higher product cost. This implies that it is not necessarily true that emerging technologies for improving supply reliability should be first adopted for products with the highest unit contribution margin. Finally, we show that a product with a lower marketing cost function always benefits more from improved supply reliability than a product with a higher marketing cost function. This finding suggests that the priority of adopting new technologies should be given to situations where the firm can effectively induce greater demand through promotional effort.

On the Simultaneous Optimization of Server and Work Allocations in Production Line Systems with Variable Processing Times

The allocation of servers and the allocation of work are two important decision variables in designing production line systems. Previous studies of each variable in isolation have found that throughput is maximized by using an allocation that gives preferential treatment to interior stations especially center stations over the two end stations. In this paper we study the simultaneous optimization of server and work allocations and obtain some surprising results of a different nature. One key finding is the L-phenomenon, whereby the throughput is maximized by assigning all extra servers over one per station to just one of the end stations and then adjusting the work allocation so that this station has by far the greatest amount of work per server. Another key finding is the multiple-server phenomenon, whereby extra servers add far more throughput per server than the initial one-per-station servers. Both findings have important implications for the design of some production line systems in ways that will greatly improve their efficiency. Similar conclusions are drawn when lower and/or upper bounds are imposed on the number of servers per station and the number of stations is included as a decision variable.