Knowledge that Transforms

In a firm that makes schedule, orders are always processed within a fixed time frame. In a congested facility, such a situation would be impossible using conventional queuing logic. We propose a conceptual model of a firm in which workers make schedule by rushing jobs, if necessary, with potential quality consequences. Hence, time and quality are substitutes, a feature that we recognize explicitly in our definition of the firm's capacity. Production yields are not exogenous parameters but endogenously determined by workers responding to schedule pressures. The plant manager can authorize overtime to relieve this pressure or live with the quality consequences of rushing. The model reveals the close relationships among the firm's workforce policies, the integrity of the inspection system, and the cost performance of the firmas its volume and/or product line expands. We consider pure congestion (driven by volume) and pure complexity (driven by product line breadth) effects in the context of our plant performance model. We consider two root causes of complexity costs: time and quality. The time effects of complexity will only have cost consequences in congested facilities, but quality effects are always present. Also in contrast to time effects, quality effects of complexity can be present in nonbottleneck workstations. Hence, the quality consequences of complexity can be as, or more important than the time consequences.

This article studies the problem of sales-force compensation by considering the impact of sales-force behavior on a firm's production and inventory system. The sales force's compensation package affects how the salespeople are going to exert their effort, which in turn determines the sales pattern for the firm's product and ultimately drives the performance of the firm's production and inventory system. In general, a smooth demand process facilitates production/inventory planning. Therefore, it is beneficial for a firm to induce its salespeople to exert effort in a way that actually smoothes the demand process. The article proposes a compensation package to induce such behavior. It evaluates and compensates the sales force on a moving-time-window basis, where the length of the time window is determined by the production lead time. Numerical examples show that the proposed package is beneficial to the firm relative to a widely used compensation plan based on annual quotas.

We consider a problem faced by a contract assembler that both assembles finished goods and procures the associated component parts for one of its major customers. Because of rapid changes in technology and ongoing engineering changes, all parts subject to obsolescence are purchased only for the current customer order. The procurement lead times of the components are random. Moreover, although the order for the finished product has a defined due date, the contract allows the customer to change the order quantity. Consequently, the assembler also faces a random demand. The assembler must determine how much to order and when to order each component part. The objective is to minimize the total expected cost, including the cost of holding components prior to their assembly, penalties for tardiness visa-vis the assembly due date, and overage and underage costs in satisfying the demand quantity. We present some structural results and discuss insights regarding optimal policies. We also present several simple heuristic policies and compare them to optimal policies. Computational results indicate that ignoring lead time variability can be costly, but relatively simple heuristics that consider lead time variability perform quite well.

The principal-agent paradigm, in which a principal has a primary stake in the performance of some system but delegates operational control of that system to an agent, has many natural applications in operations management (OM). However, existing principal-agent models are of limited use to OM researchers because they cannot represent the rich dynamic structure required of OM models. This paper formulates a novel dynamic model that overcomes these limitations by combining the principal-agent framework with the physical structure of a Markov decision process. In this model one has a system moving from state to state as time passes, with transition probabilities depending on actions chosen by an agent, and a principal who pays the agent based on state transitions observed. The principal seeks an optimal payment scheme, striving to induce the actions that will maximize her expected discounted profits over a finite planning horizon. Although dynamic principal-agent models similar to the one proposed here are considered intractable, a set of assumptions are introduced that enable a systematic analysis. These assumptions involve the “economic structure” of the model but not its “physical structure.” Under these assumptions, the paper establishes that one can use a dynamic-programming recursion to derive an optimal payment scheme. This scheme is memoryless and satisfies a generalization of Bellman's principle of optimality. Important managerial insights are highlighted in the context of a two-state example called “the maintenance problem”.

A returns policy, which specifies a schedule of rebates from manufacturer to retailer for product left over at the end of the selling season, encourages larger order quantities and can increase manufacturer profit. One downside from a manufacturer's perspective is the possibility of very low profit due to high rebate expense when demand is lower than expected. We take the viewpoint of a manufacturer selling a short life-cycle product to a single risk-neutral retailer and describe returns policies that, when compared to no returns, satisfy two conditions: (1) the retailer's expected profit is increased and (2) the manufacturer's profit is at least as large as when no returns are allowed. We call such a returns policy risk-free.

Does cross-training workers allow a firm to achieve economies of scale when there is variability in the content of work, or does it create a workforce that performs many tasks with consistent mediocrity? To address this question we integrate a model of a stochastic service system with models for tenure- and experience-based service quality. When examined in isolation, the service system model confirms a well-known “rule of thumb” from the queueing literature: Flexible or cross-trained servers provide more throughput with fewer workers than specialized servers. However, in the integrated model these economies of scale are tempered by a loss in quality. Given multiple tasks, flexible workers may not gain sufficient experience to provide high-quality service to any one customer, and what is gained in efficiency is lost in quality. Through a series of numerical experiments we find that low utilization in an all-specialist system can also reduce quality, and therefore the optimal staff mix combines flexible and specialized workers. We also investigate when the performance of the system is sensitive to the staffing configuration choice. For small systems with high learning rates, the optimal staff mix provides significant benefits over either extreme case (a completely specialized or completely flexible workforce). If the system is small and the rate of learning is slow, flexible servers are preferred. For large systems with high learning rates, the model leans toward specialized servers. In a final set of experiments, the model analyzes the design options for an actual call center.

Manufacturing and service operations decisions depend critically on capacity and resource limits. These limits directly affect the risk inherent in those decisions. While risk consideration is well developed in finance through efficient market theory and the capital asset pricing model, operations management models do not generally adopt these principles. One reason for this apparent inconsistency may be that analysis of an operational model does not reveal the level of risk until the model is solved. Using results from option pricing theory, we show that this inconsistency can be avoided in a wide range of planning models. By assuming the availability of market hedges, we show that risk can be incorporated into planning models by adjusting capacity and resource levels. The result resolves some possible inconsistencies between finance and operations and provides a financial basis for many planning problems. We illustrate the proposed approach using a capacity-planning example.

We investigate a simple dynamic model of firm behavior in which firms compete by investing in capacity that is used to provide a good or service to their customers. There is a fixed total market of customers whose demands for the good or service are random and who divide their patronage between the firms in each period. Periodically, the market shares of the two firms can change based on the realized level of customer service provided in the prior period. We assume that the expected level of customer service can be expressed as a function of the (per customer) capacity of the firms' service delivery systems, and that service declines as the capacity decreases. The firms differ in their customers' willingness to defect when confronted by service failure. The primary issue we address is the firms' capacity decisions in response to customer service concerns and competitive pressure. We provide conditions under which the firms' optimal (i.e., equilibrium) capacity levels in a period are proportional to the size of their respective customer bases in that period. Further, we develop expressions for the value of a firm's customers and the implicit cost of service failure. Results for both single-period and finite-horizon problems are investigated and applied to two examples: (1) competition between Internet service providers who operate systems that we approximate by simple loss-type queueing models, and (2) competition between make-to-stock producers who operate systems that we approximate by newsvendor inventory models. For both examples, solutions are derived and interpreted.

Advances in information technology have opened new venues for companies to create flexible supply chains by offering high-speed communication and tight connectivity. A growing number of companies are taking advantage of new opportunities to outsource portions of their production and other operations. Given the importance of the supplier selection process in the ultimate success of a product, a purchasing manager must understand the different sourcing strategies that she or he can use and the suitability of each sourcing arrangements for her or him. This paper provides an overview of the research that has been done in the fields of operations research and economics on the topic of sourcing strategies. In aggregate, this paper provides a blueprint of what market characteristics can heavily influence a buyer-supplier relationship and, hence, are important to identify and incorporate into the supplier selection process.

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.