Amitabh S. Raturi

Coping with the build-to-forecast environment

Abstract High-value-added manufacturing companies today confront a competitive trend toward greater product customization in the face of reduced response times. This scenario is encountered most often in industries like machine tools, heavy construction equipment, heavy manufacturing in general and computer software and hardware. The product is highly customized, yet competition requires manufacturers to deliver it with lead times significantly shorter than the manufacturing lead time. Generally, the scheduling practice here is to release the manufacturing order before the customer order is released and subsequently match incoming customer orders to units in progress. This is referred to as the “build-to-forecast” (BTF) approach. This study investigated the coping mechanisms used by manufacturing firms to alleviate this dilemma. The tactics vary with the firms business strategy, its operating environment, and its capabilities. We report on three case studies from firms building heavy machinery. The firms are similar in terms of the range of final product values, build times, customer delivery times and the very large number of components. Also, their operations require the use of a variety of flexible and dedicated resources. Flexibility in manufacturing processes, modular bills of materials, subcontracting and expediting are some of the approaches that these firms use to help resolve the double bind of short lead times and high levels of customization. We review some of the operational problems peculiar to the build-to-forecast environment and suggest alternative approaches for dealing with them. The coping mechanisms are grouped according to the manner by which they help relieve the BTF problems severity. One set of mechanisms makes the problem less complex by simplifying products or the production process. Another set reduces the risks due to uncertainty in demand or supply. The third set provides engineering and manufacturing slack. While some or all of the mechanisms are used by the manufacturing firms studied, the predominance of particular mechanisms in each firm is explained by a contingency model developed in this paper. The case studies provide useful insights into the nature of the problem and how the firms organizational environment often dictates the choice of mechanisms used to alleviate it. For example, these firms minimized their scheduling dilemmas with modular product designs, flexible processes, informal organization structures, or formal control mechanisms for limiting customization. We conclude by framing a number of research questions whose solutions would help such firms better manage their operations.

Retail price markup commitment in decentralized supply chains

We investigate the operational decisions and resulting profits for a supply chain facing price-dependent demand under a policy where there is an ex-ante commitment made on the retail price markup. We obtain closed-form solutions for this policy under the assumption of a multiplicative demand function and we analytically compare its performance with that of a traditional price-only policy. We compare these results to results obtained when demand follows a linear additive form. These formulations are shown to be qualitatively different as the manufacturers wholesale pricing decision is independent of the retail price markup commitment in the multiplicative case, but not when demand is linear additive. We demonstrate that the ex-ante commitment can lead to Pareto-improving solutions under linear additive demand, but not under the multiplicative demand function. We also consider the effect of pricing power in the supply chain by varying who determines the retail price markup.

A comparison of tool management strategies and part selection rules for a flexible manufacturing system

An important element in the successful operation of flexible manufacturing systems (FMS) is the management of the tooling component. This paper reports on one aspect of tool management for FMS operations. Four tool allocation and scheduling strategies are compared in the presence of three part selection rules through a simulation study of a five-machine FMS with an automated tool handling system. The tool allocation strategies are similar to those used in industry while the part selection rules are synthesized from the literature on FMS scheduling under tooling constraints. The use of different tooling strategies produces significantly different outcomes in FMS performance.

On the trade-offs between risk pooling and logistics costs in a multi-plant network with commonality

This paper examines the benefits and costs of two alternative manufacturing network configurations in the presence of component commonality. We evaluate the trade-off between the decreased logistics costs and loss of risk-pooling benefits in plant networks which spread component manufacturing over each plant (product network) as compared to those that consolidate component manufacturing in a single plant (process network). We examine for conditions that mean that a product network would be chosen instead of a process network and vice-versa. We find that the risk-pooling benefit obtained by consolidating common subassembly production is reduced when the cost of acquiring common component capacity is sufficiently high or low. A post-optimality sensitivity analysis for the process network provides insights into subtle substitution effects, which are a direct outcome of cost mix differentials and network structure and complementarity effects, which are induced by the considered sequential assembly system. Our results suggest that the impact of operational cost parameters on strategic decisions can often be non-intuitive. Overall, our analysis provides a link between strategic and operational decision-making in supply chain management, in the context of multi-plant configuration.This paper examines the benefits and costs of two alternative manufacturing network configurations in the presence of component commonality. We evaluate the trade-off between the decreased logistics costs and loss of risk-pooling benefits in plant networks which spread component manufacturing over each plant (product network) as compared to those that consolidate component manufacturing in a single plant (process network). We examine for conditions that mean that a product network would be chosen instead of a process network and vice-versa. We find that the risk-pooling benefit obtained by consolidating common subassembly production is reduced when the cost of acquiring common component capacity is sufficiently high or low. A post-optimality sensitivity analysis for the process network provides insights into subtle substitution effects, which are a direct outcome of cost mix differentials and network structure and complementarity effects, which are induced by the considered sequential assembly system. Our...

Setting planned orders in master production scheduling under demand uncertainty

For single end-product master production scheduling with time-varying demand uncertainty and supply capacity, we study approaches to set replenishment quantities over the planning horizon. We present a stochastic programming model along with a simulation-based optimisation and two traditional approaches for setting order quantities. We compare these approaches to two new methods: gamma approximation and safety stock search. Computational experiments show that the gamma approximation and safety stock search perform well in terms of holding and shortage costs, with expected total cost on average, respectively, within 0.06% and 0.66% of the optimal from the stochastic program. On average, the two traditional approaches incur 12% and 45% higher cost than optimal. We provide managerial insights on the effects of parameters such as demand coefficient of variation (cv), utilisation, and target service level on the optimal total cost, the corresponding fill rate, and the relative performance of the approaches. We find that, for finite-normal demand, on average, the impact of target service level on cost is larger than that of demand cv, whose impact is larger than utilisation, except at high utilisation. We illustrate that, when demand is not normal, the gamma approximation significantly outperforms the existing normal approximation from Bollapragada and Rao (2006).

Testing the relationship between team and partner characteristics and cooperative benchmarking outcomes

Successful benchmarking can improve a companys return on investment (ROI) ratio, facilitate cost reductions, identify new business opportunities, and help develop market competitiveness. However, there is limited evidence to understand factors that contribute to successful outcomes during benchmarking. This paper tests a sequential framework that identifies the key constituents of successful benchmarking projects. We focus on cooperative benchmarking projects where the expected outcome is identifying best practices with a partner organization. We characterize the benchmarking project in terms of its context, process, and outcomes. Our framework first relates the context variables such as characteristics of the benchmarking team and the partner organization to such process variables as effective project management and teamwork. We then relate process variables to the task and group related outcome variables measuring the effectiveness of the benchmarking project. The hypotheses derived from the framework are tested through a survey instrument administered to participants in benchmarking projects. The data suggests that satisfaction with the benchmarking process and findings (outcome variables) is strongly related to the following: internal context variables such as training and experience of team members, clarity of project objectives and support from top management, and the process owner; external context variables, such as appropriateness of the benchmarking partners, and anticipation of constraints in data collection during the project; project process effectiveness including commitment of the team members and the synergy between the process owner, the team members, and the partner organization. This paper contributes to research in cooperative benchmarking by identifying the critical success factors associated with such projects. It also contributes to the project management literature by identifying the context and process variables in projects involving multiple stakeholders. Project managers of cooperative benchmarking projects must simultaneously pay attention to the needs of benchmarking team members, top management, the owner of the process being benchmarked, as well as the partner organization.

Supply-chain performance anomalies: Fairness concerns under private cost information

This work investigates how fairness concerns influence supply-chain decision making, while examining the effect of private production-cost information and touching on issues related to bounded rationality. We conduct laboratory work utilizing a supply-chain dyad with an upstream supplier feeding a downstream retailer under a simple wholesale-price contract. We perform human–computer (H–C) experiments where human subjects play the role of the supplier paired with the computerized retailer, as well as human–human (H–H) experiments where human subjects play the role of both supplier and retailer. These experiments allow us to isolate other effects like bounded rationality from the effects of fairness concerns on supply-chain decision making. We find that, compared to standard analytical model, the bounded rationality slightly reduces overall supply chain profit without changing its distribution between the supplier and the retailer, while fairness concerns lead to greater supply-chain profits and a more balanced supply-chain profit distribution. We further illustrate that under private cost information, the retailers fairness concern is suppressed by the lack of reciprocity from not being able to observe her rivals profit information, but that the suppliers fairness concern from altruism persists. Based on our experimental results, we modify classical supply-chain models to include utility functions that incorporate both bounded rationality and fairness concerns. The estimated other-regarding coefficients are significantly lower under private information than under public information for the H–H experiments, and we find no evidence of inequity aversion for the H–C experiments.

Capacitated lot sizing under setup learning

We investigate the effects of setup time and cost reduction through learning on optimal schedules in the capacitated lot sizing problem. Dynamic demand and time varying capacity is assumed. The scheduling effects are discussed, along with exact and heuristic approaches to solving these planning problems.

The effect of inventory decisions and parameters on the opportunity cost of capital

Abstract Inventory costs are typically calculated by adding the out-of-pocket cost of holding inventory and the opportunity cost of capital tied up in inventory. The concept of opportunity cost of capital is based on the business risk of the firm associated with particular decisions. Most firms use a fixed opportunity cost of capital; an assumption also made by most production and inventory models. In other words, it is assumed that the production and inventory decisions do not affect the business risk of the firm. But, this assumption may be inappropriate given that the competitive environment in most industries has altered significantly in the last decade. This is evidenced in the popularity of just-in-time manufacturing systems, concerns for justification of new manufacturing technologies such as flexible manufacturing systems and robotics, and efforts to reduce inventory and setup costs. All of these catalysts for becoming more competitive have one thing in common—they alter the business risk of the firm significantly. Hence, in adopting, implementing and sustaining any of these, it seems appropriate that the change in the business risk is reflected by varying the opportunity cost of capital. This paper uses a simple single product inventory model to present a conceptual framework to show how various inventory parameters and decisions affect the firms business risk, and hence the firms opportunity cost of capital. The inventory parameters considered are setup cost, out-of-pocket inventory holding cost, replenishment lead time, standard deviation of demand, and correlation of demand with the stock market. The decision parameters considered are lot size and safety stock decisions of the firm. The framework also discusses how this interdependency can be incorporated in the decision-making process. This paper applies existing finance theory to inventory problems to develop some new insights and discuss possible applications of these insights. Our analysis has three main results. First, the business risk, and hence the opportunity cost of capital for inventory investments, is an increasing function of setup cost, replenishment lead time, out-of-pocket inventory holding cost, standard deviation of demand, and the correlation of demand with the stock market. Second, the opportunity cost of capital is a decreasing function of variable profit per unit (selling price minus variable costs). Third, lot size reductions can increase the business risk of the firm, and hence, the opportunity cost of capital, unless accompanied by a simultaneous decrease in the setup cost. The above mentioned results have a number of interesting implications in the current manufacturing environments where firms are changing their cost accounting systems, developing new ways to measure performance, and developing better ways to evaluate investments in new technologies. First, our results suggest that inventory of products with high lead times and high setup costs should be charged a higher opportunity cost of capital. This would not only lead to a more accurate estimate of the cost of producing different products but also provide managers with incentives to reduce lead times and setup costs. Second, lower inventory levels reduce the business risk of the firm, and hence the opportunity cost of capital. Thus, investments in new manufacturing technologies that help reduce inventories should be evaluated using a lower discount rate. Third, reducing lot size while ignoring the lot size economics, can actually increase the business risk of the firm. This suggests that firms should not unilaterally reduce lot sizes, but should simultaneously seek to decrease lot sizes and setup costs. This will reduce the business risk of the firm. Fourth, our analysis indicates that another benefit of just-in-time (JIT) manufacturing philosophy is that it lowers the business risk of the firm. This is an important implication for JIT because firms with low business risk are less likely to be adversely affected by recessions than firms with high business risk. Fifth, since lower lead time reduces the business risk of the firm, investments that reduce lead time, as well as evaluation of decisions on sourcing, should incorporate the benefits of reduced business risk. Similarly, a suppliers marketing strategy should emphasize the benefits of lower business risk for customers due to its lower lead time. Finally, many researchers claim that inventory holding cost is understated as the implicit costs of holding inventory are often difficult to assess. Our analysis shows why inventory holding costs may be mis-estimated.

Estimating the opportunity cost of capital for inventory investments

Accurately estimating inventory holding cost is important for several reasons. It would lead to an improved analysis of the benefits of Just-In-Time philosophy, a more accurate specification of cost savings associated with investments in new technologies, and improved accuracy of production-inventory decisions. The inventory holding cost of a firm is typically calculated as the sum of the out-of-pocket cash flows associated with storage, and the opportunity cost of capital tied in inventories. Recently many writers have suggested that inventory holding cost may be misestimated in many firms. This paper presents an approach for estimating the opportunity cost of capital for inventory investments. The approach uses the capital asset pricing model CAPM to evaluate the risk of the cash flows associated with inventory decisions. Using the periodic review inventory model as an example, the paper shows how the opportunity cost of capital varies with lead time, ordering costs, and the time between reviews.