Ulrich W. Thonemann

Optimal procurement strategies for online spot markets

Abstract Spot markets have emerged for a broad range of commodities, and companies have started to use them in addition to their traditional, long-term procurement contracts (forward contracts). In comparison to forward contracts, spot markets offer products at essentially negligible lead time, but typically command a higher expected price for this added flexibility while also exhibiting substantial price uncertainty. In our research, we analyze the resulting procurement challenge and quantify the benefits of using spot markets from a supply chain perspective. We develop and solve mathematical models that determine the optimal order quantity to purchase via forward contracts and the optimal quantity to purchase via spot markets. We analyze the most general situation where commodities can be both bought and sold via a spot market and derive closed-form results for this case. We compare the obtained results to the reference scenario of pure contract sourcing and we include results for situations where the use of spot markets is restricted to either buying or selling only. Our approaches can be used by decision makers to determine optimal procurement strategies based on key parameters such as, demand and spot price volatilities, correlation between demand and spot prices, and risk aversion. The results of our analysis demonstrate that significant profit improvements can be achieved if a moderate fraction of the commodity demand is procured via spot markets. The results also show that companies who use spot markets can offer a higher expected service level, but that they might experience a higher variability in profits than companies who do not use spot markets. We illustrate our analytical results with numerical examples throughout the paper.

Improving supply-chain performance by sharing advance demand information

Abstract In this paper, we analyze how sharing advance demand information (ADI) can improve supply-chain performance. We consider two types of ADI, aggregated ADI (A-ADI) and detailed ADI (D-ADI). With A-ADI, customers share with manufacturers information about whether they will place an order for some product in the next time period, but do not share information about which product they will order and which of several potential manufacturers will receive the order. With D-ADI, customers additionally share information about which product they will order, but which manufacturer will receive the order remains uncertain. We develop and solve mathematical models of supply chains where ADI is shared. We derive exact expressions and closed-form approximations for expected costs, expected base-stock levels, and variations of the production quantities. We show that both the manufacturer and the customers benefit from sharing ADI, but that sharing ADI increases the bullwhip effect. We also show that under certain conditions it is optimal to collect ADI from either none or all of the customers. We study two supply chains in detail: a supply chain with an arbitrary number of products that have identical demand rates, and a supply chain with two products that have arbitrary demand rates. For these two supply chains, we analyze how the values of A-ADI and D-ADI depend on the characteristics of the supply chain and on the quality of the shared information, and we identify conditions under which sharing A-ADI and D-ADI can significantly reduce cost. Our results can be used by decision makers to analyze the cost savings that can be achieved by sharing ADI and help them to determine if sharing ADI is beneficial for their supply chains.

Supply Chain Flexibility, Uncertainty and Firm Performance: An Empirical Analysis of German Manufacturing Firms

Supply chain flexibility is widely seen as one major response to the increasing uncertainty and competition in the marketplace. Researchers and practitioners acknowledge the competitive advantage of supply chain flexibility. However, flexibility is costly. Thus, a match between flexibility and environmental uncertainty seems to be an appropriate option. Empirical research on the relationship between environmental uncertainty and supply chain flexibility is sparse. This paper contributes to the emerging body of research by addressing the relationship between environmental uncertainty, supply chain flexibility, and firm performance through a survey of German manufacturing companies. We use structural equation modeling to analyze one of the key questions of operations management: Does a match between environmental uncertainty and supply chain flexibility lead to competitive advantages? More precisely, we present evidence that in uncertain environments companies with highly flexible supply chains perform better than companies with less flexible supply chains while in certain environments the opposite holds.

Analyzing the effect of the inventory policy on order and inventory variability with linear control theory

In this paper we apply linear control theory to study the effect of various inventory policies on order and inventory variability, which are key drivers of supply chain performance. In particular, we study a two-echelon supply chain with a stationary demand pattern under the influence of three inventory policies: an inventory-on-hand policy that bases orders on the visible inventory at an installation, an installation-stock policy that bases orders on the inventory position (on-hand plus on-order inventory) at an installation, and an echelon-stock policy that bases orders on the inventory position at that installation and all downstream installations. We prove analytically that the inventory-on-hand policy is unstable in practical settings, confirming analytically what has been observed in experimental settings and in practice. We also prove that the installation-stock and echelon-stock policies are stable and analyze their effect on order and inventory fluctuation. Specifically, we show the general superiority of the echelon-stock in our setting and demonstrate analytically the effect of forecasting parameters on order and inventory fluctuations, confirming the results in other research.

Optimal Commonality in Component Design

Increased competition and more demanding customers have forced companies to offer a wide variety of products. Component commonality can help companies reduce the cost of providing product variety to their customers. However, determining the extent to which component commonality should be used is difficult. In this paper we present an approach to determine the optimal level of component commonality for end-product components that do not differentiate models from the customers perspective. The work was inspired by and applied to a wire-harness design problem faced by a major automobile manufacturer. We model the component design problem as a mathematical program that considers production, inventory holding, setup, and complexity costs (the cost in indirect functions caused by component variety). We develop two approaches to solve the problem: a branch-and-bound algorithm that can solve small- and medium-size problems optimally, and a simulated annealing algorithm that can solve large-size problems heuristically. We apply both algorithms to the wireharness design problem faced by the automobile manufacturer and to a number of randomly generated test problems. We show that an optimal design achieves high cost savings by using significantly fewer variants than a no-commonality design but significantly more variants than a full-commonality design. We apply sensitivity analysis to identify extreme conditions under which the no-commonality and full-commonality designs perform well, and we identify the key cost drivers for our application. Finally, we describe the impact of our analysis on the companys subsequent component design decisions.

Managers and Students as Newsvendors

We compare how experienced procurement managers and students solve the newsvendor problem. We find that managers broadly exhibit the same kind of pull-to-center bias as students do. Also, managers use information and task training no better than students. The performance of managers is positively affected by the level of their education and their level in the organizational hierarchy. We discuss implications for theory and for how ordering might be improved in practice. This paper was accepted by Teck Ho, decision analysis.

Designing Buyback Contracts for Irrational But Predictable Newsvendors

One of the main assumptions in research on designing supply contracts is that decision makers act in a way that maximizes their expected profit. A number of laboratory experiments demonstrate that this assumption does not hold. Specifically, faced with uncertain demand, decision makers place orders that systematically deviate from the expected profit maximizing levels. We have added to this body of knowledge by demonstrating that ordering decisions also systematically depend on individual contract parameters and by developing a behavioral model that captures this systematic behavior. We proceed to test our behavioral model using laboratory experiments and use the data to derive empirical model parameters. We then test our approach in out-of-sample validation experiments that confirm that, indeed, contracts designed using the behavioral model perform better than contracts designed using the standard model. This paper was accepted by Christian Terwiesch, operations management.

Easy Quantification of Improved Spare Parts Inventory Policies

This paper presents approximate analytical models to quantify the expected improvement in inventory investment when using a system approach to control inventory as opposed to a simpler item approach. A system approach ensures that a demand-weighted average fill rate is achieved at low inventory investment by assigning low fill rates to parts with high costs and high fill rates to parts with low costs. An item approach does not vary fill rates by parts but assigns identical fill rates to all parts. Using single-parameter functional representations of the skewness of unit costs and average demand across all parts in the system, simple approximate analytical expressions for the required inventory investment are derived for both approaches. The accuracy of the approximations is validated using data from a distribution center for computer spare parts. For these data, the solutions obtained by the approximations are very close to the exact values. The results show that inventory investments can be well approximated as a function of only a few cost and demand parameters. These expressions can be used to determine the percentage reduction in inventory investment for a particular target demand-weighted average fill rate when the superior system approach is used instead of the item approach. For increased ease of use, the percentage reduction in inventory when using a system as opposed to an item approach is computed over a range of realistic values for the key parameters of the model and a quadratic expression is fitted to the data. This fitted expression provides rough guidelines for the anticipated improvement with very limited data needed, prior to detailed modeling or implementation.

Modeling the Benefits of Sharing Future Demand Information

We analyze how sharing of future demand information (FDI) can help companies to lower cost. FDI is imperfect information on the customer demands of the upcoming period. We consider a supply chain with a single retailer and multiple customers, where customer demands are normally distributed and correlated. The retailer faces two decisions: With which customers should information be shared and how much should be ordered? We model the problem as a two-stage dynamic program, develop an optimal solution approach, and provide structural insights into the optimal extent of FDI sharing. We show that information cost and demand correlation are important factors for determining the optimal extent of FDI sharing. For a simplified version of the problem where only a single customer is contacted, we analyze how the optimal solution is affected by nonidentically distributed or nonuniformly correlated demands.

Relaxing channel separation: Integrating a virtual store into the supply chain via transshipments

Many established companies have started to use virtual stores as a direct distribution channel in addition to their existing indirect retail channels. These companies must now decide on how to integrate these channels. The alternatives are to operate dedicated distribution channels for the virtual store and the retail stores or to tightly integrate the virtual store into the existing distribution channels. In such an integrated supply chain, retail stores would continue to serve all in-store customers, but excess stock at retail stores could be used to fill some online orders. We analyze this problem from a supply chain perspective by developing and solving mathematical models for both dedicated and integrated supply chains. We characterize the optimal inventory policy and quantify the expected cost savings that can be achieved by using an integrated supply chain over a dedicated supply chain. We show that the cost savings can be significant and that both retailers and customers benefit from an integrated supply chain. We also analyze how the optimal solutions depend on the characteristics of the supply chain and identify conditions under which it would be optimal to operate the virtual store without dedicating any inventory to the virtual store. Throughout the paper, we illustrate our analytical results with numerical examples.