No abstract

Supplier selections are complex but nonetheless strategically important decisions that are influenced by numerous factors. Drawing on the resource‐based and relational view of the firm, we investigate how suppliers’ economies of scale influence the buyer's selection decision, and we illustrate how the influence of scale is contingent upon important economic, buyer, and relationship characteristics. We test the model with a large secondary dataset of actual supplier selection decisions from the automotive industry and show that economies of scale have a strongly positive but diminishing effect on the buying firm's supplier selection decision. These effects are reinforced or extenuated by economic, buyer, and relationship characteristics, with characteristics that are more specific to the buyer‐supplier situation (e.g., relationship duration and power balance) having a stronger moderating effect than do characteristics that are more global (e.g., economic cycle). Our research helps suppliers to better understand how to manage selection probabilities with buyers and provides buying firms with a better understanding of how contextual factors affect the benefit of supplier‐provided economies of scale.

T his paper has been motivated by a fleet optimization problem faced by one of the leading European cargo rail companies. The company operates a fleet of more than 100,000 rail cars and annually invests significant sums of money into new cars. Because the price tag of a new car is over 50,000 euros, planning such a fleet is an important activity at the company. In this paper, we develop and solve analytical models for fleet planning. We first describe the rental process and show how it can be modeled as a queuing loss system. We then develop a profit function and derive several structural results, such as the concavity of the profit function in the fleet size. Building on these structural results, we show how the fleet size can be optimized, how the fleet structure (i.e., the types of cars being used) can be optimized, and how a joint fleet of owned and leased cars can be optimized. Because some of the optimal methods are difficult to implement, we also develop and test an approximation that is easy to implement. To illustrate our findings and to validate our approach, we provide numerical results that are based on data of the company that motivated our research.

Every year, humanitarian organizations assign a sizable portion of their limited financial resources to procure, operate and maintain operating assets, without which service delivery would be nearly impossible. In this study, using vehicles to represent operating assets, we identify policies for sizing and allocating operational capacity to minimize the expected deprivation costs in a humanitarian development context. First, we develop a stochastic dynamic programming model, and then an efficient heuristic policy that considers the interaction of asset purchasing and operating decisions when the budget is uncertain. Based on a dataset provided by a large international organization, we estimate the parameters of our model to run numerical experiments. Results demonstrate the following: (i) Although budget uncertainty increases the expected deprivation costs and decreases capacity utilization, the negative impact of budget uncertainty is mitigated if budget savings between periods is allowed; (ii) a policy for minimizing the expected deprivation costs over time may avoid using all available assets in all periods; (iii) in situations in which the variation in the criticality of missions is large, both the expected deprivation costs and fleet utilization decrease; and (iv) in most conditions, a centralized asset procurement model outperforms a decentralized model, not only in terms of logistic costs but also in minimizing the expected deprivation costs.