Prof. Dr.
Kai Hoberg


Professor of Supply Chain and Operations Strategy

Head of Logistics Department


Journal Articles (Peer-Reviewed)

Abstract: Spare parts are a particularly interesting application for switching production from traditional manufacturing (TM) to additive manufacturing (AM). Research assessing AM has primarily addressed cost models centering on the production process or the operations management of separate spare parts. By combining case study, modelling, and design science elements, we adopt a holistic perspective and develop a design to examine the systematic leverage of AM in spare parts operations. Contextually grounded in problems faced by a leading material handling equipment manufacturer that is challenged by common characteristics of after-sales operations, we engage with practice to propose a portfolio level analysis examining the switchover share from TM to AM. Using a dataset of 53,457 spare parts over nine years, we find that up to 8% of stock keeping units (SKUs) and 2% of total units supplied could be produced using AM, even if unit production costs are four times those of TM. This result is driven by low demand, high fixed costs, and minimum order quantities in TM. Finally, we present the evaluation by the case company's management and highlight five areas of opportunity and challenge.

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Open reference in new window "Assessing the Potential of Additive Manufacturing for the Provision of Spare Parts"

DOI: 10.1111/jscm.12203 

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.

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Open reference in new window "How Supplier Economies of Scale Drive Supplier Selection Decisions"

DOI: 10.1016/j.ejor.2017.05.010 

Abstract: Firms are increasingly interested in transport policies that enable a shift in cargo volumes from road (truck) transport to less expensive, more sustainable, but slower and less flexible transport modes like railway or inland waterway transport. The lack of flexibility in terms of shipment quantity and delivery frequency may cause unnecessary inventories and lost sales, which may outweigh the savings in transportation costs. To guide the strategic volume allocation, we examine a modal split transport (MST) policy of two modes that integrates inventory controls.We develop a single-product–single-corridor stochastic MST model with two transport modes considering a hybrid push–pull inventory control policy. The objective is to minimize the long-run expected total costs of transport, inventory holding, and backlogging. The MST model is a generalization of the classical tailored base-surge (TBS) policy known from the dual sourcing literature with non-identical delivery frequencies of the two transport modes. We analytically solve approximate problems and provide closed-form solutions of the modal split. The solution provides an easy-to-implement solution tool for practitioners. The results provide structural insights regarding the tradeoff between transport cost savings and holding cost spending and reveal a high utilization of the slow mode. A numerical performance study shows that our approximation is reasonably accurate, with an error of less than 3% compared to the optimal results. The results also indicate that as much as 85% of the expected volume should be split into the slow mode.

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Open reference in new window "An Inventory Control Model for Modal Split Transport: A Tailored Base-Surge approach"

DOI: 10.1111/poms.12721 

Abstract: To be efficient, logistics operations in e-commerce require warehousing and transportation resources to be aligned with sales. Customer orders must be fulfilled with short lead times to ensure high customer satisfaction, and the costly under-utilization of workers must be avoided. To approach this ideal, forecasting order quantities with high accuracy is essential. Many drivers of online sales, including seasonality, special promotions and public holidays, are well known, and they have been frequently incorporated into forecasting approaches. However, the impact of weather on e-commerce operations has not been rigorously analyzed. In this study, we integrate weather data into the sales forecasting of the largest European online fashion retailer. We find that sunshine, temperature, and rain have a significant impact on daily sales, particularly in the summer, on weekends, and on days with extreme weather. Using weather forecasts, we have significantly improved sales forecast accuracy. We find that including weather data in the sales forecast model can lead to fewer sales forecast errors, reducing them by, on average, 8.6% to 12.2% and up to 50.6% on summer weekends. In turn, the improvement in sales forecast accuracy has a measurable impact on logistics and warehousing operations. We quantify the value of incorporating weather forecasts in the planning process for the order fulfillment center workforce and show how their incorporation can be leveraged to reduce costs and increase performance. With a perfect information planning scenario, excess costs can be reduced by 11.6% compared with the cost reduction attainable with a baseline model that ignores weather information in workforce planning.

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Open reference in new window "The Value of Weather Information for E-Commerce Operations"