Prof. Dr.
Kai Hoberg

Abstract

Demand uncertainty can lead to excess inventory holdings, capacity creation, emergency deliveries, and stock-outs. The costs of demand uncertainty may be directly borne by upstream suppliers, but can propagate downstream in the form of higher prices. To address these problems, we investigate a practical application of a fixed order commitment contract (FOCC) in which a manufacturer commits to a minimum fixed order quantity each period and receives a per unit price discount from the supplier for the commitment. We model a FOCC as a Stackelberg game in which the supplier offers a price discount anticipating the manufacturer’s response, and the manufacturer subsequently decides on the optimal commitment quantity. We show that a FOCC can smooth the orders received by the supplier, mitigating the negative consequences of demand uncertainty for the supplier, the manufacturer, and the supply chain. We extend the current literature by solving for an endogenous price discount instead of treating it as an exogenous value, and validate our model insights with our research partner, a large international materials handling equipment manufacturer. Using data on 863 parts, we evaluate the relationships between the model parameters, contract parameters, and the contract effectiveness, and show the conditions under which the FOCC generates greater cost savings for both the manufacturer and supplier. Our results help operations managers better understand how to obtain the optimal contract parameters for a FOCC and the circumstances under which such a contract is most beneficial for the company and its supply chain.

Abstract

Manufacturing firms face complex after-sales challenges, including spare part shortages. While additive manufacturing (AM) offers a solution by minimizing costs and complexity, not all firms adopt AM equally, and research on differences in AM adoption in the context of spare part shortages is surprisingly scarce. To close this knowledge gap, we apply the awareness-motivation-capability (AMC) perspective. Our comparative case study of AM applications in 17 firms identifies three approaches how firms adopt AM—the corrective, preventive, and anticipatory approach. We find that the specific configuration of contextual factors related to a spare part shortage determines the approach firms follow. Using the AMC perspective, we discover and explain why firms differ in adopting AM despite suitable spare part characteristics and similar contexts. Through uniquely analyzing spare part shortages, our study contributes to AM research by challenging the assumption that economic justification is the sole driver of AM adoption and instead revealing that it is a context-dependent process, with awareness and motivation serving as critical yet underexplored antecedents.

Abstract

In many real-world situations, multiple humans are involved in decision-making when interacting with machine recommendations. We investigated a setting where an artificial intelligence system creates demand forecasts that a human planner can either accept or revise, and a supervisor then makes the final decision about which forecast to select. We designed and conducted two experimental studies to understand decision-making by a supervisor. First, we provided the improvement probabilities of adjustments at an aggregated level and found evidence for overoptimism bias and mean anchoring. Second, we provided decomposed guidance based on two adjustment attributes, direction and magnitude, to investigate the role of salience based on the distance between the improvement probabilities and level of detail in guidance effectiveness. We found no significant difference in using less and more salient guidance provided that the detail level was fixed. However, revealing more details when the guidance was more salient increased the use of guidance.

Abstract

Slow-moving goods are common in many retail settings and occupy a vast part of retail shelves. Since stores sell these products irregularly and in small quantities, the replenishing distribution center may only place batched orders with manufacturers every few weeks. While order quantities are often fixed, the challenge for manufacturers facing such intermittent demand is to forecast the order timing. In this paper, we explore the value of Point-of-Sales (PoS) data to improve a food manufacturer’s order timing forecast for slow-moving goods. We propose an inventory modeling approach that uses the last order, PoS data from retail stores, and the expected lead time demand to estimate the retailer’s channel inventory. With this dynamic estimate, we can ‘nowcast’ the retailer’s inventory and predict his next order. To illustrate our methodology, we first conduct an experimental simulation and compare our results to a Croston variant and a moving average model. Next, we validate our approach with empirical data from a small German food manufacturer that serves a grocery retailer with a central distribution center and 53 hypermarkets. We find that, on average, our approach improves the accuracy of order-timing predictions by 10–20 percent points. We overcome a shrinkage-induced bias by incorporating an inventory correction factor. Our approach describes a new way of utilizing PoS data in multi-layered distribution networks and can complement established forecasting methods such as Croston. Particular applications arise when the order history is short (e.g., product launch) or represents a bad predictor for future demand (e.g., during COVID-19).

Abstract

The emergence of digital technologies across all aspects of operations management has enabled shifts in decision making, shaping new operational dynamics and business opportunities. The associated scholarly discussions in information systems and operations management span digital manufacturing, the digitalization of operations management and supply chain management, platform outcomes, and economies of collaboration. For such changes to be successful, however, there is a need for organizations to go beyond the mere adoption of digital technologies. Instead, successful changes are transformational, delving into digital transformation endeavors, which in turn can enable operational improvements in organizational performance, lead to structural changes in operations processes, and may result in new business models being deployed. Our aim here, thus, is to provide an epistemic platform to advance our understanding of how such endeavors, including the adoption of digital technologies, business model innovations, and innovations in collaboration mechanisms and methods of operations improvement, can affect various aspects of operations management.