All Publications
Internet-of-Things-enabled systems that monitor usage and inventory are the latest technological advancement in demand forecasting and inventory control. Unlike traditional systems that record sales via cash registers or RFID technology at the point-of-sale, these novel systems can track product usage via smart, connected devices at the point-of-consumption, i.e., directly at the end user. This usage data promises to be a valuable basis for smart, automated replenishment services. We study such a service in the context of commercial coffee machines through collaboration with a large manufacturer in the coffee industry. Our data set contains information on more than 75 million drinks recorded since late 2017 by nearly 6,500 IoT-enabled coffee machines for commercial customers such as office kitchens, restaurants, and gas stations. The nature of the problem and data at the point-of-consumption warrants the development of synergetic models for demand forecasting, inventory control, and correction of inventory record inaccuracy. The resulting models are distinct from the state-of-the-art approach at the point-of-sale as they are uniquely integrated and involve an alternative strategy to mitigate inventory record inaccuracies. Overall, we contrast different approaches to manage smart replenishment systems, test their forecasting, inventory control, and inaccuracy correction performance, and pave the path to implementation in the field. Our findings suggest important implications for manufacturers who wish to engage in direct relationships with the end users of their products.
Firms are exposed to varying levels of supply chain risk and engage in efforts to resolve such risk. This paper examines how disclosures of supply chain risk and resolution during earnings calls affect firms? stock returns. Using natural language processing, we develop measures of supply chain risk and resolution from quarterly earnings call transcripts for a total of 129,981 firm-quarter observations between 2008 and 2019. We find that higher levels of supply chain risk are associated with lower stock returns around earnings calls, while disclosures of supply chain risk resolution attenuate these negative effects. In particular, stock returns of firms in the highest supply chain risk quintile are 1.07% lower compared to the stock returns of firms in the lowest quintile, and regression analyses indicate that a one-standard-deviation increase in supply chain risk is associated with a 0.56% decline in stock returns. The stock returns of firms in the highest supply chain risk resolution quintile are 0.12% higher compared to the stock returns of firms in the lowest quintile. A one-standard-deviation increase in resolution increases stock returns by 0.08%, and to 0.29% for the subsample of observations where the resolution measure is positive. Exploratory analyses indicate that the effect of supply chain risk on stock returns is significantly greater for smaller firms than for larger firms. In addition, when there is evidence that larger firms? resolution-related statements are mere rhetoric, the effect of resolution on stock returns is diminished.
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.
Many customers complain when informed that their order will not be fulfilled as originally confirmed, while other customers may be able to tolerate deviations. However, for suppliers, such complaints can be an early indicator of bad publicity, customer churn, and lost sales; and suppliers can prioritise orders to avoid these negative consequences. Ideally, they would know in advance if any order fulfilment change will trigger a customer complaint. To analyse how suppliers can predict these infrequent events in a business-to-business context, we leverage machine learning models on a large real-world dataset from a global semiconductor manufacturer. Our findings demonstrate that extreme gradient boosted trees effectively address the prediction problem. We explore the impact on model performance for different sampling approaches and cutoff values, as tuning the decision threshold is a meaningful calibration strategy before practical implementation. Our feature importance analysis provides evidence that high order fulfilment quality lowers complaint tendencies. Bridging the gap between advanced analytics and customer behaviour prediction, our research contributes to understanding the influence of subpar order fulfilment on customer satisfaction and offers insights into efficient order management despite disruptions. Our empirical study lays the groundwork for proactive supply chain operations when order fulfilment is at risk.
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.
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.
Sustainable Aviation Fuel (SAF) is crucial for aviation decarbonization, but its current pre-blending process at refineries presents challenges, including fixed blending ratios, higher transportation costs and long lead times. This study explores the potential of an innovative technology that enables on-site SAF blending at airports. By postponing blending to the point of use, this approach offers customization opportunities. However, the precise benefits and trade-offs of this concept remain unclear. The research aims to assess the impact of on-site blending on fuel price, lead time, carbon emissions and supply chain costs.This empirical study evaluates the effects of SAF postponement using case analyses of Singapore-Seletar and Maastricht airports. The analysis incorporates cost modeling, lead time assessment and carbon impact calculations to quantify the implications of shifting blending downstream to airport sites. Data sources include industry reports, airport-specific logistics information and SAF supply chain parameters. A comparative analysis is conducted to determine optimal airport conditions for SAF postponement, highlighting key enablers and barriers to implementation.The results indicate that on-site SAF blending can create competitive advantages by reducing supply chain costs and lowering carbon emissions. The benefits are contingent on airport-specific factors, such as Hydroprocessed Esters and Fatty Acids availability, logistics infrastructure and regulatory conditions. The findings suggest that certain airports, particularly those with strategic locations and favorable cost structures, are better suited for adopting SAF postponement. By shifting production downstream, airports can achieve greater flexibility in SAF blending ratios while minimizing logistical inefficiencies.To the best of the authors’ knowledge, this study is among the first to empirically examine the feasibility of postponing SAF blending to the airport level. While existing literature focuses on SAF production and distribution, the concept of downstream blending has not been systematically analyzed. The research provides new insights into how mass customization principles can be applied to SAF supply chains, potentially reshaping fuel logistics in the aviation industry. By identifying critical factors for successful implementation, this study contributes to both academic discussions and practical decision-making in sustainable aviation fuel management.
Recent advances in process mining technology have extended its applicability beyond traditional domains such as healthcare, finance and manufacturing, making it increasingly relevant for addressing problems in supply chain management. In our research, we explore the integration of process mining techniques within the domain of supply chain management, focusing on uncovering inefficiencies, ensuring compliance, and identifying opportunities for improvement. Therefore, we first review the technological advances in process mining relevant to supply chain management and outline six relevant approaches. We then combine the identified methodologies with expert interviews to derive and validate six specific use cases where process mining can significantly contribute to supply chain efficiency and resilience. The paper presents a detailed description of these use cases, demonstrating how process mining can provide actionable insights for a wide range of supply chain
problems. We discuss the implications of our findings for practitioners, who benefit from enhanced visibility and optimisation opportunities, and researchers, who are provided with a roadmap for exploration of this promising interdisciplinary field. To the best of our knowledge, this is the first work to explore potential use cases for process mining in a supply chain context, providing a comprehensive perspective on the potential benefits and challenges.
ABSTRACT Little is known about the careers of supply chain executives. This study revisits and extends the logistics and supply chain executive career pattern research by Flöthmann and Hoberg to take a fresh look at supply chain talents and to guide future research on this important topic. We collect new data to understand how supply chain executive careers evolve and how the next generation of logistics and supply chain executives differs from its predecessors. In this editorial study, we find support for the notion that logistics and supply chain management is a genuinely cross-functional profession, as more than half of the 293 executives identified in the original study are now working in other functions. This proliferation of talent can help firms increase supply chain awareness and spread expertise. Further, we find that 44.0% of the logistics and supply chain executives have advanced their hierarchical level, with 10.6% making it to the board level. We show with a panel of new supply chain executives that the six career patterns previously identified still hold. However, the average career spent inside the supply chain area has increased significantly from 12.0% to 20.2%. Similarly, the cluster composition has evolved toward ?Homegrowns,? making it the most frequent cluster.
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.
Complete and accurate data is an important enabler of effective supply chain decision making. Despite the increasing efforts to fully automate data collection processes using advanced sensors and scanners, human operators are still in charge of data entry tasks in most industries. Unfortunately, operators do not often comply with the standard operating procedures (SOPs) and do not always exhibit the consistency and commitment required to collect high-quality data. In fact, data collection is often perceived as a non-value-adding activity that increases workloads and lowers productivity. We aim to empirically study the extent to which compliance with SOPs for data collection is affected by some of the key factors. Using a large dataset obtained from a leading postal service provider in Australia, we find that an operator’s workload, fatigue, and related work experience directly impact the compliance levels. We also find that a company’s compliance reinforcement intervention to improve compliance behavior can moderate these impacts.
Purpose - Disruptions and shortages of drugs have become severe problems in recent years, which has triggered strong media and public interest in the topic. However, little is known about the factors that can be associated with the increased frequency of shortages. In this paper, we analyze the drivers of drug shortages using empirical data for Germany, the fourth largest pharmaceutical market.
Design/methodology/approach - We use a dataset provided by the German Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte [BfArM]) with 425 reported shortages for drug substances (DSs) in the 24-month period between May 2017 and April 2019 and enrich the data with information from additional sources. Using logistic and negative binomial regression models, we analyze the impact of (1) market characteristics, (2) drug substance characteristics and (3) regulatory characteristics on the likelihood of a shortage.
Findings - We find that factors like market concentration, patent situation, manufacturing processes or dosage form are significantly associated with the odds of a shortage. We discuss the implications of these findings to reduce the frequency and severity of shortages.
Originality/value – We contribute to the empirical research on drug shortages by analyzing the impact of market characteristics, DS characteristics and regulatory characteristics on the reported shortages. Our analysis provides a starting point for better prioritizing efforts to strengthen drug supply as it is currently intensely discussed by healthcare authorities.
When the COVID-19 pandemic began in 2020, the medical product industry faced an unusual demand shock for personal protective equipment (PPE), including face masks, face shields, disinfectants, and gowns. Companies from various industries responded to the urgent need for these potentially life-saving products by adopting ad hoc supply chains in an exceptionally short time: They found new suppliers, developed the products, ramped-up production, and distributed to new customers within weeks or even days. We define these supply chains as ad hoc supply chains that are built for a specific need, an immediate need, and a time-limited need. By leveraging a unique sampling, we examined how companies realize supply chain agility when building ad hoc supply chains. We develop an emergent theoretical model that proposes dynamic capabilities to enable companies building ad hoc supply chains in response to a specific need, moderated by an entrepreneurial orientation allowing firms to leverage dynamic capabilities at short notice and a temporary orientation that increases a company's focus on exploiting the short-term opportunity of ad hoc supply chains.
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).
