Knowledge that Transforms

In an election campaign, candidates must decide how to optimally allocate their efforts/resources optimally among the regions of a country. As a result, the outcome of the election will depend on the players' strategies and the voters' preferences. In this work, we present a zero‐sum game where two candidates decide how to invest a fixed resource in a set of regions, while considering their sizes and biases. We explore the two voting systems, the Majority System (MS) and the Electoral College (EC). We prove equilibrium existence and uniqueness under MS in a deterministic model; in addition, their closed form expressions are provided when fixing the subset of regions and relaxing the non‐negative investing constraint. For the stochastic case, we use Monte Carlo simulations to compute the players' payoffs. For the EC, given the lack of equilibrium in pure strategies, we propose an iterative algorithm to find equilibrium in mixed strategies in a subset of the simplex lattice. We illustrate numerical instances under both election systems, and contrast players' equilibrium strategies. We show that polarization induces candidates to focus on larger regions with negative biases under MS, whereas candidates concentrate on swing states under EC. Finally, we calibrate the analyzed models with real data from the US 2020 presidential election.

Inventory optimization approaches typically optimize steady‐state performance, but do not consider the transition of an initial state to the optimized state. In this study, we address this transition. Our research is motivated by a company that implemented an improved inventory policy for its spare parts division. The improved policy suggested new base stock levels for the majority of the parts. For parts with increased base stock levels, inventory increases were realized after the part lead times, but for low‐demand parts with decreased base stock levels, inventory reductions were slow. As a result, inventory cost increased over the first months after the new inventory policy had been introduced and exceeded the inventory budget substantially. To avoid such undesirable effects, base stock level changes must be phased in. We consider a multi‐item spare parts inventory system, initially operating under an item approach inventory policy that achieves identical fill rates for all parts. Our approach addresses the transition to a superior system approach inventory policy that maximizes the system fill rate. We model the inventory transition as a finite‐horizon optimization problem and apply column generation and a marginal analysis heuristic to determine transient base stock levels for all parts. Using data from the company that motivated our research, we illustrate how the transition can be controlled to quickly improve fill rates without exceeding the initial inventory budget.

Global firms often attempt to utilize modular product architectures to ease the distribution of project work among subsidiaries. However, empirical findings on the effectiveness of this approach are mixed. Seeking to clarify this matter, we investigate the conditions under which modular product platform use is associated with cross‐subsidiary distribution of project work. Through an in‐depth case study of a global corporation, we find that a firm’s ability to leverage modular product platforms for distributing project work is positively associated with project interface match (i.e., the extent to which meeting the requirements of a derivative project does not entail modifications of the interfaces embedded in the firm’s modular product platforms). However, this association is attenuated, and eventually muted, by the project technical manager’s related technical experience (i.e., the manager’s personal experience with the technical solution addressing a focal project’s customer requirements). Such attenuation effect originates because lower levels of project interface match do not necessarily imply greater expected project coordination effort. Such expectation is reduced to the extent that the technical project manager has related technical experience. Triangulation of qualitative insights from four embedded cases with a proprietary database of 97 projects supports our contention. These findings contribute to the literatures on product and organizational modularity, distributed work, and project management.

We study a novel newsvendor‐type problem where the information on demand quantity is not exogenously given. The customer needs to make the booking decision based on her estimation on demand, which is affected by the value of shortage penalty cost. The problem is motivated by low‐cost airline service practices where passengers need to book baggage allowance for their travel. The baggage overweight price affects the accuracy of passengers' baggage weight estimation and thus their booking quantities. Through stochastic decision models, we analytically characterize the impact of shortage penalty cost on passengers' booking decisions as well as airline's profit. We consider various modeling settings, including a system with multiple passengers and a system where passengers have stochastic inconvenience costs on not‐carrying overweight baggage. The results and insights from our study provide guidelines for firms to set their optimal penalty prices.

Biotechnology and pharmaceutical firms invest billions of dollars in R&D, primarily in new drug discovery. Nonetheless, the industry is facing declining returns on R&D investments. Failure to discontinue less‐promising new drug discovery projects is a key driver of this decreased productivity. Hence, firms are scrambling to restructure the new drug discovery process to improve decision‐making and to limit the attrition rate to the early stages of drug discovery. Drawing on insights from the exploration–exploitation literature, our study addresses this timing problem by using a formal model and empirically testing its implications on how the time to discontinuation of new drug discovery projects are impacted by project‐ and firm‐specific characteristics, so that a firm's resources can be promptly redeployed to more fruitful endeavors. Our findings, based on an analysis of 1274 early‐stage drug discovery projects worldwide, suggest that the time to discontinuation of early‐stage drug discovery projects requires careful consideration of these project‐ and firm‐specific characteristics. These findings hold important implications for the industry, which is undergoing tremendous stress and transformation. The results also contribute to the exploration–exploitation literature by modeling and testing the time‐allocation decision between exploratory and exploitative activities.

Buyers are increasingly pressured to ensure sustainability of their suppliers, but they are also under pressure for low‐cost procurement. To make a more informed procurement decision, a buyer can choose to invest in sustainability assessments, and select a supplier based on their price bids and cost markup terms informed by the sustainability assessments. However, sustainability assessments are costly, and whether to use them is at the discretion of the buyer. Hence, the buyer can instead choose to forgo the assessments and select a supplier based on price only. In this study, we explore this trade‐off. We find that the value of assessments depends on the buyer’s business environment in some surprising ways. For example, although sustainability assessments are used to identify the suppliers’ sustainability levels, greater ex ante variability and a decrease in suppliers’ average sustainability levels (e.g., facing a supplier base in a country with looser sustainability regulations) can decrease the value of sustainability assessments. We find that the presence of an outside option (e.g., internal production) alters the assessment policy significantly. We also explore when the buyer may prefer to assess only a subset of her suppliers. Although motivated by the use of sustainability assessments, our results are generalizable to settings where the buyer has the option to invest in total‐cost assessments on her potential suppliers’ unknown, non‐biddable, differentiator‐type attributes.

Humanitarian operations play a crucial role in alleviating human and social losses caused by natural disasters. The best way to know responders’ preparedness and ability to conduct efficient and effective humanitarian operations is to perform an evaluation. When evaluating humanitarian operations, the focus is mainly on their outcomes while the option of concentrating on the process is only mentioned, without examining in‐depth the subject nor providing specific tools for its analysis. This study tries to fill this gap by proposing and testing an outcome/process integrated approach for the evaluation of disaster operations management. The output analysis and the process analysis of disaster operations management are performed jointly by means of a questionnaire and a modeling tool, respectively. The integrated framework proposed has been applied to the emergency response of a small non‐profit organization to a flood. It has been shown that the two methods applied separately could give a distorted or partial picture of the operations under study, while the integrated framework proposed has proved to be effective, since it has brought to a deeper understanding of the processes. The approach can be used by practitioners to evaluate disaster operations management, and accurately and efficiently identify the key elements, strengths, and main weaknesses of relief operations.

We investigate whether labor unionization of customer firms affects the operating performance of their dependent suppliers. Using a sample of U.S. union elections, our regression discontinuity tests show that passing a union election leads to a 6.9 percentage‐point decline in supplier operating margin in the following year. Such negative effects are more pronounced for customers with stronger bargaining power vis‐à‐vis dependent suppliers. Additional tests show that the reduced supplier operating margins are due to weakened top lines and, more specifically, to squeezed selling prices. Finally, consistent with increased labor costs, unionization is shown to significantly increase cost of goods sold and slow labor‐force downsizing among customer firms. Overall, our evidence suggests that increased labor costs and financial inflexibility due to unionization induce customers to price‐squeeze their dependent suppliers.

Inspired by the World Food Programme's activity in the post‐civil war food crisis in Angola, this study proposes a systematic approach to address the location distribution problem in not‐for‐profit settings, where a limited volume of supply has to be allocated to different demand regions. The use of utility functions is key in our framework because it allows the decision‐maker to establish priorities by representing the heterogeneous effects of distributing supply to different demand locations (location effect) and to different individuals in the same demand location (diminishing returns effect). We propose the use of two fractional objectives with the utility functions embedded into them: an efficiency measure and a new inequity measure related to the Gini coefficient. The suggested problem has the form of a bi‐objective integer linear fractional program and our resolution optimization technique is designed to solve for multiple fractional objective measures. Novel analytical results for the worst‐case performance of the proposed resolution technique are provided. Our numerical experiments assess computational efficiency and provide concrete managerial prescriptions. Finally, an illustrative application of our approach in the context of the food crisis in Angola is presented based on an efficiency‐inequity trade‐off analysis.

Vaccination is a very effective measure to fight an outbreak of an infectious disease, but it often suffers from delayed deliveries and limited stockpiles. To use these limited doses of vaccine effectively, health agencies can decide to cooperate and share their doses. In this study, we analyze this type of cooperation. Typically cooperation leads to an increased total return, but cooperation is only plausible when this total return can be distributed in a stable way. This makes cooperation a delicate matter. Using cooperative game theory, we derive theoretical sufficient conditions under which cooperation is plausible (i.e., the core is non‐empty) and we show that the doses of vaccine can be traded for a market price in those cases. We perform numerical analyses to generalize these findings and we derive analytical expressions for market prices that can be used in general for distributing the total return. Our results demonstrate that cooperation is most likely to be plausible in case of severe shortages and in case of sufficient supply, with possible mismatches between supply and demand. In those cases, trading doses of vaccine for a market price often results in a core allocation of the total return. We confirm these findings with a case study on the redistribution of influenza vaccines.