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In today’s “new world of work,” knowledge workers are often given considerable flexibility regarding where and when to work (i.e., time-spatial flexibility) and this has become a popular approach to redesigning work. Whilst the adoption of such practices is mainly considered a top-down approach to work design, we argue that successful utilization of time-spatial flexibility requires proactivity on the part of the employee in the form of time-spatial job crafting. Previous research has demonstrated that time-spatial flexibility can have both positive and negative effects on well-being, performance, and work-life balance; yet remains mute about the underlying reasons for this and how employees can handle the given flexibility. Drawing on research from work design, we posit that in order for employees to stay well and productive in this context, they need to engage in time-spatial job crafting (i.e., a context-specific form of job crafting that entails reflection on time and place), which can be considered a future work skill. We propose a theoretical model of time-spatial job crafting in which we discuss its components, shed light on its antecedents, and explain how time-spatial job crafting is related to positive work outcomes through a time/spatial-demands fit.

In online peer-to-peer fundraising, individual fundraisers, acting on behalf of nonprofit organizations, mobilize their social networks using social media to request donations. Whereas existing studies focus on networks of donors to explain success, we examine the role of the networks of fundraisers and their effect on fundraising outcomes. By drawing on social capital and network theories, we investigate how social capital derived from social media networks and fundraising groups explains individual fundraising success. Using the Movember health campaign on Twitter as an empirical context, we find that fundraising success is associated with a moderate level of centrality in social media networks and moderate group network size. In addition, we find that fundraisers interact only marginally on social media but prefer to connect with each other outside these platforms and engage in group fundraising. Our article contributes to research on fundraising and social networks and provides recommendations for practice.

International audience; This paper investigates an integrated production inventory routing problem with time windows where a central depot is responsible for supplying single type of perishable food to multiple retailers within the planned time horizon. A mixed integer linear programming (MILP) model aiming at maximizing the total profit is formulated with explicitly tracing the food quality. To strengthen the formulation, a series of valid inequalities are introduced. Randomly generated instances with up to 40 retailers and 3 time periods are used to verify the effectiveness and the complexity of the proposed model, which is solved by the linear programming solver CPLEX. The computational results show that the proposed model is able to provide integrated plan for the decision makers, and instances with 20 retailers and 3 time periods are optimally solved with 102.97s on average. The results also indicate that the introduced valid inequalities are useful in helping CPLEX generate better upper bounds (maximization problem) for 20 out of 23 instances that are not optimally solved within the time limit.

Executive committees make critical decisions regarding project selection in different ways. In a committee operating under a democratic governance structure, all members have the right to vote, but the majority of voters ultimately decide which projects to undertake. Alternatively, in a committee operating under an elite governance structure, the decision is made only by a restricted number of voters. Therefore, in an elite governance, there are decision makers or “elite” members, and non-decision makers or “common” members. In this article, we study how committee members under either a democratic or elite governance structure interact and communicate information to each other, and ultimately make a decision about a project with uncertain revenues. We find that the efficient committee governance structure, i.e., the one that maximizes the expected surplus of the committee, can be determined by focusing on one specific communication between elite and common committee members. Further, we establish a sufficient condition on the revenues distribution for each governance structure to be efficient. Finally, when this sufficient condition on the revenues distribution does not hold, we find that governance efficiency depends on the probability of the decision makers to learn the true value of the revenues being sufficiently high or the one of the non-decision makers being sufficiently low or both these two conditions.

In this article, we study the effect of peer influence on products that exhibit positive network externalities to non-adopters (i.e., products that benefit adopters’ friends even if they do not adopt). Contrary to products that exhibit positive network externalities upon adoption, this structure of incentives likely results in negative peer influence: the more friends that adopted the product, the smaller the incentives to adopt. We measure this effect empirically by using observational data from a large mobile carrier serving 5.7 million users. We estimate the effect of peer influence across five different products of this type. A naive approach to do so results in a positive estimate for peer influence, due to unobserved homophily. We follow two approaches to address this issue. First, we suggest using the number of friends that end up adopting the product as a proxy for unobserved user fixed effects. Second, we control for homophily by applying a shuffle test (i.e., we compare the effect of peer influence from the original data with the effect obtained from comparable, randomly generated data without peer influence). We obtain negative estimates from both approaches, which provides robustness to our findings. Finally, we show that even for these products, the effect of peer influence associated with the first friends that adopt the product is positive, which arises because they still convey useful information about reducing uncertainty. The negative effect of peer influence arises only for the subsequent friends that adopt the product. These friends are unlikely to convey new information about the product, but each of them decreases the economic incentive to adopt, resulting in a negative aggregate effect of peer influence.

With the ongoing increase of variable renewable energy sources (VRES), such as wind or solar power, weather dependent production profiles induce uncertainty on the supply side and drastically change operations in power markets. In this paper, we study how an increasing market share of VRES affects spot power price dynamics and the forward price premium. Using data from simulated power markets, we analyse the forward premium in three identical power markets with a varying market share of VRES supplied to the system. We demonstrate that markets with a high share of supply from VRES yield a significantly lower forward premium than markets with a low market share of wind or solar supply. Where our results confirm that, regardless of the market share of supply from VRES, forward power prices contain information about future spot power prices, the study also provides evidence for a shift in the sign of the forward premium, from positive in a market with no VRES to negative when the share of VRES is high. These insights generate important implications for producers, retailers and other market participants exposed to wholesale price risk.

Multi-channel sales strategies have become common due to the usage of advanced information technologies. Yet, how one trading mechanism can influence the outcome of another, especially in the B2B market, is largely underexplored. This paper investigates the effect of price and quantity information from a pre-sales posted price channel on the performance of the century-old sequential Dutch auctions system. The pre-sales channel is conducted online preceding the auctions. Sellers can control the price and make a proportion of their stock available in the pre-sales. Anything left after the pre-sales is sold via the auctions. Our analysis of nearly 1.5 million flower lots reveals a positive effect where there are higher auction prices and total revenue for lots listed in the pre-sales than for lots that are not. The result holds even for lots with no actual sales in the pre-sales, indicating that buyers pay close attention to the additional information from the pre-sales posted price channel. By teasing out the information effect of pre-sales prices and pre-sales quantity on the auction prices, we evaluate a number of pricing strategies. The results suggest that selling at a high price in the pre-sales is still more beneficial than selling more by discounting prices.

In e-commerce fulfilment centres, storage assignment is critical to ensure short response times. To achieve this, many online retailers have moved to product dispersion in combination with product turnover-based slotting. However, commonly used policies do not fully utilise the historical customer demand information to optimise the storage assignment. This paper addresses a comprehensive approach to estimate the joint effects of ‘turnover frequency’, ‘product correlation’, and ‘inventory dispersion’ storage strategies on the expected order picking travel time in automated (robotic), parts-to-picker systems. Additionally, it provides a thorough analysis of the impact of product correlation and turnover frequency on storage policies’ performance. We develop a mixed-integer linear program for optimal product-to-cluster and cluster-to-zone allocation to minimise the robot's expected travel time. The travel time expressions are developed for different zone and station configurations. An efficient construction and improvement heuristic method is proposed and applied to a real dataset of a personal care products distributor. The analytical results show that the correlated dispersed assignment leads to a shorter expected travel time than the benchmark policies for order sets with sufficiently large order size. The demand correlation plays a major role in the performance of the models in the cases we tested.

Problem definition : We examine a dynamic assignment problem faced by a large wireless service provider (WSP) that is a Fortune 100 company. This company manages two warranties: (i) a customer warranty that the WSP offers its customers and (ii) an original equipment manufacturer (OEM) warranty that OEMs offer the WSP. The WSP uses devices refurbished by the OEM as replacement devices, and hence their warranty operation is a closed-loop supply chain. Depending on the assignment the WSP uses, the customer and OEM warranties might become misaligned for customer-device pairs, potentially incurring a cost for the WSP. Academic/practical relevance : We identify, model, and analyze a new dynamic assignment problem that emerges in this setting called the warranty matching problem. We introduce a new class of policies, called farsighted policies, which can perform better than myopic policies. We also propose a new heuristic assignment policy, the sampling policy, which leads to a near-optimal assignment. Our model and results are motivated by a real-world problem, and our theory-guided assignment policies can be used in practice; we validate our results using data from our industrial partner. Methodology : We formulate the problem of dynamically assigning devices to customers as a discrete-time stochastic dynamic programming problem. Because this problem suffers from the curse of dimensionality, we propose and analyze a set of reasonable classes of assignment policies. Results : The performance metric that we use for a given assignment policy is the average time that a replacement device under a customer warranty is uncovered by an OEM warranty. We show that our assignment policies reduce the average uncovered time and the expected number of out-of-OEM-warranty returns by more than 75% in comparison with our industrial partner’s current assignment policy. We also provide distribution-free bounds for the performance of a myopic assignment policy and of random assignment, which is a proxy for the WSP’s current policy. Managerial implications : Our results indicate that, in closed-loop supply chains, being completely farsighted might be better than being completely myopic. Also, policies that are effective in balancing short-term and long-term costs can be simple and effective, as illustrated by our sampling policy. We describe how the performance of myopic and farsighted policies depend on the size and length of inventory buildup.

This paper presents a new class of multiple-follower bilevel problems and a heuristic approach to solving them. In this new class of problems, the followers may be nonlinear, do not share constraints or variables, and are at most weakly constrained. This allows the leader variables to be partitioned among the followers. We show that current approaches for solving multiple-follower problems are unsuitable for our new class of problems and instead we propose a novel analytics-based heuristic decomposition approach. This approach uses Monte Carlo simulation and k-medoids clustering to reduce the bilevel problem to a single level, which can then be solved using integer programming techniques. The examples presented show that our approach produces better solutions and scales up better than the other approaches in the literature. Furthermore, for large problems, we combine our approach with the use of self-organising maps in place of k-medoids clustering, which significantly reduces the clustering times. Finally, we apply our approach to a real-life cutting stock problem. Here a forest harvesting problem is reformulated as a multiple-follower bilevel problem and solved using our approach, This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289

PurposeThis “impact pathways” paper argues that operations and supply chain management (OSCM) could help address the worsening drug shortage problem in high-income countries. This significant societal problem poses difficult challenges to stakeholders given the complex and dynamic nature of drug supply chains. OSCM scholars are well positioned to provide answers, introducing new research directions for OSCM in the process.Design/methodology/approachTo substantiate this, the authors carried out a review of stakeholder reports from six European countries and the academic literature.FindingsThere is little academic research and no fundamental agreement among stakeholders about causes of shortages. Stakeholders have suggested many government measures, but little evidence exists on their comparative cost-effectiveness.Originality/valueThe authors discuss three pathways of impactful research on drug shortages to which OSCM could contribute: (1) Developing an evidence-based system view of drug shortages; (2) Studying the comparative cost-effectiveness of key government interventions; (3) Bringing supply chain risk management into the government and economics perspectives and vice versa. Our study provides a baseline for future COVID-19-related research on this topic.

We analyse how the introduction of the same renewable energy technology at different parts of the electricity supply chain has different price formation effects on wholesale power markets. We develop a multi-stage competitive equilibrium model to evaluate the effects on short-term price formation of a technology shift from conventional to both large-scale renewable energy production (e.g. wind and solar farms) and distributed renewable energy sources (e.g. rooftop solar). We find that wind and solar technologies oppositely affect the forward risk premium, and this is related to technology-varying, risk-related hedging pressures of producers and retailers. We form a multi-factor propositional framework and empirically validate the model by analyzing data from California and Britain; two markets which recently experienced significant increases of renewable power, in terms of utility scale and distributed sources. The work is innovative in showing theoretically and empirically how different types of renewable technologies influence market price formation differently. This has implications for market participants facing wholesale price risks, as well as regulators and policy-makers.

Microgrids are an increasingly relevant technology for integrating renewable energy sources into electricity systems. Based on a microgrid implementation in California, we investigate microgrid operation under real-world conditions. These conditions have not yet been considered in combination and encompass energy charges, demand charges, export limits, as well as uncertainty about future electricity demand and generation in the microgrid. Under these conditions, we evaluate the performance of two frequently applied groups of strategies for microgrid operation. The first group is composed of proactive strategies that optimize decisions based on forecasts of future electricity generation and demand. The second group includes reactive strategies that make operational decisions based exclusively on the current state of the microgrid. We evaluate the performance of the strategies under varying operational parameters, forecast accuracies, and microgrid configurations—well beyond our Californian showcase. Our results confirm the expectation that proactive strategies outperform reactive ones in the majority of settings. Yet, reactive strategies can perform better under short control intervals or under moderate prediction errors of PV generation or demand. Furthermore, the interplay between real-world conditions and operational strategies reveals several additional insights for research on microgrid operation. First, we find that demand charges and export limits decisively affect microgrid performance. Second, the impact of forecast errors is highly non-linear and non-monotonous. Third, escalating negative interactions between forecast errors and demand charges make proactive strategies benefit from longer control intervals. This result is contrary to existing best practice, which promotes short control intervals to minimize the impact of uncertainty.

International audience; Form postponement means delaying the commitment of inventory to the final configuration of a product as long as possible. Many firms today are striving to redesign their products and/or their manufacturing and supply chain processes to implement form postponement. Opportunities for form postponement, however, are sometimes lost in the companies' production-planning processes. By focusing on the deferring of product mix decisions in the master production scheduling process, this paper shows that form postponement opportunities can be divided into two components: one whose pursuit necessarily requires product and/or transformation process redesign, the other that can be pursued by changing the sales forecasting and master production scheduling process alone. We develop an operational procedure to identify and quantify, for a given product family, all opportunities for form postponement and their two respective components. Then, we discuss and empirically illustrate how the proposed measurement procedure may support companies in changing their decision-making routines to implement form postponement. Finally, we set future research directions on form postponement suggested by our results.

Scheduling the availability of order pickers is crucial for effective operations in a distribution facility with manual order pickers. When order-picking activities can only be performed in specific time windows, it is essential to jointly solve the order picker shift scheduling problem and the order picker planning problem of assigning and sequencing individual orders to order pickers. This requires decisions regarding the number of order pickers to schedule, shift start and end times, break times, as well as the assignment and timing of order-picking activities. We call this the order picker scheduling problem and present two formulations. A branch-and-price algorithm and a metaheuristic are developed to solve the problem. Numerical experiments illustrate that the metaheuristic finds near-optimal solutions at 80% shorter computation times. A case study at the largest supermarket chain in The Netherlands shows the applicability of the solution approach in a real-life business application. In particular, different shift structures are analyzed, and it is concluded that the retailer can increase the minimum compensated duration for employed workers from six hours to seven or eight hours while reducing the average labor cost with up to 5% savings when a 15-minute flexibility is implemented in the scheduling of break times.

By bringing team members with different cultural backgrounds together, teams in international business can benefit from cultural diversity and reach higher levels of performance. Cultural diversity also brings challenges, however, and diversity research has identified the need to consider moderating influences in the diversity–performance relationship. Team leadership should be particularly important in this respect and drawing on a theoretical analysis that puts an understanding of cultural diversity center stage, we propose that factors that reflect leaders’ experience with cultural diversity positively moderate the relationship between cultural diversity and team performance. We identify leader cultural background (local vs. foreign to the host culture) and leader team tenure as such factors. We predict that the influence of team nationality diversity (a form of cultural diversity) on team performance is more positive with a leader who is foreign to the host country than with a local leader, and with a leader with longer team tenure. In addition, we predict that the one moderating influence substitutes for the other, such that the effect of leader cultural background is stronger for leaders with shorter tenure with the team. Results from a survey of N = 66 teams ( N = 336 individuals) from a multinational company support these hypotheses and inform our discussion of ways forward in the study of leadership and team diversity.

Highlights • We develop a framework to assess public-sector prescriptive analytics initiatives. • Six guiding questions link analytics to value, legitimacy, and operational capacity. • We present an illustrative case study for public charge point placement. • Data-driven recommendations are projected to increase utilization by 15%. • Initiatives benefit from scale, entrepreneurial spirit and collaboration., In this work, we investigate the challenges public-sector organizations face when seeking to leverage prescriptive analytics and provide insights into the public value such data-driven tools and methods can provide. Using the strategic triangle of value, legitimacy, and operational capacity as a starting point, we derive a framework to assess public-sector prescriptive analytics initiatives, along with six guiding questions that structure the assessment process. We present a case study applying prescriptive analytics to the placement of charge points in urban areas, a critical challenge many municipalities are currently facing in the transition towards electric mobility. Reflecting on the analytics application as well as its development and implementation process through the guiding questions, we derive key lessons for public-sector organizations seeking to apply prescriptive analytics.

Purpose: This paper, dedicated to the selection of strategic sourcing options as a part of the supply chain design, aims to provide supply chain designers with guidelines for selecting the best strategic sourcing option for each item (i.e., products, modules, parts and services). Design/methodology/approach: The study is based on a literature review of the sourcing options, buyer-supplier relationships, factors and criteria for selecting a sourcing strategy and the authors’ personal experience in this field. Findings: We propose a multi-step process by considering the three dimensions of the strategic sourcing options (i.e., who and how many will make the item; in cases of outsourcing, the kind of relationship between buyer and supplier(s); and where the item is to be made) along with the characteristics of the context, demand and product, and the relevant criteria to evaluate them. Originality/value: The main contributions are: the consideration of three dimensions to characterise the strategic sourcing options; a review of the factors and criteria relevant to decision-making in this respect for each item (i.e., products, modules, parts and services); and the guidelines provided for each step of the process

A key step in data-driven decision making is the choice of a suitable mathematical model. Complex models that give an accurate description of reality may depend on many parameters that are difficult to estimate; in addition, the optimization problem corresponding to such models may be computationally intractable and only approximately solvable. Simple models with only a few unknown parameters may be misspecified, but also easier to estimate and optimize. With such different models and some initial data at hand, a decision maker would want to know which model produces the best decisions. In this paper we propose a decision-based model-selection method that addresses this question.

Shop-floor employees play a key role in manufacturing innovation. In some companies, up to 75% of all productivity gains are the result of bottom-up employee ideas. In this paper, we examine how employee interplant assignments—short problem-solving jobs at other manufacturing plants within the same firm—influence employee-driven manufacturing innovation. Using unique idea-level data from a large European car parts manufacturer, we show that interplant assignments significantly increase the value of employees’ improvement ideas due to the short-term transfer of production knowledge and long-term employee learning. Both effects are amplified by assignments to plants that have high functional overlap (i.e., plants producing similar products using similar processes and machinery). One implication is that, for the purpose of employee-driven manufacturing innovation, assignments between peripheral plants with high functional overlap can be more effective than assignments to and from central plants. These findings are robust to several econometric tests. Our study provides novel and detailed empirical evidence of manufacturing innovation, and goes beyond previous research on the learning curve (learning by doing) by investigating how interplant assignments affect the value of employees’ improvement ideas (learning by moving). This paper was accepted by Charles J. Corbett, operations management.

The proliferation of mobile devices and the emergence of the Internet of Things are leading to an unprecedented availability of operational data. In this article, we study how leveraging this data in conjunction with data science methods can help researchers and practitioners in the development and evaluation of new operational policies. Specifically, we introduce a two-stage framework for exploratory data science consisting of a policy identification stage and an ex-ante policy assessment stage. We apply the framework to the context of free-floating carsharing—a novel mobility service that is an example of data-rich smart city services. Through data exploration, we identify a novel preventive user-based relocation policy and provide an ex-ante assessment regarding the feasibility of its implementation. We discuss practical implications of our approach and results for shared-mobility providers as well as the relationship between data science and operations management research.

In research and practice, public transportation planning is executed in a series of steps, which are often divided into the strategic, the tactical, and the operational planning phase. Timetables are normally designed in the tactical phase, taking into account a given line plan, safety restrictions arising from infrastructural constraints, as well as regularity requirements and bounds on transfer times. In this paper, however, we propose a timetabling approach that is aimed at decision making in the strategic phase of public transportation planning and to determine an outline of a timetable that is good from the passengers’ perspective. Instead of including explicit synchronization constraints between train runs (as most timetabling models do), we include the adaption time (waiting time at the origin station) in the objective function to ensure regular connections between passengers’ origins and destinations. We model the problem as a mixed integer quadratic program and linearise it. Furthermore we propose a heuristic to generate starting solutions. We illustrate the type of solutions found by our approach on two case studies based on the Dutch railway network and analyse trade-offs that are made to balance dwell times and regularity of trains.

This article studies the creation and consolidation of a trademark system tantamount to market integration and commercial specialization of Spanish regions from 1850 to 1920. We analyse the first 47,000 registrations, their geographical distribution and the drivers behind this trademark expansion. By using a lineal probability model, we find that knowledge spillovers across regions are associated with their relative trademark specialization and diversification. We incorporate the role played by transport infrastructure by calculating generalized transport costs. The results clarify the origins and evolution of geographical differences in commercial innovation and regional specialization in the first country to institute modern trademark legislation.

This paper focuses on hypothesis testing for the input of a Levy-driven storage system by sampling of the storage level. As the likelihood is not explicit we propose two tests that rely on transformation of the data. The first approach uses i.i.d. ‘quasi-busy-periods’ between observations of zero workload. The distribution of the duration of quasi-busy-periods is determined. The second method is a conditional likelihood ratio test based on the Bernoulli events of observing a zero or positive workload, conditional on the previous workload. Performance analysis is presented for both tests along with speed-of-convergence results, that are of independent interest.

Research summary: The transaction costs logic suggests that innovators cannot gain value from incumbents' unconditional termination rights in their R&D alliances with them because incumbents use these rights either to protect their own interests or as bargaining chips ex post. By contrast, the real options logic suggests that innovators can gain value from these rights because they provide them with an opportunity to release their resources from their troubled alliances. We reconcile the two logics by showing that incumbents' unconditional termination rights are their exit options in their R&D alliances and they are value-enhancing for innovators; yet, innovators' gains from them depend on the likelihood of their opportunistic uses by incumbents at their exercises. The tests in the biopharmaceutical R&D alliances setting support our hypotheses. Managerial summary: Incumbents form R&D alliances with innovators to explore new technologies and innovators join these alliances to commercialize their technologies. Yet, innovators typically possess alternative commercialization options activated once their alliances are terminated. In such R&D intensive alliances, granting unconditional termination rights to incumbents help innovators gain value from their R&D alliances. This is because incumbents' unconditional termination rights operate as their exit options and they enable easy and timely terminations of R&D alliances. This, in turn, allows innovators not to persist in troubled R&D alliances and to begin with exploring alternative commercialization options. However, the likelihood of opportunism at exit option exercises matters. The innovators' gains from exit options are likely to be higher as partner-specific alliance experience increases because the joint history mitigates opportunism.

The World Food Programme (WFP) is the largest humanitarian agency fighting hunger worldwide, reaching approximately 90 million people with food assistance across 80 countries each year. To deal with the operational complexities inherent in its mandate, WFP has been developing tools to assist its decision makers with integrating supply chain decisions across departments and functional areas. This paper describes a mixed integer linear programming model that simultaneously optimizes the food basket to be delivered, the sourcing plan, the delivery plan, and the transfer modality of a long-term recovery operation for each month in a predefined time horizon. By connecting traditional supply chain elements to nutritional objectives, we are able to make significant breakthroughs in the operational excellence of WFP’s most complex operations. We show three examples of how the optimization model is used to support operations: (1) to reduce the operational costs in Iraq by 12% without compromising the nutritional value supplied, (2) to manage the scaling-up of the Yemen operation from three to six million beneficiaries, and (3) to identify sourcing strategies during the El Niño drought of 2016.

Online communities have been identified as key platforms for innovation and knowledge sharing. While many studies have consistently identified that social capital and cultural factors are important for online knowledge sharing, their joint effect has to date received less attention. Addressing this gap helps us to move away from a one-size-fits-all approach of managing online communities to one which takes into account that social capital (i.e., trust, reciprocity, and a shared vision) may have differential effects on the sharing of high-quality knowledge. We therefore ask: To what extent does national culture shape the effect of social capital on perceived knowledge quality? We use survey data from two online communities from Germany and the Netherlands and demonstrate that the relationship between social capital and perceived knowledge quality differs in the two different national cultures, in particular for effects of reciprocity and shared vision. Besides practical contributions, we add to the literature by first integrating a social capital and online knowledge sharing lens and highlighting the moderating effect of national culture. Second, we provide a fine-grained understanding of the influence of national culture on knowledge sharing by delving deeper into differences between national cultures often regarded as similar.

Warehouses are an inevitable component in any supply chain and a vividly investigated object of research. Much attention, however, is absorbed by warehousing systems dedicated to the special needs of online retailers in the business-to-consumer segment. Due to the ever increasing sales volumes of e-commerce this focus seems self-evident, but a much larger fraction of retail sales are still realized by traditional brick-and-mortar stores. The special needs of warehouses servicing these stores are focused in this paper. While e-commerce warehouses face low-volume-high-mix picking orders, because private households tend to order just a few pieces per order from a large assortment, distribution centers of retail chains rather have to process high-volume-low-mix orders. We elaborate the basic requirements within both business segments and identify suited warehousing systems for brick-and-mortar stores (e.g., fully-automated case picking). The setup of each identified warehousing system is described, elementary decision problems are discussed, and the existing literature is surveyed. Furthermore, we identify future research needs.

In this study we explore the drivers and consequences of micro-level instances of knowledge sharing for innovation. We do so by focusing on the temporally bounded conversations that colleagues have about new ideas and we study specifically how the strength of ties between these colleagues influences the duration and breadth of knowledge sharing in the idea-related conversations they have over time. A 14-month on-site field study in a multinational company, in which we mapped 496 dyadic relationships regarding 17 new product ideas, shows that knowledge sharing can be explained by the ties between people being either strong or weak, rather than intermediate. We also discover that characteristics of the idea itself shape how tie strength influences the duration and breadth of knowledge sharing in idea conversations. Finally, we provide initial evidence to show how important conversations are for the success of an idea. Our study sheds light on micro-level instances of knowledge sharing for innovation and provides important insights into how managers can foster an environment in which weak and strong ties can be utilised optimally for sharing knowledge about ideas.

Background: First-line durvalumab plus etoposide with either cisplatin or carboplatin (platinum–etoposide) showed a significant improvement in overall survival versus platinum–etoposide alone in patients with extensive-stage small-cell lung cancer (ES-SCLC) in the CASPIAN study. Here we report updated results, including the primary analysis for overall survival with durvalumab plus tremelimumab plus platinum–etoposide versus platinum–etoposide alone. Methods: CASPIAN is an ongoing, open-label, sponsor-blind, randomised, controlled phase 3 trial at 209 cancer treatment centres in 23 countries worldwide. Eligible patients were aged 18 years or older (20 years in Japan) and had treatment-naive, histologically or cytologically documented ES-SCLC, with a WHO performance status of 0 or 1. Patients were randomly assigned (1:1:1) in blocks of six, stratified by planned platinum, using an interactive voice-response or web-response system to receive intravenous durvalumab plus tremelimumab plus platinum–etoposide, durvalumab plus platinum–etoposide, or platinum–etoposide alone. In all groups, patients received etoposide 80–100 mg/m2 on days 1–3 of each cycle with investigator's choice of either carboplatin area under the curve 5–6 mg/mL/min or cisplatin 75–80 mg/m2 on day 1 of each cycle. Patients in the platinum–etoposide group received up to six cycles of platinum–etoposide every 3 weeks and optional prophylactic cranial irradiation (investigator's discretion). Patients in the immunotherapy groups received four cycles of platinum–etoposide plus durvalumab 1500 mg with or without tremelimumab 75 mg every 3 weeks followed by maintenance durvalumab 1500 mg every 4 weeks. The two primary endpoints were overall survival for durvalumab plus platinum–etoposide versus platinum–etoposide and for durvalumab plus tremelimumab plus platinum–etoposide versus platinum–etoposide in the intention-to-treat population. Safety was assessed in all patients who received at least one dose of study treatment. This study is registered at ClinicalTrials.gov, NCT03043872. Findings: Between March 27, 2017, and May 29, 2018, 972 patients were screened and 805 were randomly assigned (268 to durvalumab plus tremelimumab plus platinum–etoposide, 268 to durvalumab plus platinum–etoposide, and 269 to platinum–etoposide). As of Jan 27, 2020, the median follow-up was 25·1 months (IQR 22·3–27·9). Durvalumab plus tremelimumab plus platinum–etoposide was not associated with a significant improvement in overall survival versus platinum–etoposide (hazard ratio [HR] 0·82 [95% CI 0·68–1·00]; p=0·045); median overall survival was 10·4 months (95% CI 9·6–12·0) versus 10·5 months (9·3–11·2). Durvalumab plus platinum–etoposide showed sustained improvement in overall survival versus platinum–etoposide (HR 0·75 [95% CI 0·62–0·91]; nominal p=0·0032); median overall survival was 12·9 months (95% CI 11·3–14·7) versus 10·5 months (9·3–11·2). The most common any-cause grade 3 or worse adverse events were neutropenia (85 [32%] of 266 patients in the durvalumab plus tremelimumab plus platinum–etoposide group, 64 [24%] of 265 patients in the durvalumab plus platinum–etoposide group, and 88 [33%] of 266 patients in the platinum–etoposide group) and anaemia (34 [13%], 24 [9%], and 48 [18%]). Any-cause serious adverse events were reported in 121 (45%) patients in the durvalumab plus tremelimumab plus platinum–etoposide group, 85 (32%) in the durvalumab plus platinum–etoposide group, and 97 (36%) in the platinum–etoposide group. Treatment-related deaths occurred in 12 (5%) patients in the durvalumab plus tremelimumab plus platinum–etoposide group (death, febrile neutropenia, and pulmonary embolism [n=2 each]; enterocolitis, general physical health deterioration and multiple organ dysfunction syndrome, pneumonia, pneumonitis and hepatitis, respiratory failure, and sudden death [n=1 each]), six (2%) patients in the durvalumab plus platinum–etoposide group (cardiac arrest, dehydration, hepatotoxicity, interstitial lung disease, pancytopenia, and sepsis [n=1 each]), and two (1%) in the platinum–etoposide group (pancytopenia and thrombocytopenia [n=1 each]). Interpretation: First-line durvalumab plus platinum–etoposide showed sustained overall survival improvement versus platinum–etoposide but the addition of tremelimumab to durvalumab plus platinum–etoposide did not significantly improve outcomes versus platinum–etoposide. These results support the use of durvalumab plus platinum–etoposide as a new standard of care for the first-line treatment of ES-SCLC. Funding: AstraZeneca.

Multimodal carriers are third-party logistics providers who utilize multiple modes of transportation to deliver timely door-to-door services to their customers. Notwithstanding frequent service disruptions, customers have ever-increasing expectations for on-time deliveries, posing significant challenges to tactical planning. Inspired by the operations of actual carriers, we study the tactical decision planning of multimodal freight networks, considering operational disruptions, timetabling restrictions, and explicit customer service requirements. Despite our focus on tactical planning, our models integrate long-term commitments by penalizing deviations from strategic goals with last-minute operational planning decisions, which are recourse actions aiming to accommodate service disruptions. After improving the dual bound of our initial formulation, we provide reduced-sized models for the special cases of nonpropagating delays and nonreactive last-minute decisions. This latter formulation is utilized to construct scenario-based and trip-based heuristics, which we combine in a hybrid search loop. Our computational study illustrates that our approach attains high-quality solutions for real-sized instances that are otherwise unsolvable by off-the-shelf optimization software, and a significant improvement over a nontrivial rolling horizon benchmark.

Timetabling for railway services often aims at optimizing travel times for passengers. At the same time, restricting assumptions on passenger behavior and passenger modeling are made. While research has shown that discrete choice models are suitable to estimate the distribution of passengers on routes, this has not been considered in timetabling yet. We investigate how to integrate a passenger distribution into an optimization framework for timetabling and present two mixed integer linear programs for this problem. Both approaches design timetables and simultaneously find a corresponding passenger distribution on available routes. One model uses a linear distribution model to estimate passenger route choices. The other model uses an integrated simulation framework to approximate a passenger distribution according to the logit model, a commonly used route choice model. We compare both new approaches with three state-of-the-art timetabling methods and a heuristic approach on a set of artificial instances and a partial network of Netherlands Railways (NS). Our experiments provide insights into the impact of considering multiple routes instead of a single route, and of integrated route choice versus predetermined route assignment with respect to the solution quality.

Warehouse automation is increasingly adopted to manage throughput fluctuations in e-commerce order fulfillment. This work develops queuing network models and solution methodologies for performance analysis of a stock-to-picker system that connects an upstream automated storage system to a downstream pick station. We focus on the pick station process, quantifying the throughput differences between a pick station that employs a static versus dynamic batching strategy. We consider a waveless order release environment where the item totes are requested from the storage system only when an order arrives. We capture throughput performance in this environment for single as well as multi-line orders with/without item commonality by developing closed-queuing network models. The consolidation of multiple product-lines for a multi-line order is modeled using fork–join synchronization stations within the closed queuing network. For analyzing such queuing networks, we develop a network-decomposition based solution methodology. We validate the models using a simulation model of the upstream storage and downstream order-picking system. We find that in waveless order release environment, dynamic batching always outperforms static batching in terms of system throughput. However, for single-line orders, the percentage gain in the throughput (by implementing dynamic batching) decreases for smaller item tote inter-arrival times. For multi-line orders, dynamic batching increases the system throughput by 37–43%. We also analyze the effect of batch size on the throughput performance. The results indicate that the system throughput increases with an increase in the batch size under both batching policies. But, the marginal benefit reduces as we increase the batch size.

The sharing economy has radically reshaped marketing thought and practice, and research has yet to examine whether and how platform-level buyer protection insurance (PPI) affects buyers and sellers in this economy. The authors exploit a natural experiment involving an unexpected system glitch during a PPI launch and estimate difference-in-differences models using over 5.4 million data points from a food sharing platform. Results suggest that PPI significantly increases buyer spending and seller revenue, affirming the benefits of this platform-level insurance in the sharing economy. The authors also uncover multifaceted buyer-side and seller-side responses that enable such benefits. PPI increases buyer spending by boosting product orders and variety-seeking behavior. Furthermore, it enhances seller revenue by increasing customer retention and acquisition. This work contributes to the literature by (1) putting a spotlight on the topic of PPI, a platform governance policy that reduces consumer risks and improves the efficacy of sharing platforms; (2) accounting for how PPI alters buyer and seller behaviors on a platform; (3) addressing what types of buyers and sellers benefit more or less from PPI; and (4) offering guidance for managers to improve platform reputation, marketplace efficiency, and consumer welfare in the context of the sharing economy.

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.

In the battle against climate change, electrification plays an increasingly large role in our society. The growing use of electricity networks requires advanced coordination mechanisms to avoid the tragedy of the commons. In this paper, we explore the effects of optimum time-dependent pricing (TDP) on supplier surplus within electricity markets, using a parameterized optimization model. Varying not only the prices themselves, but also their announcement horizon, we show how the suppliers’ optimal decision depends on risk aversion, forecasting quality, and end-user flexibility. The inclusion of procurement risk in our model shows that TDP can be beneficial for suppliers when they want to actively manage risk, even if expected profits are lower. At the same time, the shifting of end-user demand to low-cost times reduces the overall system cost, and potentially carbon emissions.

The mechanisms by which social networks and organizational vocabularies combine jointly to affect communication patterns across organizational boundaries remain largely unexplored. In this paper, we examine the mutually constitutive relation between the network ties through which organizational members communicate with each other and the vocabularies that they use to describe their organization. We suggest that the dynamic structure of social networks and organizational vocabularies is contingent on the formal design of organizational subunits. Within subunit boundaries, members who interact with each other are more likely to develop similar vocabularies over time. Interestingly, between subunits, the more two members share similar organizational vocabularies, the more likely they are to form a tie over time. We find empirical evidence for these arguments in a longitudinal study conducted among the managers of a multiunit organization. Organizational vocabularies, we suggest, may sustain communication patterns across organizational boundaries, thus bridging cultural holes within organizations.

In this study, we aim to compare the performance of systems and forensic facial comparison experts in terms of likelihood ratio computation to assess the potential of the machine to support the human expert in the courtroom. In forensics, transparency in the methods is essential. Consequently, state‐of‐the‐art free software was preferred over commercial software. Three different open‐source automated systems chosen for their availability and clarity were as follows: OpenFace, SeetaFace, and FaceNet; all three based on convolutional neural networks that return a distance (OpenFace, FaceNet) or similarity (SeetaFace). The returned distance or similarity is converted to a likelihood ratio using three different distribution fits: parametric fit Weibull distribution, nonparametric fit kernel density estimation, and isotonic regression with pool adjacent violators algorithm. The results show that with low‐quality frontal images, automated systems have better performance to detect nonmatches than investigators: 100% of precision and specificity in confusion matrix against 89% and 86% obtained by investigators, but with good quality images forensic experts have better results. The rank correlation between investigators and software is around 80%. We conclude that the software can assist in reporting officers as it can do faster and more reliable comparisons with full‐frontal images, which can help the forensic expert in casework.

__Problem definition:__ We consider a shipper transporting and selling a short-life-cycle product to a destination market. Customers in the destination market obtain higher utility if they receive the product earlier but their time preferences are heterogeneous. Two transportation service providers (i.e., carriers) offer distinct speeds and competing freight rates. This study analyzes the shipper’s optimal shipping strategy under carrier competition. __Academic/Practical relevance:__ Perishable products are commonly shipped via multiple means of transport. The faster the mode of transport is, the more expensive it is, but speed enables the product to reach the market with higher quality. In addition to the trade-off between speed and cost, the competition between carriers can also influence the shipper’s transportation procurement strategies. Our model highlights the implications of carrier competition in a dual sourcing problem. __Methodology:__ We study a two-stage game-theoretical framework: Carriers first compete on freight rates, and then the shipper determines the shipping schedule. __Results:__ The shipper may benefit from product differentiation via dual-mode shipping, in which the shipment that arrives earlier is sold at a premium price. In equilibrium, the shipper’s profit can be U-shaped in the speed difference between carriers. Dual sourcing may be inferior to simply restricting a single shipping service in a winner-take-all fashion. __Managerial implications:__ This study reveals an underlying trade-off between the operational advantage from product differentiation and the cost advantage from carrier competition. To benefit from either of these advantages, a shipper should use two carriers with either very distinct or very similar speeds. Single sourcing may bring an additional cost advantage that outweighs the value of production differentiation through dual sourcing.

Drawing from visionary leadership and strategy process research, we theorize and test the mechanism through which middle and lower-level managers’ visionary leadership affects their teams’ strategic commitment. The management literature extols the virtues of visionary leadership. In contrast to this positive stance, we reveal a dark side to visionary leadership. Our theoretical framework suggests that team manager visionary leadership harms team strategic consensus when the manager is not strategically aligned with the CEO, which in turn diminishes team commitment to the strategy. In contrast, when a team manager is strategically aligned with the CEO, team manager visionary leadership is positively related to team strategic consensus and subsequently to team strategic commitment. Data from 136 teams from two organizations support our moderated mediation model. A supplemental analysis of the content of strategic consensus and additional qualitative interviews with managers and employees in one of these organizations provide additional insights concerning the meaning of the theorized relations in practice.

A transition to electric vehicles (EVs) is widely assumed to be an important step along the road to environmental sustainability. However, large-scale adoption of EVs may put electricity grids under critical strain, since peaks in electricity demand are likely to increase radically. Efforts to manage demand peaks through pricing schemes may create new peaks at low-price periods, if large numbers of EV owners use smart charging to benefit from low prices. This effect is expected to be amplified when EV owners adopt smart decision support to assist them with optimal charging decisions. Therefore, energy policymakers are interested in advanced pricing schemes that can smooth demand or induce demand that comes as close as possible to a desired profile. We show, through simulations calibrated with real-world data, that current approaches to electricity pricing are limited in their ability to induce desired demand profiles. To address this challenge, we present adaptive pricing, a method to learn from EV owner reactions to prices and adjust announced prices accordingly. Our method draws on the Green Information Systems principles and can assist grid operators in ensuring the reliable operation of the grid. We evaluate our results in simulations, where we find that adaptive pricing outperforms current electricity pricing schemes, yielding results close to the theoretically optimal ones. We test our method in inducing both flat and extremely volatile demand profiles, and we see that in both cases it manages to induce EV charging close to the ideal scenario under perfect information.

International audience; We study a recently introduced smart compact automated parking system (CAPS), deploying straddle carriers, which require little footprint area. The straddle carriers can elevate and move a car over other cars in the horizontal direction in deep storage lanes, thereby considerably increasing the car storage capacity. Customers can reserve a parking space with a mobile application, or just drive in. Customers with booking by mobile application receive priority over the drive-in customers. We build priority queueing network models to evaluate the performance of a CAPS with priority booking under two storage policies. We then develop approximation methods to solve the analytical models and validate them through simulation. Numerical experiments show that the waiting time of a customer with booking can be reduced by 9.2% and 3.4%, respectively, for the dedicated and shared storage policies, compared with systems without priority booking. Then we compare the dedicated and shared storage policies and conduct a sensitivity analysis on the arrival rates of two types of cars. To minimise the expected retrieval time, the optimal ratio of width to height should be around 1.3. Finally, we calculate the investment cost of a CAPS and compare it with a competitive cylindrical automated parking tower.

Although the relationship between competition and firm innovation has long been of scholarly interest, prior research has predominantly considered changes in internal research and development (R&D) as a strategic response to competitors’ actions. In this study, we focus on one of the most important and commonly observed contractual mechanisms used to acquire external technologies: technology licensing. Surprisingly, licensing has been mostly overlooked by prior studies examining the effect of competition on firms’ allocation of R&D. We take into account the unique properties of licensing and systematically link them to the demands arising from the competitive pressure caused by rivals’ launches of new products. Furthermore, we discuss how licensing-in decisions ultimately shape a firm’s subsequent innovation in areas where they are threatened by competitors and how such innovation depends on the cumulative R&D investments inside the organization into which licensed knowledge is added. We test our theoretical model through a longitudinal design that tracks the licensing-in and innovation outcomes of firms in the global biopharmaceutical industry. Accounting for the endogenous selection of firms into licensing, our findings illustrate that licensing-in is motivated by competitive pressures. We also find that licensing-in increases a firm’s capacity to innovate in areas where competitors have exerted pressure, particularly in the presence of cumulative R&D investments. In so doing, the paper anchors technology licensing as a key organizational action that helps increase our understanding of the important relationship between competition and innovation.

Human resource professionals increasingly enhance their assessment tools with game elements—a process typically referred to as “gamification”—to make them more interesting and engaging for candidates, and they design and use “serious games” that can support skill assessment and development. However, commercial, off-the-shelf video games are not or are only rarely used to screen or test candidates, even though there is increasing evidence that they are indicative of various skills that are professionally valuable. Using the strategy game Civilization, this proof-of-concept study explores if strategy video games are indicative of managerial skills and, if so, of what managerial skills. Under controlled laboratory conditions, we asked forty business students to play the Civilization game and to participate in a series of assessment exercises. We find that students who had high scores in the game had better skills related to problem-solving and organizing and planning than the students who had low scores. In addition, a preliminary analysis of in-game data, including players’ interactions and chat messages, suggests that strategy games such as Civilization may be used for more precise and holistic “stealth assessments,” including personality assessments.

textabstractLearning by doing is a fundamental driver of productivity among knowledge workers. As workers accumulate experience working on certain types of tasks (i.e., they become specialized), they also develop proficiency in executing these tasks. However, previous research suggests that organizations may struggle to leverage the knowledge workers accrue through specialization because specialized workers tend to lose interest and reduce effort during task execution. This study investigates how organizations can improve specialized workers’ performance by mitigating the dysfunctional effects of specialization. In particular, we study how other sources of task experiences from the worker’s immediate manager as well as the organization itself help manage the relationship between worker specialization and performance. We do so by analyzing a proprietary dataset that comprises of 39,162 software service tasks that 310 employees in a Fortune 100 organization executed under the supervision of 92 managers. Results suggest that the manager role experience (i.e., the manager’s experience supervising workers) is instrumental in mitigating the potential negative effect of worker specialization on performance, measured as task execution time. Such influence, however, is contingent on cases in which organizational task experience (i.e., the organization’s experience in executing tasks of the same substantive content as the focal task) is limited. Taken together, our research contributes to multiple streams of research and unearths important insights on how multiple sources of experience beyond the workers themselves can help capture the elusive benefits of worker specialization.

Public transport operators (PTOs) increasingly face a challenging problem in switching from conventional diesel to more sustainable battery electric buses (BEBs). In this study, we optimize the opportunity fast-charging schedule of transit BEB networks in order to minimize the charging costs and the impact on the grid. Two mixed-integer linear programming (MILP) formulations that use different discretization approaches are developed and compared. Discrete-Time Optimization (DTO) resembles a time-expanded network that discretizes the time and decisions to equal discrete slots. Discrete-Event Optimization (DEO) discretizes the time and decisions into nonuniform slots based on arrival and departure events in the network. In addition to the DEO’s higher practicability, the comparative computational study carried out on the transit-bus network in the city of Rotterdam, Netherlands, shows that the DEO is superior to the DTO in terms of computational performance. To show the potential benefits of the optimal schedule, it is compared with two reference common-sense greedy strategies: First-in-First-Served and Lowest-Charge-Highest-Priority.

Firms compete in an increasingly omnichannel environment. Customer journeys no longer follow a single-channel linear path but look much “messier,” involving many channels, firm-owned and external, which are seamlessly integrated through technology. The associated changes in consumer behavior and the ways in which firms interact with consumers have led many firms to reshape the way they innovate their product portfolio. This paper presents a structured overview of some of the most striking changes to firms’ new product development (NPD) process. Starting from the well-known new product funnel, we describe how the omnichannel environment and the technologies that enable it affect the speed and execution of each NPD stage. We illustrate the changes we describe with examples from, among other industries, consumer packaged goods, consumer technology, and fashion. We derive learnings for practitioners involved in product innovation as well as promising avenues for academic research.

Standard-setting organizations (SSOs) establish goal-directed networks for innovators to jointly shape technology and markets through standards. The degree to which this can succeed depends to a large extent on network characteristics, which may differ substantially between SSOs. Many technological fields face intense competition between SSOs. Choosing the right one is thus a key strategic decision for innovators. Simultaneously, SSOs must reflect members’ preferences in their network set-ups and governance. Yet, little is known about these preferences. Based on extant literature, we derive hypotheses about how three themes of network attributes (membership base, rules, transaction costs) and contextual factors drive decision makers’ preferences. We conduct a comprehensive choice experiment with 141 standardization professionals in the Internet of Things field. Based on our data, we provide a more realistic indication of what firms value in SSOs than has been previously available. We also discuss our results’ implications for studying networks in other contexts.