To understand whether retailers should consider consumer returns when merchandising, we study how the optimal assortment of a retailer is influenced by its return policy. The retailer selects its assortment from an exogenous set of horizontally differentiated products. Consumers make purchase and keep/return decisions in nested multinomial logit fashion. Our main finding is that the optimal assortment has a distinct structure for relatively strict return policies: It is optimal to offer a mix of the most popular and most eccentric products when the refund amount is sufficiently low, which can be viewed as a form of risk sharing between the retailer and consumers. In contrast, if the refund is sufficiently high, or when returns are disallowed, optimal assortment is composed of only the most popular products (a common finding in the literature). We provide preliminary empirical evidence for one of the key drivers of our results: more eccentric products have higher probability of return – conditional on purchase. In light of our analytical findings and managerial insights, we conclude that retailers should take returns into account when merchandising.

Operations management methods have been applied profitably to a wide range of technology portfolio management problems, but have been slow to be adopted by governments and policy makers. We develop a framework that allows us to apply such techniques to a large and important public policy problem: energy technology R&D portfolio management under climate change. We apply a multi-model approach, implementing probabilistic data derived from expert elicitations into a novel stochastic programming version of a dynamic integrated assessment model. We note that while the unifying framework we present can be applied to a range of models and data sets, the specific results depend on the data and assumptions used, and therefore may not be generalizable. Nevertheless, the results are suggestive, and we find that the optimal technology portfolio for the set of projects considered is fairly robust to different specifications of climate uncertainty, to different policy environments, and to assumptions about the opportunity cost of investing. We also conclude that policy makers would do better to over-invest in R&D rather than under-invest. Finally, we show that R&D can play different roles in different types of policy environments, sometimes leading primarily to cost reduction, other times leading to better environmental outcomes.

Despite being theoretically suboptimal, simpler contracts (such as price-only contracts and quantity discount contracts with limited number of price blocks) are commonly preferred in practice. Thus, exploring the tension between theory and practice regarding complexity and performance in contract design is especially relevant. Using human subject experiments, Kalkanc¹, Chen, and Erhun (2011) showed that such simpler contracts perform effectively for a supplier interacting with a computerized buyer under asymmetric demand information. We use a similar set of experiments with the modification that a human supplier interacts with a human buyer. We show that human interactions strengthen the supplier's preference for simpler contracts. We find that suppliers have fairness concerns even when they interact with computerized buyers. These fairness concerns tend to be even stronger when suppliers interact with human buyers, particularly when the complexity of the contract is low. We also find that suppliers are more prone to random decision errors (i.e.,bounded rationality) when interacting with human buyers. In the absence of social preferences, Kalkanc¹, Chen, and Erhun identified reinforcement and bounded rationality as key biases that impact suppliers’ decisions. In human-to-human experiments, we find evidence for social preference effects. However, these effects may be secondary to bounded rationality.

This study characterizes the class of Pareto optimal returns policies between a manufacturer and a retailer who receives consumer returns. The manufacturer may take a costly hidden action that reduces the expected number of products returned by consumers, which when realized is hidden information known only to the retailer. When faced with consumer returns, the retailer must decide whether to send the product back to the manufacturer, who harvests a low salvage value, or to engage in costly refurbishment that permits the returned product to be resold to consumers. We find that the optimal returns policies may be implemented through the payment by the manufacturer of a full refund to the retailer of the wholesale price for any returns as well as a bonus paid to the retailer that is decreasing in the number of returns to the manufacturer.

We consider two competing supply chains, each consisting of supplier, a manufacturer, and a retailer. The suppliers exert effort to improve product quality, and the retailers sell products competitively. Each manufacturer chooses one of the three strategies: forward integration, backward integration, or no vertical integration. We seek for a subgame perfect Nash equilibrium and study the resulting market structure. Moreover, we characterize the effect of vertical integration on profitability, product price, and quality in a competitive setting. Existing literature has shown that, when manufacturers consider only forward integration, they may choose not to vertically integrate in equilibrium. In contrast, we find that, when both forward and backward integration options are considered, disintegration cannot be an equilibrium outcome. In this case, both manufacturers either forward or backward integrate, and the degree of product perishability, cost of quality, and how much consumers value quality are critical for the chosen direction of integration. Furthermore, competition increases attractiveness of backward integration relative to forward integration. We show that, while integrating backward unilaterally is always beneficial, unilateral forward integration can harm a manufacturer's profitability. Finally, vertical integration can result in a better quality product sold at a lower price.

Despite the spread of cost-driven outsourcing practices, academic research cautions that suppliers' cost advantage may weaken manufacturers' bargaining positions in negotiating outsourcing agreements, thereby hurting their profitability. In this study, we attempt to further understand the strategic impact of low-cost outsourcing on manufacturers' profitability by investigating the contractual form of outsourcing agreements and the industry structure of the upstream supply market. We consider a two-tier supply chain system, consisting of two competing manufacturers, who have the option to produce in-house or to outsource to an upstream supplier with lower cost. To reach an outsourcing agreement, each manufacturer engages in bilateral negotiation with her supplier, who may be an exclusive supplier or a common supplier serving both manufacturers. Our analysis shows that wholesale-price contracts always mitigate the competition between manufacturers regardless of whether they compete with price or quantity. In contrast, two-part tariffs intensify the competition when the manufacturers compete with quantity, but soften it when they compete with price. As a result, when outsourcing with two-part tariffs, the manufacturers may earn lower profits than they would from in-house production, although the suppliers are more cost efficient. This suggests that managers have to be wary about the downside of using coordinating contracts such as two-part tariffs when pursuing low-cost outsourcing strategies. Our analysis also sheds some light on the profitability of using an exclusive supplier for outsourcing. When outsourcing with wholesale-price contracts, the competing manufacturers are better off outsourcing to an exclusive supplier. However, when outsourcing with two-part tariffs, the manufacturers may earn higher profits by outsourcing to a common supplier than to an exclusive one when the manufacturers' bargaining power is sufficiently strong (weak) under quantity (price) competition.

Recent years have witnessed the pervasive supply disruptions and their impacts on supply chain performance. In this study, we investigate the optimal procurement design with supply disruptions and heterogeneous beliefs between the buyer and the supplier. We examine the impact of information asymmetry on the supplier's belief, the control right of the backup production, and the verifiability of supply disruption. The belief heterogeneity creates speculative gains and losses because the buyer and the supplier hold different estimates of the disruption probability. We demonstrate that the buyer's incentive to exploit this belief heterogeneity leads to real production inefficiencies in different scenarios. The production efficiency is not necessarily improved with more transparent information. Moreover, a very pessimistic supplier may have no incentive to invest in improving the reliability even if this is costless, and the supplier may produce more when the expected production cost becomes higher. When the buyer sees some value in using the supplier's estimate to update his own belief, we find that the main results hold unless the buyer completely abandons his belief.

In a three-tier supply chain comprising an original equipment manufacturer (OEM), a contract manufacturer (CM), and a supplier, there exist two typical outsourcing structures: control and delegation. Under the control structure, the OEM contracts with the CM and the supplier respectively. Under the delegation structure, the OEM contracts with the CM only and the CM subcontracts with the supplier. We compare the two outsourcing structures under a push contract (whereby orders are placed before demand is realized) and a pull contract (whereby orders are placed after demand is realized). For all combinations of outsourcing structures and contracts, we derive the corresponding equilibrium wholesale prices, order quantities, and capacities. We find that the equilibrium production quantity is higher under control than under delegation for the push contract whereas the reverse holds for the pull contract. Both the OEM and the CM prefer control over delegation under the push contract. However, under the pull contract, the OEM prefers control over delegation whereas the CM and the supplier prefer delegation over control. We also show that for a given outsourcing structure, the OEM prefers the pull contract over the push contract. In extending our settings to a general two-wholesale-price (TWP) contract, we find that when wholesale prices are endogenized decision variables, the TWP contract under our setting degenerates to either a push or a pull contract.

Systems thinking has proven useful in project management planning activities and has been suggested as a critical driver of a range of beneficial organizational behaviors. Yet, empirical evidence on the myriad of ways in which systems thinking can impact internal project dynamics and performance remains limited. This study focuses on one aspect of systems thinking in particular: the ability to recognize and understand the dynamics of systems and their features (e.g., feedback and delay). It makes use of a unique, large-scale interview data set along with objective and structured survey data drawn from multiple sources associated with supply chain system implementation projects. Analysis suggests that an individual's understanding of system dynamics as well as the similarity of such understanding to that typical of their team is, in fact, a strong predictor of both perceptions of psychological safety and information sharing quality in project work. These outcomes appear to mediate the relationship between system dynamics understanding and performance.

As public awareness of environmental hazards increases, a growing concern for corporations is the potential negative environmental impact of their products and the chemicals these products contain. In this study, we analyze the optimal decisions of a firm when a substance within its product is identified as potentially hazardous. Although the substance is not currently regulated, regulation may occur in the future. Therefore, the firm must devise a strategy for the development and implementation of a replacement substance. In an environment where replacement costs can be millions of dollars, regulation is uncertain, and both consumer and non-governmental organization pressures exist, a carefully developed plan that balances costs and risks is critical for a firm. Our results demonstrate that as long as a threat of regulation exists, a firm should always dedicate resources toward developing a replacement substance. However, it is not always optimal for a firm to implement a developed replacement. Regarding competitive dynamics, we find that competition between firms can offset a low chance of a shift in consumer perception about a substance and compel firms to replace; however, competition can lead to inefficient outcomes in which firms incur avoidable costs to implement ahead of potential regulation.

Two laboratory experiments on a single-echelon inventory task show that inventory durability interacts with transit lags to create order volatility that exceeds demand volatility. Thus, inventory durability and transit lags cause managers to deviate from inventory decision optimality. Durability creates a large increase in order volatility because players adjust orders insufficiently to reflect current inventory and backlogs, much as they adjust orders insufficiently to reflect holding and backlog costs in newsvendor studies (e.g., Schweitzer and Cachon 2000). Transit lags exacerbate non-optimal ordering by interfering with players' ability to correct prior errors. Our results suggest that non-optimal inventory decisions can be driven by inventory and supply chain characteristics, even in the absence of the coordination and information sharing problems studied by Croson et al. (2005) and Sterman (1989a,b). We also examine the influence of features related to personality. We find little evidence that the interactive effects of durability and transit lags are altered by need for cognition, impulsiveness, or locus of control, suggesting that these features make supply chain management extremely difficult. These results imply that retailers and their upstream partners must consider the characteristics of their product and supply chains when interpreting demand signals received from downstream partners.

The debate of net neutrality and the potential regulation of net neutrality may fundamentally change the dynamics of data consumption and transmission through the Internet. The existing literature on economics of net neutrality focuses only on the supply side of the market, that is, a broadband service provider (BSP) may charge content providers for priority delivery of their content to consumers. In this article, we explore a complete spectrum of broadband network management options based on both the supply and demand sides of the market. We find that although the BSP always prefers the non-neutral network management options, it does not always discriminate both sides of the market. From the social planner's perspective, we find that some network management options maximize the social welfare under certain market conditions while other options reduce the social welfare. Using the terminology from a recent Federal Communications Commission report and order, we categorize the social welfare maximizing options as “reasonable network management” and the social welfare reducing options as “unreasonable discrimination.” We also identify conditions under which the BSP's network management choices deviate from the social optimum. These conditions help establish the criteria under which the social planner might wish to regulate the BSP's actions.

We develop and evaluate a modeling approach for making periodic review production and distribution decisions for a supply chain in the processed food industry. The supply chain faces several factors, including multiple products, multiple warehouses, production constraints, high transportation costs, and limited storage at the production facility. This problem is motivated by the supply chain structure at Amy's Kitchen, one of the leading producers of natural and organic foods in the United States. We develop an enhanced myopic two-stage approach for this problem. The first stage determines the production plan and uses a heuristic, and the second stage determines the warehouse allocation plan and uses a non-linear optimization model. This two-stage approach is repeated every period and incorporates look-ahead features to improve its performance in future periods. We validate our model using actual data from one factory at Amy's Kitchen and compare the performance of our model to that of the actual operation. We find that our model significantly reduces both inventory levels and stockouts relative to those of the actual operation. In addition, we identify a lower bound on the total costs for all feasible solutions to the problem and measure the effectiveness of our model against this lower bound. We perform sensitivity analysis on some key parameters and assumptions of our modeling approach.

We study several important aspects of using environmental taxes to motivate the choice of innovative and “green" emissions-reducing technologies as well as the role of fixed cost subsidies and consumer rebates in this process. In our model, a profit-maximizing monopolistic firm facing price-dependent demand selects emissions control technology, production quantity, and price in response to the tax, subsidy, and rebate levels set by the regulator. The available technologies vary in environmental efficiency as well as in the fixed and variable costs. Both the optimal policy for the firm and the social-welfare maximizing policy for the regulator are analyzed. We find that the firm's reaction to an increase in taxes may be non-monotone: while an initial increase in taxes may motivate a switch to a greener technology, further tax increases may motivate a reverse switch. For the regulator, we compare the social welfare achievable in the centralized system (which serves as an upper bound) to the highest level achievable under different classes of environmental policies. If the regulator is limited to a tax-only policy, then when the regulator is moderately concerned with environmental impacts, the tax level that maximizes social welfare simultaneously motivates the choice of clean technology and closes the gap to the upper bound; however, both low and high levels of societal environmental concerns may lead to the choice of dirty technology and significant welfare losses as compared to the centralized case. Supplementing the environmental taxation with fixed cost subsidies and consumer rebates can eliminate this effect, expanding the range of parameters over which the green technology is chosen and often closing the welfare gap to the centralized solution.

Customers are averse to disappointment that arises when economic outcomes fall short of expectations. In this study, we study a two-period model in which the firm may create rationing in either period. In the anticipation of possible disappointment due to stock-outs, strategic customers decide when to purchase and the firm determines the prices and rationing levels in each period. We explore the impact of disappointment aversion on customers' strategic purchasing behavior and the firm's pricing and rationing decisions. Without disappointment aversion, it is optimal for the firm to adopt a uniform pricing policy without rationing. However, when strategic customers are averse to disappointment, a firm may be able to increase profits with an appropriate level of rationing. We analyze both the mark-up and mark-down policies. We show that, in a mark-down scenario, the firm always benefits from disappointment aversion behavior by using an appropriate level of rationing in a low-price period. However, in a mark-up scenario, whether it is beneficial for the firm to induce disappointment aversion behavior depends on how customers frame payoffs in different periods when forming utilities. Particularly, when customers compartmentalize payoffs in different periods to form utilities, the firm should not induce disappointment aversion behavior.

We present a method for forecasting sales using financial market information and test this method on annual data for US public retailers. Our method is motivated by the permanent income hypothesis in economics, which states that the amount of consumer spending and the mix of spending between discretionary and necessity items depend on the returns achieved on equity portfolios held by consumers. Taking as input forecasts from other sources, such as equity analysts or time-series models, we construct a market-based forecast by augmenting the input forecast with one additional variable, lagged return on an aggregate financial market index. For this, we develop and estimate a martingale model of joint evolution of sales forecasts and the market index. We show that the market-based forecast achieves an average 15% reduction in mean absolute percentage error compared with forecasts given by equity analysts at the same time instant on out-of-sample data. We extensively analyze the performance improvement using alternative model specifications and statistics. We also show that equity analysts do not incorporate lagged financial market returns in their forecasts. Our model yields correlation coefficients between retail sales and market returns for all firms in the data set. Besides forecasting, these results can be applied in risk management and hedging.

I consider pricing and ordering decisions faced by a retailer selling a perishable product with a two-period shelf life over an infinite horizon. In the first period, the product is “new”; in the next, it becomes “old.” The new product is perceived by customers to have a higher quality than the old product. Every period, the retailer makes three decisions: prices for the new and old products and how much new product to order. I first show, with some simple cases, that demand uncertainty can make the sale of the old product profitable. I then consider a more realistic case with dynamic demand substitution among customers. I recognize that the retailer's decisions may be constant or may vary across different periods, under different contexts. For instance, varying the price of the new product can sometimes be difficult due to the negative impact it generates among customers. I find that (i) the benefit obtained from selling the old product with constant decisions is much higher than the benefit from allowing all the decisions to vary; (ii) the former benefit increases with a higher procurement cost, a higher quality of the new product, and higher demand volatility; however, the latter benefit is non-monotone in these parameters; (iii) most of the latter benefit can be obtained by just changing the order quantity; and (iv) as the inventory of the old product increases, when all the decisions vary, the optimal price of the new product may increase or decrease.

Introducing environmental innovations in product and process design can affect the product's cost and demand, as well as the environmental impact in different stages of its life cycle (such as manufacturing and use stages). In this article, we advance understanding on where such design changes can be most effective economically to the firm and examine their corresponding environmental consequences. We consider a profit maximizing firm (newsvendor) deciding on the production quantity as well as its environmentally focused design efforts. We focus our results along the two dimensions of demand characteristics and life-cycle environmental impact levels, specifically functional vs. innovative products, and higher manufacturing stage environmental impact vs. higher use stage environmental impact. We also discuss the environmental impact of overproduction and how it relates to the different types of products and their salvage options. We find that although the environmental impact per unit always improves when firms use eco-efficient or demand-enhancing innovations, the total environmental impact can either increase or decrease due to increased production quantities. We identify the conditions for such cases by looking at the environmentally focused design efforts needed to compensate for the increase in production. We also show that the environmental impact of overproduction plays an important role in the overall environmental impact of the firm. We conclude by applying our model to different product categories.

In the industry with radical technology push or rapidly changing customer preference, it is firms' common wisdom to introduce high-end product first, and follow by low-end product-line extensions. A key decision in this “down-market stretch” strategy is the introduction time. High inventory cost is pervasive in such industries, but its impact has long been ignored during the presale planning stage. This study takes a first step toward filling this gap. We propose an integrated inventory (supply) and diffusion (demand) framework and analyze how inventory cost influences the introduction timing of product-line extensions, considering substitution effect among successive generations. We show that under low inventory cost or frequent replenishment ordering policy, the optimal introduction time indeed follows the well-known “now or never” rule. However, sequential introduction becomes optimal as the inventory holding gets more substantial or the product life cycle gets shorter. The optimal introduction timing can increase or decrease with the inventory cost depending on the marketplace setting, requiring a careful analysis.

Accurate inventory records are key to effective store execution, affecting forecasting, ordering, and replenishment. Prior empirical research, however, shows that retailer inventory records are inherently inaccurate. Radio Frequency Identification (RFID) enables visibility into the movement of inventories in the supply chain. Using two different field experiments, the current research investigates the effectiveness of this visibility in reducing retail store inventory record inaccuracy (IRI). Study 1 used an interrupted time-series design and involved daily physical counts of all products in one category in 13 stores (8 treatments and 5 controls) of a major global retailer over 23 weeks. Results indicate a significant decrease in IRI of approximately 26% due to RFID-enabled visibility. Using an untreated control group design with pre-test and post-test, Study 2 expands the number of categories to five and the number of stores to 62 (31 treatment and 31 control stores). Results show that the effectiveness of RFID in reducing IRI varies by category (ranging from no statistically significant improvement to 81%). Results also suggest that RFID ameliorates the effects of known determinants of IRI and provide the key insight that the technology is most effective for product categories characterized by these determinants.

The field of Production and Operations Management (POM) is increasingly perceived as a rigorous but narrow field, antiquated and not very relevant to the current challenges and concerns of managers in job-creating growth companies vital to our economies. I argue that a narrower positioning of POM in the past is responsible for its perceived limited utility to growth firms and global economies. POM at its core is about “doing more with less,” which is very well aligned with the context and needs of resource-constrained entrepreneurial companies. My discussion is focused on how the research paradigm of POM is and can be relevant to meeting the emerging challenges of growth companies of tomorrow. Specifically, I examine how POM can help meet the needs of these organizations to become scalable and sustainable. The objective is to stimulate thought and discussion and encourage early-stage POM scholars to seriously consider the contexts of technology commercialization, entrepreneurship, and growth companies as avenues for future research.

Recent years have seen a drastic transformation in the organization of wholesale and retail markets. Where once clear distinctions between wholesale suppliers and retail competitors existed, now an era of blurring boundaries has emerged. This transformation has been marked by the introduction of online channels for suppliers to provide products directly to consumers while, at the same time, traditional retailers too persist. Thus, retailers are both wholesale customers and retail competitors of many manufacturers. The consequences of the rapid emergence of instances of such partial forward integration by suppliers are not yet fully known. To this end, we study how partial forward integration can affect competing firms' strategic investments. We find that integration shifts the environment from being one in which firms invest to undercut retail rivals to one in which firms invest more in boosting demand, even that of their competitors. A case in point is the tendency for a manufacturer to invest broadly in brand promotion (benefiting both itself and its retail competitor), rather than heavy promotion of its own sales channel. The shift in the nature of strategic investments arising from partial forward integration implies that such integration can benefit firms and consumers alike, even the firm which finds itself reliant on a competitor for supplies.

A fundamental aspect of designing systems with dedicated servers is identifying and improving the system bottlenecks. We extend the concept of a bottleneck to networks with heterogeneous, flexible servers. In contrast with a network with dedicated servers, the bottlenecks are not a priori obvious, but can be determined by solving a number of linear programming problems. Unlike the dedicated server case, we find that a bottleneck may span several nodes in the network. We then identify some characteristics of desirable flexibility structures. In particular, the chosen flexibility structure should not only achieve the maximal possible capacity (corresponding to full server flexibility), but should also have the feature that the entire network is the (unique) system bottleneck. The reason is that it is then possible to shift capacity between arbitrary nodes in the network, allowing the network to cope with demand fluctuations. Finally, we specify when certain flexibility structures (in particular chaining, targeted flexibility, and the “N” and “W” structures from the call center literature) possess these desirable characteristics.

We consider the retail planning problem in which the retailer chooses suppliers and determines the production, distribution, and inventory planning for products with uncertain demand to minimize total expected costs. This problem is often faced by large retail chains that carry private-label products. We formulate this problem as a convex-mixed integer program and show that it is strongly NP-hard. We determine a lower bound by applying a Lagrangian relaxation and show that this bound outperforms the standard convex programming relaxation while being computationally efficient. We also establish a worst-case error bound for the Lagrangian relaxation. We then develop heuristics to generate feasible solutions. Our computational results indicate that our convex programming heuristic yields feasible solutions that are close to optimal with an average suboptimality gap at 3.4%. We also develop managerial insights for practitioners who choose suppliers and make production, distribution, and inventory decisions in the supply chain.

This article studies a joint stocking and product offer problem. We have access to a number of products to satisfy the demand over a finite selling horizon. Given that customers choose among the set of offered products according to the multinomial logit model, we need to decide which sets of products to offer over the selling horizon and how many units of each product to stock so as to maximize the expected profit. We formulate the problem as a nonlinear program, where the decision variables correspond to the stocking quantity for each product and the duration of time that each set of products is offered. This nonlinear program is intractable due to its large number of decision variables and its nonseparable and nonconcave objective function. We use the structure of the multinomial logit model to formulate an equivalent nonlinear program, where the number of decision variables is manageable and the objective function is separable. Exploiting separability, we solve the equivalent nonlinear program through a dynamic program with a two dimensional and continuous state variable. As the solution of the dynamic program requires discretizing the state variable, we study other approximate solution methods. Our equivalent nonlinear program and approximate solution methods yield insights for good offer sets.

This note discusses the impact of collection cost structure on the optimal reverse channel choice of manufacturers who remanufacture their own products. Using collection cost functions that capture collection rate and collection volume dependency, we show that the optimal reverse channel choice (retailer- vs. manufacturer-managed collection) is driven by how the cost structure moderates the manufacturer's ability to shape the retailer's sales and collection quantity decisions.

There is a growing trend in the retail industry to improve customer experience. In this article, we study retailer-initiated strategies to increase consumer valuation for a product under duopoly. In such a setting, it is possible that a consumer's valuation may be increased by one retailer; however, the consumer may decide to buy the product from the competitor. We consider a two-stage game where retailers first decide whether to invest in improvements in customer valuation and then engage in price competition. We computationally explore the Nash equilibria in terms of both investment and pricing. We find that in the majority of cases retailers price in a manner to discourage their local customers to buy from the competitor. Next, we focus on the pricing game and theoretically characterize the pricing Nash equilibrium. We find that a retailer could overcome competitive effects by improving consumer valuation beyond a certain threshold. We also find that a retailer who does not invest could benefit from competition in situations where his competitor increases consumer valuation beyond a threshold. Finally, we explore through a computational study the Nash equilibria of the two-stage game using an alternate model to establish the robustness of our findings.

In this study, we use a game-theory-based framework to model power in a supply chain with random and price-dependent demand and examine how power structure and demand models (expected demand and demand shock) affect supply chain members' performance. We demonstrate that whether a firm benefits from its power depends on the expected demand model but not on demand shock model. A firm benefits from its power only for linear but not for constant elasticity expected demand. The impact of power structure on supply chain efficiency depends on the models of both expected demand and demand shock. With additive shock, supply chain efficiency is highest (lowest) when neither firm dominates for linear (constant elasticity) expected demand. With multiplicative shock, the supply chain efficiency is highest with a power retailer (manufacturer) for linear (constant elasticity) expected demand. The manufacturer always benefits from a reduction in demand uncertainty. However, the retailer loses (benefits) from demand uncertainty reduction for linear (constant elasticity) expected demand. With a power retailer, the retail price is always on the higher end for linear expected demand, and the customer service level is the lowest for constant elasticity expected demand. Consequently, consumers do not necessarily benefit from a power retailer.

In this article, we examine in-store logistics processes for handling dairy products, from the incoming dock to the shelves of supermarkets and hypermarkets. The efficient execution of the in-store logistics related to such fast-moving, sensitive, and essential items is challenging and crucial for grocery retailers' sales, profits, and image. In our empirical study, we survey in-store logistics processes in 202 grocery supermarkets and hypermarkets belonging to a major retail chain in central Europe. Using a data envelopment analysis (DEA) and simulation, we facilitate process benchmarking. In particular, we identify ways of improving in-store logistics processes by showing the performance impacts of different managerial strategies and tactics. The DEA results indicate different efficiency levels for different store formats; the hybrid store format of the small hypermarket exhibits a comparatively worse performance in the analyzed execution of in-store logistics processes. The process simulation analysis reveals that the strategic and tactical design of in-store logistics processes (such as store locations/layouts, capacity management, reorder time, order period, and safety stock factors) lead to substantial service performance improvements (such as higher on-shelf availability combined with reduced inventory obsolescence costs). The results also show marginal improvements in the performance figures when delivery delays and damage to products are reduced.

Design rework is a core phenomenon in new product development (NPD). Yet carrying out design rework presupposes recognizing the need for it. I characterize the types of interpersonal knowledge transfer that help developers realize the need for design rework in NPD. As predicted by the NPD literature, I find that individuals who interact frequently with colleagues to address their task interdependences are more likely to realize the need for rework. I also learn that interacting with colleagues who have different expertise in process-related knowledge (as opposed to product-related knowledge) facilitates realizing the need for rework. However, to develop a deeper understanding of how individuals recognize the need for rework when interacting with others, we must expand our views beyond task interdependence and expertise-related factors. In particular, organizational variables—both formal and informal—play a significant role. With respect to formal hierarchical structures, actors of superior rank are less likely to realize the need for rework regardless of whether or not their interacting partner is of superior rank; however, actors of superior rank are more likely to trigger realizing the need for rework when interacting with partners of subordinate rank. By examining an organization's informal structure, I discover that the social “embeddedness” of developers (i.e., the energy and attention invested in a dyadic relationship) significantly influences their propensity to realize the need for rework. Several hypotheses are tested in a sociometric study conducted within the development department of a software company, and I discuss the implications for behavioral operations in NPD.

The changing climate and concerns over food security are prompting a new look at the supply chain reliability of products derived from agriculture, and the potential role of contract farming as a mechanism to address climate and price risk while contributing toward crop diversification and water use efficiency is also emerging. In this study, the decision problem of a farmer associated with allocating his land among different crops with varying water requirements is considered, given that a subset of the crops may be associated with a forward contract that is being offered by a buyer. The problem includes a decision to acquire a certain amount of irrigation water capacity prior to the season and to allocate this capacity as irrigation water to be applied during the season to each of the crops selected. Rainfall in the growing season and the market price of each crop at the end of the season are considered to be random variables. Two stochastic programming models are developed to consider facets of this problem and to understand how contracts that reduce market price uncertainty from the problem may change the farmer's decision. The structural properties of these models are discussed, and selected implications are illustrated through an application to data from the Ganganagar district in Rajasthan, India.

I consider a channel with one manufacturer selling the same product to two retailers engaged in imperfect competition. The retailers are asymmetric because one has a lower marginal selling cost (or a higher demand potential) than the other. I design the manufacturer's optimal selling mechanism, whereby the manufacturer must offer the same contract options to both retailers. I fully characterize the manufacturer's optimal selling mechanism for varying degrees of retailer asymmetry and competition intensity. I find that under certain conditions, the manufacturer is better off selling a larger quantity through the high-cost (or low-demand potential) retailer. I also show how the optimal mechanism can be implemented using a menu of two-part tariffs with quantity controls.

We analyze the market entry problem faced by startups that must integrate their service or product with one or more complementary technologies. The problem is especially challenging when the complementary technologies have uncertain cost reduction potentials. The entrepreneurship literature suggests that startups should pursue focused strategies for various reasons, including bounded rationality and budget constraints, but generally overlooks startups entering markets with complementary technologies. The advice for mature firms investing in complementary technologies is often to diversify investment across multiple complements to manage technological uncertainty. Given competing guidance, we seek to extend the entrepreneurship literature by modeling startups' entry decisions for markets in which complementary technologies exhibit strong learning effects. We find that, consistent with the extant entrepreneurship literature, startups generally achieve higher expected returns by channeling their integration investment to only one complementary technology. However, the mechanisms driving our results differ significantly by hinging on nonlinear feedback effects that occur when firms concentrate integration investment in only one complementary technology. Interestingly, this focused strategy often does not yield the highest market share or the lowest likelihood of bankruptcy. We characterize the situations under which each finding holds and describe the implications of these findings for theory, practice, and policy.

In this article, we study the price partitioning decisions of online retailers regarding shipping and handling (S&H) fees. Specifically, we analyze two partitioning formats used by retailers in this context. In the first scenario, retailers present customers with a price that is partitioned into a product price and a separate S&H surcharge (the PS strategy); in the second, customers are offered free shipping through a non-partitioned format where the product price already includes the shipping cost (the ZS strategy). We first develop a stylized game-theoretic model that captures the competitive dynamics between (and within) these two formats. Analysis of the model provides insights into how both firm and product level characteristics drive a retailer's strategic choice regarding which partitioning format to adopt and, hence, determines the equilibrium market structure in terms of proportion of ZS and PS retailers. Subsequently, we conduct empirical analyses, based on product and S&H prices data for two different product categories (digital cameras and printers) collected from online retailers, to validate all the results of our theoretical model. We establish that PS retailers charge lower product prices than ZS ones, but the total price (product + S&H) charged is higher for the first group. The S&H charge for PS retailers can be significant—it is, on average, 5.4% (printers) and 3.0% (digital cameras) for our two product categories. Furthermore, retailers which are popular and/or face risky cost environment are more likely to opt for the ZS strategy, while retailers whose portfolio mostly includes large or heavy products with high cost (S&H)-to-price ratios usually choose the PS strategy. Lastly, our empirical study also illustrates that the price adjustment behavior of retailers is affected by their shipping-fee policies—for example, ZS retailers change their product prices almost 1.5 times more frequently than PS ones.

This special issue contains articles that exemplify the role of operations management across the entrepreneurial value chain. This value chain encompasses all stages of the entrepreneurial phenomenon, including technology commercialization, where discovery, commitment, organization, and growth must take place. We report on a literature search that identifies research questions categorized with respect to topics crucial to operations management scholars and classify these questions under each stage of this value chain. The search guides the development of an evolutionary path for the use of resources, routines, and reputation (3Rs), often lacking in this process, and enables us to propose modeling and topical gaps in the literature. We offer a framework to set up exemplars for operational tradeoffs uniquely associated with the entrepreneurial value chain. We also articulate how five contributed articles in this issue tackle some of these tradeoffs, prior to introducing four perspective pieces. We hope this discussion motivates follow-on work and triggers a significant increase in the flow of articles that make it to both entrepreneurship and operations management top-tier academic and practitioner publications.

To achieve technology innovation and commercialization (TIC) success under complex, protracted, and uncertain product development cycles, entrepreneurial firms engage in downstream alliance partnerships with mainstream industry players. In this study, we examine two specific characteristics of the entrepreneurial firm's downstream alliance portfolio (depth and scope) and their impact on TIC success. Employing a sample of 728 biotech firms and their partnerships with pharmaceutical companies, we find that while portfolio depth and scope separately have positive impact on success, the relationship between portfolio scope and success is additionally moderated by portfolio depth. Further, insights from post hoc interviews also suggest that though it is challenging for entrepreneurial firms to incorporate both depth and scope in alliance partnerships, those that optimally combine both can achieve higher TIC success.

In this paper, we consider a retailer adopting a “money-back-guaranteed” (MBG) sales policy, which allows customers to return products that do not meet their expectations to the retailer for a full or partial refund. The retailer either salvages returned products or resells them as open-box items at a discount. We develop a model in which the retailer decides on the quantity to procure, the price for new products, the refund amount, as well as the price of returned products when they are sold as open-box. Our model captures important features of MBG sales including demand uncertainty, consumer valuation uncertainty, consumer returns, the sale of returned products as open-box items, and consumer choice between new and returned products and possibility of exchanges when restocking is considered. We show that selling with MBGs increases retail sales and profit. Furthermore, the second-sale opportunity created by restocking returned products enables the retailer to generate additional revenues. Our analysis identifies the ideal conditions under which this practice is most beneficial to the retailer. Offering an MBG without restocking increases the new product price. We show that if the retailer decides to resell the returned items as open-box, the price of the new product further increases, while open-box items are sold at a discount. On the other hand, customers enjoy more generous refunds along with lower restocking fees. The opportunity to resell returned products also generally decreases the initial stocking levels of the retailer. Our extensive numerical study substantiates the analytical results and sharpens our insights into the drivers of performance of MBG policies and their impact on retail decisions.

This article studies the performance of wholesale pricing when the supply chain partners' fairness concerns are private information. We find that some properties of wholesale pricing established under complete information hold under incomplete information as well. First, wholesale pricing can coordinate the supply chain, despite the information asymmetry, when fairness concerns are strong enough. Second, in the case when an equitable profit split does not imply that the retailers profit must be higher than that of the supplier, the suppliers' equilibrium offer is never rejected. Overall, the study makes two primary contributions. First, it provides a partial characterization of the equilibrium when the conditions required for coordination do not hold, that is, when fairness concerns are mild. In this case, the model predicts that the expected market price must be exactly the same as under complete information. The channel efficiency, nevertheless, is strictly lower than under complete information. The distribution-free lower bound on channel efficiency suggests that this efficiency loss should be quite small, though. Second, it provides an experimental test of the models' predictions as well as a direct validation of the assumptions of preferences heterogeneity and mildness by obtaining the empirical distribution of the preferences.

Traditional inventory models fail to take into account the dynamics between the retail sales floor and the backroom, commonly used by retailers for extra storage. When a replenishment order for a given item arrives at a retail store, it may not fit on the allocated shelf space, making backroom storage necessary. In this article, we introduce the backroom effect (BRE) as a consequence of misalignment of case pack size, shelf space, and reorder point. This misalignment results from the fragmented nature of inventory policy decision making in the retail industry and affects basic trade-offs in inventory models. We specify conditions under which the BRE exists, quantify the expected amount of backroom inventory, derive an optimal short-term inventory policy, and assess the impact of the BRE on the optimal inventory policy and total costs. Our results indicate that ignoring the BRE leads to artificially high reorder points and higher total costs. The paper concludes with a discussion of theoretical and managerial implications.

An important difference between both manufacturing and wholesaling vs. retail is the information available concerning inventory. Typically, far less information characterizes retail. Here, an extreme environment of information shortfall is examined. The environment is technically termed “unattended points of sale,” but colloquially called vending machines. Once inventory is loaded into a machine, information on demand and inventory level is not observed until the scheduled reloading date. Technological advances and business process changes have drawn attention to the value of information (VOI) in retail inventory in many venues. Moreover, technology is now available that allows unattended points of sale to report inventory information. Capturing the value of this information requires changes in current business practice. We demonstrate the value of capturing information analytically in an environment with restrictive demand assumptions. Experiments in an environment with realistic demand assumptions and parameter values show that the VOI depends greatly on operating characteristics and can range from negligible effects to increasing profitability 30% or more in actual practice.

Collaboration is an essential element of new product development (NPD). This research examines the associations between four types of information technology (IT) tools and NPD collaboration. The relationships between NPD practices and NPD collaboration are also examined. Drawing on organizational information processing theory, we propose that the relationships between IT tools and NPD collaboration will be moderated differently by three project complexity dimensions, namely, product size, project novelty, and task interdependence, due to the differing nature of information processing necessitated by each project complexity dimension. Likewise, the moderation effects of the project complexity dimensions on the relationship between NPD practices and NPD collaboration will also be different. We test our hypotheses using data from a sample of NPD projects in three manufacturing industries. We find that IT tools are associated with collaboration to a greater extent when product size is relatively large. In contrast, IT tools exhibit a smaller association with collaboration when project novelty or task interdependence is relatively high. NPD practices are found to be more significantly associated with NPD collaboration under the contingency of high project novelty or high task interdependence. The findings provide insights about circumstances where several popular IT tools are more likely to facilitate collaboration, thus informing an NPD team's IT adoption and use decisions.

Customers who face a stockout situation often decide to purchase a different product in the same category. We analyze the resulting dynamic substitution problem in a retail environment, where customers serve themselves from the store shelves, such that the sequence of customer arrivals affects how scarce products are allocated to customers. We consider a setting with constrained shelf space, and we study how a retailer should optimally allocate such space between substitute products. We characterize environments where the sequence of customer arrivals can have a substantial impact on profitability.

We study the stock market reaction to announcements of global green vehicle innovation over a 14-year time span (1996–2009) using the event study methodology. We document that the stock market generally reacts positively to automakers' announcements of environmental innovations, consistent with prior research on the wealth effects of innovation announcements. Our results indicate that crucial green product development decisions such as innovation type and market segment choices exert direct influence on a firm's market value. These results hold after controlling for firm size, leverage, profitability, R&D intensity, and oil price changes.

In this article, we study behavior in a series of two-player supply chain game experiments. Each player simultaneously chooses a capacity before demand is realized, and sales are given by the minimum of realized demand and chosen capacities. We focus on the differences in behavior under fixed pairs and random rematching. Intuition suggests that long-run relations should lead to more profitable outcomes. However, our results go against this intuition. While subjects' capacity choices are better aligned (i.e., closer together) under fixed pairs, average profits are more variable. Moreover, learning is slower under fixed pairs—so much so that over the last five periods, average profits are actually higher under random rematching. The underlying cause for this finding appears to be a “first-impressions” bias, present only under fixed matching, in which the greater the misalignment in initial choices, the lower are average profits.

Although project portfolio management has been an active research area over the past 50 years, budget allocation models that consider competition are sparse. Faced with the competition, firms contemplating budget allocation for their project portfolio cannot limit their attention to the returns from their projects' target markets, as is the case for monopoly firms, but must also anticipate the competitive effects on these returns. Assuming firms allocate their budgets between projects offering incremental innovation targeting a mature market and projects offering radical innovation targeting an emerging market, we show that while the monopoly firm bases its budget allocation decision solely on the marginal returns of the markets, competing firms—as they take into account their counterparts' investment decisions—need to also consider the projects' average returns from their respective markets. This drives competing firms into incrementalism: faced with competition, firms invest larger portions of their budgets into projects targeting mature markets. This effect is amplified as the number of competing firms increases and firms allocate an even greater share of their budget into projects targeting a mature market. We further demonstrate the effects that changes to firms' individual budgets, as well as to market characteristics, have on firms' budget allocation decision.

Despite the widely held belief of the importance of innovation, the connection between innovation and firm performance is empirically inconclusive, partially owing to the limitations of existing innovation measures, which tend to ignore the effectiveness of innovation programs. In this study, we use the winning of innovation awards as a proxy for the effective execution of innovation. We conducted event-study analyses based on data from more than 1000 publicly traded firms that won innovation awards between 1998 and 2003. Our statistical tests provide strong evidence that the performance of award-winning firms is significantly higher as compared with several sets of control firms. Over an 8-year period, starting from 4 years before to 3 years after the year of winning the first innovation award, the test sample's mean (median) change in return on assets is nearly 33% (24%) higher than that of a control sample. The evidence also suggests that effective innovation programs can increase firms' revenue, cost efficiency, and market valuation. Over the period, the control-adjusted mean (median) change in sales, cost per dollar of sales, and Tobin's Q are 39.28% (20.71%), −5.52% (−3.80%), and 23.70% (3.16%), respectively. Panel data regression analysis provides additional insights on the performance impact of effective innovation programs. The results show that award winners are not only financially more successful but also enjoy an indirect benefit through better R&D execution, which increases firm profitability in both the short term and long term.

Open innovation, fuelled by the rise of the Internet, has made it feasible and cheaper for firms to open themselves up to a wide range of external sources of innovative ideas. The explosive growth of open innovation intermediary networks, such as InnoCentive or Linked-in, enables the rapid pairing of firms seeking knowledge to address a wide range of business challenges (seekers) with other firms or individuals who already have relevant knowledge (solvers or knowledge brokers). These intermediary networks allow procurement departments to source codified and un-codified knowledge from firms or individuals outside their traditional supplier networks using one-off transactional relationships. Although sourcing ideas in this way theoretically poses problems for knowledge search and transfer, we have found that companies can draw on processes and integration mechanisms developed by procurement and design engineering to develop effective organizational learning routines. These routines are strategically vital to source new ideas through open innovation using intermediary networks and create competitive advantage.

The impact of information technology (IT) on the performance of distributed projects is not well understood. Although prior research has documented that dispersion among project teams has an adverse effect on project performance, the role of IT as an enabler of communication to bridge the spatial distance among team members in distributed networks has not been empirically studied. We focus on the role of IT as a moderator of the relationship between team dispersion and project performance using projects as the unit of analysis. We find that IT mitigates the negative effect of team dispersion on project performance, especially in high information volume projects. Our central contribution is the development of an empirically tested model to improve the understanding of the operational impact of IT as a vehicle to bridge spatial dispersion among distributed teams that are engaged in knowledge-intensive work.

We experimentally study the role of reputation in procurement using two common mechanisms: price-based and buyer-determined auctions. While buyers are bound to buy from the lowest bidder in price-based auctions, they can choose between bidders in buyer-determined auctions. Only the latter buyers can consider the reputation of bidders. We find that bidders supply higher quality in buyer-determined auctions leading to higher market efficiencies in these auctions. Accordingly, buyers prefer the buyer-determined auction over the price-based auction, while only half of the bidders do so. A more detailed analysis of buyers' and bidders' behavior and profits provides insights into their mechanism choice.

Failure modes and effects analysis (FMEA) is one of the most frequently used tools in process and product design: it is used in quality and reliability planning, and event and failure mode analysis. It has a long history of use and is a formally prescribed procedure by a number of prominent standards organizations. In addition, it's popular use has evolved as a less formal and widely interpreted tool in the area of Lean/Six Sigma (LSS) process improvement. This paper investigates one of the most important issues related to FMEA practice—the quality of individual vs. group performance in ranking failure modes. In particular, we compare FMEA rankings generated by: (i) individuals, (ii) group consensus, and (iii) non-collaborative aggregation of group input (a synthesized group ranking). We find that groups outperform individuals and that synthetic groups perform as well as group consensus. We explain the implications of this result on the coordination of the design of products and processes amongst distributed organizations. The increasing distribution of product design efforts, both in terms of geography and different organizations, presents an opportunity to improve coordination using distributed synthetic group-based FMEA.

In a controlled field experiment, we examine pairs of auctions for identical items under different conditions. We find that auction design features that are under the control of the auctioneer—including information transparency, number of simultaneous auctions, and the degree of overlap between simultaneous auctions—affect bidder search and choice. Clickstream data show that a significant relationship between information transparency and price dispersion can be linked to search. Specifically, the effect of information transparency on price dispersion is fully mediated by lookup behavior. Combining these findings, we make auction design recommendations regarding the provision of product and value information.

Explicit formal mechanisms dominate the discussion about incentives in Operations Management, yet many other mechanisms exist. Social comparison between peers may provide strong implicit incentives for individuals. Social comparison arises naturally in all social settings and may thus be unintended; however, many companies deliberately use it to motivate employees. In this study, we model a social context in which purchasers evaluate their performance relative to their peers; a feeling of inferiority results in a negative contribution to utility, whereas a feeling of superiority results in a positive contribution. We find that social comparison induces characteristic deviations from the newsvendor optimum ordering decision: if fear of inferiority outweighs anticipation of superiority, then purchasers herd together; the converse scenario incites actors to polarize away from each other. In both cases, actors will deviate from ordering the newsvendor optimum in order to satisfy social goals. Demand correlation and profit margins moderate the extent of the deviation.

Quality-related incidents involving contract manufacturers (CMs) are becoming increasingly prevalent. The quality management (QM) literature, however, has focused mostly on QM within a single firm. Thus, the need for data-driven research on managing quality with outsourced production is evident. We investigate the use and effectiveness of external failure penalties and audits of CMs’ facilities to manage inter-firm quality. Building on agency theory and extant QM literature, this study addresses two research questions: (i) whether the control mechanisms of quality audits and contractual external quality failure penalties are substitutes or complements in use and (ii) whether they are substitutes or complements in their effectiveness at aligning the quality interests of customers and their CMs. Our analysis uses dyadic data gathered from brand-owning firms and their CMs representing 95 contract manufacturing relationships in Food and Drug Administration (FDA)-regulated industries. The results indicate that more severe external failure penalties correspond to a lower use of facility audits (i.e., they are substitutes-in-use). We also find that both external failure penalties and facility audits have a unique positive effect on the CM's perception of relative quality importance. Finally, some evidence supports the hypothesis that each mechanism is more effective in the presence of the other (i.e., they are complements-in-effectiveness).

Many emerging entrepreneurial applications and services connect two or more groups of users over Internet-based information technologies. Commercial success of such technology products requires astute business practices related to product line design, price discrimination, and launch timing. We examine these issues for a platform firm that serves two markets—labeled as user and developer markets—such that the size of each market positively impacts participation in the other. In addition, our model allows for sequential unfolding of consumer and developer participation, and for uncertainty regarding developer participation. We demonstrate that product versioning is an especially attractive strategy for platform firms, that is, the trade-off between market size and margins is tilted in the direction of more versions. However, when expanding the product line carries substantial fixed costs (e.g., marketing cost, cost of additional plant, increased distribution cost), then the uncertainty in developer participation adversely impacts the firm's ability to offer multiple versions. We show that for established firms with lower uncertainty about developer participation, the choice is essentially between an expanded or minimal product line. Startups and firms that are entering a new product category are more likely to benefit from a “wait and see” deferred expansion strategy.

Most models of product reuse do not consider the fact that firms might be required to innovate their products over time in order to continue to appeal to the tastes of customers. We consider how the rate of this required innovation, which might be fast or slow depending on the product, affects reuse decisions. We consider two types of reuse—remanufacturing to original specifications, and upgrading used items by replacing components that have experienced innovation since the item was originally produced. We find that optimal reuse decreases with the rate of innovation, implying that models that ignore innovation overestimate the optimal amount of reuse that a company should pursue. Furthermore, we show that reuse can be encouraged in two ways—the intuitive approach of increasing end-of-life costs, and the less intuitive approach of raising the cost to make items reusable. We also examine the environmental impact of reuse, measured in terms of virgin material usage, finding that reuse can actually increase total virgin material usage in some cases. In an extension, we show how the results and insights change when the rate of innovation is uncertain.

We synthesize research from operations management, entrepreneurship, organizational science, and strategy to investigate the performance-enhancing benefits of knowledge management activities throughout the entrepreneurial process of a high-tech venture from idea conception to commercialization. We adopt a dynamic learning perspective of entrepreneurship to understand how knowledge management activities change throughout four phases of the venture's life cycle. We introduce a framework that identifies a set of knowledge-based capabilities that enhance the entrepreneurial venture's success. In the context of the first phase, we discuss knowledge as a key driver of entrepreneurial alertness and creativity, both of which impact the quality and quantity of opportunities and innovations discovered. Second, we describe how knowledge enables the entrepreneur to make decisions under uncertainty such as determining which opportunity to pursue. For Phase 3 of the life cycle, we explore the challenges of managing knowledge during the development of the product or technology including the trade-off between exploration and exploitation. In the final phase, we explore how knowledge impacts the market entry decision, survival, and the value captured at commercialization. We conclude the article with suggestions for future research.

Technology entrepreneurship is an important driver of economic growth, although entrepreneurs must maintain cooperative ties with the owners of any technology they hope to bring to market. Existing studies show that fairness perceptions have a great influence on this cooperation, but no research investigates its precise mechanisms or dynamic patterns. This study explores the development of 17 ventures that cooperated with a university-owner of technology and thereby identifies different cooperation patterns in which fairness perceptions influence the degree of cooperation. These perceptions also change over time, partly as a function of accumulated experience and learning. A system dynamics model integrates insights from existing literature with the empirical findings to reveal which cooperation mechanisms relate to venture development over time; the combinations of individual experience, fairness perceptions, and market circumstances lead to four different patterns. This model can explain changes in entrepreneurial cooperation as a result of changes in fairness perceptions, which depend on learning effects and entrepreneurial experience. Each identified cooperation pattern has implications for research and offers insights for practitioners who need to manage relationships in practice.

Inter-customer interactions are important to the operation of self-services in retail settings. More specifically, when self-service terminals are used as part of customers’ checkout processes in retail operations without the explicit involvement of retailers as the direct service providers, inter-customer interactions become a significant managerial issue. In this article, we examine the impact of inter-customer interactions at retail self-service terminals on customers’ service quality perceptions and repeat purchase intentions at retail stores. We conduct a scenario-based experimental design (N = 674) using a 2 × 2 factorial design in which inter-customer interactions are divided into “positive” vs. “negative” and occur during the “waiting” or during the actual “transaction” stages of self-services at a retail store. We use attribution theory to develop the hypotheses. The results demonstrate that, through their interactions, fellow customers can exert influences on a focal customer's quality perceptions and repeat purchasing intentions toward a retail store. Furthermore, these influences were impacted by how customers attribute blame or assign responsibility toward the retail store. Service operations managers should leverage these interactions by designing into self-service settings the capacities and interfaces that are best suited for customers’ co-production of their self-service experiences.

Early writings in economics describe the entrepreneur's role in terms of bearing the uncertainty inherent in new undertakings. Much of the research published in the pages of Production and Operations Management deals with management under uncertainty. The shared concerns over the impacts of multiple types of uncertainty suggest that research on Operations Management (OM) can play a role in the development of theory in entrepreneurship. We discuss aspects of such a role from two perspectives. First, we consider several topics in the OM literature that have clear applications or parallels in entrepreneurship. These topics include innovation, the management of technology, new product development, flexibility, and hedging strategies. Understanding these topical connections should aid in the development of tools and applications central to the practice of entrepreneurship. On another level, when we consider how the approaches to many of these topics in OM are grounded in theory adapted from Operations Research and Economics we argue that these same roots can be used as starting points for the development of theory in entrepreneurship. As examples, we will argue that the theoretical bases supporting robust optimization, stochastic dynamic programming, and even Total Quality Management can also serve as foundations of theories about the roles, practice, and behaviors of entrepreneurs.

Inspired by recent empirical work on inventory record inaccuracy, we consider a periodic review inventory system with imperfect inventory records and unobserved lost sales. Record inaccuracies are assumed to arrive via an error process that perturbs physical inventory but is unobserved by the inventory manager. The inventory manager maintains a probability distribution around the physical inventory level that he updates based on sales observations using Bayes Theorem. The focus of this study is on understanding, approximating, and evaluating optimal forward-looking replenishment in this environment. By analyzing one- and two-period versions of the problem, we demonstrate several mechanisms by which the error process and associated record inaccuracy can impact optimal replenishment. Record inaccuracy generally brings an incentive for a myopic manager to increase stock to buffer the added uncertainty. On the other hand, a forward-looking manager will stock less than a myopic manager, in part to improve information content for future decisions. Using an approximate partially observed dynamic programming policy and associated bound, we numerically corroborate our analytical findings and measure the effectiveness of an intelligent myopic heuristic. We find that the myopic heuristic is likely sufficiently good in practical settings targeting high service levels.

We study the impact of product recovery on a firm's product quality choice, where quality is defined as an observable performance measure that increases a consumer's valuation for the product. We consider three general forms of product recovery: (i) when product recovery reuses (after reprocessing) quality inducing components or material (e.g., remanufacturing), (ii) when product recovery does not reuse quality inducing components or material but it is overall profitable (e.g., cell phone recycling), and (iii) when product recovery is costly (but mandated by legislation, e.g., recycling of small appliances in the European Union). Using a stylized economic model, we show that the form of product recovery, recovery cost structure, and the presence of product take-back legislation play an important role in quality choice. Generally speaking, product recovery increases the firm's quality choice, except for some instances of recovery form (ii). In addition, we find that product take-back legislation can lead to higher quality choice as opposed to voluntary take-back. We further demonstrate that both the firm and the consumers benefit from recovery form (ii), while both are worse off with recovery form (iii). However, environmental implications of the three recovery modes differ from their impact on consumer surplus and firm profit. While recovery forms (i) and (iii) reduce consumption and increase environmental benefits, the same is not true with recovery form (ii), which can increase consumption, potentially resulting in higher environmental impact.

In this article, we introduce the notion of operational entrepreneurship—the selection and management of transformation processes for recognizing, evaluating, and exploiting opportunities for potential value creation—to offer examples of research opportunities at the interface of entrepreneurship and operations management. Specifically, we believe that operations management has been under-utilized for gaining a deeper understanding of (i) the knowledge and motivation required for opportunity recognition, (ii) evaluations of a recognized opportunity to determine if it represents an opportunity for the specific entrepreneur, and (iii) the role that feedback from an exploitation of a current opportunity plays in the recognition and evaluation of subsequent opportunities. We also introduce (but not develop) the notion of entrepreneurial operations.

This study develops a comprehensive framework to optimize new product introduction timing and subsequent production decisions faced by a component supplier. Prior to market entry, the supplier performs process design activities, which improve manufacturing yield and the chances of getting qualified for the customer's product. However, a long delay in market entry allows competitors to enter the market and pass the customer's qualification process before the supplier, reducing the supplier's share of the customer's business. After entering the market and if qualified, the supplier also needs to decide how much to produce for a finite planning horizon by considering several factors such as manufacturing yield and stochastic demand, both of which depend on the earlier time-to-market decision. To capture this dependency, we develop a sequential, nested, two-stage decision framework to optimize the time-to-market and production decisions in relation to each other. We show that the supplier's optimal market entry and qualification timing decision need to be revised in real time based on the number of qualified competitors at the time of market-entry decision. We establish the optimality of a threshold policy. Following this policy, at the beginning of each decision epoch, the supplier should optimally stop preparing for qualification and decide whether to enter the market if her order among qualified competitors exceeds a predetermined threshold. We also prove that the supplier's optimal production policy is a state-dependent, base-stock policy, which depends on the time-to-market and qualification decisions. The proposed framework also enables a firm to quantify how market conditions (such as price and competitor entry behavior) and operating conditions (such as the rate of learning and inventory/production-related costs) affect time-to-market strategy and post-entry production decisions.

Click tracking is gaining in popularity, and the practice of web analytics is growing fast. Whether strategic customers are willing to visit a website when they know their clicks may be tracked is an important yet complex problem, which depends on various factors. Using a newsvendor framework, we examine this problem by focusing on the operational factor: how product availability induces strategic customers to voluntarily provide advance demand information. We find that a strong Nash equilibrium exists where every customer is willing to click, and customer incentives to click are robust to noise. Hence, we demonstrate the promise of strategic customer behavior in the context of click tracking, contrary to the conventional wisdom that it is typically a peril for the firm. Notably, click tracking is typically advantageous to both the firm and its customers, compared with other strategies such as advance selling, quantity commitment, availability guarantees, and quick response. Lastly, we extend to two settings by including marketing decisions, price-sensitive demand and markdown pricing, and discuss how operations and marketing decisions interact in influencing the value of click tracking.

This study addresses the challenges of finding and implementing profitable energy efficiency (EE) projects, a critical foundation for sustainable operations. We focus on manufacturing enterprises, but many of our findings apply also to the back office of service operations. Our starting point is that, in nearly every industrial enterprise, there are many profitable EE projects that could be implemented but are not. An oft-cited hindrance to implementation is the lack of an internal management framework in which to find, value, and execute these projects. Using a conceptual approach, we rely on proven sustainable operations tools to develop such a framework. We identify three major value drivers of EE projects: savings intensity, “green” image, and project complexity. We then describe a framework for understanding the context of EE projects in industry, with an underlying analytic foundation in optimal portfolio analysis. A case study of a large manufacturing site is used to illustrate emerging best practices—based on Kaizen management principles—for integrating EE project management with operations, engineering, and strategy.

This study examines the effect that verbal scripts have on customer perceived service quality for two distinct service process types. We designed a video experiment that varied the level of verbal scripting for standardized and customized service encounters. We found that in standardized service encounters, an increase in the level of verbal scripting had no effect on perceived service quality. However, for customized encounters, perceived service quality was impacted. More specifically, a predominantly scripted encounter for customized service processes, on average, resulted in the lowest perception of service quality by respondents. Since verbal scripting was shown to impact customer perceptions of service quality, we suggest that a service provider's decision regarding the degree of verbal scripting is an important service design consideration.

Using predictive global sensitivity analysis, we develop a structural equations model to abstract from the details of a large-scale mixed integer program (MIP) to capture essential design trade-offs of global manufacturing and distribution networks. We provide a conceptual framework that describes a firm's network structure along three dimensions: market focus, plant focus, and network dispersion. Normalized dependent variables are specified that act as proxies for a company's placement into our conceptual network classification via the calculation of just a few key independent variables. We provide robust equation sets for eight cost structure clusters. Many different product types could be classified into one of these groups, which would allow managers to use the equations directly without needing to run the MIP for themselves. Our numerical tests suggest that the formulas representing the network structure drivers—economies of scale, complexity costs, transportation costs, and tariffs—may be sufficient for managers to design their strategic network structures, and perhaps more importantly, to monitor them over time to detect potential need for adjustment.

Self-storage is a booming industry. Both private customers and companies can rent temporary space from such facilities. The design of self-storage warehouses differs from other facility designs in its focus on revenue maximization. A major question is how to design self-storage facilities to fit market segments and accommodate volatile demand to maximize revenue. Customers that cannot be accommodated with a space size of their choice can be either rejected or upscaled to a larger space. Based on data of 54 warehouses in America, Europe, and Asia, we propose a new facility design approach with models for three different cases: an overflow customer rejection model and two models with customer upscale possibilities, one with reservation and another without reservation. We solve the models for several real warehouse cases, and our results show that the existing self-storage warehouses can be redesigned to generate larger revenues for all cases. Finally, we show that the upscaling policy without reservation generally outperforms the upscaling policy with reservation.

This article examines the pricing policy of a monopolist seller who may sell in advance of consumption in a market that comprises of myopic consumers, forward-looking consumers, and speculators. The latter group has no consumption value for the goods and is in the market with the sole objective of making a profit by reselling the purchased goods shortly after. Consumers, although homogeneous in terms of their valuations, are different with respect to their perspectives. We show that in an “upward” market where the expected valuation increases over time, the optimal pricing policy is an ex ante “static” one where the seller “prices into the future” and prices the myopic consumers out of the advance market. However, in a “downward” market where the expected valuation decreases over time, the seller adopts a dynamic pricing strategy except for the case when higher initial sales can trigger more demand subsequently and when the downward trend is not too high. In this case, the seller prefers an ex ante “static” pricing strategy and deliberately prices lower initially to sell to speculators. We identify the conditions under which the seller benefits from the existence of speculators in the market. Moreover, although the presence of entry costs is ineffective as an entry deterrence, we determine the conditions under which exit costs can rein in speculative purchase.

Modular design allows several generations of products to co-exist in the installed base as product designs change to take advantage of improved performance via modular upgrades. Use of a common base platform and modular design approach allows a firm to offer updates for improved performance and flexibility via remanufacturing when products have multiple lifecycles. However, as the product evolves through multiple lifecycles, the large pool of product variants leads to the curse of product proliferation. In practice, product proliferation causes high levels of line congestion and results in longer lead times, higher inventory levels, and lower levels of customer service. To offer insights into the product proliferation problem, the authors employ a delayed differentiation model in a multiple lifecycle context. The delayed differentiation model gives flexibility to balance trade-offs between disassembly and reassembly costs by adaptively changing the push-pull boundary. An adaptive, evolving push-pull boundary provides flexibility for a remanufacturing firm to meet changing customer demands. The delayed differentiation model includes both a mixed-integer linear program and an analytical investigation of the evolutionary nature of the push-pull boundary. Both field observations and experimental results show that the nature of product proliferation and changing demand structures play significant roles in the cost and flexibility of the evolving delayed differentiation system.

We study a multi-product firm with limited capacity where the products are vertically (quality) differentiated and the customer base is heterogeneous in their valuation of quality. While the demand structure creates opportunities through proliferation, the firm should avoid cannibalization between its own products. Moreover, the oligopolistic market structure puts competitive pressure and limits the firm's market share. On the other hand, the firm has limited resources that cause a supply-side fight for adequate and profitable production. We explicitly characterize the conditions where each force dominates. Our focus is on understanding how capacity constraints and competition affect a firm's product-mix decisions. We find that considering capacity constraints could significantly change traditional insights (that ignore capacity) related to product-line design and the role of competition therein. In particular, we show that when the resources are limited, the firm should offer only the product that has the highest margin per unit capacity. We find that this product could be the diametrically opposite product suggested by the existing literature. In addition, we show that for intermediate capacity levels, whereas the margin per unit capacity effect dominates in a less competitive market, proliferation and cannibalization effects dominate in a more competitive market.

In this note, we study the price-setting newsvendor problem. We use three conditions, the log-convexity of the coefficient of variation, the log-concavity of the deterministic profit function, and the log-convexity of the random noise's expectation conditional on having leftover inventory to establish the log-concavity of the retailer's expected profit function. This new result is complementary to existing results and removes some assumptions in the pricing and inventory coordination literature. It also addresses the conjecture made by Petruzzi and Dada (1999), and can be applied in the pricing game.

This article examines the choice of pricing policy (posted pricing or negotiation) toward end customers in a supply chain. Many retailers actively decide whether or not to encourage negotiation on the shop floor. Of course, the retailer's pricing policy influences not only the retailer's profit, but also the profits of the manufacturers who sell through the retailer. However, little is known about the forces that shape the pricing policy when two self-interested parties interact in a supply chain. We consider two alternative models depending on who has the power to decide the pricing policy: the manufacturer or the retailer. We find that an increase in the wholesale price weakens the retailer's ability to price discriminate through negotiation. Therefore, the retailer prefers negotiation at lower wholesale prices and posted pricing at higher wholesale prices. We also find that whenever the retailer prefers negotiation, the manufacturer does too. Therefore, the retailer's discretion over the pricing policy causes friction only when the retailer wants to use posted pricing, while the manufacturer wishes the retailer to use negotiation. We show that such friction arises only when product availability or the cost of negotiation is moderate. In this case, we show that the manufacturer may offer a substantial discount to persuade the retailer to negotiate. Surprisingly, in this region of friction, a decrease in the supply chain's capacity or an increase in negotiation costs (both of which are typically considered as worsening the retailer's business environment) translates into higher profit for the retailer.

One of the important objectives of supply chain S&OP (Sales and Operations Planning) is the profitable alignment of customer demand with supply chain capabilities through the coordinated planning of sales, production, distribution, and procurement. In the make-to-order manufacturing context considered in this paper, sales plans cover both contract and spot sales, and procurement plans require the selection of supplier contracts. S&OP decisions also involve the allocation of capacity to support sales plans. This article studies the coordinated contract selection and capacity allocation problem, in a three-tier manufacturing supply chain, with the objective to maximize the manufacturer's profitability. Using a modeling approach based on stochastic programming with recourse, we show how these S&OP decisions can be made taking into account economic, market, supply, and system uncertainties. The research is based on a real business case in the Oriented Strand Board (OSB) industry. The computational results show that the proposed approach provides realistic and robust solutions. For the case considered, the planning method elaborated yields significant performance improvements over the solutions obtained from the mixed integer programming model previously suggested for S&OP.

We analyze contracting behaviors in a two-tier supply chain system consisting of competing manufacturers and competing retailers. We contrast the contracting outcome of a Stackelberg game, in which the manufacturers offer take-it-or-leave-it contracts to the retailers, with that of a bargaining game, in which the firms bilaterally negotiate contract terms via a process of alternating offers. The manufacturers in the Stackelberg game possess a Stackelberg-leader advantage in that the retailers are not entitled to make counteroffers. Our analysis suggests that whether this advantage would benefit the manufacturers depends on the contractual form. With simple contracts such as wholesale-price contracts, which generally do not allow one party to fully extract the trade surplus, the Stackelberg game replicates the boundary case of the bargaining game with the manufacturers possessing all the bargaining power. In contrast, with sophisticated contracts such as two-part tariffs, which enable full surplus extraction, the two games lead to distinct outcomes. We further show that the game structure being Stackelberg or bargaining critically affects firms' preferences over contract types and thus their equilibrium contract choices. These observations suggest that the Stackelberg game may not be a sufficient device to predict contracting behaviors in reality where bargaining is commonly observed.

We consider a setting of two firms and one capacity agent. Each firm serves a primary market, and the capacity agent sustains a common market to draw demand for capacity from the external firms. The firms can partner with the capacity agent under her contract to serve the common market. When they use the common market mainly as an outlet for their unused capacities, the capacity agent will only specify a variable fee for each capacity unit deployed through her, and prefer to partner with one firm in most circumstances. When the firms adjust capacities to accommodate the businesses created by serving the common market, the capacity agent will specify a lump-sum payment and a variable fee, and will be more likely to incentivize only one firm to partner with her, when the common market is sufficiently large or the demands in the common and primary markets are strongly correlated. She will always use a fixed fee to extract, while not necessarily all, the profit gains to the firms serving the common market, but will use a variable fee only when partnering with both firms. The key results are robust with respect to market configuration and contract type.

Traditional approaches in inventory control first estimate the demand distribution among a predefined family of distributions based on data fitting of historical demand observations, and then optimize the inventory control using the estimated distributions. These approaches often lead to fragile solutions whenever the preselected family of distributions was inadequate. In this article, we propose a minimax robust model that integrates data fitting and inventory optimization for the single-item multi-period periodic review stochastic lot-sizing problem. In contrast with the standard assumption of given distributions, we assume that histograms are part of the input. The robust model generalizes the Bayesian model, and it can be interpreted as minimizing history-dependent risk measures. We prove that the optimal inventory control policies of the robust model share the same structure as the traditional stochastic dynamic programming counterpart. In particular, we analyze the robust model based on the chi-square goodness-of-fit test. If demand samples are obtained from a known distribution, the robust model converges to the stochastic model with true distribution under generous conditions. Its effectiveness is also validated by numerical experiments.

As the global competitive landscape intensifies, firms have looked to their supply chain organizations to improve cost, visibility, and cycle time performance across functions, products, and markets. As a result, the scope of supply chain related operations have increasingly cut across organizational boundaries. To understand and capture such cross-organizational activities, researchers have broadened the focus of their studies and included multiple stakeholders in their analysis (e.g., integration, sustainability, and buyer-supplier relationships). However, multi-stakeholder research has also increased the complexity and effort required to conduct studies across organizational boundaries. Unfortunately, many studies that use multi-stakeholder constructs fail to fully address their multi-sided nature during both construct conceptualization and data collection. Several studies suggest that neglecting the multi-sided nature of certain constructs can affect the research validity and reliability and may invalidate research inferences and results, although such concerns have not been empirically demonstrated. The current study addresses this gap by performing a series of tests using data from 105 matched pairs of buyers and their suppliers to illustrate key methodological considerations for conducting multi-stakeholder research. This study also offers practical guidance regarding assumptions routinely made in single rater research and proposes when single rater data may be appropriate for multi-stakeholder research.

A performance metric and goal-setting procedure is defined for an order fulfillment operation. In this operation, order requests arrive continuously, and filled orders are shipped at a specific time each day. The metric links the continuous operation of order fulfillment to the scheduled shipment times. To prescribe goals against the metric, a performance model is developed that incorporates the motivational effect of a goal. Goal-Setting Theory is used to establish the performance goal and to show how to match arriving orders to deadlines based on their arrival times and expected processing times. Monte Carlo simulation on data from a large distribution center is used to demonstrate that setting these two parameters in the light of motivational research yields quite different results than doing so with an intuitive method. Moreover, a motivational goal leads to better operational performance; that is, correctly setting up the metric causes more customers to receive their orders sooner.

Urban freight contributes significantly to pollution, noise disturbance, traffic congestion, and safety problems in city centers. In many cities, local governments have introduced policy measures, in particular time-access restrictions, to alleviate these problems. However, setting time windows is very challenging due to the conflicting interests and objectives of the stakeholders involved. In this article, we examine whether it is possible to develop time-window policies that enhance environmental sustainability and distribution efficiencies, while meeting the objectives of the municipalities. We develop a framework for balancing retailer (costs), municipality (satisfaction), and environmental (emissions) objectives, using data envelopment analysis, under different urban time-window policies. The approach is illustrated by a case study of three Dutch retail organizations, with a large number of stores affected by such time windows. On the basis of an evaluation of 99 different time-window policies, our results show that harmonizing time windows between neighboring cities leads to the best overall performance. The currently used time-window policy appears to perform reasonably well, but can be improved on all dimensions. However, harmonizing time-window policies may be difficult to realize in practice.