Knowledge that Transforms

Increasingly many firms have started to implement programs intended to improve the workers' health and the psychosocial work‐environment, as well as other attributes of labor quality. Motivated by the need for evaluating to what extent the programs affect a firm's productivity performance, this study discusses a model for analyzing the contribution of labor quality attributes toward firm productivity. To assess the contribution from the labor quality attributes, we model firm productivity as the outcome of two separate processes within a firm: the physical production process and the labor quality process. Firm productivity is measured by a Malmquist‐like productivity index and is computed by Data Envelopment Analysis. Based on bootstrap methods we analyze potential statistical bias and provide bias‐corrected productivity estimates. The labor quality attributes are first modeled at an individual worker level as latent variables using Item Response Theory, and then aggregated to a firm‐level. The model is empirically validated using data from three manufacturing plants that participated in a coordinated worksite health promotion program. Over a 4‐year period (2000–2003), we observed a general improvement in efficiency of 2–5%, half of which could be attributed to an improvement in workers' health and psychosocial work‐environment. A key benefit with the model is that it is practical, easy to implement, and very fast to compute. The model also constructively contributes to the discourse on sustainability by providing a framework for deriving meaningful metrics and providing tangible measurements on the effect of sustainability‐related issues.

This study examines how time spent in problem definition affects problem solving in projects such as Six Sigma projects. Our hypotheses are tested using data collected from 1558 Six Sigma projects in a company. The results show evidence of a U‐shaped relationship between the amount of time spent in the Define phase and project duration. This finding suggests that spending too little time on problem definition potentially causes poor problem formulation, which leads to deficient problem solving and lengthens overall project time. On the other hand, too much time spent on problem definition can lead to unneeded delays in project completion due to diminishing returns on problem definition efforts. Furthermore, the optimal balance between spending too little and too much time depends on prior project experience and project complexity. Prior project experience reduced project completion time and weakened the U‐shaped effect. Conversely, complex projects took longer and appeared to show some evidence of a stronger U‐shaped effect; this suggests balancing the time spent in the Define phase was more challenging for complex projects. Our study also underscores the importance of managing project duration, as projects that were completed faster tended to be associated with higher project savings.

This study considers a supply chain with two heterogeneous suppliers and a common retailer whose type is either low‐volume or high‐volume. The retailer's type is unknown to the suppliers. The flexible supplier has a high variable cost and a low fixed cost, while the efficient supplier has a low variable cost and a high fixed cost. Each supplier offers the retailer a menu of contracts. The retailer chooses the contract that maximizes its expected profit. For this setting, we characterize the equilibrium contract menus offered by the suppliers to the retailer. We find that the equilibrium contract menus depend on which supplier–retailer match can generate the highest supply chain profit and on how much information rent the supplier may need to pay. An important feature of the equilibrium contract menus is that the contract assigned to the more profitable retailer will coordinate the supply chain, while the contract assigned to the less profitable retailer may not. In addition, in some circumstances, the flexible supplier may choose not to serve the high‐volume retailer, in order to avoid excessive information rent.

Forecast sharing among trading partners lies at the heart of many collaborative and contractual supply chain management efforts. Even though it has been praised in both academic and practitioner circles for its critical role in increasing demand visibility, some concerns remain: The first one is related to the credibility of forecast sharing, and the second is the fear that it may turn into a competitive disadvantage and induce suppliers to increase their price offerings. In this study, we explore the validity of these concerns under a supply chain with a competitive upstream structure, focusing specifically on (i) when and how a credible forecast sharing can be sustainable, and (ii) how it impacts on the intensity of price competition. To address these issues, we develop a supply chain model with a buyer facing a demand risk and two heterogeneous suppliers competing for order allocation from the buyer. The extent of demand is known only to the buyer. The buyer submits a buying request to the suppliers via a commonly used procurement mechanism called request for quotation (RFQ). We consider two variants of RFQ. In the first type, the buyer simply shares the estimated order quantity with no further specifications. In the second one, in addition to this, the buyer also specifies minimum and/or maximum order quantities. We fully characterize equilibrium decisions and profits associated with them under symmetric and asymmetric information scenarios. Our main findings are that the buyer can use a RFQ with quantity restrictions as a credible signal for forecast sharing as long as the degree of demand information asymmetry is not too high, and that, contrary to above concerns, the equilibrium prices that emerge between competing suppliers under asymmetric information may indeed increase if the buyer can not share forecast information credibly with its upstream partners.

Should capacitated firms set prices responsively to uncertain market conditions in a competitive environment? We study a duopoly selling differentiated substitutable products with fixed capacities under demand uncertainty, where firms can either commit to a fixed price ex ante , or elect to price contingently ex post , e.g., to charge high prices in booming markets, and low prices in slack markets. Interestingly, we analytically show that even for completely symmetric model primitives, asymmetric equilibria of strategic pricing decisions may arise, in which one firm commits statically and the other firm prices contingently; in this case, there also exists a unique mixed strategy equilibrium. Such equilibrium behavior tends to emerge, when capacity is ampler, and products are less differentiated or demand uncertainty is lower. With asymmetric fixed capacities, if demand uncertainty is low, a unique asymmetric equilibrium emerges, in which the firm with more capacity chooses committed pricing and the firm with less capacity chooses contingent pricing. We identify two countervailing profit effects of contingent pricing under competition: gains from responsively charging high price under high demand, and losses from intensified price competition under low demand. It is the latter detrimental effect that may prevent both firms from choosing a contingent pricing strategy in equilibrium. We show that the insights remain valid when capacity decisions are endogenized. We caution that responsive price changes under aggressive competition of less differentiated products can result in profit‐killing discounting.

Lateral transshipments are a method of responding to shortages of stock in a network of inventory‐holding locations. Conventional reactive approaches only seek to meet immediate shortages. The study proposes hybrid transshipments which exploit economies of scale by moving additional stock between locations to prevent future shortages in addition to meeting immediate ones. The setting considered is motivated by retailers who operate networks of outlets supplying car parts via a system of periodic replenishment. It is novel in allowing non‐stationary stochastic demand and general patterns of dependence between multiple item types. The generality of our work makes it widely applicable. We develop an easy‐to‐compute quasi‐myopic heuristic for determining how hybrid transshipments should be made. We obtain simple characterizations of the heuristic and demonstrate its strong cost performance in both small and large networks in an extensive numerical study.

In this study, I investigate supply chain contracts in a setting where a supplier uses its inventory to directly satisfy a retailer's demand. These “pull” contracts have increased in popularity in practice but have not been studied experimentally. In a controlled laboratory setting, I evaluate a wholesale price contract and two coordinating contracts. The data suggest that the benefit of the two coordinating contracts over the wholesale price contract is less than the standard theory predicts, and that retailers, in the two coordinating contracts, exhibit a systematic bias of setting the coordinating parameter too low, and the wholesale price too high, relative to the normative benchmarks. In an effort to explain this deviation, I explore three behavioral models and find that loss aversion and reference dependence fit the data well. I empirically test this result in a follow‐up experiment, which directly controls for loss aversion and reference dependence, and observe that retailers make significantly better decisions. Lastly, I administer a number of experiments which reduce the complexity of the problem, curtail the amount of risk, and increase the level of decision support, and find that none improve decisions relative to the treatment that controls for loss aversion and reference dependence.

Coordinating knowledge transfer within multi‐plant manufacturing networks is a challenging task. Using a computational model, we examine when it is beneficial to create production knowledge within a central unit, the “lead factory,” and transfer it to geographically dispersed plants. We demonstrate that the knowledge transfer generates a trade‐off between a positive cost‐saving effect due to fewer adaptations in each plant, and a negative transfer cost effect due to the costly knowledge transfer itself. The complexity of the production process moderates the performance implications of the knowledge transfer because it determines the relative strength of these two effects. For production processes with low complexity, knowledge transfer can engender superior network performance. Here, an optimal extent of knowledge transfer exists, and thus, a complete knowledge transfer is not performance maximizing. For production processes with medium and high levels of complexity, performance is reduced rather than enhanced through knowledge transfer so that it is optimal not to transfer any knowledge from the lead factory to the plants. While we analyze knowledge transfer within a manufacturing network, our results are transferable to other settings that consist of a knowledge sending and receiving unit.

High‐tech organizations often struggle to manage different types of R&D projects. Evidence from research and practice suggests that managers frequently categorize and manage projects based on the extent of change triggered in product, process, technology, and market dimensions. However, this can create challenges in high‐tech organizations. This study investigates how high‐tech organizations manage R&D projects based on their learning goals. First, we argue for the benefits of categorizing R&D projects based on the degree of exploration and exploitation learning goals. A qualitative case study from four high‐tech business units involving 10 R&D projects helps understand the different types of projects based on their learning goals. The case study shows that R&D projects in high‐tech organizations typically fall into three categories based on their learning goals: Radical innovation projects , Incremental innovation projects , and Hybrid projects . Second, we iterate between literature and evidence from our qualitative data to theorize how project context and organizational context affect project performance depending on the type of project. The data for the empirical analysis come from a multilevel survey of 110 R&D projects across 34 high‐tech business units. Results show the importance of designing project and organizational context differently for the three types of R&D projects. Collectively, this study offers a new perspective on how to manage high‐tech R&D projects.

We consider a situation in which shippers (customers) can purchase ocean freight services either directly from a carrier (service provider)in advance or from the spot market just before the departure of an ocean liner. The price is known in the former case, while the spot price is uncertain ex‐ante in the latter case. Consequently, some shippers are reluctant to book directly from the carrier in advance unless the carrier is willing to “partially match” the realized spot price when it is lower than the regular price. This study is an initial attempt to examine if the carrier should bear some of the “price risk” by offering a “fractional” price matching contract that can be described as follows. The shipper pays the regular freight price in advance; however, the shipper will get a refund if the realized spot price is below the regular price, where the refund is a “fraction” of the difference between the regular price and the realized spot price. By modeling the dynamics between the carrier and the shippers as a sequential game, we show that the carrier can use the fractional price matching contract to generate a higher demand from the shippers compared to no price matching contract by increasing the “fraction” in equilibrium. However, as the carrier increases the “fraction,” the carrier should increase the regular price to compensate for bearing additional risk. By selecting the fractional price matching contract optimally, we show that the carrier can afford to offer this price matching mechanism without incurring revenue loss: the optimal fractional price matching contract is “revenue neutral.”