Knowledge that Transforms

We provide empirical evidence that the volatility of inventory productivity relative to the volatility of demand is a predictor of future stock returns in a sample of publicly listed U.S. retailers over the period 1985–2013. This key performance indicator, entitled demand–supply mismatch (DSM), captures the fact that low variation in inventory productivity relative to variation in demand is indicative of the superior synchronization of demand‐ and supply‐side operations. Applying the Fama and French (1993) three‐factor model augmented with a momentum factor (Carhart 1997), we find that zero‐cost portfolios formed by buying the two lowest and selling the two highest quintiles of DSM stocks yield abnormal stock returns of up to 1.13%. These strong market anomalies related to DSM are observed over the entire sample period and persist after controlling for alternative inventory productivity measures and firm characteristics that are known to predict future stock returns. Further, we reveal that DSM is indicative of lower future earnings and lower sales growth and provide evidence that the observed market inefficiency results from investors’ failure to incorporate all of the information that inventory contains into the pricing of stocks.

Prototyping allows firms to evaluate the technical feasibility of alternative product designs and to better estimate their costs. We study a collaborative prototyping scenario in which a manufacturer involves a supplier in the prototyping process by letting the supplier make detailed design choices for critical components and provide prototypes for testing. While the supplier can obtain private information about the costs, the manufacturer uses target costing to gain control over the design choice. We show that involving the supplier in the prototyping process has an important influence on the manufacturer's optimal decisions. The collaboration results in information asymmetry, which makes parallel prototyping less attractive and potentially reverses the optimal testing sequence under sequential prototyping: It may be optimal to test designs in increasing order of attractiveness to avoid that the supplier does not release technically and economically feasible prototypes for strategic reasons. We also find that the classical target costing approaches (cost‐ and market‐based) need to be adjusted in the presence of alternative designs: Due to the strategic behavior of suppliers, it is not always optimal to provide identical target costs for designs with similar cost and performance estimates, nor to provide different target costs for dissimilar designs. Furthermore, the timing is important: While committing upfront to carefully chosen target costs reduces the supplier's strategic behavior, in some circumstances, the manufacturer can take advantage of this behavior by remaining flexible and specifying the second prototype's target costs later.

Capitalizing on the operational concept of division‐of‐labor, clinics often reduce physician service time by off‐loading some of his/her clinical activities to lower‐cost personnel. These personnel, such as nurse practitioners and physician assistants, are often collectively referred to as “mid‐level providers” (MLPs) and can perform many patient‐consultation tasks. The common rationale is that using an MLP allows the physician to serve more patients, increase patients’ access to care, and, due to MLPs’ lower salaries, improve the clinic's financial performance. An MLP is typically integrated into the outpatient clinic process in one of two modes: as an “ice‐breaker,” seeing each patient before the physician, or as a “standalone” provider, a substitute for the physician for the entirety of some patients’ visits. Despite both of these modes being widely used in practice, we find no research that identifies the circumstances under which either one is preferable. This study examines these two modes’ effects on operational performance, such as patient flow and throughput, as well as on financial measures. Using queueing and bottleneck analysis, discrete‐event simulation, and profit modeling, we compare these two deployment modes and identify the optimal policies for deploying MLPs as either ice‐breakers or as standalone providers. Interestingly, we also find there exists a range of scenarios where not hiring an MLP at all (i.e., the physician works alone) is likely to be most profitable for the clinic. Implications for practice are discussed.

We use a behavioral experiment to examine whether the use of templates facilitates or hinders a faster production ramp‐up after a process move. If the environment in which a template is applied closely resembles the environment in which the template was developed, template use promotes knowledge transfer, resulting in increased performance while ramping up the process. However, when the environments at the origin and destination of the template differ, these benefits do not occur. We further show that making the template too rigid, i.e., not allowing any changes to the template after a production start, decreases its performance benefits. These findings help characterize when and how the encoding of existing knowledge through a template is a worthwhile investment of time and resources to facilitate a production process move.

In this paper, we study quality‐of‐service (QoS) based pricing schemes that serve as incentive mechanisms to induce sharing behaviors in Peer‐to‐Peer (P2P) networks. We incorporate operational QoS metrics into users’ utility functions and demonstrate how they affect individual users’ content sharing decisions. Using a game‐theoretic model, our study reveals how organizations respond to the changes of operational QoS metrics in their design of pricing schemes for various business objectives at different stages of network evolution. Our results show that a higher upload capacity can foster rational sharing to start when the network is small; however, it also discourages sharing behaviors when the network becomes large. In order to induce a socially optimal behavior, a pricing scheme will not charge users for requesting content while compensating them for sharing content. Such compensation is found to increase faster with the network size when the network is large. In order to maximize the profit of a monopolistic provider, however, a pricing scheme will charge content requests with a positive price while providing less compensation to sharing users compared to the socially optimal scheme. When the network size is small, such compensation can be even negative, which implies that a monopolistic provider discourages content sharing when the network is small, but encourages it when the network becomes larger. In addition, we find that more information about peer upload capacity discourages peers to share.

The ability to accurately forecast and control inpatient census, and thereby workloads, is a critical and long‐standing problem in hospital management. The majority of current literature focuses on optimal scheduling of inpatients, but largely ignores the process of accurate estimation of the trajectory of patients throughout the treatment and recovery process. The result is that current scheduling models are optimizing based on inaccurate input data. We developed a Clustering and Scheduling Integrated (CSI) approach to capture patient flows through a network of hospital services. CSI functions by clustering patients into groups based on similarity of trajectory using a novel semi‐Markov model (SMM)‐based clustering scheme, as opposed to clustering by patient attributes as in previous literature. Our methodology is validated by simulation and then applied to real patient data from a partner hospital where we demonstrate that it outperforms a suite of well‐established clustering methods. Furthermore, we demonstrate that extant optimization methods achieve significantly better results on key hospital performance measures under CSI, compared with traditional estimation approaches, increasing elective admissions by 97% and utilization by 22% compared to 30% and 8% using traditional estimation techniques. From a theoretical standpoint, the SMM‐clustering is a novel approach applicable to any temporal‐spatial stochastic data that is prevalent in many industries and application areas.

Large‐scale, web‐based service marketplaces have recently emerged as a new resource for customers who need quick resolutions for their short‐term problems. Due to the temporary nature of the relations between customers and service providers (agents) in these marketplaces, customers may not have an opportunity to assess the ability of an agent before their service completion. On the other hand, the moderating firm has a more sustained relationship with agents, and thus it can provide customers with more information about the abilities of agents through skill screening mechanisms. In this study, we consider a marketplace where the moderating firm can run two skills tests on agents to assess if their skills are above certain thresholds. Our main objective is to evaluate the effectiveness of skill screening as a revenue maximization tool. We, specifically, analyze how much benefit the firm obtains after each additional skill test. We find that skill screening leads to negligible revenue improvements in marketplaces where agent skills are highly compatible and the average service times are similar for all customers. As the compatibility of agent skills weakens or the customers start to vary in their processing time needs, we show that the firm starts to experience sizable improvements in revenue from skill screening. Apparently, the firm can reap the most of these substantial benefits when it runs only one test. For instance, in marketplaces where agents posses uncorrelated skills, the second skill test only brings an additional 2% improvement in revenue. Accounting for possible skill screening costs, we then show the optimality of offering only one test when the compatibility between agent skills is sufficiently low. The results of this study also have important implications in terms of the right level of intervention in the marketplaces we study.

Many automated manufacturing systems use robotic cells, which consist of a set of machines served by a robot. Robotic cells with a single‐gripper robot have been extensively studied in the literature. By contrast, cells with a dual‐gripper robot, although more productive, have received less attention, perhaps because of their inherent complexity. We consider the problem of scheduling operations in dual‐gripper robotic cells that have the machines configured in a circular layout and that produce identical parts repetitively. A typical objective in practice is to find a 1‐unit cyclic sequence of robot moves that maximizes the throughput. We show that dual‐gripper cycles are not optimal in all cases. We establish conditions in which the problem of finding an optimal 1‐unit cycle in dual‐gripper cells is NP‐hard. We show that the remaining cases are solvable by polynomial‐time algorithms either optimally or within a guaranteed bound of the optimum. These results are extended to linear cells. A computational study demonstrates that the algorithm performs much better on average than this worst‐case bound suggests. Our theoretical studies facilitate research into the complexity status of the corresponding domain. They also provide practical insights that are useful in maximizing productivity for any combination of cell parameters and either type of robot.

The efforts of not‐for‐profit organizations (NPOs) toward enabling health care delivery in underserved communities across the globe are well‐acknowledged. To date, little is known about the workings and sustenance of NPO‐enabled long‐term health care delivery in underserved communities, and even less about NPO‐enabled surgical care delivery. Further, health care delivery in underserved communities is a context that is often characterized by primitive and poor record keeping, data scarcity and poor data quality. Therefore, conducting research on health care delivery in such contexts is particularly challenging. The central question motivating this study is: How can international NPOs enable the long‐term delivery of surgical care in underserved communities? To overcome the data challenges, we use multi‐method triangulation as the research design to conduct this study. Specifically, we adopt a grounded theory approach with small sample statistical analysis for empirical validation. We triangulate insights from qualitative and quantitative data analyses to develop and validate an integrative framework for an international NPO‐enabled long‐term surgical care delivery in underserved communities. The setting for this study is Children's HeartLink, an international medical non‐profit organization based in the United States, which partners with health care delivery organizations in developing countries to enable the diagnosis and treatment of children with congenital heart disease (CHD). The focus of this study is the Children HeartLink's partnership with the First Hospital of Lanzhou University (FHLU) located in the underserved and impoverished Gansu province of China. In Gansu province, the CHD incidence rate is six times China's average. The time‐period of this study is 11 years, 1998–2008. The key contribution of this study is the integrative framework for an international NPO‐enabled long‐term surgical care delivery in underserved communities. The framework demonstrates how an international NPO's efforts related to affordability, provider awareness, and physical access are interdependent, and how the efforts interact and impact the surgical care delivery outcome. Contrary to conventional wisdom, the framework provides new and nuanced insights such as the following: (i) an international NPO's efforts related to affordability (e.g., funding) or provider awareness (e.g., knowledge transfer) alone has limited direct impact on long‐term surgical care outcome; (ii) instead of having a direct impact on the volume of surgeries, an international NPO's effort related to affordability moderates (positively interacts with) the positive association between its efforts related to provider awareness and physical access to surgical care delivery infrastructure, thus impacting the volume of surgeries; and (iii) an international NPO's efforts related to provider awareness and physical access are complementary for long‐term surgical care delivery – i.e., the effort related to provider awareness moderates by way of strengthening the positive association between the effort related to physical access and the volume of surgeries. Limitations of the study – e.g., small sample size for empirical validation of the grounded theory research findings and issues of generalizability – are acknowledged, and the directions of future research to address the limitations are discussed.

Due to yields of less than 50% during the production of curved glass for the displays on their new cell phone series, Samsung has to deal with higher than expected production costs of several million dollars. Where there is random yield, production costs as well as holding costs can be reduced by introducing quality inspections, in which defective items are discarded before further production. To achieve the greatest cost savings, it is important to determine the optimal number and positions of these inspections across the production process which, due to several influencing parameters, is not simple. We show how the positions of inspection within a production process influence the safety stock level that is required to buffer against uncertainties due to demand and yield randomness. Our approach is the first one, combining decisions about the number and positions of inspections with inventory control strategies in a warehouse. We achieve a maximum safety stock reduction of more than 30% in our examples, which can be even larger depending on the parameter setting. For a company like Intel, reporting inventories for finished goods of nearly 1.5 billion dollars in the 2014 annual report, this allows for significant savings.