Knowledge that Transforms

The issue you are about to browse offers an opportunity to share many thanks, some observations, and a few forward‐looking thoughts about the department I have had the honor to edit over the last 4 years. It also offers an opportunity to expand the discussion beyond the boundaries of the particular department of Production and Operations Management (POM), and to take stock of the evolution path of the academic community that has formed around the department and its research topics over the past 10 years.

Extended enterprises face many challenges in managing the product quality of their suppliers. Consequently characterizing the quality risk posed by value‐chain partners has become increasingly important. There have been several recent efforts to develop frameworks for rating the quality risk posed by suppliers. We develop an analytical model to examine the impact of such quality ratings on suppliers, manufacturers, and social welfare. While it might seem that quality ratings would benefit high‐quality suppliers and hurt low‐quality suppliers, we show that this is not always the case. We find that such quality ratings can hurt both types of suppliers or benefit both, depending on the market conditions. We also find that quality ratings do not always benefit the most demanding manufacturers who desire high‐quality suppliers. Finally, we find that social welfare is not always improved by risk ratings. These results suggest that public policy initiatives addressing risk ratings must be carefully considered.

Most service systems consist of multidepartmental structures with multiskill agents that can deal with several types of service requests. The design of flexibility in terms of agents' skill sets and assignments of requests is a critical issue for such systems. The objective of this study was to identify preferred flexibility structures when demand is random and capacity is finite. We compare structures recommended by the flexibility literature to structures we observe in practice within call centers. To enable a comparison of flexibility structures under optimal capacity, the capacity optimization problem for this setting is formulated as a two‐stage stochastic optimization problem. A simulation‐based optimization procedure for this problem using sample‐path gradient estimation is proposed and tested, and used in the subsequent comparison of the flexibility structures being studied. The analysis illustrates under what conditions on demand, cost, and human resource considerations, the structures found in practice are preferred.

We study a newsvendor who can acquire the services of a forecaster, or, more generally, an information gatherer (IG) to improve his information about demand. When the IG's effort increases, does the average ex ante order quantity rise or fall? Do average ex post sales rise or fall? Improvements in information technology and in the services offered by forecasters provide motivation for the study of these questions. Much depends on our model of the IG and his efforts. We study an IG who sends a signal to a classic single‐period newsvendor. The signal defines the newsvendor's posterior probability distribution on the possible demands and the newsvendor uses that posterior to calculate the optimal order. Each of the possible posteriors is a scale/location transform of the same base distribution. When the IG works harder, the average scale parameter drops. Higher IG effort is always useful to the newsvendor. We show that there is a critical value of order cost. For costs on one side of this value more IG effort leads to a higher average ex ante order and for costs on the other side to a lower average order. But for all costs, more IG effort leads to higher average ex post sales. We obtain analogous results for a “regret‐averse” newsvendor who suffers a penalty that is a nonlinear function of the discrepancy between quantity ordered and true demand.

We look at a simple service system with two servers serving arriving jobs (single class). Our interest is in examining the effect of routing policies on servers when they care about fairness among themselves, and when they can endogenously choose capacities in response to the routing policy. Therefore, we study the two‐server game where the servers’ objective functions have a term explicitly modeling fairness. Moreover, we focus on four commonly seen policies that are from one general class. Theoretical results concerning the existence and uniqueness of the Nash equilibrium are proved for some policies. Further managerial insights are given based on simulation studies on servers’ equilibrium/off‐equilibrium behaviors and the resulting system efficiency performance under different policies.

This paper studies the optimal policy for a periodic‐review inventory system in which the production costs consist of a fixed cost and a piecewise linear convex variable cost. Such a cost function can arise from alternate sources of supply or from the use of overtime production. We fully characterize the structure of the optimal policy for the single‐period problem. For the multi‐period problem, the optimal policy can have disconnected production regions and complicated optimal produce‐up‐to levels, which implies that implementation of the optimal policy may not be practical. Fortunately, careful investigation shows that the optimal policy has some interesting properties. The structure of the optimal policy outlined by these properties leads to a practical and close‐to‐optimal heuristic policy. In an extensive numerical study, the average gap is only 0.02% and the worst gap is 1.37%.

As the Obama administration steps up oversight of high-risk IT projects, contracting organizations must take greater responsibility to provide a level of confidence in the services they offer. That is where one of the latest offerings from the Software Engineering Institute at Carnegie Mellon University can help. (Sacks 2010). A number of US federal government information technology (IT) initiatives (e.g., implementation of the Health Insurance Marketplace or Obamacare, development of navigation systems in missiles and unmanned vehicles) are frequently organized in the form of IT projects (Kundra 2010). The Office of Management and Budget (OMB), which tracks the progress of all federal IT projects indicates that such projects face significant schedule and cost overruns. Nearly 25% of federal IT projects with a cumulative budget exceeding $10 billion and spread over 28 government agencies are facing moderate to severe problems in meeting their schedule and budgetary targets (Source: www.itdashboard.gov). Additionally, the US Government Accountability Office (GAO) reports that nearly 72% of federal IT projects, with a total budget of $27 billion, are poorly planned and face significant schedule and cost overruns (US GAO Report 2010). Despite the evidence of schedule and budget overruns in federal IT projects, the challenges associated with the management of such projects, and more generally, of IT projects in the public sector, have received limited attention in both practice and research. Public sector projects differ from private sector projects in a number of ways (Boyne 2002). First, federal IT projects are primarily funded with taxpayer's money and are aimed at maximizing public utility, instead of maximizing profits, as in private sector IT projects. Hence they face greater scrutiny from the media, the US Congress, and any number of watchdog organizations. Second, federal IT projects face challenges and risks due to increased complexity, technological uncertainty, significant resource requirements, governmental rules and regulations, and the frequent involvement of multiple stakeholders with disparate and sometimes conflicting goals. To date, much of our understanding of IT project management has been drawn from studies that have focused on the private sector (McKinsey 2012). Given the notable differences between federal and private sector IT projects and growing calls in political and media circles for the efficient utilization of tax payer contributions (Fortune 2011, P. 56), an empirical investigation of challenges in federal IT projects presents a fruitful area of research with significant implications for practitioners. Our study has two major objectives. First, we identify and conceptualize a set of salient risks in federal IT projects using a lifecycle framework. As Figure 2 indicates, we focus on the planning and execution processes within an IT project and identify three distinct types of risk—namely, complexity risk and contracting risk that arise in the planning process, and execution risk that arises in the execution process. We define each of the three risks below. As per industry standards and federal legislation (i.e., the Clinger-Cohen Act of 1996), the performance of federal IT projects is reported to the OMB using earned value management (EVM) metrics for schedule and cost performance. EVM is a project planning and control approach which compares actual accomplishment of scheduled work and associated cost against an integrated schedule and budget plan on a periodic basis. In this study, we examine the performance impact of each of three risks using a composite earned value metric, that is, schedule-cost performance index (SCPI). Second, prior research has emphasized the need for mature processes to manage IT projects; which leads to improvements in the control and predictability of project outcomes. In the context of federal IT projects, the vendor's capability to reliably deliver mission-critical IT solutions (i.e., the vendor's use of mature processes within a project) is assessed using the Capability Maturity Model Integration (CMMI) framework developed by the Software Engineering Institute (SEI). The framework consists of five levels (levels 1 – 5) which assess the evolution of a firm's processes from immature and informal to mature and formal, and define the related infrastructure necessary to support these processes at an organizational level (CMMI for Development 2010). As a signal of process excellence, CMMI level 3 represents a significant step toward process maturity, with vendor certification at this level being frequently used as a key qualifying criterion by the federal government for awarding project contracts. Given the considerable commitment of time and organizational resources required to obtain CMMI certification and the performance challenges associated with federal IT projects, we examine whether higher levels of process maturity—that is, level 3 and higher—play a significant role in mitigating the effects of risk on performance in federal IT projects. The projects for this study are drawn from a proprietary database of technology projects from Lockheed Martin, a Fortune 100 global technology firm that specializes in the development of large aerospace, defense, and security systems for the federal government (i.e., the client organization). Time-series panel data are collected across 519 quarterly time periods from 82 federal IT projects that were completed during the period 2002-2012. The firm uses a rigorous two-step procedure for collecting data on federal IT projects. In the first step, tactical and project specific details are collected on a monthly basis as part of a monthly review process. The review process is typically conducted by a panel consisting of project managers, deputy project managers, and vice presidents in the IT domain within the firm. In the second step, the monthly data are aggregated to form quarterly status reports that are used for strategic review and evaluation of project performance. In addition, these reports are used to track data for internal auditing purposes and to create lessons learned. To ensure accuracy in data collection, the data are triangulated through multiple sources (e.g., interviews with project managers, project documents, etc.). Given the time-series nature of the data, we use the generalized least squares (GLS) regression method that corrects for both panel-specific autocorrelation and heteroskedasticity in the analysis. In addition, we control for a number of factors pertaining to project characteristics (e.g., project budget, project size, project priority) in our analysis. The results provide empirical support for our arguments that each of the three types of risks—complexity, contracting, and execution risks—reduce a project's ability to meet its cost and schedule targets. More importantly, our results highlight the effect of higher CMMI levels in attenuating the negative effects of project risks on performance in federal IT projects. In addition, the attenuating effect of CMMI on the risk-performance relationship is stronger at high risk levels; at low risk levels, projects with higher maturity levels (e.g., levels 4 and 5) exhibit inferior performance on schedule and cost metrics compared to projects with lower (e.g., level 3) maturity levels. To demonstrate the economic impact of increasing process maturity levels in federal IT projects, we conduct post-hoc analysis to examine the magnitude of savings (and overruns) in project costs across different levels of CMMI and project risks. This analysis is conducted in three steps. In the first step, we estimate the dependent variable (SCPI) at low (-2 SD), average, and high levels (+2 SD) of project risks across different process maturity levels, holding all control variable values at their means. Next, based on a median project budget of $35 million in the sample, we determine the estimated cost of completion (EAC) using the formula: EAC = (ProjectBudget/SCPI) × 100. Finally, we examine the differences in EAC values across different maturity and risk levels, to determine potential savings in project budgets. The results, shown in Table 1 below, highlight the potential cost savings that may result for executing projects at higher maturity levels when project risk levels are high. Specifically, given a project budget of $35 million (based on the median project budget value in our sample), executing the project at CMMI 4 or CMMI 5 when project risks are high is associated with potential savings of $11.87 million and $8.56 million, respectively, compared to executing the project at CMMI 3. In contrast, when project risk levels are low, executing the project at CMMI 4 or CMMI 5 is associated with potential cost overruns amounting to $8.27 million and $7.26 million, respectively, compared to executing the project at CMMI 3. Findings from our study make the following important contributions to the extant literature. First, our study focuses on an important and largely understudied area of research in the OM literature—the management of public sector operations (Verma et al. 2005), and particularly, the context of federal IT projects. The second contribution of our study arises from identifying and positioning IT project risks in the context of a lifecycle framework. The importance of identifying and planning for risks has been widely discussed in the extant project management literature. Our study represents a concerted attempt to conceptualize key risks in an IT project by using the project lifecycle framework, which allows us to identify and map risks by processes associated with specific project phases. The third contribution lies in developing a nuanced understanding of the mode by which process maturity influences project performance. This is important to both theory and practice given the significant investment of resources and time that is required to acquire CMMI certification. Toward this end, our results provide the following insights to managers of federal IT projects - while the implementation of CMMI 4 relative to CMMI 3 attenuates the negative performance effects of risks in the planning process only; the implementation of CMMI 5 relative to CMMI 3 level attenuates the negative performance effects of risks in both planning and execution processes. The fourth contribution of our study is based on our results that the intrinsic benefits of CMMI implementation in federal IT projects become particularly salient at high levels of project risk; at low risk levels, the benefits of higher maturity levels (i.e., CMMI 4 and CMMI 5) on project schedule and cost metrics are inferior compared to projects with CMMI 3 maturity level. We surmise that at low levels of project risk, the improvements in project performance accruing from increased levels of process maturity, may not fully compensate the costs of implementing higher CMMI levels, thereby diminishing overall project performance. The study's final contribution arises from our use of an integrated measure of schedule-cost performance (SCPI) in the context of earned value management (EVM). While vendors working on federal IT projects are mandated to use EVM for tracking and reporting project progress as per industry standards and federal legislations, the use of EVM has also grown significantly in the private sector. Though widely used in project management, there is a dearth of studies that have used EVM for evaluating project performance. Therefore, our study provides a welcome addition to the project management literature with respect to earned value management.

We consider an inventory system under continuous review with two demand classes that are different in terms of service level required (or penalty cost incurred for backordering of demand). Prior literature has proposed the critical level rationing (CLR) policy under which the demand from the lower priority class is backordered once inventory falls below the critical level. While this reduces the penalty cost for the higher demand class, the fill rate achieved for the lower priority demand class gets compromised. In this study, we propose a new class of two‐bin (2B) policy for the problem. The proposed 2B policy assigns separate bins of inventory for the two demand classes. The demand for each class is fulfilled from its assigned bin. However, when the bin intended for the higher demand class is empty, the demand from the higher class can still be fulfilled with the inventory from the other bin. The advantage of the 2B policy is that better fill rates are achieved, especially for the lower demand class. Computational results show that the proposed policy is able to provide a much higher service level for the lower priority class demand without increasing the total cost too much and without affecting the service level for the higher priority class. When a service level constrained optimization problem is considered, the 2B policy dominates the CLR policy when the service level difference for the two classes is not too high or the service levels required for both the classes are relatively lower.

This study examines a deterministic material requirements planning (MRP) problem where lead times at subsequent ordering moments differ. Adequate replenishment methods that can cope with lead time differences are lacking because of the order crossover phenomenon, that is, replenishment orders are not received in the sequence they are ordered. This study specifies how to handle order crossovers and recalculate planned order releases after an update of gross requirements. The optimal ( s , S ) policy is based on dynamic programing. The state space is kept to a minimum due to three fundamental insights. The performance of the optimal solution approach is compared with two heuristics based on relaxations and a benchmark approach in which order crossovers are ignored. A numerical analysis reveals that average cost savings up to 25% are possible if the optimal policy is used instead of the benchmark approach. The contribution of this study is threefold: (1) it generalizes theory on MRP ordering, allowing for lead time differences and order crossovers; (2) it develops new fundamental insights and an optimal solution procedure, leading to substantial cost saving; and (3) it provides good‐performing heuristics for a general and realistic replenishment problem that can replace the current replenishment methods within MRP.

We analyze if and when symmetric Bayes Nash equilibrium predictions can explain human bidding behavior in multi‐object auctions. We focus on two sealed‐bid split‐award auctions with ex ante split decisions as they can be regularly found in procurement practice. These auction formats are straightforward multi‐object extensions of the first‐price sealed‐bid auction. We derive the risk‐neutral symmetric Bayes Nash equilibrium strategies and find that, although the two auction mechanisms yield the same expected costs to the buyer, other aspects of the two models, including the equilibrium bidding strategies, differ significantly. The strategic considerations in these auction formats are more involved than in single‐lot first‐price sealed‐bid auctions, and it is questionable whether expected utility maximization can explain human bidding behavior in such multi‐object auctions. Therefore, we analyzed the predictive accuracy of our equilibrium strategies in the laboratory. In human subject experiments we found underbidding, which is in line with earlier experiments on single‐lot first‐price sealed‐bid auctions. To control for regret, we organize experiments against computerized bidders, who play the equilibrium strategy. In computerized experiments where bid functions are only used in a single auction, we found significant underbidding on low‐cost draws. In experiments where the bid function is reused in 100 auctions, we could also control effectively for risk aversion, and there is no significant difference of the average bidding behavior and the risk‐neutral Bayes Nash equilibrium bid function. The results suggest that strategic complexity does not serve as an explanation for underbidding in split‐award procurement auctions, but risk aversion does have a significant impact.