Knowledge that Transforms

This study examines the tradeoffs in sourcing and sales strategies (i.e., upstream and downstream supply chain strategies) by considering them as components of an integral end‐to‐end supply chain strategy. We evaluate four end‐to‐end supply chain strategies under various scenarios using a newsvendor model, and compare the model's predictions against the prescriptions in Fisher's ( 1997 ) framework, which recommends “cost‐efficient” supply chains for “functional” products and “responsive” supply chains for “innovative” products. We considered combinations of offshore vs. nearshore sourcing, and online vs. brick‐and‐mortar retailing. This study's key finding is that sourcing and sales strategies are not completely modular: an integral end‐to‐end strategy may not decompose into an optimal sourcing strategy and a separately computed optimal sales strategy. Our analyses sharpen strategic supply chain thinking by identifying realistic conditions in which an end‐to‐end strategy with cost‐efficient components could outperform one with responsive components for innovative products, or when one with responsive components could be more profitable than one with cost‐efficient components for functional products.

In recent years, peer‐to‐peer (P2P) networks have become an increasingly popular method for distributing digital content. In this study, we consider the development of optimal contracts for a P2P network by a profit‐seeking provider to support the operations of an online file exchange service. By utilizing the principal‐agent model of incentive theory, we propose appropriate reward and pricing schemes for profit‐seeking P2P content distribution networks. We show that when peers are homogeneous, upload compensation increases with propagation delay uncertainty, maximum uploading nodes allowed, peers' provision cost and disutility of download delay, but decreases with the network size and content availability. We also characterize a general contracting model where there are a countable number of peer classes which are heterogeneous in their provisioning costs. For the case of two peer classes where optimal delays are separable, we derive the optimal upload compensations under different scenarios and show that the impact of operational parameters is quite similar to the case of homogeneous peers, lending support to the robustness of our analysis.

We consider production and service systems that consist of parallel lines of two types: (i) M/ M/1 lines and (ii) lines that have no buffers (loss systems). Each line is assumed to be controlled by a dedicated supervisor. The management measures the effectiveness of the supervisors by the long run expected cost of their line. Unbalanced lines cause congestion and bottlenecks, large variation in output, unnecessary wastes and, ultimately, high operating costs. Thus, the supervisors are expected to join forces and reduce the cost of the whole system by applying line‐balancing techniques, possibly combined with either strategic outsourcing or capacity reduction practices. By solving appropriate mathematical programming formulations, the policy that minimizes the long run expected cost of each of the parallel‐lines system, is identified. The next question to be asked is how to allocate the new total cost of each system among the lines' supervisors so that the cooperation's stability is preserved. For that sake, we associate a cooperative game to each system and we investigate its core. We show that the cooperative games are reducible to market games and therefore they are totally balanced, that is, their core and the core of their subgames are non‐empty. For each game a core cost allocation based on competitive equilibrium prices is identified.

The Braess Paradox (BP) illustrates an important counterintuitive observation that adding links to a directed transportation network with usage externalities may raise the costs of all users. Research on the BP traditionally focuses on congestible networks. We propose and experimentally test a new and more dramatic version of the BP, where the network exhibits both congestion (negative externalities) and cost‐sharing (positive externalities) characteristics. Our design also involves experimental manipulation of choice observability, where players choose routes simultaneously in one condition and sequentially in the other. We report robust behavioral evidence of the BP in both conditions. In nine of 10 sessions in the basic network, subjects coordinated successfully to achieve the welfare‐maximizing equilibrium. But once the network was augmented with a new link, coordination failure resulted in a major proportion of subjects switching to a new route, resulting in a 37% average increase in individual travel cost across conditions.

Studying causal effects is central to research in operations management in manufacturing and services, from evaluating prevention procedures, to effects of policies and new operational technologies and practices. The growing availability of micro‐level data creates challenges for researchers and decision makers in terms of choosing the right level of data aggregation for inference and decisions. Simpson's paradox describes the case where the direction of a causal effect is reversed in the aggregated data compared to the disaggregated data. Detecting whether Simpson's paradox occurs in a dataset used for decision making is therefore critical. This study introduces the use of Classification and Regression Trees for automated detection of potential Simpson's paradoxes in data with few or many potential confounding variables, and even with large samples (big data). Our approach relies on the tree structure and the location of the cause vs. the confounders in the tree. We discuss theoretical and computational aspects of the approach and illustrate it using several real applications in e‐governance and healthcare.

Service level agreements (SLAs) are widely adopted performance‐based contracts in operations management practice, and fill rate is the most common performance metric among all the measurements in SLAs. Traditional procedures characterizing the order‐up‐to level satisfying a specified fill rate implicitly assume an infinite performance review horizon. However, in practice, inventory managers are liable to maintain and report fill rates over a finite performance review horizon. This horizon discrepancy leads to deviation between the target fill rate and actual achieved fill rate. In this study, we first examine the behavior of the fill rate distribution over a finite horizon with positive lead time. We analytically prove that the expected fill rate assuming an infinite performance review horizon exceeds the expected fill rate assuming a finite performance review horizon, implying that there exists some inventory “waste” (i.e., overstocking) when the traditional procedure is used. Based on this observation and the complexity of the problem, we propose a simulation‐based algorithm to reduce excess inventory while maintaining the contractual target fill rate. When the lead time is significant relative to the length of the contract horizon, we show that the improvement in the inventory system can be over 5%. Further, we extend our basic setting to incorporate the penalty for failing to meet a target, and show how one can solve large‐scale problems via stochastic approximation. The primary managerial implication of our study is that ignoring the performance review horizon in an SLA will cause overstocking, especially when the lead time is large.

A cellular bucket brigade is a way to coordinate workers along an aisle with work content on both sides. Each worker in a cellular bucket brigade works on one side of the aisle when he proceeds in one direction, and he works on the other side when he proceeds in the reverse direction. Although the cellular bucket brigade eliminates the unproductive walk‐back, it requires more hand‐offs to assemble a product than a traditional (serial) bucket brigade. These hand‐offs may waste significant production capacity as each of them requires an exchange of work, which can be complicated and time‐consuming in practice. This motivates us to investigate the impact of hand‐off times on the cellular bucket brigade's performance. We identify sufficient conditions to ensure no workers are idle in the long run and for the system to self‐balance in a model with hand‐off times. Our results suggest that even with significant hand‐off times, the cellular bucket brigade can remain substantially (about 50%) more productive than the traditional bucket brigade especially if the team size is small and the workers’ work velocities are close to their walk velocity.

We develop a three‐step methodology to restructure a product line by quantifying the restructuring's likely effects on revenues and costs: (i) Constructing migration lists to capture customer preferences and willingness to substitute; (ii) Explicitly capturing the (positive and negative) cost of complexity across different functional areas, using statistical analysis of cost data; and (iii) Integrating these tools within a mathematical optimization program to produce a final product line, incorporating the possibility of differentiating products by lead‐time (into different lanes). Our methodology is highly flexible—each step can be tailored to a company's particular setting, data availability and strategic needs, so long as it produces the necessary output for the next step. We report on the successful application of our methodology to the Backhoe Loader product line at Caterpillar: In collaboration with Caterpillar, we were able to significantly simplify this line, reducing the number of configurations from 37,920 to 135, in three lanes, while increasing sales by almost 7%.

We examine a contingent price markdown (CM) mechanism with guaranteed reservation under which a retailer sells multiple units to forward‐looking consumers who arrive over time according to a Poisson process. During the early part of the selling season, each arriving consumer can either purchase a unit by paying the regular price or reserve a unit at the discount price. Reserved units can only be claimed later when the number of guaranteed reservations meets a pre‐specified threshold, or at the end of the selling season, whichever comes first. Immediately after the number of guaranteed reservations meets the pre‐specified threshold, the retailer will reduce its selling price to the discount price so that all subsequent arriving consumers can take immediate possession by paying the low price. We study the consumer purchasing behavior in equilibrium when the retailer adopts such a selling mechanism, and compare the performance of our mechanism against two benchmarks: fixed price (FP) and pre‐announced discount (PD). Through an extensive numerical analysis, we identify market conditions under which CM dominates both FP and PD in terms of the retailer's revenue and consumer's surplus. Finally, through a fluid approximation to the stochastic model, we simplify the computation of the equilibrium strategy and the optimal revenues, and verify that the key insights obtained from the stochastic model still hold.

In project portfolio selection, the aim is to choose projects which are expected to offer most value and satisfy relevant risk and other constraints. In this study, we show that uncertainties about how much value the projects will offer, combined with the fact that only a subset of the proposed projects will be selected, lead to inaccurate risk estimates about the aggregate value provided by the selected project portfolio. In particular, when downside risks are measured in terms of lower percentiles of the distribution of portfolio value, these risk estimates will exhibit a systematic bias. For deriving unbiased risk estimates, we present a calibration framework in which the required calibration can be presented in closed‐form in some cases or, more generally, derived by using Monte Carlo simulation to study a large number of project selection decisions. We also show that when the decision must comply with risk constraints, the introduction of tighter (more demanding) risk constraints can counterintuitively aggravate the underestimation of risks. Finally, we present how the calibrated risk estimates can be employed to align the portfolio with the decision maker's risk preferences while eliminating systematic biases in risk estimates.