Knowledge that Transforms

This study examines the optimal reporting time a regulator should choose for firms to report their information when the firms' effort choices influence the outcome of projects. Our analysis shows that, the regulator's optimal choice of reporting time to maximize overall efficiency is contingent on a trade‐off between motivating a firm's effort to improve the outcome and saving the liquidation value for the creditor to reduce the firm's financing cost. We also find that to induce the firm's effort, the reporting time should be either early enough or late enough—depending on the effectiveness of the effort to turn bad projects into successes. Furthermore, we examine the regulator's optimal choice of reporting time for an economy with heterogenous firms/industries, and we show that the optimal reporting time changes nonmonotonically in the probability of good projects in the economy.

This paper considers price competition in a market where two firms sell a homogeneous service to a continuum of customers differing with respect to some exogenous characteristic. Our paper's novelty consists of explicitly acknowledging a distinctive property of many services in that firms incur customer‐specific service costs after the contract is signed. Hence, not only the customers' willingness‐to‐pay and as such demand but also the firms' supply are related to customer characteristics. In this paper, we shed light on the implications thereof for optimal pricing and market segmentation strategies in a monopoly as well as a duopoly market. Importantly, we stress the profitability of services by demonstrating that firms in highly competitive industries still earn positive expected profits in equilibrium.

We consider a firm selling heterogeneous products with prices customized for each customer and the final price is set by negotiations between the seller and the customer. This type of pricing modality is referred to as customized pricing with discretion and is commonly used in insurance, consumer loans, mortgages, and many business‐to‐business markets. We assume that each sales agent has a reserve price and each customer has a willingness‐to‐pay , which are jointly drawn from a distribution and, if the transaction is successful, they agree on a price between these two values based on their relative bargaining power. Given the outcomes of a series of negotiations, our goal is to estimate the underlying joint distribution of reserve price and willingness‐to‐pay, and predict the outcomes of future transactions. We assume that the price that prevails as the outcome of the negotiation can be represented as a generalized Nash bargaining equilibrium. We develop a structural estimation method based on the expectation‐maximization algorithm to estimate the parameters of reserve price and willingness‐to‐pay distribution. Using a real‐world data set from indirect auto‐lending industry, we show that our proposed method, which accounts for heterogeneity in sales agent's reserve price and customer's willingness‐to‐pay, improves the predictive accuracy of final price (APR) and take‐up probability (i.e., probability of a customer accepting the loan) on real‐world test data by about 8.70% and 3.68%, respectively, compared to a (Tobit‐based) model, which accommodates unobserved heterogeneity in customer's willingness‐to‐pay only. Using the structural EM estimates, we conduct counterfactual analyses to understand the impact of different pricing policies, which vary in the amount of discretion provided to the sales agent during the negotiation process. For example, we find that the lender may increase profits by 13.63% compared to the status quo by optimally imposing a customized minimum price (annual percentage rate) below which a loan should not be offered to the customer.

New product and service introductions require careful joint planning of production and marketing campaigns. Consequently, they typically utilize multiple information channels to stimulate customer awareness and resultant word‐of‐mouth (WOM), availing of standard budget allocation tools. By contrast, when enacting strategic allocation decisions—which must align with other management imperatives—dividing expenditures across channels is far more complex. To this end, we formulate a multichannel demand model for new products (or services), amenable to analysis of inter‐ and intrachannel interaction patterns and with the WOM process, without building such interactions directly into the modeling framework. To address the notorious complexity of media planning over time, we propose a novel decomposition of the multichannel dynamic programming problem into two distinct “tiers”: the strategic tier addresses how to allocate total expenditure across channels, while the tactical tier studies how to allocate the channel‐specific budgets (determined in the strategic tier) over time periods. This decomposition enables optimal media strategies to sidestep the curse of dimensionality and renders the model pragmatically estimable. Strategic tier analysis suggests a variety of novel insights, primarily that funds should not be allocated based on (relative) channel effectiveness alone but also systematically aligned with WOM generation. Specifically, each channel can face a “chasm‐crossing” threshold, abruptly transitioning the adoption process from lead‐users to mass‐market penetration. Moreover, the model provides actionable managerial insights into when, and which, channel interactions are synergistic versus substitutive. Specifically, a channel's interactions are governed primarily by its own “leverage” (potential demand impact) and the WOM‐based demand “momentum” (market penetration) it can generate, affording a novel basis for channel typography and firm action. The modeling framework is illustrated by examining camera sales for two media channels (free‐standing inserts and radio) and their effects over 28 months. We use Bayesian machinery to estimate a highly flexible diffusion‐based model, along with forecasts, media plans, and both theoretical and empirically‐based qualitative insights.

A dramatically changing business landscape characterized by volatility and uncertainty (which in turn are driven by unpredictable alterations of conditions by such factors as geopolitical events, climate changes, disasters—natural and human causes, reduction in food, potable water, and critical materials supply along with environmental contamination), has created a situation whereby a rethink of Production and Operations Management (POM) strategies and practices is considered timely and appropriate. Against a backdrop of current POM strengths and weaknesses, and expected POM futures, a response path for POM adoption to deal with such uncertainties and risks is proposed and described in some detail. This involves the treatment of POM as a more interconnected and integrated system than has hitherto been the case. Such an interconnected approach is described as a POM symphony where all key players would be playing from the same script and the POM leader must serve as a coach‐coordinator, that is, the symphony's conductor.

A retailer places orders periodically for items that are shipped by a wholesaler. Items that are not sold perish randomly and independently of one another, with the perish probability depending on the age class. We consider a first‐in‐first‐out policy for depleting items. We model this problem as a Markov decision process with stochastic demand, unit holding, outdating and ordering costs, plus unit penalty costs for lost sales. We prove convexity for the penultimate period and show convexity may not hold any earlier. A dynamic program can be solved optimally for small instances. We introduce both a one‐stage‐lookahead heuristic and a heuristic which is a combination of two existing standard approaches, the newsvendor and periodic review models. For simulated data, we compare these heuristics to the optimal solution for small problem instances and to further lookahead policies for larger problem instances. We show that the two new heuristics achieve results close to optimal. Our numerical study, which includes real data from a large European retail chain, highlights that products perishing independently from each other strongly affect model behavior compared to existing approaches from the literature.

We study how to admit and schedule heterogeneous patients by using simple, interpretable, yet effective policies when capacity is scarce, no‐show behavior is patient‐ and time‐dependent, service duration and reward are deterministic but patient‐dependent, and overtime is costly. Our work is motivated by the aforementioned operational challenges that typically face adopters of new technologies in the healthcare sector. We anchor our study on a partnership with the proton therapy center of Massachusetts General Hospital (MGH), which offers a new radiation technology for cancer patients. We formulate the problem as a nonlinear integer optimization problem. However, as the solution to this formulation lacks both tractability and interpretability, to be relevant to practice, we limit our study to simple and interpretable policies. In particular, we propose a simple index‐based rule and derive analytical performance guarantees for it. We also calibrate our model using empirical data from our partner hospital, and conduct a series of experiments to evaluate the performance of our proposed policy under practical circumstances. The analytical performance guarantees and our numerical experiments demonstrate (a) the strong performance of the proposed policies, and (b) their robustness to various practical considerations (e.g., to potential misspecification of no‐show probabilities). Our results show that our proposed policy, despite being a simple and interpretable index‐based rule, is capable of improving performance by about 20% at an organization such as MGH, and of delivering results that are not far from being optimal across a wide range of parameters that might vary between organizations. This suggests that the proposed policy can be viewed as an effective “one‐fits‐all” capacity allocation rule that can be used in a variety of environments in which operational challenges such as no‐shows and overtime costs need to be navigated using simple and interpretable rules.

The path for elevating the role of manufacturing in the company strategy in the last few decades has been rather clear: Improve the basic production capabilities—typically quality, reliability, lead times, and cost efficiency of production processes. Leading Japanese companies, like Toyota, showed the way. But as many have heeded the advice and followed suit, this approach has become essentially a defensive strategy; you must do it not to fall behind. Has manufacturing lost its potential to create capabilities on which a company's strategy can rest? Our answer is absolutely not. In fact, unlike before, manufacturing has multiple paths for creating a competitive advantage and these paths require development of new and often nontraditional capabilities. We identify five sets of new capabilities, and since it is hard to excel in all of them, we provide a framework for choosing the right mix depending on the company's business strategy. The framework focuses on the implications of two recent trends: increasing information density embedded in products and increasing connectedness of manufacturing processes. We suggest specific mixes of the five groups of capabilities that can support and accelerate a company's strategy to exploit these trends. We use examples from three multinationals to illustrate the process. These new opportunities change the traditional role of manufacturing executives. Their focus will need to shift exceedingly to collaborating and interfacing with colleagues in other functions as well as managing relationships beyond the boundaries of the company.

Firms that sell products over a limited selling season often have only imperfect information about (a) the exact timing of that season, (b) the demand volume to expect, and (c) the temporal distribution of demand over the selling season. Given these uncertainties, firms must determine not only how much inventory to stock but also when to make that inventory available to customers. We thus ask: What is a firm's optimal inventory quantity and timing for products sold during a stochastic selling season? Although the newsvendor literature has developed a thorough understanding of the firm's optimal inventory quantity, it has failed to inform decision‐makers about choosing the optimal inventory timing. We address this issue by developing a theoretical model of a firm that sells a product over a stochastic selling season, and we study how this firm should choose its inventory timing and inventory quantity so as to maximize expected profits. We also identify the effects of optimal inventory timing on the firm's ability to satisfy customer demand and show how early inventory timing can be detrimental to customer service. Our core results imply three immediate recommendations for managers. First, optimal inventory timing is an effective weapon for combating both high inventory holding costs and high levels of uncertainty in the firm's customer demand pattern. Second, to be effective, a firm's inventory timing must be carefully aligned with the firm's inventory quantity. Third, naïve decision rules (e.g., “earlier is better”) may reduce not only the firm's profits but also its capacity to serve customer demand.

Online retail fulfillment is increasingly performed by semiautomated fulfillment systems in which inventory is stored in mobile pods that are moved by robotic drives. In this paper, we develop a model that explores the benefits of velocity‐based stowage policies for semiautomated fulfillment systems, also known as robotic mobile fulfillment systems. The stowage policies decide which pods to replenish with the received inventory. Specifically, we model policies that account for the velocity of the units being stowed. By stowing higher (lower) velocity units on higher (lower) velocity pods, we expect to increase the heterogeneity of the pod velocities. Greater heterogeneity in pod velocities can yield a greater reduction in pod travel distance from velocity‐based storage policies for the pod. Reducing pod travel distance decreases the number of robotic drives that are needed for the system to maintain a certain throughput rate. We analyze an M ‐class velocity‐based stowage policy. We stow units from each velocity class onto pods dedicated to that velocity class; each class of pods then has its own storage zone, where the zones are ordered based on the distance to the stationary pick and stow stations. We characterize the pod travel distance as a function of the skewness of the demand distribution and the number and size of the classes. We find that with two or three classes we can achieve most of the benefits possible from a velocity‐based stowage and storage policy. For representative demand distributions, we find that a two‐class policy achieves 75% of the maximum possible travel‐time reduction and that a three‐class policy improves this to 90%.