Does the payment scheme have an effect on inventory decisions in newsvendor problem? Keeping net profit structure constant, we examine three schemes that can be interpreted as newsvendor's order being financed by herself (scheme O), supplier through delayed S), and customer advanced revenue C). In a laboratory study, find quantities exhibit consistent decreasing pattern of O, S, C, with S close to expected-profit-maximizing solution. These observations are inconsistent model, contradict what...

Even when human point forecasts are less accurate than data-based algorithm predictions, they can still help boost performance by being used as inputs. Assuming one uses judgment indirectly in this manner, we propose changing the elicitation question from traditional direct forecast (DF) to what call private information adjustment (PIA): how much thinks should adjust its account for has that is unused algorithm. Using stylized models with and without random error, theoretically prove error...

Some environments constrain the information that managers and decision makers can observe. We examine judgment in censored where a constraint, censorship point, systematically distorts observed sample. Random instances beyond point are at whereas uncensored their true value. Many important managerial decisions occur environments, such as inventory, risk taking, employee evaluation decisions. In this research, we demonstrate bias—individuals tend to rely too heavily on sample, biasing belief...

Most operations models assume individuals make decisions based on a perfect understanding of random variables or stochastic processes. In reality, however, are subject to cognitive limitations and systematic errors. We leverage established psychology sample naivete model individuals’ forecasting errors biases in way that is portable models. The has one behavioral parameter embeds rationality as special case. use the mathematically characterize point error forecast behavior, reflecting an...

Over the last two decades, researchers in operations management have increasingly leveraged laboratory experiments to identify key behavioral insights. These inform theories of management, impacting domains including inventory, supply chain queuing, forecasting, and sourcing. Yet, until now, replicability most insights from these has been untested. We remedy this with first large-scale replication study management. With input wider community, we 10 prominent experimental papers published...

Stockpiling inventory is an essential strategy for building supply chain resilience. It enables firms to continue operating while finding a solution unexpected event that causes disruption or demand surge. While extremely valuable when actually deployed, stockpiles incur large holding costs and usually provide no benefits until such time. To help reduce this cost, study presents new approach managing stockpiles. We show if leveraged intelligently, can also organization better meet its own...

Globalization, innovation, social media, and exposure to natural man-made disasters have increased organizations’ need cope with demand surges: random, significant increases in an otherwise relatively stable environment. To build supply chain capabilities, organizations face a choice between two fundamentally different sourcing strategies—reactive capacity safety stock. We develop framework guide the joint strategy that minimizes long-run average cost under target service level. A salient...

Even if algorithms make better predictions than humans on average, may sometimes have private information that an algorithm does not access to can improve performance. How we help effectively use and adjust recommendations made by in such situations? When deciding whether how override algorithm’s recommendations, hypothesize people are biased toward following naïve advice-weighting (NAW) behavior; they take a weighted average between their own prediction the prediction, with constant weight...

Providing patients with timely care from the appropriate unit involves both correct clinical evaluation of patient needs and making admission decisions to effectively manage a limited capacity in face stochastic arrivals lengths stay. We study human decision behavior latter operations management task. Using behavioral models controlled experiments which physicians MTurk workers simulated hospital unit, we identify cognitive environmental factors that drive systematic bias. report on two main...

We study decision behavior in the selection, forecasting, and production for a new product. In stylized behavioral model five experiments, we generate insight into when why this combination of tasks can lead to overconfidence (specifically, overestimating demand). theorize that cognitive limitations noisy interpretations signal information, which itself is noisy. Because people are statistically naive, they directly use their interpretation information as forecast, thereby underaccounting...

Even if algorithms make better predictions than humans on average, may sometimes have "private" information which an algorithm does not access to that can improve performance. How we help effectively use and adjust recommendations made by in such situations? When deciding whether how override algorithm's recommendations, hypothesize people are biased towards following a naïve advice weighting (NAW) heuristic: they take weighted average between their own prediction the algorithm's, with...

Most service settings involve some degree of variability in the quality customers’ experiences. An understudied mechanism is analyzed by which this can reduce firm revenues when customers do not know true but rather learn about that over time based on their Essentially, get stuck with low-quality opinions and low purchase likelihoods after unusually bad However, high-quality good experiences are quickly corrected. The authors show dynamic pricing help mitigate effect. Specifically, if set...

Existing evidence suggests that managers exhibit a censorship bias: demand beliefs tend to be biased low when lost sales from stockouts are unobservable (censored demand) compared they observable (uncensored demand). We develop non‐constraining, easily implementable behavioral debias technique help mitigate this tendency in forecasting and inventory decision‐making settings. The debiasing has individuals record estimates of outcomes (REDO): participants explicitly self‐generated estimate...

Problem definition: How can one adapt multiechelon inventory models to capture the cash flows generated by various payment-timing contracts? What competitive policy behavior arises under these arrangements? Academic/practical relevance: Technology advancements are increasing variety of payment triggers between supply chain stages. However, traditional models, which were originally developed for vertically integrated systems, do not explicitly account payments flowing up nor issues timing...

We examine a fundamental feature of choice under uncertainty: Overestimating an alternative makes one more likely to choose it. If people are naive this structural feature, then they will tend have erroneously inflated expectations for the alternatives choose. In contrast theories motivated reasoning, theory suggests that individuals overestimate chosen even before make their choice. four studies, we found students and managers exhibited behavior consistent with naïveté toward relationship...

This study relates circuit board design features to assembly yields. Data used were collected over a period of one year from two shops at AT&T. Design parameters that may affect the yield identified using knowledge process. These then quantified for set designs and related actual by statistical regression models artificial neural network (ANN) models. are able predict with root mean square (RMS) error less than 5%. They can be new on same line. Alternatively, they compare performance...

Globalization, innovation, social media, and exposure to natural man-made disasters have increased organizations’ need cope with demand surges: random, significant increases in an otherwise relatively stable environment. To build supply chain capabilities, organizations face a choice between two fundamentally different sourcing strategies – reactive capacity safety stock. We develop framework guide the joint strategy that minimizes long-run average cost under target service level. A salient...

Over the past two decades, researchers in operations management have increasingly leveraged laboratory experiments to identify key behavioral insights. These inform theories of management, impacting domains including inventory, supply chain queuing, forecasting, and sourcing. Yet, until now, replicability most insights from these has been untested. We remedy this with first large-scale replication study management. With input wider community, we ten prominent experimental papers published...

Problem definition: People often make service-quality judgments based on information about the quality of each server even though they care primarily customer experiences. When and how do server-level metrics differ from customer-experienced ones? Can people properly account for these differences, or drive human judgment decision biases? Academic/practical relevance: Biased service can cause governments to fund programs suboptimally, organizations promote wrong employees, customers...

Advancements in algorithms hold promise to better operations by improving users’ decision-making. However, humans may exhibit so-called “algorithm aversion,” which would be a barrier achieving such improvements. Using the bolus calculator (algorithm) use behavior over 306,000 insulin decisions from field experiment on Type 1 Diabetes self-management (Aleppo et al. 2017), we contribute analysis identify drivers of algorithm use. We first focus an influential experimental finding Dietvorst...

Problem definition: What is your research problem? How can one adapt multiechelon inventory models to capture the cash flows generated by various payment timing contracts? competitive policy behavior arises under these arrangements?