Biography
Nikolay Osadchiy is an Associate Professor of Information Systems & Operations Management at Emory University's Goizueta Business School. His research interests are in supply chain management, where he studies productivity, risk, and resiliency in supply networks, and in revenue management where he studies the impact of behavioral regularities on pricing. He has published in the leading academic journals including Management Science, Operations Research, Manufacturing and Service Operations Management, and Production and Operations Management. He serves as a Senior Editor at Production and Operations Management. Nikolay's practice-focused work has been published in Harvard Business Review and MIT Sloan Management Review. He regularly contributes to the media commenting on the current issues and developments in supply chains.
Nikolay has taught Supply Chain Management and Process and Systems Management courses in the BBA, MBA, and Professional MBA programs, Supply Chain Analytics in the MSBA program, and an Operations Management seminar in the PhD program. He holds a PhD in Operations Management from the New York University Stern School of Business.
Education
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PhD in Operations ManagementNew York University Leonard N. Stern School of Business
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MPhil in Operations ManagementNew York University Leonard N. Stern School of Business
Inventory Productivity and Stock Returns in Manufacturing Networks
Manufacturing and Service Operations Management
February 22, 2025
2023
We provide a novel, supply network-based perspective on inventory productivity and incentives for its improvement. Using data from 2003 to 2019, we find that inventory productivity is lower materially and statistically for firms located upstream in the supply network, and higher for high degree and more central firms. Firms with high inventory productivity show high equity valuations and abnormal returns, with both valuations and abnormal returns amplified for upstream, low degree, and peripheral firms. Moreover, the difference in valuations and abnormal returns between best and worst performing firms is greater upstream, suggesting that financial markets offer outsized rewards for improving inventory productivity to upstream firms. We show that the information about firm’s upstreamness and centrality in the supply network is a valuable predictor of its inventory productivity and financial performance. Our methods for evaluating upstreamness are useful for that purpose. For operations managers and firm executives, our results highlight strong incentives for inventory productivity improvement upstream in the supply network. For investors, we show that supply network position data can sharpen inventory-based arbitrage opportunities.
We provide a novel, supply network-based perspective on inventory productivity and incentives for its improvement. Using data from 2003 to 2019, we find that inventory productivity is lower materially and statistically for firms located upstream in the supply network, and higher for high degree and more central firms. Firms with high inventory productivity show high equity valuations and abnormal returns, with both valuations and abnormal returns amplified for upstream, low degree, and peripheral firms. Moreover, the difference in valuations and abnormal returns between best and worst performing firms is greater upstream, suggesting that financial markets offer outsized rewards for improving inventory productivity to upstream firms. We show that the information about firm’s upstreamness and centrality in the supply network is a valuable predictor of its inventory productivity and financial performance. Our methods for evaluating upstreamness are useful for that purpose. For operations managers and firm executives, our results highlight strong incentives for inventory productivity improvement upstream in the supply network. For investors, we show that supply network position data can sharpen inventory-based arbitrage opportunities.
Sourcing for online marketplaces with demand and price uncertainty
Production and Operations Management
February 22, 2025
2023
Our paper is motivated by a manufacturer that sells a seasonal product through multiple retailers competing on an online marketplace, such as the Amazon marketplace. Demand and selling price uncertainty are key features of the online marketplace. Sourcing choices are differentiated by cost and available lead times—delaying shortens the lead time which is more expensive but yields more accurate information about future selling price and demand. Thus, ahead of the season, each retailer faces a continuous-time decision problem about when to place an order with the manufacturer and in what quantity. The manufacturer is interested in knowing the ordering pattern of the retailers in order to plan production. We consider two sourcing strategies varying in the flexibility of order timing: an optimal precommitted ordering time strategy and an optimal time-flexible ordering strategy. We prove that the former is optimal when the selling price is constant and the latter when the selling price is uncertain. We show that time-flexible ordering can be mutually beneficial for the retailer and the manufacturer in a wide range of scenarios and that the manufacturer can favorably influence order timing by adjusting its wholesale price trajectory. The predictions of our model are consistent with the experience of a large U.S. manufacturer that motivated our study.
Our paper is motivated by a manufacturer that sells a seasonal product through multiple retailers competing on an online marketplace, such as the Amazon marketplace. Demand and selling price uncertainty are key features of the online marketplace. Sourcing choices are differentiated by cost and available lead times—delaying shortens the lead time which is more expensive but yields more accurate information about future selling price and demand. Thus, ahead of the season, each retailer faces a continuous-time decision problem about when to place an order with the manufacturer and in what quantity. The manufacturer is interested in knowing the ordering pattern of the retailers in order to plan production. We consider two sourcing strategies varying in the flexibility of order timing: an optimal precommitted ordering time strategy and an optimal time-flexible ordering strategy. We prove that the former is optimal when the selling price is constant and the latter when the selling price is uncertain. We show that time-flexible ordering can be mutually beneficial for the retailer and the manufacturer in a wide range of scenarios and that the manufacturer can favorably influence order timing by adjusting its wholesale price trajectory. The predictions of our model are consistent with the experience of a large U.S. manufacturer that motivated our study.
The Right Way to Mix and Match Your Customers
MIT Sloan Management Review
February 22, 2025
2021
“The costs of demand variability can put you out of business.” That blunt assessment, recently offered to us by the director of sales and operations planning at a Fortune 500 company, reflects what managers already know: Peaks in demand can drive high overtime costs, stockouts, and lost sales, while slowdowns leave capacity idle and increase excess inventory. The impact on customer service levels — not to mention the bottom line — can be significant. But how can companies best manage this variability, especially when deciding which potential new customers to target?
“The costs of demand variability can put you out of business.” That blunt assessment, recently offered to us by the director of sales and operations planning at a Fortune 500 company, reflects what managers already know: Peaks in demand can drive high overtime costs, stockouts, and lost sales, while slowdowns leave capacity idle and increase excess inventory. The impact on customer service levels — not to mention the bottom line — can be significant. But how can companies best manage this variability, especially when deciding which potential new customers to target?
Why It’s So Hard to Map Global Supply Chains
Harvard Business Review
February 22, 2025
2022
Supply chain disruptions in the last decade have generated lots of recommendations for companies to map their supply chains, identify sources of the most costly risks, and then take steps to mitigate them. But a study of industries’ supply chains for semiconductors reveals that doing so is enormously challenging. The study found that these networks are vast, dense, and dynamic.
Supply chain disruptions in the last decade have generated lots of recommendations for companies to map their supply chains, identify sources of the most costly risks, and then take steps to mitigate them. But a study of industries’ supply chains for semiconductors reveals that doing so is enormously challenging. The study found that these networks are vast, dense, and dynamic.
The Bullwhip Effect in Supply Networks
Management Science
February 22, 2025
2021
We offer a new network perspective on one of the central topics in operations management—the bullwhip effect (BWE). The topic has both practical and scholarly implications. We start with an observation: the variability of orders placed to suppliers is larger than the variability of sales to customers for most firms, yet the aggregate demand variability felt by suppliers upstream does not amplify commensurably. We hypothesize that changes to the supplier’s customer base can smooth out its aggregate demand. We test the hypothesis with a data set that tracks the evolution of supply relationships over time. We show that the effect of customer base management extends beyond the statistical benefits of aggregation. In particular, both the formation and the dissolution of customer-supplier relationships are associated with the smoothing of the aggregate demand experienced by suppliers. This provides fresh insight into how firms may leverage their customer-supplier relationships to mitigate the impact of the BWE.
We offer a new network perspective on one of the central topics in operations management—the bullwhip effect (BWE). The topic has both practical and scholarly implications. We start with an observation: the variability of orders placed to suppliers is larger than the variability of sales to customers for most firms, yet the aggregate demand variability felt by suppliers upstream does not amplify commensurably. We hypothesize that changes to the supplier’s customer base can smooth out its aggregate demand. We test the hypothesis with a data set that tracks the evolution of supply relationships over time. We show that the effect of customer base management extends beyond the statistical benefits of aggregation. In particular, both the formation and the dissolution of customer-supplier relationships are associated with the smoothing of the aggregate demand experienced by suppliers. This provides fresh insight into how firms may leverage their customer-supplier relationships to mitigate the impact of the BWE.
Supply Network Drivers of Risk and Performance
The Oxford Handbook of Supply Chain Management
February 22, 2025
2020
This chapter reviews historical and contemporary research in economics, operations management, and finance that adopts a network perspective for modeling interactions between agents. It argues that incorporating extended network characteristics in the analysis can yield unique insights compared to the analysis done at the level of dyads or local neighborhoods. The chapter explains how new network-based models contribute to the academic debate and advance our understanding of supply network drivers of performance and risk. This includes a discussion on the structural configuration of a firm’s interconnected portfolio of upstream supplier and downstream customer relationships and its role in influencing financial and operational performance as well as its innovation.
This chapter reviews historical and contemporary research in economics, operations management, and finance that adopts a network perspective for modeling interactions between agents. It argues that incorporating extended network characteristics in the analysis can yield unique insights compared to the analysis done at the level of dyads or local neighborhoods. The chapter explains how new network-based models contribute to the academic debate and advance our understanding of supply network drivers of performance and risk. This includes a discussion on the structural configuration of a firm’s interconnected portfolio of upstream supplier and downstream customer relationships and its role in influencing financial and operational performance as well as its innovation.
Systematic Risk in Supply Chain Networks
Management Science
February 22, 2025
2016
Industrial production output is generally correlated with the state of the economy. Nonetheless, during times of economic downturn, some industries take the biggest hit, whereas at times of economic boom they reap most benefits. To provide insight into this phenomenon, we map supply networks of industries and firms and investigate how the supply network structure mediates the effect of economy on industry or firm sales...
Industrial production output is generally correlated with the state of the economy. Nonetheless, during times of economic downturn, some industries take the biggest hit, whereas at times of economic boom they reap most benefits. To provide insight into this phenomenon, we map supply networks of industries and firms and investigate how the supply network structure mediates the effect of economy on industry or firm sales...
Behavioral Anomalies in Consumer Wait-or-Buy Decisions and Their Implications for Markdown Management
Operations Research
February 22, 2025
2016
A decision to buy an item at a regular price or wait for a possible markdown involves a multi-dimensional trade-off between the value of the item, the delay in getting it, the likelihood of getting it and the magnitude of the price discount. Such trade-offs are prone to behavioral anomalies by which human decision makers deviate from the discounted expected utility model.
A decision to buy an item at a regular price or wait for a possible markdown involves a multi-dimensional trade-off between the value of the item, the delay in getting it, the likelihood of getting it and the magnitude of the price discount. Such trade-offs are prone to behavioral anomalies by which human decision makers deviate from the discounted expected utility model.
Are Patients Patient? The Role of Time to Appointment in Patient Flow
Production and Operations Management
February 22, 2025
2016
The current state of outpatient healthcare delivery is characterized by capacity shortages and long waits for appointments. Yet a substantial fraction of valuable doctors’ capacity is wasted due to no-shows. In this paper, we examine the effect of wait to appointment on patient flow, specifically on a patient’s decision to schedule an appointment, and subsequently arrive to it. These two decisions may be dependent, because appointments are more likely to be scheduled by patients who are more patient and are thereby more likely to show-up. To estimate the effect of wait on these two decisions, we introduce the willingness to wait (WTW), an unobservable variable that affects both bookings and arrivals for appointments. Using data from a large healthcare system, we estimate WTW with a state of the art non-parametric method. The WTW in turn allows us to estimate the effect of wait on no-shows. We observe that the effect of increased wait on the likelihood of no-show is disproportionately greater among patients with low WTW. Thus, although reducing the wait to appointment will enable a provider to capture more patient bookings, the effects of wait time on capacity utilization can be non-monotone. Contrary to the previously reported findings, our results suggest that increasing wait can sometimes be beneficial for reducing no-shows.
The current state of outpatient healthcare delivery is characterized by capacity shortages and long waits for appointments. Yet a substantial fraction of valuable doctors’ capacity is wasted due to no-shows. In this paper, we examine the effect of wait to appointment on patient flow, specifically on a patient’s decision to schedule an appointment, and subsequently arrive to it. These two decisions may be dependent, because appointments are more likely to be scheduled by patients who are more patient and are thereby more likely to show-up. To estimate the effect of wait on these two decisions, we introduce the willingness to wait (WTW), an unobservable variable that affects both bookings and arrivals for appointments. Using data from a large healthcare system, we estimate WTW with a state of the art non-parametric method. The WTW in turn allows us to estimate the effect of wait on no-shows. We observe that the effect of increased wait on the likelihood of no-show is disproportionately greater among patients with low WTW. Thus, although reducing the wait to appointment will enable a provider to capture more patient bookings, the effects of wait time on capacity utilization can be non-monotone. Contrary to the previously reported findings, our results suggest that increasing wait can sometimes be beneficial for reducing no-shows.
Sales forecasting with financial indicators and experts' input
Production and Operations Management
February 22, 2025
2013
We present a method for forecasting sales using financial market information and test this method on annual data for US public retailers. Our method is motivated by the permanent income hypothesis in economics, which states that the amount of consumer spending and the mix of spending between discretionary and necessity items depend on the returns achieved on equity portfolios held by consumers.
We present a method for forecasting sales using financial market information and test this method on annual data for US public retailers. Our method is motivated by the permanent income hypothesis in economics, which states that the amount of consumer spending and the mix of spending between discretionary and necessity items depend on the returns achieved on equity portfolios held by consumers.
Selling with binding reservations in the presence of strategic consumers
Management Science
February 22, 2025
2010
We analyze a revenue management problem in which a seller endowed with an initial inventory operates a selling with binding reservations scheme. Upon arrival, each consumer, trying to maximize his own utility, must decide either to buy at the full price and get the item immediately or to place a nonwithdrawable reservation at a discount price and wait until the end of the sales season where the leftover units are allocated according to first-come-first-serve priority...
We analyze a revenue management problem in which a seller endowed with an initial inventory operates a selling with binding reservations scheme. Upon arrival, each consumer, trying to maximize his own utility, must decide either to buy at the full price and get the item immediately or to place a nonwithdrawable reservation at a discount price and wait until the end of the sales season where the leftover units are allocated according to first-come-first-serve priority...