Big Data and Service Operations Cohen, Maxime C.
Production and operations management,
September 2018, Letnik:
27, Številka:
9
Journal Article
Recenzirano
This study discusses how the tremendous volume of available data collected by firms has been transforming the service industry. The focus is primarily on services in the following sectors: ...finance/banking, transportation and hospitality, and online platforms (e.g., subscription services, online advertising, and online dating). We report anecdotal evidence borrowed from various collaborations and discussions with executives and data analysts who work in management consulting or finance, or for technology/startup companies. Our main goals are (i) to present an overview of how big data is shaping the service industry, (ii) to describe several mechanisms used in the service industry that leverage the potential information hidden in big data, and (iii) to point out some of the pitfalls and risks incurred. On one hand, collecting and storing large amounts of data on customers and on past transactions can help firms improve the quality of their services. For example, firms can now customize their services to unprecedented levels of granularity, which enables the firms to offer targeted personalized offers (sometimes, even in real‐time). On the other hand, collecting this data may allow some firms to utilize the data against their customers by charging them higher prices. Furthermore, data‐driven algorithms may often be biased toward illicit discrimination. The availability of data on sensitive personal information may also attract hackers and gives rise to important cybersecurity concerns (e.g., information leakage, fraud, and identity theft).
This paper studies government subsidies for green technology adoption while considering the manufacturing industry’s response. Government subsidies offered directly to consumers impact the supplier’s ...production and pricing decisions. Our analysis expands the current understanding of the price-setting newsvendor model, incorporating the external influence from the government, who is now an additional player in the system. We quantify how demand uncertainty impacts the various players (government, industry, and consumers) when designing policies. We further show that, for convex demand functions, an increase in demand uncertainty leads to higher production quantities and lower prices, resulting in lower profits for the supplier. With this in mind, one could expect consumer surplus to increase with uncertainty. In fact, we show that this is not always the case and that the uncertainty impact on consumer surplus depends on the trade-off between lower prices and the possibility of underserving customers with high valuations. We also show that when policy makers such as governments ignore demand uncertainty when designing consumer subsidies, they can significantly miss the desired adoption target level. From a coordination perspective, we demonstrate that the decentralized decisions are also optimal for a central planner managing jointly the supplier and the government. As a result, subsidies provide a coordination mechanism.
This paper was accepted by Yossi Aviv, operations management
.
Two‐sided platforms have become omnipresent. In this context, firms compete not only for customers but also for flexible self‐scheduling workers who can work for multiple platforms. We consider a ...setting where two‐sided platforms simultaneously choose prices and wages to compete on both sides of the market. We assume that customers and workers each follow an endogenous generalized attraction model that accounts for network effects. In our model, the behavior of an agent depends not only on the price or wage set by the platforms, but also on the strategic interactions among agents on both sides of the market. We show that a unique equilibrium exists and that it can be computed using a tatônnement scheme. The proof technique for the competition between two‐sided platforms is not a simple extension of the traditional (one‐sided) setting and involves different arguments. Armed with this result, we study the impact of coopetition between two‐sided platforms, that is, the business strategy of cooperating with competitors. Motivated by recent practices in the ride‐sharing industry, we analyze a setting where two competing platforms engage in a profit sharing contract by introducing a new joint service. We show that a well‐designed profit sharing contract (e.g., under Nash bargaining) will benefit every party in the market (platforms, riders, and drivers), especially when the platforms are facing intensive competition on the demand side. However, if the platforms are facing intensive competition on the supply side, the coopetition partnership may hurt the profit of at least one platform.
Feature-Based Dynamic Pricing Cohen, Maxime C; Lobel, Man; Leme, Renato Paes
Management science,
11/2020, Letnik:
66, Številka:
11
Journal Article
Recenzirano
We consider the problem faced by a firm that receives highly differentiated products in an online fashion. The firm needs to price these products to sell them to its customer base. Products are ...described by vectors of features and the market value of each product is linear in the values of the features. The firm does not initially know the values of the different features, but can learn the values of the features based on whether products were sold at the posted prices in the past. This model is motivated by applications such as online marketplaces, online flash sales, and loan pricing. We first consider a multidimensional version of binary search over polyhedral sets and show that it has a worst-case regret which is exponential in the dimension of the feature space. We then propose a modification of the prior algorithm where uncertainty sets are replaced by their Löwner-John ellipsoids. We show that this algorithm has a worst-case regret which is quadratic in the dimension of the feature space and logarithmic in the time horizon. We also show how to adapt our algorithm to the case where valuations are noisy. Finally, we present computational experiments to illustrate the performance of our algorithm.
This paper was accepted by Yinyu Ye, optimization.
Price discrimination strategies, which offer different prices to customers based on differences in their valuations, have become common practice. Although it allows sellers to increase their profits, ...it also raises several concerns in terms of fairness (e.g., by charging higher prices (or denying access) to protected minorities in case they have higher (or lower) valuations than the general population). This topic has received extensive attention from media, industry, and regulatory agencies. In this paper, we consider the problem of setting prices for different groups under fairness constraints. We first propose four definitions: fairness in price, demand, consumer surplus, and no-purchase valuation. We prove that satisfying more than one of these fairness constraints is impossible even under simple settings. We then analyze the pricing strategy of a profit-maximizing seller and the impact of imposing fairness on the seller’s profit, consumer surplus, and social welfare. Under a linear demand model, we find that imposing a small amount of price fairness increases social welfare, whereas too much price fairness may result in a lower welfare relative to imposing no fairness. On the other hand, imposing fairness in demand or consumer surplus always decreases social welfare. Finally, no-purchase valuation fairness always increases social welfare. We observe similar patterns under several extensions and for other common demand models numerically. Our results and insights provide a first step in understanding the impact of imposing fairness in the context of discriminatory pricing.
This paper was accepted by Jayashankar Swaminathan, operations management.
Funding:
A. N. Elmachtoub was supported by the Division of Civil, Mechanical and Manufacturing Innovation Grants 1763000 and 1944428.
Supplemental Material:
The data files and online appendix are available at
https://doi.org/10.1287/mnsc.2022.4317
.
Consumer Surplus Under Demand Uncertainty Cohen, Maxime C.; Perakis, Georgia; Thraves, Charles
Production and operations management,
February 2022, Letnik:
31, Številka:
2
Journal Article
Recenzirano
Consumer Surplus is traditionally defined for the case where demand is a deterministic function of the price. However, demand is usually stochastic and hence stock‐outs can occur. Policy makers who ...consider the impact of different regulations on Consumer Surplus often ignore the impact of demand uncertainty. We present a definition of the Consumer Surplus under stochastic demand. We then use this definition to study the impact of demand and supply uncertainty on consumers in several cases (additive and multiplicative demand noise). We show that, in many cases, demand uncertainty hurts consumers. We also derive analytical bounds on the ratio of the Consumer Surplus relative to the deterministic setting under linear demand. Our results suggest that ignoring uncertainty may severely impact the Consumer Surplus value.
The service industry has become increasingly competitive. One of the main drivers for increasing profits and market share is service quality. When consumers encounter a bad experience, or a
...frustration
, they may be tempted to stop using the service. In collaboration with the ride-sharing platform Via, our goal is to understand the benefits of proactively compensating customers who have experienced a frustration. Motivated by historical data, we consider two types of frustrations: long waiting times and long travel times. We design and run three field experiments to investigate how different types of compensation affect the engagement of riders who experienced a frustration. We find that sending proactive compensation to frustrated riders (i) is profitable and boosts their engagement behavior, (ii) works well for long waiting times but not for long travel times, (iii) seems more effective than sending the same offer to nonfrustrated riders, and (iv) has an impact moderated by past usage frequency. We also observe that the best strategy is to send credit for future usage (as opposed to waiving the charge or sending an apologetic message).
This paper was accepted by Vishal Gaur, operations management.
Data Aggregation and Demand Prediction Cohen, Maxime C.; Zhang, Renyu; Jiao, Kevin
Operations research,
09/2022, Letnik:
70, Številka:
5
Journal Article
Recenzirano
High accuracy in demand prediction allows retailers to effectively manage their inventory and mitigate stock-outs and excess supply. A typical retail setting involves predicting the demand for ...hundreds of items simultaneously, some with abundant historical data and others with scarce data. In “Data Aggregation and Demand Prediction,” Cohen, Zhang, and Jiao propose a novel practical method, called
data aggregation with clustering
(DAC), which balances the tradeoff between data aggregation and model flexibility. DAC empowers retailers to predict demand while optimally identifying the features that should be estimated at the item, cluster, and aggregate levels. Theoretically, DAC yields a consistent estimate, along with improved prediction errors relative to the benchmark that estimates a different model for each item. Practically, DAC yields a higher demand prediction accuracy relative to many common benchmarks using a real data set from a large online retailer.
We study how retailers can use data aggregation and clustering to improve demand prediction. High accuracy in demand prediction allows retailers to effectively manage their inventory as well as mitigate stock-outs and excess supply. A typical retail setting involves predicting demand for hundreds of items simultaneously. Although some items have a large amount of historical data, others were recently introduced and, thus, transaction data can be scarce. A common approach is to cluster several items and estimate a joint model for each cluster. In this vein, one can estimate some model parameters by aggregating the data from several items and other parameters at the individual-item level. We propose a practical method referred to as
data aggregation with clustering
(
DAC
), which balances the tradeoff between data aggregation and model flexibility.
DAC
allows us to predict demand while optimally identifying the features that should be estimated at the (i) item, (ii) cluster, and (iii) aggregate levels. We show that the
DAC
algorithm yields a consistent and normal estimate, along with improved prediction errors relative to the decentralized benchmark, which estimates a different model for each item. Using both simulated and real data, we illustrate
DAC
’s improvement in prediction accuracy relative to a wide range of common benchmarks. Interestingly, the
DAC
algorithm has theoretical and practical advantages and helps retailers uncover meaningful managerial insights.
Governments use consumer incentives to promote green technologies (e.g., solar panels and electric vehicles). Our goal in this paper is to study how policy adjustments over time will interact with ...production decisions from the industry. We model the interaction between a government and an industry player in a two-period game setting under uncertain demand. We show how the timing of decisions affects the risk sharing between the government and the supplier, ultimately affecting the cost of the subsidy program. In particular, we show that when the government commits to a fixed policy, it encourages the supplier to produce more at the beginning of the horizon. Consequently, a flexible subsidy policy is on average more expensive, unless there is a significant negative demand correlation across time periods. However, we show that the variance of the total sales is lower in the flexible setting, implying that the government’s additional spending reduces the adoption level uncertainty. In addition, we show that for flexible policies, the supplier is better off in terms of expected profits, whereas the consumers can either benefit or not depending on the price elasticity of demand. Finally, we test our insights with a numerical example calibrated on data from a solar subsidy program.
This paper was accepted by Gad Allon, operations management.
This paper considers a traditional problem of resource allocation: scheduling jobs on machines. One such recent application is cloud computing; jobs arrive in an online fashion with capacity ...requirements and need to be immediately scheduled on physical machines in data centers. It is often observed that the requested capacities are not fully utilized, hence offering an opportunity to employ an
overcommitment policy
, that is, selling resources beyond capacity. Setting the right overcommitment level can yield a significant cost reduction for the cloud provider while only inducing a very low risk of violating capacity constraints. We introduce and study a model that quantifies the value of overcommitment by modeling the problem as bin packing with chance constraints. We then propose an alternative formulation that transforms each chance constraint to a submodular function. We show that our model captures the risk pooling effect and can guide scheduling and overcommitment decisions. We also develop a family of online algorithms that are intuitive, easy to implement, and provide a constant factor guarantee from optimal. Finally, we calibrate our model using realistic workload data and test our approach in a practical setting. Our analysis and experiments illustrate the benefit of overcommitment in cloud services and suggest a cost reduction of 1.5% to 17%, depending on the provider’s risk tolerance.
The online appendices are available at
https://doi.org/10.1287/mnsc.2018.3091
.
This paper was accepted by Yinyu Ye, optimization.
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