Data Analytics in Operations Management: A Review

Research in operations management has traditionally focused on models for understanding, mostly at a strategic level, how firms should operate. Spurred by the growing availability of data and recent advances in machine learning and optimization methodologies, there has been an increasing application of data analytics to problems in operations management. In this paper, we review recent applications of data analytics to operations management, in three major areas -- supply chain management, revenue management and healthcare operations -- and highlight some exciting directions for the future.Comment: Forthcoming in Manufacturing & Services Operations Managemen

Dealing with the Dimensionality Curse in Dynamic Pricing Competition: Using Frequent Repricing to Compensate Imperfect Market Anticipations

Most sales applications are characterized by competition and limited demand information. For successful pricing strategies, frequent price adjustments as well as anticipation of market dynamics are crucial. Both effects are challenging as competitive markets are complex and computations of optimized pricing adjustments can be time-consuming. We analyze stochastic dynamic pricing models under oligopoly competition for the sale of perishable goods. To circumvent the curse of dimensionality, we propose a heuristic approach to efficiently compute price adjustments. To demonstrate our strategy's applicability even if the number of competitors is large and their strategies are unknown, we consider different competitive settings in which competitors frequently and strategically adjust their prices. For all settings, we verify that our heuristic strategy yields promising results. We compare the performance of our heuristic against upper bounds, which are obtained by optimal strategies that take advantage of perfect price anticipations. We find that price adjustment frequencies can have a larger impact on expected profits than price anticipations. Finally, our approach has been applied on Amazon for the sale of used books. We have used a seller's historical market data to calibrate our model. Sales results show that our data-driven strategy outperforms the rule-based strategy of an experienced seller by a profit increase of more than 20%

On Policies for Single-leg Revenue Management with Limited Demand Information

In this paper we study the single-item revenue management problem, with no information given about the demand trajectory over time. When the item is sold through accepting/rejecting different fare classes, Ball and Queyranne (2009) have established the tight competitive ratio for this problem using booking limit policies, which raise the acceptance threshold as the remaining inventory dwindles. However, when the item is sold through dynamic pricing instead, there is the additional challenge that offering a low price may entice high-paying customers to substitute down. We show that despite this challenge, the same competitive ratio can still be achieved using a randomized dynamic pricing policy. Our policy incorporates the price-skimming technique from Eren and Maglaras (2010), but importantly we show how the randomized price distribution should be stochastically-increased as the remaining inventory dwindles. A key technical ingredient in our policy is a new "valuation tracking" subroutine, which tracks the possible values for the optimum, and follows the most "inventory-conservative" control which maintains the desired competitive ratio. Finally, we demonstrate the empirical effectiveness of our policy in simulations, where its average-case performance surpasses all naive modifications of the existing policies

Dynamic Pricing (and Assortment) under a Static Calendar

This work is motivated by our collaboration with a large consumer packaged goods (CPG) company. We have found that while the company appreciates the advantages of dynamic pricing, they deem it operationally much easier to plan out a static price calendar in advance. We investigate the efficacy of static control policies for revenue management problems whose optimal solution is inherently dynamic. In these problems, a firm has limited inventory to sell over a finite time horizon, over which heterogeneous customers stochastically arrive. We consider both pricing and assortment controls, and derive simple static policies in the form of a price calendar or a planned sequence of assortments, respectively. In the assortment planning problem, we also differentiate between the static vs. dynamic substitution models of customer demand. We show that our policies are within 1-1/e (approximately 0.63) of the optimum under stationary (IID) demand, and 1/2 of the optimum under non-stationary demand, with both guarantees approaching 1 if the starting inventories are large. We adapt the technique of prophet inequalities from optimal stopping theory to pricing and assortment problems, and our results are very general, holding relative to the linear programming relaxation and holding even if fractional amounts of inventory can be consumed at a time. Under the special case of IID single-item pricing, our results improve the understanding of irregular and discrete demand curves, by showing that a static calendar can be (1-1/e)-approximate if the prices are sorted high-to-low. Finally, we demonstrate on both data from the CPG company and synthetic data from the literature that our simple price and assortment calendars are effective

Dynamic Pricing with Variable Order Sizes for a Model with Constant Demand Elasticity

In this paper we investigate a dynamic pricing model for constant demand elasticity where customers have a probability distribution on the number of items they order. This is a generalization from standard models which restrict customers to buy only one item at a time. For the generalized model, we first obtain a closed form expression for the optimal expected revenue and optimal pricing strategy. This expression involves a recursively defined term for which we investigate the behavior. We call comparable models those which have the same demand, which is the customer arrival rate times the average order size. In fact, the average order size plays an important role for results for the generalized model. An important result we show is that comparable models have the same asymptotic pricing behavior. Numerical results also show that comparable models are relatively close even for low inventory levels. Lastly, we prove that the relative difference between comparable models is governed not by the customer arrival rate, but solely by their order size distributions.Comment: 29 pages, submitted to European Journal of Operational Researc

Static Pricing: Universal Guarantees for Reusable Resources

We consider a fundamental pricing model in which a fixed number of units of a reusable resource are used to serve customers. Customers arrive to the system according to a stochastic process and upon arrival decide whether or not to purchase the service, depending on their willingness-to-pay and the current price. The service time during which the resource is used by the customer is stochastic and the firm may incur a service cost. This model represents various markets for reusable resources such as cloud computing, shared vehicles, rotable parts, and hotel rooms. In the present paper, we analyze this pricing problem when the firm attempts to maximize a weighted combination of three central metrics: profit, market share, and service level. Under Poisson arrivals, exponential service times, and standard assumptions on the willingness-to-pay distribution, we establish a series of results that characterize the performance of static pricing in such environments. In particular, while an optimal policy is fully dynamic in such a context, we prove that a static pricing policy simultaneously guarantees 78.9% of the profit, market share, and service level from the optimal policy. Notably, this result holds for any service rate and number of units the firm operates. Our proof technique relies on a judicious construction of a static price that is derived directly from the optimal dynamic pricing policy. In the special case where there are two units and the induced demand is linear, we also prove that the static policy guarantees 95.5% of the profit from the optimal policy. Our numerical findings on a large testbed of instances suggest that the latter result is quite indicative of the profit obtained by the static pricing policy across all parameters

Pricing Policies for Selling Indivisible Storable Goods to Strategic Consumers

We study the dynamic pricing problem faced by a monopolistic retailer who sells a storable product to forward-looking consumers. In this framework, the two major pricing policies (or mechanisms) studied in the literature are the preannounced (commitment) pricing policy and the contingent (threat or history dependent) pricing policy. We analyse and compare these pricing policies in the setting where the good can be purchased along a finite time horizon in indivisible atomic quantities. First, we show that, given linear storage costs, the retailer can compute an optimal preannounced pricing policy in polynomial time by solving a dynamic program. Moreover, under such a policy, we show that consumers do not need to store units in order to anticipate price rises. Second, under the contingent pricing policy rather than the preannounced pricing mechanism, (i) prices could be lower, (ii) retailer revenues could be higher, and (iii) consumer surplus could be higher. This result is surprising, in that these three facts are in complete contrast to the case of a retailer selling divisible storable goods Dudine et al. (2006). Third, we quantify exactly how much more profitable a contingent policy could be with respect to a preannounced policy. Specifically, for a market with $N$ consumers, a contingent policy can produce a multiplicative factor of $\Omega(\log N)$ more revenues than a preannounced policy, and this bound is tight.Comment: A 1-page abstract of an earlier version of this paper was published in the proceedings of the 11th conference on Web and Internet Economics (WINE), 201

Commodity Market Dynamics and the Joint Executive Committee (1880-1886)

Using weekly spot and future commodity prices in Chicago and New York, we construct expected transportation rates for grain between these two cities, expected inventory levels in New York, and realized errors in the expectations of such variables. We incorporate these exogenous com- modity market dynamics into Porter’s (1983) structural modeling of the Joint Executive Committee Railroad Cartel. As in Porter, we model mar- ginal cost as a parametric function of (instrumented) output, among other factors. Unlike Porter, we model pricing over marginal cost as a nonparamet- ric function of a set of variables, which include expectations of deterministic demand cycles and cartel stability. We estimate the pricing and demand equation simultaneously and semiparametrically. Our estimated weekly markups during periods of cartel stability are shown to reflect optimal collu- sive pricing over deterministic business cycles, as modeled in Haltiwanger and Harrington (1991). Periods of cartel instability are proven to be triggered by realized mistakes in expectations of New York grain prices

Scarcity Rents in Car Retailing: Evidence from Inventory Fluctuations at Dealerships

Price variation for identical cars at the same dealership is commonly assumed to arise because dealers with market power are able to price discriminate among their customers. In this paper we show that while price discrimination may be one element of price variation, price variation also arises from inventory fluctuations. Inventory fluctuations create scarcity rents for cars that are in short supply. The price variation due to inventory fluctuations thus functions to efficiently allocate particular cars that are in restricted supply to those customers who value them most highly. Our empirical results show that a dealership moving from a situation of inventory shortage to an average inventory level lowers transaction prices by about 1% ceteris paribus, corresponding to 15% of dealers' average per vehicle profit margin or $250 on the average car. Shorter resupply times also decrease transaction prices for cars in high demand. For traditional dealerships, inventory explains 49% of the combined inventory and demographic components of the predicted price. For so-called 'no-haggle' dealerships, the percentage explained by inventory increases to 74%.

Efficiency and marginal cost pricing in dynamic competitive markets with friction

This paper examines a dynamic general equilibrium model with supply friction. With or without friction, the competitive equilibrium is efficient. Without friction, the market price is completely determined by the marginal production cost. If friction is present, no matter how small, then the market price fluctuates between zero and the "choke-up" price, without any tendency to converge to the marginal production cost, exhibiting considerable volatility. The distribution of the gains from trading in an efficient allocation may be skewed in favor of the supplier, although every player in the market is a price taker.Dynamic general equilibrium model with supply friction, choke-up price, marginal production cost, welfare theorems