Smart Pricing: Linking Pricing Decisions with Operational Insights

The past decade has seen a virtual explosion of information about customers and their preferences. This information potentially allows companies to increase their revenues, in particular since modern technology enables price changes to be effected at minimal cost. At the same time, companies have taken major strides in understanding and managing the dynamics of the supply chain, both their internal operations and their relationships with supply chain partners. These two developments are narrowly intertwined. Pricing decisions have a direct effect on operations and visa versa. Yet, the systematic integration of operational and marketing insights is in an emerging stage, both in academia and in business practice. This article reviews a number of key linkages between pricing and operations. In particular, it highlights different drivers for dynamic pricing strategies. Through the discussion of key references and related software developments we aim to provide a snapshot into a rich and evolving field.supply chain management;inventory;capacity;dynamic pricing;operations-marketing interface

Smart Pricing: Linking Pricing Decisions with Operational Insights

The past decade has seen a virtual explosion of information about customers and their preferences. This information potentially allows companies to increase their revenues, in particular since modern technology enables price changes to be effected at minimal cost. At the same time, companies have taken major strides in understanding and managing the dynamics of the supply chain, both their internal operations and their relationships with supply chain partners. These two developments are narrowly intertwined. Pricing decisions have a direct effect on operations and visa versa. Yet, the systematic integration of operational and marketing insights is in an emerging stage, both in academia and in business practice. This article reviews a number of key linkages between pricing and operations. In particular, it highlights different drivers for dynamic pricing strategies. Through the discussion of key references and related software developments we aim to provide a snapshot into a rich and evolving field

A Dynamic Pricing Model for Coordinated Sales and Operations

Recent years have seen advances in research and management practice in the area of pricing, and particularly in dynamic pricing and revenue management. At the same time, researchers and managers have made dramatic improvements in operations and supply chain management. The interactions between pricing and operations/supply chain performance, however, are not as well understood. In this paper, we examine this linkage by developing a deterministic, finite-horizon dynamic programming model that captures a price/demand effect as well as a stockpiling/consumption effect – price and market stockpile influence demand, demand influences consumption, and consumption influences the market stockpile. The decision variable is the unit sales price in each period. Through the market stockpile, pricing decisions in a given period explicitly depend on decisions in prior periods. Traditional operations models typically assume exogenous demand, thereby ignoring some of the market dynamics. Herein, we model endogenous demand, and we develop analytical insights into the nature of optimal prices and promotions. We develop conditions under which the optimal prices converge to a constant. In other words, price promotion is suboptimal. We also analytically and numerically illustrate cases where the optimal prices vary over time. In particular, we show that price dynamics may be driven by both (a) revenue effects, due to nonlinear market responses to prices and/or inventory, and (b) cost effects, due to economies of scale in operations. The paper concludes with a discussion of directions for future research

Implementation of the Newsvendor Model with Clearance Pricing: How to (and How Not to) Estimate a Salvage Value

The newsvendor model is designed to decide how much of a product to order when the product is to be sold over a short selling season with stochastic demand and there are no additional opportunities to replenish inventory. There are many practical situations that reasonably conform to those assumptions, but the traditional newsvendor model also assumes a fixed salvage value: all inventory left over at the end of the season is sold off at a fixed per-unit price. The fixed salvage value assumption is questionable when a clearance price is rationally chosen in response to the events observed during the selling season: a deep discount should be taken if there is plenty of inventory remaining at the end of the season, whereas a shallow discount is appropriate for a product with higher than expected demand. This paper solves for the optimal order quantity in the newsvendor model, assuming rational clearance pricing. We then study the performance of the traditional newsvendor model. The key to effective implementation of the traditional newsvendor model is choosing an appropriate fixed salvage value. (We show that an optimal order quantity cannot be generally achieved by merely enhancing the traditional newsvendor model to include a nonlinear salvage value function.) We demonstrate that several intuitive methods for estimating the salvage value can lead to an excessively large order quantity and a substantial profit loss. Even though the traditional model can result in poor performance, the model seems as if it is working correctly: the order quantity chosen is optimal given the salvage value inputted to the model, and the observed salvage value given the chosen order quantity equals the inputted one. We discuss how to estimate a salvage value that leads the traditional newsvendor model to the optimal or near-optimal order quantity. Our results highlight the importance of understanding how a model can interact with its own inputs: when inputs to a model are influenced by the decisions of the model, care is needed to appreciate how that interaction influences the decisions recommended by the model and how the model’s inputs should be estimated

Revenue management models in the manufacturing industry

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2005.Includes bibliographical references (p. 107-110).In recent years, many manufacturing companies have started exploring innovative revenue management technologies in an effort to improve their operations and ultimately their bottom lines. Methods such as differentiating customers based on their sensitivity to price and delays are employed by firms to increase their profits. These developments call for models that have the potential to radically improve supply chain efficiencies in much the same way that revenue management has changed the airline industry. In this dissertation, we study revenue management models where customers can be separated into different classes depending on their sensitivity to price, lead time, and service. Specifically, we focus on identifying effective models to coordinate production, inventory and admission controls in face of multiple classes of demand and time- varying parameters. We start with a single-class-customer problem with both backlogged and discretionary sales. Demand may be fulfilled no later than N periods with price discounts if the inventory is not available. If profitable, demand may be rejected even if the inventory is still available.(cont.) For this problem we analyze the structure of the optimal policy and show that it is characterized by three state-independent control parameters: the produce-up-to level (S), the reserve-up-to level (R), and the backlog-up-to level (B). At the beginning of each period, the manufacturer will produce to bring the inventory level up to S or to the maximum capacity; during the period, s/he will set aside R units of inventory for the next period, and satisfy some customers with the remaining inventory, if expected future profit is higher; otherwise, s/he will satisfy customers with the inventory and backlog up to B units of demands. Then, we analyze a single-product, two-class-customer model in which demanding (high priority) customers would like to receive products immediately, while other customers are willing to wait in order to pay lower prices. For this model, we provide a heuristic policy characterized by three threshold levels: S, R, B.(cont.) In this policy, during each period, the manufacturer will set aside R units of inventory for the next period, and satisfy some high priority customers with the remaining inventory, if expected future profit is higher; otherwise, s/he will satisfy as many of the high priority customers as possible and backlog up to B units of lower priority customers. Finally, we examine production, rationing, and admission control policies in manufacturing systems with both make-to-stock(MTS) and make-to-order(MTO) products. Two models are analyzed. In the first model, which is motivated by the automobile industry, the make-to-stock product has higher priority than the make-to-order product. In the second model, which is motivated by the PC industry, the manufacturer gives higher priority to the make-to-order product over the make-to-stock product. We characterize the optimal production and order admission policies with linear threshold levels. We also extend those results to problems where low-priority backorders can be canceled by the manufacturer, as well as to problems with multiple types of make-to-order products.by Tieming Liu.Ph.D

Optimal Allocation of Inventory and Demand for Managing Supply Chain Revenues

This dissertation focuses on three distinct yet related problems that are motivated by practices of electronics manufacturers, who satisfy stochastic demand from multiple markets and multisource parts from several suppliers. The first problem investigates joint replenishment and allocation decisions for a supplier who satisfies stochastic demand from a primary market and a spot market. We formulate the problem as a multi-period stochastic dynamic program and show that the optimal policy is characterized by two quantities: the critical produce-up-to level and the critical retain-up-to level. We establish bounds for these two quantities, discuss their economic interpretation, and use them to construct a new and effective heuristic policy. We identify two practical benchmark policies and establish thresholds on the unit revenue earned from the spot market such that one of the two benchmark policies is optimal. Using a computational study, we quantify the benefits of the optimal policy relative to the benchmark policies and examine the effects of demand correlation. The second problem investigates an important extension where a supplier faces stochas- tic demand from Class 1 along with price-sensitive stochastic demand from Class 2. We investigate the supplier’s joint replenishment, allocation and pricing problem by formulating it as a multi-period, two-stage stochastic dynamic program. We show that a dynamic pricing policy is optimal at stage 2, and the stage 1 optimal policy is characterized by two quantities: the critical produce-up-to level and the critical amount of inventory to be protected from Class 1. In contrast to the optimal policy, myopic policies are less costly to evaluate, and hence, are more practical. We establish two sufficient conditions under which a myopic joint inventory and pricing policy is optimal. Using a computational study, we show that the benefits of dynamic pricing to Class 2 are higher than the benefits of discretionary sales to Class 1. While the first two problems consider a supplier’s decision under stochastic demand from multiple markets, the third problem considers decisions of a buyer who satisfies stochastic demand by multi-sourcing parts with percentage supply allocations (PSAs). We define PSA as a pre-negotiated percentage of a multi-sourced part’s total demand that the buyer should allocate to a supplier. During recent industry collaboration, we observed that in such settings the buyer’s demand allocation decisions are challenging due to operational changes needed for (temporarily) switching suppliers, and lead to the bullwhip effect. Demand allocation policies that can meet PSAs and the resulting bullwhip effect have not been investigated in the literature before. We contribute to the existing literature by introducing and analyzing the concept of bullwhip effect under multi-sourcing. In addition, we propose and investigate three demand allocation policies: (i) random allocation policy (RAP), which benchmarks the current practice, (ii) time-based (CCP-T) and (iii) quantity-based cyclic consumption (CCP-Q) policies. We show that while RAP and CCP-T always lead to bullwhip effect, the bullwhip ratio under CCP-Q can be less than 1. We demonstrate that CCP-T and CCP-Q can reduce the supplier’s bullwhip effect without increasing the buyer’s expected long-run average number of supplier switches compared to RAP

Economic evaluation in decision models: a critical review and methodological propositions

International audienceDecision models of industrial management articles are often based on an economic criterion to find the proposed solution. They use economic parameters that are generally imported from the firm cost accounting system. When cost information is not adapted to the decision, the obtained solution of the model may be invalid. In this article, we deal with a critical literature review to report the methodological problems encountered in industrial management articles vis-à-vis the used costs. Finally we suggest methodological propositions to be kept in mind by authors when they are using costs in decision models

Inventory Signals

Among practitioners, inventory is often thought to be the root of all evil in operations management. The stock market hates it, the media abhors it, and managers have come to fear it. But high inventory levels can also be the result of strategic buying and high-availability strategies. The problem is that when the market sees lots of inventory, it cannot tell whether it is because of poor or smart operations. We hypothesize that inventory has a signaling role. In our model, publicly- traded firms use inventory levels to signal their operational competence to the market. There is a separating equilibrium that leads some firms to maintain inventory levels below what their capability could achieve. We offer this as one explanation why, for example, stock-outs are pervasive even among operationally competent firms. We provide empirical evidence for the assumptions behind this inventory signaling hypothesis: (1) the market cannot tell the difference between “good” and “bad” inventory; and (2) the counterfactual: the market punishes firms when it can tell that their inventory is bad, such as when they write off supplies. Consistent with these assumptions, we find that inventory levels do not explain firm value. And on average, stocks suffer an abnormal negative return of 7% in the month of announcing inventory write-offs.Inventory, signaling, operations management, asymmetric information

Essays on Macroeconomics and Business Cycles

This dissertation consists of three essays on macroeconomics and business cycles. In the first chapter, written with Nicolas Crouzet, we ask whether news shocks, which change agents' expectations about future fundamentals, are an important source of business-cycle fluctuations. The existing literature has provided a wide range of answers, finding that news shocks can account for 10 percent to 60 percent of the volatility of output. We show that looking at the dynamics of inventories, so far neglected in this literature, cleanly isolates the role of news shocks in driving business cycles. In particular, inventory dynamics provide an upper bound on the explanatory power of news shocks. We show, for a broad class of theoretical models, that finished-good inventories must fall when there is an increase in consumption and investment induced by news shocks. When good news about future fundamentals lowers expected future marginal costs, firms delay current production and satisfy the increase in demand by selling from existing inventories. This result is robust across the nature of the news and the presence of different types of adjustment costs. We therefore propose a novel empirical identification strategy for news shocks: negative comovement between inventories and components of private spending. Estimating a structural VAR with sign restrictions on inventories, consumption and investment, our identified shock explains at most 20 percent of output variations. Intuitively, since inventories are procyclical in the data, shocks that generate negative comovement between inventories and sales cannot account for the bulk of business-cycle fluctuations. The second chapter looks into the dynamics of durables over the business cycle. Although transactions of used durables are large and cyclical, their interaction with purchases of new durables has been neglected in the study of business cycles. I fill in this gap by introducing a new model of durables replacement and second-hand markets. The model generates a discretionary replacement demand function, it nests a standard business-cycle model of durables, and it verifies the Coase conjecture. The model delivers three conclusions: markups are smaller for goods that are more durable and more frequently replaced; markups are countercyclical for durables, resolving the comovement puzzle of Barsky, House, and Kimball (2007); and procyclical replacement demand amplifies durable consumption. In the third chapter, written with Ricardo Reis, we study the macroeconomic implications of government transfers. Between 2007 and 2009, government expenditures increased rapidly across the OECD countries. While economic research on the impact of government purchases has flourished, in the data, about three quarters of the increase in expenditures in the United States (and more in other countries) was in government transfers. We document this fact, and show that the increase in U.S. spending on retirement, disability, and medical care has been as high as the increase in government purchases. We argue that future research should focus on the positive impact of transfers. Towards this, we present a model in which there is no representative agent and Ricardian equivalence does not hold because of uncertainty, imperfect credit markets, and nominal rigidities. Targeted lump-sum transfers are expansionary both because of a neoclassical wealth effect and because of a Keynesian aggregate demand effect

Joint Inventory and Fulfillment Decisions for Omnichannel Retail Networks

With e-commerce growing at a rapid pace compared to traditional retail, many brick-and-mortar firms are supporting their online growth through an omnichannel approach, which integrates inventories across multiple channels. We analyze the inventory optimization of three such omnichannel fulfillment systems for a retailer facing two demand streams (online and in-store). The systems differ in the level of fulfillment integration, ranging from no integration (separate fulfillment center for online orders), to partial integration (online orders fulfilled from nearest stores) and full integration (online orders fulfilled from nearest stores, but in case of stockouts, can be fulfilled from any store). We obtain optimal order-up-to quantities for the analytical models in the two-store, single-period setting. We then extend the models to a generalized multi-store setting, which includes a network of traditional brick-and-mortar stores, omnichannel stores and online fulfillment centers. We develop a simple heuristic for the fully-integrated model, which is near optimal in an asymptotic sense for a large number of omnichannel stores, with a constant approximation factor dependent on cost parameters. We augment our analytical results with a realistic numerical study for networks embedded in the mainland US, and find that our heuristic provides significant benefits compared to policies used in practice. Our heuristic achieves reduced cost, increased efficiency and reduced inventory imbalance, all of which alleviate common problems facing omnichannel retailing firms. Finally, for the multiperiod setting under lost sales, we show that a base-stock policy is optimal for the fully-integrated model.With e-commerce growing at a rapid pace compared to traditional retail, many brick-and-mortar firms are supporting their online growth through an omnichannel approach, which integrates inventories across multiple channels. We analyze the inventory optimization of three such omnichannel fulfillment systems for a retailer facing two demand streams (online and in-store). The systems differ in the level of fulfillment integration, ranging from no integration (separate fulfillment center for online orders), to partial integration (online orders fulfilled from nearest stores) and full integration (online orders fulfilled from nearest stores, but in case of stockouts, can be fulfilled from any store). We obtain optimal order-up-to quantities for the analytical models in the two-store, single-period setting. We then extend the models to a generalized multi-store setting, which includes a network of traditional brick-and-mortar stores, omnichannel stores and online fulfillment centers. We develop a simple heuristic for the fully-integrated model, which is near optimal in an asymptotic sense for a large number of omnichannel stores, with a constant approximation factor dependent on cost parameters. We augment our analytical results with a realistic numerical study for networks embedded in the mainland US, and find that our heuristic provides significant benefits compared to policies used in practice. Our heuristic achieves reduced cost, increased efficiency and reduced inventory imbalance, all of which alleviate common problems facing omnichannel retailing firms. Finally, for the multiperiod setting under lost sales, we show that a base-stock policy is optimal for the fully-integrated model.http://deepblue.lib.umich.edu/bitstream/2027.42/136157/1/1341_Govindarajan.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136157/4/1341_Govindarajan_Apr2017.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136157/6/1341_Govindarajan_Jan18.pdfDescription of 1341_Govindarajan_Apr2017.pdf : April 2017 revisionDescription of 1341_Govindarajan_Jan18.pdf : January 2018 revisio