Download or read book Data Driven Dynamic Pricing and Inventory Management of an Omni Channel Retailer in an Uncertain Demand Environment written by Shiyu Liu and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, omni-channel retailing has become immensely popular among both retailers and consumers. In this approach, retailers often leverage their brick-and-mortar stores to fulfill online orders, leading to the need for simultaneous decision-making on replenishment and inventory rationing. This inventory strategy presents significant complexities in traditional dynamic pricing and inventory management problems, particularly in unpredictable market environments. Therefore, we have developed a dynamic pricing, replenishment, and rationing model for omni-channel retailers using a two-level partially observed Markov decision process to visualize the dynamic process. We design a deep reinforcement learning algorithm, called Maskable LSTM-Proximal Policy Optimization (ML-PPO), which integrates the current observations and future predictions as input to the agent and uses the invalid action mask to guarantee the allowable actions. Our simulation experiments have demonstrated the ML-PPO's efficiency in maximizing retailer profit and service level, along with its generalized ability to tackle dynamic pricing and inventory management problems.

Download or read book Data driven Pricing and Inventory Management with Applications in Fashion Retail written by Mila Nambiar and published by . This book was released on 2019 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fashion retail is typically characterized by (1) high demand uncertainty and products with short life cycles, which complicates demand forecasting, and (2) low salvage values and long supply lead times, which penalizes for inaccurate demand forecasting. In this thesis, we are interested in the design of algorithms that leverage fashion retail data to improve demand forecasting, and that make revenue-maximizing or cost-minimizing pricing and inventory management decisions. First, we study a multi-period dynamic pricing problem with feature information. We are especially interested in demand model misspecification, and show that it can lead to price endogeneity, and hence inconsistent price elasticity estimates and suboptimal pricing decisions. We propose a "random price shock" (RPS) algorithm that combines instrumental variables, well known in econometrics, with online learning, in order to simultaneously estimate demand and optimize revenue. We demonstrate strong theoretical guarantees on the regret of RPS for both IID and non ID features, and numerically validate the algorithm's performance on synthetic data. Next, we present a case study in collaboration with Oracle Retail. We extend RPS to incorporate common business constraints such as markdown pricing and inventory constraints. We then conduct a counterfactual analysis where we simulate the algorithm's performance using fashion retail data. Our analysis estimates that the RPS algorithm will increase by 2-7% relative to current practice. Finally, we study an inventory allocation problem in a single-warehouse multiple-retailer setting with lost sales. We show that under general conditions this problem is convex, and that a Lagrangian relaxation-based approach can be applied to solve it in a computationally tractable, and near-optimal way. This analysis allows us to prove structural results that give insights into how the allocation policy should depend on factors such as the retailer demand distributions, and demand learning.

Download or read book Operations in an Omnichannel World written by Santiago Gallino and published by Springer Nature. This book was released on 2019-10-15 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: The world of retailing has changed dramatically in the past decade. Sales originating at online channels have been steadily increasing, and even for sales transacted at brick-and-mortar channels, a much larger fraction of sales is affected by online channels in different touch points during the customer journey. Shopper behavior and expectations have been evolving along with the growth of digital channels, challenging retailers to redesign their fulfillment and execution processes, to better serve their customers. This edited book examines the challenges and opportunities arising from the shift towards omni- channel retail. We examine these issues through the lenses of operations management, emphasizing the supply chain transformations associated with fulfilling an omni-channel demand. The book is divided into three parts. In the first part, “Omni-channel business models”, we present four studies that explore how retailers are adjusting their fundamental business models to the new omni-channel landscape. The second part, “Data-driven decisions in an omni-channel world”, includes five chapters that study the evolving data opportunities enabled by omni-channel retail and present specific examples of data-driven analyses. Finally, in the third part, “Case studies in Omni-channel retailing”, we include four studies that provide a deep dive into how specific industries, companies and markets are navigating the omni-channel world. Ultimately, this book introduces the reader to the fundamentals of operations in an omni-channel context and highlights the different innovative research ideas on the topic using a variety of methodologies.

Download or read book Study of Customer Behavior in a Revenue Management Setting Using Data driven Approaches written by Sareh Nabi-Abdolyousefi and published by . This book was released on 2018 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this study is to propose novel dynamic pricing mechanisms in the presence of strategic customers using data-driven approaches. Dynamic pricing is the latest trend in pricing strategies and allows optimal response to real-time demand and supply information. Firms often face uncertainties when making pricing decisions. One of the uncertainties often involved is unknown demand. Therefore, businesses seek to optimize revenue while learning demand and reducing the uncertainty involved in setting prices. Understanding consumer decision-making is another crucial aspect of pricing in revenue management. One of the detrimental effects of dynamic pricing is that it invokes a type of behavior in customers that is referred to as forward-looking, or strategic, in revenue management literature. The strategic customer considers future price decreases, and purchases the product if his or her discounted surplus is higher than the immediate surplus. In chapters 1 and 2, we study a retailer who is pricing dynamically to maximize his expected cumulative revenue. We assume that the retailer has no information regarding expected demand nor the type of customers he is facing, whether they are myopic or strategic in their shopping behavior. In the problem of dynamic pricing under demand uncertainty, we face an inherent trade-off between the exploration involved in learning demand and the exploitation which occurs due to revenue maximization. One way of modeling this trade-off is using the multi-arm bandit modeling approach. Many algorithms have been proposed to solve stochastic multi-arm bandit problems. Our focus is on the Thompson Sampling (TS) algorithm which takes a Bayesian approach and was introduced by William R. Thompson. We propose a pricing mechanism called Strategic Thompson Sampling algorithm which is built upon the TS algorithm. Our main contribution in these two chapters is to merge the literature on strategic behavior with the literature on dynamic pricing and demand learning based on the classical multi-arm bandit modeling approach. In these chapters, the retailer is applying our proposed Strategic Thompson Sampling algorithm to learn expected demand in an exploration-versus-exploitation fashion. We start our analysis with a Bernoulli demand scenario in chapter 1 and extend our work to a Normal demand scenario in chapter 2. For both Bernoulli and Normal demand scenarios, we demonstrate numerically that the retailer's long run price offer decreases as the patience level of the strategic customer increases. We further show that the retailer can be better off in terms of his expected cumulative revenue when facing strategic customers. One potential explanation for this observation is the retailer's lower exploration of non-optimal arms in the presence of strategic customers rather than myopic ones. Our intuition is analytically and numerically confirmed for both Bernoulli and Normal demand scenarios. We further provide and compare expected regret bounds on the retailer's expected cumulative revenue for both types of customers. We conclude that the retailer's regret is lower when facing strategic customers as compared to myopic ones. Our objective in chapter 3 is to improve our starting point by building an informative prior and more specifically, an empirical Bayes prior for the Bayesian online learning algorithm that performs binary prediction. The underlying model used in this chapter is a Bayesian Linear Probit (BLIP) model which performs binary classification on a public data set called "Census Income Data Set". Our goal is to build an informative prior using a portion of the training data set and start the BLIP model with the built-in prior rather than the non-informative standard Normal distributions. We further compare the prediction accuracies of the BLIP model with informative and non-informative priors. An empirical Bayes model (Blip with empirical Bayes prior) has been implemented recently in the production system of one of the largest online retailers. The web-lab experiment is currently running.

Download or read book Dynamic Pricing Under Demand Uncertainty in the Presence of Strategic Consumers written by Yinhan Meng and published by . This book was released on 2011 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study the effect of strategic consumer behavior on pricing, inventory decisions, and inventory release policies of a monopoly retailer selling a single product over two periods facing uncertain demand. We consider the following three-stage two-period dynamic pricing game. In the first stage the retailer sets his inventory level and inventory release policy; in the second stage the retailer faces uncertain demand that consists of both myopic and strategic consumers. The former type of consumers purchase the good if their valuations exceed the posted price, while the latter type of consumers consider future realizations of prices, and hence their future surplus, before deciding when to purchase the good; in the third stage, the retailer releases its remaining inventory according to the release policy chosen in the first stage. Game theory is employed to model strategic decisions in this setting. Each of the strategies available to the players in this setting (the consumers and the retailer) are solved backward to yield the subgame perfect Nash equilibrium, which allows us to derive the equilibrium pricing policies. This work provides three primary contributions to the fields of dynamic pricing and revenue management. First, if, in the third stage, inventory is released to clear the market, then the presence of strategic consumers may be beneficial for the retailer. Second, we find the optimal inventory release strategy when retailers have capacity limitation. Lastly, we numerically demonstrate the retailer's optimal decisions of both inventory level and the inventory release strategy. We find that market clearance mechanism and intermediate supply strategy may emerge as the retailers optimal choice.

Download or read book Dynamic Pricing and Inventory Management written by Renyu Philip Zhang and published by . This book was released on 2016 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop the models and methods to study the impact of some emerging trends in technology, marketplace, and society upon the pricing and inventory policy of a firm. We focus on the situation where the firm is in a dynamic, uncertain, and (possibly) competitive market environment. The market trends of particular interest to us are: (a) social networks, (b) sustainability concerns, and (c) customer behaviors. The two main running questions this dissertation aims to address are: (a) How these emerging market trends would influence the operations decisions and profitability of a firm; and (b) What pricing and inventory strategies a firm could use to leverage these trends. We also develop an effective comparative statics analysis method to address these two questions under different market trends. Overall, our results suggest that the current market trends of social networks, sustainability concerns, and customer behaviors have significant and interesting impact upon the operations policy of a firm, and that the firm could adopt some innovative pricing and inventory strategies to exploit these trends and substantially improve its profit. Our main findings are: (a) Network externalities (the monopoly setting). We find that network externalities prompt a firm to face the tradeoff between generating current profits and inducing future demands when making the price and inventory decisions, so that it should increase the base-stock level, and to decrease [increase] the sales price when the network size is small [large]. Our extensive numerical experiments also demonstrate the effectiveness of the heuristic policies that leverage network externalities by balancing generating current profits and inducing demands in the near future. (Chapter 2.) (b) Network externalities (the dynamic competition setting). In a competitive market with network externalities, the competing firms face the tradeoff between generating current profits and winning future market shares (i.e., the exploitation-induction tradeoff). We characterize the pure strategy Markov perfect equilibrium in both the simultaneous competition and the promotion-first competition. We show that, to balance the exploitation-induction tradeoff, the competing firms should increase promotional efforts, offer price discounts, and improve service levels. The exploitation-induction tradeoff could be a new driving force for the fat-cat effect (i.e., the equilibrium promotional efforts are higher under the promotion-first competition than those under the simultaneous competition). (Chapter 3.) (d) Trade-in remanufacturing. We show that, with the adoption of the very commonly used trade-in remanufacturing program, the firm may enjoy a higher profit with strategic customers than with myopic customers. Moreover, trade-in remanufacturing creates a tension between firm profitability and environmental sustainability with strategic customers, but benefits both the firm and the environment with myopic customers. We also find that, with either strategic or myopic customers, the socially optimal outcome can be achieved by using a simple linear subsidy and tax scheme. The commonly used government policy to subsidize for remanufacturing alone, however, does not induce the social optimum in general. (Chapter 4.) (d) Scarcity effect of inventory. We show that the scarcity effect drives both optimal prices and order-up-to levels down, whereas increased operational flexibilities (e.g., the inventory disposal and inventory withholding opportunities) mitigate the demand loss caused by high excess inventory and increase the optimal order-up-to levels and sales prices. Our extensive numerical studies also demonstrate that dynamic pricing leads to a much more significant profit improvement with the scarcity effect of inventory than without. (Chapter 5.) (e) Comparative statics analysis method. We develop a comparative statics method to study a general joint pricing and inventory management model with multiple demand segments, multiple suppliers, and stochastically evolving market conditions. Our new method makes componentwise comparisons between the focal decision variables under different parameter values, so it is capable of performing comparative statics analysis in a model where part of the decision variables are non-monotone, and it is well scalable. Hence, our new method is promising for comparative statics analysis in other operations management models. (Chapter 6.)

Download or read book Near optimal Data driven Approximation Schemes for Joint Pricing and Inventory Control Models written by Hanzhang Qin (S. M.) and published by . This book was released on 2018 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: The thesis studies the classical multi-period joint pricing and inventory control problem in a data-driven setting. In the problem, a retailer makes periodic decisions of the prices and inventory levels of an item that the retailer wishes to sell. The objective is to match the inventory level with a random demand that depends on the price in each period, while maximizing the expected profit over finite horizon. In reality, the demand functions or the distribution of the random noise are usually unavailable, whereas past demand data are relatively easy to collect. A novel data-driven nonparametric algorithm is proposed, which uses the past demand data to solve the joint pricing and inventory control problem, without assuming the parameters of the demand functions and the noise distributions are known. Explicit sample complexity bounds are given, on the number of data samples needed to guarantee a near-optimal profit. A simulation study suggests that the algorithm is efficient in practice.

Download or read book Omni Channel Retailing and Its Requirements in the Supply Chain written by Carina Sauter and published by GRIN Verlag. This book was released on 2015-12-30 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bachelor Thesis from the year 2014 in the subject Business economics - Supply, Production, Logistics, grade: 1,1, IE Business School, Madrid, course: Production & Supply Chain Management, language: English, abstract: This thesis is a description of the state of the art of the Omnichannel retail strategy with a focus on the changes necessary in supply chain design. Its purpose is to provide a comprehensive overview on omnichannel retailing that can serve as a first source of information for companies thinking about adapting this strategy. There is no single external source that combines the description of omnichannel retailing with details of how to implement this strategy yet, so this thesis makes it significantly easier for retailers to familiarize with the topic and get impulses for further research. The introduction shows the developments that led to the strategic move, which eases understanding the concept and its purpose. The thesis finds that technological innovations, changing shopping behavior, increasing expectations, and intensifying online competition were the major drivers affecting this shift. It continues to describe the common omnichannel initiatives before it gets into more detail on what supply chain and logistics changes are necessary to support them. First, it shows that the application of RFID technology and IT platforms creates an end-to-end transparent supply chain, which delivers the core capability to pursue this strategy: complete inventory visibility. Second, the solutions to improve fulfillment speed are presented. Both upgrades in order processing inside the warehouse and innovative last mile solutions are discussed in detail. Describing the benefits of omnichannel retailing, the paper shows that it perfectly meets the requirements of today’s retailers. Not only does the strategy improve profitability and productivity, it also helps them meet expectations, learn more about their customers and use this knowledge to sustainably compete in the market. It also finds that there are significant challenges to overcome before reaping these benefits. Large investments and added complexity need to be faced during setup and a non-aligned organization and the inability to engage employees are major problems during execution. As the retail environment evolves at a rapid pace, the paper finally presents strategies for companies that have fully developed omnichannel capabilities. These ensure that retailers can also compete once omnichannel is the new normal.

Download or read book Data Based Dynamic Pricing and Inventory Control with Censored Demand and Limited Price Changes written by Boxiao Chen and published by . This book was released on 2020 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt: A firm makes pricing and inventory replenishment decisions for a product over T periods to maximize its expected total profit. Demand is random and price sensitive, and unsatisfied demands are lost and unobservable (censored demand). The firm knows the demand process up to some parameters and needs to learn them through pricing and inventory experimentation. However, due to business constraints the firm is prevented from making frequent price changes, leading to correlated and dependent sales data. We develop data-driven algorithms by actively experimenting inventory and pricing decisions and construct maximum likelihood estimator with censored and correlated samples for parameter estimation. We analyze the algorithms using the T-period regret, defined as the profit loss of the algorithms over T periods compared with the clairvoyant optimal policy that knew the parameters a priori. For a so-called well-separated case, we show that the regret of our algorithm is O(T^{1/(m+1)}) when the number of price changes is limited by m >= 1, and is O( log T) when limited by beta log T for some positive constant beta>0; while for a more general case, the regret is O(T^{1/2}) when the underlying demand is bounded and O(T^{1/2} log T) when the underlying demand is unbounded. We further prove that our algorithm for each case is the best possible in the sense that its regret rate matches with the theoretical lower bound.

Download or read book Omni channel Retailing Impacts and challenges on the supply chain written by Cindy Schröder and published by GRIN Verlag. This book was released on 2019-01-21 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: Seminar paper from the year 2017 in the subject Business economics - Operations Research, grade: 1,0, European School of Business Reutlingen, language: English, abstract: The aim of this case study is to identify impacts and challenges of an omni-channel business model on the retail supply chain. Based on this, recommendations and possible solutions will be presented which the retail should adapt along its supply chain in order to respond to the identified challenges.

Download or read book Dynamic Pricing and Inventory Control written by Elodie Adida and published by VDM Publishing. This book was released on 2007 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: (cont.) We introduce and study a solution method that enables to compute the optimal solution on a finite time horizon in a monopoly setting. Our results illustrate the role of capacity and the effects of the dynamic nature of demand. We then introduce an additive model of demand uncertainty. We use a robust optimization approach to protect the solution against data uncertainty in a tractable manner, and without imposing stringent assumptions on available information. We show that the robust formulation is of the same order of complexity as the deterministic problem and demonstrate how to adapt solution method. Finally, we consider a duopoly setting and use a more general model of additive and multiplicative demand uncertainty. We formulate the robust problem as a coupled constraint differential game. Using a quasi-variational inequality reformulation, we prove the existence of Nash equilibria in continuous time and study issues of uniqueness. Finally, we introduce a relaxation-type algorithm and prove its convergence to a particular Nash equilibrium (normalized Nash equilibrium) in discrete time.

Download or read book Supply Chain Challenges for Retailers in an Omni channel Environment written by Xiaomeng Guo and published by . This book was released on 2016 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: To capitalize on the e-commerce growth, many retailers are making the necessary investments that will allow them to sell their merchandise online. Traditionally, the online channel has been viewed as a separate way to sell products. Nowadays, many firms have realized the need to provide consumers with a seamless shopping experience, which leads to the "omni-channel" retailing. Recent surveys and studies show that consistent products and consistent pricing have been considered as the top 2 most critical attributes of "omni-channel" retailing by consumers. Although a number of theories suggest efficiency and strategic differences between channels, there is virtually no work on combining these into an "omni-channel" studies. In the first chapter, we undertake to close this gap with a theoretical study that focuses on comparing the omni-channel retailing and the traditional multi-channel retailing from the perspective of consistent product and pricing.To do this, we consider a market where there is a single manufacturer who is capable of producing up to two products versions. The manufacturer sells his products through his own online channel and a retailer's traditional brick-and-mortar store; both channels face uncertainty market size and compete against an outside retail market. Under an omni-channel setting, the manufacturer's online channel and the retailer's brick-and-mortar store are required to offer the same product at the same retail price; while under a traditional dual-channel setting, the products and retail prices across the two channels are allowed to be different. We characterize situations when an omni-channel strategy could benefit the manufacturer and the retailer. We first study the centralized supply chain where the manufacturer and the retailer are managed by an integrated firm, and then examine the decentralized supply chain where the manufacturer owns the online channel and an independent retailer owns the brick-and-mortar retail store.We find that in a centralized supply chain, the integrated firm is always worse off under the omni-channel setting since the channel consistency requirement constraints the integrated firm's product offering and pricing decisions. However, in a decentralized supply chain, the omni-channel strategy could benefit both the manufacturer and the retailer in the situations where the competition between the manufacturer's online channel and the retailer's brick-and-mortar is intense and neither channel has clear advantage over the other. This is because through synchronizing product and pricing across channels, both the manufacturer and the retailer are able to reduce competition between the two channels.Besides studying firms' strategies about managing multiple channels, this dissertation also examines firms' product-line expansion strategies and the effects of consumers' fairness behavior on firms' quality and pricing strategies.In the second chapter, we study manufacturers' product line expansion strategies in a supply chain. To expand sales, many manufacturers try to develop and sell product lines. Frequently, however, the distribution of a product line to consumers creates tensions between a manufacturer and a retailer as the retailer may choose to stock only some product versions from a product line created by the manufacturer. To mitigate this tension, previous literature has shown that if a manufacturer (he) wants to sell his product line through a retailer (she) who faces deterministic demand, then he needs to customize the product line design according to her requirements. Also, the design requirements may change across retailers. In contrast, in this chapter we show that if demand is stochastic, then a manufacturer can mitigate the same tension merely by re-allocating inventory risk in the supply chain. Surprisingly, this strategy can be so powerful that it is possible to find cases where the equilibrium product line includes more product versions when the manufacturer sells through a retailer than when he sells directly to consumers.The model in this chapter is a bilateral supply chain with a manufacturer capable of producing multiple product designs and a retailer who faces stochastic consumer demand. The manufacturer sells his output through the retailer using one of the following variations on the classical wholesale contract: push (PH), pull (PL), or instantaneous fulfillment (IF). With PH and PL (IF), wholesale prices and quantities are decided before (after) demand is revealed. Retail prices are always set after demand is revealed. With PH (PL) the retailer (manufacturer) carries retail inventory.Taking the manufacturer's point of view, we characterize the equilibrium product line length and equilibrium contracting strategy. Our answers are determined by three important drivers: demand variability, product substitutability, and the retailer's outside option. Low outside option and low (high) substitutability imply that the manufacturer maximizes his expected profit by offering the retailer longer (shorter) product line using the IF contract. As outside option increases, the equilibrium contract will be either PH or PL. High demand variability and low substitutability imply that the manufacturer should be expected to sell a longer product line with a PH contract. Low demand variability and high substitutability imply that the manufacturer should be expected to sell a shorter product line with a PL contract.In the third chapter, we study the effects of consumers' fairness concerns on firms' quality and pricing decisions. Empirical evidence and behavioral research suggest that consumers may perceive a firm's price as unfair when its profit margin is too high relative to consumers' surplus. Consumers with inequity aversion experience some psychological disutility when buying products at unfair prices.In this chapter, we develop an analytical framework to investigate the effects of consumers' inequity aversion on a firm's optimal pricing and quality decisions. We highlight several findings. First, because of consumers' uncertainty about the firm's cost, the firm's optimal quality may be non-monotone with respect to the degree of consumers' inequity aversion. Second, stronger inequity aversion makes an inefficient firm worse off, but may benefit an efficient firm. Third, stronger inequity aversion by the consumer can actually lower the consumer's monetary payoff (economic surplus) because the firm may reduce its quality to a greater extent than it reduces its price. Lastly, as the expected cost-efficiency in the market decreases, both the expected quality and the social surplus may increase rather than decrease.

Download or read book Dynamic Pricing With Infrequent Inventory Replenishments written by Boxiao Chen and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider a joint pricing and inventory control problem where pricing can be adjusted more frequently, such as every period, than inventory ordering decisions, which are made every epoch that consists of multiple periods. This is motivated by many examples, especially for online retailers, where price is indeed much easier to change than inventory level, because changing the latter is subject to logistic and capacity constraints. In this setting, the retailer determines the inventory level at the beginning of each epoch and solves a dynamic pricing problem within each epoch with no further replenishment opportunities. The optimal pricing and inventory control policy is characterized by an intricate dynamic programming (DP) solution. We consider the situation where the demand-price function and the distribution of random demand noise are both unknown to the retailer, and the retailer needs to develop an online learning algorithm to learn those information and at the same time maximize total profit. We propose a learning algorithm based on least squares estimation and construction of an empirical noise distribution under a UCB framework and prove that the algorithm converges through the DP recursions to approach the optimal pricing and inventory control policy under complete demand information. The theoretical lower bound for convergence rate of a learning algorithm is proved based on the multivariate Van Trees inequality coupled with some structural DP analyses, and we show that the upper bound of our algorithm's convergence rate matches the theoretical lower bound.

Download or read book Data driven Retailing written by Louis-Philippe Kerkhove and published by Springer Nature. This book was released on 2022-10-05 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides retail managers with a practical guide to using data. It covers three topics that are key areas of innovation for retailers: Algorithmic Marketing, Logistics, and Pricing. Use cases from these areas are presented and discussed in a conceptual and comprehensive manner. Retail managers will learn how data analysis can be used to optimize pricing, customer loyalty and logistics without complex algorithms. The goal of the book is to help managers ask the right questions during a project, which will put them on the path to making the right decisions. It is thus aimed at practitioners who want to use advanced techniques to optimize their retail organization.

Download or read book Retail Analytics written by Anna-Lena Sachs and published by Springer. This book was released on 2014-12-10 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the challenging task of demand forecasting and inventory management in retailing. It analyzes how information from point-of-sale scanner systems can be used to improve inventory decisions, and develops a data-driven approach that integrates demand forecasting and inventory management for perishable products, while taking unobservable lost sales and substitution into account in out-of-stock situations. Using linear programming, a new inventory function that reflects the causal relationship between demand and external factors such as price and weather is proposed. The book subsequently demonstrates the benefits of this new approach in numerical studies that utilize real data collected at a large European retail chain. Furthermore, the book derives an optimal inventory policy for a multi-product setting in which the decision-maker faces an aggregated service level target, and analyzes whether the decision-maker is subject to behavioral biases based on real data for bakery products.

Download or read book Inventory Management and Demand Fulfilment in Omni channel Retail written by and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Joint Inventory and Fulfillment Decisions for Omnichannel Retail Networks written by Aravind Govindarajan and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With e-commerce growing at a rapid pace compared to traditional retail, many brick-and-mortar firms are supporting their online growth through an integrated omnichannel approach. Such integration can lead to reduction in cost that can be achieved through efficient inventory management. A retailer with a network of physical stores and fulfillment centers facing two demands (online and in-store) has to make important, interlinked decisions - how much inventory to keep at each location and where to fulfill each online order from, as online demand can be fulfilled from any location. We consider order-up-to policies for a general multi-period model with multiple locations and zero lead time, and online orders fulfilled multiple times in each period. We first focus on the case where fulfillment decisions are made at the end of each period, which allows separate focus on the inventory decision. We develop a simple, scalable heuristic for the multi-location problem based on analysis from the two-store case, and prove its asymptotic near-optimality for large number of omnichannel stores under certain conditions. We extend this to the case where fulfillment is done multiple times within a period and combine it with a simple, threshold-based fulfillment policy which reserves inventory at stores for future in-store demand. With the help of a realistic numerical study based on a fictitious retail network embedded in mainland USA, we show that the combined heuristic outperforms a myopic, decentralized planning strategy under a variety of problem parameters, especially when there is an adequate mix of online and in-store demands. Extensions to positive lead times are discussed.