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Author: Mengxin Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Revenue management decisions often have both strategic and tactical components. Strategic decisions happen first and set the broad and long-term operational context in which tactical decisions are frequently and repeatedly made, often on a weekly or daily basis. We consider a joint optimization of both strategic and tactical decisions. Specifically, we examine a setting in which the strategic decision is to choose product designs (e.g., price, capacity, return eligibility, and other characteristics) and the tactical decision involves the dynamic assortment optimization over a selling season. Our formulation has many applications, including optimizing products' return eligibility and determining product discounts. We formulate an optimization problem that combines the impact on the expected revenue of both strategic and tactical decisions. To determine the product design, we reformulate the choice-based deterministic linear program, solve its continuous relaxation, and round the resulting solution. By using value function approximations, we obtain a dynamic assortment policy whose expected revenue is at least a constant fraction of the choice-based deterministic linear program, for every product design decision. Combining these two results, we show that our approach provides an approximate solution to the joint optimization with performance guarantees. Numerical experiments based on real transaction data from a major U.S. retailer show that our method has 95%-97% effectiveness, which represents a 10%-13% advantage over methods that fail to fully integrate strategic and tactical decisions. The experiments also demonstrate the prominent role of product design, which explains 85.4% of the total variation of empirically observed effectiveness across different methods.
Author: Mengxin Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Revenue management decisions often have both strategic and tactical components. Strategic decisions happen first and set the broad and long-term operational context in which tactical decisions are frequently and repeatedly made, often on a weekly or daily basis. We consider a joint optimization of both strategic and tactical decisions. Specifically, we examine a setting in which the strategic decision is to choose product designs (e.g., price, capacity, return eligibility, and other characteristics) and the tactical decision involves the dynamic assortment optimization over a selling season. Our formulation has many applications, including optimizing products' return eligibility and determining product discounts. We formulate an optimization problem that combines the impact on the expected revenue of both strategic and tactical decisions. To determine the product design, we reformulate the choice-based deterministic linear program, solve its continuous relaxation, and round the resulting solution. By using value function approximations, we obtain a dynamic assortment policy whose expected revenue is at least a constant fraction of the choice-based deterministic linear program, for every product design decision. Combining these two results, we show that our approach provides an approximate solution to the joint optimization with performance guarantees. Numerical experiments based on real transaction data from a major U.S. retailer show that our method has 95%-97% effectiveness, which represents a 10%-13% advantage over methods that fail to fully integrate strategic and tactical decisions. The experiments also demonstrate the prominent role of product design, which explains 85.4% of the total variation of empirically observed effectiveness across different methods.
Author: Chenhao Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Although assortment optimization has been extensively studied, not much is known about how it is affected by advertising. In this paper, we address this gap by considering a novel joint advertising and assortment optimization problem. To capture the effect of advertising in the context of assortment planning, we assume that one can increase the preference weight of a product by advertising it, and the degree of improvement is decided by the effectiveness of advertising, which could be product-specific, and the amount of advertising efforts allocated to that product. Given budget constraints on advertising, our objective is to find a solution, which is composed of an advertising strategy and an assortment of products, that maximizes the expected revenue. We analyze the structural properties of this problem and derive effective solutions under different settings. If there is no capacity constraint on the number of products displayed to consumers, we show that revenue-ordered assortments still maintain optimality, and we leverage this result to derive an optimal solution. For the cardinality constrained case, it is difficult to solve the optimization problem directly; therefore, we show by relaxation that a near-optimal solution can be found efficiently.
Author: Xi Chen
Publisher: Springer Nature
ISBN: 3031019261
Category : Business & Economics
Languages : en
Pages : 444
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Book Description
This book examines recent developments in Operations Management, and focuses on four major application areas: dynamic pricing, assortment optimization, supply chain and inventory management, and healthcare operations. Data-driven optimization in which real-time input of data is being used to simultaneously learn the (true) underlying model of a system and optimize its performance, is becoming increasingly important in the last few years, especially with the rise of Big Data.
Author: Junyu Cao
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Due to the sheer number of available choices, online retailers frequently use tiered assortment to present their products. In this case, groups of products are arranged across multiple pages or sections, and a customer clicks on "next'' or "load more'' to access them sequentially. Despite the prevalence of such assortments in practice, this topic has not received much attention in the existing literature. In this work, we focus on a sequential choice model which characterizes customers' behavior when product recommendations are presented in tiers. We analyze different variants of tiered assortments by imposing "no-duplication'' and/or capacity constraints, and establish the hardness result on the computation of the optimal solution. For the offline version with known customers' preferences, we characterize the properties of the optimal tiered assortment and propose an algorithm that improves the computational efficiency compared to an existing benchmark. To the best of our knowledge, we are the first to study the online version of the tiered assortment optimization problem. In particular, we consider both non-contextual and contextual settings and quantify their respective regret bound. Lastly, we perform numerical experiments to demonstrate the efficacy our proposed algorithms.
Author: Mohammed Ali Aouad
Publisher:
ISBN:
Category :
Languages : en
Pages : 256
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Book Description
Finding optimal product offerings is a fundamental operational issue in modern retailing, exemplified by the development of recommendation systems and decision support tools. The challenge is that designing an accurate predictive choice model generally comes at the detriment of efficient algorithms, which can prescribe near-optimal decisions. This thesis attempts to resolve this disconnect in the context of assortment and inventory optimization, through theoretical and empirical investigation. First, we tightly characterize the complexity of general nonparametric assortment optimization problems. We reveal connections to maximum independent set and combinatorial pricing problems, allowing to derive strong inapproximability bounds. We devise simple algorithms that achieve essentially best-possible factors with respect to the price ratio, size of customers' consideration sets, etc. Second, we develop a novel tractable approach to choice modeling, in the vein of nonparametric models, by leveraging documented assumptions on the customers' consider-then-choose behavior. We show that the assortment optimization problem can be cast as a dynamic program, that exploits the properties of a bi-partite graph representation to perform a state space collapse. Surprisingly, this exact algorithm is provably and practically efficient under common consider-then-choose assumptions. On the estimation front, we show that a critical step of standard nonparametric estimation methods (rank aggregation) can be solved in polynomial time in settings of interest, contrary to general nonparametric models. Predictive experiments on a large purchase panel dataset show significant improvements against common benchmarks. Third, we turn our attention to joint assortment optimization and inventory management problems under dynamic customer choice substitution. Prior to our work, little was known about these optimization models, which are intractable using modern discrete optimization solvers. Using probabilistic analysis, we unravel hidden structural properties, such as weak notions of submodularity. Building on these findings, we develop efficient and yet conceptually-simple approximation algorithms for common parametric and nonparametric choice models. Among notable results, we provide best-possible approximations under general nonparametric choice models (up to lower-order terms), and develop the first constant-factor approximation under the popular Multinomial Logit model. In synthetic experiments vis-a-vis existing heuristics, our approach is an order of magnitude faster in several cases and increases revenue by 6% to 16%.
Author: Anran Li
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
This thesis handles fundamental problems faced by retailers everyday: how do consumers make choices from an enormous variety of products? How to design a product portfolio to maximize the expected profit given consumers’ choice behavior? How to frame products if consumers’ choices are influenced by the display location? We solve those problems by first, constructing mathematical models to describe consumers’ choice behavior from a given offer set, i.e., consumer choice models; second, by designing efficient algorithms to optimally select the product portfolio to maximize the expected profit, i.e., assortment optimization. This thesis consists of three main parts: the first part solves assortment optimization problem under a consideration set based choice model proposed by Manzini and Mariotti (2014) [Manzini, Paola, Marco Mariotti. 2014. Stochastic choice and consideration sets. Econometrica 82(3) 1153-1176.]; the second part proposes an approximation algorithm to jointly optimize products’ selection and display; the third part works on optimally designing a product line under the Logit family choice models when a product’s utility depends on attribute-level configurations.
Author: Lihua Bai
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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This paper presents a mathematical model for jointly optimizing base stock levels for the multiple items subject to service level goals. The proposed model uses the expected demand and substitution probabilities between products as inputs and has been used to analyze the effects of demand variability on profitability under service level constraint. The results of the analysis demonstrate that neglecting customer-driven substitution or excluding the impacts of variability and correlations in demand leads to significantly inefficient assortments.
Author: Felipe Caro
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Companies such as Zara and World Co. have recently implemented novel product development processes and supply chain architectures enabling them to make more product design and assortment decisions during the selling season, when actual demand information becomes available. How should such retail firms modify their product assortment over time in order to maximize overall profits for a given selling season? Focusing on a stylized version of this problem, we study a finite horizon multiarmed bandit model with several plays per stage and Bayesian learning. Our analysis involves the Lagrangian relaxation of weakly coupled dynamic programs, results contributing to the emerging theory of DP duality, and various approximations. It yields a closed-form dynamic index policy capturing the key exploration vs. exploitation trade-off, and associated suboptimality bounds. While in numerical experiments its performance proves comparable to that of other closed-form heuristics described in the literature, our policy is particularly easy to implement and interpret. This last feature enables extensions to more realistic versions of our motivating dynamic assortment problem that include implementation delays, switching costs and demand substitution effects.
Author: Ameera Ibrahim
Publisher:
ISBN:
Category :
Languages : en
Pages : 75
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Book Description
Central to modern-time, consumer-focused retailing is the ability to provide attractive and reasonably-priced product assortments for different customer profiles. To this end, retailers can benefit from the use of data analytics in order to identify distinct customer segments, each characterized by their buying power, shopping behavior, and preferences. Further, retailers can also benefit from a careful examination of alternative procurement options and cost levers associated with products that are considered for inclusion in the assortment. Issues of assortment planning lie at the interface of operations and marketing. Profitable planning trade-os can be identified using an optimization methodology and are simultaneously driven by consumer preferences and supply cost considerations. This dissertation proposes and investigates novel, integrated optimization models for assortment planning with the following overarching objectives: (i) To reveal insights into assortment decisions under product substitutability or complementarity and multiple customer segments; (ii) to improve the computational tractability of (nonlinear discrete) optimization models that arise in such contexts and to demonstrate their efficacy for large-scale data instances. In the first essay, we investigate the joint optimization of assortment and pricing decisions for complementary retail categories with relatively popular products having high and stable sales volumes, such as fast-moving consumer goods. Each category comprises substitutable items (e.g., different coffee brands) and the categories are related by cross-selling considerations that are empirically observed in marketing studies to be asymmetric in nature. That is, a subset of customers who purchase a product from a primary category (e.g., coffee) can typically opt to also buy from one or several complementary categories (e.g., sugar and/or coffee creamer). We propose a mixed-integer nonlinear program that maximizes the retailer's profit by jointly optimizing assortment and pricing decisions for multiple categories using a deterministic maximum-surplus consumer choice model. A linear mixed-integer reformulation is developed, which effectively enables an exact solution to large, industry-sized problem instances using commercial optimization solvers. Our computational study indicates that overlooking cross-selling between retail categories can result in substantial profit losses, suboptimal (narrower) assortments, and inadequate prices. The demonstrated tractability of the proposed model paves the way for "store-wide" optimization of categories that exhibit significant complementarity, which retailers can infer from market basket analysis. The second essay addresses an assortment packing problem where a decision maker optimizes the assortment and release times of products that belong to different categories over a multi-period planning horizon. Products in a same category are substitutable, whereas products across categories may exhibit complementarity relationships. All products have a longevity over which their attractiveness gradually decays (e.g., electronics or fashion products), while being positively or negatively impacted by the specific mix of substitutable or complementary products that the retailer has introduced. Our proposed 0-1 fractional program employs an attraction demand model and subsumes recent assortment packing models in the literature. We highlight the effect of overlooking cross-selling and cannibalization on the profit using an illustrative example. We develop linearized reformulation that afford exact solutions to small-sized problem instances. Furthermore, a linear programming-based heuristic approach is devised and is demonstrated to yield near-optimal solutions for large-scale computationally challenging problem instances in manageable times. Model extensions are discussed, especially in the context of the movie industry where exhibitors have to decide on the assortment of movies to display and their optimal display times.
Author: Narendra Agrawal
Publisher: Springer
ISBN: 1489975624
Category : Business & Economics
Languages : en
Pages : 454
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Book Description
This new edition focuses on three crucial areas of retail supply chain management: (1) empirical studies of retail supply chain practices, (2) assortment and inventory planning and (3) integrating price optimization into retail supply chain decisions. The book has been fully updated, expanding on the distinguishing features of the original, while offering three new chapters on recent topics which reflect areas of great interest and relevance to the academic and professional communities alike - inventory management in the presence of data inaccuracies, retail workforce management, and fast fashion retail strategies. The innovations, lessons for practice, and new technological solutions for managing retail supply chains are important not just in retailing, but offer crucial insights and strategies for the ultimate effective management of supply chains in other industries as well. The retail industry has emerged as a fascinating choice for researchers in the field of supply chain management. It presents a vast array of stimulating challenges that have long provided the context of much of the research in the area of operations research and inventory management. However, in recent years, advances in computing capabilities and information technologies, hyper-competition in the retail industry, emergence of multiple retail formats and distribution channels, an ever increasing trend towards a globally dispersed retail network, and a better understanding of the importance of collaboration in the extended supply chain have led to a surge in academic research on topics in retail supply chain management. Many supply chain innovations (e.g., vendor managed inventory) were first conceived and successfully validated in this industry, and have since been adopted in others. Conversely, many retailers have been quick to adopt cutting edge practices that first originated in other industries. Retail Supply Chain Management: Quantitative Models and Empirical Studies, 2nd Ed. is an attempt to summarize the state of the art in this research, as well as offer a perspective on what new applications may lie ahead.
