Optimization of a Dual-Channel Retailing System with Customer Returns

A plethora of retailers have begun to embrace a dual-channel retailing strategy wherein items are provided to consumers through both an online store and a physical store. As a result of standards and competitive measures, many retailers provide buyers who are unhappy with their purchases with the ability to achieve a full refund. In a dualchannel retailing system, full reimbursements can be done through what is called a crosschannel return, when a buyer purchases a product from an online store and returns it to a physical store. They can also be done through what is called a same-channel return, when a buyer purchases a product from a physical store and returns it back to the physical store, or purchases a product from an online store and returns it back to the online store. No existing research has examined all common types of customer returns in the context of a dual-channel retailing system. Be notified that the practice of cross-returning an item purchased from the physical store back to the online store is not common. Thus, it is not considered in this dissertation. We first study the optimal pricing policies for a centralized and decentralized dual-channel retailer (DCR) with same- and cross-channel returns. We consider two factors: the dual-channel retailer’s performance under centralization with unified and differential pricing schemes, and the dual-channel retailer’s performance under decentralization with the Stackelberg and Nash games. How dual-channel pricing behaviour is impacted by customer preference and rates of customer returns is discussed. In this study, a channel’s sales requests is a linear function of a channel’s own pricing strategy and a cross-channel’s pricing strategy. The second problem is an extension of the first problem. The optimal pricing policies and online channel’s responsiveness level for a centralized and decentralized dual-channel retailer with same- and cross-channel returns are studied. Indeed, the online store is normally the distribution centre of the enterprise and is not limited to the customers in its neighbourhood. Also, the online store experiences a much higher return rate compared to the physical store. Thus, it has the capability and the need to optimize its responsiveness to customer returns along with its pricing strategy. A channel’s sales requests, in the second problem, is a linear function of a channel’s own price, a crosschannel’s price, and the online store’s responsiveness level. The third problem studies the dilemma of whether or not to allow unsatisfactory online purchases to be cross-returned to the physical store. If not properly considered, those returns may create havoc to the system and a retailer might overestimate or underestimate a channel’s order quantity. Therefore, we study and compare between four vi different strategies, and propose models to determine optimal order quantities for each strategy when a dual-channel retailer offers both same and cross-channel returns. Several decision making insights on choosing between the different cross-channel return strategies and some properties of the optimal solutions are presented. From the retailer’s perspective of outsourcing the e-channel’s management to a third party logistics and service provider, we finally study three different inventory strategies, namely transaction-based fee, flat-based fee, and gain sharing. For each strategy, we find both channels’ optimal inventory policies and expected profits. The performances of the different strategies are compared and the managerial insights are given using analytical and numerical analysis. Methodologies, insights, comparative analysis, and computational results are delivered in this dissertation for the above aforementioned problems

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

Substitution Effects in Supply Chains with Asymmetric Information Distribution and Upstream Competition

Inventory management in markets with substituting customers is extremely challenging, not only for a downstream wholesaler, but also for upstream manufacturers. Motivated by the structures in the agrochemical market, we analyze the optimal production and stocking quantities of a manufacturer and a wholesaler, respectively, in a two-stage supply chain with upstream competition and vertical information asymmetries. We characterize a monopolistic wholesaler's optimal stocking quantities and show that these quantities are not necessarily monotonic, neither in the available production quantities nor in the customers' substitution rates. We further derive the optimal production quantities of a monopolistic and a competitive manufacturer when they are incompletely informed about the wholesaler's stocking quantities. We find that the introduction of competition may lead to decreasing production quantities for some products. Furthermore, a product's end-of-season inventories at the manufacturer which arise due to information asymmetries may decrease even when initial production levels increase. Key words: customer substitution; supply chain; asymmetric information; competition; inventory managemen

Essays on E-Commerce and Omnichannel Retail Operations

The advent of e-commerce has impacted the retail industry, as retail firms have innovated in response to customers increasingly preferring to purchase products online. This dissertation studies operational problems that accompany such retail innovations, and provides tractable heuristic solutions developed using stochastic and robust optimization methods. In particular, the first two chapters focus on the value of fulfillment flexibility - online orders can be fulfilled from any node in the firm's fulfillment network. The first chapter is devoted to omnichannel retailing, where e-commerce demand is integrated with the physical network of stores through ship-from-store fulfillment. For a retailer with a network of physical stores and fulfillment centers facing two demands (online and in-store), we consider the following interlinked decisions - how much inventory to keep at each location and where to fulfill each online order from. We show that the value of considering fulfillment flexibility in inventory planning is highest when there is a moderate mix of online and in-store demands, and develop computationally fast heuristics with promising asymptotic performance for large scale networks, which are shown to improve upon traditional strategies. The second chapter considers a pure play e-commerce fulfillment network, and studies the inventory placement decision. As e-commerce demands are volatile due to a variety of factors (price-matching, recommendation engines, etc.), we consider a distributionally robust setting, where the objective is to minimize the worst-case expected cost under given mean and covariance matrices of the underlying demand distribution. For this NP-hard problem, we develop computationally tractable heuristic in the form of a semi-definite program, with dimension quadratic in the size of the network. In the face of distribution uncertainty, we show that the robust heuristic outperforms inventory solutions that assume incorrect distributions. The final chapter offers a new take on a classic problem in retail - customer returns, which has grown to be an important issue in recent times with firms competing to provide lenient and convenient return policies to boost their e-commerce sales. However, several customers take advantage of such policies, which can lead to loss in revenue and increase in inventory costs. We study different return policies that a firm can employ depending on the information about customers' return behavior that is available to the firm. We derive the structure of the optimal return policies and show that personalizing return policies based on customers' historical data can significantly improve the firm's profits, but allows the firm to extract all customer surplus.PHDBusiness AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153348/1/arav_1.pd

Open source solution approaches to a class of stochastic supply chain problems

This research proposes a variety of solution approaches to a class of stochastic supply chain problems, with normally distributed demand in a certain period of time in the future. These problems aim to provide the decisions regarding the production levels; supplier selection for raw materials; and optimal order quantity. The typical problem could be formulated as a mixed integer nonlinear program model, and the objective function for maximizing the expected profit is expressed in an integral format. In order to solve the problem, an open source solution package BONMIN is first employed to get the exact optimum result for small scale instances; then according to the specific feature of the problem a tailored nonlinear branch and bound framework is developed for larger scale problems through the introduction of triangular approximation approach and an iterative algorithm. Both open source solvers and commercial solvers are employed to solve the inner problem, and the results to larger scale problems demonstrate the competency of introduced approaches. In addition, two small heuristics are also introduced and the selected results are reported

Application of Optimization in Production, Logistics, Inventory, Supply Chain Management and Block Chain

The evolution of industrial development since the 18th century is now experiencing the fourth industrial revolution. The effect of the development has propagated into almost every sector of the industry. From inventory to the circular economy, the effectiveness of technology has been fruitful for industry. The recent trends in research, with new ideas and methodologies, are included in this book. Several new ideas and business strategies are developed in the area of the supply chain management, logistics, optimization, and forecasting for the improvement of the economy of the society and the environment. The proposed technologies and ideas are either novel or help modify several other new ideas. Different real life problems with different dimensions are discussed in the book so that readers may connect with the recent issues in society and industry. The collection of the articles provides a glimpse into the new research trends in technology, business, and the environment

Enhancing robustness and sparsity via mathematical optimization

Esta tesis se centra en derivar métodos robustos o dispersos bajo la perspectiva de la optimización para problemas que tradicionalmente se engloban en los campos de la Estadística o de la Investigación Operativa. Concretamente, el objetivo de esta tesis doctoral es fusionar técnicas de optimización con conceptos estadísticos para desarrollar metodologías innovadorass que puedan mejorar a los métodos ya existentes y que aúnen las matemáticas teóricas con los problemas de la vida real. Por una parte, los métodos robustos propuestos facilitarán un nuevo enfoque sobre el modelado y la interpretación de problemas clásicos del área de la Investigación Operativa, produciendo soluciones que sean resistentes a varios tipos de incertidumbre. Por otra parte, las estrategias dispersas desarrolladas para resolver problemas notorios del área de Estadística tendrán forma de Problemas No Lineales Mixtos (es decir, problemas de optimización con algunas variables enteras o binarias y función objetivo no lineal, denotados MINLP a partir de ahora). Se mostrará que los métodos propuestos no solamente son manejables computacionalmente, sino que además realzan la interpretabilidad y obtienen una buena calidad de predicción. Específicamente, el Capítulo 1 se centra en descubrir causalidades potenciales en series temporales multivariantes. Esto se lleva a cabo formulando el problema como un MINLP donde las restricciones modelan distintos aspectos de la dispersión, incluyendo restricciones que no permiten la aparición de relaciones espúreas en el modelo. El método muestra un buen rendimiento en términos de poder de predicción y de recuperación del modelo original. Análogamente, el objetivo del Capítulo 2 es descubrir cuáles son los predictores relevantes en un problema de regresión lineal, sin llevar a cabo tests de significación ya que éstos pueden fallar si existe multicolinealidad. Para ello, se formulan MINLPs que restringen los métodos de estimación seleccionados, añadiendo restricciones que miden la importancia de los predictores y que están diseñadas para evitar los problemas que produce la multicolinearidad en los datos. Los modelos restringidos muestran un buen equilibrio entre interpretabilidad y precisión. Por otra parte, en el Capítulo 3 se generaliza el problema clásico del vendedor de periódicos, asumiendo demandas correladas. En particular, una estrategia de inventario robusta, donde no se asumen hipótesis distribucionales sobre la demanda, se formula como un problema de optimización. Para el modelado de dicho problema se hace uso de técnicas que ligan conceptos estadísticos con conjuntos de incertidumbre. Las soluciones obtenidas son robustas ante la presencia de ruido con alta variabilidad en los datos, mientras evitan el exceso de conservadurismo. En el Capítulo 4 se extiende esta formulación para series temporales multivariantes. El escenario es, además, más complejo: no solamente se busca fijar los niveles de producción, sino que se quiere determinar la localización de instalaciones y la asignación de clientes a las mismas. Empíricamente se muestra que, para diseñar una cadena de suministros eficiente, es importante tener en cuenta la correlación y la variabilidad de los datos multivariantes, desarrollando técnicas basadas en los datos que hagan uso de métodos de predicción robustos. Un examen más exhaustivo de las características específicas del problema y de los conjuntos de incertidumbre se lleva a cabo en el Capítulo 5, donde se estudia el problema de selección de portfolios con costes de transacción. En este capítulo se obtienen resultados teóricos que relacionan los costes de transacción con diferentes maneras de protección ante la incertidumbre de los retornos. Como consecuencia, los resultados numéricos muestran que calibrar la penalización de los costes de transacción produce resultados que son resistentes a los errores de estimación.This thesis is focused on deriving robust or sparse approaches under an optimization perspective for problems that have traditionally fell into the Operations Research or the Statisics fields. In particular, the aim of this Ph.D. dissertation is to merge optimization techniques with statistical concepts, leading to novel methods that may outperform the classic approaches and bridge theoretical mathematics with real life problems. On one hand, the proposed robust approaches will provide new insights into the modelling and interpretation of classic problems in the Operations Research area, yielding solutions that are resilient to uncertainty of various kinds. On the other hand, the sparse approaches derived to address some up-to-the-minute topics in Statistics will take the form of Mixed Integer Non-Linear Programs (i.e. optimization problems with some integer or binary variables and non linear objective function, denoted as MINLP thereafter). The proposed methods will be shown to be computationally tractable and to enhance interpretability while attaining a good predictive quality. More specifically, Chapter 1 is focused on discovering potential causalities in multivariate time series. This is undertaken by formulating the estimation problem as a MINLP in which the constraints model different aspects of the sparsity, including constraints that do not allow spurious relationships to appear. The method shows a good performance in terms of forecasting power and recovery of the original model. Analogously, in Chapter 2 the aim is to discover the relevant predictors in a linear regression context without carrying out significance tests, since they may fail in the presence of strong collinearity. To this aim, the preferred estimation method is tightened, deriving MINLPs in which the constraints measure the significance of the predictors and are designed to avoid collinearity issues. The tightened approaches attain a good trade-off between interpretability and accuracy. In contrast, in Chapter 3 the classic newsvendor problem is generalized by assuming correlated demands. In particular, a robust inventory approach with distribution-free autoregressive demand is formulated as an optimization problem, using techniques that merge statistical concepts with uncertainty sets. The obtained solutions are robust against the presence of noises with high variability in the data while avoiding overconservativeness. In Chapter 4 this formulation is extended to multivariate time series in a more complex setting, where decisions over the location-allocation of facilities and their production levels are sought. Empirically, we illustrate that, in order to design an efficient supply chain or to improve an existent one, it is important to take into account the correlation and variability of the multivariate data, developing data-driven techniques which make use of robust forecasting methods. A closer examination of the specific characteristics of the problem and the uncertainty sets is undertaken in Chapter 5, where the portfolio selection problem with transaction costs is considered. In this chapter, theoretical results that relate transaction costs with different ways of protection against uncertainty of the returns are derived. As a consequence, the numerical experiments show that calibrating the transaction costs term yields to results that are resilient to estimation error

A multi-criteria inventory management system for perishable & substitutable products

Perishable products represent a vital area in the retail industry and our daily lives. However, when considered with product substitution (which provides more choices) the short lifetime of perishable products creates significant challenges for the inventory management (e.g., one-third of food products are wasted). The main question is: what is the suitable ‘inventory policy’ when we have products that are both perishable and substitutable? Appropriate performance metrics are proposed to evaluate the whole system and provide a robust solution while also being easy for professionals to understand and adopt. Therefore, this paper proposes to use multi-metric approach, including Order Rate Variance Ratio, Average Inventory, and Fill Rate. The paper extends inventory theory to consider inventory management of products where they possess multi-period lifetime, positive lead time, required customer service level, and each item is treated separately. Under these circumstances, as the first research adopting these easily captured and analysed performance metrics, the proposed model will enable management of realistic scenarios by incorporating multiple inventory characteristics that support cross-functional continuous improvement

Joint determination of process mean, price differentiation, and production decisions with demand leakage: A multi-objective approach

The selection of an optimal process mean is an important problem in production planning and quality control research. Most of the literature in this area has focused on the single objective problem of maximizing the profit for a fixed exogenous price. However, it is known that considering multiple objectives (such as gross income from sales, profit, and expected product uniformity) while allowing process mean, production and pricing to vary can significantly improve the profitability and performance of a firm. This article addresses this multi-objective problem while allowing the firm to sell two classes of products at differentiated prices based on their quality characteristics. These products are sold at differentiated prices depending upon their quality characteristics into primary and secondary markets at full and discounted prices respectively. Any nonconforming items are reworked at an additional cost. Due to customers heterogeneity, the firm experiences demand leakage between the two market segments. The proposed joint decision control for the firm includes the joint determination of full and discounted prices, the process mean selection, and the production quantities for each of the two product classes along with expected reworked items. A mathematical formulation of the objectives is first provided and then the multi-objective problem is transformed into a goal-programming problem. A solution procedure is developed using simulation-based optimization to identify Pareto-optimal solutions. Some important characteristics of the solution procedure are discussed and the performance of the approach is corroborated through detailed numerical experiments. 2016 Elsevier Inc.This publication was made possible by the support of an NPRP grant no. 4-173-5-025 from the Qatar National Research Fund . The statements made herein are solely the responsibility of the authors.Scopu