The Role of the Mangement Sciences in Research on Personalization

We present a review of research studies that deal with personalization. We synthesize current knowledge about these areas, and identify issues that we envision will be of interest to researchers working in the management sciences. We take an interdisciplinary approach that spans the areas of economics, marketing, information technology, and operations. We present an overarching framework for personalization that allows us to identify key players in the personalization process, as well as, the key stages of personalization. The framework enables us to examine the strategic role of personalization in the interactions between a firm and other key players in the firm's value system. We review extant literature in the strategic behavior of firms, and discuss opportunities for analytical and empirical research in this regard. Next, we examine how a firm can learn a customer's preferences, which is one of the key components of the personalization process. We use a utility-based approach to formalize such preference functions, and to understand how these preference functions could be learnt based on a customer's interactions with a firm. We identify well-established techniques in management sciences that can be gainfully employed in future research on personalization.CRM, Persoanlization, Marketing, e-commerce,

Optimising supermarket promotions of fast moving consumer goods using disaggregated sales data: A case study of Tesco and their small and medium sized suppliers

The use of price promotions for fast moving consumer goods (FMCG’s) by supermarkets has increased substantially over the last decade, with significant implications for all stakeholders (suppliers, service providers & retailers) in terms of profitability and waste. The overall impact of price promotions depends on the complex interplay of demand and supply side factors, which has received limited attention in the academic literature. There is anecdotal evidence that in many cases, and particularly for products supplied by small and medium sized enterprises (SMEs), price promotions are implemented with limited understanding of these factors, resulting in missed opportunities for sales and the generation of avoidable promotional waste. This is particularly dangerous for SMEs who are often operating with tight margins and limited resources. A better understanding of consumer demand, through the use of disaggregated sales data (by shopper segment and store type) can facilitate more accurate forecasting of promotional uplifts and more effective allocation of stock, to maximise promotional sales and minimise promotional waste. However, there is little evidence that disaggregated data is widely or routinely used by supermarkets or their suppliers, particularly for those products supplied by SMEs. Moreover, the bulk of the published research regarding the impact of price promotions is either focussed on modelling consumer response, using claimed behaviour or highly aggregated scanner data or replenishment processes (frameworks and models) that bear little resemblance to the way in which the majority of food SMEs operate. This thesis explores the scope for improving the planning and execution of supermarket promotions, in the specific context of products supplied by SME, through the use of dis-aggregated sales data to forecast promotional sales and allocate promotional stock. An innovative case study methodology is used combining qualitative research to explore the promotional processes used by SMEs supplying the UK’s largest supermarket, Tesco, and simulation modelling, using supermarket loyalty card data and store level sales data, to estimate short term promotional impacts under different scenarios and derive optimize stock allocations using mixed integer linear programming (MILP). ii The results suggest that promotions are often designed, planned and executed with little formalised analysis or use of dis-aggregated sales data and with limited consideration of the interplay between supply and demand. The simulation modelling and MILP demonstrate the benefits of using supermarket loyalty card data and store level sales data to forecast demand and allocate stocks, through higher promotional uplifts and reduced levels of promotional wast

Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach

Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependences, temporal dependences, and exogenous dependences need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependences within one end-to-end learning architecture. The model is stacked and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. Experimental results, validated on real-world data provided by DiDi Chuxing, show that the FCL-Net achieves better predictive performance than traditional approaches including both classical time-series prediction models and neural network based algorithms (e.g., artificial neural network and LSTM). This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.Comment: 39 pages, 10 figure

An integrated decision making model for dynamic pricing and inventory control of substitutable products based on demand learning

Purpose: This paper focuses on the PC industry, analyzing a PC supply chain system composed of onelarge retailer and two manufacturers. The retailer informs the suppliers of the total order quantity, namelyQ, based on demand forecast ahead of the selling season. The suppliers manufacture products accordingto the predicted quantity. When the actual demand has been observed, the retailer conducts demandlearning and determines the actual order quantity. Under the assumption that the products of the twosuppliers are one-way substitutable, an integrated decision-making model for dynamic pricing andinventory control is established.Design/methodology/approach: This paper proposes a mathematical model where a large domestichousehold appliance retailer decides the optimal original ordering quantity before the selling season and theoptimal actual ordering quantity, and two manufacturers decide the optimal wholesale price.Findings:By applying this model to a large domestic household appliance retail terminal, the authors canconclude that the model is quite feasible and effective. Meanwhile, the results of simulation analysis showthat when the product prices of two manufacturers both reduce gradually, one manufacturer will often waittill the other manufacturer reduces their price to a crucial inflection point, then their profit will show aqualitative change instead of a real-time profit-price change.Practical implications: This model can be adopted to a supply chain system composed of one largeretailer and two manufacturers, helping manufacturers better make a pricing and inventory controldecision.Originality/value: Previous research focuses on the ordering quantity directly be decided. Limited workhas considered the actual ordering quantity based on demand learning. However, this paper considers boththe optimal original ordering quantity before the selling season and the optimal actual ordering quantityfrom the perspective of the retailerPeer Reviewe

Demand Estimation under Uncertain Consideration Sets

To estimate customer demand, choice models rely both on what the individuals do and do not purchase. A customer may not purchase a product because it was not offered but also because it was not considered. To account for this behavior, existing literature has proposed the so-called consider-then-choose (CTC) models, which posit that customers sample a consideration set and then choose the most preferred product from the intersection of the offer set and the consideration set. CTC models have been studied quite extensively in the marketing literature. More recently, they have gained popularity within the operations management (OM) literature to make assortment and pricing decisions. Despite their richness, CTC models are difficult to estimate in practice because firms typically do not observe customers' consideration sets. Therefore, the common assumption in OM has been that customers consider everything on offer, so the consideration set is the same as the offer set. This raises the following question: When firms only collect transaction data, do CTC models provide any predictive advantage over classic choice models? More precisely, under what conditions do CTC models outperform (if ever) classic choice models in terms of prediction accuracy? In this work, we study a general class of CTC models. We propose techniques to estimate these models efficiently from sales transaction data. We then compare their performance against the classic approach. We find that CTC models outperform standard choice models when there is noise in the offer set information and the noise is asymmetric across the training and test offer sets but otherwise lead to no particular predictive advantage over the classic approach. We also demonstrate the benefits of using CTC models in real-world retail settings. In particular, we show that CTC models calibrated on retail transaction data are better at long-term and warehouse level sales forecasts. We also evaluate their performance in the context of an online platform setting: a peer-to-peer car sharing company. In this context, offer sets are even difficult to define. We observe a remarkable performance of CTC models over standard choice models therein.Este documento es la versión aceptada del artículo publicado en Operations Research (ISSN 0030-364X) en Septiembre de 202

Efficiency Improvement in Reverse Logistics and Examining the Relationships between Refund, Return Policy, Quality Policy and Pricing Strategy in E-Commerce Business.

Reverse Logistics (RL), the process of returning goods from a customer to a retail or manufacturing source, is an increasingly important yet undermanaged business function. The advent of internet and mobile technology and its rapid growth worldwide facilitates online shopping. Research shows that 54% of customers are already buying goods online. Online retail sales are expected to hit $ 4.5 trillion USD by 2020. This shift in buying pattern comes with a more worrying change in customer behaviour in the form of increasing returns, a number of that is surging at an alarming rate. In fact, statistics show that 30% of all the products ordered online are returned. Returns represent a growing cost of doing business today, and they represent unique challenges that are separate from traditional forward moving distribution channels. This thesis analyses the challenges in reverse logistics supply chain (RSLC) and provides a directional approach to overcome these challenges. The applications of emerging technologies for reverse logistics are discussed in this thesis. Also, this thesis discusses at length, return policy and its relevance in e-commerce business. A profit-maximization model is developed to obtain optimal values for refund, return policy, quality policy and pricing in terms of certain market reaction parameters. A numerical example is presented to show the applicability of the model given the parameters considered. The model provides valuable managerial insights for online apparel retailer in particular, to determine its strategic position under varying customer’s purchase and return decisions

Enhancing the Supply Chain Performance by Integrating Simulated and Physical Agents into Organizational Information Systems

As the business environment gets more complicated, organizations must be able to respond to the business changes and adjust themselves quickly to gain their competitive advantages. This study proposes an integrated agent system, called SPA, which coordinates simulated and physical agents to provide an efficient way for organizations to meet the challenges in managing supply chains. In the integrated framework, physical agents coordinate with inter-organizations\' physical agents to form workable business processes and detect the variations occurring in the outside world, whereas simulated agents model and analyze the what-if scenarios to support physical agents in making decisions. This study uses a supply chain that produces digital still cameras as an example to demonstrate how the SPA works. In this example, individual information systems of the involved companies equip with the SPA and the entire supply chain is modeled as a hierarchical object oriented Petri nets. The SPA here applies the modified AGNES data clustering technique and the moving average approach to help each firm generalize customers\' past demand patterns and forecast their future demands. The amplitude of forecasting errors caused by bullwhip effects is used as a metric to evaluate the degree that the SPA affects the supply chain performance. The experimental results show that the SPA benefits the entire supply chain by reducing the bullwhip effects and forecasting errors in a dynamic environment.Supply Chain Performance Enhancement; Bullwhip Effects; Simulated Agents; Physical Agents; Dynamic Customer Demand Pattern Discovery

DYNAMOD – A dynamic agent based modelling framework for digital businesses

Digital Businesses have become a major driver for economic growth and have seen an explosion of new startups. At the same time, it also includes mature enterprises that have become global giants in a relatively short period of time. Digital Businesses have unique characteristics that make the running and management of a Digital Business much different from traditional offline businesses. Digital businesses respond to online users who are highly interconnected and networked. This enables a rapid flow of word of mouth, at a pace far greater than ever envisioned when dealing with traditional products and services. The relatively low cost of incremental user addition has led to a variety of innovation in pricing of digital products, including various forms of free and freemium pricing models. This thesis explores the unique characteristics and complexities of Digital Businesses and its implications on the design of Digital Business Models and Revenue Models. The thesis proposes an Agent Based Modeling Framework that can be used to develop Simulation Models that simulate the complex dynamics of Digital Businesses and the user interactions between users of a digital product. Such Simulation models can be used for a variety of purposes such as simple forecasting, analysing the impact of market disturbances, analysing the impact of changes in pricing models and optimising the pricing for maximum revenue generation or a balance between growth in usage and revenue generation. These models can be developed for a mature enterprise with a large historical record of user growth rate as well as for early stage enterprises without much historical data. Through three case studies, the thesis demonstrates the applicability of the Framework and its potential applications.Fundação para a Ciência e a Tecnologia (FCT) - SFRH/BD/87286/201

The Effects of Retail Regulations on Prices Evidence form the Loi Galland

In 1997, a new legislation banning below-invoice retail prices came into force in France. Individually negotiated discounts could no longer be passed on to consumers, which is equivalent to allowing industry-wide price oors. The anti-competitive effects of such practices are well-known. The elimination of intra-brand competition is expected to lead to a sharp increase in the retail prices. Using CPI raw data, we nd evidence supporting this claim. The modification or revocation of the existing legislation (as it has been done in Ireland in December 2005) would then be expected to reduce retail prices.retail prices, pricing regulations, resale price maintenance

THE FOOD SERVICE INDUSTRY: TRENDS AND CHANGING STRUCTURE IN THE NEW MILLENNIUM

By 2010, foodservice establishments are projected to capture 53 percent of consumers' food expenditures, whereas in 1980, foodservice captured less than 40 percent. The foodservice industry accounts for approximately 4 percent of the Gross Domestic Product and about 11 million jobs. It has been rapidly changing due to economic factors, technological advances, and labor matters.1 This overview covers many of the issues and trends affecting the different segments of the foodservice supply chain including the foodservice operators, distributors and food manufacturers. Changing customer demographics are a driving force in the evolution of the foodservice industry. As the baby boomers reach middle age, they do not seem to have time to cook and their children and grandchildren do not seem to have the interest, or talent. The U.S. population in 2000 had over double ($6,500) the per capita discretionary income that it had in 1975 ($3,109) 2 and, with a high value for recreation and pleasure they are pulled out of the kitchen and into the restaurants. An ever-shrinking world also brings variety to menus as cultures and cuisines converge, introducing new flavors and textures. A tight labor market has affected the foodservice industry from top to bottom leading to a derived demand for convenience products from manufacturers. At all links in the chain, companies are experiencing mergers and acquisitions. Operators, manufacturers, and distributors are all fighting for a share of the profits as competition continues to intensify. This review of the foodservice industry incorporates interviews with industry professionals, current information from leading foodservice associations, and predictions from the top industry research firms and consultants.Agribusiness, Industrial Organization,