E-Fulfillment and Multi-Channel Distribution – A Review

This review addresses the specific supply chain management issues of Internet fulfillment in a multi-channel environment. It provides a systematic overview of managerial planning tasks and reviews corresponding quantitative models. In this way, we aim to enhance the understanding of multi-channel e-fulfillment and to identify gaps between relevant managerial issues and academic literature, thereby indicating directions for future research. One of the recurrent patterns in today’s e-commerce operations is the combination of ‘bricks-and-clicks’, the integration of e-fulfillment into a portfolio of multiple alternative distribution channels. From a supply chain management perspective, multi-channel distribution provides opportunities for serving different customer segments, creating synergies, and exploiting economies of scale. However, in order to successfully exploit these opportunities companies need to master novel challenges. In particular, the design of a multi-channel distribution system requires a constant trade-off between process integration and separation across multiple channels. In addition, sales and operations decisions are ever more tightly intertwined as delivery and after-sales services are becoming key components of the product offering.Distribution;E-fulfillment;Literature Review;Online Retailing

DSS (Decision Support Systems) in Indian Organised Retail Sector

Indian organised retail industry is poised for growth. Rapid state of change due to speedy technological developments, changing competitive positions, varying consumer behaviour as well as their expectations and liberalized regulatory environment is being observed in organized retailing. Information is crucial to plan and control profitable retail businesses and it can be an important source of competitive advantage so long as it is affordable and readily available. DSS (Decision Support Systems) which provide timely and accurate information can be viewed as an integrated entity providing management with the tools and information to assist their decision making. The study, exploratory in nature plans to adopt a case study approach to understand practices of organized retailers in grocery sector regarding applications of various DSS tools. Conceptual overview of DSS is undertaken by reviewing the literature. The study attempts to describe practices and usage of DSS in operational decisions in grocery sector and managerial issues in design and implementation of DSS. Comparision across national chain and local organized retailer in grocery sector reveals that national chain having implemented ERP partially are mostly using the same for majority of operational decisions like inventory management, CRM, campaign management. Two local players use spread sheets and in house software to make the above operational decisions. The benefits realized remain the same across the retailers. Prioritization as well as quantification of benefits was not communicated. The issues of coordination, integration with other systems in case of ERP usage, training were highlighted. Future outlook of DSS by the respondents portrayed a promising picture.

Bayesian Inference of Arrival Rate and Substitution Behavior from Sales Transaction Data with Stockouts

When an item goes out of stock, sales transaction data no longer reflect the original customer demand, since some customers leave with no purchase while others substitute alternative products for the one that was out of stock. Here we develop a Bayesian hierarchical model for inferring the underlying customer arrival rate and choice model from sales transaction data and the corresponding stock levels. The model uses a nonhomogeneous Poisson process to allow the arrival rate to vary throughout the day, and allows for a variety of choice models. Model parameters are inferred using a stochastic gradient MCMC algorithm that can scale to large transaction databases. We fit the model to data from a local bakery and show that it is able to make accurate out-of-sample predictions, and to provide actionable insight into lost cookie sales

From supply chains to demand networks. Agents in retailing: the electrical bazaar

A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version

A micro-econometric approach to geographic market definition in local retail markets: demand side considerations

This paper formalizes an empirically implementable framework for the definition of local antitrust markets in retail markets. This framework rests on a demand model that captures the trade-off between distance and pecuniary cost across alternative shopping destinations within local markets. The paper develops, and presents estimation results for, an empirical demand model at the store level for groceries in the UK

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

Multi-scale analysis of the energy performance of supermarkets

The retail sector accounts for more than 3% of the total electricity consumption in the UK and approximately 1% of total UK CO2 emissions. The overarching aim of this project was to understand the energy consumption of the Tesco estate (the market leader), identify best practice, and find ways to identify opportunities for energy reduction. The literature review of this work covered the topic of energy consumption in the retail sector, and reviewed benchmarks for this type of buildings from the UK, Europe and the US. Related data analysis techniques used in the industry or presented in the literature were also reviewed. This revealed that there are many different analysis and forecasting techniques available, and that they fall into two different categories: techniques that require past energy consumption data in order to calculate the future consumption, such as statistical regression, and techniques that are able to estimate the energy consumption of buildings, based on the specific building’s characteristics, such as thermal simulation models. These are usually used for new buildings, but they could also be used in benchmarking exercises, in order to achieve best practice guides. Gaps in the industry knowledge were identified, and it was suggested that better analytical tools would enable the industry to create more accurate energy budgets for the year ahead leading to better operating margins. Benchmarks for the organisation’s buildings were calculated. Retail buildings in the Tesco estate were found to have electrical intensity values between 230 kWh/m2 and 2000 kWh/m2 per year. Still the average electrical intensity of these buildings in 2010-11 was found to be less than the calculated UK average of the 2006-07 period. The effect of weather on gas and electricity consumption was investigated, and was found to be significant (p<0.001). There was an effect related to the day-of-the-week, but this was found to be more related to the sales volume on those days. Sales volume was a proxy that was used to represent the number of customers walking through the stores. The built date of the building was also considered to be an interesting factor, as the building regulations changed significantly throughout the years and the sponsor did not usually carry out any fabric work when refurbishing the stores. User behaviour was also identified as an important factor that needed to be investigated further, relating to both how the staff perceives and manages the energy consumption in their work environment, as well as how the customers use the refrigeration equipment. Following a statistical analysis, significant factors were determined and used to create multiple linear regression models for electricity and gas demands in hypermarkets. Significant factors included the sales floor area of the store, the stock composition, and a factor representing the thermo-physical characteristics of the envelope. Two of the key findings are the statistical significance of operational usage factors, represented by volume of sales, on annual electricity demand and the absence of any statistically significant operational or weather related factors on annual gas demand. The results suggest that by knowing as little as four characteristics of a food retail store (size of sales area, sales volume, product mix, year of construction) one can confidently calculate its annual electricity demands (R2=0.75, p<0.001). Similarly by knowing the size of the sales area, product mix, ceiling height and number of floors, one can calculate the annual gas demands (R2=0.5, p<0.001). Using the models created, along with the actual energy consumption of stores, stores that are not as energy efficient as expected can be isolated and investigated further in order to understand the reason for poor energy performance. Refrigeration data from 10 stores were investigated, including data such as the electricity consumption of the pack, outside air temperature, discharge and suction pressure, as well as percentage of refrigerant gas in the receiver. Data mining methods (regression and Fourier transforms) were employed to remove known operational patterns (e.g. defrost cycles) and seasonal variations. Events that have had an effect on the electricity consumption of the system were highlighted and faults that had been identified by the existing methodology were filtered out. The resulting dataset was then analysed further to understand the events that increase the electricity demand of the systems in order to create an automatic identification method. The cases analysed demonstrated that the method presented could form part of a more advanced automatic fault detection solution; potential faults were difficult to identify in the original electricity dataset. However, treating the data with the method designed as part of this work has made it simpler to identify potential faults, and isolate probable causes. It was also shown that by monitoring the suction pressure of the packs, alongside the compressor run-times, one could identify further opportunities for electricity consumption reduction