Query: demand forecasting fast moving consumer goods

title: Impact of Information Sharing and Forecast Combination on Fast Moving Consumer Goods Demand Forecast Accuracy
abstract: This article empirically demonstrates the impacts of truthfully sharing forecast information and using forecast combinations in a fast moving consumer goods (FMCG) supply chain. Although it is known a priori that sharing information improves the overall efficiency of a supply chain, information such as pricing or promotional strategy is often kept proprietary for competitive reasons. In this regard, it is herein shown that simply sharing the retail level forecasts this does not reveal the exact business strategy, due to the effect of omni channel sales yields nearly all the benefits of sharing all pertinent information that influences FMCG demand. In addition, various forecast combination methods are used to further stabilize the forecasts, in situations where multiple forecasting models are used during operation. In other words, it is shown that combining forecasts is less risky than "betting" on any component model.

title: Machine Learning in Predicting Demand for Fast Moving Consumer Goods: An Exploratory Research
abstract: More accurate prediction of the demand for fast moving consumer goods is a competitive factor for manufacturers and retailers, especially in the fashion, technology and fresh food sectors. This exploratory research presents the benefits of Machine Learning in sales forecasting for short shelf life and highly perishable products, as it surpasses the accuracy level of traditional statistical techniques and, as a result, improves inventory balancing throughout the chain, reducing stockout rates at points of sale, improving availability to consumers and increasing profitability.

title: Electricity Consumption Forecasting with Artificial Neural Network for Fast Moving Consumer Goods Sector
abstract: Nowadays, it is evident that electricity is an indispensable source of energy in the production sectors when industry 4.0 transformation and sustainability become important at the same time. Electricity consumption forecast has crucial importance for effective energy planning in many production sectors. It is important to predict the total consumption of energy consumption and to make a production plan according to it and therefore to make all the functions in the supply chain cost and optimization plans. In this study, Artificial Neural Networks (ANN) method is used for electricity demand estimation for production processes of cold chain product in the fast moving consumer goods sector (FMCG) The impact of the observed independent variables is analyzed on electricity consumption. Estimates in the model are made for the following periods based on the last three years' electricity consumption of the one of the big fast moving goods company located in Turkey.

title: Designing a Demand Forecasting Process in the Fast Moving Consumer Goods Context

title: Identifying problems in forecasting consumer demand in the fast moving consumer goods sector
abstract: The ability to forecast consumer demand accurately is of great importance to companies in the consumer market. The food industry, in particular, views consumer availability as the cornerstone of their business. However, many companies concede that their forecasting process does not perform as well as they would wish. A group of forecasting and demand managers from some of the leading UK food companies, with the support of Leatherhead Food RA, examined the problems associated with their functions over an 18 month period. This paper presents the key findings from their collaborative work.

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

title: FACTORS AFFECTING DISTRIBUTION OF FAST MOVING CONSUMER GOODS IN KENYA: A CASE OF EVEREADY EAST AFRICA
abstract: The general objective was to investigate factors affecting distribution of fast moving consumer goods (FMCG) in Kenya, with particular emphasis on Eveready East Africa Limited. The study specifically aimed to; determine the effect of competition; establish the effect of price; analyze the effect of promotion and investigate the effectiveness of demand forecasting on distribution of Eveready East Africa Ltd products. The study adopted a descriptive research design and the study population comprised of 120 distributors and staff assigned in market territories in Kenya. The study applied a stratified sampling technique to select a total of 120 respondents. Questionnaires were used as the main data collection instruments and a pilot study was conducted to pre test questionnaires for validity and reliability. Descriptive statistics data analysis method was applied. The study findings indicated that the major factors affecting distribution of the company fast moving consumer goods are; high competition from cheap imports, high prices of the company FMCG in comparison to the competitors FMCGs, application of ineffective promotion campaigns and lack of effective demand forecasting systems. The study recommendations were; introduction of less expensive and high quality FMCGs in the market that is dominated by cheap and poor quality imports; application of effective pricing strategies like competitor based pricing; offering of effective promotion methods such as sales promotion and continuous advertising and application of effective demand and forecasting systems such as distribution requirement system and material requirement

title: Impact of business forecasting on demand planning a strategy for improving business forecasting and reducing inventories throughout the supply chain for fast moving consumer goods in the Middle East market

title: Forecasting promotional demand volume
abstract: This research for the master thesis is performed at Unilever Netherlands in Rotterdam. Unilever is a global company selling Fast Moving Consumer Goods (FMCGs) Characteristics of FMCG market are competitive, high volumes, fast turn over, multiple product innovations. In the FMCG market the effect of promotions on sales volume is substantial. Achieving the highest product availability at the lowest costs in combination with high volumes and high inventory turnover is a big challenge. Because of this, Unilever strives to deliver the right amount of products at the right time to the right customer at the lowest cost, which is only possible with an accurate forecast of the demand volume. This emphasises the importance of an accurate forecast. That is why Unilever wants to shift towards automating demand volume forecasts of promotions using predictive modelling. Unilever has co developed a predictive modelling tool, based on a machine learning algorithm. In the implementation phase the company encountered resistance from the employees responsible for the demand forecast, because the current predictive model does not perform well at their perspective at detailed item level. As consequence, these employees modify data, resulting in an incorrect use of the workflow, leading to lower performance on forecast accuracy in the operation. Low accuracy leads to high stock costs or low fill rate do to underforecasting or overforecasting. This results in the volume forecast for product promotions not being accepted by those employees and therefore not used, which is the main problem in this research. This research is part of continious improvement programm within the company. The goal of this research is to answer the main research question: "How to improve the current forecasting method for product promotions to ensure it is accepted and internalized by the users?"

title: Synthesized model for demand forecasting and its integration with inventory decision
abstract: Accurate forecast is helpful for the enterprises to make decisions,including the production plan,the pricing and promotion decisions,etc.,so as to reduce the inventory cost and improve the service qualities. In this paper,by analyzing the factors influencing the demand for fast moving consumer goods( FMCG),a synthesized forecasting model,which involves both the time series and the multi regression methods,is established. The synthesized forecasting model is further integrated with the inventory decision,with the purpose of minimizing the overall logistics costs. To solve the integrated model where multiple parameters are involved,a variable neighborhood search( VNS) based algorithm is developed. To evaluate both the synthesized forecasting model and the integrated model of demand forecasting and inventory decision,computational studies are conducted based on some real data. The computational results show an outperformance of the synthesized forecasting model regarding forecasting accuracy,and an outperformance of the integrated model of demand forecasting and inventory decision when the logistics costs are minimized.