Regression Based Sales Data Forecasting for Predicting the Business Performance

Business plays a vital role in day-to-day life to bring the goods and services to the people. The profit ofa business highly depends on the sales. Forecasting thesales in business is essential since the sales forecast predicts the business performance.Moreover, sales forecasting is an estimation of futuresales in a business based on the past sales data. This forecasting to make better managerial decisions allows in business for improving the performance of the business. Furthermore, the sales forecasting helps to increase the revenue, reduce the operating cost, improve the working capital use, and increase the shareholder�s values. Therefore, this paper presents a sales data forecasting to predict the business performance

Winter Exponential Smoothing: Sales Forecasting on Purnama Jati Souvenirs Center

Forecasting is the process of making predictions of the future based on past and present data and most commonly by analysis of trends. In sales area, an accurate sales forecasting system will help the company to improve the customers' satisfaction, reduce destruction of products, increase sales revenue and make production plan efficiently. Purnama Jati is a typical Jember souvenir place like "prol tape", "pia tape", "brownies tape" and so forth. Every day, sales on every outlet is uncertain so Purnama Jati repeatedly send to the outlets if the stock has run out. This research will focuse on "prol tape" cake, "pia tape" cake product as the research object. In this research we will use winter exponential smoothing as a forecasting method due to suitable character with the case

Predicting product sales in fashion retailing: a data analytics approach

No mercado de retalho de moda, uma determinação errônea dos montantes a comprar de cada artigo pelos fornecedores, seja por excesso ou defeito, pode resultar em custos desnecessários de armazenamento ou vendas perdidas, respectivamente. Ambas as situações devem ser evitadas pelas empresas, como tal surge a necessidade de determinar as quantidades de compras de uma forma precisa. Atualmente, as empresas recolhem grandes quantidades de dados referentes às suas vendas e características dos seus produtos. No passado, essa informação raramente era analisada e integrada no processo de tomada de decisão. No entanto, o aumento da capacidade de processamento de informações promoveu o uso da análise de dados como meio para obter conhecimento e apoiar os responsáveis pela tomada de decisão com o objetivo de alcançar melhores resultados comerciais. Portanto, o desenvolvimento de modelos que utilizem os diferentes fatores que influenciam as vendas e produzem previsões precisas de vendas futuras representam uma estratégia muito promissora. Os resultados obtidos podem ser muito valiosos para as empresas, pois permitem que as empresas alinhem o valor a comprar aos fornecedores com as vendas potenciais.Este projeto visa explorar o uso de técnicas de extração de dados para otimizar as quantidades de compra de cada produto vendido por uma empresa de retalho de moda. O projeto resulta no desenvolvimento de um modelo que usa dados de vendas anteriores dos produtos com características semelhantes para prever a quantidade que a empresa venderá potencialmente dos novos produtos. O projeto usará como um caso de estudo uma empresa de retalho de moda portuguesa.Para validar o modelo, serão utilizadas várias medidas de regressão linear para quantificar a qualidade do modelo.In the retail context, an erroneous determination of the amounts to buy of each article from the suppliers, either by excess or defect, can result in unnecessary costs of storage or lost sales, respectively. Both situations should be avoided by companies, which promotes the need to determine purchase quantities efficiently. Currently companies collect huge amounts of data referring to their sales and products' features. In the past, that information was seldom analyzed and integrated in the decision making process. However, the increase of the information processing capacity has promoted the use of data analytics as a means to obtain knowledge and support decision makers inachieving better business outcomes. Therefore, the development of models which use the different factors which influences sales and produces precise predictions of future sales represents a very promising strategy. The results obtained could be very valuable to the companies, as they enable companies to align the amount to buy from the suppliers with the potential sales.This project aims at exploring the use of data mining techniques to optimize the amounts to buy of each product sold by a fashion retail company. The project results in the development of a model that uses past sales data of the products with similar characteristics to predict the quantity the company will potentially sell from the new products. The project will use as a case study a Portuguese fashion retail company.To validate the model it will be used several linear regression measures to quantify model quality

A Machine Learning Approach to Revenue Generation within the Professional Hair Care Industry

The cosmetic and beauty industry continues to grow and evolve to satisfy its patrons. In the United States, the industry is heavily science-driven, innovative, and fast-paced, suggesting that to remain productive and profitable, companies must seek smart alternatives to their current modus operandi or risk losing out on this multi-billion-dollar industry to fierce competition. In this paper, the authors seek to utilize machine learning models such as clustering and regression to improve the efficiency of current sales and customer segmentation models to help HairCo (pseudonym for confidentiality), a professional hair products manufacturer, strategize their marketing and sales efforts for revenue growth. The present challenge facing HairCo is the lack of models that learn from aggregated data centered on the buying behavior, demographic, and other publicly available data of end consumers tied to historical sales data of their customers, i.e., salons and stylists. The proposed clustering and regression models achieved notably improved results using the aggregated data in comparison to models solely using internal company-provided data. Recommendations on which features are most important from both models that improve customer profiling and predicting sales were presented. With these results, HairCo can increase its revenue and expand its market share

Use of factors related to the consumption of Fast Moving Consumer Goods in business intelligence system for managing orders to suppliers in retail chain

The study examines some aspects related to current trends in the modeling of business intelligent systems (BIS) specializing in retail chains for Fast Moving Consumer Goods (FMCG). Current concepts related to factors that influence business processes and their application in business intelligent order management systems in retail chains for FMCG are presented. The aim of the present study is to investigate the factors that have the strongest influence on the consumption of FMCG in retail chains and to derive the values that support business-intelligent processes for automated order management to suppliers. The studied factors presented in the presentation also include consideration of the structure of the incoming data streams, their extraction and their application in practice

Item-level RFID for enhancement of customer shopping experience in apparel retail

In the customer-oriented apparel retail industry, providing satisfactory shopping experience for customers is a vital differentiator. However, traditional stores generally cannot fully satisfy customer needs because of difficulties in locating target products, out-of-stocks, a lack of professional assistance for product selection, and long waiting for payments. Therefore, this paper proposes an item-level RFID-enabled retail store management system for relatively high-end apparel products to provide customers with more leisure, interaction for product information, and automatic apparel collocation to promote sales during shopping. In this system, RFID hardware devices are installed to capture customer shopping behaviour and preferences, which would be especially useful for business decision-making and proactive individual marketing to enhance retail business. Intelligent fuzzy screening algorithms are then developed to promote apparel collocation based on the customer preferences, the design features of products, and the sales history accumulated in the database. It is expected that the proposed system, when fully implemented, can help promote retail business by enriching customers with intelligent and personalized services, and thus enhance the overall shopping experience. © 2015 Elsevier B.V.postprin

Forecasting aggregate retail sales : the case of South Africa

Forecasting aggregate retail sales may improve portfolio investors‟ ability to predict movements in the stock prices of the retailing chains. Therefore, this paper uses 26 (23 single and 3 combination) forecasting models to forecast South Africa‟s aggregate seasonal retail sales. We use data from 1970:01 – 2012:05, with 1987:01-2012:05 as the out-of-sample period. Unlike, the previous literature on retail sales forecasting, we not only look at a wider array of linear and nonlinear models, but also generate multi-steps-ahead forecasts using a real-time recursive estimation scheme over the out-of-sample period, to mimic better the practical scenario faced by agents making retailing decisions. In addition, we deviate from the uniform symmetric quadratic loss function typically used in forecast evaluation exercises, by considering loss functions that overweight forecast error in booms and recessions. Focusing on the single models alone, results show that their performances differ greatly across forecast horizons and for different weighting schemes, with no unique model performing the best across various scenarios. However, the combination forecasts models, especially the discounted mean-square forecast error method which weighs current information more than past, produced not only better forecasts, but were also largely unaffected by business cycles and time horizons. This result, along with the fact that individual nonlinear models performed better than linear models, led us to conclude that theoretical research on retail sales should look at developing dynamic stochastic general equilibrium models which not only incorporates learning behaviour, but also allows the behavioural parameters of the model to be state-dependent, to account for regime-switching behaviour across alternative states of the economy.http://www.elsevier.com/locate/ijpehb201

Retail forecasting: research and practice

This paper first introduces the forecasting problems faced by large retailers, from the strategic to the operational, from the store to the competing channels of distribution as sales are aggregated over products to brands to categories and to the company overall. Aggregated forecasting that supports strategic decisions is discussed on three levels: the aggregate retail sales in a market, in a chain, and in a store. Product level forecasts usually relate to operational decisions where the hierarchy of sales data across time, product and the supply chain is examined. Various characteristics and the influential factors which affect product level retail sales are discussed. The data rich environment at lower product hierarchies makes data pooling an often appropriate strategy to improve forecasts, but success depends on the data characteristics and common factors influencing sales and potential demand. Marketing mix and promotions pose an important challenge, both to the researcher and the practicing forecaster. Online review information too adds further complexity so that forecasters potentially face a dimensionality problem of too many variables and too little data. The paper goes on to examine evidence on the alternative methods used to forecast product sales and their comparative forecasting accuracy. Many of the complex methods proposed have provided very little evidence to convince as to their value, which poses further research questions. In contrast, some ambitious econometric methods have been shown to outperform all the simpler alternatives including those used in practice. New product forecasting methods are examined separately where limited evidence is available as to how effective the various approaches are. The paper concludes with some evidence describing company forecasting practice, offering conclusions as to the research gaps but also the barriers to improved practice

Artificial intelligence for agricultural supply chain risk management: Preliminary prioritizations and constraints for the deployment of AI in food chains assessed by CGIAR scientists

This paper seeks to propose priorities and support the integration of artificial intelligence (AI) in agricultural supply chains for the next ten years (2020-2030), with the aim of reducing supply chain vulnerabilities and contribute to global food security. Qualitative interviews with food chains and food security specialists from the FAO, the World Bank, CGIAR, WFP and the University of Cambridge, and an exploratory quantitative survey of 72 CGIAR scientists and researchers are used to derive integrated assessments of the vulnerability of different phases of supply chains and the ease of AI adoption and deployment in these phases. The integrated assessments are structured across food chains in developed and developing regions. The research shows that respondents expect the vulnerability to risks of all but one supply chain phases to increase over the next ten years. Importantly, where the integration of AI will be most desirable, in highly vulnerable supply chain phases in developing countries, the potential for AI integration is estimate to be limited