Relevant bibliographies by topics / Sales forecasting Business forecasting

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Sales forecasting Business forecasting'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Sales forecasting Business forecasting"

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West, Douglas C. "Managing Sales Forecasting." Management Research News 20, no. 4 (April 1997): 1–10. http://dx.doi.org/10.1108/eb028556.

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Rodrigues, Aaron. "Food Sales Forecasting Using Machine Learning Techniques: A Survey." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 869–72. http://dx.doi.org/10.22214/ijraset.2021.38069.

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Abstract: Food sales forecasting is concerned with predicting future sales of food-related businesses such as supermarkets, grocery stores, restaurants, bakeries, and patisseries. Companies can reduce stocked and expired products within stores while also avoiding missing revenues by using accurate short-term sales forecasting. This research examines current machine learning algorithms for predicting food purchases. It goes over key design considerations for a data analyst working on food sales forecasting’s, such as the temporal granularity of sales data, the input variables to employ for forecasting sales, and the representation of the sales output variable. It also examines machine learning algorithms that have been used to anticipate food sales and the proper metrics for assessing their performance. Finally, it goes over the major problems and prospects for applied machine learning in the field of food sales forecasting. Keywords: Food, Demand forecasting, Machine learning, Regression, Timeseries forecasting, Sales prediction
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Lu, Chi-Jie, and Chi-Chang Chang. "A Hybrid Sales Forecasting Scheme by Combining Independent Component Analysis with K-Means Clustering and Support Vector Regression." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/624017.

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Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting.
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Murdick, Kent. "Applications Short-Term Sales Forecasting." Mathematics Teacher 89, no. 1 (January 1996): 48–52. http://dx.doi.org/10.5951/mt.89.1.0048.

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A medical-supply company asked for help in solving a warehouse-inventory problem. They wanted a computer program to track the inventory of more than one hundred medical items, such as cases of bandages and syringes, and to predict the sales for the next business period. Thus, when the company needed to order a particular item, the quantity could be calculated automatically by the program. Specifically, the problem concerned the short-term prediction of future sales.
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Bakri, Rizal, Umar Data, and Niken Probondani Astuti. "Aplikasi Auto Sales Forecasting Berbasis Computational Intelligence Website untuk Mengoptimalisasi Manajemen Strategi Pemasaran Produk." JURNAL SISTEM INFORMASI BISNIS 9, no. 2 (December 27, 2019): 244. http://dx.doi.org/10.21456/vol9iss2pp244-251.

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Business analytics plays an important role in optimizing the management of product marketing strategies. One of the most popular analytical tools in business analytics is sales forecasting. Businesses need to conduct sales forecasting to optimize marketing management in the form of product availability predictions, predictions of capital adequacy, consumer interest, and product price governance. However, the problem that is often encountered in forecasting is the number of forecasting methods available so that it makes it difficult for business people to choose the best forecasting method. The aims of this research is to develop a forecasting software tha can be accessed online based on computational intelligence, which is a software that can make forececasting with various methods and then intelligently choose the best forecasting method. The software development method used in this study is the SDLC with waterfall model. The result of this research is the Auto sales forecasting software was developed using the R programming language by combining various package and can be accessed online through the page Http://bakrizal.com/AutoSalesForecasting. This software can be used to conduct forecast analysis with various methods such as Simple Moving Average, Robust Exponential Smoothing, Auto ARIMA, Artificial Neural Network, Holt-Winters, and Hybrid Forecast. This software contains intelligence computing to choose the best forecasting method based on the smallest RMSE value. After testing the sales transaction data at the Futry Bakery & Cake Shop in Makassar, the results show that the Robust Exponantial Smoothing method is the best forecasting method with an RMSE value of 0.829
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Lauer, Joachim, and Terrence O'Brien. "SALES FORECASTING USING CYCLICAL ANALYSIS." Journal of Business & Industrial Marketing 3, no. 1 (January 1988): 25–35. http://dx.doi.org/10.1108/eb006048.

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Stormi, Kati, Teemu Laine, Petri Suomala, and Tapio Elomaa. "Forecasting sales in industrial services." Journal of Service Management 29, no. 2 (March 12, 2018): 277–300. http://dx.doi.org/10.1108/josm-09-2016-0250.

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Purpose The purpose of this paper is to examine how installed base information could help servitizing original equipment manufacturers (OEMs) forecast and support their industrial service sales, and thus increase OEMs’ understanding regarding the dynamics of their customers lifetime values (CLVs). Design/methodology/approach This work constitutes a constructive research aiming to arrive at a practically relevant, yet scientific model. It involves a case study that employs statistical methods to analyze real-life quantitative data about sales and the global installed base. Findings The study introduces a forecasting model for industrial service sales, which considers the characteristics of the installed base and predicts the number of active customers and their yearly volume. The forecasting model performs well compared to other approaches (Croston’s method) suitable for similar data. However, reliable results require comprehensive, up-to-date information about the installed base. Research limitations/implications The study contributes to the servitization literature by introducing a new method for utilizing installed base information and, thus, a novel approach for improving business profitability. Practical implications OEMs can use the forecasting model to predict the demand for – and measure the performance of – their industrial services. To-the-point predictions can help OEMs organize field services and service production effectively and identify potential customers, thus managing their CLV accordingly. At the same time, the findings imply new requirements for managing the installed base information among the OEMs, to understand and realize the industrial service business potential. However, the results have their limitations concerning the design and use of the statistical model in comparison with alternative approaches. Originality/value The study presents a unique method for employing installed base information to manage the CLV and supplement the servitization literature.
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Tavakkoli, Amirmohammad, Jalal Rezaeenour, and Esmaeil Hadavandi. "A Novel Forecasting Model Based on Support Vector Regression and Bat Meta-Heuristic (Bat–SVR): Case Study in Printed Circuit Board Industry." International Journal of Information Technology & Decision Making 14, no. 01 (January 2015): 195–215. http://dx.doi.org/10.1142/s0219622014500849.

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Sales forecasting is very beneficial to most businesses. A successful business needs accurate sales forecasting to understand the market and sales trends. This paper presents a novel sales forecasting model by integrating support vector regression (SVR) and bat algorithm (BA). Since the accuracy of SVR forecasting mainly depends on SVR parameters, we use BA for tuning these parameters because Bat is a newly introduced algorithm and has many parameters. In order to find the best set of BA parameters Taguchi method was utilized. We validated our model on four known UCI datasets. Then we applied our model in printed circuit board (PCB) sales forecasting case study. We compared the accuracy of the proposed model with Genetic algorithm (GA)–SVR, particle swarm optimization (PSO)–SVR, and classic-SVR. The experimental results show that the proposed model outperforms the others. To ensure the robustness of our proposed model, sensitivity analysis was also done using our model to find out the effects of dependent variables values on sales time series.
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Frees, Edward W., and Thomas W. Miller. "Sales forecasting using longitudinal data models." International Journal of Forecasting 20, no. 1 (January 2004): 99–114. http://dx.doi.org/10.1016/s0169-2070(03)00005-0.

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Geurts, Michael D., and J. Patrick Kelly. "Forecasting retail sales using alternative models." International Journal of Forecasting 2, no. 3 (January 1986): 261–72. http://dx.doi.org/10.1016/0169-2070(86)90046-4.

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Dissertations / Theses on the topic "Sales forecasting Business forecasting"

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Laxmidhar, Mohammad, and Dnyanesh Sarang. "Exploratory Investigation of Sales Forecasting Process and Sales Forecasting System : Case Study of Three Companies." Thesis, Jönköping University, JIBS, Business Administration, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-718.

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The future has always caught the attention of the human being. The thirst of exploring the future and to know the unknown has driven the human being toward innovativeness.

Companies are expanding their operations worldwide since the past few decades. Profit growth coupled with an effective strategy has become the primary need of global companies. Research in this area has given rise to optimization of the supply chain for higher profitability. Considering the overall strategy the company needs to plan production well in advance. The operational planning comes in picture at this moment. In order to reduce excessive inventory at each stage of the production; one should know the demand of the next stage and preferably the end customer demand. The process of sales forecasting is undertaken to predict demand at different stages. It is a complex managerial function and hence needed to be undertaken by a scientific way. The sales forecasting the function includes process of forecasting, administration, hardware, software, users and developers of forecast.

Historically sales forecasting has been considered as a side activity by most of the companies. Sales forecasting has not been considered as an important function of marketing and finance. Very few companies have seen sales forecasting by a scientific management point of view. Less research has been reported in sales forecasting in comparison to other managerial functions. Planning based on sales forecasting; may be part of a selected strategy for growth and profitability. These facts have attracted us to study sales forecasting as a managerial function.

The purpose of this study is to describe and analyze the sales forecasting process, sales forecasting system, sales forecasting methods and techniques. Further proposing possibilities of improvements in existing forecasting process is also purpose of this study.

We have selected three manufacturing companies for this study based on purposive sampling. Considering research interest in phenomenon study; we have selected a qualitative research strategy for this study. We have selected a case study method for our research as it is the most appropriate tool to study the relation between theory and phenomenon. For this research, we have collected the data by semistructured interviews based on a pre formed questionnaire. The questionnaire has been prepared with respect to our research purpose and open ended questions were used to gather extensive data. The data gathered during interviews, have been analyzed by the use of ‘Flow model’ suggested by Miles and Huberman (1994).

Results from this study shows that there is a need to see ‘sales forecasting’ as a management function rather than a computer activity. To achieve the best information integration throughout the supply chain, increased information visibility is needed. To achieve accuracy in both forecasting and planning; collaborative forecasting may be used. Forecasting software needs to have a suite of methods towards product specific forecasting. The need of customized softwares has also been indicated by this study. The need to measure performance of forecasting by means of accuracy, cost and customer relationship has been concluded.

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Yenilmez-Dramali, Demet. "Moderating effect of forecasting methods between forecasting criteria and export sales forecasting effectiveness : an empirical model for UK organizations." Thesis, Kingston University, 2013. http://eprints.kingston.ac.uk/26591/.

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Over the last three decades important advances have been made in developing sales forecasting methods that more accurately reflect market place conditions. However, surveys of sales forecasting practice continue to report only marginal gains in sales forecasting effectiveness. This gap between theory and practice has been identified as a significant issue for sales forecasting research. The literature suggests that this gap should be addressed by examining new factors in sales forecasting. Accuracy, bias, timeliness, cost and environmental turbulence are the most studied forecasting criteria in sales forecasting effectiveness. There are some literatures which address how these factors are affected by the forecast methods the firm uses. Empirical evidence on such a role of the forecasting method is lacking, and existing literature does not take into account whether forecasting criteria's influence on export sales forecasting effectiveness vary depending on the forecasting methods used by the firm. This is the first research gap identified during the literature review. Furthermore, the role of export sales forecasting. effectiveness on export market performance have received only limited attention to date. Linking the forecasting effectiveness to the business performance was reported to be critical in evaluating and improving the firm's sales forecasting capability and sales forecasting climate. However, empirical evidence of this linkage is missing and this is the second gap this study addresses. A conceptual model is proposed and multivariate analysis technique is used to investigate the relationship between dependent (forecasting effectiveness and export performance) and independent variables (forecasting criteria, forecasting methods, managerial characteristics, organizational characteristics and export market orientation). Our finding revealed the impact of bias, timeliness and cost on forecasting effectiveness varies depending on the forecasting methods used by the firm. But no moderating impact of forecasting methods has been found for accuracy and environmental turbulence. Moreover, this study reported the linkage between forecasting effectiveness and export performance when composite forecasting method is used. Identifying the relative importance of all the factors (i.e. accuracy, bias, cost, timeliness, forecasting methods, etc) it becomes possible to set priorities directly reflecting managerial preferences for different forecast criteria. If implementation of such priorities is seen to contradict principles of good forecasting practice, action can be taken to inform managers of the potential negative consequences.
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Renner, Nancy A. (Nancy Ann). "Forecasting Quarterly Sales Tax Revenues: A Comparative Study." Thesis, North Texas State University, 1986. https://digital.library.unt.edu/ark:/67531/metadc501220/.

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The purpose of this study is to determine which of three forecasting methods provides the most accurate short-term forecasts, in terms of absolute and mean absolute percentage error, for a unique set of data. The study applies three forecasting techniques--the Box-Jenkins or ARIMA method, cycle regression analysis, and multiple regression analysis--to quarterly sales tax revenue data. The final results show that, with varying success, each model identifies the direction of change in the future, but does not closely identify the period to period fluctuations. Indeed, each model overestimated revenues for every period forecasted. Cycle regression analysis, with a mean absolute percentage error of 7.21, is the most accurate model. Multiple regression analysis has the smallest absolute percentage error of 3.13.
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Postiglioni, Renato. "Sales forecasting within a cosmetic organisation : a managerial approach." Thesis, Stellenbosch : Stellenbosch University, 2006. http://hdl.handle.net/10019.1/21980.

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Thesis (MBA)--Stellenbosch University, 2006.
Although most businesses require accurate sales forecasts in order to survive and to be successful, very little attention has been devoted to examine how sales forecasting processes should be managed, and the behavioural factors associated with the management of forecasting. Sales forecasting activities and research have by and large concentrated on the techniques or on the systems used, rather than on the forecasting management philosophy, which considers the organisational, procedural, and personnel aspects of the process. Both forecasting modelling and IT systems form the basis for the forecasting process, but the third element, namely the organisation, is potentially the most important one. Researchers have argued that improvements in this area could have a greater impact on the level of forecasting accuracy than improvements with regard to other aspects. After developing predetermined forecasting standards and principles, an audit on the author's organisation was conducted. This revealed that no formal forecasting --- existed, and that a number of business practices were in effect contaminating procedures and possibly affecting the integrity of the data. Very little forecasting knowledge existed, sales were predicted very sporadically, and simple averaging techniques were adopted. Life cycles of products, trends, seasonality or any other cyclical activity were never modelled. This obviously resulted in a very poor level of forecast accuracy, affecting a number of business activities. A decision was made to research the topic of forecasting management, develop a best practice model, and apply it to the organisation. The best practice model was based predominantly on the research work of Armstrong and Mentzer. This model requires the forecasting process to be developed in two specific phases, namely a strategic phase, in which the forecast is aligned to the organisation, the internal processes and the people, and the operational phase, in which more tangible aspects of the forecasting process are identified and constructed. This new forecasting approach and a dedicated forecasting software programme were successfully implemented, improving the overall accuracy level of the forecast.
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Jessen, Andreas, and Carina Kellner. "Forecasting Management." Thesis, University of Kalmar, Baltic Business School, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hik:diva-1868.

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In a world that is moving faster and faster, a company’s ability to align to market changes is becoming a major competitive factor. Forecasting enables companies to predict what lies ahead, e.g. trend shifts or market turns, and makes it possible to plan for it. But looking into the future is never an easy task.

“Prediction is very difficult, especially if it’s about the future.” (Niels Bohr, 1885-1962)

However, progress in the field of forecasting has shown that it is possible for companies to improve on forecasting practices. This master thesis looks at the sales forecasting practices in MNCs primarily operating in emerging and developing countries. We examine the whole process of sales forecasting, also known as forecasting management, in order to develop a comprehensive model for forecasting in this type of companies. The research is based on a single case study, which is then later generalized into broader conclusions.

The conclusion of this master thesis is that forecasting is a four-step exercise. The four stages we have identified are: Knowledge creation, knowledge transformation, knowledge use and feedback. In the course of these four stages a company’s sales forecast is developed, changed and used. By understanding how each stage works and what to focus on, companies will be able to improve their forecasting practices.

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Dwyer, Michael Edward. "Impact of implementing a self-managed work team on high sales force turnover and low productivity : a field experiment." Thesis, Swansea University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.607454.

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Aronsson, Henrik. "Modeling strategies using predictive analytics : Forecasting future sales and churn management." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-167130.

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This project was carried out for a company named Attollo, a consulting firm specialized in Business Intelligence and Corporate Performance Management. The project aims to explore a new area for Attollo, predictive analytics, which is then applied to Klarna, a client of Attollo. Attollo has a partnership with IBM, which sells services for predictive analytics. The tool that this project is carried out with, is a software from IBM: SPSS Modeler. Five different examples are given of what and how the predictive work that was carried out at Klarna consisted of. From these examples, the different predictive models' functionality are described. The result of this project demonstrates, by using predictive analytics, how predictive models can be created. The conclusion is that predictive analytics enables companies to understand their customers better and hence make better decisions.
Detta projekt har utforts tillsammans med ett foretag som heter Attollo, en konsultfirma som ar specialiserade inom Business Intelligence & Coporate Performance Management. Projektet grundar sig pa att Attollo ville utforska ett nytt omrade, prediktiv analys, som sedan applicerades pa Klarna, en kund till Attollo. Attollo har ett partnerskap med IBM, som saljer tjanster for prediktiv analys. Verktyget som detta projekt utforts med, ar en mjukvara fran IBM: SPSS Modeler. Fem olika exempel beskriver det prediktiva arbetet som utfordes vid Klarna. Fran dessa exempel beskrivs ocksa de olika prediktiva modellernas funktionalitet. Resultatet av detta projekt visar hur man genom prediktiv analys kan skapa prediktiva modeller. Slutsatsen ar att prediktiv analys ger foretag storre mojlighet att forsta sina kunder och darav kunna gora battre beslut.
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Calitz, P. G. "Die ontwikkeling van 'n vooruitskattings-model vir die voorspelling van verkope." Thesis, Stellenbosch : Stellenbosch University, 1985. http://hdl.handle.net/10019.1/80768.

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Thesis (MBA)--Stellenbosch University, 1985.
Aangesien historiese data geredelik beskikbaar was, is 'n kwantitatiewe vooruitskattingsmetode gebruik met die doel om gebeure in die verlede te bestuur. Sodoende kon die onderliggende struktuur van die data beter begryp word en daarom kon 'n model daargestel word om die nodige inligting te verskaf vir bestuursbesluitneming. Die klassieke vermenigvuldigende tydreeks is gebruik om die toekomstige verkope van Stodels Nurseries (Edms.) Bpk. te projekteer. Aangesien die maatskappy se verkope onderhewig is aan hewige seisoenskommelings, is kontantvloeibeplanning van kardinale belang vir die finansiele bestuur van die maatskappy.
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Feliciano, Ricardo Alexandre. "Uma proposta de gerenciamento integrado da demanda e distribuição, utilizando sistema de apoio à decisão (SAD) com business intelligence (BI)." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/3/3136/tde-05062009-091032/.

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Os avanços na Tecnologia da Informação e a proliferação de itens de consumo, entre outros aspectos, mudaram o cenário e o desempenho das previsões. Os processos de previsão devem ser reexaminados, estabelecendo mecanismos de comunicação formais que compartilhem a informação entre os diferentes níveis hierárquicos dentro da organização, eliminando ou reduzindo o desconforto das previsões paralelas e desconexas oriundas de níveis hierárquicos diferentes. O objetivo deste trabalho é propor um sistema de apoio à decisão baseado em métodos matemáticos e sistemas de informação, capaz de integrar as previsões de vários níveis hierárquicos de uma empresa por um repositório de dados (Data Warehouse ou DW) e um Sistema de Apoio à Decisão (SAD) com sistema Business Intelligence (BI), onde os níveis hierárquicos acessem as informações com o nível de detalhe apropriado dentro do processo de decisão, alinhado às expectativas corporativas de crescimento. Assim, a modelagem realizada neste trabalho teve como foco a geração de cenários para criar um sistema de apoio à decisão, prevendo demandas agregadas e individuais, gerando uma estrutura de integração entre as previsões feitas em diferentes níveis e alinhando valores oriundos de métodos quantitativos e julgamento humano. Uma das maiores preocupações foi verificar qual método (séries temporais, métodos causais) teria destaque em um processo integrado de previsão. Entre os diferentes testes efetuados, pode-se destacar os seguintes resultados: (1) a suavização exponencial tripla proporcionou melhor ajuste (dos dados passados) de séries históricas de demandas mais agregadas e proporcionou previsões mais precisas de representatividades agregadas. Para séries históricas de demanda individual e representatividade individual, os outros métodos comparados apresentaram desempenho muito próximo; (2) a criação de diferentes cenários de previsão, fazendo uso de um repositório de dados e sistema de apoio à decisão, permitiu análise de uma gama de diferentes valores futuros. Uma forma de simulação para apoiar a formulação das expectativas da diretoria foi adaptada da literatura e sugerida; (3) os erros de previsão nas abordagens top-down ou bottom-up são estatisticamente iguais no contexto desta pesquisa. Conclui-se que o método de suavização exponencial tripla traz menos erros às previsões de séries mais agregadas, se comparado com outros métodos abordados no trabalho. Esse fato está de acordo com asserções encontradas na literatura pesquisada de que o método de suavização exponencial é cada vez mais utilizado na previsão, em detrimento dos métodos causais como a regressão múltipla. Conclui-se, principalmente, que os sistemas SAD e BI propostos deram suporte aos vários níveis hierárquicos, proporcionando variedades de estilos de decisão e que podem diminuir o hiato entre o raciocínio qualitativo adotado em nível estratégico e o aspecto quantitativo mais comum em níveis operacionais em qualquer empresa.
Advances in Information Technology (IT), and the increase of consumption items, among other things, changed the performance in the forecasts predictions. It is not uncommon that organizations will perform parallel forecasts within the various hierarchical levels without communicating with each other. The objective of this work is to build an integrated \"infrastructure\" for forecasting through a repository of data (Data Warehouse or DW) and a Decision Support System (DSS) with Business Intelligence (BI) where the hierarchical levels have access to the information with the appropriate level of detail within the process, aligned to the corporate growth expectations. The modeling in this work focused in the generation of scenarios to create a decision support system, predicting individual and aggregate demand, create a structure for integrating and aligning the estimated forecast generated by quantitative and qualitative methods. After a series of experimental tests, main results found were: (1) triple exponential smoothing provided the best fit using historical aggregated demand, and provided a more precise estimate of aggregate representation. For historical series of individual demand and individual representation, the other methods used for comparison performed similarly; (2) the creation of different scenarios for prediction, using data repository and decision support system, allowed for analysis of a range of different future values. The simulation to support management expectations has been adapted from the literature; (3) the prediction errors in the top-down and bottom-up approaches are statistically the same in the context of this research. In conclusion, the method of triple exponential smoothing has fewer errors in the forecasts of aggregated series when compared to other methods discussed in this work. Moreover, the DSS and BI systems provided decision-making support to the various hierarchical levels, reducing the gap between qualitative and quantitative decision processes thus bridging the strategic and operational decision making processes.
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SESKAUSKIS, ZYGIMANTAS, and ROKAS NARKEVICIUS. "Sales forecasting management." Thesis, Högskolan i Borås, Akademin för textil, teknik och ekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-10685.

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The purpose of this research is to investigate current company business process from sales forecasting perspective and provide potential improvements of how to deal with unstable market demand and increase overall precision of forecasting. The problem which company face is an unstable market demand and not enough precision in sales forecasting process. Therefore the research questions are:  How current forecasting process can be improved?  What methods, can be implemented in order to increase the precision of forecasting? Study can be described as an action research using an abductive approach supported by combination of quantitative and qualitative analysis practices. In order to achieve high degree of reliability the study was based on verified scientific literature and data collected from the case company while collaborating with company’s COO. Research exposed the current forecasting process of the case company. Different forecasting methods were chosen according to the existing circumstances and analyzed in order to figure out which could be implemented in order to increase forecasting precision and forecasting as a whole. Simple exponential smoothing showed the most promising accuracy results, which were measured by applying MAD, MSE and MAPE measurement techniques. Moreover, trend line analysis was applied as well, as a supplementary method. For the reason that the case company presents new products to the market limited amount of historical data was available. Therefore simple exponential smoothing technique did not show accurate results as desired. However, suggested methods can be applied for testing and learning purposes, supported by currently applied qualitative methods.
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Books on the topic "Sales forecasting Business forecasting"

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1955-, Moon Mark A., ed. Sales forecasting management: A demand management approach. 2nd ed. Thousand Oaks, Calif: Sage Publications, 2005.

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Marino, Kenneth E. Forecasting sales and planning profits: A no-nonsense guide for the growing business. Chicago, Ill: Probus Pub. Co., 1986.

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Sales analytics guide: Sales development and category management practices for enhancing business performance. [Philadelphia?]: Xlibris, 2009.

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The next big thing: Spotting and forecasting consumer trends for profit. Philadelphia: Kogan Page Limited, 2009.

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The Next Big Thing. London: Kogan Page Publishers, 2009.

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Waters, Robyn. The Trendmaster's Guide. New York: Penguin USA, Inc., 2009.

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Kotsiopulos, Antigone. Development of a sales forecasting model for small businesses within a service industry. Ann Arbor, Mi: University Microfilms International, 1985.

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Naumov, Vladimir. Markets information and communication technology and sales organization. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/21026.

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In the textbook sets out the basic information about the structure of markets, information and communication technologies (ICT), the methods of their research, assessing the attractiveness and forecasting, criteria and methods of segmentation. Deals with the organization of the sales Department of an IT company, involving analysis of organizational forms, population division, methods of remuneration and non-material incentives for experts dealing with sales of ICT products. Sets out the methodology for strategic sales of complex IT solutions, the technique of negotiation and the basics of neurolinguistic programming. The textbook pays attention to the peculiarities of the sales and promotion of ICT products through the Internet, the possibilities of the use of CRM systems. The principles of the organization of partnerships with clients. This methodical approaches to the assessment of the efficiency of the sales Department of an IT company and its sales staff. Discusses the economic evaluation of the project implementation in selling IT solutions. The textbook is prepared in accordance with the requirements of Federal state educational standard of higher education of the last generation. Designed for students enrolled in training 38.03.05 "Business-Informatics", but it can be useful to students from other disciplines and practitioners working in the field of information and communication technologies.
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D, Lawrence Kenneth, and Guerard John, eds. Forecasting sales. Greenwich, Conn: JAI Press, 1994.

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Holmes, Peter. Sales forecasting. Sheffield: Centre for Statistical Education, 1987.

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Book chapters on the topic "Sales forecasting Business forecasting"

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Lancaster, Geoffrey A., and Robert A. Lomas. "Forecasts applied to business." In Forecasting for Sales and Materials Management, 157–65. London: Macmillan Education UK, 1985. http://dx.doi.org/10.1007/978-1-349-17851-3_8.

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Armando, Enrico, and Giuseppe Craparotta. "A Meta-Model for Fashion Retail Category Sales Forecasting." In Business Models and ICT Technologies for the Fashion Supply Chain, 79–93. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98038-6_7.

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Zhang, Ying, Chunnan Zhang, and Yu Liu. "An AHP-Based Scheme for Sales Forecasting in the Fashion Industry." In Springer Series in Fashion Business, 251–67. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1014-9_12.

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Kuo, R. J., Tung-Lai Hu, and Zhen-Yao Chen. "Sales Forecasting Using an Evolutionary Algorithm Based Radial Basis Function Neural Network." In Lecture Notes in Business Information Processing, 65–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01112-2_8.

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Hsieh, Pei-Hsuan. "A Study of Models for Forecasting E-Commerce Sales During a Price War in the Medical Product Industry." In HCI in Business, Government and Organizations. eCommerce and Consumer Behavior, 3–21. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22335-9_1.

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Petrochilos, George A. "Sales Forecasting." In Managerial Economics, 94–135. London: Macmillan Education UK, 2004. http://dx.doi.org/10.1007/978-1-137-10771-8_5.

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Meldrum, Mike, and Malcolm McDonald. "Forecasting Future Sales." In Key Marketing Concepts, 219–23. London: Macmillan Education UK, 1995. http://dx.doi.org/10.1007/978-1-349-13877-7_41.

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Donaldson, Bill. "Sales Forecasting and Budgeting." In Sales Management, 128–46. London: Macmillan Education UK, 1998. http://dx.doi.org/10.1007/978-1-349-26354-7_7.

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Sajtos, Laszlo. "Sales planning and forecasting." In Sales Management, 173–203. London: Macmillan Education UK, 2011. http://dx.doi.org/10.1007/978-1-137-28574-4_8.

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Taylor, Sonia. "Forecasting." In Business Statistics for non-mathematicians, 278–92. London: Macmillan Education UK, 2007. http://dx.doi.org/10.1057/978-0-230-20685-4_13.

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Conference papers on the topic "Sales forecasting Business forecasting"

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Nunnari, Giuseppe, and Valeria Nunnari. "Forecasting Monthly Sales Retail Time Series: A Case Study." In 2017 IEEE 19th Conference on Business Informatics (CBI). IEEE, 2017. http://dx.doi.org/10.1109/cbi.2017.57.

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Wang, Yingzi, Nicholas Jing Yuan, Yu Sun, Chuan Qin, and Xing Xie. "App Download Forecasting: An Evolutionary Hierarchical Competition Approach." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/415.

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Product sales forecasting enables comprehensive understanding of products' future development, making it of particular interest for companies to improve their business, for investors to measure the values of firms, and for users to capture the trends of a market. Recent studies show that the complex competition interactions among products directly influence products' future development. However, most existing approaches fail to model the evolutionary competition among products and lack the capability to organically reflect multi-level competition analysis in sales forecasting. To address these problems, we propose the Evolutionary Hierarchical Competition Model (EHCM), which effectively considers the time-evolving multi-level competition among products. The EHCM model systematically integrates hierarchical competition analysis with multi-scale time series forecasting. Extensive experiments using a real-world app download dataset show that EHCM outperforms state-of-the-art methods in various forecasting granularities.
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Sakdiyah, Siti Holifahtus, Nurafni Eltivia, Nur Indah Riwajanti, and Kurnia Ekasari. "Forecasting Analysis on the Impact of Pandemic Towards Cigarette Sales." In 2nd Annual Management, Business and Economic Conference (AMBEC 2020). Paris, France: Atlantis Press, 2021. http://dx.doi.org/10.2991/aebmr.k.210717.053.

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Shan, Wenhui. "Research on Refined Sales Management, Data Analysis and Forecasting under Big Data." In 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI). IEEE, 2020. http://dx.doi.org/10.1109/mlbdbi51377.2020.00065.

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Brzeczek, Tomasz. "Sales Forecasting And Newsboy Model Techniques Integrated For Merchandise Planning And Business Risk Optimization." In 34th International ECMS Conference on Modelling and Simulation. ECMS, 2020. http://dx.doi.org/10.7148/2020-0111.

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Gevorgyan, Rita. "Development and Implementation of the Model for Sales Volume Forecasting for the Brewing Company." In ICBIM '18: The 2nd International Conference on Business and Information Management. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3278252.3278291.

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Iurasov, Aleksei, and Giedre Stanelyte. "Study of different data science methods for demand prediction and replenishment forecasting at retail network." In 11th International Scientific Conference „Business and Management 2020“. VGTU Technika, 2020. http://dx.doi.org/10.3846/bm.2020.604.

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The demand prediction becoming an essential tool to remain or even lead in the competitionamong the retail businesses. A well-done demand prediction model could help retailer to track the level ofinventory, orders and sales in the most effective way in which the best results could be achieved. However,there are many different methods and opinions of how to create a demand prediction model. In this paper,we will analyse the most commonly used methods of Linear regression, Logistic Regression, ProbabilisticNeural Network, Bayesian Additive Regression Trees, Random Forest and Fuzzy Logic with their specificationsand limitations found in studies of authors. After review performed all methods will be compared accordingto characteristics selected. Moreover, in order to get more practical results the accuracy of LogisticRegression and Random Forest methods will be compared based on data of milk sales collected from retailnetwork. For constructing of decision support system for retail network, we need to go beyond demandprediction one-step to replenishment forecasting. It was concluded that there is no best method to forecastreplenishment and results can differ based on the data and conditions analysing. In every situation authorsseeking to select the method with the highest accuracy and the lowest number of errors possible. Limitationsof research: limited number of goods and stores included in the modelling.
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Hirt, Robin, Niklas Kuhl, Yusuf Peker, and Gerhard Satzger. "How to Learn from Others: Transfer Machine Learning with Additive Regression Models to Improve Sales Forecasting." In 2020 IEEE 22nd Conference on Business Informatics (CBI). IEEE, 2020. http://dx.doi.org/10.1109/cbi49978.2020.00010.

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Qiao, Zihan. "Walmart Sale Forecasting Model Based On LightGBM." In 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI). IEEE, 2020. http://dx.doi.org/10.1109/mlbdbi51377.2020.00020.

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Wang, Steve Hsueh-Ming, and Teresa J. Williams. "Feasibility Analysis of Using Local Remanufactured Products: A Case Study of Industrial Starters and Alternators." In ASME 2015 International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/msec2015-9397.

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Reported by the U.S. International Trade Commission, production of remanufactured goods had a total annual amount of approximately $43 billion and accounted for about 2% of total manufacturing annual sales from 2009–2011. The remanufacturing industry of motor vehicle parts was the third largest of the remanufacturing sectors and had a production of remanufactured goods with an annual total of approximately $6.2 billion in 2011. Reliable replacement engine parts for heavy duty equipment in Alaska are a high need. Remanufactured engine parts are one way to fulfill that need. While remanufactured industrial starters and alternators are available in Alaska they are currently remanufactured out of state and shipped to a local Anchorage, Alaska business to be sold. The purpose of this paper is to determine what the best method of obtaining industrial starters and alternators is. To that end a variety of forecasting analysis is performed using data from an Anchorage, Alaska business. The results indicate that while remanufacturing industrial starters and alternators in Anchorage, Alaska is possible, there are some problems such as core availability and employee utilization that need to be overcome in order to make it a viable option.
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Reports on the topic "Sales forecasting Business forecasting"

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Dueker, Michael J., and Katrin Wesche. Forecasting Macro Variables with a Qual VAR Business Cycle Turning Point Index. Federal Reserve Bank of St. Louis, 2001. http://dx.doi.org/10.20955/wp.2001.019.

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Orekhvo, D. O., and R. V. Khrunichev. Remote training course Distance learning course «Mathematical methods of business forecasting", training direction 38.03.05"Business Informatics». OFERNIO, June 2018. http://dx.doi.org/10.12731/ofernio.2018.23677.

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Kim, Eundeok. A Service-Learning Project with a Local Apparel Business Integrated into Trend Analysis and Forecasting Class. Ames: Iowa State University, Digital Repository, November 2015. http://dx.doi.org/10.31274/itaa_proceedings-180814-78.

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