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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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Besharat, Pour Shiva. "Hierarchical sales forecasting using Recurrent Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290892.
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Sales forecasting equips businesses with the essential basis for planning future investments, controlling costs, and production. This research is in cooperation with a property development company for the purpose of improving the accuracy of manual sales forecasting. The objective is to investigate the effects of using the underlying factors that affect the individual sales of the company in forecasting the company’s income. One approach uses an aggregation of the estimates of the individual sales to approximate the company’s income. This approach uses the underlying hierarchical factors of the company’s individual sales to forecast future sales, which is known as the bottom-up approach. Another approach, known as the direct approach, uses the history of the company’s income instead. The bottom-up approach estimates the income of the company in the chosen target quarter, Q4 2019, with a percentage error of 33 percent. On the contrary, the direct approach provides an estimate of the company’s income inQ4 2019 with a percentage error of 3 percent. The strength of the bottom-up approach is in providing detailed forecasts of the individual sales of the company. The direct approach, however, is more convenient in learning the overall behavior of the company’s earnings.Försäljningsprognoser ger företag förutsättningar för planering av framtida investeringar och kontroll av både kostnader och produktion. Denna forskning har skett i samarbete med ett fastighetsutvecklingsföretag i syfte att förbättra noggrannheten i manuell försäljningsprognostisering. Målet är att undersöka effekterna av att använda de bakomliggande faktorer som påverkar enskild försäljning i prognoser för företagets intäkter. Ett av tillvägagångssätten som undersöks använder en sammanstallning av enskilda historiska försäljningar för att förutse företagets kommande intäkter. Detta tillvägagångssätt använder de bakomliggande hierarkiska faktorerna för företagets individuella försäljning för att prognostisera framtida försäljning, och metoden är känd som botten-upp-metoden. Ett annat tillvägagångssätt, känt som direktmetoden, använder företagets historiska inkomster som data i stället. Botten-upp-metoden användes för att upp- skatta företagets intäkter under Q4 2019 och gav ett procentuellt fel på 33 pro- cent. Direktmetoden, ˚a andra sidan, gav en uppskattning av företagets intäkter under Q4 2019 med ett procentuellt fel på 3 procent. Styrkan med botten- upp-metoden ¨ar att den kan tillhandahålla detaljerade prognoser för företagets individuella försäljning, samtidigt som direktmetoden ¨ar mer praktisk för att uppskatta företagets totala inkomster.
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Fredén, Daniel, and Hampus Larsson. "Forecasting Daily Supermarkets Sales with Machine Learning." Thesis, KTH, Optimeringslära och systemteori, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276483.
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Improved sales forecasts for individual products in retail stores can have a positive effect both environmentally and economically. Historically these forecasts have been done through a combination of statistical measurements and experience. However, with the increased computational power available in modern computers, there has been an interest in applying machine learning for this problem. The aim of this thesis was to utilize two years of sales data, yearly calendar events, and weather data to investigate which machine learning method could forecast sales the best. The investigated methods were XGBoost, ARIMAX, LSTM, and Facebook Prophet. Overall the XGBoost and LSTM models performed the best and had a lower mean absolute value and symmetric mean percentage absolute error compared to the other models. However, Facebook Prophet performed the best in regards to root mean squared error and mean absolute error during the holiday season, indicating that Facebook Prophet was the best model for the holidays. The LSTM model could however quickly adapt during the holiday season improved the performance. Furthermore, the inclusion of weather did not improve the models significantly, and in some cases, the results were worsened. Thus, the results are inconclusive but indicate that the best model is dependent on the time period and goal of the forecast.Förbättrade försäljningsprognoser för individuella produkter inom detaljhandeln kan leda till både en miljömässig och ekonomisk förbättring. Historiskt sett har dessa utförts genom en kombination av statistiska metoder och erfarenhet. Med den ökade beräkningskraften hos dagens datorer har intresset för att applicera maskininlärning på dessa problem ökat. Målet med detta examensarbete är därför att undersöka vilken maskininlärningsmetod som kunde prognostisera försäljning bäst. De undersökta metoderna var XGBoost, ARIMAX, LSTM och Facebook Prophet. Generellt presterade XGBoost och LSTM modellerna bäst då dem hade ett lägre mean absolute value och symmetric mean percentage absolute error jämfört med de andra modellerna. Dock, gällande root mean squared error hade Facebook Prophet bättre resultat under högtider, vilket indikerade att Facebook Prophet var den bäst lämpade modellen för att förutspå försäljningen under högtider. Dock, kunde LSTM modellen snabbt anpassa sig och förbättrade estimeringarna. Inkluderingen av väderdata i modellerna resulterade inte i några markanta förbättringar och gav i vissa fall även försämringar. Övergripande, var resultaten tvetydiga men indikerar att den bästa modellen är beroende av prognosens tidsperiod och mål.
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Zbib, Imad J. (Imad Jamil). "Sales Forecasting Accuracy Over Time: An Empirical Investigation." Thesis, University of North Texas, 1991. https://digital.library.unt.edu/ark:/67531/metadc332526/.
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This study investigated forecasting accuracy over time. Several quantitative and qualitative forecasting models were tested and a number of combinational methods was investigated. Six time series methods, one causal model, and one subjective technique were compared in this study. Six combinational forecasts were generated and compared to individual forecasts. A combining technique was developed. Thirty data sets, obtained from a market leader in the cosmetics industry, were used to forecast sales. All series represent monthly sales from January 1985 to December 1989. Gross sales forecasts from January 1988 to June 1989 were generated by the company using econometric models. All data sets exhibited seasonality and trend.
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Jedeikin, Jonathan. "An adaptive agent architecture for exogenous data sales forecasting." Master's thesis, University of Cape Town, 2006. http://hdl.handle.net/11427/6403.
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Includes bibliographical references (leaves 108-111).In a world of unpredictability and complexity, sales forecasting is becoming recognised as essential to operations planning in business and industry. With increased globalisation and higher competition, more products are being developed at more locations, but with shorter product lifecycles. As technology improves, more sophisticated sales forecasting systems are developed which require increasing complexity. We tum to adaptive agent architectures to consider an alternative approach for modelling complex sales forecasting systems. This research proposes modelling a sales forecasting system using an adaptive agent architecture. It additionally investigates the suitability of Bayesian networks as a sales forecasting technique. This is achieved through BaBe, an adaptive agent architecture which employs Bayesian networks as internal models. We develop a sales forecasting system for a meat wholesale company whose sales are largely affected by exogenous factors. The company's current sales forecasting approach is solely qualitative, and the nature of their sales is such that they would benefit from a reliable exogenous data sales forecasting system. We implement the system using BaBe, and incorporate a Bayesian network representing the causal relationships affecting sales. We introduce a learning adjustment component to adjust the estimated sales towards closer approximations. This is required as BaBe is currently unable to use continuous data, resulting in a loss of accuracy during discretisation. The learning adjustment additionally provides a feedback aspect, often found in adaptive agent architectures. The adjustment algorithm is based on the mean error calculation, commonly used as sales forecasting performance measures, but is extended to incorporate a number of exogenous variables. We test the system using the holdout procedure, with a 5-fold cross validation data-splitting approach, and contrast the accuracy of the estimated sales, provided by the system, with sales estimated using a regression approach. We additionally investigate the effectiveness of the learning adjustment component.
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Hood, Nicholas Andrew. "Evaluating alternative methods for forecasting convenience grocery store sales." Thesis, University of Leeds, 2016. http://etheses.whiterose.ac.uk/16281/.
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Convenience grocery stores have become more commonplace in grocery retailing in Great Britain since the 1990s, with a substantial increase in the proportion of stores operated by the largest grocery retailers in Great Britain that can be defined as convenience grocery stores. Geographically, the convenience networks operated by the largest retailers are more spatially concentrated than their overall grocery networks bringing them into direct competition with retailers more traditionally associated with convenience retailing in some, but not all areas of Great Britain. As convenience stores have grown, so too has interest in site location research in finding techniques to best predict their success. This thesis is carried out with the support of Sainsbury’s and GMAP Ltd and specifically considers location based decision making for convenience grocery stores in Great Britain. Grocery retailers and their location planning teams employ models that are adept at predicting supermarket revenue. However, they find it more difficult to consistently estimate revenue to new or existing convenience store locations. From the outset of this research it was hypothesised that different locations in which convenience grocery stores are found may, in theory, require a different optimal methodology for forecasting revenue accurately. This thesis first offers a segmentation of the convenience market into 7 statistically distinct location types to begin to address this problem. Using the 7 location types as a framework, three methodologies for forecasting grocery sales are tested for their suitability for predicting convenience grocery sales in the different locations in which convenience grocery stores are found. These are: GIS buffer and overlay modelling, regression modelling and spatial interaction modelling. The different methods were found to have mixed success in predicting convenience store revenue. The regression model was found to be the most effective model on average whilst the spatial interaction model was found to be the best model for generating very good revenue forecasts. Contrary to popular belief, the GIS buffer and overlay model was outperformed by the regression model and spatial interaction model in the majority of locations in which convenience grocery stores are found. Overall, the modelling frameworks presented in this thesis provide a plausible kitbag of techniques which can be applied in different convenience location circumstances.
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Liao, Kua-ping. "Feedforward neural network forecasting model building evaluation : theory and application in business forecasting." Thesis, Lancaster University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310532.
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Alsterman, Marcus. "Transfer Learning for Sales Volume Forecasting Using Convolutional Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255007.
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Improved time series forecasting accuracy can enhance demand planning, and therefore save money and reduce environmental impact. The idea behind this degree project is to explore transfer learning for time series forecasting. This has boiled down to two concrete goals. The first one is to examine if transfer learning can improve the forecasting accuracy when using a convolutional neural network (CNN) with dilated causal convolutions. The second goal is to investigate whether transfer learning makes it possible to forecast time series with less historical data.In this project, time series describing sales volume and price from three different consumer appliances are used. The length of the time series is about three years. Two transfer learning techniques are used: shared-hidden-layer CNN and pre-training. To tackle the first goal, the two transfer learning techniques are benchmarked against a CNN. The second goal is investigated conducting an experiment where the training set size varies for both a CNN and the two transfer learning techniques.The results from the first experiment indicate that transfer learning neither increase nor decrease forecasting accuracy. Interestingly, the second experiment however show that only 60 % (40 % for the SHL-CNN) of training samples is optimal for all models. This goes against the intuition that more training data leads to better model performance and this is most likely a phenomenum related specifically to time series forecasting. However, the percentage of 60 % most likely is application specific, we also find that pre-training, from any of the other products, improves the forecasting accuracy. Finally, reducing the training set further (20 % of training samples) affect the model differently. One pre-training model performs better than the rest, which perform very similar. This indicates that there are cases when transfer learning allows for forecasting smaller time series. However, further studies are required to establish how general these observations are.Bättre tidsserieprediktion kan förbättra planering av en försörjningskedja, därmed spara pengar och minska miljöpåverkan. Tanken bakom detta examensprojekt är att utforska transfer learning för prognos av tidsserier. Detta resulterar i två konkreta mål. Det första är att undersöka hur transfer learning kan förbättra prognosnoggrannheten när ett faltningsnätverk (CNN) med utvidgning och kausalitet används. Det andra målet är att undersöka om transfer learning gör det möjligt att förutspå tidsserier med mindre historisk data. De använda tidsserierna består av försäljningsvolymer och priser från tre hushållsapparater av samma slag. Tidsseriernas längd är cirka tre år. Två transfer learning tekniker används: delade dolda lager CNN (SHL-CNN) och förträning av ett CNN.För att ta itu med det första målet, så jämförs prognosnoggrannheten mellan de två transfer learning teknikerna och ett CNN. Det andra målet undersöks genom ett experiment där storleken av träningsuppsättningen varieras för ett CNN och de båda transfer learning teknikerna.Resultat ifrån det första experimentet indikerar att transfer learing varken försämrar eller förbättrar prognosnoggrannheten. Det andra experimentet visar att när antalet träningsexempel minskas till 60 % (40 % för SHL-CNN) så förbättras prediktionerna för alla modeller. Detta är inte intuitivt och är sannolikt ett fenomen specifikt för prediktion av tidsserier. Vidare så är proportionen 60 % specifik för detta projekt och vi finner även att vid denna proportion så är prediktionerna från förträning bättre än de från faltningsnätverket. Den sista upptäckten är att när antalet träningsexempel krymper till 20 % så presterar förträningsmodellen bättre än de andra. Detta pekar på att transfer learning i vissa fall kan göra det möjligt att förutspå tidsserier med mindre historisk data.
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Bajracharya, Dinesh. "Econometric Modeling vs Artificial Neural Networks : A Sales Forecasting Comparison." Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-20400.
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Econometric and predictive modeling techniques are two popular forecasting techniques. Both ofthese techniques have their own advantages and disadvantages. In this thesis some econometricmodels are considered and compared to predictive models using sales data for five products fromICA a Swedish retail wholesaler. The econometric models considered are regression model,exponential smoothing, and ARIMA model. The predictive models considered are artificialneural network (ANN) and ensemble of neural networks. Evaluation metrics used for thecomparison are: MAPE, WMAPE, MAE, RMSE, and linear correlation. The result of this thesisshows that artificial neural network is more accurate in forecasting sales of product. But it doesnot differ too much from linear regression in terms of accuracy. Therefore the linear regressionmodel which has the advantage of being comprehensible can be used as an alternative to artificialneural network. The results also show that the use of several metrics contribute in evaluatingmodels for forecasting sales.Program: Magisterutbildning i informatik
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Bally, Cortney. "Commodity pork price forecasting for Hormel fresh pork sales team." Thesis, Kansas State University, 2011. http://hdl.handle.net/2097/19762.
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Master of AgribusinessDepartment of Agricultural Economics
Glynn Tonsor
To remain competitive in an ever changing pork industry, Hormel Foods required careful evaluation of advertising forecast accuracy. This study determines forecasting accuracy for bone-in loins, boneless loins, butts, and ribs pricing within Hormel Foods and determines the relationship between forecast horizon (how many weeks forward in pricing) and forecasting accuracy of these products. The challenge required the data collection of the advertising pricing quotes for the sale price in comparison to the forecasted price. Several different forecasting combinations were examined to determine the ideal combination. The focus of this research was to determine which forecast or combination of forecasts was preferable for Hormel Foods. Findings include that each commodity and weeks out front have a different preferred forecast or combination of forecasts when analyzing root mean square errors. Four forecasts (three forecast companies and the United States Department of Agriculture actual markets at the time of forecasts) were observed with one forecast company rarely utilized in the preferred forecasting combinations and therefore the potential exists for a cost savings that affect the bottom line profitability of the division. In addition, economic models presented in this study explain the errors (both raw and percentage based) in relation to the forecast companies, weeks out front forecasted, and specific commodity differences.
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Venishetty, Sai Vineeth. "Machine Learning Approach for Forecasting the Sales of Truck Components." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18812.
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Context: The context of this research is to forecast the sales of truck componentsusing machine learning algorithms which can help the organization in activity oftrade and business and it also plays a major role for firms in decision-making operationsin the areas corresponding to sales, production, purchasing, finance, and accounting. Objectives: This study first investigates to find the suitable machine learning algorithmsthat can be used to forecast the sales of truck components and then theexperiment is performed with the chosen algorithms to forecast the sales and to evaluatethe performances of the chosen machine learning algorithms. Methods: Firstly, a Literature review is used to find suitable machine learningalgorithms and then based on the results obtained, an experiment is performed toevaluate the performances of machine learning algorithms. Results: Results from the literature review shown that regression algorithms namely Supports Vector Machine Regression, Ridge Regression, Gradient Boosting Regression, and Random Forest Regression are suitable algorithms and results from theexperiment showed that Ridge Regression has performed well than the other machine learning algorithms for the chosen dataset. Conclusion: After the experimentation and the analysis, the Ridge regression algorithmhas been performed well when compared with the performances of the otheralgorithms and therefore, Ridge Regression is chosen as the optimal algorithm forperforming the sales forecasting of truck components for the chosen data.
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Harrington, Robert P. "Forecasting corporate performance." Diss., Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/54515.
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For the past twenty years, the usefulness of accounting information has been emphasized. In 1966 the American Accounting Association in its State of Basic Accounting Theory asserted that usefulness is the primary purpose of external financial reports. In 1978 the State of Financial Accounting Concepts, No. 1 affirmed the usefulness criterion. "Financial reporting should provide information that is useful to present and potential investors and creditors and other users..."
Information is useful if it facilitates decision making. Moreover, all decisions are future-oriented; they are based on a prognosis of future events. The objective of this research, therefore, is to examine some factors that affect the decision maker's ability to use financial information to make good predictions and thereby good decisions.
There are two major purposes of the study. The first is to gain insight into the amount of increase in prediction accuracy that is expected to be achieved when a model replaces the human decision-maker in the selection of cues. The second major purpose is to examine the information overload phenomenon to provide research evidence to determine the point at which additional information may contaminate prediction accuracy.
The research methodology is based on the lens model developed by Eyon Brunswick in 1952. Multiple linear regression equations are used to capture the participants’ models, and correlation statistics are used to measure prediction accuracy.Ph. D.
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Haensly, Paul J. "The Application of Statistical Classification to Business Failure Prediction." Thesis, University of North Texas, 1994. https://digital.library.unt.edu/ark:/67531/metadc278187/.
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Bankruptcy is a costly event. Holders of publicly traded securities can rely on security prices to reflect their risk. Other stakeholders have no such mechanism. Hence, methods for accurately forecasting bankruptcy would be valuable to them. A large body of literature has arisen on bankruptcy forecasting with statistical classification since Beaver (1967) and Altman (1968). Reported total error rates typically are 10%-20%, suggesting that these models reveal information which otherwise is unavailable and has value after financial data is released. This conflicts with evidence on market efficiency which indicates that securities markets adjust rapidly and actually anticipate announcements of financial data. Efforts to resolve this conflict with event study methodology have run afoul of market model specification difficulties. A different approach is taken here. Most extant criticism of research design in this literature concerns inferential techniques but not sampling design. This paper attempts to resolve major sampling design issues. The most important conclusion concerns the usual choice of the individual firm as the sampling unit. While this choice is logically inconsistent with how a forecaster observes financial data over time, no evidence of bias could be found. In this paper, prediction performance is evaluated in terms of expected loss. Most authors calculate total error rates, which fail to reflect documented asymmetries in misclassification costs and prior probabilities. Expected loss overcomes this weakness and also offers a formal means to evaluate forecasts from the perspective of stakeholders other than investors. This study shows that cost of misclassifying bankruptcy must be at least an order of magnitude greater than cost of misclassifying nonbankruptcy before discriminant analysis methods have value. This conclusion follows from both sampling experiments on historical financial data and Monte Carlo experiments on simulated data. However, the Monte Carlo experiments reveal that as the cost ratio increases, robustness of linear discriminant rules improves; performance appears to depend more on the cost ratio than form of the distributions.
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Winklhofer, A. M. "The practice of export sales forecasting : an investigation of UK exporters." Thesis, Swansea University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.636672.
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Export sales forecasting involves additional problems compared to general sales forecasting. Despite the large number of empirical studies focusing on general and sales forecasting practices and activities, no investigation has specifically dealt with export sales forecasting. In order to close this gap, a survey of UK exporters was undertaken. The results from this survey are used to provide insights into issues such as forecasting level, time horizon, use of forecasting techniques, organisation of the forecasting function, the parties involved in preparing, approving and using export sales forecasts, forecast revision, the preparation of alternative forecasts, forecast accuracy and bias. In addition, the data are used to estimate and test a conceptual model of export sales forecasting practice and performance highlighting direct and indirect linkages between firm characteristics, export characteristics, forecasting practices, and overall forecast performance. Finally, the data are used to test specific hypotheses, at a detailed level, concerning forecast accuracy and bias. Several implications of the findings for researchers and practitioners are discussed and a comprehensive agenda for further research is developed.
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Orrebrant, Richard, and Adam Hill. "Increasing sales forecast accuracy with technique adoption in the forecasting process." Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH, Industriell organisation och produktion, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-24038.
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Abstract Purpose - The purpose with this thesis is to investigate how to increase sales forecast accuracy. Methodology – To fulfil the purpose a case study was conducted. To collect data from the case study the authors performed interviews and gathered documents. The empirical data was then analysed and compared with the theoretical framework. Result – The result shows that inaccuracies in forecasts are not necessarily because of the forecasting technique but can be a result from an unorganized forecasting process and having an inefficient information flow. The result further shows that it is not only important to review the information flow within the company but in the supply chain as whole to improve a forecast’s accuracy. The result also shows that time series can generate more accurate sales forecasts compared to only using qualitative techniques. It is, however, necessary to use a qualitative technique when creating time series. Time series only take time and sales history into account when forecasting, expertise regarding consumer behaviour, promotion activity, and so on, is therefore needed. It is also crucial to use qualitative techniques when selecting time series technique to achieve higher sales forecast accuracy. Personal expertise and experience are needed to identify if there is enough sales history, how much the sales are fluctuating, and if there will be any seasonality in the forecast. If companies gain knowledge about the benefits from each technique the combination can improve the forecasting process and increase the accuracy of the sales forecast. Conclusions – This thesis, with support from a case study, shows how time series and qualitative techniques can be combined to achieve higher accuracy. Companies that want to achieve higher accuracy need to know how the different techniques work and what is needed to take into account when creating a sales forecast. It is also important to have knowledge about the benefits of a well-designed forecasting process, and to do that, improving the information flow both within the company and the supply chain is a necessity. Research limitations – Because there are several different techniques to apply when creating a sales forecast, the authors could have involved more techniques in the investigation. The thesis work could also have used multiple case study objects to increase the external validity of the thesis.
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Amethier, Patrik, and André Gerbaulet. "Sales Volume Forecasting of Ericsson Radio Units - A Statistical Learning Approach." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288504.
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Demand forecasting is a well-established internal process at Ericsson, where employees from various departments within the company collaborate in order to predict future sales volumes of specific products over horizons ranging from months to a few years. This study aims to evaluate current predictions regarding radio unit products of Ericsson, draw insights from historical volume data, and finally develop a novel, statistical prediction approach. Specifically, a two-part statistical model with a decision tree followed by a neural network is trained on previous sales data of radio units, and then evaluated (also on historical data) regarding predictive accuracy. To test the hypothesis that mid-range volume predictions of a 1-3 year horizon made by data-driven statistical models can be more accurate, the two-part model makes predictions per individual radio unit product based on several predictive attributes, mainly historical volume data and information relating to geography, country and customer trends. The majority of wMAPEs per product from the predictive model were shown to be less than 5% for the three different prediction horizons, which can be compared to global wMAPEs from Ericsson's existing long range forecast process of 9% for 1 year, 13% for 2 years and 22% for 3 years. These results suggest the strength of the data-driven predictive model. However, care must be taken when comparing the two error measures and one must take into account the large variances of wMAPEs from the predictive model.Ericsson har en väletablerad intern process för prognostisering av försäljningsvolymer, där produktnära samt kundnära roller samarbetar med inköpsorganisationen för att säkra noggranna uppskattningar angående framtidens efterfrågan. Syftet med denna studie är att evaluera tidigare prognoser, och sedan utveckla en ny prediktiv, statistisk modell som prognostiserar baserad på historisk data. Studien fokuserar på produktkategorin radio, och utvecklar en två-stegsmodell bestående av en trädmodell och ett neuralt nätverk. För att testa hypotesen att en 1-3 års prognos för en produkt kan göras mer noggran med en datadriven modell, tränas modellen på attribut kopplat till produkten, till exempel historiska volymer för produkten, och volymtrender inom produktens marknadsområden och kundgrupper. Detta resulterade i flera prognoser på olika tidshorisonter, nämligen 1-12 månader, 13-24 månader samt 25-36 månder. Majoriteten av wMAPE-felen för dess prognoser visades ligga under 5%, vilket kan jämföras med wMAPE på 9% för Ericssons befintliga 1-årsprognoser, 13% för 2-årsprognerna samt 22% för 3-årsprognoserna. Detta pekar på att datadrivna, statistiska metoder kan användas för att producera gedigna prognoser för framtida försäljningsvolymer, men hänsyn bör tas till jämförelsen mellan de kvalitativa uppskattningarna och de statistiska prognoserna, samt de höga varianserna i felen.
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Niu, Mu. "Supply chain dynamics and forecasting." Thesis, Northumbria University, 2009. http://nrl.northumbria.ac.uk/1606/.
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Nowadays, the global supply chain system needs to respond promptly to changes in customer demand and adapt quickly to advancements in technology. Supply chain management becomes an integral approach which links together producers, distributors and customers in collaborative management of the whole system. The variability in orders or inventories in supply chain systems is generally thought to be caused by exogenous random factors such as uncertainties in customer demand or lead time. Studies have shown, however, that orders or inventories may exhibit significant variability, even if customer demand and lead time are deterministic. Most researchers have concentrated on the effects of the ordering policy on supply chain behaviour, while not many have paid attention to the influences of applying different forecasting to supply chain planning. This thesis presents an analysis of the behaviour of a model of a centralised supply chain. The research was conducted within the manufacturing sector and involved the breathing equipment manufacturer Draeger Safety, UK. The modelling process was embedded in the organization and was focused on the client's needs. A simplified model of the Draeger Safety, UK centralised supply chain was developed and validated. The dynamics of the supply chain under the influence of various factors: demand pattern, ordering policy, demand-information sharing, and lead time were observed. Simulation and analysis were performed using system dynamics, non-linear dynamics and control theory. The findings suggest that destructive oscillations of inventory could be generated by internal decision making practices. To reduce the variation in the supply chain system, the adjustment parameters for both inventory and supply line discrepancies should be more comparable in magnitude. Counter- intuitively, in certain fields of decision, sharing demand information can do more harm than good. The linear forecasting ARMA (autoregression and moving average) model and the nonlinear forecasting model Wavelet Neural Network were applied as the supply chain forecasting methods. The performance was tested against supply chain costs. A management microworld was developed, allowing managers to experiment with different decision policies and learn how the supply chain performs.
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Koottatep, Pakawkul, and Jinqian Li. "Promotional forecasting in the grocery retail business." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/36142.
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Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2006.Includes bibliographical references (leaves 84-85).
Predicting customer demand in the highly competitive grocery retail business has become extremely difficult, especially for promotional items. The difficulty in promotional forecasting has resulted from numerous internal and external factors that affect the demand patterns. It has also resulted from multiple levels of hierarchy that involve different groups in the organization as well as different methods and systems. Moreover, judgments from the forecasters are critical to the accuracy of the forecasts, while the value of tweaking the forecast results is yet to be determined. In this business, the forecasters generally have a high incentive to over-forecast in order to meet the corporate goal of maximizing customer satisfaction. The main objective of this thesis is to analyze the effectiveness of promotional forecasting, identify the factors contributing to forecast accuracy, and propose suggestions for improving forecasts. In light of this objective, we used WMPE and WMAPE as the measures of forecast accuracy, and conducted analysis of promotional forecast accuracy from different point of views.
(cont.) We also verified our results with regression analysis, which helped identify the significance of each forecasting attribute so as to support the promotion planning without compromising forecast accuracy. We suggest several approaches to improve forecast accuracy. First, to improve store forecasts, we recommend three models: the bias correction model, the adaptive bias correction model, and the regression model. Second, to improve replenishment forecasts, we propose a new model that combines the top-down and bottom-up approaches. Lastly, we suggest a framework for measuring accuracy that emphasizes the importance of comparing the accuracy of forecasts generated from systems and from judgments.
by Pakawkul Koottatep and Jinqian Li.
M.Eng.in Logistics
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Feng, Ning. "Essays on business cycles and macroeconomic forecasting." HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/279.
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This dissertation consists of two essays. The first essay focuses on developing a quantitative theory for a small open economy dynamic stochastic general equilibrium (DSGE) model with a housing sector allowing for both contemporaneous and news shocks. The second essay is an empirical study on the macroeconomic forecasting using both structural and non-structural models. In the first essay, we develop a DSGE model with a housing sector, which incorporates both contemporaneous and news shocks to domestic and external fundamentals, to explore the kind of and the extent to which different shocks to economic fundamentals matter for driving housing market dynamics in a small open economy. The model is estimated by the Bayesian method, using data from Hong Kong. The quantitative results show that external shocks and news shocks play a significant role in this market. Contemporaneous shock to foreign housing preference, contemporaneous shock to terms of trade, and news shocks to technology in the consumption goods sector explain one-third each of the variance of housing price. Terms of trade contemporaneous shock and consumption technology news shocks also contribute 36% and 59%, respectively, to the variance in housing investment. The simulation results enable policy makers to identify the key driving forces behind the housing market dynamics and the interaction between housing market and the macroeconomy in Hong Kong. In the second essay, we compare the forecasting performance between structural and non-structural models for a small open economy. The structural model refers to the small open economy DSGE model with the housing sector in the first essay. In addition, we examine various non-structural models including both Bayesian and classical time-series methods in our forecasting exercises. We also include the information from a large-scale quarterly data series in some models using two approaches to capture the influence of fundamentals: extracting common factors by principal component analysis in a dynamic factor model (DFM), factor-augmented vector autoregression (FAVAR), and Bayesian FAVAR (BFAVAR) or Bayesian shrinkage in a large-scale vector autoregression (BVAR). In this study, we forecast five key macroeconomic variables, namely, output, consumption, employment, housing price inflation, and CPI-based inflation using quarterly data. The results, based on mean absolute error (MAE) and root mean squared error (RMSE) of one to eight quarters ahead out-of-sample forecasts, indicate that the non-structural models outperform the structural model for all variables of interest across all horizons. Among the non-structural models, small-scale BVAR performs better with short forecasting horizons, although DFM shows a similar predictive ability. As the forecasting horizon grows, DFM tends to improve over other models and is better suited in forecasting key macroeconomic variables at longer horizons.
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DICKHUT, LENA. "BUSINESS CASE DEVELOPMENT : CATEGORIZATION AND CHALLENGES." Thesis, KTH, Nationalekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199203.
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Every new product launching industrial company faces the difficulties of forecasting future success or failure of a new product before launch. Before launch it is common to develop a business case in order to estimate future quantities and set prices. In the present paper the challenges of developing a standardized business case tool for a large industrial construction and mining company are presented. Few academic studies have been conducted on the challenges and complexities of developing business cases. The research question under which this study is done is: What are the challenges associated with developing an effective standardized business case tool for a large industrial construction and mining company? Due to the different subject areas of the business case for new product launch, the challenges are categorized by topics developed by the researcher in the course of this project: process and team, data gathering and validation, quantity forecast and price forecast. The main challenges found in these categories by the researcher are: finding and motivating experts for the project of developing a standardized business case, gathering and selecting all data necessary without including redundant data, ensuring that different potential new products can be forecasted and designing the price forecast to be profit-maximizing. Solutions to these challenges are provided in the context of a case company by using methods suggested by the academic literature and the evaluation of expert interviews inside the case company
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Colcombe, Steven J. "Forecasting model for future needs requirements for spare parts in FMS sales." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2000. http://handle.dtic.mil/100.2/ADA386375.
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PARKASH, MOHINDER. "THE IMPACT OF A FIRM'S CONTRACTS AND SIZE ON THE ACCURACY, DISPERSION AND REVISIONS OF FINANCIAL ANALYSTS' FORECASTS: A THEORETICAL AND EMPIRICAL INVESTIGATION." Diss., The University of Arizona, 1987. http://hdl.handle.net/10150/184093.
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The evidence presented in this study suggests that the dispersion, accuracy and transitory component in revisions of financial analysts' forecasts (FAF) are determined by production/investment/financing decisions, accounting choices as well as firm specific characteristics including the type of control, debt to equity ratio and size of the firm. Firms with managers control (owners control), high (low) debt to equity ratio and large (small) size are hypothesized to have higher (lower) dispersion, forecast error and transitory components in revisions of FAF. These hypotheses are motivated by the contracting cost and political visibility theories. The information availability theory is included as a contrast to the political visibility hypothesis. The information availability hypothesis predicts large (small) firms to have lower (higher) dispersion, forecast error and transitory component in revisions of FAF. The regression results are sensitive to deflated and undeflated measures of the dispersion and accuracy of FAF and size of the firm. The appropriateness of the two measures of firm's size, the book value of total assets and the market value of common stock plus long-term debt, as well as the deflated and undeflated measures of dispersion and accuracy of FAF are investigated. It is concluded that deflated measures of the dispersion and forecast errors and the market value as measure of firm size are misspecified in the present context. The current year forecast revisions are assumed to consist of the transitory and permanent components. The second year forecast revisions are used to represent the long-term forecast revisions and are used as a control for the permanent component of forecast revisions. The regression results are consistent with the contracting and political visibility hypotheses. The firm specific characteristics are hypothesized to influence forecast errors and dispersion directly and indirectly through business risk and accounting policy choices. The links between firm characteristics and business risk, accounting policy choices, dispersion and forecast errors are established and path analysis is used to test these relationships. These relationships are observed to be consistent with predictions and significant.
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Pacella, Claudia. "Essays on Forecasting." Doctoral thesis, Universite Libre de Bruxelles, 2020. https://dipot.ulb.ac.be/dspace/bitstream/2013/307579/4/CP_ToC.pdf.
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In this thesis I apply modern econometric techniques on macroeconomic time series. Forecasting is here developed along several dimensions in the three chapters. The chapters are in principle self-contained. However, a common element is represented by the business cycle analysis. In the first paper, which primarily deals with the problem of forecasting euro area inflation in the short and medium run, we also compute the country-specific responses of a common business cycle shock. Both chapters 2 and 3 deal predominately with business cycle issues from two different perspectives. The former chapter analyses the business cycle as a dichotomous non-observable variable and addresses the issue of evaluating the euro area business cycle dating formulated by the CEPR committee, while the latter chapter studies the entire distribution of GDP growth.Doctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished
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Elmasdotter, Ajla, and Carl Nyströmer. "A comparative study between LSTM and ARIMA for sales forecasting in retail." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229747.
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Food waste is a major environmental issue. Expired products are thrown away, implying that too much food is ordered compared to what is sold and that a more accurate prediction model is required within grocery stores. In this study the two prediction models Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) were compared on their prediction accuracy in two scenarios, given sales data for different products, to observe if LSTM is a model that can compete against the ARIMA model in the field of sales forecasting in retail. In the first scenario the models predict sales for one day ahead using given data, while they in the second scenario predict each day for a week ahead. Using the evaluation measures RMSE and MAE together with a t-test the results show that the difference between the LSTM and ARIMA model is not of statistical significance in the scenario of predicting one day ahead. However when predicting seven days ahead, the results show that there is a statistical significance in the difference indicating that the LSTM model has higher accuracy. This study therefore concludes that the LSTM model is promising in the field of sales forecasting in retail and able to compete against the ARIMA model.Matsvinn är ett stort problem för miljön. Utgångna produkter slängs, vilket implicerar att för mycket mat beställs jämfört med hur mycket butikerna säljer. En mer precis modell för att förutsäga försäljningssiffrorna kan minska matsvinnet. Denna studie jämför modellerna Long Short-Term Memory (LSTM) och Autoregressive Integrated Moving Average (ARIMA) i deras precision i två scenarion. Givet försäljningssiffror för olika matvaruprodukter, undersöks ifall LSTM är en modell som kan konkurrera mot ARIMA-modellen när modellerna ska förutsäga försäljningssiffror för matvaruprodukter. Det första scenariot var att förutse försäljningen en dag i framtiden baserat på given data, medan det andra scenariot var att förutse försäljningen varje dag under en vecka i framtiden baserat på given data. Genom att använda måtten RMSE och MAE tillsammans med ett T-Test visade resultaten av studien att skillnaden mellan LSTM- och ARIMA-modellen inte var av statistik signifikans i fallet då modellerna skulle förutsäga försäljningen en dag i framtiden. Däremot visar resultaten på att skillnaden mellan modellerna är av signifikans när modellerna skulle förutsäga försäljningen under en vecka, vilken implicerar att LSTM-modellen har en högre precision i detta scenario. Denna studie drar därmed slutsatsen att LSTM-modellen är lovande och kan konkurrera mot ARIMA-modellen när det kommer till försäljningssiffror av matvaruprodukter.
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Lombard, Daniel. "An integrated demand-planning and sales forecasting model : a case study in Parmalat S. A." Thesis, 2005. http://hdl.handle.net/10413/5382.
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This study specifically deals with finding a pragmatic solution to the problem of sales forecasting and demand planning in a very dynamic industry, the dairy industry, in the fast moving consumer goods (FMCG) market. Two projects surrounding the Parmalat supply chain were commissioned, the first dealing with sales forecasting, and the second dealing with distribution replenishment planning. This dissertation handles the former and sought to find solutions and integrate the strategic or long-term planning process with the operational forecasting process, and effectively integrate both these into the Parmalat supply chain management process. Of great importance to us during the project was the organizations maturity and level of business discipline currently prevalent, you would therefore constantly find reference to improvements required in other business process in support of a more sophisticated world class Supply Chain Management (SCM) system.Thesis (MBA)-University of KwaZulu-Natal, 2005.
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Müller, Gert Hendrik. "The importance of demand planning in the management of a fast moving consumer goods supply chain." Thesis, 2012. http://hdl.handle.net/10210/6202.
Full textAbstract:
M.Comm.As part of supply chain management, the handling of market demand information forms one of the most important concepts in any supply chain. One of the specific goals of supply chain management is to manage and co-ordinate the flow of information from the original source to the final customer. If consumer demand forms the activating element in the supply chain, it becomes clear that the process of demand planning can play an active role in improving the effectiveness of a supply chain. The correct management of information can thus greatly influence the level of integration, the responsiveness, level of customer service and value added to the end product. This is however not a one-sided approach where demand planning can be used as the tool to facilitate supply chain synchronization. The opposite effect can also be found that certain efforts to synchronize the supply chain can greatly improve the demand planning process. The fast moving consumer goods (FMCG) industry relies heavily on forecasted demand figures due to the structure of this industry 5. Developing demand forecasts forms a great part of the demand planning process and the accuracy, timely flow, interpretation and final format of the information is of the utmost importance. A well controlled forecasting process can form a solid foundation to address supply chain problems, reduce the level of wastage, increase the product value to the customer and improve the level of supply chain agility. With this background, the aim of this study will be: To explore the subject of Demand Planning in the synchronization of a FMCG supply chain. It will aim to show how an effective demand planning process can positively influence the supply chain management process and form an active element in supply chain synchronization. To investigate certain supply chain strategies on demand planning to indicate the level of integration between these two processes. In order to do this, a theoretical study needs to be done on Demand Planning and into the elements thereof. Within this structure it will be possible to formulate a structure to evaluate the concept of Demand Planning.
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Lin, Chi-Cheng, and 林啟正. "A Research of the Influence of Sales Forecasting on Business Inventory Policy - Taiwanese Automotive Industry as an Example." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/me2yw4.
Full textAbstract:
碩士中原大學
企業管理研究所
102
Due to production constraints, the automobile industry relies entirely on sales forecasts. However, these forecasts are mostly subjective, based upon expert opinions carried out and given by various sales departments. It adopts prior period results as a basis, tied in with assumed factors such as future political and economic variables. These, combined with new product launch plans and company policy qualitative forecasts result in forecast discrepancy. These forecast errors lead to the distress of production and inventory management. This research, to provide an effective stock control model, focuses on an important variable in inventory management - the sales forecast. In the use of regression analysis, three variables highly relevant to car sales are selected among eleven industrial economic variables. (Japanese Yen exchange rate, Unemployment rate, Economic growth rate, Stock market index, gasoline price, U.S. dollar exchange rate, Real wages, National savings rate, Consumer price index, National Industrial production index and Bank interest rate) Computing the variables with multiple regression analysis, three independent variables, ‘stock market index ‘, ‘gasoline price’ and ‘real wages’, remain to develop a company inventory regression management model. The findings come from these three variables are: 1.There are negative correlation between the stock market index in our country and the sales of the case company. This is different to other research results that are relevant to the variables affected by car sales in our country. 2.It shows that liability is negative correlation to gasoline price and the sales of the case company. 3.The real wages of our people positively correlate with the sales of the case company.
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Majidi, Alireza. "A conceptual framework of sales forecast : in business processes dependent on the actual location of sales with analysis of past data and coming information about future days from valid online resources." Master's thesis, 2019. http://hdl.handle.net/10362/70659.
Full textAbstract:
Dissertation presented as partial requirement for obtaining the Master’s degree in Information Management, specialization in Information Systems and Technologies ManagementDespite the advent of cyberspace in the provision of services and online sales, still, some of them require customer presence, such as restaurant food or medical and health services in clinics. For the high efficiency of the service provider and greater profitability and customer satisfaction, obviously, the capacity of service or sale of the goods should be proportional to the demand of customers. The number and type of customers of these service providers are affected by various factors, some of them may be fixed and pre-booked, as well as some of them are casualties. On the other hand, in addition of the time and energy required to provide service and products in these types of businesses, some of the raw materials necessary to produce the final product or service might have a short life-cycle that even modern warehousing, supply systems and enterprise resource planning are not responsive. For example, cooking some foods and serving at a certain restaurant may require fresh meat or fresh ingredients that cannot be stored in the restaurant’s stockpiles, so they should be supplied from original supplier in the same day. Therefore, forecasting the number of services and sales in the coming days can be beneficial. Already, there are several ways to forecast sales and service providing. In this study, with a brief overview of them, a conceptual framework provided that focuses on the precise analysis of data of sales in the past days and the identification of effective factors on sales in abnormal days as well as the acquisition of accurate information from online information resources about the status of similar coming days. Then by the case study that was a good restaurant in Lisbon, with analysis of the data history in the sales system over the course of a year, we were able to identify the factors affecting on the number of sales by this method.
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Brand, Trevor Stanley. "The development of a sustainable and cost effective sales and distribution model for FMCG products, specifically non alcoholic beverages, in the emerging markets of the greater Durban area." Thesis, 2005. http://hdl.handle.net/10413/2269.
Full textAbstract:
ABI has a sophisticated and effective distribution fleet which delivers canned and bottled non alcoholic beverages to 12000 wholesale and retail outlets in the Durban Metropole and to 46000 outlets nationally. Delivery is normally executed once per week, 48 hours after a separate order is taken by an account manager. In the more rural or "emerging market" areas traditional retail outlets such as supermarkets and superettes are scarce and reliance is made on spaza and house shops. Cash flow and storage space is limited. The sales and distribution calls are expensive, relative to the size order that the spaza would place. Spaza shop owners rely on distributors or collect from wholesalers. These outlets often run out of stock. Sales revenue is thus not maximized. Outlet development is marginal. The writer embarked on a research project to develop a sustainable and cost effective Sales and Distribution model in order to address these constraints in the Emerging Market territories of ABI Durban. Traditional theory turns to channel distribution as a means to effectively reaching an entire retail market. Levels are thus added to the distribution channel. The research however showed that service levels are sometimes compromised. The model that was developed returns ABI to DSD (direct service delivery) via specially designed vehicles and combines the function of "preseller" and "delivery merchandiser" on a dedicated route. Although a marginal increase in cost per case has been experienced, deliveries are direct to store, at least twice per week. Sales growth in these routes have been in excess of 85% while the total Umlazi area grows at 13%. Customer service levels, as surveyed, are exceptional. Although the model was specifically designed by ABI Durban for use in Durban, the concept has been adopted as a best practice and is being "rolled out" across the business. By the end of 2005, 10% of ABl's fleet nationally will function as MOTD (Merchandiser Order Taker Driver) routes. Additional vehicles have been ordered for delivery during the period July 2005 to September 2005 in order for this to be achieved. This model has assisted ABI in achieving its goal of maximizing DSD and lifting service levels to its customers (retailers). Revenue has increased significantly along with volume in these areas. Invariably MOTD acts as a significant barrier to competitor entry in those geographic areas where it is utilized. The Merchandiser Order Taker Driver (MOTD) model is successful and has potential for wider use, even in more developed markets.Thesis (MBA)-University of KwaZulu-Natal, 2005.
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Ku, Li-Ting, and 辜莉婷. "Applying Hierarchical Forecasting Methodology to TV-shopping Sales Forecasting." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/45727307552231581004.
Full textAbstract:
碩士國立臺灣大學
商學研究所
94
For those companies who sell multiple products to end users, demand forecasting is a tough but important issue. In consumer product industry, retailers have to coordinate the supply chain to become more competitive. In generally, aggregated demand forecasting is more accurate than individual item forecasting. However, if we have only the aggregated demand, we will not be able to get useful information for decision-making in the operation level. If we forecast each end item’s demand, it will be too time consuming and the difficulty of forecasting will increase. As a result, this research makes use of the hierarchical forecasting methodology to make the demand forecasting more efficient. In this research, the study of the hierarchical forecasting model is conducted in several steps: 1.Use regression and regression tree model to build the forecasting model. 2.Aggregate the lower level of product hierarchy or disaggregate the higher level to other levels to generate the forecasting values for other levels. 3.Compare the error rates and identify the best forecasting levels and forecasting methods. To validate the proposed model, sales data of a local TV-shopping company are collected. Three different forecasting methods are compared, Top-down, Middle-out and Bottom-up, and the results show that ”Bottom-up” is the best forecasting method. In forecasting the sales at the bottom level, regression tree model has a better prediction accuracy than that by regression model due to the “re-classification” feature. Furthermore, the sales forecasting model built in the research performs better than the current forecasting methods of the TV-shopping company.
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Lin, Wei-Ting, and 林威廷. "Applying Hierarchical Forecasting Methodology to Seasonal Sales Forecasting in Appliance Industry." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/63047138764787177203.
Full textAbstract:
碩士臺灣大學
商學研究所
95
The results of demand planning highly influence the quality of supply chain network planning. It reveals that demand planning plays a very important role in business operations. However, demand information is often distorted through supply chain. The phenomenon of asymmetric demand information seriously harms the quality of supply chain planning. As a result of product diversity and shorter product life cycle, forecasting for every single product item will cost too much in physical and time aspect, and also unlikely obtain accurate forecasts. Although forecasting for aggregating demand is more accurate, it has difficulty to handle operations planning and decisions in more details.. In order to enhance the benefits of demand forecasting, managers must take advantage of aggregation and disaggregation strategies in demand forecasting. This research takes seasonal data from monthly sales data of an air conditioner manufacturer in last four years as an example. We apply hierarchical forecasting methodology with seasonal ARIMA transfer function to aggregate and disaggregate data between product levels, and come up a effective forecasting model. At first, we forecast for sales at different levels respectively, and then aggregate upwards or disaggregate downwards to other levels by using Top-down, Middle-out and Bottom-up approaches. Finally, we obtain forecasts of all items in the hierarchical product structure. After establishing this complete hierarchical forecasting model of seasonal data, we compare forecast predictability generated by different forecasting approaches in order to find the most suitable forecasting approach. After comparing three forecasting approaches, Top-down, Middle-out and Bottom- up, we find that Bottom-up is the most suitable forecasting approach for sales of air conditioner. The forecasting model we build up not only improves the forecasting predictability of the firm but also gets a grip of the pattern of sales series such that the firm can make accurate decision in product planning and shipment allocation.
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SU, YU-TING, and 蘇御廷. "Empirical Study on Cars Sales Forecasting." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/c227nz.
Full textAbstract:
碩士朝陽科技大學
企業管理系
105
This study is concerned with sales analysis and prediction of empirical research, well-known brands (Toyota, Mitsubishi, Nissan) of automobiles. First of all Toyota, Mitsubishi, Nissan and its car sales analysis, the study of the analysis of the sales model for the judge, select the appropriate forecasting methods for the brand and there vehicle forecast analysis, and error measurement standards assessment, view predict the accuracy of the results to confirm the applicability of the forecasting method.Its excellent forecast results will be used as an important basis for the future development of business strategy.In this study, the time series method is used as the main prediction method.The prediction methods are as follows:Simple Time Series Techniques、Simple Exponential Smoothing、Trend Analysis Method 、Seasonal Analysis Method 、Triple Exponentially Smoothing. In this study, sales analysis and forecasting of Toyota, Mitsubishi and Nissan and its sales figures between 2010 and 2016 were conducted.The study found that: medium-sized car with a significant trend of sales performance of the vehicle suitable for the use of linear trend analysis to predict and forecast its future sales performance has gradually increased trend, said medium-sized car there is still a certain degree of growth space;Which is suitable for the use of linear trend analysis method to predict, the forecast results are declining trend, indicating that large-scale car in Taiwan market gradually declining; and the sale of the trend of the ride is also quite significant, the same linear trend analysis to predict the results Showing the possibility of sustainable growth in the market in Taiwan market; finally the three brands in the annual sales forecast for each month, with triple index smoothed analysis of the results of the prediction accuracy is better, and the results can be found in January and July sales better performance, and February and August sales of the performance is relatively low. Therefore, the choice of the above-mentioned brands and vehicles, the choice of forecasting methods and the forecast results will be used as an important basis for future business development strategy.
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Lee, Chio-Hui, and 李昭慧. "Intelligent forecasting models for sales volume." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/bfa4x2.
Full textAbstract:
博士元智大學
資訊管理學系
106
Sales forecasting is a vital cornerstone of a company's budget in manufacturing industries. The future direction of the company may rest on the accuracy of sales forecasting. In manufacturing firms, product demand is forecast for the upcoming production period, and the production is planned in accordance with the forecast to avoid inventory shortage or excess. Therefore, sales forecasting is crucial for manufacturing industries. In this study—which used the monthly sales volumes of two car manufacturers in Taiwan, economic variables analyzed using grey theory (GRA), and opinion scores of sentiment analysis analyzed by coefficient of determination (R2), and the Google Trend from 2011 to 2017 as the research data—Least Square Support Vector Regression (LSSVR) optimized by Particle Swarm Optimization (PSO) was applied to forecast monthly car sales volume. The selected features together with Google Trend and analyzed opinion score are then used as the inputs to the LSSVR to build the various models. Finally, to increase model accuracy, the parameter values of the LSSVR are optimized through PSO. The mean absolute percentage Error (MAPE) is adopted to evaluate the forecasting accuracy. Our experimental results indicate that the most precise forecasts are given as a result of using the selected economic variables by GRA, analyzed opinion scores by R2, and Google Trend.
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Day, Min-Yuh, and 戴敏育. "Research Of Applying Genetic Algorithms To Fuzzy Forecasting-Focus On Sales Forecasting." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/98418171464602711307.
Full textAbstract:
碩士淡江大學
資訊管理研究所
83
The major purpose of this study is applying Genetic Algorithms(GAs) to developing fuzzy forecasting in order to increase the accuracy of forecasting. Genetic algorithm is a parallel goal-oriented search technique for optimization and can be used to easily find out the global or nearly global optima for optimization problems. In this study, we focus on sales forecasting and propose a dynamic forecasting model by using Genetic Algorithms in searching the optimal linguistic variables and partition intervals, and finding out the most fitness model basis w of fuzzy time series in different cases. Finally, we propose adding the expert opinions served as leading indicators in the fuzzy time series for forecsting value. Results show that the accuracy of the forecasting results is significantly improved, it proved the effectiveness of the fuzzy forecasting model we proposed.
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Yang, Yu Chuang, and 莊楊裕. "Computer server sales forecasting using cluster-based forecasting model with different linkage strategies." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/41152233888692300027.
Full textAbstract:
碩士健行科技大學
工業管理系碩士班
103
Sales forecasting is crucial for every company since it is an important task for manufacturing, inventory management and marketing. In this study, a computer server sales forecasting model using clustering method with support vector regression (SVR) and extreme learning machine (ELM) with different linkage strategies is proposed. The proposed scheme first uses k-means algorithm to partition the whole training sales data into several disjoint clusters. Then, for each group, the SVR and ELM is applied to construct forecasting model. Finally, for a given testing data, three linkage methods are used to find the cluster which the testing data belongs to and then employee the corresponding trained SVR model and ELM model to generate prediction result. A real data of computer server sales collected from a Taiwanese multinational electronics company is used as illustrative examples to evaluate the performance of the proposed model. Experimental results revealed that the proposed clustering-based sales forecasting scheme outperforms the single method and seasonal naive forecasting models and hence is an effective alternative for sales forecasting.
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Li, Jian Huei, and 黎鑑輝. "An Empirical Study on Motorcycles Sales Forecasting." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/10213855277681050698.
Full textAbstract:
碩士長庚大學
企業管理研究所
95
英文摘要 " KYMCO " , " SYM " and " PGO " which not only own independent R&D but also sell products to markets throughout the world are few brands with international well-knownness. The existing vehicle industry researches focus more on automobile than motorcycle. Besides, the relevant researches of the motorcycle emphasized more on traffic management and control than markets sales volume, which the industry really cares.¬ In recent years, the development of substitute transportation and the saturation of vehicle market have made the motorcycle sales no longer continuous growth but undulation. This study which adopts Trend Forecast Method, ARIMA Method and Grey Prediction Method and applies A.D. 1989~2006 motorcycle sales data targeting at motorcycle industry probes how the different complexity prediction methods and quantity data influence the forecasting accuracy. The results of the researches showed: Different length of data and different prediction methods obviously influence the results of forecasting, but none of them has absolute advantages in different period of prediction or length of data. Prediction methods with excellent in-sample MAPE do not mean they have good out-sample prediction. How to choose the best method still needs the synthetical judgement of the researcher. The outcomes of complicated methods do not lead you to more accurate predictions. However, thorough estimation and verification do relatively stabilize the prediction results.
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LIU, CHIA-HSIN, and 劉佳欣. "Vehicle Sales Forecasting by Sentiment Analysis Data." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/n2sr45.
Full textAbstract:
碩士國立暨南國際大學
資訊管理學系
106
The automobile industry plays an important role when it comes to economic development of a country. America’s automobile industry has been a bright spot in the global economy. It is related to a wide range of neighboring industries, such as steel, transportation, and component supply. The increase of capacity utilization can create more job opportunities that will have a far-reaching impact on economic structure. For years, social media websites have been spreading widely, and make it easier for people to share their comments about products and services of brands. People are now able to seek information and exchange their opinions in social media, that eventually may effect one’s purchase intention and behavior. For these reasons, many researchers and practitioners increasingly resort to social media to obtain valuable information and potential business opportunities. In this study, we focus on monthly new vehicle sales in the US. The dataset consists of three kinds of input variables. One is sentiment data set, another is stock market index data set and the other is the combination of the first two data sets. The sentiment data set use tweets data to get the public opinion about buying cars by sentiment analysis. Dow Jones Industrial Average (DJI) and Standard & Poor's 500 (S&P 500) are selected as the performance of purchasing power and market economy. The aim of this study is to predict light vehicle and total vehicle sales in the US, and the Least Squares Support Vector Regression (LSSVR) method is used to construct a vehicle sales forecasting model. For forecasting accuracy comparison, the mean absolute percentage error (MAPE) and root mean square error (RMSE) are computed. Then, compare with five time series models: Naïve、Exponential smoothing、Holt-Winters、ARIMA and SARIMA. Furthermore, this paper explores the usefulness of raw data and seasonally adjusted data for vehicle sales forecasting. Our empirical analysis indicates that using the combination of sentiment and stock market index data as the input variables of LSSVR model has the best predictive results. Comparisons of prediction accuracy demonstrate that our model outperforms other time series models. Moreover, LSSVR models with seasonally adjusted data perform better than unadjusted raw data, and the prediction accuracy of the proposed method is improved by approximately 35%.
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Barreira, Jose. "Development of a sales forecasting model for canopy windows." Thesis, 2014. http://hdl.handle.net/10210/11467.
Full textAbstract:
M.Com. (Business Management)Forecasting is an important function used in a wide range of business planning or decision-making situations. The purpose ofthis study was to build a sales forecasting model that would be practical and cost effective, from the various forecasting methods and techniques available. Various forecast models, methods and techniques are outlined in the initial part of this study by the author. The author has outlined some of the fundamentals and limitations that underline the preparations of forecasting models. It is not the purpose of this study to microscopically dissect each forecasting model, method or technique. Various forecasting options were assessed in a manner that could provide some relevance to the study, thus providing a general framework for the construction of the specific sales forecasting model. Appropriate data sources were described and analysed. The data was further tested using the author's chosen quantitative forecasting techniques. Results were interpreted, and included into the author's untested sales model. It is the author's opinion that the sales model is practical, cost effective and gives a general sales forecast.
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Shih, Meng-Yu, and 施孟妤. "Applying Data Mining Techniques for Magazine Sales Forecasting." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/19204062537561282614.
Full textAbstract:
碩士國立交通大學
管理學院資訊管理學程
100
Chain of retail sells fresh food, groceries, toys, publications and so on. They distribute these goods to the franchises regularly. The demand of each franchise is different because of their geographic, population structure, residential area, weather, seasonal changes and extrinsic environmental impact. Therefore, how to accurately forecast demand for the amount of goods to increase profit is one of the most important business issues. The research focuses on the prediction about the retail demand of female fashion magazine. It is due to an observation that there is a seasonal cycle in sales curve of female fashion magazine. However, the chain of retail uses simple average in sales amount prediction is easy to undervalue the market demand. In this research, we adopt Autoregressive Tree Time Series method and Grey Prediction method to construct sales prediction model, and take the MAE as criteria to evaluate the forecasting competences of different models. According to the empirical results, if the sales series and the seasonal index have higher correlation, then the prediction accuracy of multivariable ART (Autoregressive Tree) is better than those of Univariable ART and Grey Prediction methods; otherwise, the prediction accuracy of Grey Prediction method is better than those of Univariable ART and multivariable ART methods.
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Tseng, Yin-Jung, and 曾尹蓉. "Sales Forecasting for a Chinese Apparel Retailing Store." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/07435067756859636306.
Full textAbstract:
碩士國立交通大學
工業工程與管理系所
102
Due to China’s huge apparel retailing market, more and more companies decide to set up their own stores in China. Therefore, the apparel retailers in China have become much more competitive than before, and the risk of opening a new store is also much higher than ever. Thus, this study proposed to find out the important factors related to sales performance based on a company’s history data and provide a sales forecasting model based on those factors. Through the sales forecasting model, it could reduce the risk by assist the managers in making their decisions with the predictor. Based on the experimental results, the factors chosen in this study do have a great impact on sales performance. Although the forecasting model proposed in this study is only suitable for the current company, but for other company, they can consider to include those important factors in its own sales forecasting model to enhance the forecast accuracy.
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BIN, LIN KAI, and 林楷斌. "Machine Learning Approaches for the Cosmetics Sales Forecasting." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/xd9naz.
Full textAbstract:
碩士國立高雄應用科技大學
工業工程與管理系碩士班
104
In the contemporary information society, constructing an effective sales prediction model is challenging due to the sizeable amount of purchasing information obtained from diverse consumer preferences. Many empirical cases shown in the existing literature argue that the traditional forecasting methods, such as the index of smoothness, moving average, and time series, have lost their dominance of prediction accuracy when they are compared with the modern types of forecasting approaches, such as the neural network (NN) and support vector machine (SVM) models. To verify these findings, this paper utilizes the Taiwanese cosmetic sales data to examine three forecasting models, namely, the back propagation neural network (BPNN), least-square support vector machine (LSSVM), and auto regressive model (AR). The result concludes that the LS-SVM has the smallest mean absolute percent error (MAPE) and largest Pearson correlation coefficient ( ) between model and predicted values.