Title: IT’s Role on Knowledge Management in the Sales Forecasting Process at IKEA: A Focus on Statistical Perspective Definition: Knowledge Management (KM) can be summarized as tools, methods and philosophies that are used to create, share, develop and organize knowledge within an organization. Information Technology (IT) refer to the technologies utilized in promoting Knowledge Management. Sales forecasting is the process of estimating future sales and is part of an organization's daily operations. Purpose: To offer an insight into knowledge management in sales forecasting, and the role of IT in the process. Method: This case study adopts a qualitative research method and an abductive research approach. Data has been collected through semi-structured interviews with employees at IKEA who are developing a statistical sales forecast model. A thematic analysis method has been implemented to identify themes and patterns in the collected data. Target group: Parties with an interest in ITs role to manage knowledge within an organization. Conclusion: It is found that the use of IT is crucial in the development of a statistical sales forecast model. IT is clearly required since it involves data science and machine learning. It can be said that IT has an important role in the continuous creation of sales forecasts as it provides historical sales data on which they are based. Furthermore, it appears that the development of a statistical sales forecast model requires a high level of knowledge management, and that IT serves a central role in this development. We find that the use of IT supports the knowledge-creation processes on individual and organizational levels in order to create the sales forecasting model. However, IT is not the source of knowledge creation, it can rather be seen as a support in knowledge management initiatives.

Supply chain management and logistics are two sectors currently experiencing a transformation thanks to the advent of AI(Artificial Intelligence) technologies. Leveraging predictive analytics powered by AI presents businesses with novel opportunities to streamline their operations effectively. This study utilizes sales forecasting for predictive analysis using three distinct artificial intelligence paradigms : Long Short-term Memory (LSTM), Bayesian Neural Networks (BNN) – both of which belong to the family of deep learning models – and Support Vector Regressors (SVR), a machine learning technique. The empirical data employed for this forecast stems from the historical sales data of Bactiguard, the collaborating company in this study. Subsequent to the essential data manipulation, these models are trained, and their respective results are assessed. The evaluation matrices incorporated in this study include the mean absolute error (MAE), root mean square error (RMSE), and the R2 score. Upon analysis, the LSTM model emerges as the clear frontrunner, exhibiting the lowest error rates and the highest R2 score. The BNN follows closely, demonstrating credible performance, while the SVR lags, presenting suboptimal results. In conclusion, this study highlights the accuracy and efficiency of artificial intelligence models in sales forecasting and underscores their practical, real-world applications. / Supply chain management och logistik är två sektorer som för närvarande genomgår en förändring tack vare tillkomsten av AI(artificiell intelligens) teknik. Att utnyttja prediktiv analys som drivs av AI ger företag nya möjligheter att effektivisera sin verksamhet. Denna studie använder försäljningsprognoser för prediktiv analys med hjälp av tre distinkta artificiell intelligensparadigm: Long Short-term Memory (LSTM), Bayesian Neural Networks (BNN) - som båda tillhör familjen av modeller för djupinlärning - och Support Vector Regressors ( SVR), en maskininlärningsteknik. Den empiriska data som används för denna prognos härrör från historiska försäljningsdata från Bactiguard, det samarbetande företaget i denna studie. Efter den väsentliga datamanipulationen tränas dessa modeller och deras respektive resultat utvärderas. De utvärderingsmatriser som ingår i denna studie inkluderar det genomsnittliga absoluta felet (MAE), root mean square error (RMSE) och R2-poängen. Vid analys framstår LSTM-modellen som den tydliga föregångaren, som uppvisar de lägsta felfrekvenserna och den högsta R2- poängen. BNN följer noga och visar trovärdig prestanda, medan SVR släpar efter och ger suboptimala resultat. Sammanfattningsvis belyser denna studie noggrannheten och effektiviteten hos modeller med artificiell intelligens i försäljningsprognoser och understryker deras praktiska tillämpningar i verkligheten.

Thesis (D. Tech.(Marketing)) - Central University of Technology, Free state, 2012 / A reliable sales plan and forecast is the basis for good cash flow management and capacity planning. If the sales figures are below plan, the sales manager will increase the sales efforts in order to compensate these deviations. Usually, it can be expected that these efforts should be at least partly successful in the consumer markets. This situation is expected to be different in the industrial markets, as usually the generation of sales turnover can only be achieved by either new customers or new products sold to existing customers. It is therefore expected not to be possible to immediately compensate a loss of sales turnover within the planning period by increased sales efforts. This research project investigated whether industrial markets react differently from consumer markets by investigating the sales planning and forecasting process in the Machinery & Equipment Industry, the Automotive Supplier Tier 1 and the Automotive Supplier Tier 2 Industry. It investigated several time aspects of the sales process, displayed as customer-supplier interaction. The results of the research project showed that in fact sales processes in the investigated industry sectors have such a long duration, that it is not possible for sales managers to immediately compensate low sales figures by increased sales efforts. The sales turnover raise will come in a later period and thus simply too late for the current one. This results in the fact that the reliability of the sales forecast (for the established sales plan) is reduced, if industry characteristics and special time aspects of the sales process are not taken into consideration. These time aspects can be described best by the Market Response Time (MRT). The MRT is defined as the time lag between the start of an increase of sales efforts by the supplier (first contact) and the market response in terms of increased purchase. This is at the time when the customer starts to financially respond, with the result of a sales turnover increase at the supplier’s side. If the MRT is long, sales planning and forecasting has increased importance, because sales efforts need to be planned well in advance. For this reason response times are major elements in planning and forecasting, although it was previously not very well recognised in literature and practice. Based on a qualitative empirical study with the case study methodology, 41 case studies were undertaken within the three industry sectors. The investigated companies showed that these three industry sectors have different MRTs, such as 68 weeks in the Machinery & Equipment Industry, 138 weeks in the Automotive Supplier Tier 1, and 62 weeks in the Automotive Supplier Tier 2 Industry. These different MRTs influence the companies planning and forecasting processes in different ways. This research project qualitatively showed that if time aspects were taken into consideration in sales planning and forecasting, forecast accuracy could improve. It was furthermore indicated that an adequate sales planning approach could improve forecast accuracy as well. In a second step, it was indicated that these companies, which are aware of the time aspects, have shown a better sales performance in terms of sales force productivity, growth of productivity and market position. Concluding it can be stated that the respect of time aspects, such as MRT, may increase sales performance. The study's results have some limitations, which are the research context and the research methodology. As the project only investigated the industrial context, namely the Machinery & Equipment and the Automotive Tier 1 Supplier and Tier 2 Supplier Industry, its results can only be applicable to this context. The research methodology of this project is a qualitative one, which means that the sample size is small but deep and statistical generalisations cannot be made. Based on this, further research implications of this project are that its results may further be statistically generalised by quantitative studies. Especially the sales planning and forecasting processes in the detected clusters per industry sector should be investigated on a broad sample. Thirdly, the indicated relation between market knowledge and accuracy should be further investigated. This is because it can be estimated that the forecast accuracy is the highest if the company’s information horizon is equal to the product life cycle time of the products produced. Last of all, as there are only a few research projects done in the industrial context regarding market response models and time aspects, therefore these topics should be further investigated.

Submitted by Mariana Dornelles Vargas (marianadv) on 2015-06-01T12:45:08Z No. of bitstreams: 1 otimizacao_niveis.pdf: 2029201 bytes, checksum: a40cd7a9e26627e3677b30cfdb0cdd33 (MD5) / Made available in DSpace on 2015-06-01T12:45:08Z (GMT). No. of bitstreams: 1 otimizacao_niveis.pdf: 2029201 bytes, checksum: a40cd7a9e26627e3677b30cfdb0cdd33 (MD5) Previous issue date: 2011 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Em um contexto empresarial, a competitividade entre companhias de um mesmo ramo de atividade se torna mais presente a cada dia que passa. Empresas estruturadas de forma enxuta em termos de custo podem ter maior vantagem competitiva sobre seus concorrentes. Reduzir custos é, portanto, um objetivo almejado por todas as organizações. A gestão e controle de estoques de produtos é um problema presente em diversas empresas e organizações. Diversos custos estão associados a este problema. O volume monetário relacionado é bastante grande. Assim, quanto melhor for o processo de controle e gerenciamento de estoques de uma empresa, menor será o custo para manutenção dos mesmos. Este trabalho propõe uma ferramenta para otimização dos níveis de estoque de uma rede varejista, considerando características como lucratividade, custos e atendimentos às demandas. Isto é feito através do uso de um método previsor baseado em Suavização Exponencial com Sazonalidade Multiplicativa, um módulo Otimizador baseado na metaheurística Guided Local Search, além de um Simulador Discreto Determinístico. A ferramenta conta com uma série de parâmetros que permitem a criação de diferentes cenários relativos ao sistema de estocagem da rede varejista. Os resultados obtidos durante a fase de experimentação da ferramenta demonstram sua capacidade de encontrar soluções para o problema de níveis de estoque de forma satisfatória, além de possibilitar a criação de cenários alternativos à realidade observada no sistema físico. / Competitiveness is an element that increases on a daily basis considering nowadays businesses. Companies with more efficient structures in terms of costs have an important advantage when compared to their competitors. This greatly motivates companies to reduce costs. Inventory control is an existing problem in many companies and organizations. Many types of costs are associated to this problem. Also, the amount of money involved in inventory maintenance is very considerable. So, the better the inventory control process is, the lower the costs related to it will be. This paperwork proposes a tool to optimize inventory levels on a retailer company, considering profit, costs and service level attendance. This is done by using a Forecasting Method called Exponential Smoothing with Multiplicative Seasonality, an Optimizer based on the Guided Local Search metaheuristic and a Discrete Deterministic Simulator. This tool uses a series of parameters in order to allow users to create different scenarios. The results obtained with the conducted experiments show that the tool is capable of finding good solutions to the problem of inventory levels, as well as creating alternative scenarios to operate the inventory system..

The aim of this study is to certify the legitimacy of employing quantitative methods in the day-to-day business practice. The task is approached as a case study of a real-life financial planning process. I work with the financial data of POS Media Czech Republic (a media company providing point-of-sale advertising solutions). My intention is to simulate the projection of a pro forma income statement with the use of quantitative methods. More specifically, I am applying time series prediction techniques in order to forecast POS Media's sales. The goal is, first, to demonstrate that quantitative techniques can be handled even with limited statistical background and, second, to discuss the relevancy of the obtained results. In the methodical part of my thesis I deal with the theoretical aspects of financial planning. I further describe various methods of sales forecasting (qualitative vs. quantitative). Special emphasis is put on time series prediction methods. In the application part I provide a short description of POS Media and its business. I use time series decomposition techniques to predict POS Media's sales in 2012. Consequently, I outline the rest of the pro forma income statement.

This research focuses on developing a crop decision planning model to help farmers make decisions for an upcoming crop year. The decisions consist of which crops to plant, the amount of land to allocate to each crop, when to grow, when to harvest, and when to sell. The objective is to maximize the overall profit subject to available resources under yield and price uncertainties. To help achieve this objective, we develop yield and price forecasting models to estimate the probable outcomes of these uncertain factors. The output from both forecasting models are incorporated into the crop decision planning model which enables the farmers to investigate and analyze the possible scenarios and eventually determine the appropriate decisions for each situation. This dissertation has three major components, yield forecasting, price forecasting, and crop decision planning. For yield forecasting, we propose a crop-weather regression model under a semiparametric framework. We use temperature and rainfall information during the cropping season and a GDP macroeconomic indicator as predictors in the model. We apply a functional principal components analysis technique to reduce the dimensionality of the model and to extract meaningful information from the predictors. We compare the prediction results from our model with a series of other yield forecasting models. For price forecasting, we develop a futures-based model which predicts a cash price from futures price and commodity basis. We focus on forecasting the commodity basis rather than the cash price because of the availability of futures price information and the low uncertainty of the commodity basis. We adopt a model-based approach to estimate the density function of the commodity basis distribution, which is further used to estimate the confidence interval of the commodity basis and the cash price. Finally, for crop decision planning, we propose a stochastic linear programming model, which provides the optimal policy. We also develop three heuristic models that generate a feasible solution at a low computational cost. We investigate the robustness of the proposed models to the uncertainties and prior probabilities. A numerical study of the developed approaches is performed for a case of a representative farmer who grows corn and soybean in Illinois.

Bibliography / Thesis (MPhil (Journalism))--University of Stellenbosch, 2010.

Made available in DSpace on 2016-06-02T19:50:10Z (GMT). No. of bitstreams: 1 3530.pdf: 3939391 bytes, checksum: 555a1e91c6d19c737e2b04d918e65d42 (MD5) Previous issue date: 2011-02-18 / Frequent changes in consumer behavior and the characteristics of complexity and competition involving the market demands constant innovation in products, technologies, management strategies and management systems, including the systems that make the integration between the company and its customers or suppliers, as well as systems that integrate or coordinate business processes. In this scenario, understanding and managing of the vendors and/or sales representatives of the company turn out to be an essential factor for the company to remain competitive in the market. In this light, the process of determining and allocating sales quotas appears as integral and important part of the sales management process, which allows, among other things, the evaluation of vendor performance toward the goals set by the company. The purpose of this thesis was to establish a set of procedures to forecast appropriately, sales in the footwear industry and from then on, build a model to determine sales quotas which meets the specific of a sector and integrate two important functions of the company: Sales and Production. Semi-structured interviews with sales professionals, as well as production planning and control were made to subsidize the development of the proposal. The evaluation of the proposal was made by means of a case study in a footwear company in the city of Franca (SP). It was concluded that demand management in the footwear industry, is connected to the quotas they should limit (directly or indirectly) to sales due to the production capacity available and the considered models, in this study, meet the criteria to the studied situation. / As frequentes mudanças no comportamento do consumidor e as características de complexidade e competição que envolvem o mercado demandam inovações constantes nos produtos, tecnologias, estratégias de gestão e sistemas de gestão, entre eles, os sistemas que fazem a integração entre a empresa e seus clientes ou fornecedores, bem como os sistemas que integram ou coordenam processos da empresa. Neste cenário, a compreensão e administração dos vendedores e/ou representantes de vendas da empresa revelam-se um fator essencial para a empresa manter-se competitiva no mercado. Sob esta ótica, o processo de determinação e alocação de quotas de vendas aparece como parte integrante e importante do processo de gestão de vendas, o que permite, entre outras coisas, a avaliação do desempenho do vendedor em direção aos objetivos traçados pela empresa. A proposta desta tese foi estabelecer um conjunto de procedimentos para prever, de forma adequada, as vendas na indústria de calçados e, a partir daí, construir um modelo para determinar quotas de vendas o qual atenda as especificidades do setor e integre duas importantes funções da empresa: Vendas e Produção. Para subsidiar a elaboração da proposta foram feitas entrevistas semiestruturadas com profissionais de vendas e planejamento e controle da produção. A avaliação da proposta deu-se por meio de um estudo de caso em uma empresa de calçados da cidade de Franca (S.P). Concluiu-se que a gestão de demanda na indústria calçadista está atrelada às quotas que devem limitar (direta ou indiretamente) as vendas em função da capacidade de produção disponível, e os modelos propostos neste estudo atendem bem à situação estudada.

Orientador: Antonio Batocchio / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-20T08:27:21Z (GMT). No. of bitstreams: 1 Casula_HenriqueCury_M.pdf: 1869496 bytes, checksum: 0ebc9d261c898a9363cb007fa72f0bef (MD5) Previous issue date: 2012 / Resumo: A previsibilidade é uma importante ferramenta que os tomadores de decisão buscam nas suas escolhas. Entende-se a tomada de decisão como o processo de identificação de um problema ou de uma oportunidade e a seleção de uma linha de ação para resolvê-la ou de alteração dos objetivos e metas a fim de superá-las. Visando o auxilio a decisões de dimensionamento da cadeia de suprimentos será apresentado um estudo de caso de aplicação de modelos estatísticos em séries temporais para gerar cenários futuros, os riscos inerentes e os erros de previsão. Os dados matemáticos foram ajustados com os especialistas da empresa em estudo que acrescentaram informações não presentes nas séries temporais, como informações de mercado, gerando assim a previsão fim para as decisões. O trabalho foi aplicado em uma manufatura para o auxilio no dimensionamento do seu centro de distribuição para comportar o crescimento de longo prazo / Abstract: Predictability is an important tool for decision makers in their choices. The decision-making is the process of identifying a problem or an opportunity and the selection of a course of action to solve it or change the goals and objectives in order to overcome them. In order to help of design decisions in the supply chain will be presented to the application of statistical models in time series to generate future scenarios, the risks and the forecast errors. The mathematical data were fitted with the company's experts added information not present in time series, such as market information, thereby generating the prediction order for decisions. The method was applied in a manufacturing to design your distribution center to accommodate the long-term growth / Mestrado / Materiais e Processos de Fabricação / Mestre em Engenharia Mecânica

The sales representatives primary duties are to attract wholesale and retail buyers and purchasing agents to their merchandise, and to address any of their client's questions or concerns. Aided by a laptop computer connected to the Internet, they can access the customer information and sell products to their customer immediately. This project, a Mobile Order Entry System using cellphones, will give sales representatives as state-of-the-art alternative in accessing anf selling products to their customers through cell phones instead of using laptop computers.