Long-Term Sales Forecasting Using Lee-Carter And Holt-Winters Methods

This study developed a statistical model for long-term forecasting sparkling beverage sales in the 14 provinces of Southern Thailand. Data comprised the series of monthly sales from January 2000 to December 2004 obtained from the company. We applied a classical Lee-Carter mortality forecasting approach as well as exponential smoothing Holt-Winters with additive seasonality method to log-transformed monthly sales containing season of month and branch location as factors.  The model produced excellent estimates in sales predicting for up to 24 future months of 20 branches compared with actual data in each branch during the years 2005-2006. The model also gave more accurate results than using separate forecasting method whereas it was parsimonious in the number of parameters used

Previsão de vendas na presença de um elevado número de variáveis : um estudo de caso de itens intra e inter-categoria

A presente pesquisa teve como objetivo verificar se séries históricas referentes a Intra e Inter-categorias são capazes de melhorar o modelo de previsão de vendas de curto prazo para o varejo. Trata-se de um estudo de caso utilizando regressões múltiplas e o método de seleção de variáveis LASSO (Least Absolute Shrinkage And Selection Operator). Os objetivos específicos consistiram em: (1) confirmar empiricamente a existência de itens complementares e substitutos em Intra e Intercategorias; (2) propor um modelo de previsão que leve em consideração séries de Intra e Inter-categorias; (3) comparar os resultados encontrados entre o modelo de previsão com somente uma série histórica e o modelo proposto com Intra e Intercategorias; (4) identificar se existe diferença entre os resultados do modelo com séries Intra e Inter-categorias. Assim, os principais resultados identificados revelam que foi comprovada a existência de itens complementares e substitutos em Intra e Inter-categorias no nível de gramatura. Além disso, os resultados demonstraram maior prevalência de itens complementares, o que representa em média 88,8% das interações, sendo os demais 11,2% substitutos; os resultados apontam que 83,8% da melhoria dos resultados do RMSE são provenientes das séries Intra-categoria, o que representa a maioria expressiva da contribuição. Dentro deste percentual, a redução média do RMSE foi de 56,30%. Entretanto, o estudo destaca que séries Inter-categorias também são capazes de contribuir com 16,2% para melhorar a acurácia, demonstrando assim uma redução do erro e comprovando a existência de interação entre séries ao longo das categorias. Por fim, conclui-se que a utilização de séries pertencentes apenas a Intra-categoria para compor o modelo de previsão consegue melhorar a acurácia na maioria dos casos, e que a redução alcançada atinge resultados satisfatórios.FAPE

Estimating retail market potential using demographics and spatial analysis for home improvement in Ontario

Alongside the breadth of literature on retail location theory, retail market assessment, and consumer analysis, two major topics are addressed. First, a framework for estimating market demand spatially using consumer expenditures for retail is put forward. This is a foremost step in identifying suitable locations for retail stores, as it gives an indication into the power of the market at the location. Coupled with reported retail sales, the approach evaluates what proportion of the market share remains for capture. Benchmarking the aggregate estimated market expenditures against provincially reported sales reveals that the estimation performance of one method is within one percent of the provincial sales. Spatial analysis results for Ontario show that market demand for home improvement is clustered in Census Metropolitan Areas, where 90% of the provincial expenditures are located. A regression analysis identified three demographic variables as drivers of home improvement expenditures: count of households with income over $100,000, average monthly shelter costs for owned dwellings, and count of owned dwellings. The market demand estimation is necessary for profitability analyses, site suitability analyses and gravity modelling. Such a framework for market demand estimation can be used by retailers and local governments to inform policy creation for future development. The second topic addressed by this thesis was the characterization of situational and demographic variables contained in the service areas of home improvement chain stores. The characterization facilitated a statistical comparison between chains to identify similarities and differences in store formats and demographics. Results showed that big-box chains are fairly similar in their situational characteristics and statistically significant similarities were observed when comparing the demographic variables contained in the service areas of these chains’ stores. Chains that employ various store formats exhibit statistically significant differences both in situational and demographic characteristics. The spatial distribution of the chain stores was assessed using spatial statistics and showed that the chains exhibited different spatial patterns in Ontario. Store level sales were estimated using a mathematical model that employed store area and demographics. Future work on chain characterization using more demographic categories would allow for the segmentation of target markets and the characterization of the landscape for optimal retail locations

Sales forecasting and demand management for agricultural machinery : an Artificial Neural Network approach proposal

Orientador: Luis Antônio de Santa-Eulália, Aníbal Tavares de AzevedoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Ciências AplicadasResumo: O presente estudo propõe um modelo baseado em Redes Neurais Artificiais (RNA) para Previsão de Vendas adequado a um processo de Gestão da Demanda. Para tanto, realizamos a avaliação e aperfeiçoamento geral do processo de negócio da Gestão da Demanda (GD), tendo como foco principal os métodos de Previsão de Vendas (PV) e servindo-nos do processo e dos dados históricos de uma grande empresa de máquinas agrícolas. Após uma revisão sobre o processo de GD, uma avaliação dos métodos de PV atuais da empresa foi realizada. A partir da revisão bibliográfica sobre ambos os temas, um modelo matemático de RNA foi desenvolvido em sintonia com um processo de GD, ambos aplicados à empresa. O modelo de previsão é considerando aqui como parte de um processo de GD, sendo função de variáveis distintas de mercado, levando em considerações diversas características, incluindo horizonte de planejamento, padrões da demanda, acurácia e a aplicabilidade dos métodos. Os resultados dos modelos testados de RNA foram comparados aos métodos de Regressão Múltipla e Suavização Exponencial. O modelo de Suavização, o qual faz uso apenas da série histórica de vendas para realizar a previsão, foi o que apresentou performance menos satisfatória. Os métodos causais de Regressão Múltipla e Redes Neurais Artificiais, por sua vez, exibiram uma boa performance, semelhantes entre si. A Regressão Múltipla, cuja aplicação é mais usual na literatura, exibiu desvios menores que a RNA em algumas das medições de acurácia utilizadas. A pesquisa apresenta tanto contribuição de natureza teórica, uma vez que a literatura não abrange métodos previsão de vendas para produtos similares, como de natureza prática, pois colabora de forma concreta para o meio empresarial, em particular para uma empresa de grande porte. Contribui ainda com o uso empírico da técnica de Inteligência Artificial que, apesar de ser conhecida há mais de meio século, ainda é tida como um método de difícil implantaçãoAbstract: The presented study proposes a model based in Artificial Neural Networks (ANNs) for sales forecasting, suitable for the Demand Management process. We performed the valuation and improvement of the business process for demand management, focusing on sales forecasting methods and using the data series from a large company of agricultural machinery. After the review of the current process we we evaluated the current forecasting method. An algorithm model of ANNs has been developed in parallel to the process. The forecasting models were considered as part of the demand management and have distinct market variables in its main function. Some of the characteristics considered include planning horizon, demand patterns, accuracy and applicability of methods. The results of the ANNs models were compared with the exponential smoothing and multiple regression methods. The first, which uses only the sales history series to perform the forecast, was the one with worst results. The multiple regression and the artificial neural networks, which are known as causal methods, presented good performance, similar to each other. The multiple regression even showed less errors than the ANNs in some of the error measures we have used. This research presents contribution for the theory of forecasting methods, once there is not much of research for similar products. The contribution of this study is also practical, since it collaborates with a company's reality, in particular a large enterpriseMestradoPesquisa OperacionalMestra em Pesquisa Operaciona

Retail forecasting: research and practice

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

Prévision et réapprovisionnement dynamiques de produits de consommation à cycle rapide

L’industrie du commerce de détail est en plein bouleversement, de nombreuses bannières ont annoncé récemment leur fermeture, telles que Jacob, Mexx, Danier, Smart Set et Target au Canada, pour n’en nommer que quelques-unes. Pour demeurer compétitives et assurer leur pérennité, les entreprises en opération doivent s’adapter aux nouvelles habitudes d’achat des consommateurs. Nul doute qu’une meilleure connaissance de la demande s’impose. Or comment estimer et prévoir cette demande dans un contexte où la volatilité croit constamment, où la pression de la concurrence est omniprésente et globale, et où les cycles de vie des produits sont de plus en plus courts ? La gestion de la demande est un exercice difficile encore aujourd’hui, même avec le développement d’outils de plus en plus sophistiqués. L’environnement dynamique dans lequel évoluent les organisations explique, en partie, cette difficulté. Le client, depuis les 30 dernières années, est passé de spectateur passif à acteur de premier plan, modifiant inévitablement la relation consommateur-entreprise. Le développement technologique et la venue du commerce en ligne sont aussi largement responsables de la profonde mutation que subissent les entreprises. La façon de faire des affaires n’est plus la même et oblige les entreprises à s’adapter à ces nouvelles réalités. Les défis à relever sont nombreux. Les entreprises capables de bien saisir les signaux du marché seront mieux outillées pour prévoir la demande et prendre des décisions plus éclairées en réponse aux besoins des clients. C’est donc autour de ce thème principal, à travers un exemple concret d’entreprise, que s’articule cette thèse. Elle est divisée en trois grands axes de recherche. Le premier axe porte sur le développement d’une méthode de prévision journalière adaptée aux données de vente ou de demande présentant une double saisonnalité de même que des jours spéciaux. Le second axe de recherche, à deux volets, présente d’abord une méthode de prévision par ratios permettant de prévoir rapidement les ventes ou demandes futures d’un très grand nombre de produits et ses variantes. En deuxième volet, il propose une méthode permettant de calculer des prévisions cumulées de vente ou demande et d’estimer la précision de la prévision à l’aide d’un intervalle de confiance pour des produits récemment introduits dans les magasins d’une chaîne. Enfin, le troisième axe traite d’un outil prévisionnel d’aide à la décision de réapprovisionnement et propose des recommandations de taille de commande basées sur les résultats d’une analyse prévisionnelle, sur le déploiement des produits ciblés et sur l’analyse de la demande et des inventaires des produits substituts potentiels. Mots-clés : Prévision; saisonnalité; effet calendaire; produit sans historique; intervalle de confiance; décision de réapprovisionnement; réseau de détail.The retail industry is in upheaval. Many banners have recently announced their closure, such as Jacob, Mexx, Danier, Smart Set and Target in Canada, to name a few. To remain competitive and ensure their sustainability, companies have to adapt themselves to new consumer buying habits. No doubt that a better understanding of demand is needed. But how to estimate and forecast demand in a context with constantly increasing volatility and ever shorter product lifecycles, where competitive pressure is pervasive and global? Managing demand is a difficult exercise, even in this age when numerous sophisticated tools have been developed. The dynamic environment in which organizations evolve explains in part the difficulty. Through the past 30 years, the customer has gone from passive spectator to leading actor, inevitably changing the consumer-business relationship. Technological development and the advent of e-commerce are also largely responsible for profound changes experienced by businesses. The way of doing business is not the same and forces companies to adapt to these new realities. The challenges are important. Companies able to seize market signals will be better equipped to anticipate demand and make better decisions in response to customer needs. This thesis is articulated according to three main lines of research around this main theme, exploiting a real business testbed. The first theme concerns the development of a daily forecast method adapted to sales data with a double seasonality as well as special days. The second twofold research first presents a forecasting method for using ratio to quickly forecast sales or future demands of a very large number of products and their variations. Then it proposes a method to determine cumulative sales forecasts and confidence intervals for products newly introduced in the chain stores. Finally, the third axis proposes a predictive method to help reorder launching and sizing decision based on the results of a predictive analysis, deployment of targeted products and inventory of potential substitute products. Keywords : Forecasting; seasonality; calendar effect; products without demand history; confidence intervals; replenishment decision; retail network

Diseño de una herramienta para la previsión de demanda basada en modelos causales para una empresa del sector de distribución de alimentos

[ES] La previsión de la demanda constituye una de las entradas fundamentales a diversos procesos clave de las empresas. Es por ello que una previsión precisa contribuirá, sin duda, a aumentar la eficiencia global de la empresa. En el sector de distribución de alimentos alcanzar el anterior objetivo resulta más complicado que en otros sectores por las propias características de los productos y otros factores que, como las promociones, pueden tener sobre la demanda. Para dar respuesta a esta situación, el presente TFM tiene como objetivo el diseño de una herramienta de previsión de demanda basada en modelos de programación matemática que incorporan factores causales en empresas de distribución del sector de la alimentación. Posterioremente, se procederá a la validación de los modelos a través de su aplicación a una importante empresa del sector.[EN] Demand forecasting forms a fundamental input for different key processes of companies. Because of this, an accurate forecast contribute, for sure, to increase the global efficiency of the company. On retail sector of food, to achieve before objective becomes more complicated than other sectors because of own characteristics of products and other factors, like promotions, that could affect to demand. In order to give an answer to this situation, this Final Master Thesis has as aim to design a demandforecasting tool that is based on mathematical programing models that adds causal factors in distribution sector of food retail. Later on, this method will be tested through its application to an important company of the sector.Fuentes Pinel, A. (2016). Diseño de una herramienta para la previsión de demanda basada en modelos causales para una empresa del sector de distribución de alimentos. http://hdl.handle.net/10251/73126.TFG

Cash Flow Forecasting Process and its Impact on Capital Budgeting: Evidence from Libya

This study highlights the role of cash flow forecasting process in capital budgeting decisions, where the forecasting process starts with identifying the procedures and methods used in forecasting, and ends by estimating future cash flow required by managers for decision-making. This study utilised questionnaire survey to collect data from 69 manufacturing and oil companies operating in Libya within contingency and new institutional sociology theories, which are commonly used in capital budgeting research. Further, this study seeks to ascertain the key variables associated with the forecasting process in capital budgeting decisions. In this regard, this study examined the contingent and institutional variables influencing the use of forecasting procedures and methods associated with the adoption of different capital budgeting processes. Consequently, the results of this study explored the forecasting procedures, methods and the capital budgeting techniques used in manufacturing and oil companies operating in Libya. The researcher found that most manufacturing and oil companies depend on personal and management's subjective estimates in forecasting their future cash flows. In terms of the extent of use of capital budgeting techniques, the findings indicate that most Libyan manufacturing and oil companies use the payback period (PB) and accounting rate of return (ARR) to evaluate and select the investment opportunities, as well as rely upon subjective assessments in evaluating the project risk inherent within capital budgeting decisions. In addition, this study applied the partial least squares structural equation modelling (PLS-SEM) technique to test the research hypotheses. Using the same sample of Libyan manufacturing and oil companies, the findings are as follows. First, the use of forecasting procedures/methods and components of cash flow are positively associated with the extent of use of capital budgeting techniques. Second, the forecasting horizon and the use of multiple data sources in forecasting are significantly associated with the use of forecasting procedures and methods. Third, the presence of qualified persons responsible for estimating future cash flow is positively associated with the use of forecasting procedures and methods. Fourth, the findings suggest that the influence of contingent variables differs from public to private companies. Fifth, the study findings also suggest that coercive, mimetic and normative pressures are significantly associated with the use of forecasting procedures and methods. Finally, the research findings revealed that there is a significant relationship between the procedures and methods used in forecasting (PMUF) and the firms’ financial performance (PERF), whilst the study does not find any evidence that the extent of use of capital budgeting techniques improves the firms’ financial performance. The findings of this study offer new important insights and contributions to the existing literature, as well as have useful implications for practitioners and researchers