Purpose – Forecasting is a firm's ability to anticipate or predict the future demand givenon a set of assumptions. For a company to implement an appropriate forecast model whichcan make accurate assumptions, the model needs to be aligned with the company's businesssituation and enhanced through supply chain relationships. Therefore, the purpose of thisstudy is: Investigate how small sized wholesalers benefit from demand forecasting. The purpose is divided into two research questions RQ1: How can a company influenced by a seasonal demand select an appropriateforecast model according to its business environment? RQ2: Why do information sharing issues between supply chain partners occur and howcan wholesalers overcome this resistance? Method – The researchers executed a singular case study at one of the local small-sizedfurniture wholesalers in Sweden. The data collection methods implemented in this studyare interviews, document analysis and a survey addressed towards downstream membersof the wholesalers’ chain, retailers (five participants). The combination of both qualitativeas well as quantitative methods was based on a triangulation principle which helped theresearchers provide a comprehensive understanding of the problem as well as increasevalidity and credibility of the study. Findings – The result of the study raises the importance of selecting a forecast model inaccordance with the company's business situation. Furthermore, by the help of a selfdesigned four-step forecast process the company could identify its influencing factors(seasonality, lead-times, lack of information sharing, etc.), available data, and finally selectthe appropriate model corresponding with the business situation. In this study the Holt-Winters model was selected due to the promotion of simplicity considering the casecompany. Also, the issue regarding information sharing among supply chain partners wasidentified where retailers promotes the performance of the whole supply chain anddemands a partnership as a requirement for sharing information. Implications – As every firm is unique and different in its nature it therefore requires itsown specific forecast process in which can select the appropriate model. However, thestudy revealed how selecting the appropriate forecast model can enhance the businessmeeting their seasonal demand. Additionally, the fact that small-sized companies need toestablish a partnership to receive demand information from their retailers. Based on theresult, the study reveals how companies can enhance their situation through demandforecasting. Limitations - As each model is based on each specific company the results regarding theselected forecast model can be questioned. Furthermore, due to the limited time-period ofthe research a specific forecast process had to be constructed which could only cover thescope of the research and not how the forecast model performed over time. Therefore, alonger time-period of the research could have included extra activities in the forecastprocess which would have validated the model.

This thesis examines how to design a mathematical inventory model for a ”Fast Moving Consumer Goods”-company (FMCG-company), which determines the optimal reorder point and order quantity such that the average inventory cost is minimized. The thesis was made in collaboration with a ”Software as a Service”- company which provided the data containing information about the products and inventory management of one of their customers, a FMCG-company. The thesis first considers a basic EOQ-model, with constant demand rate, that suggests a reorder time and order quantity for the products. Since constant demand rate might be an unrealistic assumption for a FMCG-company, the thesis also considers a (R,Q)-model, where the demand was based on a forecast made by using the Holt-Winters model on previous sales history. The solutions were found by investigating the singular points and comparing them to the critical point. The thesis shows that the EOQ-model gives useful results for the most indemand products, while the reorder times for the less popular products are instead impractically high. The (R,Q)-model showed more stable solutions for all products and therefore proves to be a better inventory model for FMCG-companies, as expected. Simulations of the (R,Q)-model showed various inventory cases, where some showed a mismatch between the inventory level and the demand. The cases shows how demand planning can be applied for different products for when to consider changing inventory strategy or discontinuing products and how the orders can be made optimally. / Detta examensarbete undersöker hur en matematisk lagermodell kan utformas för ett ”Fast Moving Consumer Goods”-företag (FMCG-företag), som bestämmer den optimala beställningspunkten och orderkvantiteten så att den genomsnittliga lagerkostnaden minimeras. Examensarbetet gjordes i samarbete med ett ”Software as a Service”-företag som tillhandahöll data innehållandes information om produkter och lagerhantering hos en av deras kunder, ett FMCG-företag. Avhandlingen behandlar först en grundläggande EOQ-modell, med konstant efterfrågan, som föreslår en återbeställningstid och orderkvantitet för produkterna. Eftersom att en konstant efterfågan kan anses vara ett orealistiskt antagande för ett FMCG-företag, tar avhandlingen även upp en (R,Q)-modell, där efterfrågan baserades på en prognos gjord med hjälp av Holt-Winters-modellen på tidigare försäljningshistorik. Lösningarna hittades genom att undersöka de singulära punkterna och jämföra dem med den kritiska punkten. Avhandlingen visar att EOQ-modellen ger användbara resultat för de mest efterfrågade produkterna medan beställningstiderna för de mindre populära produkter är ofta opraktiskt höga. (R,Q)-modellen visade mer stabila lösningar för alla produkter och visar sig därmed vara en bättre lagermodell för FMCGföretag, som förväntat. Simuleringar av (R,Q)-modellen visade olika fall, där vissa visade en obalans mellan lagernivån och efterfrågan. De olika fallen visar hur efterfrågeplanering kan tillämpas för olika produkter för när man ska överväga att ändra lagerstrategi eller avveckla produkter och hur beställningarna kan göras optimalt

[pt] As partículas finas (PM2.5) são um dos principais poluentes atmosféricos associados a problemas de saúde. Estas partículas penetram no sistema respiratório, carreando desde metais traços a substâncias orgânicas. Apesar disso, a legislação ambiental brasileira ainda não tem estabelecido padrões para este poluente. Entretanto, Agencia Ambiental dos Estados Unidos (US.EPA) já tem adotado limites para exposições de curto (25 (micro)g m-3/diário) e longo (15 (micro)g m-3/anual) prazo. Esta tese teve quatro principais objetivos: (1) investigar a relação das condições meteorológicas, sazonalidade e bacias aéreas sobre as concentrações de PM2.5 na atmosfera; (2) avaliar modelos de previsão de qualidade do ar inovadores para estimar concentração de PM2.5 em locais com diferentes fontes de emissão; (3) validar método de extração e determinação pseudototal de metais traços presentes no material particulado, com espectrômetro de emissão ótica por plasma indutivamente acoplado (ICP-OES) de acordo com critérios estabelecidos pelo INMETRO; (4) quantificar carbono orgânico e metais traços presentes no material particulado fino para entender melhor como a atmosfera do estado do Rio de Janeiro tem sido afetada, devido aos vários tipos de emissão e condições meteorológicas. Amostradores de grandes volumes coletaram todas as amostras de PM2.5. Estes amostradores foram operados por 24 h, a cada seis dias, em locais com diferentes fontes de emissão (industrial, veicular, poeira do solo, etc.), no estado do Rio de Janeiro. As amostras foram coletadas pelo Instituto Estadual do Ambiente (INEA), no período de janeiro/11 até dezembro/13. Variáveis meteorológicas próximas (d(menor que)2 km) aos pontos de monitoramento de PM2.5 também foram obtidas na mesma frequência e período de amostragem. Em relação a este estudo, quatro resultados podem ser destacados. O primeiro, as concentrações médias diárias de PM2.5 variaram de 1-65 (micro)g m-3, ultrapassando em alguns pontos os limites adotados pela US.EPA. Estes resultados mostraram que concentrações de PM2.5 no RJ não é influenciada, em expressão, pela sazonalidade. Além disso, foi observado que as bacias aéreas definidas no Rio de Janeiro não têm sido confirmadas, e os locais mostraram uma semelhança de comportamento em função da sua fonte de emissão. O segundo, a aplicação do modelo Holt-Winters para previsão de PM2.5 simulou melhor a zona industrial, com RMSE (raiz do erro quadrático médio) entre 5,8-14,9 (micro)g m-3. Em contrapartida, a rede neural artificial associada a variáveis meteorológicas estimou melhor os resultados das zonas urbanas e rurais, com RMSE entre 4,2-9,3 (micro)g m-3. O terceiro, o método de extração e determinação pseudototal de metais por ICP-OES atendeu aos critérios de validação estabelecidos pelo INMETRO. Além disso, mostrou-se ser equivalente ao método US.EPA IO-3.1. Finalmente, as concentrações de carbono orgânico solúvel em água variaram de 0,8-4,9 (micro)g m-3. Os principais metais determinados foram: Na (5,8-13,6 (micro)g m-3), Al (1,6-6,7 (micro)g m-3) e Zn (1,9-6,6 (micro)g m-3). Foi verificado também que os fenômenos meteorológicos de superfície aumentam em 30 por cento a explicação da variância do modelo receptor (PCA), quando adicionados aos dados das substâncias químicas analisadas do PM2.5. Contudo, é crucial a aplicação de ferramentas quimiométricas para ajudar na caracterização e estimava das concentrações de poluentes atmosféricos. / [en] Fine particulate matters (PM2.5) are one of the primary air pollutants associated with health problems. These particles penetrate in the respiratory system, loading from trace metals to organic compounds. Neverthelere4ss, the Brazilian environmental legislation has not yet established standards for this pollutant. However, the US Environmental Agency (US.EPA) has already adopted limits for short-term (25 (micro)g m-3/daily) and long-term (15 (micro)g m-3/annual) exposures. This thesis had four main objectives: (1) to investigate the relation of weather conditions, seasonality and air basins on PM2.5 concentrations in the atmosphere; (2) to evaluate innovative air quality forecast models to estimate PM2.5 concentration in sites with different emission sources; (3) to validate method to extract and pseudo total determinate trace metals present in the particulate matter by inductively coupled plasma optical emission spectrometer (ICP-OES) according to criteria established by INMETRO; (4) to quantify organic carbon and trace metals present in fine particulate matter to better understand how the Rio de Janeiro State (RJ) atmosphere has been affected due to the various types of emission and weather conditions. High volumes samplers PM2.5 collected all PM2.5 samples. These samplers were operated for 24 h, every six days, in places with different emission sources (industrial, vehicular, soil dust, et caetera), in the Rio de Janeiro State. The samples were collected by the State Environmental Institute (INEA) during the period from January/2011 still December/2013. Meteorological variables nearby (d(less than)2 km) to PM2.5 monitoring points were also obtained at the same frequency and sampling period. Regarding this study, four results can be highlighted. The first one, the PM2.5 dailly concentrations average ranged from 1-65 (micro)g m-3, exceeding in some sites the limits adopted by US.EPA. These results showed that PM2.5 concentrations in RJ is not influenced, in expression, by the seasonality. In addition, it was observed that the defined RJ air basins have not been confirmed, and the local showed a similar performance according to their emission sources. The second one, the application of the Holt-Winters model for PM2.5 forecast simulated best industrial zone, with RMSE (root mean square error) between 5.8 to 14.9 (micro)g m-3. On the others hand, the artificial neural network associated with meteorological variables estimated best results from urban and rural areas, with RMSE between 4.2 to 9.3 (micro)g m-3. The third one, the method to extract and determine pseudo total metals by ICP-OES followed the validation criteria established by INMETRO. Furthermore, it was shown to be equivalent to US.EPA IO-3.1 method. Finally, the water-soluble organic carbon concentrations ranged from 0.8 to 4.9 (micro)g m-3. The principal metals determined were: Na (5.8-13.6 (micro)g m-3), Al (1.6-6.7 (micro)g m-3) and Zn (1.9-6.6 (micro)g m-3). It was also found that the surface meteorological phenomena increase at 30 percent the explicated variance of the receiver model (PCA) when added to PM2.5 chemical analysis data. Therefore, it is crucial the application of chemometric tools to help in the characterization and estimated air pollutant concentrations.