Indústria de máquinas agrícolas no Brasil: um modelo para estimação da demanda de tratores para o triênio 2016 – 2018

O objetivo desta dissertação foi estimar a demanda de tratores agrícolas para o mercado brasileiro no triênio 2016-2018, utilizando-se para isto de técnicas de econometria de séries temporais, neste caso, modelos univariados da classe ARIMA e SARIMA e ou multivariados SARIMAX. Justifica-se esta pesquisa quando se observa a indústria de máquinas agrícolas no Brasil, dados os ciclos econômicos e outros fatores exógenos aos fundamentos econômicos da demanda, onde esta enfrenta muitos desafios. Dentre estes, a estimação de demanda se destaca, pois exerce forte impacto, por exemplo, no planejamento e custo de produção de curto e médio prazo, níveis de inventários, na relação com fornecedores de materiais e de mão de obra local, e por consequência na geração de valor para o acionista. Durante a fase de revisão bibliográfica foram encontrados vários trabalhos científicos que abordam o agronegócio e suas diversas áreas de atuação, porém, não foram encontrados trabalhos científicos publicados no Brasil que abordassem a previsão da demanda de tratores agrícolas no Brasil, o que serviu de motivação para agregar conhecimento à academia e valor ao mercado através deste. Concluiu-se, após testes realizados com diversos modelos que estão dispostos no texto e apêndices, que o modelo univariado SARIMA (15,1,1) (1,1,1) cumpriu as premissas estabelecidas nos objetivos específicos para escolha do modelo que melhor se ajusta aos dados, e foi escolhido então, como o modelo para estimação da demanda de tratores agrícolas no Brasil. Os resultados desta pesquisa apontam para uma demanda de tratores agrícolas no Brasil oscilando entre 46.000 e 49.000 unidades ano entre os anos de 2016 e 2018.

Gestão de estoques: uma proposta de reposição contínua para material médico hospitalar e medicamentos em um hospital universitário federal do estado do Rio Grande do Norte

The inventory management in hospitals is of paramount importance, since the supply materials and drugs interruption can cause irreparable damage to human lives while excess inventories involves immobilization of capital. Hospitals should use techniques of inventory management to perform replenishment in shorter and shorter intervals, in order to reduce inventories and fixed assets and meet citizens requirements properly. The inventory management can be an even bigger problem for public hospitals, which have restrictions on the use of resources and decisionmaking structure more bureaucratized. Currently the University Hospital Onofre Lopes (HUOL) uses a periodic replacement policy for hospital medical supplies and medicines, which involves one moment surplus stock replenishment, the next out of stock items. This study aims to propose a system for continuous replenishment through order point for inventory of medical supplies and medicines to the hospital HUOL. Therefore, a literature review of Federal University Hospitals Management, Logistics, Inventory Management and Replenishment System in Hospitals was performed, emphasizing the demand forecast, classification or ABC curve and order point system. And also, policies of inventory management and the current proposal were described, dealing with profile of the mentioned institution, the current policy of inventory management and simulation for continuous replenishment order point. For the simulation, the sample consisted of 102 and 44 items of medical and hospital drugs, respectively, selected using the ABC classification of inventory, prioritizing items of Class A, which contains the most relevant items in added value, representing 80 % of the financial value in 2012 fiscal year. Considering that it is a public organization, subject to the laws, we performed two simulations: the first, following the signs for inventory management of Instruction No. 205 (IN 205 ), from Secretary of Public Administration of the Presidency ( SEDAP / PR ), and the second, based on the literature specializing in inventory management hospital. The results of two simulations were compared to the current policy of replenishment system. Among these results are: an indication that the system for continuous replenishment reorder point based on IN 205 provides lower levels of safety stock and maximum stock, enables a 17% reduction in the amount spent for the full replenishment of inventories, in other words, decreasing capital assets, as well as reduction in stock quantity, also the simulation made from the literature has indicated parameters that prevent the application of this technique to all items of the sample. Hence, a change in inventory management of HUOL, with the application of the continuous replenishment according to IN 205, provides a significant reduction in acquisition costs of medical and hospital medicine

Importância dos fatores não-tecnológicos na implementação do CPFR

De acordo com o Voluntary Interindustry Commerce Standards [VICS], o Collaborative Planning, Forecasting, and Replenishment [CPFR] se baseia na padronização, registro e sincronização de dados eletronicamente, apoiado pela gestão colaborativa existente entre as empresas (VICS, 2004). A partir desta definição, pode-se concluir que existem dois fatores preponderantes na implementação do CPFR: um essencialmente tecnológico e outro não-tecnológico. Nesse contexto, o propósito principal deste estudo é identificar na literatura os chamados fatores não tecnológicos que envolvem o CPFR e analisá-los em situações reais. A importância desses fatores é analisada, então, por meio do estudo de dois casos reais de implementação do CPFR, respectivamente, em uma grande rede de fast food e em um grande distribuidor de alimentos, que operam no Brasil. Os resultados destacam, principalmente, que a previsão da demanda realizada pela empresa coordenadora do CPFR é preponderante sobre o entendimento da demanda por todos os elos da cadeia de suprimentos, que a cultura colaborativa é considerada muito importante no relacionamento ao longo da cadeia (embora não seja determinante para a implementação dos processos) e que o monitoramento das atividades é fundamental para o alinhamento das empresas na gestão do CPFR.

Previsão da demanda doméstica da aviação comercial européia

This paper work has as objective the study and forecasting of the demand behavior for the European commercial aviation industry. Once economy and demand has a straight relationship, the tool chosen to perform this forecast was the Econometry. In order to get a more efficient forecast, a complete analysis of the environment in which the aviation sector is, to understand all factors with influence over the market as a whole. Only then, the variables which would be tested for the correlation with the demand were picked. The final results of this study has achieved all objectives set and has given a better view over the European Commercial Aviation Market

Modelagem auto-regressiva na previsão de demanda dos produtos de uma empresa do setor químico

Considering the high competitiveness in the industrial chemical sector, demand forecast is a relevant factor for decision-making. There is a need for tools capable of assisting in the analysis and definition of the forecast. In that sense, the objective is to generate the chemical industry forecast using an advanced forecasting model and thus verify the accuracy of the method. Because it is time series with seasonality, the model of seasonal autoregressive integrated moving average - SARIMA generated reliable forecasts and acceding to the problem analyzed, thus enabling, through validation with real data improvements in the management and decision making of supply chain

Modelagem auto-regressiva na previsão de demanda dos produtos de uma empresa do setor químico

Considering the high competitiveness in the industrial chemical sector, demand forecast is a relevant factor for decision-making. There is a need for tools capable of assisting in the analysis and definition of the forecast. In that sense, the objective is to generate the chemical industry forecast using an advanced forecasting model and thus verify the accuracy of the method. Because it is time series with seasonality, the model of seasonal autoregressive integrated moving average - SARIMA generated reliable forecasts and acceding to the problem analyzed, thus enabling, through validation with real data improvements in the management and decision making of supply chain

The seasonal forecast of electricity demand: A hierarchical Bayesian model with climatological weather generator

In this paper we focus on the one year ahead prediction of the electricity peak-demand daily trajectory during the winter season in Central England and Wales. We define a Bayesian hierarchical model for predicting the winter trajectories and present results based on the past observed weather. Thanks to the flexibility of the Bayesian approach, we are able to produce the marginal posterior distributions of all the predictands of interest. This is a fundamental progress with respect to the classical methods. The results are encouraging in both skill and representation of uncertainty. Further extensions are straightforward at least in principle. The main two of those consist in conditioning the weather generator model with respect to additional information like the knowledge of the first part of the winter and/or the seasonal weather forecast. Copyright (C) 2006 John Wiley & Sons, Ltd.

The seasonal forecast of electricity demand: A hierarchical Bayesian model with climatological weather generator

In this paper we focus on the one year ahead prediction of the electricity peak-demand daily trajectory during the winter season in Central England and Wales. We define a Bayesian hierarchical model for predicting the winter trajectories and present results based on the past observed weather. Thanks to the flexibility of the Bayesian approach, we are able to produce the marginal posterior distributions of all the predictands of interest. This is a fundamental progress with respect to the classical methods. The results are encouraging in both skill and representation of uncertainty. Further extensions are straightforward at least in principle. The main two of those consist in conditioning the weather generator model with respect to additional information like the knowledge of the first part of the winter and/or the seasonal weather forecast. Copyright (C) 2006 John Wiley & Sons, Ltd.

Usage of enterprise modeling processes and information systems design to forecast demand

Managing the great complexity of enterprise system, due to entities numbers, decision and process varieties involved to be controlled results in a very hard task because deals with the integration of its operations and its information systems. Moreover, the enterprises find themselves in a constant changing process, reacting in a dynamic and competitive environment where their business processes are constantly altered. The transformation of business processes into models allows to analyze and redefine them. Through computing tools usage it is possible to minimize the cost and risks of an enterprise integration design. This article claims for the necessity of modeling the processes in order to define more precisely the enterprise business requirements and the adequate usage of the modeling methodologies. Following these patterns, the paper concerns the process modeling relative to the domain of demand forecasting as a practical example. The domain of demand forecasting was built based on a theoretical review. The resulting models considered as reference model are transformed into information systems and have the aim to introduce a generic solution and be start point of better practical forecasting. The proposal is to promote the adequacy of the information system to the real needs of an enterprise in order to enable it to obtain and accompany better results, minimizing design errors, time, money and effort. The enterprise processes modeling are obtained with the usage of CIMOSA language and to the support information system it was used the UML language.

Using demand response to deal with unexpected low wind power generation in the context of smart grid

Demand response is assumed an essential resource to fully achieve the smart grids operating benefits, namely in the context of competitive markets. Some advantages of Demand Response (DR) programs and of smart grids can only be achieved through the implementation of Real Time Pricing (RTP). The integration of the expected increasing amounts of distributed energy resources, as well as new players, requires new approaches for the changing operation of power systems. The methodology proposed aims the minimization of the operation costs in a smart grid operated by a virtual power player. It is especially useful when actual and day ahead wind forecast differ significantly. When facing lower wind power generation than expected, RTP is used in order to minimize the impacts of such wind availability change. The proposed model application is here illustrated using the scenario of a special wind availability reduction day in the Portuguese power system (8th February 2012).

Metaheuristic approach to the Holt-Winters optimal short term load forecast

Electricity short-term load forecast is very important for the operation of power systems. In this work a classical exponential smoothing model, the Holt-Winters with double seasonality was used to test for accurate predictions applied to the Portuguese demand time series. Some metaheuristic algorithms for the optimal selection of the smoothing parameters of the Holt-Winters forecast function were used and the results after testing in the time series showed little differences among methods, so the use of the simple local search algorithms is recommended as they are easier to implement.

Metaheuristhic approach to the Holt-Winters optimal short term load forecast

Electricity short-term load forecast is very important for the operation of power systems. In this work a classical exponential smoothing model, the Holt-Winters with double seasonality was used to test for accurate predictions applied to the Portuguese demand time series. Some metaheuristic algorithms for the optimal selection of the smoothing parameters of the Holt-Winters forecast function were used and the results after testing in the time series showed little differences among methods, so the use of the simple local search algorithms is recommended as they are easier to implement.

Virtual Power Player Using Demand Response to Deal with Unexpected Low Wind Power Generation

Demand response is assumed as an essential resource to fully achieve the smart grids operating benefits, namely in the context of competitive markets and of the increasing use of renewable-based energy sources. Some advantages of Demand Response (DR) programs and of smart grids can only be achieved through the implementation of Real Time Pricing (RTP). The integration of the expected increasing amounts of distributed energy resources, as well as new players, requires new approaches for the changing operation of power systems. The methodology proposed in this paper aims the minimization of the operation costs in a distribution network operated by a virtual power player that manages the available energy resources focusing on hour ahead re-scheduling. When facing lower wind power generation than expected from day ahead forecast, demand response is used in order to minimize the impacts of such wind availability change. In this way, consumers actively participate in regulation up and spinning reserve ancillary services through demand response programs. Real time pricing is also applied. The proposed model is especially useful when actual and day ahead wind forecast differ significantly. Its application is illustrated in this paper implementing the characteristics of a real resources conditions scenario in a 33 bus distribution network with 32 consumers and 66 distributed generators.

Random walk forecast of urban water in Iran under uncertainty

There are two significant reasons for the uncertainties of water demand. On one hand, an evolving technological world is plagued with accelerated change in lifestyles and consumption patterns; and on the other hand, intensifying climate change. Therefore, with an uncertain future, what enables policymakers to define the state of water resources, which are affected by withdrawals and demands? Through a case study based on thirteen years of observation data in the Zayandeh Rud River basin in Isfahan province located in Iran, this paper forecasts a wide range of urban water demand possibilities in order to create a portfolio of plans which could be utilized by different water managers. A comparison and contrast of two existing methods are discussed, demonstrating the Random Walk Methodology, which will be referred to as the â On uncertainty pathâ , because it takes the uncertainties into account and can be recommended to managers. This On Uncertainty Path is composed of both dynamic forecasting method and system simulation. The outcomes show the advantage of such methods particularly for places that climate change will aggravate their water scarcity, such as Iran.