Resumo O desenvolvimento de um Sistema de Medição de Desempenho (SMD) em uma organização é uma questão essencial para acompanhar adequadamente as operações, e possibilitar as tomadas de decisões corretas e a tempo. Porém, muitas organizações implementam os SMDs e não obtém sucesso, principalmente, por negligenciar a fase de projeto, que trata sobre o que medir. Projetar um SMD é um problema de decisão complexo que envolve vários stakeholders. O objetivo desta pesquisa é identificar e priorizar os indicadores de desempenho comerciais mais valorizados pelos stakeholders desta área em uma empresa do segmento metal-mecânico. A pesquisa foi dividida em duas etapas, sendo na primeira aplicada uma abordagem da PO Soft (construtivista): Value Focused Thinking - VFT, para identificação dos indicadores de desempenho; e na segunda etapa a aplicação do Método de Apoio a Decisão Multicritério da PO Hard (racionalista): Analytic Network Process - ANP, para modelagem e priorização destes. Como resultado, foram identificados 30 indicadores de desempenho, sendo os de maior peso: percentual do atingimento da meta, faturamento a clientes novos e percentual de crescimento de cada cliente. Os resultados encontrados mostraram-se capazes de auxiliar a definição da estratégia e gestão comercial além da identificação de indicadores não monitorados pela organização. (SMD operações tempo Porém sucesso principalmente projeto medir metalmecânico. metalmecânico metal mecânico. mecânico metal-mecânico etapas construtivista (construtivista) VFT racionalista (racionalista) ANP destes resultado 3 peso meta cliente mostraramse mostraram se (construtivista (racionalista

Abstract The development of a Performance Measurement System (PMS) in an organization is essential in order to adequately monitor operations and enable correct and timely decision-making. However, many organizations implement PMSs and are not successful, mainly because they neglect the design phase, which informs what has to be measured. Designing a PMS is a complex decision problem that involves multiple stakeholders. The objective of this research is to identify and prioritize the commercial performance indicators most valued by the stakeholders in this area in a company in the metal-mechanical segment. The research was divided into two stages, the first being a PO Soft (constructivist) approach: Value Focused Thinking - VFT, to identify performance indicators; and, in the second stage, the application of the PO Hard (Rationalist) Multicriteria Decision Support Method: Analytic Network Process - ANP, for modeling and prioritizing these. As a result, 30 performance indicators were identified, the most important ones being: percentage of target achievement, sales to new customers and percentage of growth for each customer. The results found proved to be capable of assisting the definition of strategy and business management in addition to the identification of indicators not monitored by the organization. (PMS decisionmaking. decisionmaking making. making decision-making However successful phase measured metalmechanical metal mechanical segment stages constructivist (constructivist approach VFT stage Rationalist (Rationalist Method ANP these result 3 identified achievement customer

Abstract Paper aims Investigate whether the results of time series models can be adjusted with the AHP method towards a more assertive forecast. Originality Considering demand forecasting as a complex decision-making situation, this research investigated the use of the AHP as a complement to traditional forecasting methods. Research method This applied research employed, as main procedures, literature review and mathematical modeling. Main findings Two models were proposed that presented satisfactory results: model I reduced the forecast error by 16% in January, 25% in February, 37% in March, 3% in April, and 7% in May; model II reduced it by 17% in January, 21% in February, 29% in March, 2% in April, and 5% in May. Implications for theory and practice We conclude that the AHP has the potential to correct the results of time series in the textile industry by allowing the incorporation of quantitative and qualitative variables.

ABSTRACT Performance assessment is addressed in the literature as means for continuous improvement. The objective of this study was to identify the most valued performance criteria for a Graduate Program of Biotechnology in a University of Brazil through the Value Focused Thinking (VFT) approach. The choice of the VFT was due to the holistic vision provided by it and the focus on the identification of organizational values. Among the objectives found, it is possible to notice an expansion of the vision of performance that is more focused on publications regarding the regulatory body Coordination of Improvement of Higher Level Personnel (CAPES), and contemplating among others the quality in the student’s training as evidence of other stakeholders. The VFT approach met the established assumptions, efficiently allowing strategic, fundamental and media objectives to be identified, as well as measurable attributes to be used by the program to enable more efficient management.

In general, the identification of probability distributions for time between arrivals and the covariates that affect them, get a superficial treatment in papers on production engineering. The main goal of this paper is to analyze the data on the time between arrivals for batches at the quality control department in a food company. A Weibull distribution, with two parameters, was used in the statistical modeling - a quantitative approach. Scale and shape was estimated by the Maximum Likelihood Method. A regression Weibull model was considered to evaluate inferences for the mean and median times between arrivals and to detect important factors affecting the variability in data sets. Considering some covariates, the Weibull distribution was found to have a good fit for the data. These results allow for a more reliable modeling of the quality control department for discrete event simulation.

Em geral, a identificação das distribuições de probabilidades dos tempos entre chegadas, bem como das covariáveis que os afetam é tratada de forma superficial nos artigos sobre engenharia de produção. O objetivo principal deste trabalho é analisar os dados sobre os tempos entre chegadas de lotes de receitas ao departamento de controle de qualidade de uma indústria alimentícia. Para a modelagem estatística -abordagem quantitativa- utilizou-se uma distribuição de Weibull. Os parâmetros de escala e forma da distribuição foram estimados pelo método da máxima verossimilhança. Um modelo de regressão de Weibull foi apresentado para a avaliação de inferências nos parâmetros tempos médios e medianos entre chegadas e, para detectar os fatores que afetam a variabilidade dos dados coletados. Na presença de algumas covariáveis, observou-se que a distribuição de Weibull teve boa aderência aos dados. Os resultados permitiram uma modelagem mais fidedigna do setor de controle de qualidade da empresa para a realização de simulação discreta de eventos.

Este artigo apresenta uma modelagem estatística para os dados sobre consumo específico de madeira (CEM) de uma indústria de celulose e papel. Um importante objetivo deste setor é identificar os possíveis fatores que afetam o CEM, promovendo grande variabilidade nos custos de produção. Modelos de regressão linear múltipla foram utilizados para esse fim. Com os fatores identificados, objetivou-se determinar os valores adequados (condições ideais) que minimizam a resposta CEM no processo produtivo através de técnicas de superfície de respostas. Os resultados mostraram que a densidade básica da madeira (DB), o rendimento da madeira no processo (REND) e as perdas na produção (PERDAS) influenciam significativamente o CEM. As condições ideais identificadas foram: DB > 500 kg-seco/m³-sólido; REND > 57%; PERDAS < 14%. Conclui-se que, além de trazer contribuições para o planejamento de florestas, a abordagem estatística empreendida pode ser útil para o controle dos processos das indústrias de celulose e papel.

This study presents a statistical modeling approach for wood consumption in the cellulose industry. One of the goals of the cellulose industry is to identify the factors that can affect wood consumption (CEM) and cause high variability in production costs. Multiple linear regression models were used for this purpose. After the factors of influence were identified (for optimal conditions), the goal was to determine their appropriate values to minimize CEM in the production process, using response surface methods. The results showed that three factors affect wood consumption: the basic density (DB) of the wood received, the Kraft pulp yield (REND), and wood losses that occur during the production process (PERDAS). The optimal conditions identified are DB > 500 kg-dry/m³-solid, REND > 57% and PERDAS < 13%. The statistical approach taken in this work is concluded to be useful in process control in the cellulose industry and can also provide insights useful to forest planning.

This paper presents the statistical modeling for daily counting statistics of units that arrive for quality inspection at a food company. Different Poisson regression models were considered in order to analyze the data collected, with a Bayesian focus. The main objective was to forecast the daily average count based on co-variables such as days of the week. The analysis of co-variables is very often neglected by statistical packages that come with Discrete Event Simulation software. The discovery of the factors that influence these variations was essential to a more accurate modeling (the definition of simulation calendars) and enables industrial managers to make better decisions about the reallocation of people in the department, resulting in better planning of production capacity.