Abstract Paper aims To identify factors to measure the cooperation between an agro-industrial pork slaughterhouse cooperative and input suppliers. Originality A performance measurement tool applicable in real-life industry practices related to input suppliers, based on critical success factors. Research method The performance measurement tool was calculated based on the analytic hierarchy process (AHP) procedure, constituted by two essential elements, the critical success factors and the key performance indicators, organized in a hierarchical structure. The key performance indicators are used to detail the critical success factors, measuring the supplier’s performance in relation to specific cooperation characteristics. Main findings In general, 77% of suppliers are fully or potentially cooperative with an established performance in risk sharing and information sharing factors. For the remaining 23% of suppliers classified as partially cooperatives, suggestions were proposed for changes to be implemented which improve their cooperation level performance. Finally, a direct relation between a low proportion of input discards and a established cooperative performance between suppliers and the analyzed agro-industrial pork cooperative was not verified. Implications for theory and practice To provide a performance measurement tool developed with methodological rigor to be used by researchers and agro-industrial pork slaughterhouse cooperative managers to improve the Brazilian pork slaughterhouse supply chain context.

Abstract The time interval between inspections of cold standby systems is a crucial decision to ensure the appropriated system reliability and the lowest costs possible. This paper presents a model developed to establish the optimal time interval between inspections for a two-unit cold standby system with component repair and subject to periodic inspection, considering reliability and costs. A Markov chain is used to define possible states, their transition probabilities and the mean time to system failure, as a function of the time interval between inspections. Given the mean time to system failure, the steady state availability is determined. Finally, the costs related to the system maintenance are established and a cost function is developed and optimized for the time interval between inspections. Numerical examples are presented and results for different system parameters are compared. Besides optimizing the time interval between inspections, the analyses also reveal the effect of repair time on system availability and mean time to system failure.

Este artigo apresenta um método para desenvolver análises quantitativas que orientem a revisão ou elaboração de um plano de manutenção de equipamentos em um cenário de produção just in time. O método proposto contempla: i) Identificar os conjuntos que influenciam a confiabilidade; ii) Levantar taxas de falhas; iii) Classificar os conjuntos quanto ao efeito de suas falhas; iv) Levantar parâmetros de demanda da linha; v) Identificar as distribuições de probabilidade da demanda da linha e dos tempos de bom funcionamento e tempos de reparo dos conjuntos; vi) Simular a produção/manutenção utilizando o método de Monte Carlo; vii) Realizar uma análise de sensibilidade a eventuais variações na demanda, MTBF e MTTR; e viii) Estabelecer a estratégia de manutenção e intervalos entre manutenções preventivas. A aplicação do método é ilustrada através de um estudo real realizado em uma linha de rotulagem e enchimento de galões de uma empresa do setor de tintas e corantes. A aplicação do método permitiu identificar com clareza os conjuntos e subconjuntos críticos frente ao cenário produtivo em questão.

This paper presents a method to develop quantitative analysis to support the review or preparation of an equipment maintenance plan in a just-in-time production scenario. The proposed method includes: i) Identification of parts that influence reliability; ii) Estimation of failure rates; iii) Classification of parts according the effect of their failures; iv) Estimation of line occupation parameters; v) Identification of probability distributions for occupation line, time between failures and repair times, vi) Simulation of production and maintenance using the Monte Carlo Method; vii) Accomplishment of a sensitivity analysis concerning variations in demand, MTBF and MTTR, and iv) Establishment of maintenance strategy and intervals between preventive maintenance. The method is illustrated through an application comprising the scenario of a labeling and filling gallons line at a paints and dyes production company. The method allowed to clearly identifying the critical parts of the equipment according the production scenario in question.

Este artigo investiga o suporte quantitativo necessário à operacionalização da manutenção centrada em confiabilidade (MCC). Inicialmente, foram identificadas na literatura dez atividades consideradas essenciais para a operacionalização da MCC e os métodos quantitativos que podem dar suporte a essa metodologia. A seguir, foram realizadas entrevistas em empresas manufatureiras a fim de verificar a utilização dos métodos descritos na literatura e identificar outros métodos usados. Como resultado, foi elaborado um quadro que associa as atividades essenciais da MCC aos métodos quantitativos, juntamente com uma breve descrição das contribuições de cada método. Entre os métodos quantitativos identificados, destaque para o uso de distribuições de probabilidade, que suporta várias atividades da MCC. Os métodos de engenharia econômica e a simulação de Monte Carlo também merecem destaque, pois permitem análises mais sofisticadas associadas a custo e desempenho de ençãsistemas produtivos sujeitos à manutenção.

This article investigates the quantitative support necessary for the operation of Reliability Centered Maintenance (RCM). Initially, ten essential activities for the operation of RCM and quantitative methods that can support such activities were identified in the literature. Next, interviews were conducted in manufacturing companies to verify the use of the methods described in the literature and identify other methods used. As a result, a framework that associates the RCM essential activities with the quantitative methods was created, along with a brief description of each method contribution. Among the quantitative methods identified, probability distributions stood out for supporting various RCM activities. The Economic Engineering and Monte Carlo simulation methods were also emphasized since they enable a more sophisticated analysis related to cost and performance of production systems subjected to maintenance.