Shewhart and EWMA t control charts for short production runs

Abstract

Short-run productions are common in manufacturing environments like job shops, which are characterized by a high degree of flexibility and production variety. Owing to the limited number of possible inspections during a short run, often the Phase I control chart cannot be performed and correct estimates for the population mean and standard deviation are not available. Thus, the hypothesis of known in-control population parameters cannot be assumed and the usual control chart statistics to monitor the sample mean are not applicable. t-charts have been recently proposed in the literature to protect against errors in population standard deviation estimation due to the limitation of available sampling measures. In this paper the t-charts are tested for implementation in short production runs to monitor the process mean and their statistical properties are evaluated. Statistical performance measures properly designed to test the chart sensitivity during short runs have been considered to compare the performance of Shewhart and EWMA t-charts. Two initial setup conditions for the short run fixing the population mean exactly equal to the process target or, alternatively, introducing an initial setup error influencing the statistic distribution have been modelled. The numerical study considers several out-of-control process operating conditions including one-step shifts for the population mean and/or standard deviation. The obtained results show that the t-charts can be successfully implemented to monitor a short run. Finally, an illustrative example is presented to show the use of the investigated t charts.

Avaliação De Desempenho Na Aplicação Do Controle Estatístico De Processos: Seleção De Referencial Teórico Internacional E Análise Bibliométrica

“…Dentre as constatações mais relevantes observadas constam as dos autores Kaya e Kahraman (2011); Haq, Brown e Moltchanova (2014); Chen, Lai e Nien (2010); Celano et al, (2011);Zhang et al, (2014), que mesmo reconhecendo a necessidade da gestão do CEP estar alinhada aos objetivos da organização e ter em conta suas particularidades se valem de abordagens realistas (normativistas, descritivistas ) e propõem modelos genéricos que omitem os valores, as preferências e as motivações do contexto onde os modelos de gestão do CEP são aplicados. Tais trabalhos fomentam a utilização de diversos indicadores, porém não há uma organização entre eles que possibilite a identificação de quais níveis estratégicos, táticos e operacionais se propõem mensurar seu desempenho, tornando estes critérios de difícil convergência com os objetivos empresariais.…”

“…A gestão dos Controles Estatístico dos Processos (CEP) assume um grau de complexidade diferente quando as linhas de produção necessitam trabalhar sincronizadas com outras atividades; necessitam atender a múltiplos produtos de múltiplos clientes com importâncias diferentes e dinâmicas, informações difusas, tempo e insumos escassos, mão de obra e equipamentos limitados, e consequências financeiras e imagem afetadas pelas decisões do CEP (CELANO et al, 2011;KAYA;KAHRAMAN, 2011;HAQ;BROWN;MOLTCHANOVA, 2014). Para a gestão de CEP, pesquisadores como Hsu e Shu (2008), Chen, Lai e Nien (2010) e Al-Refaie e Bata (2010) têm alertado para a necessidade do uso de ferramentas de avaliação de desempenho em apoio à gestão de CEP que ademais da qualidade e produtividade clássicas (visão ontológica) tenham em conta os valores e as preferências dos gestores e as particularidades dos ambientes organizacionais (visão epistemológica).…”

Selection of the Most Suitable Statistical Process Control Approach for Short Production Runs: A Decision-Model

“… In repetitive production industries where frequent setup changes occur [8] to accommodate different product types.  When the rate of production and/or inspection can impose constraints in the number of samples to be inspected [9].  In low-volume production processes motivated by the adoption of JIT systems [10].…”

“…These charts, intended to monitor the process mean were first proposed by Zhang et al [30], by presenting an X-bar t-chart and a EWMA t-chart. The applicability of these two charts to short production runs was described by Celano et al [9], who also studied their statistical properties. A t-chart does not require a preliminary estimation of the in-control process standard-deviation, so it also can be used to monitor the start-up of a process.…”

A STUDY OF THE MEDIAN RUN LENGTH (MRL) PERFORMANCE OF THE EWMA t CHART FOR THE MEAN

“…Since then numerous extensions on EWMA charts have been made. Among the more recent extensions on EWMA charts are those by Su et al [2], Graham et al [3], Castagliola et al [4], Tsai & Yen [5], Celano et al [6], Yang et al [7], Lee & Apley [8], Perry & Pignatiello [9], Lin & Chou [10], Pascual [11], Ozsan et al [12], Simoes et al [13], Epprecht et al [14], Khoo et al [15], Li et al [16], Spliid [17], Capizzi & Masarotto [18], and Thaga & Yadavalli [19]. The EWMA X chart is excellent in detecting small persistent changes in the mean.…”