Control charts for monitoring the time between events can be applied in various areas. In this study, we focus on the exponential control chart and consider the phase II problem (when process parameters are known) as well as the phase I problem (when process parameters are unknown). An exponential chart designed with the conventional approach has the disadvantage that the Average Run Length (ARL) value may increase when the process deviates from the nominal state. An ARL-unbiased design approach is therefore proposed for both phase II and phase I exponential charts. A sequential sampling scheme is adopted for the phase I exponential chart. The proposed ARL-unbiased design approach has several advantages over the conventional one, as it provides a self-starting feature and can significantly improve the ARL performance. Specific guidelines are suggested regarding the time to stop updating the estimates of parameters and control limits based on the actual false alarm rate. The phase I exponential chart can be calibrated to a constant in-control ARL value for each successive event accumulated to date. Simulated and real data examples are given to demonstrate the use and efficiency of the proposed design approach.

Six Sigma as a quality improvement framework cannot remain static if it is to sustain its value for businesses beyond the first waves of applications. This paper explores the possibilities of enhancing the usefulness and effectiveness of Six Sigma by the integration of established Operations Research/Management Science (OR/MS) techniques. In this paper, we elucidate the needs for OR/MS techniques to enhance Six Sigma deployment in operational and transactional environments and propose a new training roadmap for core Six Sigma professionals (Six Sigma Black Belts) which incorporates these techniques. A matrix relating the components of the proposed training curriculum to the actual deliverables during implementation for a hybrid of operational and transactional environments is also presented. A practical case study is also presented to demonstrate the usefulness of the OR/MS tools in a typical transactional environment.

No abstract

No abstract

Production, yield and maintenance are three key components for sustaining the competitiveness of a manufacturing firm. In this paper, we investigate a joint production and maintenance planning problem in a periodic review environment subject to stochastic demand and random yields. The manufacturing system deteriorates from period to period according to a discrete-time Markov chain. The objective is to find an integrated lot sizing and maintenance policy for the system such that the aggregate cost associated with production, holding, backlogging and maintenance is minimised. We formulate this integrated planning problem as a Markov decision process and analyse the structural properties of the optimal policies. We prove that the optimal production and the maintenance policies both exhibit a control limit structure and show that the solution to the finite-horizon problems converges to that of the infinite-horizon problem.