Statistical Process Control and Maintenance Planning are critical factors in controlling any manufacturing process. Although these two research fields have common goals, traditionally they are studied separately both in business practice and scientific researches. Their common objective is to achieve a high quality product, lower downtimes, and lower expenses by controlling variances in the process. Therefore, single or separated parallel applications of these fields would not be an appropriate way. Some studies are performed in this field to combine such concepts; nevertheless they are entirely concentrated on utilizing control charts. Since such kinds of charts are not appropriate for small differences where results in large second error type and also some more advanced charts such as EWMA are utilized in sensitive processes, hence, in this thesis we have considered the problem of integrated design of EWMA control charts and maintenance policies. Also we have proposed a mathematical model in order to combine these research fields. In this thesis, we develop a generalized analytic model to determine the optimal policy to coordinate Statistical Process Control and Planned Maintenance to minimize total expected cost. We propose integrating these two management practices into a coordinated Monitoring-Maintenance Model. In our model, Statistical Process Control monitors the equipment and provides signals indicating equipment deterioration, while Planned Maintenance is scheduled at regular intervals to preempt equipment failure. The determination of an unstable process, via Statistical Process Control, results in an early Reactive Maintenance to restore the equipment. Otherwise a Planned Maintenance occurs after a specified period of operation. In this sense, we propose an ‘‘adaptive’’ maintenance policy, where the maintenance schedule adapts to the stability of the process. The completion of maintenance returns the process to its original operating condition and results in a process renewal. The model derives and optimal policy to minimize the cost per unit time. The derivation of the coordinated cost function follows a renewal-reward process.
