ON SOME FACTORS AFFECTING THE JUST-IN-TIME PRODUCTION SYSTEM OUTPUT VARIABILITY - A SIMULATION STUDY USING TAGUCHI TECHNIQUES

The main objectives of this research were to analyze the effect of different maintenance policies, machines unreliability, and some other factors on the Just-In-Time (JIT) production system output variability or stability. Maintenance policies of type I and II which were introduced by Proshan and Hunter, and maintenance policy III, which was introduced by Makabi Hajime et al., were considered in this study and their effect on the JIT production system were analyzed. In this research, mathematical predictive and integrative models were developed to demonstrate the effects of different maintenance policies (A), machine unreliability (B), ratio of preventive maintenance time to processing time (D), production line size (C), processing time variability (E) and ratio of minimal repair time to preventive maintenance time (F) on the production line output variability (PLV) in a JIT production system environment. Functional relationship between the dependent variable PLV and die independent variables A, B, C, D, E and F were developed. Using the Taguchi approach in this experimental design all main factors used in this experiment were tested at three levels each using on L27 orthogonal plan, then all main factors and their first order interactions were tested using an L32 orthogonal plan of experiment. Discrete-event simulation, using the GPSS/H simulation language, was employed in order to collect the needed data. The analysis of the data showed that under different situations, different maintenance policies do not have the same effect on the production line performance. With regard to the PLV, maintenance policy III led to a higher variation than policy II, except at the level where the number of machines is less than or equal to 5. The results of this study also demonstrate that the production line performance under policy II is more sensitive to the change on the ratio of minimal repair time to preventive maintenance time when under policy III the same production line performance is more sensitive to production line size. Therefore, the results of this study should help the user in choosing a maintenance policy as a function of the production process parameters in order to decrease production line output variability and improve productivity and stability.