Taguchi regarded the concept of quality as ‘total loss to society due to fluctuations in quality characteristics from the time of supplied to the customer.’ The loss function is a representative tool that can quantitatively convert the loss that occurs due to the deviation of the quality characteristic value from the target value. This has been utilized in various studies with the advantage that it can change the social loss caused by fluctuation of quality characteristics to economic cost. The loss function has also been used extensively in the study of producer specification limits. However, in previous studies, only the second order loss function of Taguchi is used. Therefore, various types of losses that can occur in the process can’t be considered. In this study, we divide the types of losses that can occur in the process considering the first and second loss functions and the Spiring’s reflected normal loss function, and perform total inspection before delivering the customer to determine the optimal producer specification limit that minimizes the total cost. Also, we will divide the quality policy for the products beyond the specification limits into two. In addition, we will show the illustration of expected loss cost change of each model according to the change of major condition such as customer specifications and maximum loss cost.

In the manufacturing industry fields, thousands of quality characteristics are measured in a day because the systems of process have been automated through the development of computer and improvement of techniques. Also, the process has been monitored in database in real time. Particularly, the data in the design step of the process have contributed to the product that customers have required through getting useful information from the data and reflecting them to the design of product. In this study, first, characteristics and variables affecting to them in the data of the design step of the process were analyzed by decision tree to find out the relation between explanatory and target variables. Second, the tolerance of continuous variables influencing on the target variable primarily was shown by the application of algorithm of decision tree, C4.5. Finally, the target variable, loss, was calculated by a loss function of Taguchi and analyzed. In this paper, the general method that the value of continuous explanatory variables has been used intactly not to be transformed to the discrete value and new method that the value of continuous explanatory variables was divided into 3 categories were compared. As a result, first, the tolerance obtained from the new method was more effective in decreasing the target variable, loss, than general method. In addition, the tolerance levels for the continuous explanatory variables to be chosen of the major variables were calculated. In further research, a systematic method using decision tree of data mining needs to be developed in order to categorize continuous variables under various scenarios of loss function.

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