- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Purpose – The purpose of this paper is to clarify several commonly used quality cost models based on Juran’s characteristic curve. Through mathematical deduction, the lowest point of quality cost and the lowest level of quality level (often depicted by qualification rate) can be obtained. This paper also aims to introduce a new prediction model, namely discrete grey model (DGM), to forecast the changing trend of quality cost. Design/methodology/approach – This paper comes to the conclusion by means of mathematical deduction. To make it more clear, the authors get the lowest quality level and the lowest quality cost by taking the derivative of the equation of quality cost and quality level. By introducing the weakening buffer operator, the authors can significantly improve the prediction accuracy of DGM. Findings – This paper demonstrates that DGM can be used to forecast quality cost based on Juran’s cost characteristic curve, especially when the authors do not have much information or the sample capacity is rather small. When operated by practical weakening buffer operator, the randomness of time series can be obviously weakened and the prediction accuracy can be significantly improved. Practical implications – This paper uses a real case from a literature to verify the validity of discrete grey forecasting model, getting the conclusion that there is a certain degree of feasibility and rationality of DGM to forecast the variation tendency of quality cost. Originality/value – This paper perfects the theory of quality cost based on Juran’s characteristic curve and expands the scope of application of grey system theory.
More and more enterprises are beginning to realise the importance of quality cost in quality control. Different quality cost prediction optimisation models can quantitatively forecast and simulate quality costs at different quality management levels. When there is a lack of information, a DGM can be used to predict the fluctuation trend of quality cost based on Juran’s quality cost characteristic curve. What’s more, the randomness of time series can be weakened by a weakening buffer operator before modelling, which can effectively reduce the factor perturbation and reduce the randomness of the data.
The purpose of the forecast is to control the quality of the enterprise better, and the effective control and management of the cost is an important guarantee for normal capital flows. Cost controls throughout the operation of the enterprise have always been implemented to establish a long and effective accounting system to strengthen scientific and rational cost accounting. At the same time, in the process of cost forecasting and control, we must consider all aspects of uncertainty derived from unknown factors, which is the only way to make the forecast more feasible for the quality of enterprise management to provide more practical guidance.
With the rise of “zero defect” management and total quality management (Dong et al., 2017), most companies are no longer using a single quality cost minimum target for quality control and management, but a large number of external factors should be taken into account by quality of cost models when establishing the actual dynamic cost forecasting model of an enterprise. The optimal quality level obtained by Juran’s quality cost curve is required to accept an unqualified product rate for the firm. With the passage of time and the improvement of management methods, the limitations of this quality cost model have become more and more obvious. In these circumstances, we should use new theories to guide quality cost management in enterprises.