Abstract
Estimating the unit cost of each product precisely and accurately is a prerequisite to determining the profitability of a manufacturer, which is usually addressed by fitting the underlying learning process. However, existing methods for this purpose often deal with a logarithmic or log-sigmoid value, rather than the original value, of the unit cost. To resolve this problem, in this study, a new fuzzy collaborative intelligence (FCI) approach is proposed by considering the original value of the unit cost directly. The effectiveness of the new FCI approach is validated with a real dynamic random access memory (DRAM) case. The experimental results showed that the new FCI approach outperformed two existing methods in improving the fitting accuracy in terms of MAE and MAPE and also in reducing the average range of the fitted unit costs.
Highlights
The unit cost of each product is a critical performance measure for a factory [12]
Compared with other performance measures such as yield and productivity, the unit cost is special for the following reasons: 1. Since the profit is derived by subtracting the price by the unit cost, a lower unit cost immediately increases the profit
The performances of the two methods with regard to root mean squared error (RMSE) were quite close. These results revealed that the estimating accuracy could be truly optimized only if the original value of the unit cost was considered directly
Summary
The unit cost of each product is a critical performance measure for a factory [12]. Compared with other performance measures such as yield and productivity, the unit cost is special for the following reasons: 1. Since the profit is derived by subtracting the price by the unit cost, a lower unit cost immediately increases the profit. In this way, the estimating accuracy or precision with respect to the original value of the unit cost has not truly been optimized To resolve this problem, a new fuzzy collaborative intelligence (FCI) approach that can deal with the original value of the unit cost is proposed in this study. A new fuzzy collaborative intelligence (FCI) approach that can deal with the original value of the unit cost is proposed in this study This is a homogeneous FCI method because all models are built by solving the same, i.e., mathematical programming (MP), problems. Some studies proposed fuzzy methods, especially FCI methods, to estimate the unit cost of a product. The FCI methods gather several experts that apply fuzzy methods to estimate the unit cost of a product [24].
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