China 
Africa 
Explore chapters and articles related to this topic
Six Sigma and Lean Project Management
Published in Adedeji Badiru, Abidemi Badiru, Adetokunboh Badiru, Industrial Project Management, 2007
Adedeji Badiru, Abidemi Badiru, Adetokunboh Badiru
The philosophy of Taguchi loss function defines the concept of how deviation from an intended target creates a loss in the production process. Taguchi’s idea of product quality analytically models the loss to the society from the time a product is shipped to customers. Taguchi loss function measures this conjectured loss with a quadratic function known as quality loss function (QLF), which is mathematically represented as: L(y)=k(y−m)2
Quality Management
Published in Adedeji B. Badiru, Abidemi S. Badiru, Adetokunboh I. Badiru, Mechanics of Project Management, 2018
Adedeji B. Badiru, Abidemi S. Badiru, Adetokunboh I. Badiru
The philosophy of Taguchi loss function defines the concept of how deviation from an intended target creates a loss in the production process. Taguchi’s idea of product quality analytically models the loss to the society from the time a product is shipped to customers. Taguchi loss function measures this conjectured loss with a quadratic function known as Quality Loss Function (QLF), which is mathematically represented as:L(y)=k(y−m)2
Concept of Quality
Published in Sunil Luthra, Dixit Garg, Ashish Agarwal, Sachin K. Mangla, Total Quality Management (TQM), 2020
Sunil Luthra, Dixit Garg, Ashish Agarwal, Sachin K. Mangla
Genichi Taguchi’s contributions translate into a robust design in the area of product development and optimised product manufacturing. The Taguchi loss function, the Taguchi method (experiment design), and other methodologies have largely contributed to reducing variations and greatly improving the quality of engineering and productivity. This method optimises the results by taking different parameters. By careful and purposeful consideration of these factors, the reduction of failures in the field, and, ultimately, Taguchi’s methodologies will help to ensure client satisfaction.
Multi objective optimization of parameters in EDM of Mirrax steel
Published in Materials and Manufacturing Processes, 2023
If the product result differs from the target value, the losses are detected and deviations are calculated. The Taguchi loss function is the deviation among the empirical value and the expected target value and is calculated by converting it to a signal-to-noise-ratio(S/N). The Taguchi method uses three key performance characteristics that depend on the S/N ratio. As part of this study, since the aim was to minimize the Ra and the amount of electrode wear, the “Lowest-is-the-Best” was used for the performance feature according to the S/N ratio. To maximize the MRR, the “Highest-is-the-Best” value was used for the performance feature according to the S/N ratio. Equations (3) and (4) were used to calculate the “Lowest-is-the-Best” and “Highest-is-the-Best” performance characteristics, respectively.[26,27]
Two-stage optimization model for process/supplier selection, component allocation, and quality improvement
Published in Cogent Engineering, 2018
Cucuk Nur Rosyidi, Mega Aria Pratama
The objective function of Stage II model is to maximize the ROI, in which the ROI is the common measure of investment effectiveness in project selection. The aim of the model is to determine optimal proportion value of variance reduction. Hence the company has to set the variance reduction target of a component and the target will be allocated to the selected process/suppliers. The variance reduction will have investment consequence. Hence, the manufacturer has to maximize the ROI to obtain optimal variance reduction. Three variables will affect the ROI, namely the initial quality loss, quality loss cost due to variance reduction, and total learning investment. Equation (11) expresses the quality loss function (manufacturers and suppliers) (Feng et al., 2001). Taguchi Loss function is used to quantify the quality loss which measure the loss of customer due to the bias and variance of a certain product quality characteristics. Since we assume that the nominal target value can be achieved by each process, then the quality loss will only depend on the variance of a certain product quality characteristic. In Equation (11) the right side of the equation has been defined in Equation (4):