Continuous improvement is the prime requirement for all industries to sustain and grow in the competitive global market. This paper is a case study of a manufacturing industry in Taiwan, facing the problem of rejection in the brushless motor product. The DMAIC (Define, Measure, Analyze, Improve, and Control) cycle was used to improve processes to reduce the rejection rate. In the Define phase, anything related to processes, products, suppliers, customers, and customer needs related to good quality products were determined. SIPOC diagrams (Supplier, Input, Process, Output, and Customer) and Critical to Quality (CTQ) were identified. The Measure phase focused on data collection by determining the baseline of the process and determining the root causes of the process. DPMO and the Control Chart were applied in this phase. In the Analyze phase, the causes of production process failures that result in defective products were identified. Tools like Pareto Diagram, Fishbone Diagram, and FMEA were used in this phase. In the Improve phase, the improvement solutions in overcoming priority problems were determined by using the 5W + 1H tool. Several improvement solutions were implemented, such as improvement in inspection methods, re-selection for the supplier, increasing the number of workers, providing training to workers, and others. In the control phase, the Six Sigma values were improved. In January–May 2022, the Six Sigma level increased from 5.11 to 5.44.

The Six Sigma program has been widely adopted by industries worldwide since the late-1990s due to GE’s successful implementation, though Motorola initiated it in 1987. Several quality experts, such as Juran and Feigenbaum and several researchers, have concluded that the poor quality in product design will cause vast and chronic costs, which the Six Sigma improvement may not eliminate. The focus of new product development is needed to transfer the quality improvement from the later phases of the Design Life Cycle to the early grades. It is necessary to adopt the “Design for Six Sigma” (DFSS) methodology to achieve the best performance in the early stages and focus on ‘prevention’ instead of ‘problem-solving. In this research, we develop a new DFSS process, including Define, Identify, Measure, Design, Optimize, and Verify (DIMDOV), based on the in-depth study of DFSS and the author’s rich consultant experience. The new DFSS process can be confirmed to be a robust methodology, used to assure the design quality of the product design and manufacturing process design by the application results.

The growth of financial literacy is significant nowadays. Because of this, more people are becoming increasingly responsible in their financial planning, investments, and living expenses. In developing new technology, it is necessary to know the technological acceptance of the prospective users of the technology itself. This study aims to identify the primary factors influencing the technology acceptance levels of lower-middle socio-economic users for a digital financial literacy application. The proposed model in this research was developed based on UTAUT, TAM, and Usability model, and it consists of six primary constructs: (1) Performance Expectancy; (2) Effort Expectancy; (3) Social Influence; (4) Resources and Cost; (5) Satisfaction; (6) Behavior Intention. All the hypotheses used in this study were statistically measured using SmartPLS tools. This study found that because many lower-middle socio-economic users lack sufficient understanding of technology to properly utilize it, that a digital platform is not the right tool to teach them financial literacy.

This study presents a breakthrough in sustainable injection molding by uniquely combining a backpropagation neural network (BPNN) with particle swarm optimization (PSO) to overcome traditional optimization challenges. The BPNN’s exceptional ability to learn complex nonlinear relationships between six key process parameters (including melt temperature and holding pressure) and product quality is amplified by PSO’s intelligent search capability, which efficiently navigates the high-dimensional parameter space. Together, this hybrid approach achieves what neither method could accomplish alone: the BPNN accurately models the intricate process-quality relationships, while PSO rapidly converges on optimal parameter sets that simultaneously meet strict quality targets (66–70 g weight, 3–5 mm thickness) and minimize energy consumption. The significance of this integration is demonstrated through three key outcomes: First, the BPNN-PSO combination reduced optimization time by 40% compared to traditional trial-and-error methods. Second, it achieved remarkable prediction accuracy (RMSE 0.8229 for thickness, 1.5123 for weight) that surpassed standalone BPNN implementations. Third, the method’s efficiency enabled SMEs to achieve CAE-level precision without expensive software, reducing setup costs by approximately 25%. Experimental validation confirmed that the optimized parameters decreased energy use by 28% and material waste by 35% while consistently producing parts within specifications. This research provides manufacturers with a practical, scalable solution that transforms injection molding from an experience-dependent craft to a data-driven science. The BPNN-PSO framework not only delivers superior technical results but does so in a way that is accessible to resource-constrained manufacturers, marking a significant step toward sustainable, intelligent production systems. For SMEs, this framework offers a practical pathway to achieve both economic and environmental sustainability, reducing reliance on resource-intensive CAE tools while cutting production costs by an estimated 22% through waste and energy savings. The study provides a replicable blueprint for implementing data-driven sustainability in injection molding operations without compromising product quality or operational efficiency.

Technologies such as machine learning, big data, and Industry 4.0 have become the trends in the development of science and technology in various countries in recent years. This research hopes to establish a predictive model through data analysis to help die-casting plants determine whether there are defects in the castings and improve the production competitiveness of domestic die-casting plants. Data was taken from the domestic automobile industry and used actual production data as the basis for analysis. In this study, relevant parameters of die-casting manufacturing as independent variables were chosen and determined whether there were defects in the castings as strain numbers. Afterward, the researchers constructed Artificial Neural Network, Support Vector Machines, and Random Forests as three prediction models. Three prediction models with the Taguchi Methods are used to find the best parameter configuration of each model. AUC (Area Under Curve)- Receiver Operating-Characteristic (ROC) evaluates the strength and weaknesses of the three models and, in the end, finds the most suitable network prediction model.

PT ABC President Indonesia is a manufacturing company engaged in the food sector by producing ABC noodles and Nu Greentea. At PT ABC President Indonesia, the production scheduling process is one of the main processes carried out to determine the number of products that must be produced. Based on the problems that occur regarding stock, it is proposed that the best forecasting method is based on the time series of each Distribution Centre (DC) and processing Distribution Centre (DC) requests using the Distribution Requirement Planning (DRP) method. Where the DRP method is used, so that the company can meet the demand of every DC so that there is no shortage of products and the production process continues to run without the company experiencing losses. In addition, with the use of DRP method there will be additional stock so that if there is a defect, the company still has stock remaining so that it can still meet the customer demands for each DC and the product distribution process runs more regularly.