The research aims to give practical instructions for applying Plan–Do–Check–Act (PDCA) cycle in a packaging process. Eco-friendly, recycled material and a new packaging method for quality improvement and cost efficiency of heavily fragile product packaging are studied in this paper. A case study was conducted at GPEM laboratory, Vietnamese German University, Vietnam. In this case study, the current packaging style with Styrofoam material was analyzed and replaced by new packaging material and methods after applying the PDCA cycle for continuous quality improvement. Targets of the research were to find the new packaging method using friendly environment materials, to improve the quality, and to reduce the defect ratio due to packaging for fine-stone round surface fountains. Moreover, the extra cost should not be higher than 20% compared with the current packaging cost. The article proposes a simplified way that focuses on the combination of quality tools in the PDCA multiple phases to solve these problems. The quality tools are applied effectively through the PDCA cycle from collecting data, defining, analysis, testing, evaluation, and making decisions. New packaging design was been produced and tested successfully. One hundred percent of new packaging boxes for the mid-weight fountains (under 15 kg) passed the dropping test condition. Nearly 10% of the heavier weight products (above 15 kg) still had some small cracks on their top and bottom due to drop tests. Another PDCA cycle is recommended to continue applying for achieving a thorough solution. The conducted results show that PDCA is an effective method to tackle the damage product issue due to inappropriate packaging material and technique. It also brings good solutions for balancing sustainable packaging improvement and reducing the cost to ensure profit for companies. Besides contributing a guide reference for PDCA deployment, the authors intend to inspire practitioners and researchers to broaden exploration of the PDCA applications for sustainable packaging methodology. The research analysis shows that the PDCA methodology should be applied for defect reduction and quality enhancement in the packaging field. The field currently lacks systematic guidance for continuous improvement.

This paper presents practice and application of Design of Experiment techniques and Genetic Algorithm in single and multi-objective optimization with low cost, robustness, and high effectiveness through 3D printing case studies. 3D printing brings many benefits for engineering design, product development, and production process. However, it faces many challenges related to parameters control. The wrong parameter setup can result in excessive time, high production cost, waste material, and low-quality printing. This study is conducted to optimize the parameter sets for 3D Fused Deposition Modelling (FDM) products. The parameter sets, i.e., layer height, infill percentage, printing temperature, printing speed with different levels are experimented and analyzed to build mathematic models. The objectives are to describe the relationship between the inputs (parameter values) and the outputs (printing quality in term of weight, printing time and tensile strength of products). Single-objective and multi-objective models according to user’s desire are constructed and studied to identify the optimal set, optimal trade-off set of parameters. Besides, an integrated method of response surface methodology and Genetic algorithm to deal with multi-objective optimization is discussed in the paper. 3D printer, testing machines, and quality tools are used for doing experiments, measurement and collecting data. Minitab and Matlab software aid for analysis and decision-making. Proposed solutions for handling multi-objective optimization through 3D Fused Deposition Modelling product printing case study are practical and can extend for other case studies.

The research presents an analysis and comparison of the Taguchi Design and Response Surface Methodology (RSM) in optimizing the laser cutting machine parameters on dimension accuracy for stainless steel products. The paper studies effects of input factors such as cutting speed, nitrogen pressure, power, and frequency on the quality cutting of stainless steel (304) specimens. In this paper, two objectives are examined: targeting laser-cut edge to perpendicular 90 degrees and maximizing the cutting accuracy. The paper proposes a simple formula to optimize both targets by minimizing one new function definition, i.e., dimensional error. An L9 orthogonal array of Taguchi Methodology is adopted to minimize the number of experiments and shorten analysis time to achieve the optimal parameters. These results are compared with RSM. Box–Behnken Design (BBD) type of RSM requires more experiments than the Taguchi approach. RSM regression models as the quadratic functions of the control factors are developed to minimize the dimensional error of cutting products. Then, Analysis of Variance (ANOVA) and graphs will be analyzed to determine the influences of variables on the responses. Both Taguchi’s method and RSM found that the most influential factor on dimension accuracy is cutting speed, followed by laser power. While Taguchi provides good graphic visualization for quickly predicting the optimum condition, it cannot examine the interaction effects as RSM due to the lack of data. Besides, RSM reveals the percentage contribution of factors on dimensional error. Cutting speed has a maximum contribution, i.e., 39% of the total. The interaction of cutting speed and power contributes 16% of the total. In this study, RSM can predict optimum conditions more accurately than Taguchi. There are misleading results from the Taguchi method compared with RSM. However, the difference between these objective values is insignificant. The validation experiments show that the Taguchi method can be a practical approach for optimization problems. It can help reduce cost and time and achieve the desired optimal outputs. With cutting problems requiring high precision, the RSM method is highly recommended for identifying optimal parameter settings and interaction effects. With problems that their experimental runs consume high cost and time, Taguchi can be a suitable method for screening the significant variables. Although Taguchi and RSM are used widely for optimization problems in many fields, choosing the right methodology for various objectives is still a concern with different arguments and needs further research. Therefore, this study could be an adequate reference for parameter optimization problems in various fields.