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.

In small apparel manufacturing, unit price determination is often based on production duration given by customers and design complexity rather than information relating to internal labor resources. However, labor expertise and skills are critical factors that outweigh the machinery and technology in small and medium apparel companies. The quality of the product greatly depends on the experience and delicacy of the tailors. Using data on labor skill and wage levels in the planning process will benefit human resource utilization, increasing productivity, and profits effectively. This paper proposes a general mathematical model for task allocation and cost optimization for small and medium apparel companies. The model handles task allocation and cost minimization problems that must ensure processing time requirements and balance workloads for operators. The developed model tests two case studies in a published paper. The results prove that although the proposed model is simple, it has high applicability and efficiency in solving allocation optimization problems. The authors then integrate the formulations into a Standalone desktop app in the MATLAB “App designer” module. With a standalone desktop app, end users can enjoy the application. This app has a user-friendly design. Users unfamiliar with computers or planners with no background in programming can use the app to tackle similar optimization problems. The proposed mathematical model can further expand to include more complex issues in apparel companies and can also be a good reference for other fields.