Selecting the best quality inspection alternative based on the quality, economic and environmental considerations

Abstract

The use of inspection is unavoidable in many situations. For example, inspection is required to satisfy government regulation, guarantee quality parameter is met by the products supplied by new suppliers whose quality history has not been built, and mitigate product liability risks. There are three quality inspection alternatives, 100 percent inspection, no inspection, and sampling inspection. No inspection brings a considerable risk of shipping non-conforming products to the consumer. 100 percent inspection involves a slight risk of delivering non-conforming products to the consumer but consumes resources significantly. Sampling inspection requires the producer to judge products based on a random sample. Therefore, it still contains the risk of sending non-conforming products to the consumer. The risk and resource consumptions can cause the producer to bear for the cost and environmental impact due to rework, scrap, and transportation. This paper proposes a mathematical model and algorithm to select the best inspection alternative based on quality, economic, and environmental considerations. We apply the model and algorithm to solve a real industrial case study. The results of the case study show that the proposed method results in a lower expected total cost and CO₂ emission and a better level of protection for the producer and consumer than the single sampling plan method widely used. We expect that the proposed model and algorithm can assist practitioners in the industry to select the best inspection alternative. It is essential to note that sampling inspection is not a substitute for proper process monitoring and control.

Keywords:

• Algorithm
• cost
• environmental impact
• inspection
• quality

Additional information

Funding

We would like to thank the Journal Publication Grant of the Faculty of Engineering, Andalas University, Contract No. 014/UN.16.09.D/PL2020, for its financial support during the revision process.

Notes on contributors

Feri Afrinaldi

Feri Afrinaldi is a faculty member at the Department of Industrial Engineering, Andalas University, Padang, Indonesia. He received his Ph.D in Industrial Engineering from Texas Tech University, Lubbock, TX, USA. His research interest is the application of Operations Research in Sustainable Manufacturing.

Harly Bobby Pratama

Harly Bobby Pratama was an undergraduate research assistant at the Industrial Systems Planning and Optimization Laboratory, Department of Industrial Engineering, Andalas University, Indonesia, when this research was conducted. His undergraduate thesis is in the area of quality control and assurance. Now, he is a practitioner in the industry.