Framework of decision in data modeling for quality improvement

CMMI based fuzzy logic approach to assess the digital manufacturing maturity level of manufacturing industries

Purpose

This research study aims to introduce a maturity model based on capability maturity model integration (CMMI) that can assess the digital manufacturing maturity level of manufacturing companies.

Design/methodology/approach

A CMMI model for the manufacturing industry is designed to assess the digitalisation level of manufacturing industries. The model is developed exclusively for the process area “organisational process focus” (OPF), and the digitalisation level is quantified using fuzzy logic by employing a case study approach.

Findings

The CMMI is successfully employed to assess the digitalisation level of a manufacturing organisation using the fuzzy logic approach. The triangular fuzzy number of the Fuzzy CMMI Measure Index (FCMI) is obtained as (6.08, 7.33, 8.52). The transformation of FCMI into linguistic terms discloses the digitalisation level of the manufacturing organisation as “Capability Maturity Level 4” (CML 4).

Originality/value

The authors tested the suitability of CMMI in the manufacturing sector. The operational concept introduced in this research sets forth a unique framework to quantify the digitalisation level of manufacturing industries.

Statistical process monitoring for e-waste based on beta regression and particle swarm optimization

Purpose

E-waste management can reduce relevant impact of the business activity without affecting reliability, quality or performance. Statistical process monitoring is an effective way for managing reliability and quality to devices in manufacturing processes. This paper proposes an approach for monitoring the proportion of e-waste devices based on Beta regression model and particle swarm optimization. A statistical process monitoring scheme integrating residual useful life techniques for efficient monitoring of e-waste components or equipment was developed.

Design/methodology/approach

An approach integrating regression method and particle swarm optimization algorithm was developed for increasing the accuracy of regression model estimates. The control chart tools were used for monitoring the proportion of e-waste devices from fault detection of electronic devices in manufacturing process.

Findings

The results showed that the proposed statistical process monitoring was an excellent reliability and quality scheme for monitoring the proportion of e-waste devices in toner manufacturing process. The optimized regression model estimates showed a significant influence of the process variables for both individually injection rate and toner treads and the interactions between injection rate, toner treads, viscosity and density.

Originality/value

This research is different from others by providing an approach for modeling and monitoring the proportion of e-waste devices. Statistical process monitoring can be used to monitor waste product in manufacturing. Besides, the key contribution in this study is to develop different models for fault detection and identify any change point in the manufacturing process. The optimized model used can be replicated to other Electronic Industry and allows support of a satisfactory e-waste management.