Assessment of CMMI level of manufacturing industry using fuzzy logic approach: a case study

Thomas, T.; Saleeshya, P.G.; Suresh, M.

doi:10.1108/jm2-09-2020-0229

Cited by 5 publications

(14 citation statements)

References 20 publications

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“…As a result, these Process areas are relevant to and valuable in the manufacturing industry. For instance, in the case studies by Raghuram et al (2020) and Thomas et al (2021), the CMMI approaches were employed in the process appraisal of manufacturing industries. Among the different process areas of CMMI, OPF is selected for the investigation considering the process enhancement for the growth of digitalisation in the manufacturing sector.…”

Section: Methodsmentioning

confidence: 99%

“…The extensive literature review revealed the requisite facilities, resources and managerial skills required in the process area OPF pertaining to digital manufacturing. The multi-level CMMI model for the process area OPF was adopted from the literature (Thomas et al, 2021) and modified to assess the digital manufacturing maturity of an organisation. The CMMI multilevel model comprises three enablers, nine criteria and 57 attributes.…”

Section: Methodsmentioning

confidence: 99%

“…CMMI is a successful tool kit used by software industries to guide process appraisal most effectively, and it is considered one of the influential, long-standing and often-studied standards to schedule performance index (Staple and Niazi, 2008). CMMI has been profoundly generalised over the years, and various investigations have tested the suitability of CMMI in the manufacturing sector, which include the studies conducted by Thomas et al (2021), Raghuram et al (2020), De Carolis et al (2017, Kundu and Manohar (2012) and Li and Lin (2011). Though ample research and development are progressing in assessing the CMMI level of software organisations, none of the studies has extended it to manufacturing industries to assess the digital manufacturing maturity level.…”

Section: Introductionmentioning

confidence: 99%

“…The CMMI model employed to assess the digital manufacturing maturity level for the process area OPF is based on three enablers, nine criteria and 57 attributes. The multilevel CMMI model is adapted from the literature (Thomas et al, 2021). The model's enablers, criteria and attributes are the measures for assessing the digital manufacturing maturity level.…”

Section: Introductionmentioning

confidence: 99%

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

Thomas

Saleeshya

2023

TQM

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PurposeThis 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/approachA 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.FindingsThe 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/valueThe 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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Thomas

Saleeshya

2023

TQM

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“…The expert questionnaire sheet for gathering expert opinion has been shown in Appendix. To handle imprecise data in multi-criteria decision making, Pythagorean and Intuitionistic fuzzy sets were used frequently; however, it is difficult for them to explain the information which is ambiguous and complex (Thomas et al, 2021). Such complex information can be easily expressed well using triangular fuzzy numbers.…”

Section: Methodsmentioning

confidence: 99%

Benchmarking Industry 4.0 readiness evaluation using fuzzy approaches

Wankhede

Vinodh

2022

BIJ

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PurposeThe purpose is to assess Industry 4.0 (I4.0) readiness index using fuzzy logic and multi-grade fuzzy approaches in an automotive component manufacturing organization.Design/methodology/approachI4.0 implies fourth industrial revolution that necessitates vital challenges to be dealt with. In this viewpoint, this article presents the evaluation of I4.0 Readiness Index. The evaluation includes two levels with appropriate criteria and factors. Fuzzy logic approach is used for assessment. Furthermore, the results obtained from fuzzy logic have been benchmarked with multi-grade fuzzy approach.FindingsThe proposed assessment model has successfully utilized fuzzy logic approach for assessment of I4.0 readiness index of automotive component manufacturing organization. Based on fuzzy logic approach, readiness index of I4.0 has been found to be (4.74, 6.26, 7.80) which is further benchmarked using multi-grade fuzzy approach. Industry 4.0 readiness index obtained from multi-grade fuzzy approach is 6.258 and thus, validated. Furthermore, 20 weaker areas have been identified and improvement suggestions are provided.Research limitations/implicationsThe assessment module include two levels (Six Criteria and 50 Factors). The assessment model could be expanded based on advancements in industrial developments. Therefore, future researchers could utilize findings of the readiness model to further develop multi-level assessment module for Industry 4.0 readiness in organization. The developed readiness model helped researchers in understanding the methodology to assess I4.0 readiness of organization.Practical implicationsThe model has been tested with reference to automotive component manufacturing organization and hence the inferences derived have practical relevance. Furthermore, the benchmarking strategy adopted in the present study is simple to understand that makes the model unique and could be applied to other organizations. The results obtained from the study reveal that fuzzy logic-based readiness model is efficient to assess I4.0 readiness of industry.Originality/valueThe development of model for I4.0 readiness assessment and further analysis is the original contribution of the authors. The developed fuzzy logic based I4.0 readiness model indicated the readiness level of an organization using I4RI. Also, the model provided weaker areas based on FPII values which is essential to improve the readiness of organization that already began with the adoption of I4.0 concepts. Further modification in the readiness model would help in enhancing I4.0 readiness of organization. Moreover, the benchmarking strategy adopted in the study i.e. MGF would help to validate the computed I4.0 readiness.

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Productivity development enablers in the infrastructure sector: capability maturity model integration approach

Laitinen,

Luhtala,

Örmä

et al. 2024

BEPAM

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PurposeInsufficient productivity development in the global and Finnish infrastructure sectors indicates that there are challenges in genuinely achieving the goals of resource efficiency and digitalization. This study adapts the approach of capability maturity model integration (CMMI) for examining the capabilities for productivity development that reveal the enablers of improving productivity in the infrastructure sector.Design/methodology/approachCivil engineering in Finland was selected as the study area, and a qualitative research approach was adopted. A novel maturity model was constructed deductively through a three-step analytical process. Previous research literature was adapted to form a framework with maturity levels and key process areas (KPAs). KPA attributes and their maturity criteria were formed through a thematic analysis of interview data from 12 semi-structured group interviews. Finally, validation and refinement of the model were performed with an expert panel.FindingsThis paper provides a novel maturity model for examining and enhancing the infrastructure sector’s maturity in productivity development. The model brings into discussion the current business logics, relevance of lifecycle-thinking, binding targets and outcomes of limited activities in the surrounding infrastructure system.Originality/valueThis paper provides a new approach for pursuing productivity development in the infrastructure sector by constructing a maturity model that adapts the concepts of CMMI and change management. The model and findings benefit all actors in the sector and provide an understanding of the required elements and means to achieve a more sustainable built environment and effective operations.

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Assessment of CMMI level of manufacturing industry using fuzzy logic approach: a case study

Cited by 5 publications

References 20 publications

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

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

Benchmarking Industry 4.0 readiness evaluation using fuzzy approaches

Productivity development enablers in the infrastructure sector: capability maturity model integration approach

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