Many companies embrace Industry 4.0 technologies to enable operational sustainability against increasing climate change effects, decreasing natural resources, and raising consumer awareness of environmental issues. Even though readiness and maturity assessment of smartness and sustainability concepts are nested, no study simultaneously focuses on these concepts. As pioneering research, we propose a novel model titled “Smart and Sustainable Supply chain Readiness and Maturity model (S3RM)” and validate it by conducting a case study in the automotive industry. We design our model upon the triple-bottom-line (TBL) approach consisting of smartness and sustainability dime5nsions. Our study introduces the TBL of smartness covering availability, integrity, and adaptability sub-dimension. TBL of sustainability includes social, environmental, and economic sub-dimensions. The proposed model calculates the Smart and Sustainable Readiness and Maturity Index by averaging sustainability scores’ summation and smartness scores’ multiplication. Each sub-dimension consists of items measured by a readiness and maturity scale. The findings suggest how smartness and sustainability items create strengths, weaknesses, opportunities, and threats for the supply chain operations. Our model provides managerial implications in assessing the readiness and maturity of Industry 4.0 tools and sustainability indicators. This study offers a road map to managers on smart and sustainable supply chains’ defined target areas.

Departments are assessed using performance evaluation systems (PES) based on their important performance criteria. The sum of the weighted KPIs’ actualization scores creates the departments' performance ratings, which are used to rank departments from highest to lowest performing. However, this strategy does not account for departmental variances. We used multi-attribute decision-making (MADM) techniques in this study to ascertain the performance of several divisions inside a business. Our study's structure is divided into two distinct parts. In the first stage, pair-wise evaluation matrices are built and attribute weights are established using the Analytic Hierarchical Process (AHP) to determine attribute precedence. The second step employs the Elimination and Choice Expressing the Reality (ELECTRE) methodology to rank the departments using the attribute weights generated in the first stage using the AHP method. At the conclusion of the paper, a case study is presented to demonstrate how departments are organized in accordance with performance management principles within a corporation that use the balance-scorecard program for performance evaluation.