Facilitating decision-making for the adoption of smart manufacturing technologies by SMEs via fuzzy TOPSIS

The fourth industrial revolution or Industry 4.0 has changed today's manufacturing scenario. The need to make manufacturing systems agile, adaptive, resilient, and robust, due to the pandemic, has expediated the adoption and implementation of smart manufacturing technologies. Despite the interest of manufacturers in smart manufacturing, the adoption rate has been slow. Small- and medium-sized enterprises (SMEs) can be especially hindered in adoption due to the lack of a transition strategy and identification of relevant technologies required to achieve a smart factory. Although there is literature that provides maturity and readiness models and toolkits for adoption, the decision-making models for SMEs are inadequate. This paper proposes a multi-criteria decision-making model as a tool to provide a means for evaluating a large range of smart manufacturing technologies while considering the status quo for SMEs. The aim of this project is to aid SMEs in the adoption of smart manufacturing technologies by providing a roadmap to assess performance parameters and identify an appropriate smart manufacturing technology for adoption. The recommended technology is tailored to the requirements of the SME using fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The fuzzy TOPSIS technique aggregates the opinions of decision makers and uses a fuzzy environment to account for their subjectivity. The inclusion of personnel as provided by the model from various hierarchical levels promotes favourable implementation by insertion in the transition process while also educating the personnel of the technologies. An industry case study with individuals from an SME, Levil Technology, and Florida's Manufacturing Extension Partnership (MEP) Center, FloridaMakes, is conducted to assess the preference for five smart manufacturing technologies over a range of eleven criteria pertaining to performance, sustainability, quality, cost and maintenance. The results give clarity regarding the preference for critical manufacturing criteria by assigning weightage, and identifies the most relevant technology catering to the preferred criteria. Predictive analytics for asset health monitoring was found to be most preferred followed by a digitally connected factory for visibility into production operations. The determination of rank will allow manufacturers to assess the manufacturing alternatives with respect to the key performance indicators for transition to Industry 4.0.