Paradigm shift in implementing smart technologies for machinery optimisation in manufacturing using decision support system

There is a revolution taking place in the industrial sector as a result of intelligent technologies that optimise machinery. In light of the requirements of the present day, the traditional methods are not going to suffice. Within the scope of this research, a multi-criteria decision-making (MCDM) framework is proposed for the purpose of selecting appropriate smart technologies. We develop four novel operators: q-rung fuzzy Schweizer–Sklar power average (q-RFSSPA), q-rung fuzzy Schweizer–Sklar power weighted average (q-RFSSPWA), q-rung fuzzy Schweizer–Sklar power geometric (q-RFSSPG), and q-rung fuzzy Schweizer–Sklar power weighted geometric (q-RFSSPWG). These operators handle uncertainty and ambiguity in decision-making. We explore their properties to demonstrate their robustness. Using the logarithmic percentage change-driven objective weighing (LOPCOW) technique, we objectively determine the criteria weights. The weighted aggregated sum product assessment (WASPAS) method ranks alternatives, combining the benefits of the Weighted Sum and Weighted Product Models. Applied to a mid-sized manufacturing company, we evaluate five smart technologies with eight. The findings demonstrate that GV (Guided Vehicles) emerge as the most effective alternative. GV vehicles significantly enhance productivity by automating material transportation, reducing human error, and minimising transport time. This selection illustrates the framework’s practical value in optimising machinery operations and provides actionable guidance for businesses aiming to improve their production efficiency. The study contributes to the field by offering a robust methodology for technology selection and practical insights for both industry professionals and policymakers.