A Fuzzy Inference Model to Identify the Current Industry Maturity Stage in the Transformation Process to Industry 4.0

Abstract

The fourth industrial revolution has been developed and promoted unlike any other. In this regard, the measurement of achievements on every Industry IX.0 is subject to two major sources of imprecision, namely ambiguity and vagueness. In this paper, we introduce an Industry X.0 Fuzzy Inference Engine that, for a given industrial plant, it computes the technological footprint on Industries I1.0–I4.0; i.e., how much technology of each Industry IX.0 is present in that plant, therefore revealing its current maturity level in the transformation process to become an Industry 4.0. The model is scalable, and implements a control strategy inspired in resource allocation problems. It addresses the overlap issues common to I3.0 and I4.0, and assigns each criterion with a distinct importance. Our work stands out for bringing formal analytical methods supported by a theoretical background. To date, no work in the field has ever faced this problem as approached in the paper. Simulation results applied to hypothetical plants show the consistency and effectiveness of the proposed model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The need for leveraging all of the technologies currently available has made industrial plants to retrofit their facilities towards Industry 4.0. In order to properly assess the current stage of this transformation process, in real-world heterogeneous plants, we should measure the implementation extent of each automation technology, while considering where they fit in the industrial timeline. Existing models overlook important features of automated systems, such as control architecture and communication protocols, that among others determine whether a plant qualifies for one or more IX.0. Thus, in this paper we propose a Fuzzy inference engine that allows a thorough assessment and treatment of information, in order to state the current maturity stage (technological footprint) of a given plant with respect to Industries I1.0–4.0. The matrices are scalable, i.e., various plants may be evaluated all together for comparative purposes, the knowledge base can be improved, and other future IX.0 may be incorporated. The use of Fuzzy set and Fuzzy logic provided a suitable framework for modeling, quantifying, and reasoning under imprecise and unclear development stages of a transition for I4.0.