We need a theoretical framework for the modernization of industrial legacy systems

Industrial informatics brings computational intelligence to industry, powering the “software-ization” of manufacturing processes. However, when faced with the myriad of legacy systems that cannot be fully replaced cost-effectively, practitioners must retrofit computational intelligence into legacy systems. This modernization of legacy industrial systems is deceptively challenging: poor retrofitting can cause more harm than good, hindering overall metrics. We argue for a theoretical framework for modernizing legacy industrial systems. We illustrate the challenge within the context of the real-time performance of industrial cyber-physical systems by depicting a formalization of the problem and illustrating its impact through Monte Carlo methods. We show how knowledge of extant system internals constrains possible optimizations. We conclude by highlighting several research directions, including some recommendations, that must be pursued to establish a common theoretical underpinning that can inform practitioners.