Assessing Artificial Intelligence Capabilities in Manufacturing Operations
Artificial intelligence (machine intelligence or AI) is described as providing machines with such aspects of human intelligence as “cognition,” “learning” or “creative problem solving.” Its applications in manufacturing refer to instruments or machines driven by computers. In working with AI as an applied tool in industry, capability in math and logic is not required, but one must be capable in two other areas. These are, 1) to understand and operate both the new and unique outputs or results of AI (such as machine learning and dynamic query resolution), and 2) to apply the consequences of these outputs in such business activities as process development, operations, quality and regulatory systems. The Xavier University AI in Operations team has composed an AI Maturity Model to assess and measure both the functional AI capability of an organization in defined operations or categories, as well as its current capability to improve in these areas over time. It assesses the practical ability of an organization’s people, departments and culture to understand, implement and operate applied AI tools or instrumentation. Rather than assessing a capability to create, critique or maintain integral AI algorithms, the maturity model qualitatively measures the functional capabilities of an organization to work with AI empowered tools, processes, structures and objects. Despite the newness of AI in many manufacturing environments, this tool is envisioned to be employed in the ways other maturity models are. That is, used in diverse teams and units and in many degrees of formality and levels of stringency.
