Smart manufacturing, today, is the ability to continuously maintain and improve performance, with intensive use of information, in response to the changing environments. Technologies for creating smart manufacturing systems or factories are becoming increasingly abundant. Consequently, manufacturers, large and small, need to correctly select and prioritize these technologies correctly. In addition, other improvements may be necessary to receive the greatest benefit from the selected technology. This paper proposes a method for assessing a factory for its readiness to implement those technologies. The proposed readiness levels provide users with an indication of their current factory state when compared against a reference model. Knowing this state, users can develop a plan to increase their readiness. Through validation analysis, we show that the assessment has a positive correlation with the operational performance.

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Various techniques are used to diagnose problems throughout all levels of the organization within the manufacturing industry. Often times, this root cause analysis is ad-hoc with no standard representation for artifacts or terminology (i.e., no standard representation for terms used in techniques such as fishbone diagrams, 5 why's, etc.). Once a problem is diagnosed and alleviated, the results are discarded or stored locally as paper/digital text documents. When the same or similar problem reoccurs with different employees or in a different factory, the whole process has to be repeated without taking advantage of knowledge gained from previous problem(s) and corresponding solution(s). When discussing the diagnosis, personnel may miscommunicate over terms used in the root cause analysis leading to wasted time and errors. This paper presents a framework for a knowledge-based manufacturing diagnosis system that aims to alleviate these miscommunications. By learning from diagnosis methods used in manufacturing and in the medical community, this paper proposes a framework which integrates and formalizes root cause analysis by categorizing faults and failures that span multiple organizational levels. The proposed framework aims to enable manufacturing operations by leveraging machine learning and semantic technologies for the manufacturing system diagnosis. A use case for the manufacture of a bottle opener demonstrates the framework.