{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:15:42Z","timestamp":1777853742252,"version":"3.51.4"},"reference-count":62,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T00:00:00Z","timestamp":1708646400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Integrated Design and Process Science: Transactions of the SDPS, Official Journal of the Society for Design and Process Science"],"published-print":{"date-parts":[[2024,2,23]]},"abstract":"<jats:p>\u00a0Manufacturers perpetually adapt their systems to meet unforeseen events, new objectives, competition, and improved understanding of processes. In that human-directed work, models mediate an enduring relationship between production resources and engineers. Accommodating new understanding in the models controlling production can lead to more effective manufacturing. That work has previously been the province of quality programs such as Six Sigma, but is now fertile ground to study human-computer interaction about that enduring relationship mediated by models. Can AI augment human capability in the arcane work of formulating and refining models? This question is relevant to complex system engineering generally, not just manufacturing. In answering this question, this paper adapts Klein\u2019s flexecution for use in adaptable manufacturing systems. Theory flexecution, the methodical refinement of models, points to human-computer interactions that emphasize the roles of models, explanation, and machine agents that recognize the engineer\u2019s goals. This perspective article illustrates these ideas with an example of formulating models for production scheduling.<\/jats:p>","DOI":"10.3233\/jid-230035","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T11:32:09Z","timestamp":1708687929000},"page":"71-82","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Cognitive work in future manufacturing systems: Human-centered AI for joint work with models"],"prefix":"10.1177","volume":"27","author":[{"given":"Peter","family":"Denno","sequence":"first","affiliation":[{"name":"National Institute of Standards and Technology, Gaithersburg, MD, USA"}]}],"member":"179","published-online":{"date-parts":[[2024,2,23]]},"reference":[{"key":"ref001","unstructured":"Achinstein,P.(1983).\n                      The Nature of Explanation\n                      . 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