Automatic root cause analysis in manufacturing: an overview &amp; conceptualization

Oliveira, Eduardo e; Miguéis, Vera L.; Borges, José

doi:10.1007/s10845-022-01914-3

Cited by 36 publications

(11 citation statements)

References 45 publications

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“…Furthermore, root cause analysis makes the prediction actionable, since the user can derive countermeasures for non-compliant future events through acting on the root causes. Researchers in the application domains of engineering and cyber systems, i.e., IT systems, typically develop root cause analysis for explaining the root causes of machine faults or system anomalies to the operation manager [83,195].…”

Section: Widening the Scope: Event Prediction Methods In The Pcm Systemmentioning

confidence: 99%

A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas

Benzin¹,

Rinderle-Ma²

2023

Preprint

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Event prediction is the ability of anticipating future events, i.e., future real-world occurrences, and aims to support the user in deciding on actions that change future events towards a desired state. An event prediction method learns the relation between features of past events and future events. It is applied to newly observed events to predict corresponding future events that are evaluated with respect to the user's desired future state. If the predicted future events do not comply with this state, actions are taken towards achieving desirable future states. Evidently, event prediction is valuable in many application domains such as business and natural disasters.The diversity of application domains results in a diverse range of methods that are scattered across various research areas which, in turn, use different terminology for event prediction methods. Consequently, sharing methods and knowledge for developing future event prediction methods is restricted. To facilitate knowledge sharing on account of a comprehensive classification, integration, and assessment of event prediction methods, we combine taxonomies and take a systems perspective to integrate event prediction methods into a single system, elicit requirements and assess existing work with respect to the requirements. Based on the assessment, we identify open challenges and discuss future research directions.

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Section: Widening the Scope: Event Prediction Methods In The Pcm Systemmentioning

confidence: 99%

A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas

Benzin¹,

Rinderle-Ma²

2023

Preprint

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“…It applies human factors engineering principles (Reason, 1995) and serves as one of the core building blocks in an organization's continuous improvement efforts (ASQ, n.d.‐c). Initially developed to analyze serious incidents in high‐hazard industries such as aviation and nuclear power (Carroll, 1998), RCA has been widely used in manufacturing (Kumari et al., 2020; Oliveira et al., 2022; Yang & Xiao, 2012), transportation (White, 2005), marine operations (Kececi & Arslan, 2017; Kum & Sahin, 2015), construction (Rosenfeld, 2014), the food industry (Song et al., 2018), retail (Teller et al., 2018), the criminal justice system (Friend et al., 2020; Holloway et al., 2018), and numerous other settings as a method of identifying the root causes of faults, problems, accidents, and sentinel events.…”

Section: Root Cause Analysis Overviewmentioning

confidence: 99%

Teaching root cause analysis in healthcare management with Grey's Anatomy

Gray,

Petrova

2023

Decision Sci J Innov Edu

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Over the past 20 years, root cause analysis (RCA) has become one of the most widely used retrospective methods for detecting safety hazards in medicine and healthcare. Despite its wide use in management practice and growing popularity in academic research, there is currently a dearth of coverage of RCA in popular healthcare management textbooks and pedagogical publications. This paper addresses this gap by presenting innovative instructional materials and recommendations for teaching RCA in healthcare management. Using an episode of Grey's Anatomy TV show, this experiential learning exercise takes students through the steps of RCA and makes use of a range of RCA tools, including high‐level flowchart, fishbone diagram, “five whys”, corrective action plan, and others. The exercise, which was classroom tested and received positive student feedback, can be adapted to a range of healthcare management classes. Because RCA can have widespread, lifesaving consequences, healthcare management courses can significantly benefit from integrating this exercise into classroom pedagogies.

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“…Specifically, we utilize CV to efficiently determine the wear states of products. As machine learning (ML) and DL has been shown to yield benefits in the related field of smart manufacturing (Wang et al, 2018a;Flath and Stein, 2018;Miguéis et al, 2022), it promises to be effective for the proposed approach.…”

Section: Introductionmentioning

confidence: 99%