Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning

Wuest, Thorsten

doi:10.1007/978-3-319-17611-6

Cited by 26 publications

(15 citation statements)

References 192 publications

(297 reference statements)

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“…Blockchain technology enables food traceability to the item level, not just batch level, so that participants can trace each item in the supply chain (Wuest, 2015). Walmart's blockchain pilot identified which data were relevant to capture and compiled a list of mandatory attributes (lot number, pack date, quantity shipped, unit of measure, purchase order number, shipment identifiers) and a list of optional attributes (carton serial numbers, pallet number, harvest date, buyer identifier, vendor/supplier identifier).…”

Section: Discussionmentioning

confidence: 99%

Food Traceability on Blockchain: Walmart’s Pork and Mango Pilots with IBM

Kamath¹

2018

The JBBA

322

154

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Section: Discussionmentioning

confidence: 99%

Food Traceability on Blockchain: Walmart’s Pork and Mango Pilots with IBM

Kamath¹

2018

The JBBA

322

154

View full text Add to dashboard Cite

“…Some EHM applications are also unsupervised. At the other extreme, analytics can focus on determining correlations between "input" and "output" datasets [26,27]. As an example, PdM and VM determine relationships between equipment data (trace or processed, e.g., through FD) and maintenance and metrology measurement data, respectively.…”

Section: Dimensions Of Analytics Capabilitiesmentioning

confidence: 99%

“…The predominant method used here is PCA, with other methods employed including one-class support vector machine (SVM), k-nearest neighbors (k-NN), auto-associative neural network, and hidden Markov models (HMMs) [26,[31][32][33][34]. When an alarm occurs, a decomposition of the MVA model can provide a Pareto of contributors to a fault, aiding in classification.…”

Section: Emerging Apc Analytics Applications and Analytics Trends In mentioning

confidence: 99%

Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing

2017

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Smart manufacturing (SM) is a term generally applied to the improvement in manufacturing operations through integration of systems, linking of physical and cyber capabilities, and taking advantage of information including leveraging the big data evolution. SM adoption has been occurring unevenly across industries, thus there is an opportunity to look to other industries to determine solution and roadmap paths for industries such as biochemistry or biology. The big data evolution affords an opportunity for managing significantly larger amounts of information and acting on it with analytics for improved diagnostics and prognostics. The analytics approaches can be defined in terms of dimensions to understand their requirements and capabilities, and to determine technology gaps. The semiconductor manufacturing industry has been taking advantage of the big data and analytics evolution by improving existing capabilities such as fault detection, and supporting new capabilities such as predictive maintenance. For most of these capabilities: (1) data quality is the most important big data factor in delivering high quality solutions; and (2) incorporating subject matter expertise in analytics is often required for realizing effective on-line manufacturing solutions. In the future, an improved big data environment incorporating smart manufacturing concepts such as digital twin will further enable analytics; however, it is anticipated that the need for incorporating subject matter expertise in solution design will remain.

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“…sensor data), the high dimensionality and variety (e.g. due to different sensors or connected processes) of data as well as the NP complete nature of manufacturing optimization problems (Wuest, 2015) present a challenge.…”

Section: Challenges Of the Manufacturing Domainmentioning

confidence: 99%

“…manufacturing cost estimation and/or process optimization, better understanding of the customer's requirements, etc., support is needed to handle the high dimensionality, complexity, and dynamics involved (Davis et al, 2015;Loyer, Henriques, Fontul, & Wiseall, 2016;Wuest, 2015). New developments in certain domains like mathematics and computer science (e.g.…”

Section: Open Accessmentioning

confidence: 99%

Machine learning in manufacturing: advantages, challenges, and applications

Wuest

Weimer²,

Irgens

et al. 2016

Production & Manufacturing Research

Self Cite

808

438

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The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means available. One area, which saw fast pace developments in terms of not only promising results but also usability, is machine learning. Promising an answer to many of the old and new challenges of manufacturing, machine learning is widely discussed by researchers and practitioners alike. However, the field is very broad and even confusing which presents a challenge and a barrier hindering wide application. Here, this paper contributes in presenting an overview of available machine learning techniques and structuring this rather complicated area. A special focus is laid on the potential benefit, and examples of successful applications in a manufacturing environment.

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Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning

Cited by 26 publications

References 192 publications

Food Traceability on Blockchain: Walmart’s Pork and Mango Pilots with IBM

Food Traceability on Blockchain: Walmart’s Pork and Mango Pilots with IBM

Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing

Machine learning in manufacturing: advantages, challenges, and applications

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