Prescriptive Analytics for Big Data

Soltanpoor, Reza; Sellis, Timos

doi:10.1007/978-3-319-46922-5_19

Cited by 45 publications

(19 citation statements)

References 9 publications

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“…(2 (Pedersen, 2017). The recent advances in the area of business analytics in terms of prescriptive analysis provides organisations with adaptive, automated, and time-dependent courses of actions to take advantage of likely business opportunities (Soltanpoor and Sellis, 2016).…”

Section: Quality Control and Performance Of Qualitymentioning

confidence: 99%

“…(Pedersen, 2017). The recent advances in the area of business analytics in terms of prescriptive analysis provide organizations with adaptive, automated and time-dependent courses of actions to take advantage of likely business opportunities (Soltanpoor and Sellis, 2016). Prescriptive analytics algorithms in quality management can provide two levels of human intervention for decision-making (Hagerty, 2017).…”

Section: Handling Big Datamentioning

confidence: 99%

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Essential ingredients for the implementation of Quality 4.0

Sony

Antony

Douglas

2020

TQM

189

166

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PurposeQuality 4.0 is concerned with managing quality in the Industry 4.0 era. Specifically, its focus is on which digital tools are used to enhance an organization’s ability to reliably give customers high-quality products. The purpose of this paper is to investigate the key ingredients for the effective implementation of Quality 4.0.Design/methodology/approachA narrative literature review was conducted on the extant works to collate and analyse previous studies in this relatively new field.FindingsThe study revealed eight key ingredients for the effective implementation of Quality 4.0 in organizations, namely: (1) handling big data, (2) improving prescriptive analytics, (3) using Quality 4.0 for effective vertical, horizontal and end-to-end integration, (4) using Quality 4.0 for strategic advantage, (5) leadership in Quality 4.0, (6) training in Quality 4.0, (7) organizational culture for Quality 4.0 and, lastly, (8) top management support for Quality 4.0. These findings have provided a steer for the future research agenda of Quality 4.0.Practical implicationsOrganizations can use the eight ingredients to perform a self-assessment on the current state of each element within their own organization. When implementing Quality 4.0, each ingredient should be effectively analysed, and measures taken so that the implementation of Quality 4.0 is effective.Originality/valueThe paper makes the first attempt to present the key ingredients an organization should possess to effectively implement Quality 4.0.

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Section: Quality Control and Performance Of Qualitymentioning

confidence: 99%

Section: Handling Big Datamentioning

confidence: 99%

Essential ingredients for the implementation of Quality 4.0

Sony

Antony

Douglas

2020

TQM

189

166

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“…Dies beinhaltet beschreibende, vergangenheitsorientierte sowie vorhersagende und erklärende Analysen zur zukunftsorientierten Betrachtung von Ereignissen [7]. Dynamische Szenarien und Simulationen dienen dabei der Entscheidungsfindung [8]. Die Funktionsumfänge von BI-Systemen schwanken stark, sodass komplexe Analysen nur von spezialisierten Systemen abgedeckt werden.…”

Section: Ddo-dimension 2: Datenverarbeitende Werkzeugeunclassified

Durchblick statt Bauchgefühl – Transformation zur Data-Driven Organization

Körppen

Ullrich

Bertheau

2021

Wirtsch Inform Manag

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Um in der digitalisierten Wirtschaft mitzuspielen, müssen Unternehmen, Markt und insbesondere Kunden detailliert verstanden werden. Neben den "Big Playern" aus dem Silicon Valley sieht der deutsche Mittelstand, der zu großen Teilen noch auf gewachsenen IT-Infrastrukturen und Prozessen agiert, oft alt aus. Um in den nächsten Jahren nicht gänzlich abgehängt zu werden, ist ein Umbruch notwendig. Sowohl Leistungserstellungsprozesse als auch Leistungsangebot müssen transparent und datenbasiert ausgerichtet werden. Nur so können Geschäftsvorfälle, das Marktgeschehen sowie Handeln der Akteure integrativ bewertet und fundierte Entscheidungen getroffen werden. In diesem Beitrag wird das Konzept der Data-Driven Organization vorgestellt und aufgezeigt, wie Unternehmen den eigenen Analyticsreifegrad ermitteln und in einem iterativen Transformationsprozess steigern können.

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“…This means that predictive analytics tells what will happen, while prescriptive analytics provides the cause factors and suggests what to do. Predictive and prescriptive analytics with big data are becoming more and more prevalent in industries [Soltanpoor and Sellis, 2016;Vahn, 2014]. To the best of our knowledge, there has not been any particular study which incorporates these two types of analytics together to provide a framework to address flood issues.…”

Section: Introductionmentioning

confidence: 99%

Rapid spatio-temporal flood prediction and uncertainty quantification using a deep learning method

Fang

Pain

et al. 2019

Journal of Hydrology

141

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Recently accrued attention has been given to machine learning approaches for flooding prediction. However, most of these studies focused mainly on time-series flooding prediction at specified sensors, rarely on spatio-temporal prediction of inundations. In this work, an integrated long short-term memory (LSTM) and reduced order model (ROM) framework has been developed. This integrated LSTM-ROM has the capability of representing the spatio-temporal distribution of floods since it takes advantage of both ROM and LSTM. To reduce the dimensional size of large spatial datasets in LSTM, the proper orthogonal decomposition (POD) and singular value decomposition (SVD) approaches are introduced. The LSTM training and prediction processes are carried out over the reduced space. This leads to an improvement of computational efficiency while maintaining the accuracy. The performance of the LSTM-ROM developed here has been evaluated using Okushiri tsunami as test cases. The results obtained from the LSTM-ROM have been compared with those from the full model (Fluidity). In predictive analytics, it is shown that the results from both the full model and LSTM-ROM are in a good agreement whilst the CPU cost using the LSTM-ROM is decreased by three orders of magnitude compared to full model simulations. Additionally, prescriptive analytics has been undertaken to estimate the uncertainty in flood induced conditions. Given the time series of the free surface height at a specified detector, the corresponding induced wave conditions along the coastline have then been provided using the LSTM network. Promising results indicate that the use of LSTM-ROM can provide the flood prediction in seconds, enabling us to provide real-time predictions and inform the public in a timely manner, reducing injuries and fatalities.

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Prescriptive Analytics for Big Data

Cited by 45 publications

References 9 publications

Essential ingredients for the implementation of Quality 4.0

Essential ingredients for the implementation of Quality 4.0

Durchblick statt Bauchgefühl – Transformation zur Data-Driven Organization

Rapid spatio-temporal flood prediction and uncertainty quantification using a deep learning method

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