Big Data Analytics for Predictive Manufacturing Control - A Case Study from Process Industry

Krumeich, Julian; Jacobi, Sven; Werth, Dirk; Loos, Peter

doi:10.1109/bigdata.congress.2014.83

Cited by 29 publications

(13 citation statements)

References 15 publications

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“…In most cases, the existing contributions focus on the changes of process scheduling caused by CPS. The authors primarily deal with the effects for PPC systems and describe theoretical potentials as well as technical hurdles [14]. Mieth et al [15], for example, describe how real-time data can improve the simulation of production.…”

Section: Basics and Related Researchmentioning

confidence: 99%

“…Present production process data (10), supplier data (11), production data (12), order data (13) FR rcs 14 Correlate data FR rcs 15 Present current production schedule…”

Section: Requirementmentioning

confidence: 99%

“…Create production schedules FR rtd [2][3] Define objectives (2) and priority rules (3) for the production schedule FR rt 4 Define events not relevant for simulation FR rtd 5 Define possible measures to be taken FR rtd 6 Prioritize orders FR rtd 7 Simulate schedules FR rtd [8][9][10][11] Assess (8), present (9), sort (10), and filter (11) the alternative production schedules FR rtd 12 Show details of the alternative production schedules FR rtd [13][14] Edit (13) and select (14) an alternative production schedule…”

Section: Requirement Description Fr Rtdmentioning

confidence: 99%

See 2 more Smart Citations

Design and Implementation of a Decision Support System for Production Scheduling in the Context of Cyber-Physical Systems

Freier¹,

Schümann²

2020

WI2020 Zentrale Tracks

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The use of cyber-physical systems in production promises great potential for production scheduling since a larger information base is available for the scheduling of production orders. However, the mere acquisition of realtime data does not inherently lead to improvements. On the contrary, a targeted preparation of the data is required in order to prevent an information overload. Decision support systems that support decision makers in production scheduling can perform this task. However, the design of such systems in combination with cyber-physical systems has hardly been investigated so far. In this paper, we therefore design and implement a corresponding decision support system in a design science approach. For this, we identify meta-requirements based on a literature analysis and an interview study. Finally, we evaluate the created meta-artifact in a laboratory setting in order to obtain generalizable knowledge about building such a decision support system.

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Section: Basics and Related Researchmentioning

confidence: 99%

“…Present production process data (10), supplier data (11), production data (12), order data (13) FR rcs 14 Correlate data FR rcs 15 Present current production schedule…”

Section: Requirementmentioning

confidence: 99%

Section: Requirement Description Fr Rtdmentioning

confidence: 99%

See 1 more Smart Citation

Design and Implementation of a Decision Support System for Production Scheduling in the Context of Cyber-Physical Systems

Freier¹,

Schümann²

2020

WI2020 Zentrale Tracks

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“…• Availability improvement -Predictive Maintenance [12], [13], [14] -Prognostics and Health Management [13], [15] • Performance improvement -Predictive Production Planning [16] -Predictive Manufacturing Control [17] • Quality improvement -Predictive Quality Control [18] -Control Chart Pattern Recognition [19], [20] These concepts provide theoretical foundations and require additional specifications for direct applicability. For this purpose, we propose a specified process model focusing on improving availability, since the costs for keeping a high availability represent 15 -60 % of total costs in manufacturing [21].…”

Section: Kdd In Manufacturingmentioning

confidence: 99%

How to Discover Knowledge for Improving Availability in the Manufacturing Domain?

Utz¹,

Neumann²,

Omid³

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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This paper presents a specific process model for Knowledge Discovery in Databases (KDD) projects aiming at availability improvement in manufacturing. For this purpose, Overall Equipment Efficiency (OEE) is analyzed and used, since it is an approved approach to monitor and improve the degree of availability in manufacturing. To define the specific process model, we use the generic CRISP-DM reference model and conduct a mapping for availability improvement. We prove the applicability of our model in the context of a specific KDD project in a large enterprise in the manufacturing industry.

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“…The method validation took place at a German steel producer. Continuously growing service expectancy and increasingly complex customer requirements for individualization present new challenges for companies in the age of digitalization [42]. Particularly the companies in the German steel industry are in demanding times.…”

Section: Framework Application At a German Steel Producermentioning

confidence: 99%

A Simulation-Based Evaluation Approachfor Digitalization Scenarios in Smart SupplyChain Risk Management

Schlter

Hetterscheid

Henke

2017

JIEMS

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To increase the security of modern, volatile supply chains a proactive risk management based on real-time risk related information transparency is required. At this time, none or only limited empirical/objective information about digitalization benefits for supply chain risk management is available. A method is needed, which draws conclusion on the estimation of costs and benefits of digitalization initiatives. The paper presents a flexible simulationbased approach for evaluating digitalization scenarios prior to realization. The evaluation approach is integrated into a framework and its applicability will be shown in a case study of a German steel producer, evaluating digitalization effects on the Mean Lead time-at-risk.

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Big Data Analytics for Predictive Manufacturing Control - A Case Study from Process Industry

Cited by 29 publications

References 15 publications

Design and Implementation of a Decision Support System for Production Scheduling in the Context of Cyber-Physical Systems

Design and Implementation of a Decision Support System for Production Scheduling in the Context of Cyber-Physical Systems

How to Discover Knowledge for Improving Availability in the Manufacturing Domain?

A Simulation-Based Evaluation Approachfor Digitalization Scenarios in Smart SupplyChain Risk Management

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