Big data analytics for operations management in engineer-to-order manufacturing

Kozjek, Dominik; Vrabič, Rok; Rihtaršič, Borut; Butala, Peter

doi:10.1016/j.procir.2018.03.098

Cited by 18 publications

(16 citation statements)

References 8 publications

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“…4 Title: Big-data analytics for operations management in engineer-to-order (ETO) manufacturing (Kozjek et al 2018b) Description: The objective of the research is to investigate manufacturing data which are collected by a MES during operations and to develop data-driven tools for supporting operations management in ETO manufacturing. The developed tools can be used for the simulation of production and the forecasting of potential resource overloads.…”

Section: Methodsmentioning

confidence: 99%

“…This section demonstrates the use of a conceptual framework on the selected studies of introducing data analytics in manufacturing systems. Five existing case studies of developing data-analytics solutions in manufacturing systems, i.e., (Kozjek et al 2017a(Kozjek et al , 2017b(Kozjek et al , 2018a(Kozjek et al , 2018bVrabič, Kozjek, and Butala 2017), are selected. Data-analytics solutions developed within these projects are either innovative ways of reducing the incompleteness of information and discovering new knowledge through additional use of data or they enable the more efficient reduction of information incompleteness than the conventional approaches.…”

Section: Demonstrating the Use Of A Conceptual Framework On Selected Studiesmentioning

confidence: 99%

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Advancing manufacturing systems with big-data analytics: A conceptual framework

Kozjek

Vrabič

Rihtaršič

et al. 2020

International Journal of Computer Integrated Manufacturing

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With the intensive development and implementation of information and communication technologies in manufacturing, large amounts of heterogeneous data are now being generated, gathered and stored. Handling large amounts of complex dataoften referred to as big datarepresents a challenge as there are many new approaches, methods, techniques, and tools for data analytics that open up new possibilities for exploiting data by converting them into useful information and/or knowledge.However, the application of advanced data analytics in manufacturing lags behind in terms of penetration and diversity in comparison with other domains such as marketing, healthcare and business, meaning that the available data often remain unexploited. This paper proposes a new conceptual framework for systematically introducing big-data analytics into manufacturing systems. To this end, the paper defines a new stepwise procedure that identifies what knowledge and skills, and which reference models, software and hardware tools, are needed for the development, implementation and operation of data-analytics solutions in manufacturing systems. The feasibility of the proposed conceptual framework is demonstrated in a case study from an engineer-to-order company and by mapping the framework to several previous data-analytics projects.

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Section: Methodsmentioning

confidence: 99%

Section: Demonstrating the Use Of A Conceptual Framework On Selected Studiesmentioning

confidence: 99%

Advancing manufacturing systems with big-data analytics: A conceptual framework

Kozjek

Vrabič

Rihtaršič

et al. 2020

International Journal of Computer Integrated Manufacturing

Self Cite

View full text Add to dashboard Cite

show abstract

“…A production-scenario simulation tool, presented in [28], is used during the experiments. The simulation tool is based on pre-processed Manufacturing Execution System (MES) data (Table 1).…”

Section: Case Studymentioning

confidence: 99%

“…7. Resulting distributions of the work-order delay time, adapted according to [28] Focused on the proposed approach, a comparison of the simulation results indicates that introducing additional communications between the functions in the cyber system of the CPPS enables a better production performance with respect to the selected performance measures.…”

Section: Case Studymentioning

confidence: 99%

A Cyber-Physical Approach to the Management and Control of Manufacturing Systems

Hozdić

Kozjek

Butala

2019

SV-JME

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Cyber-physical systems (CPSs) open up new perspectives for the design, development, implementation, and operation of manufacturing systems and will enable a paradigm shift in manufacturing. The objective of this research is to develop a new concept of cyber-physical production systems (CPPSs) and, on this basis, to address the issue of management and control, which is crucial for the effective and efficient operation of manufacturing systems. A new model of CPPS is proposed. The model integrates digitalized production planning, scheduling, and control functions with a physical part of manufacturing system and enables the self-organization of the elements in production. A case study demonstrates feasibility of the approach through the use of simulation experiments, which are based on real industrial data collected from a company that produces industrial and energy equipment. Keywords: cyber-physical production system, production planning and control, self-organization Highlights • A conceptual model of cyber-physical production systems (CPPSs) is developed. • A cyber-physical approach to the production planning and control (PPC) of manufacturing systems is presented. • The presented approach to the production planning, scheduling and the self-organization in the CPPS is demonstrated through the use of simulation experiments.

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“…Then this data can be considered big data which needs data-analytics and machine learning tools to discover knowledge that is hidden in this data. [12]. There are previous studies that introduced the use of smart logistics solutions using data analytics, artificial intelligence and machine learning techniques to solve the SLAP under different conditions.…”

Section: Introductionmentioning

confidence: 99%

Smart Solution For Enhancing Storage Location Assignments In WMS Using Genetic Algorithm

Saleet¹

2020

International Journal of Engineering Research and Technology

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Big data analytics for operations management in engineer-to-order manufacturing

Cited by 18 publications

References 8 publications

Advancing manufacturing systems with big-data analytics: A conceptual framework

Advancing manufacturing systems with big-data analytics: A conceptual framework

A Cyber-Physical Approach to the Management and Control of Manufacturing Systems

Smart Solution For Enhancing Storage Location Assignments In WMS Using Genetic Algorithm

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