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

Abstract

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.

Looking Ahead: Business Intelligence & Analytics Research in the Post-Pandemic New Normal

“…(1) New data pipelines: Data pipelines exist to infuse different sources of data to existing BI&A storage for data driven decision making. They ingest data from data sources of interest to the organization via an extraction, transformation and loading paradigm to the data infrastructure in BI&A such as a data lake or a data warehouse [33]. However, organizations are now discovering that new data sources are needed for decision making in a rapidly changing environment.…”

Impact of big data analytics on distributed manufacturing: Does big data help?

“…All practitioners and academia should be aware of this problem. These producing groups, which have regularly carried out around terminology of better industry efficiency, have adopted five typical methods -just-in-time, total quality managing, continuous learning, worker participation and ease of use (Kozjek, 2020). Wang et al (2021a) further identified typical methods involving world class production groups as complete quality, people involvement and just in time.…”

“…Asmussen et al (2021) have argued naturally that quality information is essential to look at today's source chain procedures, using organizational theories. Kozjek (2020) argued that big data and social media are complementary during the existing scenario. Wiech (2022) have additionally noted that the area of functions control remains relatively sluggish with in examining social media and BD.…”

Study on the Digital Intelligent Diagnosis of Miniature Machine Tools

“…Tian et al [16] indicated that processing is an important challenge in an internet-based environment and must facilitate distribution, autonomy, and cooperation. Kozjek et al [17] presented a study defining a new stepwise procedure. The procedure identifies what software and hardware tools are needed for the implementation of data analytics solutions in manufacturing systems.…”