Establishment of an appropriate data analytic platform for developing a wisdom manufacturing system using decision techniques
Zeferino, Emanuel Fernando Mpofu, Khumbulani Makinde, Olasumbo Ramatsetse, Boitumelo
Zeferino, Emanuel Fernando
Mpofu, Khumbulani
Makinde, Olasumbo
Ramatsetse, Boitumelo
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Abstract
In today’s global business context, data has played a critical role in ensuring accurate and appropriate decision making in manufacturing organisations. Despite the huge pool of information (i.e. data) generated by consumers, repair or maintenance shops, manufacturing job shop, scientific society on various products, which could be deployed by manufacturers in eliciting vital information towards achieving sustainable product design and development, only few manufacturers are making use of this data to generate wisdom required for sustainable manufacturing. This act is caused by lack of appropriate systems capable of integrating the available data and make wise inferences that will result in a competitive advantage of a specific organisation over its competitors. In light of this, the aim of this study is to establish a suitable data analytic platform that could be used to
sort, classify and integrate data required to generate wisdom vital for sustainable manufacturing. In order to achieve this, Analytical Hierarchy Process (AHP) was deployed to appraise various alternative data analytical platforms such as Python, Apache Spark, QlikView, Power BI, Tableau, KNIME, Excel, Talend, RapidMiner and Statistical Analysis System (SAS) using various criteria such as Data Format, Availability, Interface, Programming Intensity, Data Science Knowledge Intensity and Capabilities. The result of this decision analysis and selection exercise, revealed that KNIME data analytic platform, with the most important decision criterion; data science knowledge intensity, and a cumulative assessment score
of 80.80 is the appropriate data analytic platform that manufacturers should use to generate a knowledge advisor vital for sustainable manufacturing and product development.
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Date
2022-10-05
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Springer
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Keywords
Wisdom manufacturing, Data analytics, Analytical hierarchy process, Decision analysis