Dwika Widyantama, Andri D Setiawan


Technological developments always create new challenges for organizations to adapt so that they remain competitive. Today, organizations are dealing with rapid technological developments and the disruption of digital transformation. The characteristics of digital transformation in manufacturing are the application of the latest technology that supports so that processes and information are connected, between production machines, and products as well as the high adaptability of a production system. In achieving the main goal of Industry 4.0, namely smart manufacturing that can respond to fluctuations of market demand for high-quality products, it is necessary to apply technology that can collect and analyze data to produce intelligent solutions, which is often referred to as the use of Data Analytics. Literature study shows that there are various barriers or barriers in the implementation of Data Analytics in manufacturing companies. However, none of these studies have discussed what success factors need to be prioritized for treatment. This causes the implementation of Data Analytics in manufacturing to be less effective. The aim of this research is to provide strategic recommendations in the form of a priority sequence of success criteria that can be used by stakeholders in manufacturing companies to be able to implement effective digital transformation. Determining the priority of handling obstacles in the implementation of Data Analytics is a Multi-criteria Decision Making (MCDM) problem, the AHP method is used in this study to obtain priority success factors which are the basis for strategic recommendations in increasing the effectiveness of Data Analytics implementation in manufacturing. From the research results, it was found that the top 3 success factors were Effective data driven communication (People & Management), Technology & Infrastructure Integration (Technology) then Training & Upskilling (People & Management).

Kata Kunci

Smart Manufacturing; Data Analytics; Analytic Hierarchy Process

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