![]() ![]() ![]() New developments in certain domains like mathematics and computer science (e.g. ![]() for quality improvement initiatives, manufacturing cost estimation and/or process optimization, better understanding of the customer’s requirements, etc., support is needed to handle the high dimensionality, complexity, and dynamics involved (Davis et al., 2015 Loyer, Henriques, Fontul, & Wiseall, 2016 Wuest, 2015). Overall, it can be safely concluded, the manufacturing industry has to accept that in order to benefit from the increased data availability, e.g. distract from the main issues/causalities or lead to delayed or wrong conclusions about appropriate actions (Lang, 2007). However, it has been recognized that much information can also propose a challenge and may have a negative impact as it can, e.g. quality-related data offers potential to improve process and product quality sustainably (Elangovan, Sakthivel, Saravanamurugan, Nair, & Sugumaran, 2015). This increase and availability of large amounts of data is often referred to as Big Data (Lee, Lapira, Bagheri, & Kao, 2013). Industrie 4.0 (Germany), Smart Manufacturing (USA), and Smart Factory (South Korea). Different names are used for this phenomenon, e.g. sensor data from the production line, environmental data, machine tool parameters, etc. These data compromise a variety of different formats, semantics, quality, e.g. The manufacturing industry today is experiencing a never seen increase in available data (Chand & Davis, 2010). ![]()
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