A standard-based business repository for on-demand manufacturing execution systems

Design and deployment of an analytic artefact – investigating mechanisms for integrating analytics and manufacturing execution system

“…With the recent focus on BDA, the MES system have been identified as a competitive enabler, as it can store data from heterogeneous systems, enabling real-time data analysis via internet of things, advanced analytics and better information sharing for e.g. flexible manufacturing (Cottyn et al 2011;Weihrauch, Schindler, and Sihn 2018;Jeon et al 2017;Mansour, Millet, and Botta-Genoulaz 2018;Cupek et al 2018;De Ugarte, Artiba, and Pellerin 2009). The data gathered in the MES system can be used to make detailed planning and execution on a per production unit basis, as well as create higher degrees of traceability, and is seen as an enabler of predictive and prescriptive analytics.…”

“…One of the major challenges in integrating analytics is dealing with system and data heterogeneity. Different IT-systems will often have different data models which leads to errors (Mansour, Millet, and Botta-Genoulaz 2018;Saberi, Hussain, and Chang 2017;Cupek et al 2018). This is becoming an even greater issue as both structured and non-structured data, along with real-time data is introduced (Saberi, Hussain, and Chang 2017).…”

“…This is becoming an even greater issue as both structured and non-structured data, along with real-time data is introduced (Saberi, Hussain, and Chang 2017). Several researchers have presented frameworks and models addressing the issue of capturing, managing, and storing real time data for manufacturing companies (Weihrauch, Schindler, and Sihn 2018;Cottyn et al 2011;Jeon et al 2017;Cupek et al 2018;Mansour, Millet, and Botta-Genoulaz 2018;Jiang et al 2007), however the research have not been empirically proven. Omar, Minoufekr, and Plapper (2019) found prerequisites for enabling analytics in manufacturing data, where data must be standardised, aggregated and stored.…”

Integrating Analytics with Enterprise Information Systems: Generating Value from Enterprise Data for Operations and Supply Chain Management Companies

“…The concept of CSF in relation to implementation and assimilation has been extensively researched within the field of EIS, where some main CSF are top-management support, user skill/training, performance evaluation, context-dependent configuration (country, industry, implementation life cycle, culture and maturity [technology, people, systems]), implementing the project as a business project, and having a large budget . A main challenge with the use of analytics and EIS is dealing with both system and data heterogeneity, which stems from different IT systems with different data models (Mansour, Millet, and Botta-Genoulaz 2018;Saberi, Hussain, and Chang 2017;Cupek et al 2018;. Further, this is becoming an even greater issue with the introduction of real-time and unstructured data (Saberi, Hussain, and Chang 2017;.…”

“…Further, this is becoming an even greater issue with the introduction of real-time and unstructured data (Saberi, Hussain, and Chang 2017;. Several authors have addressed this issue by proposing conceptual frameworks (Weihrauch, Schindler, and Sihn 2018;Cottyn et al 2011;Jeon et al 2017;Cupek et al 2018;Mansour, Millet, and Botta-Genoulaz 2018;Jiang et al 2007). Some standards for structuring data have been proposed, such as the ISA-95 standard for manufacturing, but it is uncertain how to use that for analytics Cottyn et al 2011;Cupek et al 2018;Mansour, Millet, and Botta-Genoulaz 2018).…”