Digital Twin and IIoT in Optimizing Manufacturing Process and Quality Management
otherwise analyze with data from other systems . Analytics , if any , often requires manual work that are technically involved , time-consuming and unreliable , and remain to be ad hoc , incomplete , inconsistent , usually carried out too late to be useful .
• At ISA-95 Level 3 : the existing application systems tend to be built over different periods of time , often sponsored by different departments with a strong focus on solving specific domain problems such as equipment maintenance without enough thoughts given to forming holistic view and considering the interaction of different aspects of the overall operations . Worse still , these systems were contracted out to third-party software integrators who built these systems independently , often with completely different designs and data models supported by different technology stacks with few functions exposed as APIs . Consequently , these systems operate on their own and has little integration with other systems , forming stacks of independent application silos and unreachable and reusable data islands . Furthermore , there are domain areas that are not yet covered by modern software application systems ; thus , its operations still require manual data entry and tracking .
• Between ISA-95 Levels : few Level 3 application systems have comprehensive connectivity to the Level 1 and Level 2 systems making it difficult to obtain near real time data for advanced analytics in and across the application systems .
At present , the production operation management of steelmaking have been established on the basis of automation systems and IT application systems as briefly described above . However , there exist connectivity and integration barriers in the current systems preventing further improvement in operation efficiency from realization . These barriers in a major part are resulted from the technical challenges outlined above , between the production operation management application systems and the equipment automation control systems ( PLCs and SCADAs ), and among the application systems themselves .
The first type of barriers exists between the IT application systems and the automation systems because the PLCs and SCADAs are not nearly sufficiently connected to the application systems , causing many application systems separated from , or only weakly linked to , the physical reality of the production environment where equipment is in operation and products are being processed . The result is that the data in the underlying automation systems has not been fully collected and utilized , making it almost impossible to gain transparency over the status of production processes leaving alone the possibility for data and analytics-driven management of the operations .
The second type of barriers exists among the applications systems in various applications domains ( e . g ., process , quality , equipment , energy , production planning and execution ), and in various production processes and lines , ( e . g ., those described in the steelmaking processes ). This is largely the consequence of history in that the technologies of the past were not conducive to enable interworking of various applications in the very complex environments such as those in steelmaking . This leads to the situation in which application systems in different domains , or in
IIC Journal of Innovation - 63 -