Lately there has been considerable attention placed on gathering and analyzing manufacturing intelligence, from both an operational and process perspective. The process of extracting this data can be complex, especially when data is accessed from multiple sources, including machine controllers, manufacturing execution systems and, quite often, multiple databases. It is typically necessary to establish a series of integrations and/or user interfaces to achieve a holistic view of your operations intelligence. If it is possible to establish a unified data model, the task of data aggregation and reporting can be greatly simplified. Further, this type of architecture can actually improve your operational performance.
Simplified Reporting and Analytics
Significant time and effort is typically involved when building reporting application interfaces, especially when working with legacy or home grown systems. Those who have performed this task understand both the complexity as well as the fact that it is nearly impossible to have consistent reporting without addressing the challenge of data unification and standardization. It is only after these obstacles have been cleared that you can then start building consolidated reports and dashboards.
Alternatively, if you start with a standardized manufacturing execution system that already shares a common data model implemented at every site, this challenge goes away. With a common system, your data are already standardized and updated in real-time. As new processes are built or existing ones are changed, uninterrupted access is maintained to accurate, timely manufacturing intelligence, leading to better information to make better decisions.
Better Operational Performance
Now let’s think about information management from a different perspective. With disparate systems and databases, you have people performing production, warehouse, quality and maintenance operations autonomously, in virtual “silos,” using and maintaining their own, separate data. While reporting may aggregate this data daily, real-time access to a co-worker’s quality test, maintenance schedule or other process may be hidden for hours or days. Manufacturers using isolated systems (MES, LIMS/QMS, WMS/LES, etc.) are placing enormous pressure on their IT team to build and maintain a number of Enterprise Manufacturing Intelligence (EMI) interfaces between each of these systems. As updates are performed in one system, a number of changes may then be triggered in other systems, in an attempt to maintain data integrity and consistency. This approach never seems to work as well as we would like.
Those that have implemented a platform-based manufacturing execution system that shares a common data model will not experience these integration challenges. As each plant and department share a common data set, any time a new process is modeled or a data collection process is modified (such as quality metrics from a quality control test), results are instantly available. This information can then be acted upon immediately by any department or employee – across your enterprise, in any geographic region. By removing EMI integration challenges, IT is no longer a constraint to achieving real-time visibility of important operational data, simplifying not only operational challenges but reporting ones as well. Any change made in a manufacturing process can be done once, with the resulting update instantly reflected in future reporting.
There is an increasing urgency to create an agile enterprise. Considerable resources have been invested to improve the visibility of manufacturing operations. Why not consider a different approach that starts with a common data model? Not only can this strategy eliminate many reporting and analytics integration challenges, but further, can offer a path to improved operational performance on an ongoing and continuously improving basis.