A paper by Greg Cline of Aberdeen Group, “IoT and Analytics,” makes a compelling case for prescriptive analytics. The author’s point is that the more data that is available, such as from Internet of Things (IIoT) devices or other organizational systems, will ultimately drive better analytics performance. Those operating in complex discrete manufacturing industries are aware of the importance of collecting the right data at the right time. This capability is so important, it has a huge impact on elevating quality across production and sustainment activities. It leads to improved decision support based on better data.
The Search for Better Data
Best-in-class firms are eagerly searching to gain access to more data. The rationale is simple. Greater, more accurate data delivered faster contributes to improved decision support, greater efficiency, and improvement to the bottom line.
All the investment in Operational Technology (OT), Information Technology (IT), and Industrial IoT data have been part of this push to perform greater, in-depth analyses. What results is the ability to make better decisions about assets, products, processes, and operations. The benefits are numerous. As one example, by paying attention to parameters about machinery and component functionality, non-optimal operations can be detected and remedied before any unscheduled downtime occurs.
MES is Key to the Digital Transition
Aberdeen Group noted that Manufacturing Execution Systems (MES) on the factory floor form an important part in the hunt for better data. This isn’t a big leap. MES systems have been collecting a wealth of information from the shop floor for many years. When MES is fully integrated with Quality and Sustainment operations, you have a flexible and reliable foundation to drive better data collection. This knowledge then becomes a critical part of your Enterprise Quality Management and Quality Assurance programs.
Ironically, MES systems have been around for decades. Yet they continue to be a critical part of how to improve manufacturing efficiency and performance – now more than ever – as we become ever-more immersed in the digital age of manufacturing operations.
The Quest for Greater Quality
Just as importantly, harnessing IT, OT, and IIoT data with advanced analytics can result in a sharp rise in product quality. When data is more readily available and aggregated together quickly in real-time, it can drive greater value. The conclusions reached by analytics applications make it possible to optimize production, quality and sustainment processes. What results is a reduction in waste and an increase in quality. As well as forming the cornerstone of excellent customer relationships, this has everything to do with cost containment and greater profitability.
The systems of complex discrete manufacturers must be nimble enough to provide pre-configured products when applicable, yet also be capable of dealing with frequent Engineering Change Orders that are complex and often difficult to accurately track. Further, change must be managed quickly to maintain fast go-to-market objectives.
Incorporating IT, OT and IIoT Data
In order to capture the greatest success, analytics applications need to better utilize IT, OT, and IIoT data. This data can be collected from assets, components, sensors, and systems to develop a complete, real-time picture. Manufacturers require status updates on all critical assets. This includes visibility to streaming data about key performance indicators from a huge volume of data, and much more.
When tethered to advanced analytics, asset performance and product quality improves. Alerts can trigger an equipment inspection sooner than previously considered. Contextualized alerts can offer suggestions to managers and shop floor supervisors on what to do next. Analytics can also be used to make suggestions on how to improve Maintenance, Repair, and Overhaul (MRO) operations. This knowledge can then optimize staffing schedules, MRO visits, and other workflows.
Where to Begin?
In the field of MRO, it might be as simple as developing a system to first classify assets as over-maintained, under-maintained or well-maintained. The application of analytics continues to get better over time. Indicators of maintenance status on dashboards can then deliver real value to shop floor personnel.
Operators could then shift setpoints to better mirror actual real-world results. They could then quickly understand if too much time was spent maintaining certain assets at the expense of other assets that might be in danger of failure. From this perspective could come a wealth of new insights that could then be applied to your overall MRO program – letting the cycle begin once again!