What happens when MOM systems collect and distribute inaccurate data?

Much of the decision-making in plant operations (pre-shift and in-shift) requires real-time raw data collections to be accurate in terms of timing, complexity of the process and context. If any of this data is delayed, collected out of order or contains errors, the aggregated data may lead to inaccurate decisions for scheduling, resource allocation, product quality, process analysis and performance and planning metrics.

Often production order reporting data is collected manually with little to no enforcement of timely recording. And in general, even when automated or manual data collections are timely and used accurately in the applied operation-level context, the decision accuracy can be lost. How and why?

Operations metrics, events and comparative analysis typically use raw data from 15 to 50 standalone plant systems (Scheduler, Production Track/Trace, Maintenance, Quality, Warehouse, Batch, PLCs, Historians, etc.) all with different database structures. In order to allow for intelligence-based decision-making, each of these disparate data sources must be aggregated into one decision data set. This requires manual analysis and calculation, which is prone to errors.

The above issues affect many decision making metrics or KPIs including: OEE metrics, ERP cycle time standards comparisons, plant WIP inventory consumptions compared to finished goods counts, warehouse counts used for logistics scheduling and full-load shipping, NPI time-to-volume and time-to-margin, etc.

So, to come full circle to the question posed: What happens when MOM systems collect and distribute inaccurate data? It causes bad decision-making!

How do you avoid these issues? Implement a MOM solution that uses independently time-encoded streams (both for storage and retrieval) of sensor data, contexts and events. This allows the system to infer, combine and enrich information based on a model and a context of execution. A MOM solution with this technology ensures that while data is collected in real-time, analysis and calculations remain highly accurate and predictive.

The Industrial Internet of Things: data must be actionable

In a recent Automation World article titled “Constructive Disruption: A Vision of Smart Manufacturing,” Jim Wetzel, Chair of the Smart Manufacturing Leadership Coalition (SMLC) and General Mills’ Director of Global Reliability, shared his definition of smart manufacturing:

“‘For us, it is trending [data] in the cloud for traceability, transparency, and visualization,’ says Wetzel. ‘It is networking the supply chain, and shortening the time to get actionable information on the plant floor.’”

This school of thought – that smart manufacturing, at its core, is about leveraging actionable intelligence – is gaining broader adoption across the industry. Simply using sensors and software to collect massive amounts of data is NOT smart manufacturing. Collecting data, putting it into context and having agile systems in place that can respond in real time to make intelligent decisions IS smart manufacturing.

Preparing for the “Constructive Disruption” that is the Industrial Internet of Things (IIOT), by implementing systems that can make process data actionable is a must for today’s manufacturers. Leaders in the manufacturing industry, like Jim Wetzel and those associated with the SMLC, recognize that the ROI of smart manufacturing can be measured by improvements in time to market, decreases in waste, increased accuracy and efficiency of resource allocation, and better labor management.

“‘The measure of success,’ Wetzel says, ‘is having the ability to deliver value and evolve with changing business using a technology infrastructure that is supportable, quick, and delivers information that can be trusted.’”

Smart manufacturing allows us to automate continuous improvement initiatives by providing a framework for optimizing operations.

Snapping into focus: Visibility across the network

As anyone knows who has suddenly seen the individual leaves on the trees when they get new glasses, visibility is transformative. Gartner recently released its Market Guide for Manufacturing Execution Systems (MES)Software, in which the research and advisory company identified Savigent Software as a "best-of-breed MES vendor." As the Market Guide notes, one of the top reasons businesses cite to justify investing in MES is increasing visibility across the manufacturing network.

The field of “big data,” which looks for useful
relationships hidden in large data sets, is predicated on having a well-organized and clean data set on which to perform the analysis. But without visibility, without uniform access to that data, more than half of manufacturing organizations simply don’t have sufficient insight into their manufacturing processes.

Instead of basic techniques that rely on making corrections based on post-processing measurements, top MES solutions like the Savigent software suite offer innovation that captures internal tool sensor parameters individually and stores them in one highly accessible database. This allows the use of this previously unavailable data in real time to develop and implement state-of-the-art tool control methods far beyond simple statistical process control. Identifying faults and patterns and correcting them in real time means faster production cycles, decreased material waste and decreased energy use.

So an integrated MES solution offers the right blend of sensors, connectivity, data collection, analytics and real-time access to the data to allow manufacturers to develop ground-breaking tool control methods. But far beyond the plant floor, as the Gartner report underscores, MES helps manufacturers really see the big picture, enabling better decisions, reducing errors and driving optimization. 

Going Beyond MES/MOM to Smart Manufacturing

I recently created a post for AutomationWorld, titled "Going Beyond MES/MOM to Smart Manufacturing"  below is an excerpt.

Set your sights on Smart Manufacturing. I know what you might be thinking… “Great, yet another term for the space” or “How does this fit with MES/MOM?”  Click here to see the full article and find out.