Five Ways Augmented Intelligence Is Saving Manufacturers
ERP and MES software give assemblers up-to-the-moment insights into their supply chains, production capacity and product quality
Let’s not sugarcoat this. The world’s manufacturing base is at war with COVID-19 and its destructive, disruptive effects on everything from supply chains to daily production levels and, ultimately, the health of everyone. Getting ahead of this curve will require a much more aggressive approach to using data.
Running an assembly plant on Microsoft Excel alone is not going to cut it. That’s like trying to treat pneumonia with aspirin. It may thwart the symptoms and provide temporary relief, but it’s far from a cure. Instead, more than ever, manufacturers need to augment their intelligence to gain deeper insights, faster, in order to respond to rapidly changing conditions.
For manufacturers, augmented intelligence means applying advanced analytics techniques to data stored in their manufacturing execution system (MES), real-time process and product monitoring, quality management, and enterprise resource planning (ERP) systems to identify trends that aren’t readily visible simply by looking at a spreadsheet.
Think of augmented intelligence as a pre-emptive treatment plan that can thwart the pandemic’s long-term impact on manufacturing. Companies that adopt augmented intelligence to aggregate, analyze and gain value from real-time monitoring can define and monitor leading indicators of supplier performance, production efficiency and product quality.
Significantly, augmented intelligence is being applied by forward-thinking manufacturers today, such as Eldon James Corp., a world-class manufacturer of medical tubing and connection products headquartered in Denver. Using a combination of real-time monitoring, ERP, MES and quality management functionality, the company has up-to-the-moment, accurate insights into its supply chain, production capacity and efficiency, and product quality.
Through augmented intelligence, Eldon James also is able to run lights-out manufacturing across its 28 injection molding machines. Now, in the wake of COVID-19, the manufacturer is meeting the spike in demand for its products by keeping a pulse on suppliers and inventory, as well as running 24/7 operations by combining a fully lights-out shift with two manned shifts.
Why Augmented Intelligence
Eldon James points to the value of augmented intelligence, but let’s look a little closer at why it is needed now more than ever.
Typically, manufacturers have turned to augmented intelligence to solve challenging production problems and see where they could improve. This has meant capitalizing on new insights from the latest generation of smart machines, sensor-based production monitoring equipment, and the real-time process and product monitoring systems they have in place across their shop floors. To pull insights from the vast amounts of data provided by this machinery, these companies have integrated real-time process and product monitoring with their MES, ERP and quality management solutions. This has given them a unified, single system of record from which they can analyze the state of their operations and make data-based decisions.
However, to maximize the value of their data, manufacturers need to delve deeper into their operations than their pre-built dashboards, metrics and key performance indicators (KPIs) allow. More and more, this means augmenting their reporting with the real-time process and product monitoring and advanced analytics, including machine learning, to cull deeper, more timely insights into their operations. In this way, augmented intelligence enables manufacturers to go beyond knowing what is happening and to understand why.
While optimizing production has been the primary driver behind augmented intelligence, manufacturers also increasingly rely on it to react quickly to shifts in market demand, the availability and cost of raw materials, local natural disasters, and other disruptions. This enables the agility to adjust to market demand and gain a competitive edge in their markets while also building business continuity and greater resiliency. With insights based on a 360-degree view of their organizations, these manufacturers are some of the best positioned to gain the pre-emptive, predictive insights they need in responding to COVID-19’s effects on ongoing operations.
Augmented intelligence is not a replacement for human decision-making. Instead, it helps manufacturers to become genuine learning organizations. By embracing augmented intelligence, a company has the foundation for turning its manufacturing operation into one that profoundly values learning, continual improvement, and the willingness to keep questioning how every aspect of production can be improved.
Intelligence in Production
The greater the uncertainty in the global economy, the more certainty every manufacturer needs to have of its operations. COVID-19 is a harsh wake-up call that manufacturers can’t rely on disconnected, disjointed systems or, worse, attempting to run an assembly plant on Excel spreadsheets. Manufacturers that have already integrated their data and adopted augmented intelligence are well-positioned to anticipate and proactively respond to a rapidly evolving situation.
Other manufacturers need to start reinventing themselves now. A great place to begin is by looking at the approaches that have worked for other manufacturers and capitalize on their lessons learned. Here are five of the critical areas where manufacturers are relying on augmented intelligence to improve their operations.
Use augmented intelligence to synchronize diverse manufacturing systems. This is critical to breaking down the data silos that typically hold manufacturers back from greater efficiencies and cost savings.
The first step to improving production operations is to define and implement a consistent, scalable intelligence platform that can capture, aggregate and act on manufacturing data in real-time. Having a unified, real-time analytics reporting system that can pull from different data sources is critical to overcoming the challenges of orchestrating key business processes, such as new product development, across multiple parts of the organization—from design to the shop floor, sales and customer support. Eldon James’ results from this strategy include an improvement from 51 to 98 percent in on-time deliveries and a significant reduction in scrap and manufacturing rejects.
Make real-time process and product monitoring the cornerstones of augmented intelligence. When using real-time data in combination with advanced analytics, manufacturers can gain the timely insights needed to achieve more significant cost optimization, higher quality, production improvements, and optimized maintenance, repair and operations strategies.
Real-time process and product monitoring can also help pinpoint which machines on a shop floor need maintenance to improve production efficiency, product quality, yield rates, and machinery and plantwide utilization rates. For example, using this technology, Vision Plastics Inc. of Wilsonville, OR, is able to fulfill orders from global medical products manufacturers ahead of schedule even as it has grown this segment to represent more than 40 percent of its total business.
Adopt machine learning within predictive analytics applications across shop floors by using real-time process and product monitoring data sets to fuel the development of useful machine-learning models in manufacturing. Machine-learning algorithms are now available from a range of ERP, inventory management, MES and production scheduling software providers. This emerging class of machine-learning-based predictive analytics applications will lead to more efficient inventory reconciliation via more accurate tracking of production time, downtime, total parts created, rejects and parts.
Replace brute-force MES production scheduling with augmented intelligence to optimize production schedules down to the work center, operator and material level. The core components of an MES—including planning and scheduling, bill of materials, finite scheduling, shop floor control, production reporting, and quality—each produce valuable data. Aggregating the data from all of these systems to see how an MES can improve production scheduling and efficiency is possible today. This alleviates the need to take a brute-force, iterative, trial-and-error approach to enhance a production schedule over time.
Apply augmented intelligence to optimize production workflows for highly customized, engineer-to-order products while overcoming the constraint challenges of improving production efficiency. Doing so can help minimize the complexity around synchronizing production scheduling, engineering, and manufacturing to customers’ inbound order requirements. Augmented intelligence can free up manufacturers to manage custom configurations over their life cycle, instead of as a special order or project for which they have to make exceptions.
For instance, Donnelly Custom Manufacturing Co. of Alexandria, MN, has a business model that relies on real-time monitoring and process-unique metrics to keep daily production schedules on track.
“We have 36 injection molding machines; we do between 30 and 40 setups a day; we have about 400 different resins that we use; and we have one person who schedules everything using our manufacturing ERP system,” says Jerry Bienias, vice president of operations at Donnelly. “We never lock our schedule, and we make changes regularly across all shifts. This is possible because our system manages the complexity of the short-run business and makes data on the schedule, material requirements, and inventory available to everyone in real time.”
The greater the uncertainty manufacturers face, the more they need to consider how they can apply augmented intelligence to become more resilient. Only by re-engineering or reinventing themselves by first creating a unified data platform that provides a 360-degree view of all the production operations can they grow competitively stronger in an uncertain world. Augmented intelligence is the path to knowing how every phase of production contributes to the goals they need to achieve now and in the future.