Driving Efficiency by Connecting Machine Data
An MES empowers metal formers and fabricators to connect their islands of information. It effectively serves as an operational hub, pulling data from individual machines and contextualizing that data across the shop floor to produce actionable insights. It also provides real-time performance reporting, improves communication between devices and eliminates manual intervention for routine production analysis. Using an MES in this way delivers several advantages, but four top benefits stand out:
- Improved real-time decision making. An MES centralizes machine-performance and process metrics, so key parameters can be monitored anywhere. This empowers managers to pre-emptively act on trends rather than react to breakdowns or inefficiencies. For example, a centralized MES can alert teams if multiple machines are running below standard output, allowing them to act swiftly to avoid missed deadlines.
- Streamlined scheduling. Dynamic production adjustments become feasible when an MES ties machine availability and productivity data directly to enterprise job orders or schedules. Consider how an MES can identify which machines operate below standard and recalibrate schedules to keep projects on track—even with delayed performance. An MES also can provide “runs best” data on machines and employees, helping managers to allocate and schedule resources for different production runs.
- Enhanced resource management. An MES tracks job-specific processes and correlates this data with material consumption and tool wear. For instance, some metal alloys and lubricants can accelerate wear on tooling. In this case, the MES provides a quantified history that managers can use to alter strategies for maintenance and performance expectations.
- Comprehensive reporting. Real-time reporting supports both immediate operational needs and long-term analysis, including identifying recurring inefficiencies and improving compliance with audits in heavily regulated industries.
Driving Predictive Maintenance and Quality Assurance
We’ve reviewed the general efficiencies gained by integrating an MES with smart machines. Now let’s examine how metal formers can apply MES and smart-machine data to improve two critical functions in manufacturing: predictive maintenance (PM) and quality assurance (QA).
Machine failures impose a significant cost on manufacturing operations—from added costs for replacement parts to delayed deliveries, dissatisfied customers and lost revenue. PM, facilitated by an MES, eliminates surprises by monitoring critical equipment variables to identify anomalies, anticipate maintenance needs and provide early warnings. Let’s look at five ways that an MES enables PM.
1. Centralized monitoring of critical parameters. Every machine operates with specific variables that serve as indicators of its performance. Examples of such metrics include motor operating temperature, hydraulic pressure, input amperage and vibrations. A machine may show increasing motor amperage over weeks of operation. On its own this trend could go unnoticed, but with an MES tracking and analyzing the data managers quickly can identify that the machine is straining beyond its usual operating conditions and requires corrective action.
2. Detecting trends and anomalies in real time. An MES does not just collect data; it contextualizes and analyzes the data. By comparing real-time metrics to historical baselines and performance standards, an MES can immediately flag anomalies. Consider a stamping press producing parts with inconsistent dimensions. Process monitoring might reveal that motor temperature and amperage draw have started fluctuating, which could indicate worn tooling and varying blank thickness. Acting swiftly based on these insights prevents further defects and unplanned downtime.
3. Scheduling maintenance at optimal times. A planned maintenance schedule ensures routine checks on machines, but an MES takes this one step further by enabling dynamic, condition-based maintenance. If vibration sensors indicate unusual stress on a machine, for example, the MES can signal immediate inspection or maintenance. In addition, the MES can connect this alert with the production schedule to identify the least disruptive time to complete the repair.
4. Storage of key historical data for analysis. An MES continuously builds a historical archive of equipment performance, linking data to specific tools, jobs and conditions. When failures occur, this data becomes a powerful resource for root-cause analysis to determine, for example, if downtime may be linked to certain materials or job types, or if running a harder material led to earlier wear of specific machine components.
5. Detecting interplays across systems—PM with an MES is not limited to individual machines. The system draws data from across the shop floor, making connections between equipment-performance data and broader operational variables. For instance, an MES can identify environmental factors, such as shop-floor temperature causing inefficiencies, or even pinpoint how personnel shifts affect machine outputs. This holistic oversight reduces guesswork in troubleshooting.
Improving QA with Holistic Data
Traditional QA often relies on post-production inspection, which only acts as damage control. An MES empowers manufacturers to monitor parts in real time and confirm that they meet specifications throughout the production cycle, preventing defects from reaching inspection lines in the first place. Let’s review four ways that an MES improves QA.
Monitoring parameters against specifications. At the heart of real-time process monitoring is the ability to continuously compare production metrics against predefined recipes. If an MES detects deviations from acceptable thresholds, it triggers alerts that allow line operators to intervene.
Reducing dependence on manual inspections. Automated monitoring reduces human errors during inspection. An MES ties data from machines to parts, giving managers confidence that every unit produced adheres to quality standards.
Detecting trends and preventing failures. An MES goes beyond spotting individual defects, to help managers identify trends over time. For example, while changes in vibration levels of a stamping press may not immediately lead to defects, it could signal worsening conditions.
Providing holistic data for quality analysis. An MES connects shop floor data with enterprise-level systems (such as ERP software) to provide a comprehensive view of the production process, linking quality issues with associated jobs, materials or even operators.
Integrating Smart Machines with an MES: Best Practices
Metal formers planning to integrate their smart machines with an MES should start by developing a scalable and flexible roadmap to optimize their return on investment (ROI). Consider the following seven steps in creating such a roadmap.
1. Define integration goals, identify the metrics and processes that bring the highest value, and focus the company’s early implementations there. Not all parameters are equally relevant. Choose the parameters that have the greatest impact on the process and machines being monitored.
2. Consider which machines to integrate. Integration has a cost, and not all machines will hold equal value for a manufacturer’s specific needs. Evaluate the role and performance of the machines on the shop floor and consider how they support the company’s objectives in order to determine which machines should be integrated with the MES.
3. Evaluate connectivity options, recognizing that machines must have some form of communications technology in place to connect with the MES. Ideally, a machine will support standard communication protocols such as OPC Unified Architecture, Message Queuing Telemetry Transport or MTConnect to streamline the MES integration. Two other factors to consider:
- When buying a new machine, ask for the communication add-on as part of the initial purchase. While it is possible to add a communication card later, it may cost as much as double the price to do so.
- Some older machines on the shop floor may rely on older communications technology. In that case, an MES provider should offer support for integrating it.
4. Start small. Integration doesn’t happen overnight, especially for companies that operate equipment with varying ages and types. Pilot an MES integration with the most critical production lines or on machines having the highest downtime costs. Then, focus on two or three of the critical metrics identified earlier in order to show a quick ROI from the integration.
5. Establish cross-functional alignment. Collaboration between engineering, production and quality-assurance teams is essential. Gain their input to select critical data points, narrow down valuable metrics and ensure buy-in across the organization.
6. Monitor and optimize. Use historical data collected by the MES to track performance against key impact metrics such as reduced downtime, improved lead times and reduced scrap. The data collected can be used to scale usage, improve processes over time, and develop predictive and automated workflows that continuously refine operations.
7. Future-proof implementation. On the technology side, you may need to avoid using a system-specific solution offered by an equipment vendor that may limit flexibility, especially for diverse machine fleets. And, for data collection, consider maintaining more data in the MES than current usage requires. This strategy runs counter to the traditional best practice of storing only relevant information, yet recent developments in artificial intelligence provide opportunities to extract more value from data that no one could have anticipated even a few years ago.
Establishing Competitive Differentiation for the Future
The integration of an MES with smart machines offers advantages that extend far beyond immediate savings and efficiency. Such a strategy strengthens long-term positioning by enabling metal formers to set new benchmarks for quality, adapt to fluctuating customer demands, overcome operational silos and identify new opportunities for growth and innovation.
For any metal manufacturer still relying on reactive measures or struggling with isolated systems, the message is clear: Integrating an MES with smart machines is an investment not just in technology but in the future sustainability and performance of your business. MF
Industry-Related Terms: Alloys,
Blank,
Case,
Checks,
Draw,
Form,
Forming,
Lines,
Scale,
Scrap,
Shut Height,
Thickness,
Ties,
Forming,
Forming,
StampingView Glossary of Metalforming Terms
See also: Dassault Systemes Delmia Corporation
Technologies: Management, Sensing/Electronics/IOT
By using MES software to aggregate machine data and combine it with other operational information, metal formers can obtain actionable insights across the business. This, in turn, helps them to optimize efficiency, enhance quality and improve decision making. Let’s review the risks smart machines pose in creating information silos, as well as how an MES reduces islands of machine information and ensures a cohesive approach to data-driven decision-making. We’ll also explain how this approach can improve two critical operations: maintenance and quality. Finally, we’ll examine best practices for getting started.
