Process improvement techniques are essential for optimizing manufacturing tasks. And when done right, these optimized processes lead to greater efficiency. But what is the connection? And why is process optimization so crucial for achieving greater efficiency?
The answer to this lies in the effectiveness of an activity. It's not enough to have many things being produced. The tasks, checks, adjustments, and motion required for production must be orchestrated to find the most effective use of time and resources with the least amount of input.
Many companies describe this as “a path to continuous improvement”. And in the case of most manufacturing operations, that path centers around the machine. People, such as operators, mechanics, and technicians, interact with their machines out of habit and training. Most of their efforts are aimed at ensuring the maximum amount of uptime. But whether data is manual or electronic, the effectiveness of their actions is what counts. And the act of making adjustments that make that interaction more effective is called process optimization.
Process optimization consists of making improvements across several critical areas. Each of these areas cumulatively adds up to more efficient processes and more significant outputs with the least resources expended. By engaging in process optimization, manufacturers can achieve:
Downtime is the bane of every manufacturing manager's existence. And many spend much of their time managing its causes, as well as dealing with its consequences By taking a data-based approach, companies can reduce downtime and increase the overall uptime of their equipment.
One way to accomplish this is by analyzing and ranking the top reasons for unplanned downtime. This information can then be used to adapt or adjust processes to reduce or eliminate many downtime events. Because the list is in rank order of worst to least, the most egregious culprits can be tackled first.
The top downtime reasons are analyzed in the MachineMetrics Downtime pareto chart.
Once uptime has increased and stabilized, managers can methodically proceed down the list, changing process parameters as needed to bring each item in line with requirements. However, the key to this list and ranking is clean, clear data that helps prioritize what needs attention first.
Sometimes, the problem isn't the machine; it's the upstream process feeding the machine. By capturing data to visualize this, changes in WIP flow or other feedstock can be implemented to improve uptime. Another example is training, or lack thereof, with operators potentially in the wrong position at the wrong time to clear alarms and reset the machine as needed.
Again, data becomes critical in helping leaders develop better training to ensure operators are never out of position. The point is that optimizing processes improves response time at the machine level when problems occur. Using data to identify these areas, several processes can be optimized at once for a multiplier effect on uptime. Training, workflow, equipment layout, material quality, and many more issues can be optimized with clearly visualized data.
Further reading: How to Respond to Downtimes Faster with MachineMetrics
Traditional maintenance programs rely upon reactive measures. Either the equipment was allowed to run to failure, or preventative maintenance was used to keep the equipment running well.
But preventative maintenance is built on time-based assumptions. It assumes that belts and pulleys will break at a specific time based on broad averages assigned by the original equipment manufacturer (OEM). But this doesn't account for industrial equipment where the product produced is light-duty, allowing for longer lifespans for parts. And it doesn't account for heavy-duty production where parts may wear out faster than planned. In the case of the former, money is spent when it could be deferred. In the latter case, downtime may occur when it’s least expected.
Automated data collection can improve the maintenance function and increase uptime of equipment. By enabling condition-based monitoring, maintenance can be either prescriptive or predictive based on actual conditions. By deploying advanced sensing systems, data can help companies understand the current real-time state of equipment, and over time, deep analytics can help predict failures accurately and schedule the replacements at a time when it makes the most sense, such as changeovers or shutdown periods.
Further reading: The Different Types of Maintenance in Manufacturing
The goals for process optimization should include several areas:
For manufacturers looking to improve their processes, there are many solutions available:
Traditional tracking for process optimization usually meant manual paper-driven sheets and data entry into Excel. This was time-consuming, error-prone, and hard to maintain. Plus, analysis was typically based on human insight, which could be biased or wrong. This is why companies are starting to go paperless.
MachineMetrics dashboards display real-time production data that has been automatically collected from manufacturing equipment across the shop floor.
Collecting real-time data and contextualizing it for visibility by managers and operators allows insights not possible in the past; insights that can allow a proper understanding of production progress and equipment performance on the shop floor. With dashboards and relevant production analyses and reports, processes can be changed faster and result in greater efficiency.
Further reading: Manufacturing Data Collection: The Key to Optimizing the Shop Floor
Every manager knows what a bottleneck is. And most can identify a few critical areas in their operation where these blockages occur. But just as real-time data provides insights to optimize the process at a machine level, so too can it lend insight into bottlenecks that may or may not be obvious. Human operators may assign a backup to one cause when data may indicate that it is another.
With real-time data and analytics, machine and process data can identify bottlenecks and constraints within the entire ecosystem. This data allows operators to track processes to focus on those bottlenecks that cause the most downtime. Whether the blockage is a physical constraint or an operational constraint such as scheduling or missed opportunities to improve setup time and reduce changeovers, cloud-based data empowered with analytics and OEE software can free up the disruption.
A machine downtime analysis is a useful approach for highlighting the areas that need to be addressed immediately. By utilizing captured downtime data by reason, managers and operators can start with the worst offender. But the key to this analysis is the ability to access and query the data, thus understanding the cause.
Analyzing downtime reasons at the machine level provides a granular level insight into why specific equipment may be causing problems. Pictured is a "Downtime by Machine" report from MachineMetrics..
The use of Pareto charts, percentage of unplanned downtime, high and low performing shifts, and operators, Mean Time Between Failure, Mean Time to Repair, and other tools can be gleaned from the data through dashboards and used to develop strategies to reduce or eliminate the cause and optimize the process.
Perhaps one of the most valuable tools when using data-driven software, predictive analytics can supercharge your processes. This software uses machine data to diagnose and predict failure. And because it can connect all equipment within a shop floor, predictions on the entire ecosystem can be made, and solutions deployed more quickly, if not immediately.
Machine algorithms detect patterns that humans simply cannot. This allows proactive intervention before problems occur or well-planned maintenance and changeover action when they do occur. These predictive analytics can even be used to extend tool life by monitoring tool wear and predicting when a tool will fail. Custom applications may even allow automated and semi-autonomous solutions to be made at the machine level to free up operators for other tasks.
Using the MachineMetrics Industrial Data Platform, manufacturers can optimize their processes like never before. This optimization is achieved in a variety of ways:
Manual tracking of data is inherently flawed. While well-intended, paper tracking can be error-prone. It is also challenging to keep up, and gaps and missing data are common. Furthermore, it's likely that the data will be rounded and, therefore, will be far less accurate.
Manual tracking often requires data entry into Excel or some other spreadsheet to make some sense of the data, meaning the data is already outdated by the time it reaches the hands of those who need it. But the depth of that analysis is limited. And human interpretation can be biased.
With MachineMetrics, operators, managers, and other key stakeholders have immediate visibility with real-time, accurate machine data. Because data is in real-time, they have fingertip access via tablets remotely or dashboards at the machine and above the shop floor to understand the actual cause of the failure.
The dashboards and reports are intuitive, providing quick insight into the cause of the downtime and providing quick answers for action.
With MachineMetrics software, machine data can be used to trigger workflows. These powerful tools ensure that the correct information is sent to the right person or automated system for action. These actions take the form of incidents, notifications, or webhooks, and each can optimize processes by enabling fast action when issues arise.
Further reading: Top 10 Workflows for Manufacturers
An incident may trigger an email or text notification to the right person. In the past, operators needed to rely on visual cues, phones, PA systems, or other communication methods to notify others of a problem. Often, the person informed was the wrong person for the task. And in many cases, the operator may not know what the problem is. With incidents, the correct issue is reported saving time to restart or repair.
Notifications allow communication immediately, saving time and reducing the number of steps to identify and solve the problem. If a supervisor knows the message indicates a lack of feedstock, they can skip the trip to the machine and proceed directly to the bottlenecked process upstream.
Webhooks take the level of action even further. These tiny packets of data may be used to trigger a remedial action by the machine itself. Or they may be used in conjunction with a computerized maintenance management system (CMMS) to notify maintenance teams automatically, check stock for the spare part and order its issuance to the technician, speeding repair and restart.
MachineMetrics can empower a manufacturer to utilize accurate machine conditions to develop condition-based, predictive, or prescriptive maintenance programs. The health of equipment can be assessed at any moment, and operators and managers can save time and maintenance costs by leaving behind reactive maintenance programs.
The result is a vastly improved Overall Equipment Effectiveness (OEE) that reduces downtime and helps improve processes by adding strategically designed maintenance strategies into the value stream. This data is then added to the analytics to allow even more precise optimization of all processes.
Get instant access to machine conditions, health, and performance to diagnose and resolve problems, and drive higher machine utilization.
MachineMetrics allows manufacturers to harness the power of data at the machine and factory levels. With a powerful range of Edge devices combined with an AI-enabled data platform, MachineMetrics can supply apps or help you build your own to drive actionable insights based on real-time data and conditions to optimize processes in any environment. Contact MachineMetrics today so they can show you how to get the most of your data.
47 Pleasant St, Suite 2-S, Northampton, MA 01060