As Industry 4.0 continues to mature across manufacturing enterprises globally, many assume that new technologies will replace old methodologies to spur continued improvements in efficiency and production. And while that may be the case for some applications, it’s also true that these new technologies can complement existing practices.
One such methodology that aligns exceptionally well with the value offerings of Industrial IoT applications is that of Total Productive Maintenance (TPM). Built on the 5S foundation popularized by lean methodology, Total Productive Maintenance is a maintenance model that helps alleviate downtime and improve production. In this model, downtime for maintenance is included in manufacturing as part of manufacturing scheduling. Operators and technicians, those most familiar with the equipment, are given responsibility for certain aspects of regular and preventive maintenance as part of their responsibilities.
Total Productive Maintenance aims to improve productivity, efficiency, and safety while building a culture that includes basic maintenance into standard operating procedures for line staff. This consists of eight pillars of activity:
So how does Total Productive Maintenance align with the value offerings of the Industrial IoT? Rather than being a methodology at odds with new technology, it’s easy to see that programs such as Total Productive Maintenance are almost tailor-made for IIoT. A strong and comprehensive Industrial IoT platform will offer several things:
Shop floor visualization is important at multiple levels of the organization, from technicians to plant managers.
At MachineMetrics, the goal of the data is to provide you with real-time visualization and analysis that fully aligns with all three. And data is what makes MachineMetrics so good at what we do. MachineMetrics helps customers improve their Overall Equipment Effectiveness and manufacturing efficiency and identify bottlenecks in production by driving value through analytics. This improvement drives value through the use of analytics throughout the operation.
One key driver of Total Productive Maintenance is that it uses the Lean methodology tool of 5S. By creating an environment where the workplace is highly organized and standardized procedures, safety, quality, morale, maintenance, and other key performance factors are impacted.
TPM uses the Lean methodology tools of 5S and may often be a part of a Lean implementation. It may also be used for the deployment of IIoT and factory monitoring. Both require a commitment to undertake Total Productive Maintenance as an improvement process whose goal is to be incorporated into the company culture. There are five basic steps for TPM implementation.
A pilot area gives the TPM team something to use as a proving ground. It builds buy-in from employees and allows them to see the benefits at work. Buy-in is reached easier if the implementation team includes staff members from many different roles and hierarchical levels.
The choice of a pilot area will, of course, include specific equipment. One successful trick is to find an obvious, immediate improvement to get a "team win." This win doesn’t fully vet the process but instead builds confidence upon success. It also makes it easier for employees to see the path for more challenging areas of improvement.
Another way to choose the equipment for improvement is to pick a bottleneck. This bottleneck may involve several pieces of equipment and may result in some up-front downtime. But by focusing on an area where production "always" jams, the benefits could be higher.
The final way to select a pilot machine is to focus on the equipment that always seems to have the greatest maintenance downtime. It may be a legacy machine or one with fewer run hours and training. But it’s the headache everyone will enjoy resolving.
Highly anchored in 5S is the restoration of equipment effectiveness to its prime operating condition. Before a TPM program, operators, technicians, and maintenance staff may have created years of "workarounds" to address real and perceived problems. By deploying the 5S tool to the equipment itself, it’s returned to peak condition.
Steps to facilitate this process include before and after pictures. It also requires removing excess tools, equipment, and supplies not required for daily operation.
Organizing tools in shadow boxes or drop-down harnesses and cleaning the area around the machine are also important. And from this, formal 5S logs should be added with an audit step added to the production SOPs to ensure the effort remains in force over time.
Overall Equipment Effectiveness (OEE) is a great tool to establish a performance metric and set targets for improvements. OEE is not one metric. It can be calculated with formulas to measure quality, performance, or availability.
The established goals of the TPM team will determine which one, or which combination, of metrics to use.
OEE tracking is a great way to establish data-driven insights that can help inform managers and decision-makers of future goals for factory automation projects and IIoT deployments. It allows companies to benchmark and see the progress of TPM as the metrics play out over time. And it helps the team see what to work on next for further improvement.
Because unplanned downtime is the largest factor in low efficiency, an interval of two to four weeks is recommended to get a clear picture of the team's conditions and issues. It also helps build consensus for categories of downtime to be used in future automation in production monitoring.
With a reliable amount of data points over time for unplanned stops, the team can begin to work on improvements. The most obvious area to address is the largest source of unplanned downtime on the category list.
Here, other Lean and Six Sigma tools can be used, such as fishbone diagrams, Pareto charts, etc.
Root cause analysis should be a vital component of the considerations. By analyzing through observation, physical and photographic evidence, and production history and comparing those findings to possible causes, the root cause of the problem can be determined.
After changes are made, monitor and record the results. It may be required to run through the process again if the results don't work.
However, it’s also acceptable to run through the process again to try and reach higher improvement levels for changes that did work.
Once the change has been validated as effective, it can be rolled into the proactive maintenance plan.
This maintenance may include heat and temperature monitoring for equipment where the root cause identified heat failure. Or it could involve vibrational analysis for machines that were the subject of high stress or sheering forces.
It’s also at this stage that the maintenance interval should be established. This interval should be based on run time rather than calendar dates.
And records and charts should be kept to identify realistic intervals for proactively replacing wear parts or establishing predicted failure rates. These procedures, once mature, can also be added to a production monitoring IIoT software program later to optimize the system further.
TPM is different from traditional preventive maintenance. In traditional maintenance programs, a time-oriented list is utilized based on OEM recommendations. This maintenance method doesn’t factor in production materials that put an aggressive or mild strain on equipment reliability or condition, meaning that equipment malfunctions sooner than expected or much longer than expected. One adds cost in the form of breakdowns, and the other adds cost in replacing parts that are still in optimal working order. MachineMetrics helps customers with advanced machine learning, deep analytics, and intuitive and customized dashboards that allow operators, technicians, and managers to act in real-time at a level previously impossible.
Traditional maintenance isn’t predictive. It doesn’t have the ability afforded IIoT technology to schedule the maintenance in conjunction with changeovers or other scheduled downtime to reduce overall downtime. And being matrix-based rather than analytics-based, traditional maintenance relies on pre-set time to perform maintenance. We've seen this time and time again at MachineMetrics. Our software brings the benefits of Industrial IoT to the production floor, allowing customers to develop dynamic, data-driven maintenance programs that save time and money.
Intuitively, the effectiveness of TPM is made possible by the value offerings of the Industrial IoT. By providing machine performance and health data, the IIoT allows for accurate and actionable capture of information that makes the full realization of TPM possible. In doing so, the potential of the "Hidden Factory" is unlocked, and digital lean can be realized through the use of IIoT technology by building on lean principles.
Header image source: Unsplash
47 Pleasant St, Suite 2-S, Northampton, MA 01060