1. Plot the process.
Start by drawing a diagram of the system of interest and its core processes. You don’t need to break out a drawing program like Visio yet (though you may want to eventually); a “back of the envelope” drawing that captures the core processes in the system is enough to get started.
2. Identify key components.
Next, identify the key components in each process. Key components will vary by industry, but some key components of many industrial processes that you’ll want to consider are items such as motors, variable frequency drives, sensors and power supplies.
3. Assign metrics for key components.
For each of the key components that you identified in the previous step, identify some “operational metrics”. These are that you can measure that reveal important information about the state of the component.
Good candidates for metrics are information about a component that can be measured easily and that you can gather data for at a single point in time (for example, meter measurements, waveform captures, observations, and photos). Capture relevant quantitative information (for example, a temperature measurement) or qualitative information (for example, the shape of a waveform from a variable speed drive).
4. Take measurements.
Measure and record the metrics of the key components. Capturing this data doesn’t have to be a complicated process. Although you may eventually want to record and analyze component metrics in a spreadsheet or database, you can get started with pencil and paper.
Your experience and training is the best guide for both what and how to measure, (and for more ideas you can review the sidebar) but here are some simple “rules of thumb” for making effective measurements. Start measuring at the power source and follow the current flow. Work towards the furthest point or device, capturing measurements as you go, and at the input and the output of each step or key component. Keep track of key metrics over time and take measurements when the system is running at peak capacity. Measurements taken when the system is not being taxed are of much less value and be sure to look for changes (which can indicate that something may have happened) or measurements that exceed limits.
5. Create a data “dashboard”.
Statistical information such as that which you gathered in step 4 can be a powerful tool for analysis and prediction, but interpreting rows and columns of raw data can be overwhelming. An effective way to deal with this kind of data overload is to decide in advance the range of values for each metric that is OK, suspicious, serious, and extreme.
With data ranges in place, you can create a simple, color-coded status for each component in the system that you can view in a notebook, on a whiteboard, or in a spreadsheet. The simplified, high-level view of the system that results, free of distracting detail, can make data analysis and decision making for complex systems much simpler.
6. Prioritize components for attention, maintenance, and budget.
Now that you have a clear and uncluttered view of the status of the key components in the system, you can prioritize components for attention as needed.
7. Make the decision.
For each component on your prioritized list, make a decision about what to do next: Does it require attention (for example, resizing the motor on the conveyor to better handle the load demand)? Does it need replacement? Should you just keep an eye on it?
Another option is to simply let it run to failure, planning ahead by allocating budget and resources. Objective, prioritized information about key components can be a big help to the decision-making process.
8. Refine and extend, but stay flexible.
When you have a working strategy in place, you can refine and extend it as needed in an ongoing process. As your system strategy evolves, be sure to keep flexibility in mind. Taking a variety of measurements with general purpose, hand-held tools gives your system strategy the flexibility to adapt as processes, components, priorities, and your needs change.