When trying to improve an industrial or manufacturing operation, the complexity can sometimes be overwhelming for teams. The intricacy of many modern processes makes it challenging to discover the pain points and bottlenecks holding back an organization. And it can often be just as tricky to triage which problems should be addressed first.
When faced with this challenge, even highly-skilled teams can find it tough to improve productivity or discover new efficiencies. Fortunately, Management Execution Systems (MES) are here to address this very concern. An MES can flag the easiest ways to increase operating efficiency by analyzing data from across an organization’s entire manufacturing operation. Critically, an MES can also prevent problems that may threaten your efficiency by improving your approach to predictive maintenance, building resilience to supply chain crunches, and better understanding what drives productivity among employees.
In the last four parts of this in-depth series on the benefits of MES, we’ve defined these systems, explored how they optimize existing processes, discussed best practices for using an MES, and explored some common pitfalls they help resolve. This fifth blog will cover what an MES can do to help you predict and prevent issues that may hinder your operating efficiency.
Statistical Process Controls for Prevention
An essential part of an MES solution is Statistical Process Controls (SPCs). SPCs use statistical modeling and analytics to identify limitations in an industrial or manufacturing process. Once it determines the exact bottleneck, an SPC can suggest possible remedies to improve operating efficiency.
Further, SPCs leverage organizational data to anticipate issues that may erode your team’s efficiency. This way, an MES can anticipate failure points or problems well before they happen, allowing you to deploy preventative measures.
What does this mean in practice? To answer this, let’s run through an example. Imagine you work at the imaginary Acme Cup Company, one of the country’s leading cup manufacturers.
An Example of Predictive Maintenance
One of Acme’s main problems is conveyor belt failures, which can bring the factory to a halt. Your team has come to accept that while belt failures are inevitable, their timing is also unpredictable. Unfortunately, this means frequent and unscheduled drops in throughput caused by belt breaks. Even if the fix only takes 10-20 minutes, this lost output represents a considerable cost to the company.
What if you could accurately predict when a belt section will fail? With this knowledge, the Acme team could either do a preventive maintenance pass or replace the belt segment outright during planned downtime. This predictive maintenance ability is something an MES excels at via its SPC capability.
An SPC can take stock of historic belt failure rates, analyzing them in tandem with the known age of belt segments and insights into how heavily used they are. From there, the SPC can develop a model to predict when a given stretch of the conveyor belt will fail and suggest an ideal time window for technicians to repair or replace that belt segment. This predictive maintenance regimen can ensure the best use of employee time while minimizing downtime.
Data to Power Preventative MES Solutions
The case of Acme Cups is just one example of how an MES leverages SPCs to implement preventative measures. Others may include improving quality control measures, anticipating supply chain crunches, and predicting seasonal variations in productivity among employees, and more.
An MES accomplishes this by observing the frequency of various events in your operation and spotting any underlying patterns. Over time, the MES can quantify the correlation between particular events in your process and explain their impact on overarching operating efficiency.
What Sort of Data Does Your MES Need?
Going back to the example of Acme Cups and its conveyor belts, what data points would help an MES form a predictive maintenance schedule? Alongside logs of current and historic belt failures, the inputs that an MES would draw from include:
- Length of service of a particular belt segment
- Previous servicing date of a belt segment
- Tonnes of goods moving over a belt segment per day
- Average belt uptime per day
But how does an MES obtain this data? Some of it is derived automatically via integrations with actuators and sensors in the factory environment. However, not everything can be automated.
Some information, such as past service dates, requires manual input from team members. Combining automated data collection with manualized entry helps ensure your MES is a helpful pan-department logging resource for future projects.
How Does an MES Turn Data Into Prevention?
Your MES is focused on discovering correlations between events and underlying factors. The more data points are entered into management executions systems over time, the more statistically significant those uncovered correlations will be.
From there, the SPCs developed by your MES build models to predict when certain events will happen. To be confident in these models and their prescriptions, the team at ICA Engineering is keen on testing their reliability.
By “testing,” we usually mean introducing perturbations or minor errors into a system. This achieves two things:
- It allows us to line the predictions of an MES up with the results of an experiment and see how reliable the SPC’s model is.
- It generates new data points to enter into an MES, reducing the amount of time a team may need to otherwise wait to fine-tune predictions.
Deploy Industry-Leading Preventative Measures With ICA
The team at ICA is proud of our rigor when it comes to fine-tuning the predictive power of an MES. If you’d like to find out how you can improve your operating efficiency, the ICA Engineering team would be glad to help. Contact us to discuss where and how an MES solution can optimize your operations.