IN SHORT
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Implementing predictive maintenance can be done without high upfront investments
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Higher uptime and cheaper service agreements ensures satisfied customers
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Many people don't realize how much valuable data they have
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Predictive maintenance allows for better consideration of risks in operations
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Service contracts can be offered at a more competitive price thanks to predictive maintenance
Not spending a euro if it is not really necessary
The customer's initial hesitation to replace parts prematurely is quite understandable. There is a good chance that some expensive equipment will continue to run for months without intervention. Even if they wear out, use slightly too much energy or are already well past their useful life. In general, the credo is: rather not spend money if it is not really necessary.
After all, keeping a chicken slaughterhouse running is about careful balancing. This business is all about margins of pennies per product. Every minute counts. Not for nothing are service level agreements (SLAs) very common in this world: suppliers of slaughter lines guarantee uptime. If the lines are down, they share in the losses. To guarantee continuity, many parts are in stock at the abattoirs. Examples are the knives for cutting the chicken fillets. If these are blunt or they are at the end of their life, the abattoirs choose a suitable time to replace them. Nuts and bolts, fan belts and other small items are always in stock.
The trade-off is more complicated when the price of spare parts starts to run into the thousands. A catastrophic problem - such as the failure of a crucial engine - can shut down a slaughter line entirely, but keeping tons of expensive spare parts in stock at each customer location is also expensive. "There are complex tradeoffs involved," says Richard Bernards, business manager at Alten. "You can stock everything, but there's no guarantee that critical parts will work when they come out of the box after one or two years."
Hours of production loss was previously taken for granted
Until recently, the machinery supplier was looking for a balance. Cheap parts were kept in stock, while more expensive parts such as motors were only flown in when the need arose. Restarting a slaughter line in such a case may cost hours, but that was taken for granted. Alten suggested to his client that he should start predicting catastrophes with more expensive parts. If a complex system part were to break down, it could be replaced at a time when the slaughter line is shut down anyway. That story proved harder to sell than expected. Bernards: "The main argument was: we're not going to spend money if machines aren't broken."
In the customer's perception, Alten's proposal created a new problem. He was going to incur costs at an earlier stage. Bernards: "They had a different idea of predictive maintenance. They imagined, for example, that they would get a signal the moment an engine had run for three years.
If it was still running well at that time, it would be a waste. However, we saw opportunities to predict the timing of replacement quite accurately."
Alten proposed to start discovering patterns in sensor information from the slaughter line and use it to make statements about whether something was going to break down soon. In recent years the software house had built up a good relationship with the machine builder. Despite his reservations, he gave Alten the green light to delve into the matter and investigate whether there were any opportunities here.
Chicken slaughter lines generate overwhelming amount of data
One of the major advantages proved to be the overwhelming availability of data generated by existing sensors in the respective chicken slaughter lines. For example, hundreds of sensors monitor the quality of drumsticks, wings and fillets through temperature measurements. If the value is too high, the meat is immediately ejected from the line.
Bernards: "There is enough data. A slaughter line, for example, also makes choices and performs actions based on sensor data. Data is also available on power consumption and engine speeds, light barriers see passing chickens, and image sensors provide much-needed information for filleting."
Numerous sensors turned out to be able to provide more information than was needed until recently. For example, each microprocessor is equipped with a sensor that protects the electronics from overheating. If the temperature exceeds a specific value, the microprocessor shuts down.
This information was used to enrich the already available data and thus increase the quality of the predictions. The abundant availability of very diverse data proved to be a huge advantage. Indeed, the accuracy of the prediction lies mainly in combining, matching and comparing the data. If a motor starts spinning faster, it may be that it no longer feels resistance due to the lack of a transmission. It could also be that an internal shaft is broken. "A rpm sensor only tells you that the revolutions are increasing," Bernards says. "To know what's really going on, you need other data as well."
Crux lies in the smart matching of data
However, to properly estimate future failures, smart matching during data collection is not enough. "You can't say: if incident 1 and incident 2 occur at the same time, then you have to stop the machine," Bernards says. "You also have to look at the historical data, recognize patterns in it of situations where catastrophes occur, and use that to estimate what kind of action into the future you need to take." The big advantage was that this customer was already capturing large amounts of information in its latest generations of slaughter lines, centrally on a PC or in the cloud. This eventually led to a system that combines data, learns itself and keeps getting better. From that mash of data, an estimate can be made of the probability of failure. "We train the model to make a recommendation to replace something quickly, Bernards says: "We can't say that something will fail at five past four this afternoon, but we can say, for example, that there is a 90 percent chance that a specific component will fail within the next two weeks."
Predictive maintenance helps to avoid SLA penalties, ensure uptime and keep maintenance costs low. But there is still plenty of room to set priorities. For example, the machine builder can suggest to the end customer that, for a specific amount per month, he guarantee that the slaughter line will run 99 percent of the time. After all, if the guarantee is 100 percent, he must go to great lengths to incur costs and, in doing so, run the risk of replacing parts too early. Bernards: "Predictive maintenance allows you to choose the right time better. But you can still make the trade-off of intervening quickly or going to the edge." Bernards expects the technology to bring benefits to both the machine supplier and its customers. " For example, customers get a discount on their SLA if they take software versions with predictive maintenance, or higher uptime."
TIPS
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In a production process, existing sensors often already provide ample information to build valuable applications. If you also have access to historical data, then it can be a relatively simple step to build advanced applications that directly save money, such as in this case predictive maintenance in chicken slaughter lines
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Choosing to use data for a specific application, as in this case predictive maintenance, often leads directly to new opportunities. In this case, immediate cost savings were realized on maintenance and uptime for customers. As a spin-off, there was more room for maneuver in service agreements.
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