As perishable goods, meat and poultry production must run at a rapid pace. With the need for speed at scale, there is more room for error, globally, tons of carbon is emitted, energy consumption is at a high, and waste, and product loss increases in an effort to get the meat and poultry to the […]
As perishable goods, meat and poultry production must run at a rapid pace.
With the need for speed at scale, there is more room for error, globally, tons of carbon is emitted, energy consumption is at a high, and waste, and product loss increases in an effort to get the meat and poultry to the consumer.
As Canada’s largest prepared meats and poultry producer, Maple Leaf Goods is looking at digital twins to help virtualise its production lines and tackle these issues. The firm is also looking at making its operations more sustainable, eliminating waste and product loss from its supply chain.
To give you an idea of scale, one section of one of Maple Leaf Food’s plants produces about 800 million hot dogs a year, 60 million kilograms of deli meats and wieners, and hosts around 1000 employees in its 500,000 square foot facility.
The firm worked with software firm Cygnus Consulting and analytics outfit Braincube to digitally transform its huge production and create a digital twin of its heritage plant in Hannon, Canada. The process included adding IoT sensors, improving data collection, and leveraging existing data from industrial software supplier Aveva’s MES data-collection technology that it had already acquired.
At the Aveva World conference in San Francisco at the end of last year, Maple Leaf’s senior solutions architect explained what it referred to as the ‘Heritage IoT Project’ which has been three years in the making.
To digitalise, the meat manufacturer first looked at the production of its deli meat line. Typically, once the raw deli meat arrives, it is prepared, brined, stuffed for shape, cooked, chilled, sliced, and then packaged.
“If you look at where it starts in the brining area, through to the stuffing area…and into the slice halls, it was all about making consistent product,” says Blair Hembruff, chief engineer and president of Cygnus Consulting.
During the stuffing process, the meat goes through a machine and turns into ‘logs’, in which an IoT sensor vision system can measure the dimensions so that operators can adjust if necessary.
With consistent shape, the logs will cook at the same time so that they are neither undercooked and unsafe, nor overcooked and lacking in moisture, thus creating product loss.
“Through feeding this data up back up to the digital twin, it was able to come up with optimum recipes for the oven,” explained Hembruff.
Blair Hembruff, chief engineer & president, Cygnus Consulting
Then, once it was established what temperature and time the meat should stay in the oven, it had to adapt to the rest of the production line or otherwise overcook simply in waiting, even if the oven wasn’t on.
“So we built an optimiser to make sure the ovens were scheduled in an optimal manner,” says Hembruff. Which would signal the ovens to cook the meat in time to be moved to chilling, and then slicing.
According to Hembruff, while slicing machines may not seem complicated, in large-scale productions they are rather complex with recipes based on what’s being produced in the life of the machine, and what meat will need slicing at any time, whether it be ham, chicken, or beef.
So how does the firm come up with the golden recipe for the slicing machines? In this case, Maple Leaf collects its slicing information through Aveva’s sensors and sends them up to the data analytics at Braincube, which will present a digital twin dashboard of the shop floor and show real time operations, colour-coded to present any faults or issues.
“So they know exactly when they’re running off spec,” and the Braincube will adjust how the machines work depending on the flow of production.
“It iteratively pulls data up every 10 minutes and keeps refining it,” adds Andrew Thorne, senior solutions architect at Maple Leaf Foods. “Eventually, it comes out with the perfect parameters for that day and that machine on that line”
“Once we started setting machines up this way, we got the perfect batch almost every single time,” he concludes.
The deli line project is actually just one of eight cases in the ‘Heritage IoT Project’, and, according to the firm, it has been responsible for increasing gross profit up by 10 to 12%, by reducing waste such as large end piece losses on the logs; reducing overcooked logs in the ovens; improving slice parameters to minimise loss and energy consumption.
