Flipping the Script for Food Processors via Standardized Production Data Metrics

In the future, Belden said it will employ this data standardization approach to more plants and a wider array of devices.  

Scaling data analytics across plants

While other industries prolong the use of equipment in the field, greenfield plants with advanced analytics and Industry 4.0 technology represent the next step toward digital transformation.  

The food and beverage segment is crossing the chasm from digitizing operations to a digital transformation of operations. “Companies move on to the next step of digitalization, where they try to radically reorient their business processes for safe, reliable, and profitable operations,” says Prabakaran. “This requires the application of technologies that aid or support the domain experts by capturing the knowledge of the experts in the form of digital solutions.”

“The food industry is undergoing a gradual digital transformation,” adds Pennington. “Upstream and downstream processes benefit from connected technologies to solve challenges and aid in track and trace applications, digital documentation for performance verification, and lab record streamlining.”

Remote monitoring can reduce cabling costs in plants, as it has in oil and gas,  and modern MES stacks receive data packets via OPC UA, JDBC, or Modbus.Endress+Hauser

Two key elements in digital transformation for any industry are standardizing essential KPIs across multiple plants and shifting to a software-first perspective for greenfield plants.  

“Twenty years ago, it was solely about getting the equipment up and running,” says Rice. “Now, many big projects are coming in with (operations) standards where you have to reach a certain production level and start applying analytics well before the first project."

“The importance of data governance and foresight in constructing data pipelines is increasing rapidly (in other industries),” adds Pennington. “Getting data from the field to the right people, from the right assets, and in a meaningful context, are the cornerstones for scalable future infrastructure.”

Food processors are standardizing data metrics trends quickly, especially with greenfield projects. What’s helping the food industry is the recent wave of standardizing automation platforms by food manufacturers during the last decade.

This standardization trend also encompasses legacy plants and companies, as well as those with continuous improvement and proof-of-concept projects. “When we go to an oil-seed plant, as an example, we already know the standards and KPIs for baselining its performance against other sites,” says Rice. “We know what the typical production performance profile of an oil-seed plant looks like in the U.S. versus Brazil, Europe, or China.”

One approach to standardizing metrics in operations is Unified Namespace projects for legacy plants. The target metrics are mean time between failures (MTBF), mean time to repair (MTTR), asset availability and planned maintenance percentage (PMP).

“An Unified Namespace approach is helpful in situations where there are disparate pieces of proprietary equipment or a variety of different automation platforms on them,” says Bryon Hayes, Industry 4.0 Consultant at Grantek. Grantek is a system integrator that works in the pharmaceutical, life sciences, food and beverage, and CPG industries.

“One strategy for capturing and standardizing OEE, MTBF, throughput, yield, and efficiency metrics is creating a Unified Namespace with edge computing,” says Ravishankar Subramanyan, Director of Industry Solutions Manufacturing at HiveMQ.

Edge computing and the deployment of more sensors in the field, such as pipeline meters, have increased in the oil and gas, mining, and pure process industries throughout the last decade. Sensors track machine uptime, cycle times, energy consumption, and waste.

HiveMQ constructs models that unify data from multiple sources and create a data model for downtime, such as machine or PLC data indicating when downtime begins and ends.

For large food manufacturers, digital transformation is gaining momentum. This diagram illustrates the levels of autonomy within the manufacturing sector.YokogawaWorkforce innovations and data centric teams

In recent years, food manufacturers have elevated workforce challenges, but investments in automation and equipment have taken a bite out of training programs.

“The oil and gas and chemicals/specialty chemicals sectors are mainly focusing on technology-driven learning,” says Saravanan. “This includes AI-powered training platforms, virtual reality simulators, or AI-powered operation assistance, in line with ISA 106, where transient operation of the operation is handled by a supervisory system.”

In the pharmaceutical industry, there is a push to upskill workers via the International Society of Pharmaceutical Engineers (ISPE) Pharma 4.0 initiative. “Pharma 4.0 is about upskilling your teams, creating, and moving away from the rigid management structure and toward a tiger-team type scenario,” adds Hayes.

ISPE is promoting training and tools to move the industry toward a data-centric workforce. Clean rooms are ubiquitous in the pharmaceutical industry, and this is a training challenge.

“Companies are training operators on new equipment using simulation for aseptic filling due to the sterile envelope inside the machine,” says Hayes.

“Once the sterile nature of the machine breaks," he adds, "it’s much work to sample the environment multiple times until you’ve proven that you’re back to the sterile envelope within the machine.”

In addition to digital twin simulation, pharmaceutical companies are also using augmented and virtual reality technology to conduct training offline.

For food processors, a software-first and standardized data metric approach is taking shape and should persist during business uncertainty. Increased operational efficiencies have always been welcomed by food manufacturers.