The critical role of IIoT in the future of food processing

Michael Cahill, Rockwell MES Technical Consultant

As the old adage goes, an ounce of prevention is worth a pound of cure.

New Zealand food companies, that often produce high value-add product that is exported around the globe, value their brand and reputation.  Things are changing.  Customer expectations for food safety and quality are unprecedented.  Expectations from regulatory authorities are increasing to meet ever growing market regulation.  Demand for high safe products is driving growth at an unprecedented rate.

The reality is the number of food recalls has been steadily increasing over the last decade, according to FSANZ’s 2009-2018 reporting. The lack of visibility in the early stages of production is leading to an increased amount of inedible product and is posing serious food safety compliance risks.

With headline after headline, product traceability from paddock to plate is becoming more and more important.

Any deviation in quality in the product line will involve significant wastage and financial burden on the organisation.

A sustainable approach to maintain the highest standard of production quality is sought to meet these customer, regulator and growth challenges.

Connecting the dots

The rise of the Industrial Internet of Things (IIoT) has emerged as a solution to this problem. IIoT platforms and advanced analytics now enable users to fuse multiple data sources to provide contextually relevant historical and real-time data.

Add in AI and machine learning and food manufacturing is more predictive and intelligent than ever before, giving workers uncanny insight into how to solve issues and predict problems before they happen.

The problem isn’t a lack of data but a lack of connectivity from food companies. IIoT has provided the tools to better verify quality, establish batch genealogy and capture meaningful processing data from beginning to end. It’s now the job of manufacturing plants and farms to embrace these technologies.

It awards us real-time operational visibility and the brilliance of predictive maintenance. Predictive maintenance is a technology that has grown tenfold in the manufacturing sector over the years, guided by major developments in AI, Big Data Analytics and IIoT collectively.

As a tool it can trigger a work order before any downtime needs to occur and has an unprecedented ability to detect failure early.  The software uses advanced analytics to effectively become a crystal ball, giving companies a predicted future and prescripted action simultaneously.

Additionally, advancements in anomaly detection can identify the dips in performance that require immediate attention. Deploying technologies like this early in the production stage is exactly the kind of tool that could have prevented the salmonella outbreak in our supply of eggs.

The food plant of the future is undeniably an interconnected one, empowering food processing facilities with access to real-time data that can effect real change and dramatically improve quality.

It is an exciting time and many food manufacturers are still learning about these capabilities and what they can do with all of this new data at their fingertips.

And it’s imperative that we do use this information. The food sector is a sector where in many cases something going wrong is not simply a problem but a calamity. Ignoring garbage-in is a timeless recipe to have garbage-out.

Today’s advancements in IIoT technology and paperless manufacturing techniques can stop food recalls by preventing a faulty product from leaving the plant in the first place. Historically, a company may not know it has a bad batch until it is packaged because of the delays in manual reportage. Preventing recalls is critical for the health of the consumer, the company reputation and the bottom line.

‘Visibility of all manufacturing actions and statuses will ultimately drive accountability and transparency that will change internal and external expectations’.

 

 

Share this:

Leave a Reply

Your email address will not be published.