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A Vision for post-pandemic recovery

05 September 2021

The food industry has faced huge challenges in the last year as COVID-19 has changed consumer demands, limited workers’ movements and put financial pressures on food supply chains. Nigel Smith explains why 3D vision systems hold the key to adapting to change. 



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The food industry remains slow to embrace automation. In BCI’s Supply Chain Resilience Report 2021, 41.6% of organisations answered ‘no’ when asked if they have increased their use of technology to map supply chain disruption as a direct result of the COVID-19 pandemic. But, why the reluctance? 

Some manufacturers are wary of change. There are cultural expenses, in terms of adapting workers and management team to new Industry 4.0 systems. And there are opportunity costs, or the likelihood that new digitalised systems will slow things down before speeding things up and properly yielding a return on investment (ROI). 

Aside from internal resistance, manufacturers also face external pressures. They include an increase in the cost of raw material and energy, and pressure from retailers for cheaper, faster production. There are also regulations. 

Many food manufacturers understand that automation is the only way forward. According to research by Gartner, 79% of supply chain leaders think that an internet- or platform-based approach is the most critical new business model to support post-pandemic recovery. But, for manufacturers to take the Industry 4.0 plunge, these systems must demonstrate return on investment (ROI) and compatibility with existing systems and processes. 

It should be noted that resistance to change among food manufacturers isn’t a weakness in relation to Industry 4.0. Actually, it’s a strength. For example, when selecting the type of machine to introduce to a facility, considering the actual needs of the facility and processes will result in a more cost-effective and appropriate machine being installed, boosting potential ROI. It is also crucial for manufacturers to consider what kind of objects a robot will be interacting with.

This is where machine vision systems can play a vital role alongside automation systems, cobots and other Industrial Internet of Things (IIoT) devices. Vision systems do more than just scan food. With tireless automatic monitoring, they can help ensure products meet the standards demanded by legislation and retailers. 

While blind industrial robot –or those without vision systems – can complete simple repetitive tasks, robots with machine vision can react to their surroundings intuitively. Indeed, traditional IIoT systems in general can suffer from blind spots when physically executing processes in a 3D plant environment, leaving human workers to diagnose process bottlenecks or malfunctions.  

2D or 3D vision systems can counter this blindness to better monitor production environments, in combination with artificial intelligence (AI) and machine learning. 2D vision is better suited for situations where colour or texture of the target object is important, like barcode detection.

Any task where shape or position are important, like bin picking, is better served by 3D machine vision. In 3D vision, multiple cameras are used to create a 3D model of the target object.  

Better quality
Shibaura Machine’s TSVision3D system operates in this way. As a result, it doesn’t require complex CAD data to recognise objects. Instead, two integrated and high-speed stereo cameras capture continuous, real-time 3D images. 

The software can recognise any object that’s positioned in its field of vision, even for non-uniform products, like bananas or mangoes, for example. Not only can vision systems fit more flexibly into production lines, the technology is emerging as an effective and profitable way to streamline production, improve quality checking and keep consumers safe from product contamination. 

Nigel Smith is CEO at TM Robotics.


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