smart data

IoT, predictive modelling, smart data solutions, and the global food supply chain

In the digital age of the Internet of Things (IoT), technology and data management can potentially deliver greater efficiency, process management and improvements in food safety across the global food supply chain.

By taking advantage of data science, food companies can improve transparency, be better prepared for pest and weather challenges, avoid contamination and prevent wastage by ensuring optimum food safety levels.

IoT applications: Improving tracking and traceability

Enabling the tracking and traceability of goods immediately improves transparency and benefits consumers at the end of the food supply chain. In these instances, IoT devices and applications can provide support by improving tracking and traceability, providing end to end transparency.

Example: IoT devices and foodborne diseases

In the case of a foodborne disease outbreak, IoT devices enables the food supply chain to provide immediate insights such as where the contamination started, where it originated from, who might be liable and how the problem should be rectified – such as with the removal or correction of the defective/contaminated product (recall) before it can cause any more problems.

Predictive modelling techniques: Forewarning against food safety issues

Recent research from Quocirca (commissioned by Rentokil Initial), which interviewed 400 respondents responsible for the management of food safety and hygiene across the UK, US, China and Australia, found that many companies are already considering new ways of working. These include initiatives such as real-time reporting to assist in the journey to discovering the heart of food safety problems as quickly as possible.

At the same time, the research also indicates that predictive modelling presents a huge opportunity to meet the food safety challenges of the future. Being forewarned of a pest outbreak based on data around the weather forecast and prior infestations can minimise food spoilage.

Example: Predictive modelling and farming

Computational tools that predict pest movements can help farmers preempt outbreaks and manage crop rotations. Similarly, precision agriculture, based on measuring and optimising granular field operations, is a type of predictive modelling technology which increases yield.

Smart data analysis: Improving food safety through hygiene monitoring

Data, when used wisely, can enable effective hygiene management. In food processing, data on employee health and handwashing habits can be used to help employees share verified data with the company directly. This proactively mitigates the risk of spreading viruses and bacteria by preventing workers from reporting for work when contagious.

Within the processing plant itself, hyperspectral scanning can pick up where bacteria and viruses are already present, triggering the use of ultraviolet lamps and wands to treat any surfaces and to help prevent airborne cross-contamination. Any foodstuffs that are contaminated can be identified more easily and either suitably treated or removed before they cause greater problems. This is particularly useful when it comes to providing evidence of compliance and continuous improvement against major food safety standards. The availability of such data means that managers can measure and understand more about their businesses, enabling huge opportunities for improved decision-making and better performance.

Smart data solutions: Preventing food wastage through advanced automation

Efficient use of smart data can even help prevent food waste. Through analysing sales information, weather forecasts and seasonal trends, manufacturers can find an “optimum inventory level”, which they can then use to reduce the effects of food wastage.

Example: Smart data and advance automation in food processing

Technology such as advanced automated warehousing enables food to be stored in discrete specific zones to minimise wastage. Each zone can have highly specialised systems that monitor and manage areas such as pest control and hygiene.

For example, these systems can treat incoming salad foodstuffs before they get to the processing line. In doing so food manufacturers can help prevent infestations of blackfly, snails and other pests without contaminating the actual processing line itself.

The need for collaboration for smart data solutions

Making the best use of data in the age of the IoT naturally requires data sharing, as well as a culture of collaboration between IT departments and data professionals. It will only work if the data is accurate, which requires a willingness to share.

With new technologies such as blockchain, storing and sharing information across a network of users in an open virtual space becomes easier and safer. Allowing users to look at all transactions simultaneously and in real-time, can bring huge advantages for every stage within the supply chain – whilst effective use of data can provide the foundation for a more sustainable, transparent, efficient and profitable future for the food industry.

smart data

Jack Lyons

I joined the Marketing and Innovation team at Rentokil in 2015, and my mind has quickly become accustomed to the weird and wonderful world of pests. Outside of work my main hobby is music, being a huge fan of bands such as Queen and Led Zeppelin as well as being an avid drummer.

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