Poised to deliver

Simon Noakes explains how AI is transforming ‘traditional’ operations in the food manufacturing sector and introducing important and advanced new capabilities such as demand forecasting, preventative maintenance and advanced quality assurance (QA)

Artificial intelligence (AI) is on course this decade to disrupt end-to-end operations in every industry, with PwC projecting the technology will add over $15 trillion to the global economy by 2030. Food manufacturing will be no exception.

There is rising demand for greater food production in response to world population growth, at a time when health-conscious consumers are also looking for healthy alternatives and a broader range of products. To satisfy these evolving customer demands and ensure production remains at very high volumes without introducing errors into the manufacturing process or failing to comply with industry regulations, advanced technologies such as AI, machine learning and IoT will be required to improve operational efficiency.

With more data being generated within companies than ever before, AI applications in particular are set to transform every area of the food manufacturing process, from more accurate demand forecasting to preventative cleaning and maintenance. Here are six key capabilities that are already being disrupted and augmented by the introduction of AI.

Food QA and segregation
Manufactured food products have traditionally been sorted based on simple observable characteristics such as size and colour, and through visual inspection. Integrating devices such as sensors or cameras with other mechanical devices to mimic what humans can do provides an automated method to validate food products against required standards or sort them based on characteristics.

Using machine learning alongside these devices means granular characteristics can be refined and learned in such a way that AI can support more complex decisions regarding segregation. This results in far less waste for food manufacturers courtesy of more rigorous QA procedures, refined processes and a reduced need for extensive manual sorting.

Preventative maintenance and cleaning
The traditional approach to cleaning in the industry was governed by routinely scheduled cleans and checks, or through manual inspections, which today is being augmented by devices such as sensors and fluorescent imaging. When combined with other recorded parameters, food manufacturers can deploy machine learning to determine the characteristics that pre-empt cleaning activities, and in turn use AI to determine when those conditions are present so they can be promptly addressed.

These AI and machine learning applications can be expanded to monitor intricate manufacturing equipment where constant uptime is a necessity to maintain narrow profit margins. Companies can embrace a preventative maintenance strategy for the first time through accurate, 24×7 monitoring of valuable machinery.

Agile and data-driven food development
AI and machine learning are now helping food manufacturers achieve higher success rates when developing and bringing new products to market without the need for extensive trials and tests.

By deploying algorithms and machine learning based on previous patterns to support future taste preferences, historical taste and preference data from previous products and products changes can be gathered and analysed.

Enhanced product risk analysis
The risks associated with potential product issues and recalls are well-known and carry a very real threat of high costs and damage to brand reputation.

Reviewing and collating data from a variety of sources, such as social media and emails, and then applying machine learning can provide earlier indication of a potential new product issue. Continual review of information and content ensures a more rapid response and the ability to minimise the impact by taking swift remedial action.

Highly accurate demand forecasting
AI can support demand forecasting through the gathering and analysis of historical data and events that may have triggered demand changes. Based on this learning and the application of relevant algorithms, future trends can be determined – whether this is identifying emerging product opportunities, accurately forecasting seasonal demands or tracking evolving consumer trends.

Personalised online purchasing
By combining gathered user data and preferences with analysis through machine learning and behavioural science, AI-powered systems can more accurately determine consumer personal needs and tastes and create targeted offerings and nutritional plans to suit dietary needs.

This level of personalisation and tailored consumer support has the potential to be a key differentiator for food manufacturers and vendors at a time when consumers are placing significant emphasis on the customer experience.

AI is opening new doors – but make sure you open the right doors
AI offers limitless potential to transform the way food manufacturers approach ‘traditional’ operations and also opens the door to new, powerful business capabilities.

Ambitious food manufacturers will need to map out existing operations and assess where AI will have the most beneficial impact without affecting uptime or on-going manufacturing. This is where an implementation partner with extensive experience of deploying advanced technology into the food industry can be crucial in enabling companies to build a reliable framework for the adoption of AI and other emerging technologies with disruptive potential, without disrupting their day-to-day business.

Simon Noakes is SMB Director at Columbus UK, an international IT services company serving customers worldwide. It is an expert in developing and providing digital business applications that help its customers in the digital transformation of their business. Columbus is a specialist within the industries of retail, distribution, food and manufacturing, with more than 28 years of experience and over 8000 successful business cases.