In the realm of food production, technological advancements have always played a pivotal role in improving efficiency, safety and quality. One such game-changing evolution is digitalization, which has penetrated every sector, including food processing units. Digitalization refers to the integration of digital technologies into various aspects of an operation, fundamentally transforming traditional processes. In the context of food processing, this paradigm shift is poised to revolutionize the entire production process, from sourcing raw materials to delivering the final product to consumers.
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The food technology market size worldwide was USD 260 Billion in 2022. Due to advancing technologies within the food industry, as well as a demand for healthier, cheaper and safer food products, the market is forecast to exceed USD 360 Billion by 2028. Traditional food processing units operate on well-established processes that have evolved over time. However, these processes often suffer from inefficiencies, lack of real-time data and limited scalability. Inefficiencies can result in increased production costs, longer processing times and compromised product quality. Moreover, the lack of real-time data can hinder decision-making and responsiveness to changes in demand or supply chain disruptions. This is where digitalization steps in, offering solutions to address these challenges comprehensively.
There is a need for manifold interventions from multiple angles at a low cost to save on margins and increase profitability keeping quality and safety in mind. One such powerful yet niche field is advent of Artificial Intelligence (AI). AI, automation and optimization go hand in hand. AI mimics decision taking and problem-solving capability using high end Deep Learning algorithms, the crucial requirement of course being data. The challenge for AI companies is to get quality data with high granularity for them to build models, which can be immensely useful for their operations. A hybrid model of first principles physics/chemical-based process modelling combined with data-based machine learning models can bridge the gap of process data not being available to great extent. Through this, a Digital Twin of the process can be built. This is an accurate prediction model that predicts the process behaviour in the next instance and in the future. Once the future state is known, powerful AI models can work on the predicted state and build an optimization strategy for achieving energy efficiency and in turn, reduce carbon footprint.
It is critical for food processing companies to build their digital transformation infrastructure and adopt AI into their routine practice. It brings about seamless uninterrupted automated execution bringing about energy optimization thereby, saving cost. Digital Transformation infrastructure should (a) Sense i.e., real time capture of high granularity, high coverage, high quality, high variety, high volume of all relevant monitoring and controllable data points, operational, health, energy of process & utility equipment data and process, production & quality data. (b) Connect i.e., real time transmission of upstream and downstream data with no cost towards control and network cables and (c) Act i.e., real time actuation of control commands, along with centralized location for real time and historical data storage, real time data processing for business intelligence and report generation & highly interactive, intuitive visual analytics. The level of Digital Transformation infrastructure will depend on the current digital state of the plant. The beauty of centralized data repository is that it can store all the data that is generated from the process and can provide integrated Business Intelligence dashboards and reports customized to the end stakeholder like a Plant Head, Production Head, CXO’s and CEO.
The AI models deployed on the process plant learn continuously from the real time data and improve their performance over the years. Thus, the plant continues to benefit and can see reduction in energy consumption and achieve its sustainability goals. AI can help in other critical functions like Smart Inventory Management, Automated Processing, Data Driven Decision Making, Quality Control & Traceability, Predictive Maintenance & Supply Chain Integration. The adoption of digitalization/ AI in food processing units offers a multitude of benefits that have the potential to reshape the industry mainly Enhanced Efficiency, Improved Product Quality, Accurate Demand Forecasting, Cost Savings, Flexibility and Scalability, Food Safety and Market Responsiveness.
While the potential benefits of digitalization are significant, there are few challenges that must be navigated which are Initial Investment, Data Security, Workforce Transformation, Integration Complexity and Regulatory Compliance.
Thus, it the right time now for all food processing companies to invest in AI technologies which can provide both the Digital Infrastructure and the AI technology that can make a huge impact in the way the plant operates and brings in all round benefits. The early adopters of this technology stand to gain immensely and will have a great first mover advantage in this highly competitive market.
References:
• Food tech market size worldwide 2022 | Statista
