The resilience of the food supply chain has emerged stronger from the COVID-19 crisis. However, its complexities and challenges have also been exposed in recent months. The chain is nothing but a complex web of interactions between very different elements, from farmers and their suppliers to processing facilities and logistics. When the elements of a chain multiply, so do its risks. In recent years, technology and, in particular, data analysis has brought new tools to the table with which to mitigate and minimize these risks.
The food industry is one of the sectors that has experienced the most changes since the last century, when consumption habits were modified to make way for processed foods and their mass production. Today, this industry is facing another process of change and opportunity with Data Science.
If Big Data
has revolutionized the production processes of the most important sectors, the food industry could not be the exception. Data Science has had benefits in customer service, quality control and improving the operational efficiency of these companies.
• Customer service
Data Science can monitor and analyze the comments that customers make on social media platforms. Through techniques such as language processing, it is possible to categorize customer responses as positive, negative or neutral and with this implement strategies to improve the consumer experience.
• Quality control
For processed foods, maintaining a consistent taste and appearance in all your products is a must. Data Science is responsible for analyzing the factors involved in the above (such as the quantity, quality and storage of ingredients) to prevent possible problems in the quality and safety of food.
• Operating efficiency
One of the great problems, not only of the food industry but of the whole world, is the enormous waste of food that happens in these companies. With Data Science
and through the algorithms that it produces, companies have been able to analyze market trends that are reflected in demand, effects of temperature and storage on the quality of products and other factors that cause food waste. and they mean losses not only for companies but for the planet.
Application of Data analytics in food industry
Ensuring a high quality, appetizing and fresh food
This is how they should be. Our favorite dishes and delicacies, our favorite products in the refrigerated counter and salad bar or our children's school meals. To ensure this, smooth supply chain management, demand-oriented production, reliable cold chains and optimal transport routes and conditions are essential.
Companies that manufacture fresh products, process them, sell or transport them are dealing with particularly sensitive goods: Because the sensitive goods not only have to be available on time in the right quantity and in perfect quality - they are often not really storable either. In short: too few goods annoy customers and miss out on sales opportunities. Too much goods can mean that some of it spoils and the goods used do not generate any income.
Interlinking processes in the best possible way in order to design transport and sales channels in such a way that quality and freshness benefit as much as the company's results is essential. One key is professional data analysis. Depending on the company's purpose (production, food logistics, food-related services, etc.), it can be decisive or even business-critical to plan and decide on the basis of data.
Efficiency in cultivation techniques, managing stocks and warehouses, or forecasting demand for retailers are other advantages of relying on data. Ultimately, quality data and information are underpinning the food industry supply chain and anticipates a more resilient and secure future.
The big data in the food industry: control and chain transparency
Many of the risks in the food industry supply chain stem from its complexity. Technology has contributed to reinforcing the control and transparency of the chain for years, thanks to elements that today seem as basic as a barcode. However, in recent years, advances in sensorics (with the internet of things at the helm) and big data analytics capabilities are enabling unprecedented advancement.
Traceability from the source has ceased to be a chimera to become a reality. Today, it is possible to know all the ins and outs of the path traveled by a food, from the production of the raw material to the supermarket shelf with the appropriate technology.
Thanks to the data collected by devices such as RFID tags (radio frequency identification), distributed databases (and blockchain) and real-time analysis technology, it is possible to know the entire chain of suppliers involved in each product, identify their vulnerabilities and anticipate the risks derived from them.
Today, it is possible to anticipate risks in great detail and anticipate supply chain disruptions.
Another of the great advantages of big data in the food industry is the improvement of the quality of the information available for decision-making and the greater transparency of the supply chain. This in turn helps to strengthen trust with both suppliers and customers and face potential reputational risks from an advantageous position.
Finally, predictive analytics is perhaps the most promising aspect of big data analytics technology.
Based on large amounts of quality information and with the potential of analytical models (increasingly supported by artificial intelligence), it is possible to anticipate risks in great detail and anticipate interruptions in the supply chain. whether due to a cyberattack or an extreme weather event.
Whether manufacturer, refiner or service provider - data-driven corporate management in organizations in the food industry helps with the optimal use of resources, with saving working time and costs as well as with supplier and customer management. Ultimately, both companies and consumers benefit from this. Optimized production, streamlined processes and improved use of resources on the basis of data analytics are ideal prerequisites for highly available quality products at fair market prices - as well as for customer-oriented service in the service sector.