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Every food company, manufacturer and machine builder knows that it is necessary to stay up-to-date with all technical developments. Once you fall behind, it becomes harder to keep up with market changes and stay ahead of the competition. Digital innovation makes your business grow and you gain competitive advantage. To grow, data is an indispensable starting point.
The food industry is no exception when it comes to investing in digital innovation. The food industry in particular should take advantage of the opportunities technology has to offer. This industry still has a big efficiency battle to make; the population and demand for food is growing, on the other hand the labor force is decreasing and therefore productivity.
Innovation is not just about adopting new technologies. It's about how effectively and efficiently you deploy these smart technologies to create value for both production and the end customer.
Digital innovation and new technologies provide support in several areas:
To achieve this, data is essential. Every business has data. By structuring data, you get information. The more you know, the better you can manage.
The measurement, analysis and effective use of data is an essential component for growth. The ideal situation is when a company can make confident decisions based on reliable data. This enables a company to:
Here are some situations in which you can use data.
The ultimate goal, of course, is to continuously produce high-quality food products that are safe and meet quality requirements. Data can help maintain consistent quality in several ways.
You can apply technology to combat poor quality food and avoid having to throw away an entire batch. Vision AI can be used for this purpose. The technology provides solutions for automated quality inspections. Proper quality inspection reduces risk and ensures consistent product quality. Vision AI is a form of Artificial Intelligence. An essential feature of an AI-integrated system is that it generates data to improve itself.
Vision AI identifies and analyzes objects based on visual characteristics. The system mimics specific human tasks. Vision AI can have organic products recognized, categorized and sorted by specific external features, for example, apples with rotten spots. Vision AI is a self-learning system. Therefore, the system is able to recognize situations and can prevent problems.
You can monitor real-time data that provides information about production conditions, such as temperature and machine settings. If there are deviations, the system can issue alerts based on data. This allows you to react quickly to problems and prevent quality problems.
Food companies can increase their efficiency, reliability and food safety by implementing predictive maintenance timing using data analytics. With constant data collection and analysis, the system can identify problems and indicate when maintenance is needed. Predictive maintenance provides:
Data can be applied widely; in addition to predictive maintenance, processes can be optimized, think: speed, cost savings, efficiency, economy and hygiene.
The move to data-driven optimization or decision-making can impact the workplace. It requires new knowledge and skills from employees. Staff must be trained to make the best use of data analytics. They play an important role in monitoring data that contributes to food quality and safety, as well as process efficiency and productivity.
Adequate support for innovations should therefore certainly not be lacking and is often important for an innovation process to be successful. Technological changes usually have a major impact on the shop floor. It should then naturally go without saying that innovation plans should be discussed with staff in the early stages.
Creating support reduces resistance and keeps everyone motivated. In addition, you get the most valuable information from employees that will only further optimizations.
Collaboration between humans and data is and will always be crucial. In questionable situations, humans will always tie the knot and make the decision,
There are countless ways to harness data and make it work for you. The most important thing remains the collaboration between data and people. At QING, we always start with the basics. A good starting point for companies looking to get started with data is to understand what you want to analyze and examine what's going well and what's not.
Want to know more about working (together) with data? Then contact us without obligation. We would love to tell you more!
Read more about data-driven machine optimization here.
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