Taking the guesswork out of the food and beverage industry with data analytics and AI
The call to guarantee food safety and establish 100% transparency across food and beverage supply chains is urgent. Authorities in the industry, farmers, manufacturers, and distributors are making a concerted effort to ensure consumers know the minutest details of what’s on their plates and to give them confidence that the food is safe to consume. But this is a formidable task because the food and beverage industry has many moving parts, and given the perishable nature of food, it needs to move from farm to fork at different time frames.
Most food and beverage manufacturers attempt to achieve this level of transparency manually or by rolling out ad-hoc technologies that don’t integrate seamlessly – the task is Herculean, to say the least. However, data analytics and AI present some incredible solutions to the complex tasks food and beverage manufacturers must accomplish and the guesswork they take on. This blog will delve into the complexities and challenges within the industry and move into a discussion on how data analytics and AI can support you in achieving greater levels of transparency and operational efficiency.
What does the food and beverage industry have on its plate?
The food and beverage industry has many issues to contend with. Amongst these are seasonal fluctuations or variations in demand during different times of the year which can make long-term planning and staffing a challenge. Additionally, the industry has strict regulations that govern food safety, labeling, and handling, requiring businesses to stay compliant and adapt to changes in legislation.
Food and beverage businesses also need to source quality ingredients – this can be tough when supply chains are disrupted and the business deals with perishable goods. Finding and retaining skilled staff in manufacturing is also a persistent issue.
Food and beverage is a very competitive space, making it crucial for businesses to differentiate themselves and maintain customer loyalty. This too, in a climate where meeting the demands of health-conscious and sustainability-focused customers, is critical.
As we approach 2024, we see data analytics informing intricate decisions in the F&B industry. These include narrowing down on the right amounts of resources required for cultivation, data-driven menus that cater to the exact requirements of consumers, the development of plant-based products using consumer data, and analytics contributing to minimal waste with better intelligence on demand.
Given that the value of the market for Artificial Intelligence (AI) in the food and beverage industry is expected to reach a staggering $29.94 billion by 2026, let’s see where a pairing of data analytics and AI can take the food and beverage industry.
Track the trends
Not long ago, food and beverage manufacturers had to dabble in a lot of guesswork from understanding which products were selling fast and why, to taking onboard customer feedback. Today, data analytics is helping manufacturers efficiently analyze sales and transaction data, identify which products are selling well and at what times. This information helps in adjusting inventory and pricing strategies.
Data analytics can also be used to predict future demand based on historical sales data, seasonal factors, and external variables like holidays or events. Intricately tied to demand analysis is competitor analysis which helps food and beverage manufacturers monitor competitors’ pricing, promotions, and customer reviews that provide insight into market trends while ensuring they stay competitive.
Some other critical areas that data analytics helps monitor include:
- Social media and online presence analysis that delves into social media mentions and engagement, offering real-time insights into customer sentiments, trends, and emerging preferences in the food and beverage industry.
- Customer segmentation based on factors like age, location, and order history can help in tailoring marketing strategies and menu offerings to specific customer groups.
- Price elasticity analysis involves studying how changes in pricing affect demand. By analyzing these, businesses can set optimal prices that maximize profits.
Take the example of Americana Group – a Quick Service Restaurant (QSR) operator that now has over 2,000 restaurants and 25 food production sites in countries such as the UAE, KSA, Kuwait, and Egypt. The Group didn’t have a single source of truth to gain full visibility of all its restaurants. As a result, it has integrated Microsoft Azure, Azure Synapse Analytics, Azure Data Factory, SQL Server Integration Services, Azure Analysis Services, and Power BI for their ecosystem to improve visibility and take data-driven decisions. Today, the group’s finance, sales, revenue, HR, and operations teams can fast-track action as they have important data at hand and are spending 80% less time on monotonous administrative work.
Narrow down on issues
For food and beverage manufacturers, nothing is more problematic than machines that malfunction – it often causes delays that last a couple of hours to days. But AI presents a viable solution that’s bound to revolutionize food and beverage operations.
AI can analyze data from sensors and machinery to predict when equipment is likely to fail. This allows for scheduled maintenance, reducing downtime and preventing costly breakdowns. Similarly, AI can also continuously monitor data from machines to identify anomalies in performance or output. Unusual patterns can signal potential issues or deviations in product quality.
On the other hand, data analysis can reveal inefficiencies in production processes, enabling manufacturers to fine-tune operations for better resource utilization and quality control.
Incorporating data and AI into food and beverage manufacturing processes requires the integration of data-centric AI based off a well-defined analytics platform It’s essential to have a data-driven culture and the expertise to interpret the insights gained from these technologies to make informed decisions and drive operational improvements.
Focus on quality control
Ensuring the quality of food is consistent across all products is one of the most complicated aspects of the industry. Today, food and beverage manufacturers can leverage video and image recognition tools for quality control in a variety of ways, including the automated visual inspection of products on the production line. Cameras capture images of food items, and image recognition algorithms identify defects, inconsistencies, or foreign objects that may affect quality.
Image recognition can also ensure that product labels, including text and barcodes, match the intended design. This helps prevent mislabeling or packaging errors while the same technology can be used to assess the color and texture of food products. Deviations from the desired appearance are flagged as quality issues.
These tools can also be used for the following purposes:
- Packaging integrity: Image recognition can detect flaws in packaging, such as cracks, leaks, or improper sealing, which could compromise product freshness and safety.
- Shelf-life prediction: By analyzing images of food products over time, manufacturers can predict shelf life and freshness, helping to manage inventory and reduce waste.
- Quality assurance documentation: Images can be used to document and verify quality control processes for compliance and record-keeping purposes.
For Majans, an Australian-owned snack company; quality is a constant concern because the snack space is very competitive, and inconsistencies in taste result in the loss of business. By incorporating Dynamics 365 IoT Intelligence, the business is able to transform data from IoT sensors on connected production devices into actionable insights. For example, when there are inconsistencies in salt levels, the relevant officials are alerted. At the senior level, executives have access to Power BI Dashboards that give them insights that provide an added layer of intelligence. Together Dynamics 365 IoT Intelligence and Power BI Dashboards work seamlessly to provide accurate, up-to-date information that informs real-time decisions.
Data analytics can monitor energy consumption in real time and identify opportunities for energy efficiency. Manufacturers can then adjust equipment settings and schedules to reduce energy waste. Data analytics can also track water usage in the production process. By identifying areas of excessive water consumption or leaks, manufacturers can reduce water waste.
A few other areas that digital technology supports on a sustainability front include:
- Sustainable sourcing: Data analytics can help manufacturers select suppliers that adhere to sustainable practices, reducing waste associated with unsustainable sourcing.
- Waste reduction in packaging: Data can be used to optimize packaging materials and design, reducing excess packaging and its associated waste.
- Recycling and waste management: Data analytics can guide recycling and waste management processes, ensuring that waste materials are sorted and managed efficiently.
Next steps: Your data analytics and AI diet
In a world that’s very demanding of food and beverage businesses, they can no longer relegate data analytics and AI to the back seat. These technologies not only support creating greater transparency across supply chains but also help identify malfunctioning machinery and other blockers while accelerating sustainability drives across operations.
If you are ready to kickstart your data analytics journey, start by asking yourself these essential questions.