Organising data and using AI strategically can help businesses make better, faster decisions, market understanding and forecasting, se their maintenance and waste.
Food and beverage manufacturers, like companies in every sector, must address the challenges of operating in the modern business world.
They must evolve to meet changing consumer demands and address sustainability needs whilst also ensuring product freshness, preventing food spoilage, adhering to delivery and shipping schedules, and maintaining quality control.
Compound these demands with unforeseen setbacks, and it’s easy to see how even minor changes can significantly impact operations.
To stay ahead in the food and beverage industry, manufacturers can leverage the power of artificial intelligence (AI) technologies and machine learning to:
- Make smarter business decisions
- Gain consumer insights for more targeted marketing efforts
- Improve customer experience
- Enhance productivity
- Prevent human errors
- Increase efficiency
- Address food waste challenges
- Save money to offset energy, raw material and transportation costs
- Gain flexibility in sourcing and distribution strategies
- Modernise manufacturing and warehouse operations
Use cases for AI and machine learning in the industry
As AI technology matures, it is changing the way food and beverage manufacturers are able to operate. They can now use advanced analytics at every stage of their supply chain.
To inspire you with the potential of AI, here are some of the top use cases for AI in the sector:
Real-time market and brand analysis
When companies launch a new product or a variation of an existing product, it is a traditionally labour-intensive process, requiring deep market and consumer research. However, artificial intelligence can produce real-time analysis of market trends to make things more efficient.
For example, social media analytics can mitigate low responses to customer surveys. Businesses can see which products consumers are talking about and what the positive or negative qualities are associated with those products.
They can take that data and improve product development with more precise and reliable insights, assessing consumer views in real time and identifying high-value features or challenges for products and services.
Market trends forecasting
Social media analytics enabled by AI can also be used for market trend forecasting. Businesses can project patterns to better understand where markets are heading.
Although AI tools can’t fully predict the future, they can help food and beverage manufacturers better understand what is coming, identifying shifting consumer interests and trends, spotting market trends related to offerings or brand and forecasting waning or growing interest in product types.
Predictive maintenance
Every part of every machine in every warehouse or production facility has a lifespan, which poor maintenance can reduce. AI can shorten, if not eliminate, the time between when a machine begins to fail and when operators notice an issue.
Critical data, such as temperature or speed of operation, can be analysed in real-time using machine learning.
The model can identify patterns and predict when a machine needs attention. And predictive analysis, using AI, can alert businesses that maintenance is required, meaning the process becomes preventative rather than reactive, reducing production downtime and manufacturing errors and streamlining product delivery.
Supply chain optimisation
AI can enable efficient production plans for supply chain optimisation, which is especially useful when businesses are confronted with unexpected delays or shortages. It is much easier for companies to adapt to unforeseen challenges with the help of AI, which will analyse new constraints and produce a new optimised plan for the situation.
This helps businesses to:
- Maximise revenue subject to demand/production constraints
- Streamline product delivery processes
- Reduce or eliminate waste and human error
- Target delivery to predicted demand
Food and beverage manufacturers must consider many factors in production and delivery, such as demand versus capacity, how much materials cost along the supply chain, and crop and livestock management. From intelligent health monitoring to disease detection and diagnosis, AI helps optimise day-to-day operations.
One example is grading and sorting with computer vision. The process can be time-consuming, involve labour-intensive manual selection and is dependent on human expertise. But using computer vision (AI) technologies, this can be standardised and reduce human error and dependence and enable the efficient quality grading of items or products.
Addressing food waste challenges
Waste reduction is a key focus for food and beverage manufacturers. With more than 60 percent of businesses in the sector affected by increased energy costs, reducing waste can help save on operating overheads and improve sustainability.
For example, if businesses are over or under-ordering particular ingredients, not only are they missing out on opportunities to optimise production processes, but they’ll also be wasting energy within their operations.
Food waste also affects our environment. Preventing food waste and not producing food we don’t eat are crucial steps to reduce greenhouse gas emissions in the industry.
Technology such as automation, AI and machine learning can improve production efficiency and output. This allows businesses to better track ingredients across production lines so they can accurately predict when to reorder stock which, in turn, improves cost efficiency.
Automation can also help food businesses improve their supply and demand management. An end-to-end-solution can provide accurate data on the amount of stock needed based on real-time demand to cut food waste and can help maintain inventory levels, reducing carbon footprints by minimising travel throughout the supply cycle.
Rapid A/B testing to optimise marketing and sales
AI and machine learning can make A/B testing measurements faster, more precise and less costly than traditional efforts. AI-backed technology can also segment your customers. For example, they can identify groups with similar buying behaviours within a customer base. Companies can leverage these insights for marketing efforts and product launches and:
- Analyse results from rapid prototyping
- Assess sales change effects of different innovations or product features
- Narrowly target consumer demand
- Tighten development/test/feedback loops
Shorter time to market
Advanced analytics can improve the efficiency of an overall business by addressing workflow challenges and helping companies to:.
- React to market opportunities and challenges
- Build an agile development process
- Streamline approval process and overall workflow
- Automate processes
- Define marketplace and sample
- React to responses from several, combined sources
Realise the potential of AI in the food and beverage industry
These are just a few examples of how AI can benefit the food and beverage manufacturing industry.
AI and machine learning can help businesses focus on patterns and trends that the human eye might miss - and enable analysts to dive deeper by providing greater detail and data-supported forecasts.
They also allow companies to leverage information to make better, more informed decisions at every step of the process.
Working with a trusted partner can facilitate the integration of this technology into everyday use.
At Columbus, we combine our years of business and technological know-how with data strategies to help you drive growth and gain a competitive advantage in your market.
You can find inspiration on how to define valuable AI use cases for your business in our blog Applying AI: How to define valuable AI use cases.
Ready to explore AI use cases for your business? Register for our AI value workshop today, where you can discover how to identify and apply high-potential AI use cases for your business.