<img src="https://secure.leadforensics.com/133892.png" alt="" style="display:none;">

If you organize your data and use AI strategically, you can make better decisions faster. You can for example improve your market understanding and forecasting, optimize your maintenance or reduce food waste. Choose what is most important for you!

In today’s dynamic landscape, food and beverage manufacturers face many challenges. Everyone in food manufacturing must keep up with constantly evolving consumer demands while also addressing sustainability needs, ensuring product freshness, preventing food and waste spoilage, adhering to delivery and shipping schedules, and maintaining quality control.

Compound these factors with unforeseen setbacks, and it’s easy to see how even minor changes can significantly impact your 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 high energy, raw material and transportation costs
  • Gain flexibility in sourcing and distribution strategies
  • Modernize manufacturing and warehouse operations

Use cases for AI and machine learning in the industry 

The future of the food and beverage industry is coming into focus. As AI grows increasingly popular, it can rapidly change how food and beverage manufacturers operate.

You can use advanced analytics at every stage of your supply chain. To inspire you with the potential of AI, here are some of the top use cases for AI in the food and beverage industry.

Real-time market and brand analysis

When companies launch a new product or a variation on an existing product, this labor-intensive process traditionally requires deep market and consumer research. However, artificial intelligence can produce real-time analysis of market trends to make this process more efficient.

For example, social media analytics can mitigate low responses to customer surveys. You can see which products people are talking about and what the positive or negative qualities associated with those products are.

You can take that data and improve product development with more precise and reliable insights.

  • Assess consumer views in real-time
  • Identify high-value features for products/services
  • Identify challenges for products/services

Market trends forecasting 

Social media analytics enabled by AI can also be used for market trend forecasting. You can project patterns to better understand where the market is heading. Although AI tools can’t fully predict the future, they can help food and beverage manufacturers better understand what is coming.

  • Identify shifting consumer interests and trends
  • Spot market trends related to offerings or brand
  • Forecast 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 space between when a machine begins to fail and when a human starts to notice an issue. Critical data, such as temperature or speed of operation, can be analyzed in real-time using machine learning.

The model can identify patterns and predict when a machine needs maintenance. For example, when X begins to happen, then this machine needs attention. Predictive analysis using AI can alert your analysts when maintenance is required.

Predictive maintenance then becomes preventative maintenance instead of having to recover and restart from failure.

  • Streamline product delivery
  • Reduce production downtime
  • Reduce manufacturing errors
  • Optimize livestock feed algorithm

Read more about the use of automation in the food industry here.

Supply chain optimization

AI can enable the most efficient production plans for supply chain optimization, which is especially useful when confronted with unexpected delays or shortages. It’s much easier to adapt to the unexpected when AI supports your business. Simply add new constraints, and the technology produces a new optimized plan for that situation.

  • Maximize revenue subject to demand/production constraints
  • Streamline product delivery processes
  • Reduce or eliminate waste and human error
  • Target delivery to predicted demand

Your supply chain directly impacts your ability to bring ideas from conception to consumers. 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 farmers optimize their day-to-day operations.

Depending on your company’s requirements, constraints and restrictions, you can program those criteria into your AI algorithm, which will then find the best arrangement/solution. One example of this is grading and sorting with computer vision. The process can be time-consuming, involves labor-intensive manual selection and is dependent on human expertise. Using computer vision (AI) technologies, this can be standardized and reduce human error and dependence. Computer vision can also enable the efficient quality grading of items or products.


Addressing food waste challenges

Waste reduction has long been a key focus for food and beverage manufacturers. With over 60 percent of food and drink businesses affected by increased energy costs, reducing waste can help you save on operating costs and improve sustainability. For example, if you’re over or under-ordering particular ingredients, not only are you missing out on opportunities to optimize production processes, but you’ll also be wasting energy within your operations.

Food waste and loss are affecting our environment. Preventing food waste and not producing food we don’t eat are crucial steps to reducing greenhouse gas emissions in the food and beverage industry.

Technology such as automation, AI and machine learning can improve production efficiency and output. This allows you to better track ingredients across production lines so you can accurately predict when you need to reorder stock which, in turn, improves cost efficiency.

Automation can 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 down on food waste.

Automation can also help food businesses maintain inventory levels and reduce their chain carbon footprint by minimizing travel throughout the supply cycle.

Read more about how and why businesses should reduce food waste here.

Rapid A/B testing to optimize 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 behaviors within a customer base. Companies can leverage these insights for marketing efforts and product launches.

  • Analyze results from rapid prototyping
  • Assess sales change effects of different innovations or product features
  • Narrowly target consumer demand
  • Tighten development/test/feedback loops

Read more about how you can map the quality of user experiences here.

Shorter time to market 

Advanced analytics can improve the efficiency of your overall business by addressing workflow challenges and helping you make more informed business decisions.

  • 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

Realize 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. The AI tools are here; although powerful, they aren’t magic. AI and machine learning cannot arguably replace humans.

However, the technology helps you focus on patterns and trends that the human eye might miss - and enables analysts to dive deeper by providing greater detail and data-supported forecasts. AI and machine learning help teams do what they’re already doing in a more efficient way.

Gathering data and insights is only one part of the process. The other is leveraging that information to make better, more informed business 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 and 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 will discover how to identify and apply high-potential AI use cases for your business.

AI value workshop


Diskutera detta inlägg

Rekommenderad läsning

Demand forecasters do the impossible — predict what products and services customers want in the future. Their forecasts inform decision-making about production and inventory levels, pricing, budgeting, hiring and more. "While crystal balls remain imaginary, machine learning (ML) methods can give global supply chain leaders the support they need in the real world to create more accurate forecasts." The goal is to produce exactly the amount of product to meet demand. No more. No less. Demand forecasting is used to anticipate the demand with enough time to manufacture the right stock to get as close to this reality as possible. The cost is high if you don’t get it right. Your customers will go to your competitors if you don’t have what they need. Unfortunately, capacity, demand and cost aren’t always known parameters. Variations in demand, supplies, transportation, lead times and more create uncertainties. Ultimately demand uncertainties greatly influence supply chain performance with widespread effects on production scheduling, inventory planning and transportation. On the heels of the global pandemic, supply chain disruptions and a pending economic downturn, many demand forecasters wish for a crystal ball. While crystal balls remain imaginary, machine learning (ML) methods can give global supply chain leaders the support they need in the real world to create more accurate forecasts.
If you have identified possible AI use cases for your business, the next step will be to test if they are possible to implement and if they will create great value. While there is a lot of momentum and excitement about using AI to propel your business, the reality is only 54% of AI projects are deployed. How do you ensure you’re one of the businesses that does unlock the new opportunities AI promises? Your success with AI begins by discovering AI use cases that work for your business. In the first blog of our Columbus AI blog series, we shared five areas where organizations should focus their efforts to generate ideas for AI implementations based on our experience. After generating some ideas for AI use cases that could potentially benefit your company from the first step of the Columbus AI Innovation Lab, the next step is to test which AI use cases could be operationalized by evaluating them. Columbus AI Innovation Lab
Only half of the companies starting an AI pilot project are actually executing it. The key is to choose an idea that will benefit your business. Read more about how! In 2022, 27% of chief information officers confirmed they deployed artificial intelligence (AI), according to a Gartner AI survey. Even though businesses across all industries are turning to AI and machine learning, prepare your organization before jumping on the AI bandwagon by considering a few factors. Ask yourself: Is AI necessary for achieving the project requirements or is there another way? Does your team have the skills to support AI and machine learning? How will AI impact your current operations if you adopt it? How will you integrate AI with existing systems? What are the data, security and infrastructure requirements of AI and machine learning? The Gartner AI survey found only 54% of projects made it from the pilot phase to production. After significant investment in AI, why aren’t companies deploying it? We found the problem begins when companies define a use case. Too often, companies are not identifying AI use cases that benefit their businesses and end-users will adopt. The question is then, how should companies unlock the value and new opportunities AI promises? It starts with a systematic approach for each stage of the AI life cycle. We developed the Columbus AI Innovation Lab, a comprehensive method to address and account for all challenges when adding AI to your business operations and bring stakeholders into the process at the right time to help you operationalize AI.
right-arrow share search phone phone-filled menu filter envelope envelope-filled close checkmark caret-down arrow-up arrow-right arrow-left arrow-down right-arrow share search phone phone-filled menu filter envelope envelope-filled close checkmark caret-down arrow-up arrow-right arrow-left arrow-down