Food and beverage manufacturers must plan with greater speed and precision. AI technologies such as machine learning and predictive analytics are reshaping how businesses in this sector forecast demand and manage their supply chains. 

In this article, we examine the evolving role of demand and supply forecasting in the food and beverage industry, outlining its key benefits, common hurdles, and practical strategies to improve planning efficiency. To enrich the discussion, we feature insights from food manufacturing expert Bo Krogh Knudsen, Industry Practice Lead at Columbus. 

Changing role of demand forecasting in the food industry  

Food producers are increasingly leveraging real-time integrated data to make forecasting more accurate and agile. Recent research from the Institute of Food Technologists (IFT) indicates that nearly  50% of food industry companies plan to invest in AI and supply chain tracking systems in 2025. Among the top reasons cited was the ability to enhance data-driven decision-making across the organisation. 

Sustainability is also playing a much more prominent role in demand forecasting as food manufacturers aim to align with eco-friendly practices. According to IFT, over half of companies (52%) are adopting sustainability technologies to meet rising consumer demand for sustainable products, driving the need for more eco-conscious demand predictions. 

“We’re also seeing changing lifestyle trends like more people living alone, meaning a higher demand for single-serve meals,” says Bo. “Shoppers want convenience, but not ultra-processed food. They’re also looking for new flavours and aiming to cut back on animal proteins. All of this makes predicting future demand even more complicated for food manufacturers.” 

Modernising supply forecasting  

On the supply side, food and beverage companies are redesigning their networks for more regionalised and adaptive sourcing. This strengthens resilience while reducing risk from global shocks. 

“When demand shifts, supply has to shift too,” says Bo. “We’re seeing more manufacturers moving from conventional to ecological products, experimenting with new flavours, and reducing meat content to stay aligned with what consumers want.” 

External factors also remain a major challenge: “Climate change and seasonality still play a huge role in supply. Whether its flooded fields, droughts, livestock disease – these things all affect the quality and availability of raw materials.” 

Despite these growing pressures, many companies are still behind adopting new technologies that can help with forecasting. A recent McKinsey survey of over 100 global supply-chain leaders found that 37% of respondents say their APS (advanced planning and scheduling) systems aren’t being used widely enough across the organisation. 

They worry about not fully understanding complex machine learning algorithms, who's responsible for the outcomes, and in some cases, what it might mean for their jobs.” 

However, Bo explains that ML forecasting has proven that it outperforms traditional forecasting methods in several ways.  

According to a Gartner survey, top-performing supply chain organisations are using AI and machine learning (ML) to optimise processes at more than twice the rate of their lower-performing peers. Among the top five processes leveraging supply chain data to automate or optimise decisions with AI/ML, demand forecasting ranked first. Notably, 40% of survey respondents were classified as “high performers”—organisations that exceeded expectations over the past 12 months across five key supply chain performance metrics.

“Statistics like these are just one example of how machine learning is proving its value. By using advanced algorithms, data analytics, and pattern recognition, machine learning consistently delivers more accurate and actionable insights for food manufacturers,” says Bo.  

Benefits of demand forecasting in food manufacturing  

Demand forecasting offers substantial advantages in the food industry. “It’s vital for reducing costs,” says Bo. “By forecasting accurately, food manufacturers can avoid overproduction, cut down on excess inventory, and reduce waste from expired products. It means less money tied up in stock that might not sell.” 

Bo points out that accurate forecasting also helps improve how resources are allocated - from raw materials and labour to equipment usage - leading to more efficient operations and significant cost savings.  

He goes on to explain that demand forecasting can also drive revenue growth by aligning production with anticipated demand and pairing it with expected supply—for example, accounting for the size, yield, and quality of harvested fish, crops, or slaughtered animals. It reduces the risk of stockouts, making sure that products are available when customers are ready to purchase. 

“When product availability is optimised, you capture more sales opportunities and keep customer satisfaction high. Accurate demand forecasting also helps manufacturers prepare for peak periods like seasonal trends or holidays,” says Bo. 

Another key benefit Bo highlights is the reduction of lead times across the supply chain. With accurate demand forecasts, food manufacturers can better anticipate needs and plan ahead – giving them the agility to respond quickly to changes in customer demand or unexpected disruptions. 

Integrating demand forecasting into supply chain management  

Demand forecasting plays a key role in supply chain management by helping businesses anticipate future customer demand for their products. This information enables better inventory management, more accurate production planning, and a smoother distribution process, helping reduce excess inventory costs and stockouts.  

“Reducing waste is key to being sustainable, and that applies across the whole supply chain,” says Bo. “Take bread, for example – waste can happen at production, in stores if it doesn’t sell, or at home if it goes stale before it’s eaten. With more single-person households, we may soon see a rethink in how packaging sizes are designed.” 

To achieve this level of efficiency, Bo adds, demand forecasting needs to be embedded throughout every stage of the supply chain. Here are three key ways it supports more effective supply chain management: 

Plan production more accurately 

With reliable forecasts, food manufacturers can align production schedules to match demand, helping avoid both overproduction and underproduction. “Producing the right amount at the right time leads to better cost control and a more streamlined operation overall,” says Bo. 

Align inventory levels with demand 

Accurate forecasts enable manufacturers to avoid excessive stockpiling which ties up capital and storage space, as well as stockouts that lead to missed sales opportunities. “You also gain visibility into raw materials and semi-finished or finished goods that are nearing expiry,” says Bo.

By aligning inventory with anticipated demand, Bo explains, food manufacturers can strike the right balance between maintaining enough stock to meet customer needs and reducing the risk of expired or obsolete inventory. This leads to better cash flow, lower carrying costs, and improved operational efficiency.

Strengthen supplier relationships through smarter procurement

Accurate demand forecasting allows food manufacturers to work more strategically with their suppliers. With clear insights into future demand, they can plan purchases more effectively, ensure raw materials arrive on time, and minimise the risk of shortages or last-minute disruptions. 

“To avoid raw materials expiring in storage, manufacturers need a balanced supply strategy – getting the right quantities at the right time. This helps reduce waste and keeps costs down,” says Bo.

Overcoming machine learning challenges in demand forecasting 

Combining machine learning with demand forecasting provides several advantages for food manufacturers. But it also comes with certain challenges. 

Ensuring data quality and consistency  

One of the biggest hurdles according to Bo is collecting enough historical data and ensuring its quality, particularly for new products or emerging markets. But even well-established companies and products often don’t have historical data, or this data isn’t consolidated and structured. Choosing the right ML algorithm and optimising its parameters can be complex, while the interpretability of these models may be limited.  

“Manufacturers might not even know which factors are really influencing demand and supply. Take a fish producer for example, why is the harvested fish smaller, lower in quality, or affected by illness? Is it the feed, the medicine, water temperature, flow conditions, or some combination of these? Without understanding the root causes, it's difficult to build effective models,” says Bo.  

The dynamic nature of the food industry also adds to the challenge, Bo explains. Shifting consumer preferences, changing supply conditions, and economic fluctuations can all throw off even the most advanced models. To stay effective, machine learning models need to be regularly updated and retrained to reflect real-time conditions and maintain accuracy. 

Strategies to overcome forecast errors and variability  

Forecast errors and variability can be challenging, but there are several strategies Bo recommends that food manufacturers can use to improve forecast accuracy and reduce uncertainty: 

• Diversify forecasting methods – “Using a mix of techniques like time series analysis, regression, and machine learning, can give a more accurate and rounded forecast. Combining methods gives a more reliable result than relying on just one,” says Bo. 

• Prioritise data quality – “Good forecasting starts with good data. Rigorous validation and cleansing processes ensure forecasts are based on accurate inputs, which leads to better decisions.” 

• Build a culture of continuous improvement – “Models should be monitored and adjusted regularly. By learning from past performance and market changes, manufacturers can become more adaptable and reduce the impact of variability.” 

• Use AI to understand complex patterns – “AI is evolving fast, and it’s particularly useful in spotting patterns between different parameters and their impact on supply and demand. Using the fish producer example again – if you feed AI data on feed types, medicine use, water temperature, and so on, it can reveal correlations and trends. Then, based on the current conditions, you can adjust variables like feed to optimise supply.”  

By working with specialists in data science and machine learning, Bo explains, food manufacturers can tackle their business-critical challenges more effectively, and gain access to expertise that may be lacking internally.  

Bo also points out that external experts can help food manufacturers identify and hire the right talent, which is often in high demand and difficult to secure. According to a Deloitte and Manufacturing Institute study, more than 65% of respondents in the National Association of Manufacturers’ (NAM) Outlook Survey cited attracting and retaining talent as their top business challenge. 

“Bringing in external experts is a great way to close the skills gap,” says Bo. “It not only helps you get machine learning up and running faster, but also makes the move to digital forecasting smoother and more successful.” 

Transforming organisational  culture  and  managing resistance  to  change  

Introducing ML or AI models into demand forecasting can be complex, and one that requires both strategic thinking and cultural alignment to succeed. “Changing any part of your organisation can cause confusion and disruption among your employees if it’s not managed correctly,” says Bo. “Several people across your organisation will need to be involved in the decision-making process and shaping how your transformation project will fit into the business.”  

Too often, Bo explains, businesses focus heavily on the technology itself, overlooking the people side of change. If employees aren’t properly guided through the transition, the software or solution may fall short of delivering its expected value. 

“That's why change management should be an integral part of organisational change. You must involve all stakeholders from the beginning to reduce the chances of resistance and set your project up for success.  

“And it’s important to approach the implementation project as a business transformation, not just an IT project. This helps break down organisational silos, encourages collaboration between departments, and supports a more holistic understanding of business needs—all of which maximise the value that demand forecasting technologies can bring,” says Bo. 

Understanding cost and return on investment (ROI)  

Investing in machine learning and AI for demand forecasting can be a significant financial commitment. Costs often include hiring skilled professionals, implementing the right technology, and developing tailored ML/AI models. Calculating the ROI can also be challenging, especially when benefits may not be immediate, and ongoing maintenance costs need to be factored in. 

“This is where it helps to bring in someone external,” says Bo. “They can break your project into clear, manageable iterations, so you start seeing progress in weeks. Each iteration gives you the chance to evaluate the direction of your transformation project, make adjustments, and maximise the chances of delivering the outcomes you’re aiming for.” 

Drive smarter decisions with  precision  forecasting  

There are several opportunities ahead for demand forecasting in food manufacturing. Emerging technologies like the Internet of Things, continuous advancements in AI, and big data analytics are all driving change, making forecasting more accurate than ever for food manufacturers. 

At Columbus, we work with food and beverage businesses like yours, supporting every step of the value chain, from primary producers in agri or aquaculture, through harvest or slaughter, processing, and value-adding, all the way to sales and distribution to the final consumer. Together, we help you tackle industry challenges so you can enhance supply chain management, improve efficiency, streamline operations, reduce overheads, and exceed customer expectations. 
 

If you’d like more information on how we can help your business, contact us below.