Food waste is no longer just a sustainability issue for companies in the food and beverage industry. It creates additional costs, ties up resources and puts pressure on the entire supply chain. At the same time, manufacturers must ensure that products remain available and that retailers' requirements are reliably met. Especially in the FMCG sector, this creates a difficult balancing act: Producing too much leads to excess inventory, write-offs and products reaching their expiration dates. Producing too little can result in stock-outs, lost sales and dissatisfied retail partners.
AI forecasting can help companies manage this trade-off more effectively. By improving demand forecasts and aligning demand, inventory and production planning, manufacturers can reduce overproduction and avoidable food waste without compromising product availability.
Food Waste in the Food Industry: Why It Remains a Major Challenge
Food waste continues to be a significant global challenge. According to the United Nations Environment Programme's (UNEP) Food Waste Index Report 2024, approximately 1.05 billion tonnes of food were wasted worldwide in 2022 at the household, food service and retail levels. Additional food losses occur earlier in the supply chain. These figures illustrate the scale of the problem. For food manufacturers, an important consideration is that some avoidable losses can be influenced long before products reach consumers, for example through production quantities, raw material procurement, inventory levels and coordination with retailers. At the same time, planning is becoming increasingly complex. Companies must manage fluctuating demand, short-notice promotions, seasonal peaks, growing product variety and limited shelf lives. Reliable planning is therefore an important starting point for preventing food waste before it occurs.
What Causes Food Waste and Overproduction in FMCG?
Overproduction is rarely the result of a single poor decision. More often, it arises from several planning challenges that reinforce one another.
1. Inaccurate Demand Forecasts
When sales volumes are overestimated, companies produce more than is actually needed. Particularly for fresh or short-shelf-life products, this surplus can quickly lead to inventory write-offs. Conversely, underestimating demand can cause stock-outs and last-minute production runs, resulting in additional costs and planning effort.
2. Seasonal Fluctuations and Short-Term Demand Changes
Temperatures, public holidays, vacation periods and promotional campaigns significantly influence demand for food and beverages. An unusually warm spring, for example, may cause demand for certain beverages to increase earlier than expected. If actual demand subsequently falls short of expectations, manufacturers may be left with excessive inventory. Traditional forecasting approaches based primarily on historical averages can struggle to capture these changes.
3. Expiration Dates and Limited Shelf Life
Unlike durable industrial products, many food products cannot be stored indefinitely. The shorter the remaining shelf life, the less flexibility companies have to sell excess inventory through regular channels. This is why considering expected demand alone is not enough. Existing inventory, remaining shelf life and planned production quantities must also be aligned.
4. Lack of Coordination Between Sales, Production and Procurement
Even an accurate demand forecast does not automatically prevent overproduction. When sales, production, warehousing and procurement teams work with different data or planning versions, additional safety stock, unnecessary purchasing volumes and production orders that no longer match current demand can arise.
What matters is not only forecast accuracy, but also how consistently forecasts are translated into operational decisions.
How Does AI Forecasting Help Reduce Food Waste?
AI forecasting uses data-driven models to predict future demand as accurately as possible. Unlike simple projections of historical sales figures, modern AI models can analyze multiple influencing factors simultaneously and identify complex relationships. For the food industry, this creates several opportunities.
Predict Demand More Accurately
AI models analyze historical sales and demand data, identify seasonal patterns and account for changes in purchasing behavior. This enables companies to align production quantities more closely with expected demand. As a result, manufacturers can reduce the risk of consistently producing too much or accumulating unnecessarily high finished-goods inventory.
Consider Weather, Promotions and Holidays
External factors have a particularly strong influence on sales in the food and beverage industry. AI forecasting can incorporate weather conditions, public holidays, promotions and other relevant demand signals. This allows companies to assess expected demand peaks and declines more precisely instead of relying on previous-year figures alone.
Update Forecasts Regularly
A forecast should not be treated as a one-time monthly planning exercise. When demand changes, forecasts and the decisions based on them must also be adjusted. AI-powered systems can continuously evaluate new data and make changes visible earlier. This gives companies additional time to review planned production or procurement quantities.
Plan at Product and Customer Level
Not every product follows the same demand pattern. Significant differences can also exist between retail chains, regions and sales channels. Detailed forecasts at SKU, customer or regional level enable more targeted planning. Instead of applying a general decline in demand across the entire product portfolio, companies can identify the specific products and sales channels affected.
From Better Forecasts to Less Food Waste
More accurate demand forecasting is an important first step. However, the greatest potential emerges when companies connect their forecasts directly to inventory and production decisions.
Three planning areas work together:
- Demand Planning: How many units of a product are expected to be needed during a specific period?
- Inventory Optimization: How much inventory should be held, considering demand, product availability, existing stock and shelf life?
- Production Planning: When and in what quantities should products be manufactured to meet expected demand as cost-effectively as possible?
An integrated planning approach helps companies make these decisions using a shared data foundation. This not only reduces excess finished-goods inventory but also helps align raw material and packaging requirements with planned production. To learn more about the relationship between forecasting, inventory and production processes, read our article AI in the Food Industry: Optimizing Planning with AI.
Practical Example: How Better Planning Can Prevent Overproduction
A beverage manufacturer is planning production for a product line with strong seasonal demand. Based on the previous year's figures, the company expects a significant increase in sales and schedules correspondingly high production volumes. Shortly before production begins, however, new data indicates that demand will likely be lower than anticipated. A planned retail promotion has been postponed, and the weather forecast has changed. With planning based exclusively on historical data, this information might be considered too late. An AI-powered forecasting system, on the other hand, can incorporate these new signals into an updated demand forecast. When the forecast is connected to production planning, the company can assess whether planned quantities can still be reduced or production orders postponed.
The result: Lower potential excess inventory, reduced write-off risk and better alignment between demand and production. This example is illustrative. Actual savings depend on factors such as data quality, production flexibility and product shelf life.
How Optiwiser Helps Companies Reduce Food Waste
Optiwiser combines AI-powered demand forecasting with additional planning capabilities for inventory, production and procurement. The solution is designed particularly for the requirements of FMCG companies and the food and beverage industry.
AI-Powered Demand Planning
Optiwiser analyzes historical sales data and incorporates additional influencing factors such as weather, promotions, holidays, events and market trends. Its StreamWiser™ technology selects forecasting models based on their expected accuracy and adapts forecasts to different demand patterns. This provides planning teams with a more reliable foundation for estimating future demand at product, customer and regional level.
Inventory Optimization Considering Operational Requirements
In addition to demand forecasts, inventory planning takes existing stock levels and relevant operational constraints into account. The objective is to avoid unnecessary inventory while maintaining high product availability. For food manufacturers, balancing product availability with limited shelf life is particularly important.
Demand-Driven Production and Purchasing Planning
Optiwiser's AI-powered production planning considers demand, inventory, capacities, lead times and other operational constraints. Purchasing planning can also be connected to forecast demand, allowing procurement quantities to be aligned more closely with planned production. This creates a consistent link between demand forecasts and operational execution.
How Much Potential Is Hidden in Your Purchasing Planning?
Excess inventory, frequent stock shortages or time-consuming manual purchasing processes may indicate opportunities to improve existing planning. With Optiwiser's free Potential Analysis, companies can review their current situation across forecasting, inventory, production and purchasing planning and identify potential areas for optimization. Optiwiser offers this analysis as a free, non-binding 30-minute consultation.
Book Your Free Potential Analysis
What Are the Benefits of AI-Powered Planning for Food Manufacturers?
The benefits of more accurate forecasts extend beyond reducing food waste.
Less Overproduction and Fewer Write-Offs
When production quantities are better aligned with actual demand, the risk of excess products remaining unsold before their expiration dates decreases.
Lower Inventory Costs and Less Tied-Up Capital
Demand-driven inventory levels reduce unnecessary storage requirements, tied-up working capital and inventory holding costs.
Higher Product Availability
Reducing overproduction does not mean inventory should always be minimized. Effective planning also considers demand fluctuations, replenishment lead times and target service levels to ensure important products remain available.
More Efficient Use of Raw Materials and Resources
By producing according to actual demand, companies can use raw materials, packaging, storage capacity and production resources more efficiently.
Better Decision-Making
Transparent demand forecasts make collaboration between sales, procurement, production and supply chain management easier. Instead of relying exclusively on experience and intuition, teams can base decisions on current data and understandable planning assumptions.
How Can Companies Measure Success in Reducing Food Waste?
To ensure improvements are sustainable, companies should evaluate not only forecast accuracy but also the actual impact on inventory and losses. Relevant key performance indicators include:
- Food Waste Rate: The proportion of discarded food relative to the relevant production or product volume.
- Inventory Write-Off Rate: The value of written-off inventory relative to inventory value or revenue.
- Forecast Accuracy: How closely does forecast demand match actual sales?
- Forecast Bias: Are certain products systematically overestimated or underestimated?
- Days of Inventory on Hand: How long will existing inventory last based on expected demand?
- Service Level: How reliably can customer demand be fulfilled?
It is important to assess these KPIs together. Higher forecast accuracy, for example, only delivers economic value when it leads to better planning decisions. Similarly, a lower food waste rate should not come at the expense of significantly reduced product availability.
What Are the Limitations of AI Forecasting?
AI can significantly support planning, but it cannot eliminate food waste entirely. Unexpected events, production disruptions, quality issues or short-term supply chain failures cannot be fully prevented, even with accurate forecasting models. The quality of results also depends on available data and how effectively the technology is implemented within the organization. For example, if production quantities cannot easily be adjusted because of large minimum batch sizes, the potential savings may remain limited despite improved forecasts. AI forecasting should therefore not be viewed as an isolated technology, but as part of an integrated planning approach that also considers operational constraints.
Food Waste Often Starts with Planning
Food waste cannot be reduced solely through better storage or more efficient disposal and recovery processes. An important starting point lies in deciding how much should be produced and procured in the first place. AI forecasting helps food and beverage manufacturers estimate future demand more accurately, identify changes earlier and prevent unnecessary overproduction. The greatest benefits arise when companies connect demand forecasts with inventory, production and purchasing planning.
In this way, AI can help reduce food losses, use resources more efficiently and maintain reliable product availability.
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