In today's world, where sustainability and responsible resource management are of paramount importance, the food and beverage industry faces a significant challenge: reducing food waste.

 “Approximately 1.3 billion tons of food, equivalent to one-third of all food produced for human consumption, is wasted or lost globally each year.”

However, with the advent of artificial intelligence (AI) and advancements in supply chain automation, there is newfound hope for addressing this pressing issue.

These innovative solutions harness the capabilities of AI algorithms to provide accurate insights, optimise inventory management, and ultimately drive significant reductions in food waste. By leveraging the power of AI, businesses can make informed decisions, minimise overproduction, streamline supply chain operations, and ensure fresher products reach consumers while significantly reducing waste.

‍

Let’s look at 5 ways AI transforms Demand Forecasting and Inventory Optimisation

‍Achieving Accurate Demand Forecasting Accuracy

Demand forecasting is the first step in managing a supply chain. It's critical to get the accurate demand numbers in real time to plan supply accordingly. There are several factors that impact demand and it is important to factor them while forecasting future demand.

‍

AI-powered demand forecasting models analyse various data sources, including historical sales, market trends, weather patterns to predict consumer demand more accurately. By improving demand forecasting accuracy, food producers can better align their production levels with actual demand, minimising the risk of overproduction and subsequent waste.

‍Setting Standard Inventory Management Practices

It is crucial for businesses to follow standard purchase and replenishment practices to achieve efficiency and reduce waste. Supply planning requires precise timing to provide dependable, repeatable synchronisation of demand and inventory. Businesses must account for dynamically changing lead times, fill rates, price, shelf life, current inventory levels, and open orders for various categories and items in order to implement shorter purchasing cycles as businesses lower their days on hand needs.

Purchase and replenishment planning can be an exhausting task when performed manually leading to inefficiency and human error. This is where AI based inventory optimization simplifies the task. They continuously optimise inventory as demand changes eliminating any inventory imbalances. 

Providing End to End Visibility

AI based demand forecasting and inventory optimisation tools enable enhanced traceability in the supply chain. With transparent and immutable records, stakeholders can track the origin, processing, and distribution of food products. This traceability helps identify bottlenecks, minimise delays, and improve inventory management, ultimately reducing food waste.

Automation also enables seamless collaboration between various teams through inclusive demand plans and better visibility. For instance, when an employee edits purchase order quantities due to an unexpected surge in demand they can notify other users about this change by adding comments. This will allow for quicker communication and proactive actions rather than costly reactive responses.

Collaborating With Suppliers To Reduce Food Waste

Suppliers play an important role in ensuring that the right product reaches you at the right time. Continuous supplier evaluation is important for businesses to ensure that food gets from farm to fork in a timely manner to avoid food waste.

Businesses need to account for dynamically changing lead times, fill rates, pricing, shelf life, current inventory levels, and open orders for different SKUs across stores to bring in motion shorter cycles of purchasing as businesses move to lower days on hand needs  and prevent pile ups. Real time supply planning helps take into account these critical supplier metrics and recommend the best course of action to fulfil business needs. 

Liquidating Excess Inventory Before They Expire

Most businesses procure excess inventory to deal with erratic demand behaviour. As a result, they end up with unsold inventory often. This also happens when businesses don’t have visibility into real time demand changes. 

AI algorithms can analyse various factors like shelf life, expiry dates, and warehouse capacity to estimate when to clear perishable goods accurately. By implementing automated systems that monitor and analyse these parameters, businesses can optimise stock rotation, prioritise items nearing expiration, and minimise food waste associated with spoilage.

As we look into the future, the role of AI in reducing food waste can further evolve and expand. With this, there is greater potential for businesses to make more informed decisions that are also planet friendly. Further, collaborative efforts between various stakeholders like producers, suppliers and customers can contribute towards creating more efficient, responsible and  resilient food supply chain systems.

‍