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The root cause of most of these issues is one or a combination of failures around product and packaging design, process and system design or operational control and conversion. Each of these root causes can be significantly reduced with a set of fairly simple actions. Overcoming common barriers
WASTE REASON
SOLUTION
WASTE REASON
SOLUTION
Product and packaging design Example: Waste caused by inefficiently designed product or packaging specifications. Found in: Quality Control, Preparation, Planning, Operations. Issue: Innovation without understanding factory constraints can lead to specifications that effectively build repeated waste. Often product development is carried out in a test kitchen disconnected from operational teams. Designers may not realise that, at scale, certain standards add a level of unnecessary complexity and waste. As a result, manufacturers may agree specifications with retailers that are not operationally ideal. Agreements can get close to the boundaries of what is practically possible.
Connection and conversation Manufacturers Are product development teams
Process and system design Example: Systematic waste due to processes and supply chain designs that lack agility and are held back by batch requirements or outdated planning systems. Found in: Quality Control, Preparation, Giveaway, Planning, Operations, Changeover. Issue: When making complex products, waste is inevitable if systems and processes are not fully understood and constantly monitored. Look at ready meals. If five components make up a chicken curry, as soon as the first element runs out, there is no use for the other four ingredients. If the shelf life is short, manufacturers end up throwing them all away. This shortfall could be the result of any one of a multitude of assumptions being inaccurate – what yield will be achieved after the chicken is prepared, and then cooked, and then cooled, for instance.
Non-stop issue spotting and correction Given the number of items in the supply chain of a typical manufacturing process and the frequency at which those change, it is impossible to manually interrogate data and get to root causes fast. Reducing waste is rarely about finding a 20% gain every three months: it’s 100 smaller changes that create a cumulative incremental impact. There might be a theme to them, such as an incorrect piece of data that means all the yields throughout the process are out. But even then, multiple changes will be required. High frequency data analytics and AI can help spot and track exceptions, providing ongoing visibility of where loss is occurring and what needs to be solved. On one production line for instance, an AI solution identified additional edible food surplus that could be sold for human consumption over animal feed, resulting in a 15-fold increase in revenue from surplus 8 . Manufacturers should be confident their data is accurate, churn through actions to spot issues, build consensus and remedy rather than simply report when it’s too late. Even if a factory was to hit on the perfect ratio, there would be some waste as most manufacturers build in a production surplus rather than risk undersupplying. Retailers can play their part in reducing waste by discussing levels of planned surplus and agreeing a plan of what to do with it.
connected to operations so they have clarity on the constraints of the business, knowing which features are likely to cause surplus or waste issues? Can you have data-led, open discussions with retailers when setting specifications? Retailers Are you asking questions of manufacturers about which features add cost and drive product into the bin? Do customers care which way up a pepper is placed? Do sausage rolls need to be an exact width and length to the millimetre? Is the colour range agreed on loaves of bread unnecessarily tight? Use data to get clear on what customers notice and appreciate, so you can challenge assumptions about specifications. Do long-term agreements with suppliers, set thoughtful targets for waste reduction and agree how to invest to reduce waste, for instance by updating machinery?
8 Scaling AI-led food waste and surplus visibility, reduction and redistribution, Sustainable Ventures, 2026
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