Short answer: spent grain, grape pomace and distillery draff are the biggest things leaving a drinks plant, usually treated as waste. The lever is routing each batch to its highest-value use — feed, food or energy — net of haulage and spoilage. Data forecasts volumes; AI allocates; biology sets the clock.
The largest by-product on site is often handled as a chore, given away or landfilled. But high in protein and fibre, it is a circular-economy opportunity the moment you start weighing it.
Related: what to do with spent grain.
Measure first, model second
Put a scale and a moisture reading on the by-product stream and log off-take. You cannot optimise — or claim diversion — for tonnes you never weighed.
Where AI and data cut by-product waste
ML forecasts weekly by-product volume from the production schedule and allocates it across feed, food and energy routes to maximise value net of haulage distance and the spoilage clock.
Where generative AI (Claude, ChatGPT) helps
A copilot drafts the circular-economy narrative for a CSRD or GRI disclosure and answers ‘how much went to feed versus landfill?’ from the by-product ledger. The rule holds: it drafts and explains, a person verifies anything that reaches a regulator.
The rules, region by region
Across regions the levers are the same but the rules differ: the UK (SECR energy/carbon reporting, packaging EPR), the EU (CSRD, the EU ETS, and the Packaging and Packaging Waste Regulation), the USA (EPA water and Energy Star, state programmes like California’s, and TTB for labelling), and India (the Bureau of Energy Efficiency’s PAT scheme and CPCB effluent norms). Measure to your own meters first; map to whichever framework applies.
Where it breaks
Perishability beats cleverness — by-product spoils in days, so an optimiser is useless without the drying or off-take to act in time. And drying can erase the carbon win unless you use waste heat.
The bottom line
By-products are the biggest, most overlooked stream leaving a drinks plant. Weigh them, forecast them, and route each batch to its highest net value before the clock runs out.
Frequently asked questions
How can data and AI cut by-product waste? ML forecasts weekly by-product volume from the production schedule and allocates it across feed, food and energy routes to maximise value net of haulage distance and the spoilage clock.
Where do Claude and ChatGPT fit in sustainability? A copilot drafts the circular-economy narrative for a CSRD or GRI disclosure and answers ‘how much went to feed versus landfill?’ from the by-product ledger.
What can a brewery do with spent grain? Route it to its highest-value use: animal feed (cheapest, fastest), food-grade flour or protein (highest value, demand-limited), or biogas/energy (closes the loop). The right split depends on your volumes, moisture and haulage — an allocation problem AI handles well.
Part of the ESG Analytics for Beverage track.