The Optimal Batch Size Decision Board

Excel can calculate EOQ, but ChatGPT can help turn the calculation into a planner-reviewable decision board. That matters because batch size can look like a clean calculation from the outside. But in a real planning conversation, that number is only the first draft. If the batch gets smaller, the business may carry less inventory and respond faster, but it may also create more setups, more changeovers, and more planning noise. If the batch gets larger, the production run can look efficient because setup frequency drops, but the cost can come back later through trapped cash, shelf-life risk, slower response, or capacity used on the wrong product. This is where ChatGPT becomes useful if we stop treating it like a calculator and start using it to build the first version of the decision artifact. Give it the actual planning context: demand rate by SKU or family, setup or changeover cost, holding cost, current batch size, MOQ or pack-size rule, production rate, shelf life, capacity calendar, service priority, demand variability, and planner comments. Then ask for a batch-size decision board. The useful output is a review table that shows: - first-pass EOQ - smaller, current, and larger batch options - setup or changeover burden - inventory exposure - capacity fit - service risk - assumptions that need to be verified Batch size decisions often hide behind the word efficiency. Bigger runs feel efficient because setup drops, but the planner still has to ask what the business is choosing to carry in return. The value of AI here is not that it decides the perfect batch, the value is that it makes the trade-off visible before the planner changes the plan or master data. If demand changed next week, would your team know which batch size is still carrying the right trade-off?