Every EOQ example you’ll find online shows clean numbers going into a formula and a clean answer coming out. Real inventory decisions rarely stop there. This is a fictional but realistic scenario showing both halves: the calculation, and what happens when it collides with how the business actually operates.
The Setup
Coffee Bean Depot is a fictional specialty coffee distributor supplying independent coffee shops and restaurants across a five-state region. It imports green coffee directly from a dozen origin suppliers and roasts to order. Like most distributors, it carries dozens of SKUs — one bean origin, processing method, and grade per SKU — each with its own demand pattern, cost structure, and supplier relationship.
This example follows one SKU: Ethiopian Yirgacheffe, a popular single-origin bean that its coffee-shop customers use as a year-round pour-over offering.
Business profile for this SKU:
- Annual demand: 12,000 lbs
- Sold to roughly 60 of Coffee Bean Depot’s 200+ retail customers
- Imported from a single origin supplier, shipped by ocean freight in consolidated containers
- Warehouse racking allocated to this SKU: 1,000 lbs
- Demand is fairly steady year-round, with a modest bump from October through December as shops add seasonal pour-over specials
Gathering the Numbers
Annual demand (D): 12,000 lbs. Pulled directly from the past year’s sales data for this SKU. Demand has been consistent enough month to month that an annual figure is a reasonable basis for the calculation — this bean doesn’t have the sharp seasonality some of Coffee Bean Depot’s other products do.
Ordering cost (S): $180 per order. This SKU is imported, so the ordering cost is higher than a typical domestic reorder: customs clearance and brokerage fees, inbound freight coordination, and the receiving team’s time to inspect and grade the shipment on arrival. Coffee Bean Depot’s purchasing team built this number from last year’s total customs, freight-coordination, and receiving labor cost for this supplier, divided by the number of shipments received.
Holding cost (H): $3.00 per lb per year. Green coffee is a perishable, moisture-sensitive product — quality degrades over time even in good storage conditions, so obsolescence risk is real, not theoretical. This figure combines climate-controlled storage space, insurance, the cost of capital tied up in inventory, and an allowance for quality degradation on beans held longer than a few months.
Calculating EOQ
EOQ = √((2 × 12,000 × 180) / 3) = √1,440,000 = 1,200 lbsWhat this means in practice:
- Order size: 1,200 lbs per order
- Orders per year: 12,000 / 1,200 = 10 orders, roughly one every 5 weeks
- Annual cost at this order size: (12,000 / 1,200) × $180 + (1,200 / 2) × $3 = $1,800 + $1,800 = $3,600
That last line is worth pausing on: at the true EOQ, ordering cost and holding cost come out equal — $1,800 each. That’s not a coincidence; it’s exactly what the formula is built to produce, and it’s a useful sanity check that the calculation was done correctly.
The Reality Check
On paper, Coffee Bean Depot should order 1,200 lbs of Ethiopian Yirgacheffe roughly every five weeks. Two real constraints get in the way immediately.
Constraint 1: Supplier minimum order. This origin supplier consolidates shipments into full ocean containers and requires a 1,500 lb minimum per order to justify the customs and freight coordination on their end. The calculated EOQ (1,200 lbs) is below that minimum — Coffee Bean Depot literally cannot order the “optimal” amount as a single shipment.
Constraint 2: Storage capacity. The warehouse has only 1,000 lbs of climate-controlled racking allocated to this SKU — less than both the calculated EOQ (1,200 lbs) and the supplier’s minimum (1,500 lbs). Even if the supplier’s minimum weren’t an issue, Coffee Bean Depot couldn’t physically receive and store a full 1,200 lb order at once.
So the constraints don’t just override the textbook answer — they conflict with each other. The supplier wants a bigger order than EOQ suggests; the warehouse can hold less than EOQ suggests. Neither the “optimal” number nor the supplier’s minimum fits in the space available.
The Actual Decision
Coffee Bean Depot’s options, as its purchasing manager laid them out:
Option A — Order the 1,500 lb minimum, split into two receipts. Place one purchase order for 1,500 lbs, but ask the supplier to release it as two partial shipments three weeks apart — 750 lbs each, both fitting comfortably within the 1,000 lb storage limit. This meets the supplier’s minimum on paper while never exceeding available space at any one time.
Option B — Rent overflow storage temporarily. Take the full 1,500 lb shipment at once and pay for short-term third-party cold storage for the 500 lbs that doesn’t fit on-site, drawing it down as the on-site stock depletes.
Option C — Negotiate a smaller minimum. Ask the supplier whether a 1,000-1,200 lb order is workable, possibly at a modest freight surcharge, since this would align with both the calculated EOQ and existing storage.
Coffee Bean Depot chose Option A. Splitting the shipment cost nothing extra — the supplier was willing to release a single purchase order in two batches, since the total volume ordered stayed the same from their side. It kept inventory within the physical space available at every point in time, and it landed close enough to the calculated EOQ that the added holding cost from ordering slightly above 1,200 lbs (1,500 lbs total, split into two 750 lb batches) was minor — roughly an extra $75-100 per year in holding cost compared to a pure 1,200 lb order, a difference not worth renegotiating a supplier relationship over.
Lessons From This Example
The formula gave a clean, correct answer: 1,200 lbs. That answer was not directly usable, because two independent real-world constraints — a supplier minimum and a storage limit — didn’t just override it, they conflicted with each other. The useful move wasn’t picking the constraint to ignore; it was finding a third option (splitting the delivery) that satisfied both.
This is the pattern worth taking away, more than the specific numbers: EOQ tells you the cost-minimizing order size assuming no other constraints exist. The moment a real constraint shows up — and in practice, one almost always does — the job shifts from “trust the formula” to “find the option that gets you closest to the formula’s answer without violating a constraint you can’t move.” What Changed? EOQ Decision-Making in Practice generalizes this exact kind of reasoning across the constraints you’re most likely to run into — supplier minimums, storage limits, seasonality, and volume discounts.