What is Pack Size Optimization?
Pack size optimization (also called pre-pack optimization) is the process of deciding how many units of each size (XS, S, M, L, XL, etc.) go into a pre-pack or case pack. Instead of ordering each size individually, retailers often receive merchandise in cartons that contain a fixed mix of sizes.
The goal is simple: design these packs so they reflect actual customer demand by size. That means fewer cases where a store has too many fringe sizes that won’t sell, and fewer cases where the most popular sizes sell out on day one.
In practice, this involves analyzing historical sales to create a “size curve” for each store or region. That curve shows what percentage of demand belongs to each size. The planner then converts that curve into an actual pack breakdown that fits a standard pack size, like 8, 12, or 24 units.
When done right, pack size optimization helps retailers keep the right sizes in stock, reduce markdowns, and make supply chain operations smoother.
Why Does Pack Size Optimization Matter?
Better Conversion and Customer Experience
Nothing is more frustrating for a shopper than finding the perfect item but not in their size. Research shows that stockouts directly cause walkouts. Shoppers often leave empty-handed, or worse, buy from a competitor, when their size isn’t available. By optimizing size packs, you increase the odds that core sizes are on the floor when customers need them. That leads to higher full-price conversion and stronger loyalty.
Fewer Markdowns, Less Lost Sales
Oversupplying fringe sizes locks up capital in inventory that eventually goes to clearance. Undersupplying popular sizes means leaving sales on the table. Both situations are costly. Industry research estimates the global cost of inventory distortion, overstocks plus stockouts, at $1.77 trillion. Pack optimization directly tackles this by matching supply with demand at the size level. That means fewer markdown racks and fewer “sorry, we’re out of your size” moments.
Balancing Inclusivity and Efficiency
Many retailers are expanding their size ranges to be more inclusive. That’s important, but it can create excess in sizes that don’t sell in certain stores. Pack optimization balances inclusivity with reality. You can enforce business rules like “each store must receive at least one of each size,” while still tuning the pack to actual demand.
Supply-Chain Implications of Pack Size Optimization
Pack size decisions ripple through the supply chain, not just the sales floor.
Vendors and Factories
Suppliers usually work with fixed carton sizes and MOQs. Standardizing pack sizes keeps SKUs simple and reduces ticketing, labeling, and QA work. More pack types improve demand fit but add complexity.
Distribution Centers
Pre-packs reduce picking and let cartons flow through without rework. Too many pack types or variable pack sizes bring complexity back. Pack IDs in ASNs, EDI, and WMS are key to smooth flow.
Brick–and-Mortar Retail Stores
Packs aligned to demand mean less backroom clutter, fewer floor-set reworks, and fewer transfers. Studies even show pack choices affect store storage needs, tying directly to labor and space.
Sustainability
Smarter packs reduce markdowns, transfers, and wasted miles. That cuts cost and supports ESG goals by reducing overstock and stockout waste.
How Pack Size Optimization Works
At its core, this is a math problem. You start with a demand curve, decide on a pack size, and then calculate the integer mix of units per size that best matches the curve.
For example:
A store sells a dress:
30% Small, 50% Medium, 20% large
If you're building a 10-unit pack, the perfect breakdown would be:
3 Small, 5 Medium, 2 Large
But for a 12-unit pack, the math gets tricker:
3.6 Small, 6 Medium, 2.4 Large
You can't ship fractions, so you test options like:
4S-6M-3L or 3S-6M-3L
and choose the one that's closest to the target.
Modern planning systems automate this process. They also account for business rules and constraints, like minimum presentation (at least one of every size on the floor) or vendor carton requirements.
One Pack vs. Multiple Packs
Some retailers use a single “average” pack across all stores. This is simple, but it often leaves mismatches in stores with very different size demand.
Others create 2–3 different pack types, each tuned to a cluster of stores. For example, urban stores might get packs skewed toward smaller sizes, while suburban stores get packs with more large sizes. Most retailers find that 2–3 pack types capture most of the benefit without adding too much complexity.
The trick is to stop before diminishing returns. Adding a fourth or fifth pack type usually adds more operational headache than sales benefit.
How to Implement Pack Size Optimization
- Clean the Data: Gather historical sales by size. Adjust for stockouts, returns, and anomalies so the curves reflect real demand.
- Set Business Rules: Decide on pack size, number of pack types allowed, and any guardrails (like minimum 1 unit per size).
- Run Scenarios: Test single-pack vs. multi-pack solutions. Compare service rates, markdown projections, and labor impacts.
- Backtest and Validate: Use past seasons to see how new packs would have performed. Measure improvement in size-level in-stock and sell-through.
- Operationalize: Create pack SKUs, communicate requirements to vendors, and set up DC systems to handle pre-packs.
- Review Regularly: Customer size trends shift over time. Revisit size profiles each season to keep packs aligned with reality.
What to Measure
To know if pack optimization is working, track:
- Size-level in-stock % – Are core sizes available more often?
- Sell-through % by size – Are sizes selling more evenly instead of piling up?
- Markdown rate – Are fewer units going to clearance?
- Store transfers – Are you reducing rebalancing between stores?
- DC labor metrics – Are cartons flowing through with fewer touches?
When these KPIs improve, you see the real value: higher full-price sales, cleaner inventory, and happier customers.
How Technology Helps with Pack Size Optimization
Doing this at scale is nearly impossible with spreadsheets. Modern planning systems automate the heavy lifting by:
- Building accurate size curves, even with imperfect data
- Running optimization algorithms that quickly test scenarios
- Managing business rules like “at least one of each size”
- Integrating packs into assortment and allocation workflows
- Providing visibility into outcomes through dashboards and alerts
For planners, this means less guesswork and more time spent on strategy. The system does the math; you set the rules and validate the outputs.
Final Takeaway
Pack size optimization is one of those quiet but powerful levers in retail. It keeps sizes in balance, improves conversion, reduces markdowns, and smooths out the supply chain. For planners, allocators, and merchants, it means fewer headaches and better results.
In a world where every lost sale or markdown matters, getting the pack right is a simple way to make a big impact.
If you’re interested in putting pack size optimization into practice, Toolio can help. See it for yourself - Speak to an Expert to find out if it’s the right fit for your business.
