Seasonal demand planning is the work of forecasting a peak, building stock ahead of it, placing that stock where the peak will happen, and running it down before demand falls away. In retail it covers holiday and weather peaks, tourist seasons that move demand from one region to another, and fashion seasons with a fixed start and end. The plan has to decide how much to buy, when to buy it, where to put it and when to stop ordering, all for a period you have seen only a few times before.
What Seasonal Demand Planning Covers
Seasonality in retail comes in a few distinct shapes, and each one needs a slightly different plan:
- Calendar peaks: Christmas, Ramadan, back to school, national holidays. The date is known, so the questions are size and mix.
- Weather-driven demand: ice cream, beverages, sun care, heaters. The season is known, but the timing and size of each week depend on the temperature.
- Tourism and location swings: coastal, ski and holiday towns where the number of shoppers changes for a few months, and demand jumps in stores that are quiet the rest of the year.
- Fashion and seasonal ranges: products bought once, before the season, with little or no chance to reorder. What is left at the end has to be cleared.
Promotions also create peaks, but they are planned events with their own uplift, cannibalization and post-promotion dip. We cover them separately in our promotion management guide, from forecasting net lift to planning the replenishment behind each offer.
Forecasting a Season You Have Seen Only a Few Times
A product that sells every week gives 52 data points a year. A summer range sells for one stretch of weeks a year, so three years of history gives you three examples of the peak. A good seasonal forecast makes up for the thin history in a few ways.
First, it forecasts at the level where the season actually happens. A chain-level forecast can look stable while coastal stores triple and inland stores dip. Grouping stores by their seasonal profile, rather than by region or size, lets a forecast for a seasonal store borrow from other stores that behave like it.
Second, it separates the seasonal shape from the level. Last year’s peak may have been cut short by stock-outs, so the sales history understates demand. Correct for lost sales before the history becomes next year’s profile, or the plan will repeat last year’s shortfall.
New seasonal items have no history of their own. Planners usually forecast them from similar items in past seasons, matched on attributes such as category, price point and color.
Once the season starts, short-term signals take over: weather, local events, the first weeks of sell-through. Our guide to demand sensing covers how those signals adjust the near-term forecast.
Building Stock Before the Peak
A peak is usually larger than what suppliers, DCs and trucks can move in the weeks it lasts, so some of the stock has to be built or moved early. That pre-build is a trade-off: start too late and the peak runs short; start too early and you pay to store stock that might not sell.
The build plan works backward from the peak. It needs supplier lead times and capacity, DC and store space, and how many deliveries each location can take in a week. Stock bought for a seasonal build is an investment, and it is easier to manage when it is tracked apart from safety stock and cycle stock.
The Magnum Ice Cream Company faces this every year: summer demand can be five times higher, with volumes rising between May and September across 130+ distributors. Solvoyo sets weekly inventory targets for each distributor and product, simulating a year of transport flows so that the plan finds pre-shipment opportunities within distributor capacity. In peak weeks, pallet quantities are rounded to full layers, which cut warehouse handling time nearly tenfold, and planning time in 2024 fell to a third of earlier years.
Pre-Season Allocation and In-Season Replenishment
Before the season, the initial allocation decides how much of the build each store gets on day one. Each store needs enough for its presentation minimum and early sales, and the rest is held back in the DC. Holding stock centrally keeps options open, because the first weeks of selling show which stores and items the forecast got wrong.
In season, replenishment follows sell-through. Fast stores get topped up from the DC, and when the DC runs low, store-to-store transfers move stock from slow locations to fast ones. These are daily decisions across every store and item, which is too many for planners to make by hand during a peak. DeFacto, a fashion retailer in 50 countries, runs initial allocation, replenishment and transfers on Solvoyo with 150M+ weekly recommendations, and 95% of its daily recommendations are accepted.
How to Clear Seasonal Stock at the End of the Season
Unwinding starts before the peak ends. The last order for each item should land while there is still enough selling time left to sell it, so the cutoff date for each item comes from the supplier’s lead time and the expected sell-through.
After the last order, the levers are:
- Taper replenishment so stores carry less cover each week as demand falls.
- Move remaining stock to the locations that will still sell it.
- Time markdowns by store and item, deep enough to clear but no earlier than needed.
- Return stock to suppliers or carry it to next season where the terms and the product allow.
Markdown timing decides how much margin the leftover stock gives back. DeFacto uses Solvoyo’s markdown optimization across 50 countries, with 80% of markdown planning running no-touch and gross margin up 200 basis points.
Tourist Seasons: The Studenac Market Case
Studenac Market is the leading retailer on Croatia’s Adriatic coast, with 1,000+ stores, many of them seasonal, and 1,500+ seasonal products among 14,000+ active SKUs. When summer tourists arrive, demand for everyday essentials in seaside towns can rise three to five times. When they leave, the same stores go back to serving local customers.
Replenishment had been mostly manual, on inconsistent data. The project started by cleaning and connecting the data, then built one automated replenishment model for seasonal and non-seasonal products and stores, covering both DC deliveries and direct-to-store shipments. Studenac’s published results are 8x planner productivity, an 81% user acceptance rate, 55% fewer lost sales, 15% less excess stock and 8% better availability. The full story is in the Studenac case study.
How Solvoyo Approaches Seasonal Planning
Solvoyo is a supply chain decision automation platform. It forecasts, builds, allocates, replenishes and marks down from one data model, so the build plan, the store orders and the markdowns all work from the same numbers. Its recommendations already respect peak-season limits such as distributor capacity, truck loads, pack sizes and presentation minimums. Across enterprise clients, over 95% of plans execute untouched.
Planners can model a heatwave, a holiday rush or a tourist surge and see how orders, stock and margin respond before they commit. See Solvoyo demand planning for the forecasting side, or request a demo to see a season planned on your own data.
Frequently Asked Questions
Seasonal demand planning forecasts a recurring peak in demand and plans the stock around it: how much to buy, when to build it, how to allocate it to stores before the season, how to replenish during the season and how to clear what is left at the end. In retail the peaks come from holidays, weather, tourism and fashion seasons.
They forecast at the store and item level where the season happens, group stores with similar seasonal profiles so each store can borrow from its peers, correct past sales for stock-outs, and forecast new seasonal items from similar items in earlier seasons. Once the season starts, weather and early sell-through adjust the near-term forecast.
Set a last order date for each item from the supplier lead time and the selling time left after the stock arrives. After that date, taper replenishment, move remaining stock to locations that still sell it, and time markdowns by store and item so the season clears without discounting earlier than needed.
