Seasonal Demand in Retail India: Why Last Month Misleads

The order that looks right on paper

Picture a grocery store owner placing the week's order in the first week of the rains. The screen says cold drinks and ice cream moved well last month, so the suggested quantities are generous. Meanwhile besan, tea, instant noodles and mosquito coils are quietly running thin on the shelf.

Nothing on that screen is wrong. It's answering a different question: what sold over the last few weeks. The question you actually have is what will sell over the next few.

That gap is where seasonal demand in Indian retail bites. The monsoon, then Navratri, Diwali, wedding season, winter, summer. Each one bends the mix, and an order built on last month's sell-through is always running a season behind.

How a trailing average carries the last season forward

Worked example: say a soft drink sold 6 units a day through June, then the rains settled in at the start of July and it dropped to 2 a day. On 10 July, a 30-day average still holds about 20 days at 6 and 10 days at 2. That's (20 × 6 + 10 × 2) ÷ 30 = 140 ÷ 30, roughly 4.7 a day. Order a week's cover at that rate and you bring in about 33 units when 14 would do. The other 19 just sit there.

Now flip it. Suppose besan went from 3 a day to 8 a day once the pakora weather arrived. The same 30-day average works out to (20 × 3 + 10 × 8) ÷ 30, again about 4.7 a day. So you under-order by more than 3 units a day on the line customers are actually asking for.

Same arithmetic, two mistakes: too much of the fading line, not enough of the rising one. The empty shelf costs you as much as the extra stock, as we worked through in the hidden cost of out-of-stocks on perishables.

Festivals make it worse because the spike is short. The average keeps climbing through the run-up and peaks just after the festival ends. So the biggest suggested order lands right as demand falls off a cliff. That's how a store ends up with the leftover boxes we covered in Diwali stock planning.

Same weeks last year: the comparison that works

The better baseline is the same stretch last year, lined up to the event rather than the calendar date. The rains don't arrive on the same day every year, and festivals move with the lunar calendar, so Diwali can fall in October one year and November the next. Compare "the two weeks before Diwali" with "the two weeks before Diwali", not 1 October with 1 October.

For example, a simple version of the routine looks like this:

• Pull last year's weekly sales by item for the season you're about to enter, plus the few weeks before it.
• Work out last year's seasonal ratio: the daily rate during the season divided by the daily rate in the weeks just before it.
• Take this year's rate from the weeks just before the season and multiply by that ratio.
• Use the result, not the trailing average, as the planning rate for the coming weeks.

Worked example: last year besan sold 3 a day in June and 8 a day in the first three weeks of the rains, so the ratio is 8 ÷ 3, about 2.7. This June it's running at 3.5 a day. Multiply and you get a planning rate of about 9 a day. That's a number you can order against before the rains start, not after.

The ratio carries over better than the raw quantity because your store has changed. You may have added a counter, lost a neighbour, or picked up new families in the area. The ratio captures the shape of the season. This year's base rate captures your store as it is now.

Flagging lines whose sell-through has shifted

You can't run this for every item every week, so narrow it down first. The quickest filter is a shift ratio: the daily rate over the last week or two divided by the trailing month's rate.

For example, with thresholds you'd tune to your own store:

• Shift ratio below 0.7: the line is cooling faster than the average shows. Check stock on hand before the next order.
• Shift ratio above 1.4: the line is heating up. Check lead time and whether you'll run dry before the next delivery.
• Anything in between: leave it alone this week.

Then check each flagged line against last year's same weeks. If last year showed the same jump at the same point in the season, it's seasonal and you can plan with the ratio. If last year was flat, something else is going on, like a new competitor, a price change or a supplier gap. That needs a different fix.

The monsoon adds a second problem on top of demand: damp gets into atta, biscuits, salt and cartons. A line that's selling slower and sitting in humid storage is a double risk. Monsoon humidity damage covers the storage side.

Adjusting the order once a line is flagged

Once you know which lines have shifted, the order changes in three different ways.

Cooling lines. Order to the recent rate, not the average, and count how many days of cover you already hold. Worked example: 40 units on hand at 2 a day is 20 days of cover. If the product has 12 days of shelf life left, about 16 units won't sell in time, so skip this order and plan a markdown now. Markdown pricing for near-expiry products walks through how to set the discount.

Heating lines. The risk is the gap between the order and the delivery. If your distributor takes four days in the rains instead of two, your buffer has to cover the slower trip. Safety stock from your own lead times shows how to size that from your own order history.

Festival lines. Plan the exit before you plan the entry. Order for the run-up, then taper hard, so the last delivery lands well before the festival day rather than on it. Whatever's left after the festival is either a markdown or a supplier return, so check the return terms before you place the big order.

What a spreadsheet can and can't carry

All of this works on paper or in a spreadsheet for your top thirty or forty lines. Keep a sheet with last year's weekly sales, a column for the seasonal ratio, and a column for the shift ratio. Ten minutes on a Monday will catch the worst mistakes. A seasonal inventory planning calendar helps you know which season to prepare for next.

The limits are real, though. You need last year's data by week. If your system only kept monthly totals, start saving weekly exports now so next year's comparison exists. The routine also gets skipped in the weeks you're busiest, and those are the festival weeks when it matters most. And a spreadsheet won't tell you which of the two hundred lines you didn't check has quietly turned.

A daily check alongside your current system

ShelfSense by ShelfLifePro is a daily inventory agent that works alongside the system you already run. Once a day it reads your stock, batch, cost and expiry records and surfaces what to mark down, clear or reorder, ranked by money at risk. It shows the arithmetic behind every call, so you can check it like the worked examples above. It sizes reorders from sell-through and cost basis, with a safety buffer and a lead time learned from your own orders. It recommends, and you approve. It learns over time from which calls sold and which didn't.

The demand-trend view is part of the Starter plan. Starter and Act plans are listed on the ShelfSense page. If you're on Tally, Shopify or WooCommerce, export to CSV today; a direct connection is on request. ShelfSense's free audit reads one CSV export, no account needed. The Watch tier is free forever at 30 SKUs — a daily scan and a health report. You can start free on the ShelfSense page before the next season turns.