How to plan daily production for several stores

It is five in the morning. The plant has already fired up the ovens, and store orders arrive by message, each in a different format. There is no time, so the plant bakes “the same as yesterday”. At seven in the evening one store throws away two trays of bread, while another closed its display empty at five in the afternoon, with customers walking off to the competition.

The problem is not the oven or the baker: nobody calculated how much each store was going to sell that day. This guide shows you how to do it with a simple method that works in a spreadsheet.

Why “the same as yesterday” doesn’t work

Bakery sales depend heavily on the day of the week. A Saturday looks nothing like a Tuesday, and neither does a Monday after a holiday. Copying the previous day carries the error forward: if yesterday was slow, you under-bake today; if there was a fair yesterday, you end up with leftovers today.

The right planning unit is product × store × weekday.

The method in five steps

1. Record each store’s actual sales, by product and by day. Use till sales, not what you shipped: what was sent is not what was sold.
2. Calculate the baseline: the average of the same weekday over recent weeks. Four weeks is usually enough for a bakery.
3. Adjust for events you know about: fairs, school holidays, public holidays, matches, weather.
4. Subtract what the store already has and can still sell. For same-day bread, usually nothing; for packaged cookies or cakes, the stock left in the store.
5. Consolidate all stores, add special orders and convert the total into ingredients using the recipe.

The formulas

Store baseline (for one product and one weekday):

Baseline = (S₁ + S₂ + S₃ + S₄) / 4

where S₁…S₄ are the sales on that same weekday over the last four weeks. If one of those weeks had something exceptional (a holiday, a power cut), drop it from the average and divide by three.

Store order:

Order = Baseline × event factor − sellable stock in the store

The event factor is 1 on a normal day; for example, 1.10 if you expect 10% more customers.

Plant production:

Production = (Σ store orders + special orders) / (1 − expected production scrap)

Production scrap is what is lost in the plant: burnt, misshapen or rejected pieces.

Worked example with three stores

Suppose you are planning the house bread roll for next Saturday. These are the sales from the last four Saturdays:

Store	Sat 1	Sat 2	Sat 3	Sat 4	Baseline	Adjustment	Order
A	420	380	400	440	410	Nearby fair, ×1.10	450
B	300	320	280	300	300	Sold out two Saturdays, ×1.05	315
C	250	230	260	240	245	No adjustment	245
Total					955		1,010

A few notes:

• In store A, 410 × 1.10 = 451, rounded to 450.
• In store B, sales “look” stable at 300, but on two of those Saturdays the bread ran out before closing. That means real demand was higher than recorded sales, so it goes up 5%: 300 × 1.05 = 315.
• Since it is same-day bread, no store stock is subtracted.

Now the plant adds a special order of 150 units for an event. Total demand is 1,010 + 150 = 1,160. If your historical production scrap is 3%:

Production = 1,160 / 0.97 = 1,195.9 → 1,200 units

With the recipe you turn that into ingredients. If 100 units use 6 kg of flour, you need 1,200 × 0.06 = 72 kg of flour for this product. Do the same for every product of the day and add up by ingredient: that is your purchasing and warehouse requirement. When you issue ingredients, use the lot that expires first; we explain it in FEFO vs FIFO.

Leftovers and shortfalls: the two data points that close the loop

The plan only improves if each store reports two things per product at closing:

Data point	How it is recorded	What it tells you
Leftovers	Units left at closing	You sent too much, or the event adjustment was optimistic
Shortfall	Time the product sold out	Recorded sales underestimate real demand

The shortfall is the most overlooked. If the bread ran out at four in the afternoon, that day’s sales are not demand: they are the ceiling of what you sent. If you average those sales without correcting them, the plan shrinks week after week.

Indicators to check whether the plan works

• Leftover % = leftovers / units sent × 100. Suppose store A received 450 and had 27 left: 6%. Measure it by store and product, not only in total.
• Stockout rate = product-days sold out before the cutoff time / planned product-days × 100. Set a reasonable cutoff (for example, one hour before closing).
• Production scrap = units discarded in the plant / units produced × 100. If it rises, review the process before touching the plan.
• Plan accuracy = 1 − |order − actual sales| / actual sales. The closer to 1, the better.

Read them together. Low leftovers with many stockouts mean you plan too tight. High leftovers with no stockouts mean you can lower the order. Both high in the same store usually means the product mix is poorly split: one item is left over while another runs out.

Daily checklist

• Yesterday’s sales for each store are loaded by product.
• Yesterday’s leftovers and sell-out times are recorded.
• The baseline uses the same weekday and excludes atypical days.
• Tomorrow’s events are noted with their factor.
• Confirmed special orders are added.
• The total was converted into ingredients with the current recipe.
• Ingredients are issued by expiry date.

Common mistakes

1. Planning with what was shipped instead of what was sold. If you always send 300, the average will always tell you 300.
2. Ignoring stockouts. Sales on a day that sold out early are not demand; correct them upward.
3. Using the average of the whole week. It mixes Saturdays with Tuesdays and you get both wrong.
4. Letting each store order “by eye” without a baseline. The order ends up depending on who is on shift.
5. Not separating plant scrap from store leftovers. They are different problems with different fixes.
6. Outdated recipes. If the real recipe uses more flour than the recorded one, your purchases and your cost will always fall short.

If you want to automate it

With a few stores and products, this method works well in a spreadsheet. As volume grows, an ERP like MKA can suggest each store’s order from the average of the same weekday in recent weeks, consolidate it with special orders into a daily plan and explode the recipes to know what to make and what to buy (see bakeries and food).