ran a nationwide TV marketing campaign for its best-selling attire line.
The demand planner up to date the forecast on a Tuesday morning. However the provide crew solely discovered eleven days later, within the month-to-month assembly!
By then, the manufacturing facility lead time had expired, and two outlet shops had empty cabinets for the primary week of the marketing campaign.
VP of Advertising and marketing: Tens of 1000’s of {dollars} in misplaced marketing campaign income as a consequence of inadequate stock!
The advertising and marketing funds was spent. The site visitors confirmed up. The product didn’t.
That is what S&OP was supposed to repair, and in most retailers, it nonetheless has not.
This isn’t a expertise downside. It’s the means most retailers organise the dialog between Gross sales and Operations.
Built-in Enterprise Planning, also called Gross sales and Operations Planning (S&OP), is the method that interprets gross sales forecasts (“We expect to sell 50 units next month”) to merchandise on the shelf (“75 units delivered at Store 125 last week”).
In a spreadsheet world, every crew works by itself copy of the information, and a single forecast change takes weeks and the fee is measured in misplaced gross sales no person can hint again to the unique delay.
What must be a seamless course of turns into a sequence of emails and conferences, during which groups uncover modifications too late.
At a vogue retail firm I’ve been working with, it took as much as 27 days to see a change within the demand sign to be communicated to the warehouse groups.
The data was trapped in remoted Excel recordsdata shared through electronic mail, with restricted traceability and model management.
The foundation reason behind poor planning execution isn’t the forecasting mannequin alone. It’s the siloed organisation round it.
Merch Group to Demand Planning: Are you able to ship me forecast_jan2026_v12.xslx to organize tomorrow’s shopping for session?
In most firms, the reply is measured in weeks, and the fee reveals up in subsequent quarter’s margin with out anybody connecting the dots.
Within the simulation you might be about to learn, it’s one working day!
An ideal forecast that no person downstream sees in time is price nothing.
On this article, I’ll present how a linked planning platform turns that two-week delay into a same-day motion plan, and why this single change can save retailers hundreds of thousands.
We’ll use a simulation platform that I designed from scratch, the place you may experiment with how info flows throughout groups via built-in enterprise planning.
There’s a video model of this text for extra particulars,
The 5 Groups That Don’t Speak
To make this concrete, let’s take a look at SupFashion, a mid-size European vogue retailer that’s sufficiently small to be agile and large enough to really feel each planning failure in its margin.
A Mid-Measurement Trend Retailer with a Complicated Provide Chain
SupFashion is a mid-size vogue retailer primarily based in Europe.
It sells 4 product strains (wallets, baggage, baggage, attire), sources from 5 factories on three continents, and runs 12 shops throughout the US and Europe.
The model is small by trade requirements, however the planning challenges are precisely the identical as these of a world vogue large.
The 5 groups within the chain
To plan and run this community, 4 planning groups work in sequence each month, supported by a finance crew that closes the loop.
Every crew’s job is dependent upon the output of the crew earlier than it:
- The merchandiser can’t lock a purchase plan till the demand planner has signed off on the forecast.
- The provision planner can’t place a manufacturing facility order till the purchase plan is authorised.
- Finance can’t challenge margin till manufacturing and freight prices are confirmed.
Right here is the catch. None of those 5 groups works in the identical instrument!
5 Groups, 5 Spreadsheets, Zero Dialog
That is how each planning error enters the system. Not via unhealthy fashions, however via copy-and-paste.
In a SupFashion, every of those 5 groups works by itself copy of the information, in its personal instrument:
- A requirement planner has
forecast_jan2026_v22.xlsx. - The merchandiser has
buy_plan_SS26_FINAL_v3.xlsx. - The provision planner manages a Google Sheet shared with the manufacturing facility.
- Distribution runs on a Energy BI dashboard constructed three years in the past by somebody who has since left.
- Finance reconciles all of it in a separate system, two weeks after the actual fact.
When the demand planner modifications a quantity, it sits in her file till the subsequent month-to-month assembly.
From there, it travels by electronic mail and is re-typed throughout 4 groups. Two weeks later, the distribution lastly hears about it.
Are we engaged on the proper model of
XXX.xlsx?
Within the subsequent part, we’ll comply with a single forecast change intimately and see what occurs when these two weeks turn into one working day.
One Forecast Change, Two Realities
Let me introduce you to SupFashion’s end-to-end planning crew.
We’ll analyse how all the course of chain reacts to a change within the demand sign, contemplating two situations:
- State of affairs 1: The remoted spreadsheets world that SupFashion lives in at this time
- State of affairs 2: Planners engaged on the identical linked platform SupPlan
We’ll uncover how SupPlan enabled SupFashion to chop the end-to-end cycle time from 14 days to lower than 24 hours.
The set off: A Sports activities Truthful Beginning in Might
It’s Friday morning, April tenth.
Sarah, the demand planner at SupFashion, receives a message from the advertising and marketing director.
“The Base Layer Top – Black has been selected for the Southeast Summer Sports Fair in Charlotte and Atlanta (May 2026). We expect a 30% footfall increase in both stores. Please adjust the forecast accordingly.”
Sarah is aware of from previous campaigns that outlet shops in tier-2 cities reply strongest to TV promoting.
She wants to extend the forecast for Base Layer High – Black (Attire class) at two Southeast US shops for Might 2026:
- +30 models at Charlotte Outlet (94 → 124)
- +20 models at Atlanta Customary (136 → 156)
Whole adjustment: +50 models for one month, two shops.
What occurs at SupFashion at this time with spreadsheets
Sarah opens forecast_apr2026_v8.xlsx, updates the cells for Charlotte and Atlanta shops.
She saves the file and emails it to Marc, the merchandiser accountable for the North American market.
Timeline – Day 1 (Friday): Sarah sends the e-mail.
- Topic:
Up to date forecast for Base Layer High, Charlotte + Atlanta, pls evaluation.
Timeline – Day 4 (Monday): Marc opens the e-mail and
- Downloads the file, compares it with
buy_plan_SS26_FINAL_v3.xlsx - He manually re-types the brand new portions into his personal spreadsheet, however forgets Atlanta Customary.
Base Layer High - Black – (Picture by Samir Saci)The Atlanta Customary shopping for plan doesn’t account for the further demand of 20 models.
Sadly, he emailed the up to date purchase plan to Li Wei, the provision planner.
Timeline – Day 6 (Wednesday): Li Wei opens Marc’s purchase plan to replace manufacturing facility orders
- Checks the manufacturing facility capability of the attire provider, Dhaka Clothes Ltd.
- Solely updates the orders of Philadelphia Outlet.
- She discovered an issue!
With a 21-day lead time from Dhaka, she wanted to commit on March tenth to obtain items by Might 1st.
That was six days in the past.
It’s too late. The products is not going to arrive on time for the marketing campaign.
Li Wei flags the difficulty in a reply-all electronic mail, however no person responds till Wednesday.
Timeline – Day 11 (Monday): The month-to-month S&OP assembly occurs.
- Sarah presents her forecast.
- Marc presents his purchase plan with the Atlanta adjustment lacking.
- Li Wei raises the concern concerning the lead time.
The distribution crew hears concerning the change for the primary time.
Distribution Group: “Where is the store allocation?”
It doesn’t exist but!
Timeline – Day 14+ (Thursday): Distribution lastly receives the confirmed manufacturing order and begins planning the warehouse allocation.
However items will arrive in late Might on the earliest, lacking the primary two weeks of the Sports activities Truthful.
Consequence: investing in a advertising and marketing marketing campaign with out the stock to transform it
- Charlotte retailer will get late deliveries.
- Atlanta will get nothing.
A $40,000 advertising and marketing funding that drives site visitors to half-empty cabinets in Charlotte and nil inventory in Atlanta.
The misplaced gross sales from Atlanta alone exceed $1,500. The misplaced gross sales from Charlotte’s late supply push the entire properly previous the price of the marketing campaign itself.
Within the subsequent part, we’ll simulate the very same situation utilizing now SupPlan.
The identical situation, the identical 52-day constraint, the identical shops. The one variable we alter is how the data travels.
What occurs with a linked platform?
Allow us to think about our situation triggered by the identical occasion at the very same time.
However this time, Sarah makes use of the linked planning platform SupPlan as an alternative of spreadsheets.
- Sarah opens the Demand Planning web page and selects the Planner View tab.
- She filters by the SKU: Base Layer High – Black.
Timeline – Day 1, 10:00 am: Sarah Opens the Planner View
- Sarah clicks on the forecast cell for Charlotte Outlet, 2026-05, varieties the brand new worth, and presses Enter.
- She does the identical for Atlanta Outlet.
For every change on the SKU x Retailer degree, the instrument gives an summary of the potential impacts.
She has elevated the Might forecast for Base Layer High – Black by 50 models.
These two modifications are staged; which means they haven’t but cascaded to the remainder of the planning chain.
Timeline – Day 1, 11:00 am: Earlier than saving, Sarah switches to the Forecasts tab.
The Cascade Influence Preview panel opens on the proper.
This window seems to tell Sarah concerning the cascading impression of those two modifications:
- Merchandising: what’s the impression on purchase? (+50 models)
- Provide Planning: impression on manufacturing facility orders? (+50 models to supply at Dhaka Clothes, 52-day lead time)
- Finance: further income and prices? ($+3950 in income)
Sarah now sees the total downstream impression earlier than committing to something.
She saves the adjustment with the explanation: “Southeast Sports Fair – May 2026” and clicks Run E2E Cascade to tell the opposite groups.
Timeline – Day 1, 02:15 pm: Marc opens the Merchandise Planning web page.
A notification banner is already on the high of his display:
“Demand change: Base Layer Top – Black.
Forecast for Charlotte Outlet (Retail) in 2026-05 changed by +30 units (94 to 124).
Reason: TV campaign May-June, outlet stores, confirmed by marketing.”
The system generates two up to date purchase orders for Might 2026, already reflecting the brand new forecast portions.
Marc doesn’t must open an electronic mail, retype numbers, or reconcile a spreadsheet.
He merely opens the purchase plan and finds two new draft strains already ready for him with:
- Up to date portions: former baseline + Sarah’s forecast changes
- Corresponding income projections: models x (unit worth)
- Margin impression: models x (unit margin)
He simply has to approve every row and choose the top-right button, Notify Provide, to tell Li Wei.
We saved a gathering and, extra importantly, Marc is not going to miss Atlanta Customary!
Timeline – Day 1, 04:00 pm: Li Wei opens the Provide Planning web page.
The identical notification is ready for her informing her that:
- Sarah up to date the demand forecasts for the 2 shops
- Marc created new purchase orders reflecting this variation
And she will discover under two further manufacturing orders created.
Not like within the earlier situation, they’re now anticipated to be delivered on time (Might 1st, 2026).
If Li Wei confirms them at this time, the shops can be prepared for the marketing campaign!
She simply has to press Notify Distribution, so the logistics crew are knowledgeable about these further shipments.
Timeline – Day 1, 05:30 pm: Omar Hassan, our distribution planner, receives a notification
He’s notified concerning the two new manufacturing facility orders created by Li Wei.
And two further orders have been created, with a standing of deliberate, to ensure that the completed items acquired from the manufacturing facility are transferred to the shop.
Results of a totally built-in platform
4 groups aligned in a single working day, from demand forecast to distribution planning, with no single electronic mail, assembly, or re-typed quantity.
As a result of the cascade reached Li Wei on Day 1, the manufacturing order goes to Dhaka with the total 52-day lead time intact.
The product landed on the Southeast Regional DC on Might 1st and reached Charlotte Outlet and Atlanta Customary by Might third.
Shops have been replenished 2 days earlier than prospects arrived on the Sports activities Truthful.
Atlanta additionally will get its +20 models (and avoids potential misplaced gross sales) as a result of Sarah’s adjustment was by no means misplaced in another person’s inbox.
Conclusion
I constructed SupPlan to simulate the impression of built-in enterprise planning for medium-sized firms that also depend on spreadsheets and emails to coordinate their planning groups.
The SupFashion situation reveals that the issue will not be with the forecast mannequin.
It’s the time it takes for a sign to journey from one crew to the subsequent.
What we lined on this article
The cascade from demand to provide: how a single forecast change propagates via merchandise and manufacturing planning, and why lead time constraints make each misplaced day costly.
We stopped originally of distribution: Omar receives the cargo notification, however the detailed warehouse allocation, routing, and last-mile supply logic will not be but carried out.
What comes subsequent
SupPlan is a basis for testing optimisation algorithms designed by my startup LogiGreen, launched in earlier articles:
- Worth Chain Mapping for enterprise planning, together with price structure and gross sales channel optimisation
Every of those algorithms was designed in isolation. SupPlan is the setting the place they arrive collectively and the place their interactions turn into seen.
What I’ll enhance
- An entire distribution planning module with inbound flows (manufacturing facility to warehouses) and outbound flows (warehouses to shops)
- Detailed monetary flows: manufacturing prices, logistics prices, COGS breakdown, income streams by gross sales channel, stock valuation, and money move projections
The platform is open-source and publicly obtainable. You may replicate each situation from this text, change a forecast, watch the cascade, and reset the information when you find yourself achieved.
Attempt it now:
About Me
Let’s join on LinkedIn and Twitter. I’m a Provide Chain Engineer who’s utilizing knowledge analytics to enhance logistics operations and scale back prices.
Should you’re searching for tailor-made consulting options to optimise your provide chain and meet sustainability targets, please contact me.
