Sales & Operations Planning: The Planning Matters More Than The Plan

Why alignment matters more than perfect forecasts — and what our S&OP simulation reveals

Sebastian Bitter, Hieu Dang

23.3.2026

No forecast is perfect and no order strategy wins every time, but companies that plan regularly are better prepared for the unexpected — what matters is having a process that enables switching strategies when the situation changes. In practice, slight overestimation in the forecast can even be preferable to perfect accuracy, especially when stockouts are more costly than holding inventory. Ultimately, S&OP is not a forecasting tool but an alignment process that makes trade-offs explicit before they escalate into conflicts.

"Plans are worthless, but planning is everything." – Dwight D. Eisenhower

The general who planned the Normandy invasion knew: no plan survives first contact with the enemy. But those who don't plan get overwhelmed by that first contact.

It's no different in the supply chain. Forecasts miss the mark. Suppliers fail. Customers order differently than expected. Yesterday's plan is today's waste paper.

Yet successful companies keep planning. Not because they believe in perfect forecasts, but because the process of planning prepares them for the unexpected.

That's exactly what Sales and Operations Planning (S&OP) is.

What is Sales and Operations Planning?

Sales & Operations Planning is a recurring alignment process, typically monthly, though some companies run it weekly or quarterly. The goal: Sales, Production, and Finance bring their perspectives together to create a shared basis for decision-making.

The S&OP Cycle in 5 Steps

Step	Phase	Who?	Key Question
1	Data Gathering	Analytics	How Did We Perform?
2	Demand Planning	Sales	What demand do we expect?
3	Supply Planning	Production	What can we deliver?
4	Alignment	All (incl. Finance)	What trade-offs do we accept?
5	Executive Decision	Leadership	Decision and sign-off

The S&OP cycle: A monthly recurring alignment process across all departments.

We've also formalized this process structure as a navigable model in our S & OP Model in the Metapad library: Reviews produce Plans, Plans are measured by KPIs, and Action Items lead to concrete improvements. This makes the building blocks of S&OP and their relationships explicit.

Why alignment matters so much

Sales sees opportunities and wants availability. Production sees capacity limits and wants predictability. Finance sees tied-up capital and wants low inventory.

Without regular alignment, each function optimizes for itself and the overall system loses. S\&OP brings these perspectives to the table before decisions are made.

S&OP is not a tool for perfect plans. It's a process for better decisions.

Let's Make it Concrete: Our Brewery Model

To make S&OP dynamics tangible, we built a simulation. The setting: a mid-sized brewery with roughly 60,000 hectolitres of annual production.

Why a Brewery?

Beer combines all typical S&OP challenges in a single product. And what happens in a brewery happens everywhere: in pharmaceutical warehouses, automotive manufacturing, retail shelves. The details change, but the underlying dynamics remain the same.

The Levers

Three things can be varied in the simulation:

Demand scenario: How does the market behave? Constant, seasonal, with sudden shocks?
Order strategy:How does the brewery respond? Order a fixed amount, trust the forecast, or keep an eye on inventory levels?
Disruptions: What goes wrong? Supply shortage, production outage, quality issue?

The constraints

Some parameters are deliberately fixed because they reflect the typical challenges of a brewery:

Parameter	Value	Why relevant?
Lead time	2 months	Pilsner needs 6–8 weeks for fermentation and maturation. The decision in May determines what's available in July.
Sales window	3 months	Buyers won't accept beer older than 3 months. They want fresh product with remaining shelf life for their customers.
Max. capacity	8,000 hl/month	A hard upper limit. Even the best forecast won't help if production can't keep up.
Max. storage capacity	15,000 hl	Beyond this, storage costs rise due to external warehousing and overtime (€10/hl instead of €3/hl).
Seasonality	2.5:1	In July, 2.5× as much beer is consumed as in January.

Inventory cohorts of the brewery simulation

The cohort chart shows how inventory is structured by age: Fresh beer (dark), 1 month old (yellow), and 2 months old (orange). After 3 months, product is disposed. The sales window is the hardest constraint.

Measuring Success

The simulation measures success across four KPIs:

KPI	What it measures	Example
Service Level	Share of fulfilled demand	100% = every customer served, 85% = every seventh goes empty-handed
Avg. Inventory	Average stock level	More stock = more buffer, but also more tied-up capital
Stockouts	Unfulfilled demand	Costs €100/hl (lost contribution margin + customer churn)
Disposal	Expired product	Costs €50/hl (product beyond the sales window)

On top of that, the financials: Revenue, production costs, holding costs, disposal costs, and stockout costs add up to Profit.

The tension: Ordering too little leads to stockouts. Ordering too much leads to disposal. The art lies in the balance.

The core question: How much should the brewery produce when demand fluctuates, the forecast is uncertain, and every decision only takes effect two months later?

The simulation shows over 24 months how demand (blue), forecast (orange), and orders (green) interact.

Try the simulation yourself here.

Five Order Strategies

The simulation compares five order strategies, from simple to forward-looking:

Constant (Baseline): The same quantity every month, no matter what. The simplest strategy. Only works with absolutely stable demand. With seasonality: stockouts in summer, disposal in winter.
Replenishment (Reactive): Order what was sold last month. The classic ERP approach. Simple, but: it only reacts after the fact. Fatal with seasonal demand, because in May you order what was needed in April, but in July you need twice as much.
Seasonal Naive (Historical): Order what was sold in the same month last year. Captures seasonality as long as the pattern repeats. The problem: orders "blindly" and ignores what's already in stock. When disruptions hit, this leads to chaos.
Target Inventory (Stock-aware): Only order the difference between target stock and current stock.
Uses historical data like Seasonal Naive, but additionally asks: "What do I already have?" This avoids double-ordering and responds flexibly to disruptions. Downside: with structural breaks (e.g., market collapse), the strategy only adapts the following year.
Forecast-based (Predictive): Like Target Inventory, but with forecast instead of historical data. Can respond to trends and expected changes. The price: quality depends on the forecast. With systematic bias, this strategy can go dramatically wrong.

What the Simulation Shows

We systematically tested the five strategies: 6 demand scenarios (stable, seasonal, seasonal with double peak, growing, shrinking, crash) combined with 7 disruptions (none, material shortage, capacity outage, blackout, spoilage, logistics strike, hop shortage). That's 42 realistic situations, enough to recognise patterns.

Result 1: No strategy wins every time

The simulation delivers a clear message: The "best" strategy depends on the context.

Strategy	Wins	Share
Target Inventory	17/42	40%
Forecast-based	12/42	29%
Seasonal Naive	6/42	14%
Replenishment	4/42	10%
Constant	3/42	7%

Target Inventory leads, but only wins 40% of the time. That means: in 60% of situations, a different strategy is better. There's no "autopilot" for order planning. That's precisely why you need a process that regularly checks: does our strategy still fit the current situation?

When does what work?

Target Inventory dominates with seasonal patterns and disruptions. It uses historical data but responds flexibly to the current stock situation.
Forecast-based wins with structural changes: in shrinking markets, it dominates with 7/7 wins. When the world changes, looking forward is worth its weight in gold.
Seasonal Naive works with stable, recurring patterns, as long as no major disruptions occur.
Replenishment surprises with steady growth: the 1-month delay isn't a disadvantage with a continuous trend.
Constant only wins with stable demand ("Steady State") and disruptions, where the "accidental" overstock acts as a buffer.

Result 2: Stockouts are the biggest cost driver

A look at the cost structure explains why some strategies perform better:

Cost type	Share	Cost per hl
Stockout costs	57%	EUR 100
Disposal costs	22%	EUR 50
Holding costs	21%	EUR 3 per Month
↳ Overflow (>15k)		EUR 10 per Month

Stockouts dominate. One hectolitre that can't be delivered costs €100. That's the lost contribution margin (€80) plus knock-on effects like customer churn and brand damage. By comparison: one hectolitre in storage costs only €3 per month — though holding costs above the 15,000 hl capacity threshold rise to €10/hl due to external warehousing and overtime.

This explains why inventory-aware strategies (Target Inventory, Forecast-based) perform better: they systematically avoid stockouts instead of leaving them to chance.

In S&OP terms: Supply Planning needs to know what Demand Planning expects, and both need to know what's in stock.

Result 3: Why inventory awareness makes the difference

What sets Target Inventory and Forecast-based apart from the simpler approaches? They ask: "What do I need, and what do I already have?"

Constant, Replenishment, and Seasonal Naive order "blindly": they don't know what's already in the warehouse or in transit. When disruptions hit, this leads to double-ordering or gaps.

A test makes this clear: What if you had a perfect forecast (0% error, 0% bias), but simply ordered that exact quantity without checking inventory? This "forecast-without-inventory-check" strategy loses 69% of the time against Forecast-based with inventory awareness. The average profit disadvantage exceeds €300,000.

Even a perfect view of the future isn't enough. If you don't know what you have, you'll order wrong.

Result 4: The surprising bias effect

Now it gets interesting: what happens when the forecast isn't perfect?

Forecast quality	Target Inv.	Forecast-based	Winner
Perfect (0% bias)	17/42 (40%)	12/42 (29%)	Target Inventory
+10% bias (overestimate)	13/42 (31%)	19/42 (45%)	Forecast-based
-10% bias (underestimate)	17/42 (40%)	12/42 (29%)	Target Inventory
-20% bias (underestimate)	17/42 (40%)	10/42 (24%)	Target Inventory

The surprise: A slightly overestimating forecast (+10%) performs better*than a perfectly accurate one! Why? Overestimation leads to more safety stock. This buffer pays off especially during disruptions: without disruptions, both strategies are tied (3:3). With disruptions, Forecast-based jumps from 16/36 to 24/36 wins at +10% bias.

But beware: This insight applies to our model, where stockouts are more expensive than overstock. With different cost structures (e.g., higher holding costs, shorter shelf life, more expensive capital), overestimation can quickly become a problem. The bias effect is context-dependent.

Underestimation, on the other hand, is almost always dangerous: at -10% or -20% bias, Target Inventory dominates clearly (23:13).

Forecast without bias (0%): Forecast (orange) and demand (blue) track closely together. Moderate inventory levels.

Forecast with +30% bias: The forecast (orange) sits well above demand. The resulting overstock acts as a buffer against disruptions.

Forecast with -30% bias: The forecast systematically underestimates demand. Inventory is nearly empty. Stockouts follow.

The takeaway for S&OP: Not all bias is bad. But you need to know it and be able to manage it. That's exactly what S&OP is for: regular reviews make deviations visible, and joint alignment forces teams to address them rather than look the other way.

The Paradox: More wins, less profit?

One last detail that shows why numbers alone aren't enough:

Forecast-based: avg. €8.49M profit per scenario
Target Inventory: avg. €7.84M profit per scenario

Forecast-based has the higher average profit, but fewer*wins. How does that add up?

The answer: Forecast-based wins less often, but when it wins, it wins by a huge margin. In shrinking markets, the advantage reaches up to +€3M per scenario. Target Inventory wins more often, but by a smaller margin.

That's the real S&OP question: Do I want the strategy with the highest average performance? Or the most robust strategy that works in the most situations? This decision belongs in the Executive S&OP phase, where risk tolerance and strategic priorities are weighed.

It's not the method that determines success, but the quality of alignment. Which strategy fits depends on what you know about the market, forecast quality, and your own risk tolerance. S&OP brings these puzzle pieces together.

What This Means for Your S&OP Process

S&OP is more than forecasting. A good forecast matters, but it's only one building block. S&OP delivers value even without a perfect forecast: it forces alignment, makes assumptions debatable, and creates a framework for trade-offs.
Not all bias is equal. Overestimation can help during disruptions (more buffer against stockouts), but it's no free pass: higher holding costs, tied-up capital, and with perishable goods, more disposal. Regular comparison of forecast vs. actuals shows which direction the deviation goes and whether it fits the cost structure.
Trade-offs don't disappear. Sales wants availability, Finance wants low inventory. The value of S&OP lies in making this tension explicit before it turns into conflict.
Communication beats methodology. No single department can choose the right strategy alone. The choice depends on forecast quality, market stability, and risk tolerance. These questions can only be answered together.

It's not the best method that wins, but the company that communicates best.

Try It Yourself

The simulation runs in your browser. Three settings define the scenario:

Demand scenario (top left): How does the market develop?
Order strategy (below): How does the brewery respond?
Disruption (optional): What goes wrong? After every change, the charts update automatically. The KPIs at the top show service level, inventory, stockouts, and profit.

Three experiments to get started:

Experiment 1: Reactive vs. Forward-looking Scenario "Seasonal", no disruption. Compare Replenishment with Target Inventory. Watch the summer months: Replenishment lags behind, Target Inventory anticipates the peak.
Experiment 2: The price of forecasting Scenario "Seasonal", Forecast-based. Set bias to -20%. Then switch to Target Inventory. Target Inventory stays stable, Forecast-based collapses.
Experiment 3: Random vs. systematic deviation Compare Bias=0%/Error=20% with Bias=-10%/Error=0%. The random error averages out, the bias accumulates.

→ Launch the simulation

What's Next?

This article has shown: Neither the perfect forecast nor the cleverest order method guarantees success.

What counts is cross-functional alignment between Sales, Production, and Finance: the core of S\&OP.

In the next article, we move from theory to disruption: When the Plan Falls Apart. What happens when a supplier fails or logistics grind to a halt? And why S&OP makes the difference between surviving a disruption and being overwhelmed by it.

The core question: it's not the disruption that decides, but whether there's a process behind it.

Want to experience supply chain dynamics first-hand? In our Beer Distribution Game, you take on a role in the supply chain and experience the bullwhip effect for yourself.

Want to explore the structure behind S&OP? In our S&OP Process Model on Metapad, you can navigate the process from Data Review to Executive S&OP and see how Reviews, Plans, KPIs, and Action Items connect.