Manufacturer P&L Sensitivity: How Every Commercial Lever Flows to Profit

A price sensitivity sweep charts how contribution profit changes as you move the price lever across a range (e.g., -15% to +15%), holding all other variables constant.

The resulting curve is typically an inverted U-shape:
- At very low prices, volume is high but margin per unit is too thin to cover costs
- At very high prices, margin per unit is excellent but volume has collapsed
- The peak is the contribution-maximizing price point

The shape of this curve depends on three factors:
1. Elasticity — steeper elasticity makes the curve narrower (the optimal zone is tighter)
2. Contribution margin % — higher base margins make the curve taller and shift the peak rightward (you can tolerate more volume loss)
3. COGS level — lower COGS shifts the peak rightward (more room for price increases)

For a typical mainstream FMCG product with elasticity of -1.8 and 50% contribution margin, the contribution-maximizing price increase is approximately 3-6% above current levels. Beyond 8-10%, the volume loss accelerates and contribution starts declining rapidly.

This analysis explains why most successful FMCG price increases are in the 3-7% range — this is not conservatism, it is optimization.

For each price change Δ in the sweep range:

New Price = Base Price × (1 + Δ)
Volume Response = Base Volume × (1 + Elasticity × Δ)
New Net Revenue = New Price × (1 − GTN Rate) × Volume Response
New COGS = Unit COGS × Volume Response
New Contribution = New Net Revenue − New COGS − Marketing/Sales

Optimal Price Change (under constant elasticity):
Δ* = (|E| × C / (|E| − 1) − P) / P
where E = elasticity, C = variable cost, P = current price

At elasticity -1.8 and COGS/Price ratio of 40%:
Δ* = (1.8 × 0.40 / 0.8 − 1) / 1 = (0.90 − 1) = −10%
Wait — this says the optimal price is 10% lower? Only if the current price is already above optimal. In practice, most FMCG prices are slightly below optimal (they could tolerate a small increase), which is why the sensitivity sweep usually shows a peak slightly above the current price.

Consider a biscuit SKU with the CrunchField default base case: list price $4.29, 17% GTN (net price $3.56), COGS $1.72, variable cost $0.34, elasticity −1.8. Margin per unit at defaults = $3.56 − $1.72 − $0.34 = $1.50.

Run the sensitivity sweep from −10% to +10% price change. Key points on the curve:

| Price change | New net price | Volume % of base | Margin per unit | Contribution ($K) |
|---|---|---|---|---|
| −5% | $3.38 | 109% | $1.32 | $2,882 |
| 0% (base) | $3.56 | 100% | $1.50 | $3,000 |
| +3% | $3.67 | 94.6% | $1.61 | $3,045 |
| +5% | $3.74 | 91% | $1.68 | $3,054 |
| +7% | $3.81 | 87.4% | $1.75 | $3,045 |
| +10% | $3.92 | 82% | $1.86 | $3,009 |

The contribution peak sits between +3% and +7% with a broad flat top (Strategic Pricing Lesson 8 calls this the Zone of Indifference). Beyond +7%, volume loss accelerates faster than margin per unit improves.

Cross-lesson connection: The curve shape shown above is the same profit parabola taught in Strategic Pricing Lesson 8 (The Pricing Parabola) — revenue and profit peaks sit at different prices, with the profit peak approximately C/2 above the revenue peak under linear demand. The flat-top Zone of Indifference is why tuning pricing to the third decimal place is wasted effort; being in the right neighbourhood (+3% to +7% in this example) matters more than hitting an exact optimum. The elasticity value that shapes the curve comes from Strategic Pricing Lesson 1 and reflects the category-specific demand response the simulator applies.

Practitioners use price sensitivity sweeps in three key contexts:

1. Setting price increase magnitude: Run the sweep for your specific product. If the curve peaks at +5% but you are considering +8%, you know you are pushing past the optimum. If it peaks at +8% but you are only considering +3%, you are leaving money on the table.

2. Stress testing assumptions: Run the sweep with three elasticity scenarios (optimistic, expected, pessimistic). If contribution is positive across all three at your planned price increase, the decision is robust. If it turns negative under the pessimistic case, you need a contingency plan.

3. Portfolio pricing: Run sweeps for each product in your portfolio. Products where the curve peaks further right (higher optimal price) should get larger increases. Products where it peaks left or is already declining should get smaller increases or none at all. This is how you construct a differentiated portfolio pricing strategy.

The volume line on the sweep chart is equally important — it shows how many units you sacrifice at each price point. Even if contribution peaks at +7%, if the volume line shows a 15% decline at that level, your production team and retail partners may push back hard.