Why More Volume Doesn't Always Mean More Profit

The Instinct to Fill Capacity

When a manufacturing plant isn't running at full utilisation, the pressure to "sell more" is almost universal. Sales teams push for volume. Operations wants to spread fixed costs. Leadership watches capacity utilisation as a KPI. The natural solution: discount prices, attract more orders, fill the plant.

This logic is intuitive. It is also frequently wrong, and the error is usually invisible until a finance team builds the right model to surface it.

The problem is not the instinct to grow. The problem is the implicit assumption that more volume produces more profit. In a multi-product business with varying price sensitivities, this assumption can lead to decisions that destroy significant value.

The Model: Three Strategies, One Portfolio

To demonstrate this, I built a pricing and product-mix optimisation model for a five-product industrial manufacturer, a hybrid business selling both standardised and custom products. The portfolio ranges from high-volume, low-margin commodity items to low-volume, high-margin custom units.

Three pricing strategies are compared:

All three scenarios produce nearly identical revenue. But contribution, what actually funds fixed costs and generates profit, varies by $1.14 million between the best and worst strategy. The only thing that changed was pricing decisions.

The Mechanism: Price Elasticity

The driver of this difference is price elasticity, how much unit demand changes when you change price. Different products in the same portfolio can have very different elasticities, and pricing decisions that ignore this variation leave substantial value on the table.

In the portfolio modelled, Products D and E, the upper-mid and custom units, have elasticities of −0.8 and −0.6 respectively. These are the products where price increases improve contribution materially. Products A and B, the standardised items, have elasticities of −1.5 and −1.3. Discounting these attracts volume, but the margin compression more than offsets the revenue gain.

Why Discounting to Fill Capacity Destroys Value

The High Volume scenario applies discounts of 2–10% across all five products. Unit volume rises by 11% to 715 units. Revenue increases slightly to $11.6M. But total contribution falls by $559K, a 20% decline from the baseline.

Here is what is happening at the product level:

• Elastic products (A, B, C) gain the most volume from discounting. But their contribution per unit collapses because the price reduction is large and the per-unit margin was already thin.
• Inelastic products (D, E) see a smaller volume increase, but also take a margin hit they didn't need to absorb. Discounting them was unnecessary: their demand wasn't sensitive to price anyway.
• The blended contribution margin falls from 24.4% to 19.2%, a compression of 5.2 percentage points across the whole portfolio.

The cost of discounting is always higher than it appears, because it compounds across every unit sold, not just the incremental volume the discount was intended to attract.

The Optimised Strategy: Selective Price Discipline

The Optimised scenario takes the opposite approach. Prices are raised on all five products, concentrating the largest increases (10% on Product D, 6% on Product E) where demand is least sensitive.

The result: 597 units shipped, nearly identical revenue at $11.4M, and $3,363K in total contribution, a 21% improvement over the baseline and a 51% improvement over the discounting strategy.

The contribution margin rises to 29.5%, an improvement of 10.3 percentage points over the High Volume scenario.

There is a secondary efficiency gain. The model includes an Effort Index, a proxy for production burden per unit. The Optimised scenario not only produces more contribution, it does so with less total production effort, because the mix shifts slightly away from high-volume, low-margin standardised products. Contribution per unit of effort rises by 30% compared to the baseline.

From Three Scenarios to an Optimisation Engine

Everything above compares three price vectors that a human chose. That is the normal way this analysis gets done, and it has a hidden weakness: the answer can only ever be as good as the options someone thought to write down. With five products and a plausible range of price moves on each, the set of legal pricing decisions is continuous and infinite. Three of them is not a search.

So the model now contains an actual optimiser. Instead of evaluating scenarios, it treats the five price changes as decision variables, maximises total contribution, and does so subject to constraints that represent real commercial limits: how deep a discount the business would authorise, how far each product's price can be pushed before customers or competitors react, a floor under total volume, a ceiling on production effort, a floor under revenue, and a minimum volume for each product line so none is quietly abandoned.

On the same assumptions used throughout this article, and drawing from exactly the same set of allowable prices, the solved optimum finds materially more contribution than the hand-set Optimised scenario:

The gap between judgement and the optimum is $256K, roughly 8% of contribution. It was available the whole time, inside rules the business had already accepted. No one found it because no one could enumerate the alternatives by hand.

The answer is less interesting than the constraint that produced it

The more useful output is not the price list. It is which constraint the solution presses against. Here, four of the five products go straight to their pricing-power ceiling, meaning the model would price them higher if permitted, and the only binding portfolio constraint is the volume floor.

That single fact reframes the decision. Product A, the most price-elastic and lowest-contribution item, is held back to a 3.2% increase rather than its permitted 6%, because A is the cheapest place in the portfolio to buy the units the volume floor demands. The optimiser is not really recommending a price for A. It is telling you that a volume commitment is being paid for in margin, showing you the invoice, and identifying which product is picking up the bill.

Where the maths stops and judgement restarts

There is an honest limitation built into this class of model, and the workbook states it plainly rather than hiding it. For a single product with no constraints, contribution is maximised at a price of variable cost × ε ÷ (1 + ε). That optimum only exists where demand is elastic (ε below −1). Products D and E in this portfolio have elasticities of −0.8 and −0.6, so on this demand curve their contribution rises without limit as price rises: an unconstrained optimiser would price them into absurdity.

That is not a flaw to be patched. It is the whole point. When the model pins a product to your ceiling, the number it reports is your assumption about market tolerance, not a mathematical result. The optimiser has quietly handed the decision back to you and told you exactly which assumption now carries the weight. A pricing model that makes that explicit is more useful than one that produces a confident number and buries the dependency.

Five Strategic Implications for FP&A

1. 1
   Volume is not a proxy for value.

   Unit volume, revenue, and capacity utilisation are operational metrics. Contribution is the financial metric that matters. A business optimising the wrong variable will make systematically bad pricing decisions.

2. 2
   Elasticity is the missing variable in most pricing conversations.

   Most manufacturing pricing decisions are made without a quantified view of price sensitivity. Calibrating elasticity, even roughly, from historical price-volume data, transforms the quality of the pricing conversation from intuition to analysis.

3. 3
   Product mix can be managed through pricing, not just production planning.

   Raising prices on inelastic products shifts the effective mix toward higher-margin items without changing what the plant produces. Pricing is a mix lever, not just a revenue lever.

4. 4
   The cost of discounting is usually underestimated.

   The true cost is not just the margin lost on existing volume, it is the elastic response that compounds the margin compression across every unit attracted by the lower price. Quantifying this changes the risk calculus of any pricing action.

5. 5
   Comparing scenarios is not the same as optimising.

   A scenario table answers "what if we did this?" and is bounded by whatever options someone thought to write down. An optimiser answers "what is the best we could do, and what is stopping us?" Here that difference was worth 8% of contribution, entirely within rules the business had already agreed. The real deliverable is the binding constraint: it names the policy that is costing money and turns a pricing debate into a specific, priced negotiation.