Industry insights
The Supply Chain Multiple: Why mid-market Private Equity still leaves value on the table
The Supply Chain Multiple: Why mid-market Private Equity still leaves value on the table
Private equity has become extraordinarily good at financial engineering. Debt structuring, working capital sweeps, add-on consolidation, multiple arbitrages at entry: the playbook is mature, well-taught, and executed with precision across the industry. What the same playbook still handles poorly, particularly in the lower mid-market, is the supply chain sitting underneath the numbers.
That gap should bother every deal partner who has ever watched a promising platform company underperform its thesis. Supply Chain is not a support function you fix after the deal closes, if there is time. It is one of the few remaining levers a smaller PE firm can pull that a competing bidder has not already priced in, because most competing bidders never looked at it in detail.
We see this repeatedly with mid-market industrials, distributors, and consumer goods platforms: EBITDA margins of 10-15%, a supply chain that has never been formally diagnosed, and millions of euros sitting quietly in the gap between what the operation delivers and what it could deliver. In a portfolio company doing 50 million euros in revenue, that gap is rarely small. It is usually the second-largest value creation opportunity in the deal, after the commercial thesis itself, and almost nobody has a systematic way to find it.
This article lays out two distinct but connected levers. The first improves the EBITDA a portfolio company generates. The second improves the multiple a buyer is willing to pay for that EBITDA at exit. Treated separately, both levers are useful. Treated together, they change the exit conversation entirely.
Lever one: An EBITDA that deserves its multiple
Three operational dimensions do almost all the work: service level/availability, cost (i.e., COGS), and working capital. They are not new ideas. What is missing in most mid-market portfolios is not the idea but the discipline to diagnose and execute against it systematically.
Availability is revenue wearing a disguise
Lost sales rarely show up on a P&L as lost sales. They show up as flat revenue growth that management attributes to the market, the competition, or the sales team. In practice, the far more common cause is simpler: the product was not there when the customer wanted it.
A distributor running an 85 percent fill rate is not eight or ten points away from a full order book. They are losing the order entirely to a competitor who happened to have stock. Push that fill rate to 95 percent through better demand sensing and a disciplined safety stock policy, and a five to eight percent revenue uplift in year one is a realistic, defensible number. On a 50-million-euro revenue base, that is 2.5 to four million euros of top-line growth that required no new customers, no new products, and no incremental sales spend.
The diagnostic here is straightforward. What is the current fill rate, by product line and by region? How much of the forecast error is structural versus a planning process that nobody has touched in years? Where do stock-outs cluster? These are Brilliant Basics questions: unglamorous, foundational, and answered correctly less often than you would expect from companies that have been operating for decades.
Where the network stops making sense
Most mid-market companies, particularly those pursuing a buy-and-build strategy, did not design their distribution network; they inherited it: one warehouse from an acquisition, a second because a large customer once demanded faster delivery, a third nobody remembers the reason for. The result is a cost structure nobody has actually chosen.
A proper network and total-cost-to-serve assessment (mapping facilities, transportation lanes, and inventory positions against what customers need) typically uncovers 8 to 12 percent in transportation and logistics savings without a single euro of new capital. Consolidating distribution points, moving from regional silos to a hub-and-spoke model, renegotiating carrier contracts on consolidated volume: none of this is exotic. It is simply work that requires someone to look at the network as a whole rather than defend the piece they inherited.
A control tower (a single point of visibility across procurement, warehousing, and transportation) usually pays for itself inside the first year purely by eliminating the invisible waste: duplicate shipments, expedited freight ordered out of panic rather than necessity, and loads that leave half-empty because nobody could see the full picture. Five years ago, this kind of visibility required a large IT investment. Today, cloud platforms, standardized integrations and GenAI-assisted development (“vibe coding”) make lightweight solutions faster and cheaper to build, while AI agents can monitor operations, identify exceptions and recommend corrective actions. This is why control towers now belong on the agenda of a €50 million company – and not only for the enterprise accounts
The cash trapped in the middle
Working capital is the fastest of the three levers to move, and the one PE firms understand best, which makes it the natural entry point into a broader supply chain conversation, even for a partner who has never thought about supply chain as a value lever before.
The diagnostic is a single question with several parts: how many days of inventory are you carrying, on what terms are you paying suppliers, on what terms are you collecting from customers, and where exactly is the cash sitting idle in that cycle? The fix runs as a sprint, not a program: extend supplier terms where leverage allows, tighten customer terms where the relationship allows, and cut inventory days through demand sensing.
There is a wrong way to do this, and it is by far the most common one. A blanket inventory haircut, cutting every SKU by 15 percent or setting a single uniform days-of-coverage target across the portfolio, looks like working capital discipline on a monthly dashboard. In practice, it strips buffer from exactly the items that needed it and leaves it sitting on the items that did not. Availability drops. Expediting costs rise to cover the resulting stock-outs. Customers notice the service decline before management does. The balance sheet improvement is real for a quarter, maybe two, and then it reverses, this time at a higher cost than where the company started, because expedited freight and lost customer trust are both expensive to buy back.
The right way is the Brilliant Basics way: segment before you optimize. Not every SKU deserves the same safety-stock policy, and not every customer requires the same service level. A-items with long lead times and high demand variability need real buffers. C-items with predictable turnover do not. Again, this is where AI can help: making segmentation easier to implement by continuously analyzing demand, lead times, and margins across thousands of SKUs. Getting that segmentation right is unglamorous work, and it is exactly the difference between a working-capital sprint that holds and one that quietly undoes itself six months later.
A company carrying a 60-day cash conversion cycle that trims 20 days the right way frees 5-7 million euros in cash. That cash pays down acquisition debt, funds the next add-on, or goes straight back to the fund. It requires no capital expenditure and, done properly, has a 90-day payback.
Lever two: Why the multiple moves at all
Here, it's worth being precise about what actually happens. The multiple itself (determined by comparables, market conditions, and buyer appetite at the time of exit) is largely outside management's control. What is inside management's control is the quality of the EBITDA a buyer is being asked to pay that multiple for. That distinction is the entire lever.
A buyer doing diligence on a company that hit its EBITDA number through a control tower, a disciplined demand planning process, and a redesigned network sees something structurally different from a company that hit the same number through a one-time inventory liquidation or an unsustainable round of supplier squeezing. The first story is repeatable. The second is not, and sophisticated buyers discount accordingly, often by half a turn to a full turn of EBITDA, which on a mid-market deal is not a rounding error.
Risk perception moves the same way. A supply chain with a concentrated supplier base, long and unmonitored lead times, and no documented contingency plan reads as exposure to any buyer's diligence team. A supply chain with diversified sourcing, visibility into supplier health, and a demonstrated ability to absorb disruption reads as a company that has already done the buyer's risk work for them. Technology and AI make this visibility operational: integrated planning, supplier-risk and control-tower tools combine ERP, purchasing, logistics and external supplier data to flag emerging shortages, delayed orders, deteriorating supplier performance and demand deviations early enough to intervene. This makes improvements more measurable and repeatable – and the resulting EBITDA story more credible. These capabilities no longer require enterprise-scale investment, putting them within reach of mid-market companies. For a smaller PE firm competing against larger funds with deeper resources, this is one of the few places where discipline substitutes for scale: a well-run supply chain is a credibility signal that a 50-million-euro company can genuinely earn.
There is a third, quieter effect: growth optionality. A company with a tight, well-understood network and real demand visibility can scale into new geographies or channels with far less incremental capital than one that is still discovering its own cost structure as it grows. Buyers pay for that optionality too, even when nobody puts a line item on it.
None of this means the multiple is manufactured. It means the multiple is earned by making the EBITDA underneath it credible, and credibility, unlike the multiple itself, is entirely within the portfolio company's control.
The Roadmap: From diagnostic to exit-ready
For a smaller PE firm with limited internal operating resources, the sequence matters as much as the content.
- Weeks one through four: the diagnostic.
Before anything is implemented, the company needs an honest baseline: fill rates and lost-sales estimates, a total-cost-to-serve view of the network, the working capital position, supplier performance, and the visibility gaps that make all of the above hard to see in the first place. This is where an external partner earns its place, not to tell management what they already suspect, but to quantify it in numbers a buyer will later trust. - Months two through six: quick wins and the control tower.
The working capital sprint runs in parallel with control tower deployment. Both are chosen deliberately for this phase because both pay back fast and require no structural change to the business, which matters when a fund is midway through a hold period and cannot wait 18 months to see results. - Months six through 18: structural work.
Network redesign, supplier consolidation, and a rebuilt demand planning process happen here, once the baseline visibility and the quick wins have built the internal credibility and the cash to fund the bigger moves. - Throughout: AI where the data earns it.
Machine-learning-based demand forecasting routinely outperforms traditional statistical methods by ten to fifteen percent, and route optimization and predictive maintenance both have mature, provable returns. GenAI and increasingly agentic AI can further improve productivity by helping planners interpret exceptions, evaluate scenarios, and automate supplier communication. The discipline for a smaller portfolio company is resisting the AI initiative that looks impressive in a board deck but has no data behind it. Invest where the return is demonstrable. Skip the rest.
OMMAX supported an international home, garden and leisure e-commerce company in optimizing and digitalizing its end-to-end supply chain
