A PE firm closes on a manufacturer. The board wants visibility. The portfolio company's ERP holds all the data. Here is the framework for extracting the five answers your investment thesis depends on, and the warning signs that the data infrastructure isn't ready to provide them.
When a PE firm closes on a manufacturer or distributor, the first 30 days carry disproportionate weight. The acquisition thesis, whatever drove the valuation, rests on assumptions about margin performance, pricing power, and operational efficiency. The question that comes immediately is whether the data exists to validate those assumptions, or whether you're running blind.
Most portfolio companies have the data. It's sitting in their ERP system: every invoice, every purchase order, every inventory receipt, every payment. What they typically don't have is the analytics layer that surfaces that data in a form that finance leadership and PE operating partners can use to manage the business and report to a board.
This guide covers the five questions every PE operating partner should be able to answer within the first 30 days at any portfolio company, and what it means if the company can't answer them quickly.
EBITDA improvement is the primary value creation lever in PE-owned manufacturing. But EBITDA starts with gross margin, and gross margin is the most directly actionable number in the business. If you don't know gross margin by product line, you don't know where the thesis is performing and where it's eroding.
Revenue growth that comes from low-margin products or customers looks good in a top-line report and destructive in an exit process. The product line view is what separates companies that are genuinely building value from companies that are growing into a margin problem.
Every invoice line in the ERP contains both a revenue figure and a cost figure, the sale price and the cost of goods sold for that line. The ERP has everything needed to calculate gross margin at the SKU, product family, and product line level. The problem is that ERP-native reporting groups data by transaction, customer, and time period, not by product line margin trend over time. Getting to a meaningful gross margin by product line view requires extracting and restructuring the data in a way most ERP systems don't do automatically.
Pricing initiatives are one of the most frequently cited value creation levers in PE. A 3% price increase across the product line, held consistently, can represent millions of dollars in EBITDA improvement. The problem is that price increases frequently do not hold at the invoice level, because of volume discount agreements, long-standing customer relationships, sales rep discretion, or simply inconsistent enforcement.
A 3% pricing initiative that only realizes 50% of the increase is a 1.5% improvement, not a 3% improvement. If you're presenting a 3% increase to the board while the actual realized improvement is 1.5%, you have a measurement problem. Price realization is the gap between what pricing strategy says should happen and what the invoice record shows actually happened.
Invoice line records in the ERP contain the actual invoiced price for every transaction. Price realization analysis compares those actual prices against a pricing baseline, the prior period price, the list price, or the recommended price from a pricing initiative. The ERP doesn't automatically perform this comparison, but the underlying data is all there.
Working capital reduction is one of the fastest near-term cash generation levers in PE-owned businesses. Releasing cash tied up in excess inventory or past-due receivables doesn't require a pricing initiative, an operational improvement, or a customer negotiation, it requires visibility into where the cash actually is, and a plan to move it.
The challenge is that working capital is frequently reported as a single company-wide number. That number obscures the distribution, the fact that one SKU has 400 days of supply while another has four, or that one customer accounts for 40% of the 90-day AR balance while every other customer pays on time. The distribution is where the actionable insight lives.
Inventory on-hand data, quantity by SKU, by location, with associated cost, lives in the inventory module of the ERP. Accounts receivable aging, open invoices by customer, by age bucket, by collector, lives in the AR module. Both are present in the ERP and updated in real time. The question is whether they've been surfaced at the SKU and customer level with trend tracking over time.
Not all revenue is equal, and not all customers are equal, even among the top accounts. A customer doing $5M at 12% gross margin is a fundamentally different business relationship than a customer doing $5M at 32% gross margin. One is building value toward exit; the other is consuming capacity and management attention while compressing the multiple.
Revenue growth that comes disproportionately from low-margin customers makes the business harder to exit, and lower to value. The buyer at exit will look at customer-level margin during due diligence. You want to know what they'll find before they do.
Customer-level gross margin requires joining invoice line revenue data with cost of goods sold data at the line level, then aggregating by customer. This data exists in the ERP, every invoice is associated with a customer, and every invoice line has both a revenue and a cost figure. The challenge is that standard ERP reporting shows revenue by customer, not gross margin by customer. Building the customer-level margin view is a data modeling exercise, not a data collection exercise.
This question sounds administrative. It isn't. When the first question in every board meeting or management review is "which numbers are we using?", that's a data governance failure with direct consequences for decision quality and management speed.
In PE-owned businesses with multiple plants, multiple ERPs, or recent acquisitions, the most common data problem is not bad data, it's fragmented data. Finance produces one set of numbers. Operations produces another. The two reconcile manually before each board meeting, which takes days and still produces a version of truth that differs from entity to entity.
Boards make allocation decisions based on comparative company performance. If the comparison is unreliable because the underlying data isn't unified, those decisions are made on noise. The companies that perform best in PE portfolios are almost always the ones with the cleanest, most consistent management reporting.
The consistency problem is more specific than it sounds. In a five-company portfolio, each entity may have evolved its own definition of gross margin over years of independent management. One plant includes freight in cost of goods sold. Another excludes it. A third allocates overhead differently based on a costing decision made a decade ago. None of these is wrong in isolation. All of them make portfolio-level comparison meaningless without a translation step that, in most PE firms, happens manually in Excel before every board presentation.
What an operating partner actually needs is not consolidated data. It's consistently defined data. One customer master across all entities, so the same customer isn't counted four different ways across four different ERP systems. One gross margin formula applied everywhere. One working capital calculation that uses the same inventory aging methodology at every plant. The comparison only works if the underlying definitions match.
In a multi-ERP environment, the data lives in multiple places with inconsistent schemas. Customer names differ between systems, the same customer under three different naming conventions. SKUs don't cross-reference between plants. Cost allocation methods diverge. Getting to one operational view requires a data mastering layer, a unified customer master, item master, and supplier master that sits above the individual ERPs and creates a consistent view of pricing, margin, and inventory data across entities.
A PE operating partner using Marquis IQ gets portfolio-level visibility within 1–3 weeks of go-live. The ERP connections pull live transactional data from every portfolio company. The IQ Modules surface the answers to these five questions in standard dashboards, gross margin by entity and product line, price compliance by customer and rep, working capital by plant and SKU, and customer margin ranking, consistently defined across all entities.
The key distinction is that this isn't a custom analytics build. Marquis IQ is pre-built for manufacturing ERP environments, the data model, the metric definitions, and the dashboard structures are already there. What gets configured is specific to your product taxonomy, your cost structure, and your specific entities. The infrastructure that answers these five questions is ready.
The companies in a PE portfolio that can answer all five of these questions, consistently, from live data, without manual reconciliation, tend to be the ones that outperform their thesis. Not because the data creates performance. Because the data makes performance visible, accountable, and improvable at the right cadence for PE ownership.
The companies that can't answer these questions are usually not hiding bad performance. They're hiding the data infrastructure gap that's preventing them from seeing, and therefore managing, what's actually happening in the business.
We'll connect to one of your portfolio companies' ERPs and walk through Marquis IQ in 30 minutes. Pricing, margin, working capital, and inventory analytics, live on your numbers.