Return on Assets in HVAC Distribution: It's Not an Inventory Problem

HARDI's June 2026 benchmarking survey put a hard number on something most HVAC distributors already feel in their gut: inventory, cash, and returns aren't lining up the way they used to.

Warehouse sales-to-inventory ratios have settled just under 3.0x, which is down from a pre-pandemic norm closer to 4.0x. Cash conversion cycles have stretched from roughly 150 days to 180. And return on assets has now declined for three consecutive years, sliding from a pre-pandemic norm near 8% down to roughly 6%. HARDI's data ties the return on assets decline directly to the excess inventory sitting on distributor balance sheets, a position that's held steady for three straight years, long enough that it's stopped looking like a blip and started looking like the new normal.

It isn't. But the reason that it’s not has less to do with how much inventory HVAC distributors are carrying, and more to do with how those inventory decisions are getting made in the first place.

The Numbers Are a Symptom, Not the Root Cause

A declining ROA and a stretched cash conversion cycle are what it looks like when purchasing decisions are made branch by branch, SKU by SKU, without a system that can see the whole network at once. Most HVAC distributors are still running this process on ERP min/max rules, spreadsheets, and the tribal knowledge of a handful of experienced buyers, a combination that was already strained before three years of post-pandemic demand volatility, refrigerant transitions, and supplier lead time swings piled on top of it.

Pre-pandemic, ERP min/max rules and experienced buyers were close enough, because lead times, demand, and the product mix were predictable. A broken and rebuilt supply chain, a growth boom and its hangover, higher interest rates, and the A2L transition have changed that. Static rules were not built to absorb this much movement at once.

The result is a familiar pattern inside HVAC branch networks: one location sitting on excess units while another is stocked out on the same part, buyers spending hours a day running suggested orders instead of managing exceptions, and pre-season stocking decisions made months ahead of a demand signal nobody can see yet. Inventory goes up because nobody has full visibility into what the network as a whole needs, not because anyone's making bad individual calls.

That's the structural problem HARDI's numbers are describing. It's also a problem HVAC distributors have already solved, just not with a smaller purchase order.

What Moves the Needle

A declining ROA and a stretched cash conversion cycle are what it looks like when purchasing decisions are made branch by branch, SKU by SKU, without a system that can see the whole network at once. Whether that process runs on ERP min/max settings, a spreadsheet, or some blend of both, the result is the same: static reorder points that can't adapt as demand shifts. Lohmiller, cfm Distributors, and Jackson Supply all started from exactly this position before working with Blue Ridge.

Lohmiller & Company, a four-location HVAC distributor, was carrying inventory the same way, leaning on a planning process with no visibility past 30 days and no ability to account for seasonality, before deploying Blue Ridge's Demand Planning and Multi-Echelon Inventory Optimization (MEIO) across its branch network. The result: a 24% inventory reduction and an 11% improvement in fill rate, the two outcomes HARDI's data frames as being in tension. Inventory turns improved 70%, and better-positioned stock across the network cut $4 million in transfer costs. None of that came from ordering less. It came from knowing, at a network level, what was needed where.

cfm Distributors saw a similar shift after replacing Excel spreadsheets and gut-instinct purchasing with demand-driven forecasting: over-inventory dropped from roughly 52% to 32% in six months, freight costs fell 30%, and service levels improved 13%, with the company's own team describing the change as finally "leveling with where we should be from a cash flow perspective."

Jackson Supply saw the same pattern from that same similar starting point: a $3 million inventory reduction alongside a 50% cut in stockouts, proof that the inventory-versus-service tradeoff HARDI's data assumes isn't fixed by changing systems alone, it's fixed by changing what those systems can see.

The common thread isn't a smaller number on the purchase order. It's a forecast and a replenishment process that can see every branch at once and account for seasonality at the SKU level, surfacing the handful of items that need a buyer's judgment instead of asking someone to review a network's worth of SKUs by hand.

Why Forecast-Based Replenishment Fixes All Three Metrics at Once

It's worth being precise about what HARDI's three numbers (the sales-to-inventory ratio, the cash conversion cycle, and ROA) measure, because the precision is what makes the fix obvious.

The sales-to-inventory ratio is a proxy for inventory turns: the number that shows up in a distributor's financials as cost of goods sold divided by inventory. The cash conversion cycle stretching from 150 to 180 days comes almost entirely from days of inventory on hand rising by roughly 30%. This isn't a receivables problem or a payables problem, it's an excess-inventory problem wearing a different metric's name. And ROA is a function of two things multiplied together: margin (profit as a share of sales) and asset efficiency (how much sales a given level of assets is able to produce). A distributor's ROA can only fall for one of two reasons: margins are getting squeezed, or assets are growing faster than sales. HARDI's data shows both happening at once.

Forecast-based replenishment fixes the first two metrics the same way: by reducing inventory in the right places rather than everywhere. Less inventory relative to the same sales base directly raises the sales-to-inventory ratio, and since the cash conversion cycle is driven almost entirely by days of inventory on hand, the same reduction directly shortens it. ROA takes both paths at once: the inventory reduction itself improves asset efficiency, while more efficient purchasing, like fewer emergency buys, less expedited freight, and lower carrying costs, improves margin. Move both, and ROA recovers faster than either lever could manage alone.

That's also why the case results above show up on both sides of the ledger at the same time: Lohmiller's inventory reduction and fill rate improvement, cfm Distributors' freight cost reduction and service level gain. Those aren't two separate wins, they're the same mechanism showing up in the turns ratio, the cash conversion cycle, and ROA simultaneously, which is the only way all three move together.

What This Means for HVAC Distributors

HARDI's benchmarking data is a useful diagnostic: it tells distributors that the industry's cash conversion cycle and asset returns have been quietly eroding for three years running. What it can't tell a distributor is where, specifically, that erosion is happening inside their own branch network. Which locations are overstocked, which are exposed, and which purchasing decisions are driving the imbalance.

That's the visibility gap. Closing it doesn't start with a directive to buy less. It starts with a system that can see the full network well enough that both the margin lever and the asset lever move at once, and “how much to buy” stops being a guess.

This is precisely the mechanism Blue Ridge's Demand Planning and Multi-Echelon Inventory Optimization are built to run: a network-level view that moves the margin lever and the asset lever together, instead of asking a branch team to guess at both.

Ready to see what network-level visibility looks like for your branches? Book a demo with Blue Ridge.