MedTech Consumables: Why Better Models Won’t Fix the Forecasting Problem

Vatsal Tiwari - Director, Healthcare

The industry does not have a forecasting problem. It has a visibility problem. 

For years, MedTech companies have treated consumables as one of the most predictable parts of the business. The logic appeared simple: place more systems, grow procedure volume, and consumables revenue follows naturally. Compared to capital equipment, consumables offered recurring revenue, stronger margins, and long-term account monetization potential. 

Yet despite increasingly sophisticated CRM systems, distributor networks, procurement datasets, and analytics infrastructure, consumables forecasting remains surprisingly unreliable across many MedTech categories. 

The issue is not analytical capability but rather a fundamental lack of visibility into how consumables demand is actually created inside healthcare systems. Installed base data, procurement activity, and distributor sales create the appearance of precision, while the operational reality underneath remains fragmented, behaviorally driven, and economically distorted. 

1. Installed base rarely predicts real consumables demand 

One of the industry's most persistent assumptions is that installed base directly predicts consumables pull-through. On paper, the relationship feels logical: more systems deployed should lead to more procedures and higher consumables utilization. In practice, healthcare utilization rarely behaves linearly. Two hospitals operating the exact same platform can generate dramatically different consumable demand profiles despite appearing commercially similar. Procedure growth depends not only on equipment availability, but on staffing capacity, scheduling efficiency, reimbursement economics, referral flows, and broader operational constraints inside the hospital. 

A newly installed platform may remain underutilized for months because of physician onboarding delays, nursing shortages, operating room bottlenecks, or limited procedure capacity. In other cases, hospitals with similar installed bases may operate at completely different throughput levels due to differences in workflow efficiency or departmental prioritization. 

The result is that installed base often creates the appearance of forecasting precision while obscuring the operational variability that actually determines consumables demand. 

2. Consumables utilization is deeply behavioral 

Even where operational conditions appear similar, consumables utilization can vary significantly because healthcare consumption is heavily influenced by human behavior. 

Physician habit, workflow familiarity, perceived procedural risk, procurement pressure, and institutional culture all influence how products are selected, substituted, reused, or standardized across care settings. 

Within standardized procedures, there can be surprisingly large variability in consumption behavior. Two physicians performing the same procedure inside the same institution may still use products differently based on training background, procedural comfort, or personal workflow preference rather than any measurable difference in clinical outcomes. 

In wound care, for example, clinicians managing comparable patient populations have been shown to select dressings based on habit rather than clinical evidence, leading to significant divergence in consumption rates across institutions even where the underlying case mix is similar. 

3. Procurement data frequently misrepresents market reality 

Many MedTech companies continue to treat procurement activity as a reliable representation of downstream demand. The gap between purchasing behavior and clinical utilization, however, is frequently wider than commercial models acknowledge. 

Large hospital systems do not purchase consumables in neat alignment with procedural demand. Purchasing activity is influenced by tender timing, budget cycles, inventory strategy, anticipated pricing changes, and distributor relationships, many of which have little connection to real time utilization. 

As a result, large purchase orders often create the illusion of stable demand when they may simply reflect inventory buffering, procurement timing, or budget exhaustion behavior. End of fiscal year purchasing spikes, driven by budget exhaustion rather than clinical need, are a well-recognized pattern across hospital procurement systems. At an aggregate level, these timing-driven orders can meaningfully inflate apparent demand, creating significant distortion in any forecasting model that treats procurement volume as a proxy for clinical consumption. 

4. Pricing visibility is often more distorted than volume visibility 

The industry frequently frames consumables sizing as a volume challenge. Pricing visibility, however, may be equally problematic. 

Most market sizing models still assume: Market Size = Volume × Average Selling Price (ASP) 

But in MedTech consumables, neither variable is truly stable. Unlike traditional industrial markets, pricing often exists as a negotiated outcome rather than a standardized benchmark. Realized pricing can vary significantly depending on tender structures, distributor involvement, bundled contracts, competitive defense strategies, and broader capital equipment negotiations. 

In practice, consumables pricing is frequently used as a strategic lever to protect installed base, secure long term contracts, or prevent competitor entry into key accounts. This is a well-recognized dynamic in bundled contracting arrangements, where consumables are deliberately priced below standalone market rates as part of broader capital equipment deals, a practice that makes realized pricing difficult to observe and even harder to benchmark accurately across a market. 

5. Distributors often possess better market visibility than OEMs 

In several regions, distributors function as the operational intelligence layer of the consumables ecosystem, often sitting closer to the point of care than any OEM commercial team. They see which SKUs are actually being pulled off shelves versus stockpiled. They know when a hospital is quietly substituting a competing product because a procurement manager negotiated a side agreement. They observe inventory stress before it registers in any manufacturer's demand signal. They understand the real net pricing at which products are moving through the system, not the list price, not the contract price, but the realized economics after local discounting, rebates, and informal arrangements. 

This creates a structural asymmetry that most OEMs significantly underestimate. The distributor who covers a cluster of secondary tier hospitals in a given region often carries more actionable market intelligence about those accounts than the manufacturer whose name is on the product. They know which physician is driving utilization, which accounts are at risk of switching, and where unofficial product substitution is already happening. They observe these signals continuously and directly, while manufacturers typically receive filtered, aggregated, and delayed data through commercial reporting. 

The challenge is that this intelligence rarely flows upstream in a systematic way. Distributors have limited incentive to share granular market visibility with their OEM partners, particularly where that intelligence could be used to renegotiate contracts, bypass the distributor, or reduce commercial dependence. What gets reported is what the distributor chooses to report. 

For MedTech companies operating in markets heavily reliant on distributors, across much of Southeast Asia, the Middle East, Latin America, and parts of Europe, this means that commercial forecasting models are being built on data that has already been filtered through an intermediary with its own commercial interests. The manufacturer is, in effect, modeling a market it cannot directly see, using signals provided by a partner whose visibility is superior but whose incentives to share that visibility are structurally misaligned. 

It is an intelligence architecture problem, and most organizations have not yet designed for it. 

6. The future advantage will come from utilization intelligence 

The next generation of consumables leaders will likely differentiate themselves less through product breadth alone and more through their ability to build superior utilization intelligence across fragmented healthcare environments. 

Traditional commercial datasets are no longer enough. Organizations increasingly need workflow level visibility, reimbursement aware forecasting, behavioral understanding, procedural analytics, distributor intelligence integration, and deeper insight into how hospitals operationally manage demand under financial pressure. 

The companies that ultimately outperform in consumables may not necessarily be the ones with the largest installed base. They may simply be the ones that understand healthcare behavior more accurately than everyone else. 

Conclusion 

Most MedTech companies still believe they have a market sizing problem. The more uncomfortable possibility is that they have a structural intelligence problem, one that better models cannot fix. 

As long as OEMs remain dependent on procurement data, signals filtered through distributors, and installed base assumptions to understand a market that is fundamentally behaviorally and operationally fragmented, forecasting precision will remain limited regardless of analytical sophistication.  

The companies that close this gap first will not do it by refining their spreadsheets. They will do it by redesigning how commercial intelligence is sourced, structured, and acted upon, closer to the point of care, and with far less dependence on intermediaries whose visibility exceeds their own. 

At Phronesis Partners, we help MedTech companies build the market intelligence that procurement signals and installed base data cannot provide. This includes primary research into how consumables are actually utilized across clinical settings, how realized pricing moves through distributor channels, and where demand is being created or distorted inside healthcare systems. If these are live questions for your organization, we would welcome a conversation.