Most sourcing organizations eventually encounter the same problem: projected savings and realized financial results rarely match perfectly. In some cases, realized performance falls short of projections. In others, projects outperform expectations. Yet the explanation is often reduced to a single phrase such as “volume changed.” Understanding why a project deviated from projections matters for more than simply defending results.
A waterfall analysis provides a practical framework for decomposing the gap between projected and realized savings into its underlying components. By systematically isolating factors such as baseline pricing, contract pricing, volume, and item mix, organizations can better understand not only whether a project succeeded, but why its results differed from expectations.
In practice, savings variances are rarely driven by a single factor. Yet many reconciliations stop at broad explanations such as “volume increased” or “compliance was lower than expected.” While directionally accurate, these explanations often fail to describe the underlying financial mechanics that produced the variance.
Building the Waterfall: Isolating the Drivers
At its most basic level, most sourcing savings analyses are built from a relatively simple formula for both the projection and actual results:
(Old Price − New Price) × Quantity = Savings
Projected savings and realized savings often use different values for one or more components of that formula. A waterfall analysis works by isolating each variable independently and measuring how much it contributed to the variance between the projected and realized result. The waterfall begins with the original projected annualized savings and sequentially adjusts each component until arriving at the realized annualized result. Each bar in the waterfall represents the financial impact of changing a single variable while holding the remaining variables constant. This allows the organization to decompose a complex variance into a series of understandable drivers.
Driver 1: Baseline Price Variance
The first driver isolates the difference between the baseline price used in the projection and the baseline price reflected in the realized analysis. In many cases, this variance will be relatively small, particularly when the same methodology and analyst are used for both analyses. However, it remains important for two reasons. First, it ensures completeness by isolating every component of the savings formula independently. Second, when this driver becomes materially large, it can serve as an early indicator that assumptions or methodologies differ between the projected and realized analyses.
For example, differences in weighted average calculations, purchasing periods, unit conversions, or source data can all alter the effective baseline price. A significant variance here may indicate that one of the analyses is using an incorrect baseline or that purchasing behavior changed meaningfully prior to implementation. Even if the variance does not represent operational failure, identifying it early helps organizations refine expectations and improve forecasting accuracy.
Driver 2: Contract Price Variance
The second driver isolates the difference between the projected contract price and the actual realized contract price reflected in purchasing activity. Conceptually, this is often the most intuitive variance category. If the organization projected savings using one contract price but invoices reflect another, realized savings will naturally differ from expectations.
The causes may vary. The negotiated pricing may have differed from what was initially modeled, implementation timing may have delayed access to contracted pricing, or purchasing activity may not be transacting correctly through the intended agreement. In some cases, tier attainment, rebates, freight assumptions, or administrative fees may also contribute to the variance.
This driver is particularly valuable operationally because it frequently identifies actionable issues. Large variances here may indicate the need to engage purchasing teams, distributors, or vendors to determine why realized pricing is not aligning with the expected contract structure.
Although both technically originate from the quantity portion of the formula, they represent fundamentally different types of operational change and therefore should be analyzed independently.
Driver 3: Item Volume Variance
If baseline price and contract price have already been isolated, the remaining variance must logically come from quantity. However, this is where much of the complexity — and often the most valuable operational insight — begins to emerge.
In theory, baseline and contract pricing variances should generally remain relatively small. When they become significant, the issue is often methodological or operational: incorrect assumptions, pricing discrepancies, implementation failures, or purchasing alignment problems. While it is important to identify, these variances are usually not the primary drivers of long-term performance differences.
Quantity, however, is different.
This is where organizations frequently oversimplify the analysis by concluding that “volume changed.” While technically correct, that explanation often hides the underlying operational behavior that actually drove the variance. The waterfall makes this visible by separating quantity into two distinct drivers: overall line-item volume and item mix.
The third driver isolates how changes in total utilization within each individual product line affected the realized savings.
This captures straightforward changes in activity such as:
- increased procedure counts
- census growth
- expanded service lines
- seasonality
- or broader utilization changes
For example, if a converted glove SKU was projected at 10,000 units annually but actual utilization increased to 12,000 units, the additional volume would create incremental savings exposure under the same pricing assumptions. Conversely, lower-than-expected utilization would reduce realized savings.
Importantly, this driver evaluates volume changes within each individual line independently before considering whether utilization is shifted between products. It answers the question:
“How did total utilization changes affect the savings outcome if product usage patterns otherwise remained constant?”
This distinction becomes important because total quantity alone does not fully explain financial performance.
Driver 4: Item Mix Variance
The fourth driver isolates changes in how utilization shifted between products or SKUs within the analyzed category.
This is often where the most meaningful operational insight emerges.
Two projects may have identical total quantity and identical overall utilization growth, yet produce materially different savings results because the composition of that utilization changed. Increased usage of higher-cost products, physician preference items, premium configurations, or partially converted SKUs can materially alter realized savings even when total volume remains relatively stable.
For example, a project may successfully convert overall catheter utilization to contract pricing, but if clinicians disproportionately shift toward higher-cost specialty configurations within that category, realized savings may underperform projections despite stable total volume.
Conversely, favorable mix shifts toward lower-cost converted items may improve realized savings even without overall utilization growth.
By isolating item mix separately from overall volume, the waterfall prevents these effects from becoming buried within generalized “volume changed” explanations. Instead, it allows organizations to distinguish between:
- changes in how much was purchased
- and changes in what was purchased
That distinction is often critical when evaluating clinical adoption patterns, physician preference behavior, standardization efforts, and the true operational drivers behind realized financial performance.
Pulling the Drivers Together
Once each driver has been isolated, the waterfall can effectively bridge the gap between projected annualized savings and actual realized results. The key is to establish clear baseline drivers across all contracting categories so organizations can accurately distinguish true savings from other financial impacts. More importantly, this level of clarity enables the development of more precise and forward-looking savings projections, extending beyond the initial 6 to 12 months typically used to evaluate contract spend against new pricing or product mix.
Ultimately, a well-constructed waterfall analysis transforms savings reconciliation from a surface-level explanation into a meaningful operational insight tool. Rather than relying on broad assumptions, organizations gain a structured, data-driven understanding of exactly what influenced performance. This not only strengthens accountability but also enhances future decision-making, improves forecasting accuracy, and ensures that savings opportunities are fully realized and sustained over time.
Article by:
Brett J. Webster, Healthcare Supply Chain Analytics Leader, MemorialCare
Brett is a healthcare supply chain analytics leader at MemorialCare with a background in finance and quantitative analysis. His work involves translating complex sourcing activity into clear, defensible financial outcomes that strengthen alignment between supply chain and finance teams.
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