The scope is fixed at Product × Technology × Application × Channel × Region, spanning seven regions and over thirty countries. Units (value/volume) and the currency basis are defined upfront to ensure consistency. Monthly signals shape our quarterly baselines, with forecasts running at constant FX unless specified otherwise in scenario analysis.
We count shipments once at vendor-to-channel dispatch, ensuring that OEM transfers are not double-counted. Subsidiary inventory is excluded until it enters the channel, and re-exports are reassigned to the country of final use when determinable. Manufacturer self-consumption and bona-fide donations are included to maintain the integrity of our installed-base data.
ASP is captured at the distributor level, including freight, insurance, and import duties within vendor/channel pricing, while point-of-sale taxes remain outside the scope. Outliers are normalized, and prices are weighted across international, domestic, and China-based vendors according to geography and application. For bulk chemicals/intermediates, bulk distributor prices are used.
Brand share is calculated as Brand Value ÷ Category Value by geography and channel. In the absence of EPOS data, we triangulate brand value from filings and public multi-signal proxies (SRAP/PII/RHI), calibrated to disclosed anchors and bounded by corridor tests.
At FMI, we compile trade flows using HS codes, with mirror-stat checks for gap detection. Adjustments are made for re-exports through known hubs, differences between FOB and CIF valuations, and reclassification events. Data series are time-aligned and currency-normalized.
Apparent consumption is calculated as Production + Imports − Exports ± Stock Change, where stock data is observable.
Revenue and volume are allocated across direct, distributor/wholesale, retail/mass, pharmacy/specialty, e-commerce, and tender/government channels. We model margin stacks for each channel. Leakage from grey/parallel trade is estimated through guardrails and anomaly detection on pricing and availability.
We map supply chains from input to end-use, covering capacity, utilization, lead times, yield loss, scrap, lane reliability, and inventory policies. Concentration indices and single points of failure are stress-tested under demand and supply shocks.
Supplier scorecards track quality, on-time delivery, cost variance, and risk metrics, giving us a comprehensive view of supply chain performance.
Method 1: Regression-based models incorporate predictors from production, utilization, trade, capex, approvals, and price corridors. Stability tests, residual checks, and structural break handling are employed to ensure robustness.
Method 2: We apply driver growth-rate models, where weighted driver growth is applied to audited baselines. We tune weights to reflect realized outcomes, and revisions are made when drivers rebase or when shocks occur.
Triangulation: Product Category Analysis, n-per-Population intensity, and Economic Envelope guardrails ensure comprehensive scenario modeling. Outputs provide value, volume, installed base, and price scenarios with confidence bands.
Regression accuracy is monitored via MAPE (Mean Absolute Percentage Error) with residual diagnostics. Growth-rate models are validated through correlation checks and backtesting, ensuring they remain within volatility-based tolerance bands.
Reconciliation ensures closure of apparent consumption, and we rigorously test price corridors, FX limits, and capacity constraints. Every analysis undergoes a dual-analyst review, and revisions are tracked with clear rationale in change logs.
We rely on a clear set of operational KPIs to maintain consistency across categories and regions. Value is calculated as Units × ASP, while Installed Base reflects Σ Shipments − Retirements based on class-specific survival curves. Our Apparent Capacity view uses Production ÷ Capacity Factor, and Brand Share is measured as Brand Value ÷ Category Value. Channel behavior is assessed through Channel Mix = Channel Revenue ÷ Total, and the Export–Import Balance is captured as Exports − Imports. These formulas allow our models to stay transparent and fully auditable.
At FMI, we draw from structured global sources to ensure every series carries traceable lineage. Key inputs include UN Comtrade, ITC Trade Map, Eurostat/PRODCOM, US Census, BEA, BLS, OECD, IEA, EIA, USGS, UNCTADstat, FAOSTAT, and USDA. Regulatory and clinical datasets such as FDA, EMA, PMDA, CDSCO, ClinicalTrials.gov, and WHO ICTRP support our medical device pathways.
Sector-specific sources such as OICA, ACEA, SIAM, EDGAR, SEMI, GSMA, and 3GPP are used where applicable. Public retail signals are gathered from filings, store locators, circulars, app stores, and visible social activity.
Our lineage cards specify pull dates, access type, transforms applied, confidence tier, and any caveats so that every data point is traceable and reproducible.
Our cadence combines quarterly baselines with monthly micro-updates to capture high-frequency deviations in shipments, pricing, regulatory actions, or operational disruptions. Shock notes are released rapidly when we observe policy changes, outages, recalls, or price spikes that could shift demand or supply conditions.
Deliverables typically include an executive memo, analytical workbook and models, 16:9 dashboards with lineage layers, and a detailed change log. When required, we also provide weekly SRAP/PII/RHI tiles to support retail monitoring and near real-time competitive tracking.
Methodology
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