The OSI Framework
Operational Stress Index Methodology
The Operational Stress Index (OSI) is a composite scoring framework developed by Allodial to quantify operational stress in US supply distribution markets. This document describes each indicator, the composite scoring model, data sources, and limitations, openly and without proprietary obfuscation.
Introduction
What the OSI is — and what it is not
The OSI is a directional, public-signal-based framework designed to surface regional operational stress conditions for the Jan-San, paper, and facility supply distribution industry in the United States. It is not a proprietary black box. Every indicator has a defined conceptual basis, a described input model, and documented limitations.
OSI scores are derived from reviewed public data sources: government statistical series (BLS, EIA, FRED, NOAA), industry publications, and trade RSS feeds. They are supplemented with structured Allodial methodology for seasonal adjustment and signal weighting. No individual distributor customer data is used in OSI scoring.
The framework is specifically designed for JanSan and facility supply distribution, a sector with distinct demand patterns (institutional cyclicality, weather-linked usage, regulatory compliance purchasing) that general-purpose supply chain indices do not adequately capture.
The Seven Indicators
OSI-01 through OSI-07
Seven indicators cover distinct dimensions of operational stress. Each scores 0–100. The composite OSI score is a weighted average of all seven, with weights calibrated for leading-indicator value and JanSan-sector relevance.
Reactive Ordering
Percentage of reorders triggered by stockout events rather than scheduled cadence.
What It Measures
The degree to which distributor procurement is demand-driven (reactive) versus proactive. High reactive ordering means orders are placed only after inventory has already hit a critical threshold.
Why It Matters for Distributors
Reactive ordering is one of the strongest leading indicators of operational friction. When a distributor's customers are ordering reactively, it signals that safety stock discipline has broken down, creating compounding risk for distributors who supply those customers.
How It Is Calculated
Public signals referencing supply disruptions, back-order commentary, and demand-pull commentary are scored for operational relevance and severity. The indicator blends source density, recency, and reliability weighting.
Score above 65 indicates reactive ordering signals are materially elevated relative to baseline.
Stockout Proximity
Median estimated days of cover remaining on the highest-cadence SKUs in the region.
What It Measures
How close the regional supply position is to a stockout event for the highest-velocity products. Lower days-of-cover equals higher proximity to a stockout.
Why It Matters for Distributors
Stockout proximity is the most operationally acute indicator. When cover is thin across multiple distributors in a region, demand spikes or lead-time extensions can cause cascading stockouts, particularly for commoditized JanSan and paper products with high substitution friction.
How It Is Calculated
Combines signal from public supply chain commentaries, product availability mentions, and regional distribution disruption reports. Seasonal adjustment is applied for known high-demand calendar periods.
Score above 70 warrants attention. Score above 80 indicates material stockout proximity risk in the region.
Dead Inventory Pressure
Share of estimated on-hand inventory value with no observed demand in the prior 180 days.
What It Measures
Overstock concentration in slow-moving or obsolete SKUs. Elevated dead inventory pressure competes with working capital that could otherwise fund reorders of active SKUs.
Why It Matters for Distributors
Dead inventory is a lagging indicator: it builds slowly and signals that purchasing decisions have been systematically misaligned with actual demand. For distributors, high dead inventory pressure can mask a low overall stock position on active SKUs.
How It Is Calculated
Derived from public purchasing signals, commentary on inventory write-downs, and demand pattern signals from industry publications. Given its lagging nature, dead inventory pressure receives a slightly lower composite weight than leading indicators.
Score above 60 suggests meaningful dead inventory concentration. Score above 75 may indicate a structural purchasing misalignment.
Reorder Inconsistency
Coefficient of variation between observed and baseline reorder intervals for key product categories.
What It Measures
How erratically orders are being placed relative to historical cadence. High inconsistency means order timing is irregular, sometimes too early, sometimes too late.
Why It Matters for Distributors
Inconsistent reorder timing is a signal of procurement system stress or manual override behavior. It amplifies demand uncertainty upstream for manufacturers and logistics providers, and often precedes more severe supply disruptions.
How It Is Calculated
Scored from signals indicating demand variability, irregular purchasing patterns, and supply chain fragmentation commentaries. Source reliability and recency are weighted.
Score above 55 suggests elevated reorder timing variability. Score above 70 indicates significant procurement discipline breakdown.
Seasonal Stress
Year-over-year drift in operational discipline through seasonal peak weeks for the region.
What It Measures
Whether the region's operational posture is deteriorating or improving relative to the same period in prior years. High seasonal stress means this year's peak season is creating more friction than prior years.
Why It Matters for Distributors
Seasonal peaks (spring cleaning, back-to-school, holiday facility prep) create predictable but often underestimated demand surges for Jan-San and paper products. Distributors who fail to forward-position inventory before peaks face acute stockout exposure.
How It Is Calculated
Combines calendar seasonal adjustment weights with real-time signal density from sources covering seasonal demand patterns, weather-related demand spikes (via NOAA alert data where available), and institutional procurement cycles.
Calendar seasonal adjustment ranges from +2 to +8 points by month. Score above 65 in a low-season month is particularly notable as a stress signal.
Freight Pressure
30-day variability of lane cost and lead-time per region.
What It Measures
How unstable freight costs and transit times are for the region. High freight pressure means distributors face unpredictable inbound lead times and margin compression from freight costs.
Why It Matters for Distributors
Freight pressure directly extends effective lead times, compressing the window distributors have to reorder before reaching stockout proximity. It also reduces the cost-effectiveness of emergency orders and spot freight, which are the primary safety valve when standard channels fail.
How It Is Calculated
Primary signal comes from EIA regional diesel price data (where available) blended with BLS freight price indices and public source signals. EIA/BLS data is weighted at 65% and 55% respectively, with source-derived signals filling the remainder.
Score above 65 indicates freight pressure meaningfully elevated above baseline. Score above 75 suggests compounding risk to standard reorder timing.
Supplier Pressure
Upstream supplier reliability and allocation pressure across the region's primary supply base.
What It Measures
How much pressure is building at the manufacturer or upstream distributor level. High supplier pressure means lead times are extending, allocations are tightening, or supplier reliability is degrading.
Why It Matters for Distributors
Supplier pressure is a leading indicator for the entire downstream distribution chain. When manufacturers are under allocation pressure or experiencing reliability issues, the ripple effect reaches distributors within weeks, often before it is visible in any customer-specific system.
How It Is Calculated
Primary signal from BLS Producer Price Index (PPI) components for paper and sanitation products, blended with source-derived signals from industry publications. BLS PPI is weighted at 55%, source signals at 45%.
Score above 60 indicates supplier-side pressure building. Score above 70 warrants proactive forward-purchasing consideration.
Composite Scoring
How the 7 indicators combine into the overall OSI score
The composite OSI score is a weighted sum of all seven indicator scores, bounded 0–100. Indicator weights reflect their relative predictive value as leading indicators of operational disruption in the JanSan distribution sector, validated against historical supply events.
Indicator Weights (sum = 1.0)
Stress level classifications are applied to composite scores: Baseline (0–39), Watch (40–59), Elevated (60–74), and Critical (75–100).
Forecasts (7d, 14d, 30d windows) are generated using a hybrid model that blends deterministic indicator momentum, seasonal positioning, and — where available — AI-assisted directional classification (openai/gpt-5.4-mini via Vercel AI Gateway). All forecasts are capped at "medium" confidence until validated against customer ERP data.
Data Sources
What feeds the indicators
OSI indicators are computed from two classes of inputs:
Government Statistical Series
- —EIA: Regional diesel and energy price data (primary Freight Pressure input)
- —BLS: Producer Price Index (PPI) for paper and sanitation products (primary Supplier Pressure input)
- —FRED: National freight cost indices (fallback Freight Pressure input)
- —NOAA: Active weather alert counts by region (Seasonal Stress boost)
Reviewed Public Sources
- —Industry trade publications (distribution, logistics, manufacturing)
- —RSS feeds from logistics and supply chain news sources
- —Manual analyst notes on significant regional events
- —Publicly available industry reports and methodology documents
Ingested sources are processed for operational relevance score (0–100), severity classification, and indicator tagging. Reliability is classified as high (government statistical series, major trade publications), medium (regional trade sources, RSS), or low (unverified or low-circulation sources). Reliability and recency jointly determine each source's contribution weight.
Limitations
What the OSI does not tell you
OSI scores are regional, not company-specific.
A region with a Critical OSI score does not mean every distributor in that region is experiencing critical stress. Individual distributor conditions depend on customer mix, product portfolio, purchasing discipline, and ERP data, none of which are reflected in the OSI.
Public signals are directional, not predictive guarantees.
OSI scores and forecasts indicate directional pressure. They are not guarantees of operational outcomes. Supply chains can absorb pressure through inventory positioning, supplier relationships, and alternative sourcing that are not visible in public data.
Source coverage varies by region.
Regions with fewer reviewed public sources produce lower-confidence scores. Confidence levels (low, medium, high) are displayed with each score to reflect this. Low-confidence scores should be treated as weak directional signals only.
The OSI does not capture black-swan events in advance.
Unforeseen disruptions (natural disasters, port closures, manufacturer plant failures) will not appear in OSI scores until after public sources begin reporting. The OSI is a real-time signal framework, not a prediction system for unprecedented events.
No individual distributor or customer data is used.
OSI scores are derived entirely from public sources and government data. No customer inventory counts, purchasing records, or operational data from Allodial Predict customers are used in or influence OSI indicator calculations.
Beyond Public Signals
For customer-specific OSI analysis,
request an Operational Replay
An Operational Replay maps your actual ERP data against OSI signals to surface where customer-specific purchasing patterns are amplifying or masking regional stress conditions.