Exposure. P&L. Margins. Risk.
One platform, six modules.

What it does

Exposure Management consolidates every physical and derivative position your business holds into a single governed net exposure — then lets you read that number along whichever dimension the decision requires: commodity, plant, division, trade flow, period, product form, entity or portfolio.

The business problem it addresses

In most commodity-exposed businesses the exposure picture is fragmented by department. Procurement tracks budgeted purchases in one spreadsheet, trading records hedges in another, and inventory sits in the ERP. Nobody holds the consolidated view, so decisions on hedging, pricing and procurement are taken on partial information. When two teams are asked the same question they return two different answers — and the disagreement is rarely arithmetic. Each team is netting a different set of positions against a different definition of exposure.

How it works

Positions flow in automatically from ERP systems, broker statements and trading platforms. One calculation engine applies your netting rules, your conversion ratios and your bill-of-materials mappings — exploding finished-goods SKUs back into their underlying commodity components, and resolving raw material into end product and by-product at your own plant recovery rates. The consolidated position is ready before your teams start work, and every function reads it the same way.

What you can see

Enterprise total down to commodity, plant and individual lot in a few clicks. Import, domestic and export books separated by trade flow. Near-term exposure separated from forward periods. Long and short shown gross as well as net, with the hedged proportion visible against each position.

Which teams use it

Procurement sizes the next purchase against what is already committed. Trading sees physicals and derivatives in one book before executing. Finance gets a position base that reconciles. Risk gets the denominator for every limit and every VaR run. Leadership gets one number rather than four versions of it.

How it relates to the other five modules

Exposure is the base layer. P&L Analytics and Margin Intelligence values these same positions; Risk Measurement and Scenarios runs VaR and stress tests over them; Limits, Governance and Alerts measures utilisation against them; Reporting and Decision Support presents them; and Data and Integrations is what keeps them current.

What it does

This module explains how commodity prices, procurement decisions, hedges and your own operating structure landed in the P&L — and what margin your plants actually earned while doing it. It produces four P&L measures from one underlying dataset, every morning.

The four measures

Open MtM values open positions against current market prices. Margin P&L, also called structural margin, is the margin between raw material cost and finished-product realisation. Closed P&L, also called Mark-to-Sales or MtS, covers business that is contractually closed but not yet settled. Realised P&L is what has settled. Because all four come from the same dataset they reconcile to each other by construction, and nobody has to bridge them by hand.

The business problem it addresses

Finance, trading and procurement each need a different measure. When those measures are produced separately, in separate spreadsheets, they stop agreeing — and month-end becomes an argument about whose number is right instead of a discussion about what to do next. Margin is the harder half: a published crush or refining spread tells you what a notional plant would have earned in a notional market, not what your facility earned on the parcels it actually processed.

How margin is calculated

Structural margin is built from the data specific to your operation. FIFO lot costs rather than a weighted average. Plant-level yield and recovery rates rather than an industry standard. Full landed cost, including freight, insurance, duty and quality adjustment. And derivative P&L allocated back to each plant in proportion to its share of the hedged exposure. The result is a number a plant manager and a CFO can both stand behind, because it describes that facility rather than a group average.

Which teams use it

Finance uses the realised and closed picture for reporting and budget variance. Procurement uses margin to judge whether the next purchase improves or dilutes it. Trading uses it to see hedge performance. Plant management uses it for conversion economics. Leadership uses forward-period visibility to see profitability building before the period closes.

How it relates to the other five modules

It values the positions Exposure Management consolidates, using costs and prices delivered by Data and Integrations. Risk Measurement and Scenarios then asks what these numbers become if prices move, and Reporting and Decision Support is where each team meets the measure relevant to its accountability.

What it does

Quantifies commodity risk before a position is taken rather than after a limit is crossed: Value-at-Risk across the whole book, scenario analysis, stress testing and sensitivity analysis, all run on your actual portfolio.

The business problem it addresses

Plenty of organisations produce a VaR number. Far fewer can act on one. A single portfolio figure arriving the morning after tells a trader nothing about which position drove it, whether the hedge genuinely offsets the physical it was placed against, or what a trade under consideration would do to it. Risk reporting becomes a post-mortem — accurate about yesterday, silent about the decision in front of you.

Three methodologies, on your portfolio

Monte Carlo, Historical Simulation and Parametric VaR, selectable by commodity, applied to your positions rather than to a model portfolio. The framework includes Component VaR and Marginal VaR for decomposition, AVaR and PVaR, and treats basis risk and rollover risk as standard rather than as an adjustment. Outputs are backtested against realised P&L so the model itself is under review.

Beyond the headline number

Risk decomposition attributes total portfolio risk down to individual positions. Basis risk analysis compares the physical price against the hedge instrument for each hedged position. Contract rollover risk surfaces upcoming rollovers with current roll spreads and spread volatility. Pre-trade assessment shows portfolio VaR and limit utilisation both before and after a proposed trade, so the analysis changes the decision instead of explaining it afterwards.

Scenarios and stress testing

Price, volatility and exposure scenarios are applied across the full commercial book — inventory, open purchases and forward sales — and shown beside the live position, so the effect on MtM and VaR is visible before anyone commits. Extreme tail-risk scenarios are run the same way.

Which teams use it

Risk owns the methodology and the backtest. Trading uses pre-trade assessment before execution. Procurement runs seasonal what-if analysis ahead of a buying window. Finance and leadership use stress results to size the tolerance the policy should carry.

How it relates to the other five modules

It measures the exposure that Exposure Management consolidates, valued with the same prices behind P&L Analytics and Margin Intelligence. Its VaR output is one of the dimensions on which Limits, Governance and Alerts sets thresholds, and its results appear in the risk views inside Reporting and Decision Support.

What it does

Turns a written risk policy into controls that run on their own: limits defined across every dimension of the book, utilisation tracked as it changes, warnings before a breach, notification at the moment of one, and an audit trail that assembles itself.

The business problem it addresses

Having a policy is not the same as running one. Where limits are checked by hand they are checked on a cycle — weekly, or whenever someone remembers — and a limit that is crossed on Tuesday is found on Friday, after the position has moved again. The most common failure in commodity risk governance is not a rogue trader. It is inconsistency: the same limit interpreted differently by two desks, or quietly not applied at all during a busy week.

How limits are defined

Across trader, commodity, desk, division, market and VaR — the dimensions your policy already uses, so the platform enforces the document you approved rather than a simplified version of it. Thresholds, warning levels and notification recipients are configured per limit.

What runs without anyone starting it

Utilisation is tracked continuously as a percentage of each threshold. Warning alerts fire at the level you set, before the limit is reached. Breach notifications are sent the moment a threshold is crossed, to whoever the policy designates, carrying the limit, the breached value, the size of the overshoot and the position responsible. Every warning, breach and notification is time-stamped and recorded.

Why the audit trail matters

Because the record is a by-product of the control rather than something assembled afterwards, an internal review or an external examination is answered by querying it. Nobody has to reconstruct who knew what and when, from mailboxes and spreadsheet versions, months after the event.

Which teams use it

Risk defines and maintains the limit framework. Trading checks headroom before executing. Finance leadership and compliance rely on the evidence trail. Internal audit queries it directly rather than requesting a reconstruction.

How it relates to the other five modules

Utilisation is measured against the net position from Exposure Management and the VaR produced by Risk Measurement and Scenarios. Limit status, headroom and the breach log surface inside Reporting and Decision Support, and the position data behind them arrives through Data and Integrations.

What it does

Gives every team that carries commodity accountability one cockpit — the exposures, P&L, margins, risk and limit positions each of them needs, prepared overnight and waiting when they arrive.

The business problem it addresses

In many organisations the first two hours of the day go on rebuilding the same report: exporting from the ERP, pulling broker positions, refreshing prices, reconciling the differences, formatting the result. The work is repeated every morning, it is the step where most errors enter, and it consumes exactly the people who should be interpreting the numbers rather than assembling them.

Role-specific views, not filtered copies

Five dashboards — executive, risk, trading, procurement and finance — each built around a different accountability rather than being the same screen with different column filters. The executive view carries enterprise exposure, margin against budget and VaR against policy. The trading view carries position detail, hedge ratio, limit headroom and pending rollovers. The procurement view carries purchase commitments, landed cost and margin. Graphical and tabular content sit in the same screen.

Asking a new question without building a new report

OLAP-style drill-down moves from any summary to the underlying positions and pivots there. User-defined reports save automatically. Existing internal report formats can be reproduced, so adopting the platform does not force every recipient to relearn the document they have read for years. Scheduled distribution sends the right report to the right list on the right cadence, without anyone preparing it.

Decision support, beyond reporting

Purpose-built workspaces bring exposure, margin and risk together per commodity group, alongside position notes and hedge monitors. Automated MIS covers raw materials, finished goods and by-products, with plant-level mapping for management briefing — business-ready analytics rather than a data extract someone still has to interpret.

Which teams use it

Procurement, trading, finance, risk and leadership — which is the point. The same platform serves the trading floor and the boardroom, from one set of numbers.

How it relates to the other five modules

It presents what the other five produce: exposure from Exposure Management, the four measures and structural margin from P&L Analytics and Margin Intelligence, VaR and scenarios from Risk Measurement and Scenarios, utilisation and breaches from Limits, Governance and Alerts, all on data delivered by Data and Integrations.

What it does

Connects ERP, broker, market and operational data to TransRisk so positions arrive automatically, are validated before they are used, and give every other module one trusted foundation to calculate from.

The business problem it addresses

Re-keying. Positions are exported from the ERP into a spreadsheet, broker statements are retyped from their native formats, prices are pasted in from a terminal — and every hop is an opportunity to introduce an error that nobody can trace back later. Meanwhile the positions that live outside any system at all, held in someone's workbook, never reach the risk view. The daily reconciliation that follows is not analysis; it is repair work.

What connects

SAP FICO and MM through native ETL developed in-house rather than third-party middleware, with bi-directional sync and multiple SAP instances across entities and geographies supported. Oracle and other ERPs through configured pipelines. Trading systems through API connectors. Broker statements ingested in their native formats and standardised automatically into TransRisk position data. Out-of-system positions through a governed Excel upload that validates on the way in and flags rejected rows for review rather than accepting them silently.

Price data

Your own price database, custom series for proprietary grades and locations, or live feeds from authorised market providers — in any combination. Price-to-position mapping is validated before it is applied to a P&L or VaR calculation, so a mismapped series is caught rather than quietly distorting a result.

Rules, quality and control

A rules engine validates data on ingest. Conversion rules map commodity to end product, and bill-of-materials definitions map SKUs to their components. Data-quality reporting and process history make the overnight cycle inspectable rather than opaque. Role-based access with desk, commodity and field-level permissions controls exactly what each user sees.

Which teams use it

IT and data owners configure and run it. Everyone else benefits from it without touching it — which is the measure of whether it is working.

How it relates to the other five modules

Every other module reads from here. Exposure Management nets what arrives, P&L Analytics and Margin Intelligence values it, Risk Measurement and Scenarios models it, Limits, Governance and Alerts tests it against policy, and Reporting and Decision Support presents it.