management into a single CTRM system. However, this approach often runs into fundamental challenges stemming from the nature of commodity trading itself. Companies active in multiple physical commodities must account for highly specific characteristics (e.g. different crude oil grades) that influence pricing, operational processes, delivery, and, ultimately, the entire trade life cycle.
Similarly, physical storage and delivery introduce commodity-specific requirements. These include, for instance, boil-off rates in LNG transportation or the oxidative degradation of coal during storage. CTRM systems that are sufficiently flexible to accommodate the full range of such commodity-specific features are rare; when they do exist, they are typically expensive and require long implementation times.
As a result, most multi-commodity traders either adopt commodity-specific CTRM solutions or accept functional compromises, supplementing their core CTRM with spreadsheets and ancillary applications to compensate for missing capabilities.
In an environment, where multiple CTRM systems and additional supporting applications are used, the task of data consolidation and synchronization becomes complex. Despite this complexity, companies often still consider building an in-house solution, typically based on a data lake combined with BI tools drawing data from multiple CTRMs and ERPs.
In practice, such solutions often prove insufficient and show clear limitations when compared to specialized
vendor platforms. The most common issues include:
• Time to value
Internal IT teams must work closely with multiple departments and consolidate input from subjectmatter experts who are already heavily involved in daily operations. They are responsible for defining data ownership and workflows, aligning valuation logic for KPI calculations, and establishing rules for data exchange, connectivity, and normalization. In practice, some departments may not be able to provide sufficient engagement, which can result in only partial data integration. Therefore, internal initiatives often take significantly longer than planned and still deliver suboptimal results.
By contrast, software vendor teams bring experience gained across many implementations, allowing them to recommend and apply proven approaches more efficiently. Based on RadarRadar’s experience, projects supported by a vendor take typically 3 to 12 months and require a couple of hours of a subject matter expert. Whereas projects implemented entirely in-house generally take well beyond 12 months to deliver with close to full-time involvement of subject matter experts.
• Model and quality risk
Reusing existing business logic embedded in spreadsheets or bespoke, opaque models often leads to inconsistencies, a lack of standardized calibration, and suboptimal results. Inaccurate or misaligned models can produce incorrect calculations for positions, exposures, and margins,
RADARRADAR’S OFFERING
RadarRadar offers a comprehensive software suite that leverages existing data sources and flexible ways of data ingestion to enable data-driven decision-making for positions, margin, and risk management.
The Radar Engine helps customers transform enterprise-wide raw data into trusted, actionable information for commodity risk management. Whether the source is an E/CTRM, ERP, finance system, Excel sheet, or simple file, the Radar Engine can ingest and integrate all necessary data regardless of its location, origin, or volume, providing a single source of truth across the entire organization.
With master data and governance support, realtime reporting, and comprehensive consistency and completeness checks, customers gain full control over their data. Cloud services, advanced security tools, and user-permission features ensure the secure storage and use of business data. The data workflow includes the following steps:
• Mapping: converting data from disparate source formats into a unified destination format
• Harmonizing : standardizing qualitative and quantitative fields into a single composite dataset
• Cleansing: identifying and resolving errors, null values, and duplicates, then aggregating and enriching data
• Automating: streamlining the uploading, processing, and transformation of data
• Distributing: replicating and distributing data across multiple databases
• Maintaining: storing, auditing, organizing, and correcting data to ensure accuracy and accessibility
By converting raw data into reliable information, the RadarRadar Engine enables customers to address business challenges from a data-driven perspective. This supports advanced analytics, enhances reporting, and improves transparency for interdisciplinary collaboration.
These business functions are supported by RadarRadar’s analytical modules, built on top of the core data management framework:
• Position and Performance Module provides a holistic view of positions, market Prices, portfolio exposures and financial value to support decisionmaking in complex market environments.
• Risk Management Module offers both quantitative and qualitative insights into portfolio risk profiles, based on internal models and external market developments. It helps identify key risk drivers, track exposure, predict performance volatility, run scenario analyses, visualize and test option books under different conditions, assess counterparty risks, and configure alerts and notifications.
• Dynamic Margin Management Module combines portfolio and market insights to help customers understand, manage, and optimize margins. Users can analyze production planning, forward and historical margins, yield, blend and
bill-of-material calculations, create and evaluate scenarios, monitor purchases and sales, and optimize procurement and asset strategies.
• Regulatory Compliance Module helps customers manage regulatory risks across their portfolios and locations while staying ahead of compliance deadlines. The module provides customizable, on-demand tools covering global derivatives regulations. It supports transaction and position reporting, visualization of current
and historical submissions, threshold monitoring, response management, and rapid adaptation to regulatory changes, including monitoring for market abuse.
Together, these modular tools transform consolidated, trusted data into powerful applications for commodity risk management, margin optimization, and regulatory compliance.
