Case Study • AI & DATA ANALYTICS ASSISTED ASSET MANAGEMENT

Materials Master Data Quality & Spend Analytics for Power & Energy Industry

AI-assisted spares and commodity standardisation to improve asset availability, procurement efficiency, and cost control.

Focus: Spares consistency, inventory visibility, and spend optimisation Delivery: Cross-functional (SCM, Finance, IT and Engineering) Methods: AI-assisted analytics and engineering validation

Executive Summary

AQRESS supported a power-generation client to address a high-impact challenge in asset-intensive environments, including inconsistent and poor-quality materials master data that drives procurement inefficiency, unreliable spares visibility, and avoidable cost variation. The client wanted consistency in spend on common components and to reduce costs caused by inconsistent spares descriptions. AQRESS worked with Supply Chain Management and Finance teams, as well as site Engineers, to improve spares data quality and standardise commodities across different sites. We accelerated the work using AI-assisted analytics to identify duplicates, cluster similar material descriptions, and highlight inconsistencies at scale, thus enabling engineering-led validation and standardisation.

Problem Statement

In power generation, maintenance execution and plant availability depend on timely access to correctly specified spares. When the materials master contains duplicates, inconsistent descriptions, or incorrect attributes, the downstream impact is significant, including the following:

False stock-outsSpares exist in inventory but are recorded under a different description, so the system shows none available during a breakdown.

Duplicate items & fragmented demandThe same instrument/equipment is treated as different commodities, preventing demand aggregation and weakening purchasing leverage.

Inconsistent spend & price varianceIdentical items are purchased under different descriptions, creating varying prices for the same component.

Inefficient catalogue maintenanceDelays and weak governance lead to incomplete, inconsistent, and duplicate records.

Poor data entry qualityInvalid values captured in attributes reduce trust in the data and limit analytics value.

Our Solution (AI-Assisted Analytics and Engineering-Validation)

AQRESS implemented a structured data quality optimisation approach aligned to good practice in master data management and cataloguing.

1. Baseline Assessment of Materials Master Data

AQRESS assessed the current state of materials descriptions and key attributes to identify patterns such as:

Duplicates and near-duplicates
Inconsistent naming conventions across sites
Incomplete fields and attribute errors
Outliers affecting spend analysis and reporting

2. AI-Assisted Materials Description Analytics

To work effectively at scale, AQRESS used AI-assisted techniques within Python and analytics workflows to:

Identify and group likely duplicates and near-duplicates
Cluster similar descriptions to support commodity standardisation
Flag anomalies and outliers (including invalid entries in attribute fields)
Prioritise high-impact items based on usage/spend patterns

This accelerated the process and focused engineering effort where it delivered the most value.

3. Engineering-Led Commodity Standardisation

A key differentiator was combining AI signals with engineering judgement. AQRESS engineers applied process and equipment understanding to:

Confirm functional equivalence and specification-critical attributes
Standardise descriptions and attributes across sites
Reduce duplicates and improve searchability and spares visibility
Support correct spares identification during breakdowns and maintenance planning

4. Spend & Price Consistency Analytics

AQRESS supported the client in analysing purchasing and spend patterns to:

Compare pricing for equivalent items across sites and orders
Identify avoidable price variance driven by inconsistent descriptions
Improve visibility of repeat purchases and consolidation opportunities

Team Mobilised

AQRESS deployed a multidisciplinary team working jointly with Supply Chain, Finance, and Engineering stakeholders:

Data Analysts Software Developers Engineers

Results & Impact

🔎

Improved Catalogue Consistency

Improved consistency of spares descriptions across multiple sites and users.

🧬

Reduced Duplicates

Reduced duplicate items and improved catalogue usability and searchability.

👁️

Better Spares Visibility

Increased confidence in spares availability (reducing false stock-outs during breakdowns).

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Spend Transparency

Improved spend transparency for repeat materials and better analysis of price variance.

Why Partner with AQRESS

AI-assisted analytics at scaleRapidly detects duplicates, inconsistencies, and standardisation opportunities across large data sets.

Engineering-led validationEnsures catalogue decisions reflect real equipment specifications and maintenance realities.

Cross-functional deliveryAligns outcomes across Supply Chain, Finance, and Engineering stakeholders.

Actionable reportingDashboards and analytics that support procurement efficiency and asset reliability.

Sustainable governance enablementSupports practices that prevent re-introduction of duplicates and poor data quality.

Operational improvement focusImproves spares availability, purchasing performance, and asset management decision-making.

Enabling Better Asset Management Decisions

By improving materials master data quality and standardising commodities, organisations increase confidence in inventory visibility, strengthen purchasing leverage, and create a reliable foundation for ongoing analytics, governance, and operational performance improvement.

Next Steps

To extend and sustain benefits, typical next steps include strengthening validation at data entry points, formalising governance and approvals for catalogue changes, defining KPIs aligned to spares data quality and availability outcomes, and implementing continuous monitoring dashboards for duplicates, completeness, and consistency.

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