Overview
Industry: Truck Manufacturing
Geography: Global
Technologies: Data Governance, RBAC, Data Quality Controls, RACI Framework
The Situation
A large truck manufacturing company was losing ground to a problem hidden in its own data. Inaccurate and inconsistent information flowing across ERP, CRM, and WMS systems had created a compounding cycle of operational failures and the supply chain was absorbing the cost of every one.
Phantom inventory was the most visible symptom. Quantity on Hand figures in the warehouse system diverged from physical reality, creating simultaneous stockouts and overstock in the same facility. Production schedules built on those figures collapsed, triggering line stoppages and emergency inventory buildups. Faulty Bill of Materials data sent errors cascading through production planning, and inaccurate demand forecasts misaligned the entire supply chain.
Compounding the operational challenge, sensitive financial data Finished Good Unit Costs and Sale Prices and proprietary intellectual property including Manufacturing BOM IDs lacked adequate access controls. Critical data was visible to people with no legitimate business need to see it.
What Codincity Did
Codincity implemented a hub-and-spoke data governance programme centred on the Critical Data Elements essential to the organisation's inventory process establishing standards, accountability, security controls, and quality guardrails across the entire supply chain data estate.
Defined and documented over 17 Critical Data Elements including Forecasted Quantity, Finished Good Unit Cost, and Order Quantity with clear business definitions and end-to-end data lineage across CRM, ERP, and WMS systems.
Deployed 8 RACI matrices across all data domains, explicitly assigning Data Owners (such as the Head of Sales) and Data Stewards (such as Inventory Managers) to every CDE creating clear accountability for data quality for the first time.
Applied a three-tier data classification framework Confidential, Restricted, and Internal automatically triggering RBAC controls and data masking for financial data and PII. Financial figures are visible only to Cost Accounting and Executive roles; customer identifiers are masked in all non-production and analytics environments.
Implemented automated data quality validation rules at source preventing invalid data from entering the supply chain before it can propagate downstream into planning and inventory systems.
Established formal data lifecycle policies, including a permanent retention rule for Truck Model and SKU data to support service records, parts management, and recall obligations indefinitely.
Business Impact
>99% data completeness for critical fields Sales and Operations Planning now runs on complete, reliable inputs.
>98% data accuracy across CDEs phantom inventory and BOM errors dramatically reduced, with direct improvement in production reliability.
<24-hour data timeliness planning and S&OP decisions made on near real-time data, enabling faster response to supply chain changes.
Inventory carrying cost reduction through improved forecast accuracy, demand-supply alignment, and optimised inventory positioning.
Accelerated data issue resolution the RACI model ensures problems are identified and resolved at source rather than cascading through the supply chain.
Systematic PII and financial data protection enforced through classification, RBAC, and masking no manual controls required.
What It Means Going Forward
The governance framework the organisation now has in place does not just improve today's supply chain performance it protects it as the business evolves. Quality controls embedded in source systems enforce standards continuously. RACI accountability ensures data problems are owned and resolved. And lifecycle policies protect critical reference data from inadvertent loss.
Every planning cycle that runs on accurate, timely, complete data produces better outcomes than the one before. That compounding effect is what enterprise data governance delivers not a one-time fix, but a sustained operational capability.
Conclusion
Data governance delivers value when it improves operational outcomes, not just data quality metrics. By defining critical data elements, establishing clear ownership through RACI, enforcing role-based access controls, and embedding automated quality controls into source systems, Codincity helped the organization transform fragmented supply chain data into a trusted operational asset. The result is greater inventory accuracy, improved planning reliability, stronger data security, and a governance foundation that supports long-term business growth.
