D-Data: The Foundation of DOSS for Inventory Management

Introduction

In the DOSS framework, D = Data and it’s not merely a supporting element; it is the foundation. Accurate, Standardized master data is what enables reliable stock visibility, correct procurement decisions, and efficient MRO (Maintenance, Repair & Operations) inventory control. Conversely, poor data is the single biggest hidden cost in manufacturing and distribution: it creates phantom inventory, causes production delays, and drives reactive emergency buying that inflates total cost of ownership.

Common challenges faced in the Industry.

1. Decentralized plants, weak SOPs, and language barriers

When multiple plants operate with decentralized data ownership and without standardized SOPs for masterdata creation, modification, and retirement, inconsistencies multiply rapidly. Each site maintains its own spreadsheets, naming conventions, and item codes, often influenced by local practices and regional languages across states and territories. The same material may be described differently due to translation variations, abbreviations, or local terminology, resulting in multiple SKUs for a single part.

Impact: duplicate and unnecessary purchases, misaligned inter-plant stock transfers, inflated inventory values, unreliable consumption analytics, and declining trust in ERP and management reports due to uncontrolled masterdata “noise.”

2. People leaving or switching roles

Inventory managers, engineers, or procurement officers often become the informal owners of critical data. When they leave or move roles, undocumented knowledge naming conventions, cross-reference rules, and local fixes leaves with them.

Impact: orphan records, missing cross-references, and long delays while new staff re-learn local quirks.

3. Poor integration between systems

ERP, WMS, procurement portals, and condition-monitoring systems often don’t synchronize cleanly. Data transformations across interfaces change codes, units, or descriptions.

Impact: broken BOM links, phantom inventory when counts aren’t reconciled across systems.

4. Inconsistent naming and attribute standards

Free-text descriptions, varying unit-of-measure usage, and missing manufacturer/part numbers make precise matching difficult.

Impact: inefficient search, wrong item picks, and higher maintenance labor.

Examples of Real Operational Consequences

• Production halted for hours because a critical valve was recorded under a different SKU at the plant level.
• Emergency air shipments purchased at premium rates to compensate for perceived shortages caused by duplicate records.
• Planned preventive maintenance missed due to misidentified spare part sizes.

What Good Data Quality Looks Like

• Accuracy: The attribute value reflects the true, physical item.
• Completeness: Required fields (UoM, supplier, criticality, BOM usage) are populated.
• Consistency: Same item has the same ID and attributes across systems.
• Uniqueness: No duplicate records for the same SKU/part.
• Validity: Values conform to agreed formats, codes and business rules.
• Timeliness: Changes (e.g, Obsolescence, new suppliers) are updated promptly.

Data you trust shortens the decision loop; data you doubt doubles the cost of every decision.

Masterdata accuracy and data standardization are the bedrock of an effective DOSS deployment. The costs of ignoring data governance are concrete: higher carrying costs, production risk, and lost operational agility. By combining strong governance, standardized SOPs, right-sized technology (SSoT/MDM), and disciplined people processes organizations can turn masterdata from a recurring problem into a strategic asset.

Start with masterdata health-check on your critical MRO Spare Parts by submitting a sample batch of 1,000 material codes to get a clear view of your inventory data health. W2W’s Data Health Checkup Program identifies gaps, duplication, and standardization issues, enabling informed decision-making and a prioritized roadmap for data-driven inventory control.