Leading Capital Equipment Manufacturer

The Opportunity

A global leader in capital equipment manufacturing — producing complex, high-value machinery with multi-level bills of material — struggled with data quality and process gaps that had compounded during a period of rapid international growth. Disconnected systems for inventory, purchase, and manufacturing created a fragmented operational landscape: the same part number could exist with different descriptions across systems, purchase orders were tracked in spreadsheets alongside the ERP, and manufacturing processes lacked the standardized data capture needed for reliable yield analysis and capacity planning. As the company expanded into new markets, the cost of this fragmentation multiplied — every new facility onboarded inherited the data quality problems of the legacy environment, and reliable cross-plant reporting was effectively impossible.

• 01Multi-level bills of material with inconsistent part numbering and descriptions across disconnected systems.
• 02Purchase order tracking split between the ERP and manual spreadsheets — creating reconciliation gaps and audit risk.
• 03Manufacturing processes lacking standardized data capture — preventing reliable yield analysis and capacity planning.
• 04Rapid international expansion multiplying data quality problems — every new facility inheriting the fragmented legacy environment.

The Solution

We led an end-to-end ERP implementation across Inventory and Purchase — not as a technology deployment, but as a business transformation initiative. The implementation began with deep process mapping: walking the factory floor, sitting with procurement teams, and documenting how information actually flowed versus how it was supposed to flow. This revealed silent operational gaps — processes that existed in tribal knowledge but had never been formalized, data entry points where quality degraded, and handoff moments where information was lost between departments. We cleansed years of legacy data using OpenRefine and Data Wrangler, standardized master data definitions across all facilities, and instituted formal governance structures — including departmental data stewards, periodic audit processes, and escalation protocols — to ensure that data quality was sustained beyond the project lifecycle.

• 01End-to-end ERP implementation for Inventory and Purchase.
• 02Process mapping to identify and close silent operational gaps.
• 03Data sanitization using OpenRefine and Data Wrangler.
• 04Formal data governance with departmental stewards and audit processes.
• 05Master data standardization across all facilities — ensuring consistent part numbering, descriptions, and unit-of-measure definitions.
• 06Change management program ensuring user adoption — training, feedback loops, and phased rollout to minimize operational disruption.

The Impact

The project established a 'single source of truth' for the entire manufacturing lifecycle — from raw material procurement through production to finished goods inventory. For the first time, leadership had reliable, cross-plant visibility into operational metrics without manual reconciliation. Real-time adoption tracking via Tableau ensured that the ERP was not just deployed but actively used as designed, with early detection of workaround behaviors that could degrade data quality. The governance framework — stewards, audits, and escalation protocols — ensured that the gains were structural rather than temporary, creating a data foundation capable of scaling with the company's continued international expansion.

• 01100% adoption visibility via real-time Tableau monitoring.
• 02Eliminated reporting siloes across departments.
• 03Sustained data quality through institutionalized governance.
• 04Cross-plant operational visibility achieved for the first time — enabling comparative performance analysis across facilities.
• 05Foundation laid for advanced analytics: demand forecasting, predictive maintenance, and yield optimization now possible on clean, governed data.
• 06New facility onboarding accelerated through standardized master data templates and documented data capture processes.