Manufacturing BOM Cross-Analysis

KDG introduces a unique concept called R-BOM (Result BOM), enabling structural visualization of where added value is created and where it is lost by comparing planned Q-BOM estimates against actual results. Five BOMs (E-BOM, M-BOM, P-BOM, S-BOM, Q-BOM) are connected through knowledge graphs, and veterans' cost expertise is accumulated in KBMS. Knowledge-driven VA (Value Analysis) builds an executive decision platform that explains not just numbers, but the causal chains behind every variance.

Feature 01

KDG Mapping — Semantic BOM Name Reconciliation

Uses LLM-based semantic matching to reconcile different IDs for the same parts across E-BOM, M-BOM, and P-BOM with confidence scoring. Automatically discovers part correspondences across BOMs, helping reduce manual reconciliation effort.

1LLM-powered semantic name matching with confidence scores
2Cross-BOM part reconciliation across design, manufacturing, and procurement IDs
3Visual mapping interface with manual override capability

Feature 02

KDG Parts — Unified Parts Master

Provides a single consolidated view of each part across all BOM types, combining engineering specs, manufacturing processes, procurement data, and service history. Instantly grasp the full picture of any part in your portfolio.

1Unified part profile across all BOM types
2Part search with multi-dimensional filtering
3Usage analysis showing which products use each part

Feature 03

KDG Integrity — BOM Consistency Checker

Validates BOM data integrity by detecting orphaned parts, circular references, missing relationships, and structural inconsistencies. Issues are classified by severity and managed through a resolution workflow.

1Automated integrity rule checking across BOM layers
2Issue detection with severity classification
3Resolution workflow with audit trail

Feature 04

KDG ECM — Engineering Change Management

Manages ECR/ECO workflows while providing Neo4j-powered multi-hop impact propagation analysis that traces change ripple effects across all BOM types. Quantifies risk through scoring and surfaces similar historical changes.

1N-level graph impact analysis (up to 5 hops across 7 relationship types)
2Risk scoring based on impact depth, affected BOMs, and historical rework rates
3Similar-change discovery from historical patterns

Feature 05

KDG PLM — Value-Added Visualization Dashboard

Visualizes Q-BOM (planned value hypothesis) vs R-BOM (actual results) through 4-quadrant analysis, categorizing variances into Secured, Hidden Sources, Leakage, and Damage. Each variance is explained by certified KBMS knowledge with causal chains, and VA improvement activities are tracked on a timeline — quantitatively proving that the more knowledge you apply, the more value you create.

1Q-BOM vs R-BOM 4-quadrant analysis (Secured / Hidden Sources / Leakage / Damage) to structure value flow
2KBMS-certified knowledge explains causal chains — not just numbers, but evidence-based reasoning for every variance
3VA improvement timeline (T0 estimate → T3 first shipment → VA1/VA2/VA3 activities) tracking improvement per applied knowledge

Ready to Transform Manufacturing with BOM Intelligence?

Getting started with BOM cross-analysis begins with an inventory of your existing BOM data. From evaluating data structures to designing integration models and running pilot analyses, we walk alongside you every step. Reach out to start the conversation.