DFT+ for Manufacturing Organizations - Source Agnostic

Overview — DFT+ for Manufacturing Companies

Manufacturing organizations rely on multiple operational systems — ERP, MRP, PLM, quality systems, shop-floor equipment, maintenance platforms, and supply-chain applications. Each system captures transactions differently, often producing inconsistent definitions of products, costs, inventory, and production performance.

DFT+ (Dimension, Fact, Time) modeling separates operational system complexity from the analytics layer, giving manufacturers a stable and trusted foundation for reporting, operational analytics, and AI initiatives.

Within DFT+, a SPOT (Single Point of Truth) defines the authoritative dataset for each critical manufacturing concept — such as Item, Bill of Material, Work Order, Production Operation, Inventory Position, Supplier, or Customer.

Each SPOT is implemented as a curated data-warehouse table, view, or calculation that uses SQL to expose clean, clearly named fields — primary keys, foreign keys, and analysis attributes — ensuring dashboards, KPIs, and analytical models remain stable even as operational systems evolve.

Example — Item & Production SPOT

The Item SPOT consolidates all attributes required for product and manufacturing analysis.

Data may originate from multiple sources:

• ERP → Item master, standard cost, planning parameters

• MRP → production quantities, cycle times, machine performance

• PLM → engineering revisions and product hierarchy

• Quality systems → inspection results and defect tracking

• Maintenance systems → downtime and equipment reliability

The SPOT harmonizes product definitions across systems and revisions, enabling consistent analysis of:

• product profitability

• manufacturing efficiency

• production throughput

• scrap and rework rates

• cost variance analysis

• engineering change impacts

Why DFT+ Matters for Manufacturing

Manufacturers constantly face operational change:

• ERP and MRP upgrades or replacements

• plant expansions or new facilities

• product redesigns and engineering revisions

• automation and Industry 4.0 initiatives

• acquisitions and multi-plant consolidation

One of the primary goals of DFT+ is to keep reporting, analytics, and AI models stable throughout these transitions.

Instead of rebuilding reports whenever production systems change, DFT+ preserves a consistent analytics layer — allowing manufacturers to focus on improving throughput, quality, and margins rather than fixing data definitions.

Business Outcomes for Manufacturers

DFT+ enables reliable analysis across:

• production efficiency and throughput

• capacity utilization and bottlenecks

• labor and machine productivity

• standard vs. actual cost variance

• scrap, yield, and quality performance

• inventory optimization and material planning

• on-time delivery and schedule adherence

By establishing SPOT datasets, manufacturing companies gain a durable analytics foundation that supports continuous improvement, operational excellence, and data-driven decision making.

DFT+ Stages

Stage 1 — Source Mirroring: Creates a clean, fast, low-impact copy of raw source data, preserving lineage and keys.

Stage 2 — Data Warehouse: Builds the primary SPOTs (dimensions/facts) via SQL (or Python), applying cleansing and conformance, and defining clear columns plus surrogate/foreign keys..

Stage 3 — Analytics: Leverages analytics engines modeling (e.g., Domo, Looker, Power BI, Tableau), optimizing joins and aggregations where those tools perform best.

Stage 4 — Report, Dashboard, and KPI Templates: This is an extensive and customizable library of reports, dashboards, and KPIs that plug-and-play on Stage 3.

Stage 1 - Source Mirroring

DataSelf ETL+ is a powerful, easy-to-use tool for source mirroring.

You can design this Stage from scratch (click the following list of ETL+ 430+ No-code Data Sources), or accelerate optimized mirroring with our pre-mapped ETL+ Pre-mapped Source Systems.

Stage 2 - Data Warehouse

Stage 2 is housed in a SQL data warehouse (usually SQL tables/views). It transforms ERP-specific structures into the standardized model used by reports and dashboards (as in DataSelf Stages 3–4 templates).

Changing Stage 2 logic automatically propagates to all downstream reports and dashboards. For example, changing the Gross Profit calculation to absorb freight in Stage 2 will automatically show Gross Profit with freight absorbed in all downstream reports, dashboards, and AI models.

Stage 2 DFT+ - A SQL Statement Example

Example from ETL+ transformation: mapping ERP tables to the target _D_Item DFT+ table.

Stage 3 - Star and Galaxy Schemas

Implemented in conjunction of MS SQL, Power BI and/or Tableau, the DFT+ turns Stage 2 data into fast, ready-to-use datasets.

DFT+ Star Schema Example

The following exemplifies how the Sales Invoice DFT+ tables are linked in DataSelf’s out-of-the-box templates in a star schema arrangement. This model works in reporting tools such as Power BI, Tableau, and Excel.

DFT+ Galaxy Schema Example

The following exemplifies how DFT+ tables are linked in DataSelf’s out-of-the-box templates in a galaxy schema arrangement. This model works in reporting tools such as Power BI, Tableau, and Excel.

A key benefit of a galaxy schema is easy reporting across multiple fact tables in a single report. Click the image to zoom in.

Stage 4 - Downstream (Report, Dashboard, and KPI Templates)

This is comprised of KPI+, our extensive and customizable library of report, dashboard, and KPI templates that plug-and-play to Stage 3.

Click the following links to learn more:

• Power BI KPI+ Templates

• Tableau KPI+ Templates

• Packaged BI with AI by source system.

Key Words: single source of the truth, ETL templates, data warehouse templates, analytics templates, tds, tdsx, twb, twbx, pibx.