ECO Cost Savings Dashboard
Budget: $250 – $750 USD
Project Title:
Comprehensive ECO Cost Savings Dashboard (Phase 1 Consolidations)
Objective:
Develop a comprehensive data model and interactive visualizations to accurately track and quantify cost savings from ECO (Engineering Change Order) changes—particularly those involving part consolidations and cost-reduction initiatives. You will be using preexisting semantic models and some ECO data and will have to directly work on my computer through any desk. (PLS DONT ACCEPT THIS IF U ARENT OKAY W ANYDESK)
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Key Requirements:
This project focuses on capturing the true realized savings from ECO-driven part consolidations. This involves not just identifying a cheaper part via an ECO, but also calculating when those savings actually begin—only after old inventory is depleted and the new part is being consumed in production.
To achieve this, the freelancer must:
• Model complex data across messy, pre-existing semantic layers (e.g., demand plans, inventory models, and part hierarchy tables).
• Deal with fragmented data and unclear joins, requiring deep contextual logic and iterative modeling.
• Connect ECO data to real inventory usage, cost data, and forecast demand volumes to pinpoint the true savings window and magnitude.
⸻
Phase 1 Focus: Consolidation Analysis
You will need to build a model that enables tracking of the following for each ECO consolidation effort:
1. ECO Implementation Date
• When the part was officially changed or relabeled to the new (consolidated) PN.
2. Savings Realization Start Date
• Date when the old part inventory was fully depleted and the new (cheaper) part began usage.
3. Demand Volume Changes
• Quantity and % change in annual demand volume, sliced by part classification (e.g., screws, capacitors).
4. Cost Changes
• Dollar and percentage cost changes resulting from the switch to the new part.
5. 12-Month Forecasted Savings
• Calculated from the savings realization date forward, using a forward-looking demand plan for the new part.
6. ECO Type Distribution
• On average, how many ECOs are involved in each consolidation, categorized by type:
• DOC CHG
• NEW PART
• BOM/RTG CHANGE
7. Unique Consolidations by Classification
• Example: Number of consolidations in the “Screws” classification.
• These can also be rolled up into category-level savings and trends.
⸻
Current Data Environment:
Data is already loaded in Power BI via semantic models, but the structure is not clean or easily navigable. Expect:
• Messy relationships between inventory, ECO data, demand plans, and costs
• Need to leverage Parent/Child part mappings to trace ECO impact through multi-level assemblies
• Demand and Inventory models are already in place but need intelligent joins and logic filters to return meaningful savings data
⸻
Ideal Candidate:
• Strong data modeling background (Star schema, parent-child modeling, etc.)
• Hands-on experience with Power BI (semantic model editing, DAX, relationship troubleshooting)
• Comfort working in messy, fragmented datasets
• Deep understanding of cost accounting, demand forecasting, and inventory depletion logic
• Ability to clearly visualize insights via dashboards (cost savings timelines, ECO rollout maps, classification-level rollups)
Comprehensive ECO Cost Savings Dashboard (Phase 1 Consolidations)
Objective:
Develop a comprehensive data model and interactive visualizations to accurately track and quantify cost savings from ECO (Engineering Change Order) changes—particularly those involving part consolidations and cost-reduction initiatives. You will be using preexisting semantic models and some ECO data and will have to directly work on my computer through any desk. (PLS DONT ACCEPT THIS IF U ARENT OKAY W ANYDESK)
⸻
Key Requirements:
This project focuses on capturing the true realized savings from ECO-driven part consolidations. This involves not just identifying a cheaper part via an ECO, but also calculating when those savings actually begin—only after old inventory is depleted and the new part is being consumed in production.
To achieve this, the freelancer must:
• Model complex data across messy, pre-existing semantic layers (e.g., demand plans, inventory models, and part hierarchy tables).
• Deal with fragmented data and unclear joins, requiring deep contextual logic and iterative modeling.
• Connect ECO data to real inventory usage, cost data, and forecast demand volumes to pinpoint the true savings window and magnitude.
⸻
Phase 1 Focus: Consolidation Analysis
You will need to build a model that enables tracking of the following for each ECO consolidation effort:
1. ECO Implementation Date
• When the part was officially changed or relabeled to the new (consolidated) PN.
2. Savings Realization Start Date
• Date when the old part inventory was fully depleted and the new (cheaper) part began usage.
3. Demand Volume Changes
• Quantity and % change in annual demand volume, sliced by part classification (e.g., screws, capacitors).
4. Cost Changes
• Dollar and percentage cost changes resulting from the switch to the new part.
5. 12-Month Forecasted Savings
• Calculated from the savings realization date forward, using a forward-looking demand plan for the new part.
6. ECO Type Distribution
• On average, how many ECOs are involved in each consolidation, categorized by type:
• DOC CHG
• NEW PART
• BOM/RTG CHANGE
7. Unique Consolidations by Classification
• Example: Number of consolidations in the “Screws” classification.
• These can also be rolled up into category-level savings and trends.
⸻
Current Data Environment:
Data is already loaded in Power BI via semantic models, but the structure is not clean or easily navigable. Expect:
• Messy relationships between inventory, ECO data, demand plans, and costs
• Need to leverage Parent/Child part mappings to trace ECO impact through multi-level assemblies
• Demand and Inventory models are already in place but need intelligent joins and logic filters to return meaningful savings data
⸻
Ideal Candidate:
• Strong data modeling background (Star schema, parent-child modeling, etc.)
• Hands-on experience with Power BI (semantic model editing, DAX, relationship troubleshooting)
• Comfort working in messy, fragmented datasets
• Deep understanding of cost accounting, demand forecasting, and inventory depletion logic
• Ability to clearly visualize insights via dashboards (cost savings timelines, ECO rollout maps, classification-level rollups)