DCM Tool
Demand and Capacity Management
Supply chain visibility across every tier.
A demand and capacity simulation built within Aerospace-X, a 30+ company aerospace consortium led by Airbus. The five-tier engine supply chain is a working example for Use Case 1 on cross-partner demand planning, and served as the foundation for industrial application development within the programme.
Context
Why supply chain visibility breaks down
In aerospace, parts of an airplane for example contain thousands of parts sourced across multiple supplier tiers. An OEM like Airbus sees its Tier-1 partners, but rarely has direct visibility into what Tier-2 or Tier-3 suppliers are producing, committing, or struggling with.
This is fine when everything runs smoothly. It becomes a crisis when a critical component, with a long lead time (e.g. 20 weeks), can quietly fall behind. By the time the shortage surfaces in a programme review, there is no longer enough time to recover. Production stalls.
The Aerospace-X project was created to close that gap, building a common data standard across a 30+ company industry consortium led by Airbus. The DCM Tool is part of that project: a simulation that propagates demand through every tier simultaneously, so shortfalls are visible weeks or months before they become production problems.
Visibility stops at Tier 1
OEMs can see their direct suppliers, but lower tiers remain a black box. Risks building at Tier 3 or 4 are invisible until they propagate upward.
Long lead times, frozen plans
Critical components require order signals months in advance. Once a production schedule is frozen, you cannot react. The gap needs to be seen weeks or months earlier.
Each partner plans in isolation
Without a shared model, every company maintains its own forecast. Supply-demand gaps only surface when partners compare notes, often too late to act.
Bottlenecks hide across tiers
The constraint is rarely where you expect it. End-to-end simulation regularly shows the real bottleneck sitting two tiers below where it first appears.
Architecture
DCM Tool - Example Engine Supply Chain
A model of five tiers of an engine supply chain were modelled within the DCM Tool, from OEM demand down to raw material ingots. The tool tracks stock levels, lead times, and dependencies across every tier to detect bottlenecks weeks before they impact the production line. The diagram below shows a single engine supply chain, but the DCM Tool was created to model multiple supply chains simultaneously.
OEM
Demand
Qty / Week
Engine
final assembly
Module
LT: 4 wk
Disk
LT: 20 wk
Blade A
LT: 14 wk
dual-source
Blade B
LT: 16 wk
dual-source
Forging
LT: 6 wk · shared
Ingot
LT: 4 wk · raw material
End-to-end visibility
Stock levels, lead times, and supplier commits are tracked at every tier simultaneously, so a Forging shortfall at T3 triggers an alert 30+ weeks before it would halt engine assembly.
44+ weeks
Total LT horizon
2 Blade options
Dual-sourcing modeled
Disk + Blade A
Shared material inputs
Simulation
How the Simulation Works
Each week, OEM demand is propagated backwards through the full bill of materials, tier by tier, accounting for lead times, risk-adjusted delivery rates, and current stock levels to forecast exactly where and when shortfalls will occur.
Demand Propagation
OEM weekly demand is exploded through the BOM to derive material-specific requirements at every tier
Lead Time Cascade
Each derived demand is offset by the component's lead time. Forging orders must be placed 6 wk before Module assembly needs the part
Stock Accounting
Week-by-week: stock = opening stock + risk-adjusted deliveries − consumed in production. Negative stock = backlog
Bottleneck Detection
Stock below buffer triggers an alert. The most constraining material at each tier is flagged as the active production bottleneck
Adaptive Escalation
When backlogs form, the model calculates the minimum supplier commit uplift needed to recover to buffer stock within the planning horizon
What This Enables
30+ Week Early Warning
A Forging shortfall at T3 surfaces as a stock alert more than 30 weeks before it would halt engine assembly, with enough lead time to negotiate supplier recovery.
Cross-Tier Propagation
A missed supplier commit at T4 flows automatically through Forging to BladeA/Disk to Module to Engine. No manual re-calculation at each tier.
Common Planning Baseline
All supply chain partners work from the same model and numbers, replacing the fragmented per-tier tracking each party previously maintained separately.
Live Scenario Testing
Adjust a lead time, change a supplier commit, or remove a component. The downstream impact cascades through all tiers immediately.
Quantified Shortfalls
Not just 'there is a risk': the simulation gives exact units short, by material, by week, so planners can present hard numbers in supplier reviews.
Adaptive Recovery Planning
When backlogs build, the model calculates and outputs the required supplier commit uplift per material to recover within the planning horizon.
Outputs
Simulation Outputs
Four chart types generated per run, each targeting a distinct planning decision.
Module Bottleneck & Stock Levels
Component availability vs. production capacity over the planning horizon, alongside stock trajectories, immediately showing where and when constraints form.
Line of Balance: All Materials
One LoB panel per material showing cumulative requirements, supplier deliveries, and stock level. The standard format used in multi-party aerospace programme reviews.
Supply–Demand Gap Heatmap
Gap severity by material and week. Dark cells indicate critical shortfalls, enabling planners to pre-position interventions before gaps propagate upward.
Component Capacity Gap Analysis
Planned vs. required weekly capacity per material, adjusted for delivery risk. Flags which components need increased supplier commitments and by how much.
Impact
Tech Stack
Python 3.11
simulation engine
pandas
BOM explosion & data wrangling
matplotlib
LoB, bottleneck & heatmap charts
openpyxl / xlrd
Excel I/O
NumPy
vectorised weekly calculations