When this approach is needed

Typical business situations
Case 1

Demand amplification across a supply chain

Orders swing far more upstream than end-customer demand, so the business pays for overtime, excess inventory and missed service at once.

DecisionWhich delays and ordering policies drive the amplification, and which change dampens it?

Case 2

Capacity that lags demand

A step in demand produces overshoot in inventory, hiring or backlog that planning reads as forecasting error.

DecisionHow much of the overshoot is structural, and how should replenishment and hiring policies be set?

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Case 3

Exposure to chokepoints and concentrated sourcing

A large share of volume depends on one strait, port, region or supplier.

DecisionWhich sites run short first, how long can each hold out, and what does a closure cost per week?

Case 4

Sizing buffers and flexibility

The business must decide how much safety stock, second-source capacity, air-freight cover or multi-gateway contracting to pay for every year.

DecisionWhich options pay for themselves against the expected annual loss — and which do not?

Case 5

Policies with delayed side-effects

Deferred maintenance, aggressive inventory cuts or promotional pricing improve this quarter and erode performance later.

DecisionWhat are the long-run consequences of a policy that looks right in the short term?

Case 6

A contested problem, or resilience reporting to the board

Functions attribute the problem to one another, or leadership needs a quantified view of resilience rather than a heat map.

DecisionWhat shared, testable model can the leadership team agree on — and what is the expected loss under plausible disruptions?

What you receive
  • A causal map of the problem, built and agreed with the people who own it
  • A calibrated simulation model of the system and its network, with assumptions documented
  • How disruptions propagate: time-to-survive and time-to-recover by site, and expected loss by scenario
  • Policy and mitigation options compared on cost, service, stability and expected benefit
  • An executive workshop and a written recommendation
How an engagement runs
  1. ScopeDefine the problem behaviour over time and the disruptions that matter, from historical and live data.
  2. MapBuild the causal structure and the network with the team through facilitated group model building.
  3. ModelQuantify feedback, delays and capacity limits on the real network, calibrated against history.
  4. TestRun policies and disruption scenarios, find the thresholds where losses escalate, and agree the changes to make.
Reference
Approach
Recurring operational failures and escalating disruptions share a cause: the structure of delays, feedback, dependencies and capacity limits in the system. Risk registers and root-cause reviews look at events one at a time; systems analysis models the structure that connects them — to diagnose why problems return, to quantify how far a disruption travels, and to test remedies before they are implemented.
Core question
Which structure — rather than which event — produces this pattern, does a disruption stay local or escalate, and which change removes or contains it?
Method
System dynamics in the Forrester–Sterman tradition — causal loop diagrams, stock-and-flow simulation calibrated to data, group model building — combined with propagation analysis on the real network topology and flexibility options valued against expected loss.
Evidence
The Global Disruption Atlas turns live chokepoint transit data into scenarios and values flexibility options against expected annual loss; it accompanies Engineering Flexibility in Supply Chain Design. The Dynamic Cyber-Risk Model (DDRA) is a five-stock system dynamics model with a containment threshold; Supply Pulse demonstrates the bullwhip effect in a food and dairy supply chain; Lag Lab splits the overshoot after a demand step into the necessary refill, structure, policy and forecast, and shows how little of it is really forecast error; the case studies include a hospital replenishment model.
Background
PhD, Technical University of Denmark. Council member, System Dynamics Society. Former teaching assistant at MIT; lecturer and researcher at DTU. See About.
Get in touch

Describe the recurring problem or the exposure that concerns you. I will tell you whether a systems model can settle it.

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