E0239: Supply Shock Propagation Framework

Quality / Updated / COI

Quality

Reviewed

Updated

02/14/2026

Source

Citations & Trust

COI

none

TL;DR

Supply Shock Propagation Framework maps input price shock, pass through lag, and inventory buffers and supply chain concentration, energy dependency, and substitution so teams can decide on mapping how supply shocks propagate across sectors while documenting the resilience vs efficiency. It turns implicit judgment into an explicit decision record.

Applicability

Apply this framework when mapping how supply shocks propagate across sectors creates disputes about input price shock, pass through lag, and inventory buffers and the reliability of supply chain concentration, energy dependency, and substitution. It forces a single view of the resilience vs efficiency, clarifies decision rights, and creates a repeatable process for updates when conditions change.

Steps

1. Define scope and horizon, then lock metric definitions for input price shock, pass through lag, and inventory buffers so comparisons are consistent.
2. Collect supply chain concentration, energy dependency, and substitution and normalize units, timing, and ownership; document data quality gaps.
3. Run scenarios to see where resilience vs efficiency flips; record thresholds and triggers.
4. Select a preferred option, note constraints and approvals, and capture decision criteria.
5. Set monitoring cadence and review triggers tied to changes in input price shock, pass through lag, and inventory buffers and supply chain concentration, energy dependency, and substitution.

Template

Template: Objective; Scope and horizon; Success metrics (input price shock, pass through lag, and inventory buffers); Key inputs and assumptions (supply chain concentration, energy dependency, and substitution); Options A/B/C; Scenario ranges; Tradeoff summary (resilience vs efficiency); Risks and mitigations; Decision criteria; Recommendation; Owner and timeline; Review triggers; Evidence log and data refresh plan.

Pitfalls

• Misconception: treating input price shock, pass through lag, and inventory buffers as sufficient without validating supply chain concentration, energy dependency, and substitution creates false confidence.
• Overweighting one side of resilience vs efficiency leads to decisions that unravel when conditions shift.
• Stale or unowned data sources will fail governance checks and force rework during audits.

Case

Case: In an industry ministry, leaders debated mapping how supply shocks propagate across sectors but had conflicting views of input price shock, pass through lag, and inventory buffers. They used the framework to align supply chain concentration, energy dependency, and substitution, quantified where resilience vs efficiency flipped, and documented the trigger. The resulting decision log clarified accountability, reduced escalation time, and prevented repeated debates in the next planning cycle.

Citations & Trust

• The Economy (CORE Econ)
• Principles of Economics 3e (OpenStax)