E0239: Supply Shock Propagation Framework
A decision-ready template derived from the framework.
Name variants
- English
- E0239: Supply Shock Propagation Framework
- Katakana
- ショック / フレームワーク
- Kanji
- 供給 / 伝播
Quality / Updated / Source / COI
- Quality
- Reviewed
- Updated
- Source
- Citations & Trust
- COI
- none
Context
Context: mapping how supply shocks propagate across sectors often exposes disagreements about input price shock, pass through lag, and inventory buffers and the reliability of supply chain concentration, energy dependency, and substitution. Without a shared frame, the resilience vs efficiency remains implicit and accountability erodes across reviews. A structured record is needed to keep decisions consistent as market conditions change.
Options
- Option A: Keep the current approach to minimize disruption while accepting limited improvement.
- Option B: Pilot a phased change, validate against agreed metrics, and scale once thresholds are met.
- Option C: Redesign the approach end to end to pursue larger gains with higher execution risk.
Decision
Decision: Choose Option B. Validate input price shock, pass through lag, and inventory buffers early, confirm supply chain concentration, energy dependency, and substitution assumptions, and pause if the resilience vs efficiency no longer holds. Document owners, constraints, and review dates.
Rationale
Rationale: Option B balances resilience vs efficiency while preserving flexibility. It tests whether input price shock, pass through lag, and inventory buffers respond as expected to changes in supply chain concentration, energy dependency, and substitution before committing to a full rollout. This reduces the risk of locking in a costly path based on weak evidence and improves governance confidence.
Risks
- Weak data quality can hide shifts in input price shock, pass through lag, and inventory buffers and delay corrective action.
- Slow execution can magnify the downside of resilience vs efficiency and reduce credibility in reviews.
Next
Next: Assign owners for input price shock, pass through lag, and inventory buffers and supply chain concentration, energy dependency, and substitution, finalize baseline values, and publish the trigger thresholds. Schedule the first review checkpoint and define stop conditions so the decision can be revised quickly.