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Tools · Startup & strategy

Scenario Planning

Describe a decision — get distinct future scenarios + no-regret strategies.

How it works

Corpus-grounded (Shell/GBN scenario method via the strategy cluster). Isolates the key uncertainties, picks the two critical axes, builds genuinely distinct scenarios with implications, the no-regret robust strategies, and the signposts to monitor.

You bring

{ focus, cluster? }

You get

{ focus_summary, key_uncertainties[], critical_axes[]{axis, low_end, high_end}, scenarios[]{name, narrative, implications[]}, robust_strategies[], signposts[], riskiest_assumptions[], grounded_in, provenance }

Use it for

See it work

example output

Focus: should a regional grocery chain commit to automated micro-fulfillment centers over the next five years?

Scenario Planning — Micro-fulfillment for a regional grocery chain (5-year horizon)

Focus: Whether a 40-store regional grocer should invest heavily in automated micro-fulfillment centers (MFCs) for online order picking through 2031.

Key uncertainties

  • How fast online grocery share grows in the region.
  • Whether automation hardware costs fall enough to clear the labor-cost hurdle.
  • Customer willingness to pay a delivery/pickup premium.
  • Competitive moves by national chains and pure-play delivery apps.

Critical axes

  1. Online grocery demand — low: stays a niche convenience · high: becomes the default weekly shop.
  2. Automation economics — low: hardware stays costly, ROI 7+ years · high: costs fall, ROI under 3 years.

Scenarios (the 2×2)

  • "Robots Win" (high demand × good economics) — MFCs are table stakes. Implications: move first, lock in sites, risk is moving too slowly.
  • "Stranded Steel" (low demand × good economics) — cheap automation but no volume to fill it. Implications: small modular bets only; avoid large fixed MFCs.
  • "Expensive Necessity" (high demand × poor economics) — customers want online, automation doesn't pay. Implications: serve demand with store-based picking; partner rather than build.
  • "Status Quo Holds" (low demand × poor economics) — in-store shopping stays dominant. Implications: defer capital; invest in store experience.

Robust (no-regret) strategies

  • Build store-based online picking now — it pays in three of four futures and is reversible.
  • Pilot ONE modular MFC rather than committing the network.
  • Negotiate site options, not purchases, to keep the first-mover path open cheaply.

Signposts to watch

  • Regional online grocery penetration crossing ~15%.
  • MFC hardware cost-per-pick falling year over year.
  • A national competitor announcing local MFC sites.
  • Delivery-premium acceptance in customer surveys.

Riskiest assumptions

  • That demand and automation cost move independently (they may be coupled).
  • That store-based picking scales without degrading the in-store experience.

Run it now

Plan for the future

Build scenarios for a big decision — the key uncertainties, two critical axes, distinct scenarios with implications, no-regret strategies, and the signposts to watch.

Prefer code? Call it over the API or hand it to your AI agent via MCP — POST /api/bicycle/scenario-planning · plan_scenarios. API & agent access →

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