Scott Wueschinski

Case study · CPG

Global CPG leader — plan-to-cash cycle modernization

Executive-summary engagement covering plan-to-cash cycle modernization. AI orchestration cut time-to-market by 40% across the operating brand portfolio.

The situation

A global CPG leader's plan-to-cash cycle had compounded inefficiencies across decades of brand acquisitions. Each brand operated its own demand-planning + trade-promo + cash-collection system, partially integrated, mostly not. Time-to-market on new SKU launches was the visible symptom; the underlying cause was operational fragmentation across what should have been a unified plan-to-cash motion.

AI was on the table — but the C-suite needed an executive-level view of where AI orchestration would move the needle versus where it would just add cost.

What I built

  • Plan-to-cash heat-map. Where the cycle bled time and where it bled margin, broken out by brand. Specific, dollar-quantified, defensible at board level.
  • AI orchestration target map. Five concrete workloads where agentic AI would move time-to-market — demand sensing, trade-promo modeling, supply-allocation under uncertainty, exception handling, and cash collection prioritization. Five workloads where it wouldn't.
  • Three-cycle CODN model. What cycle 1, 2, and 3 of inaction would cost. The compounding curve made the case.
  • Implementation sequencing memo. Which AI orchestration workloads to ship first based on dependency + signal-quality readiness, not vendor pitch order.

What changed

  • 40% reduction in CPG time-to-market across the operating brand portfolio, attributable to the AI orchestration engine deployed against the heat-map's top three workloads.
  • Trade promotion cycle time compressed materially — the underused-AI-workload thesis from the Retail POV stream played out in production.
  • Two AI vendor pitches were declined post-engagement based on the readiness diagnostic, saving multi-year commitments that wouldn't have produced lift.

What I'd do differently

The executive summary was the right artifact for the C-suite, but the implementation sequencing memo needed to live with operations leadership for a longer period than the engagement allowed. A follow-on fractional advisory would have caught implementation drift earlier.

Stack used

Heat-map and CODN modeling in Claude + Excel-back-and-forth with finance. AI orchestration target map informed by hands-on experience with comparable agentic deployments. Implementation memo delivered as a stand-alone executive document; follow-up cadence handled inside their existing operating rhythm.

Where this engagement shape fits

This was a transformation sprint — fixed scope, defined deliverable, executive-level audience. Best fit when the C-suite needs a board-grade view of where AI moves the needle in a complex, multi-brand operation. See engagement shapes →

Methodology

Run by the CODN framework. Engagements delivered through Stravonvale.

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