Insights & news.

This article examines why semiconductor S&OP systematically underprices volatility — and how that blind spot erodes margin, working capital, and cycle performance. It reframes volatility as a financial variable, not just a planning challenge. Instead of asking how to “manage” volatility, it asks: - Where is risk not being priced into decisions? - What is that mispricing costing each cycle? - How should planning models incorporate risk premiums explicitly? The core argument: Volatility is structural in semiconductors. The competitive advantage lies in pricing it correctly — operationally and financially.

If every exception in your planning process requires a meeting, you don’t have a planning problem — you have a governance problem. Service, margin, and cash collide every day. When no one explicitly owns the tradeoffs, planning devolves into negotiation, escalation, and overrides. In this edition, I break down the real reason planning transformations stall — and what it takes to institutionalize decision ownership at enterprise scale.

In this post, we share how we think about decision maturity across people, process, data, and technology — and why most organizations struggle to move beyond “continuous improvement” into real decision intelligence. We also talk candidly about what really happens when AI-driven decision augmentation enters the room: politics, trust, and role anxiety. If you’re under pressure to deliver profitability, cashflow improvement, and resilience quarter after quarter — this one’s for you.

Most companies don’t lose money because their forecasts are “inaccurate.” They lose money because their forecasting decisions are optimized against the wrong objective. In a recent VYAN value pilot, we identified ~$3M in avoidable business impact across just a subset of SKUs — driven not by focusing simply on forecast error, but by how forecast error translated into inventory, service, cash, and operational instability. We didn’t achieve this by endlessly fine-tuning models or correcting outliers manually. We achieved it with out of the box optimization of forecasts for Cost of Forecast Error (COFE) — the economic cost of being wrong.