The research behind the method — starting with the single finding that justifies PFEP over conventional MRP.
The Variable Your ERP Doesn’t Track — and Why It’s the One That Matters Most
Every MRP item master carries lead time as a fixed number. In practice, lead time isn’t fixed — it varies, and that variance is invisible to the planning engine that’s supposed to protect you from it.
That would be a minor omission if lead-time variability were a small contributor to how much safety stock you need. It isn’t. For any fast-moving part, the effect of lead-time variability on required buffer size outweighs the effect of demand variability — and the gap widens, not narrows, as volume increases. The parts your business depends on most are exactly the ones most exposed to this blind spot.
This is the core argument for PFEP over a conventional MRP-only approach: PFEP is structured, part-by-part, to capture lead-time variability and feed it directly into buffer sizing. A standard item master can’t do that. It was never built to.
Why this matters under real-world volatility
Under stable, predictable conditions, this gap barely shows up — average lead times are close enough to reality that ignoring the variance doesn’t cost much. Under the supply volatility most manufacturers have lived through since 2020, it does. The businesses most exposed are the ones still planning as if lead time were a constant.
Want the full analysis?
The complete white paper covers the buffer-sizing mathematics in full — including the safety-stock derivation with both demand and lead-time variability — the PFEP data schema this argument is built on, and how it connects to DDMRP buffer positioning. It also sets out the schema itself: the structured set of questions every part record needs to answer, extended for volatile, uncertain conditions and for regulated manufacturing environments. If you’re evaluating whether your current PFEP data would actually support this kind of analysis, this is the document that shows what “complete” looks like.
Supply Chain Data Insights 