Insights
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.
The PFEP white paper
The full argument, with the buffer-sizing mathematics worked through — including the safety-stock derivation with both demand and lead-time variability — the PFEP data schema it’s 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 conditions and for regulated manufacturing. If you’re evaluating whether your current PFEP data would support this kind of analysis, this is the document that shows what “complete” looks like.
No form, no email address required. Read it and make your own mind up.
The working documents
Three further documents go beyond the argument into how an implementation actually runs. Available together on request.
The case for PFEP in manufacturing — executive presentation
An 11-slide executive briefing setting out PFEP as the data foundation of lean manufacturing — the data architecture, DDMRP buffer logic, the AI agent analysis layer, and a before/after comparison. Built for leadership conversations.
SIPOC for PFEP implementation — process map
An 11-slide visual process map applying SIPOC methodology to a PFEP implementation — Suppliers, Inputs, Process (P0–P5), Outputs, Customers. A working reference for implementation teams and a stakeholder alignment tool.
Hoshin Kanri AI agent — strategy briefing
How Hoshin Kanri strategy deployment works when an agent handles the detection: orphan initiatives, missing KPIs, weak ownership, and red/amber execution risk surfaced for a human to act on.
Want to know whether this applies to your parts?
A short scoping call establishes whether your current data would support this analysis, and what it would take to get there.
Supply Chain Data Insights 