Case Study · 3D Systems · 2016–2019
WALT: a dollar-weighted lead-time metric I devised
The problem
Inside a manufacturing plant fighting excess spend, management needed a way to profile and monitor long-lead-time components. The logic was sound: the further out a delivery date sits, the less likely the original forecast still holds — so long lead times quietly drive over-purchasing. But the obvious move, ranking parts by raw lead time, treats a cheap 100-day component the same as an expensive one. That misdirects effort.
What I proposed
Rather than rank by lead time alone, I weighted each component's lead time by its share of total production-line spend — turning a time measure into a dollar-aware priority score. I called it Weighted Average Lead Time.
WALT = Sum( WLT₁ + WLT₂ + … + WLTₙ )
Why it changes the answer
The reprioritization is the whole point. Ranked by raw lead time, the longest-lead part tops the list regardless of its cost. Ranked by WALT, the part carrying the most dollar exposure rises — even with a shorter lead time — because that's where shortening lead time actually reduces committed spend and inventory risk.
Ranked by lead time alone
Ranked by WALT (dollar-weighted)
Illustrative figures from the original worked example. Component B — shorter lead time but far larger spend share — becomes the true priority under WALT.
The result
WALT gave the plant a single, sortable number to focus lead-time-reduction effort where it moved the most committed dollars — decisive in an environment of high forecast uncertainty and constant engineering revisions, where keeping inventory low was survival. It's a metric I devised rather than adopted: as far as I've found, weighting lead time by spend share this way was a genuinely new framing of the problem.