Rapid obsolescence, long component lead times and model transitions drive excess-and-obsolete write-downs straight to the P&L. DemandIntel plans on your own data, deterministically, to shrink them.
In electronics and hardware, the recurring wound is excess and obsolete: a model transitions, a component is over-bought on allocation, demand softens, and the reserve lands on the P&L. These are not freak events, they are a planning signal that arrives too late.
Long component lead times and last-time-buy decisions force commitments far ahead of real demand. A smoothed forecast cannot tell a healthy buffer from the start of an obsolescence pile.
Products turn over fast. Yesterday’s buffer becomes today’s write-down when a model moves on.
Parts are committed months ahead, sometimes on allocation. The forecast must hold over that horizon or the buy is wrong.
EOL and last-time-buy decisions are high-stakes bets. Too much and you write it down; too little and you cannot service the field.
Demand moves with product cycles and sentiment, sharp and hard to smooth.
When components are expensive, a modest over-buy is a large frozen, and ageing, position.
Recurring E&O reserves are the visible cost of a forecast nobody can reproduce or defend.
inventory write-down at one chipmaker in a single quarter
more chips ordered than needed during the shortage (bullwhip effect)
Sources: Micron, Q2 FY2023 (SEC filing); McKinsey (2022).
DemandIntel focuses where the money bleeds: a pre-obsolescence view flags stock heading toward excess and obsolete before the reserve is booked, so you can redeploy, promote or stop buying while it still has value. Demand is forecast reproducibly, so a healthy buffer is not mistaken for the start of an E&O pile.
Component and last-time-buy decisions are sized on real error and long lead times, not padded, so you commit closer to true demand. It runs on-site, beside your ERP, and because every number is auditable, finance can see exactly why a buy or a reserve is what it is.
That is the primary target. The pre-obsolescence view flags at-risk stock before it is written down, early enough to act.
Those decisions are sized on real demand and lead time rather than a padded guess, so you commit closer to what the field will actually need.
It plans over the full lead-time horizon and its variability, so the commitment reflects reality, not a one-month view.
Reproducible, non-smoothed forecasting handles sharp cycles better than a moving average that is always late.
Picture your next S&OP meeting: one number on screen that nobody argues with. See it free, on your own data, in 45 minutes; and if it does not hold up, we part as friends.