“Our forecast accuracy is fine on paper, but nobody trusts the plan” is a sentence we hear in almost every demand-planning review. It is a more honest description of the problem than the accuracy metric suggests. A demand plan rarely fails in a single bad month. It drifts - cycle after cycle - until the number on the screen and the number people actually use to make decisions are two different things. By then the statistical engine is beside the point; the operating habits around it have decided the outcome.
1. The baseline is a model, not a number
When a demand planner opens the cycle to a single baseline figure with no visible assumptions behind it, every conversation that follows is an argument about opinions. A baseline that exposes its components - the statistical signal, the seasonality applied, the events layered on, the manual adjustments and who made them - turns the same conversation into a review of assumptions. The drift starts the moment the baseline becomes a black box, because a number you cannot interrogate is a number you quietly replace with your own.
The fix is not a better algorithm. It is making the baseline legible: a clear separation between the unadjusted statistical forecast and everything humans have done to it, so that the size and direction of human intervention is visible at all times.
2. Every override needs an owner and a reason
Overrides are not the enemy - unaccountable overrides are. In most estates we audit, a large share of manual adjustments have no recorded rationale and no named owner, and a meaningful fraction make accuracy worse than the untouched baseline would have been. Nobody knows this, because nobody measures the value an override actually added.
A demand plan stays honest when every adjustment carries two things: a reason code and a person. That single discipline lets you do the one analysis that changes behaviour - comparing forecast error with and without human intervention, by owner and by reason. Once planners can see which of their habits help and which hurt, the bad overrides quietly stop on their own.
3. Consensus is a challenge process, not a sign-off
The consensus meeting is where drift is supposed to be corrected and is most often ratified instead. When sales, marketing and supply each bring their own number and the meeting averages them into a figure everyone can live with, the plan becomes a negotiated truce rather than a best estimate. Truces are stable and usually wrong.
A consensus step earns its place only when it is set up to challenge: one baseline as the starting point, each proposed change argued against it with evidence, and disagreements resolved by assumption rather than by splitting the difference. The test of a healthy consensus process is simple - if it never moves the number, it is theatre; if it moves the number without a recorded reason, it is the drift.
4. Measure bias before you measure error
Most teams track error - MAPE, WMAPE - and stop there. Error tells you how far off you were; it does not tell you whether you are structurally optimistic or pessimistic. Bias does, and bias is what compounds. A plan that is consistently 6% high will overstock the whole network quarter after quarter, and no amount of error reduction will surface that pattern. Tracking bias by product family and by planner is the early-warning system that catches drift while it is still small enough to correct cheaply.
Where to start
- Make the baseline legible: separate the statistical signal from every human adjustment, and show the gap.
- Require a reason code and an owner on every override - then report error with and without intervention.
- Reframe consensus as a challenge against one baseline, not an average of several.
- Put bias next to error on every dashboard, split by family and by planner.
None of this requires new tooling - it is achievable inside any serious planning platform, Anaplan included, and most of it is configuration and operating discipline rather than data science. A focused engagement to instrument these four habits usually runs four to six weeks. The payoff is not a one-off accuracy gain; it is a plan that stops drifting between the cycles, which is where the trust - and the inventory - actually lives.
