When the Business Case No Longer Matches the Business
A logistics firm approved a change to replace its depot scheduling system. The original business case promised a 15% reduction in overtime costs, based on a pilot at two depots where supervisors volunteered to use the new tool. Six months in, rollout has reached nine of thirty depots. Overtime has fallen 4%, not 15%, and supervisors at the new depots are using the old system for about a third of shifts because the new tool does not handle split shifts, which the pilot depots rarely had. The finance director is asking whether the programme should continue. The change lead owns the benefits tracking.
1. Why is the original 15% figure not a fair test of the change, and what should the change lead do about it?
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The 15% was derived from a volunteer pilot in depots with a simpler shift pattern, so it measured an optimistic, self-selected sample, not the wider population. Comparing 4% against 15% treats two different conditions as equivalent, which is misleading.
What the change lead should do:
- Reconstruct the benefit logic: what specifically drove the pilot's 15%? If it was reduced manual scheduling time, that driver is weaker where split shifts force workarounds.
- Separate the effect of the missing split-shift feature from the effect of adoption resistance. Right now both are tangled in the 4%.
- Re-forecast benefits per depot type (simple vs split-shift) rather than as one blended number.
The judgement: a benefits shortfall is evidence, not a verdict. The change lead's job is to explain which assumption failed before anyone decides whether to stop.
2. The finance director wants a stop/go recommendation. How should the change lead frame it?
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Frame it around whether the remaining value justifies the remaining cost, given what is now known, not around whether the original 15% was hit.
Structure the recommendation:
- Known value: 4% overtime reduction across nine depots, with a credible path to more once split shifts are handled.
- Known blocker: the split-shift gap causes a third of shifts to fall back to the old system, capping adoption and therefore benefits.
- Cost to close the gap: estimate the split-shift feature work and the re-training for depots already live.
- Options: (a) fix split shifts then continue rollout, (b) continue rollout only at simple-shift depots and pause complex ones, (c) stop and bank the 4% where it exists.
Recommend the option where incremental benefit exceeds incremental cost. The change lead should state the uncertainty in the estimate rather than presenting a single confident number, because the pilot already showed how optimistic early figures can be.
3. What should the change lead change about how benefits are tracked for the rest of the rollout?
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Move from a single blended target to benefits tracked by condition, so the next review is not another apples-to-oranges comparison.
Concretely:
- Report overtime reduction split by depot type and by whether split shifts occur, so the feature gap's effect is visible.
- Track leading indicators alongside the lagging cost figure: proportion of shifts entered in the new tool, supervisor time per schedule, and workaround frequency. These move before overtime does and give early warning.
- Set a review trigger: if adoption at a depot stays below a threshold after a defined period, escalate rather than waiting for the quarterly number.
- Record the revised forecast and the reason for the revision, so the audit trail shows the change was managed, not the target quietly moved.
The point is that benefits realisation is a feedback loop, and the change lead designs it to detect the next surprise early.