As many businesses are discovering, buying forecasting software is not the end of their forecasting problems; excessive inventory, high levels of obsolescence, poor customer service and costly late changes to production and shipping schedules are much harder to eradicate. The resulting finger-pointing and witch hunts are symptomatic of questions remaining unanswered:
- How do I create an objective, single ‘source of truth’ about forecast performance, outside the spreadsheet jungle and free from statistical jargon?
- How do I know whether my forecast performance is good or bad?
- How much value is the forecast process adding?
- How much of my forecast error is avoidable, and how much does that inflate inventory levels and costs?
- How can I meaningfully compare the quality of forecasts between different products, territories and channels?
- How do I ensure that I am using statistical processes and judgement in the right areas and in the right way?
- Can I be sure my forecast engine is working as it should?
- Are judgemental interventions adding or destroying value?
- How can I engage everyone in this forecast improvement process – including sales/marketing who provide the market intelligence to “improve” the statistical forecasts?
- How can I convince senior management of the need to improve forecasting?
- How can I trace and diagnose the source of problems?
- If the “forecasting blame game” isn’t working, how do I break this cycle?