Demand Forecasting and Planning
- How about measuring the cost of forecast error status quo?
- In real $ terms. How do I know which forecast model to use?
- How do I know what proportion of demand ‘happened’ because of promotional event?
- What do I lose by forecasting in weekly buckets instead of monthly?
- What do I gain out of it? Do I?
- Should I have centralized demand planning or regional demand planning?
- How does competition do it?
- What does ‘best-in-class’ do?
- Should I forecast at SKU level or brand level?
- What do I lose or gain in doing so?
- What really is (should be) the purpose of forecasting?
- How about new products?
Your customers do NOT care how good or bad is your forecast error. They bother about delivery service level. A reliable predictable service level. But the good thing is it doesn’t cost much to improve the forecast accuracy that is only one of the determinants of service level. Yet the benefits of forecast accuracy are counter intuitive and highly non-linear in nature. Studies say that a mere 5 % improvement in forecast accuracy and staying good at that can potentially reduce inventory investments by order of magnitude upto 35% or more over an year. Without compromising on the service levels.
Apart from deep subject matter expertise on model building and demand side analytics, The Management Technician brings diverse insights on demand planning practices, processes and organizations from a variety of industry contexts. We work on diverse applications like SAP APO, SAS, i2, Demantra and other best of breed applications to make the solution deliver superior results. Our custom model tuning and macro services have objectively benefitted our customers by achieve higher forecast accuracy over time and more importantly a feeling of trust in the solution. Something we excel at.
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