Integrated Business Planning for Demand
Integrated Business Planning for Demand Stephan Kreipl, Chief Product Owner Demand Management, SAP SE April, 2015 SAP Integrated Business Planning for demand SAP Supply Chain Control Tower Sales and operations planning Demand Inventory Supply Response SAP HANA platform © 2015 SAP SE or an SAP affiliate company. All rights reserved. For internal SAP and partner use only 2 Simplified “definition”: integrated business planning for demand Integrated business planning (IBP) for demand = “Traditional” demand planning + Demand sensing/short-term forecasting + Predictive/forecasting analytics techniques © 2015 SAP SE or an SAP affiliate company. All rights reserved. For internal SAP and partner use only 3 SAP Integrated Business Planning for demand Demand planning vs. demand sensing Demand planning: For example, executed monthly in weekly or monthly buckets Demand sensing: In general done daily in daily buckets Difference between short-term forecast and consensus mid-long-term forecast Forecast wk 1 wk 2 wk 3 wk 4 © 2015 SAP SE or an SAP affiliate company. All rights reserved. … wk 52 For internal SAP and partner use only Time 4 What planning processes does demand sensing impact? Illustration of the different planning horizons Deployment and transportation decisions 1 2 © 2015 SAP SE or an SAP affiliate company. All rights reserved. Production and packaging sequences 3 4 Material purchasing 5 6 Future week For internal SAP and partner use only 5 Simplification – what we heard “Demand planners aren’t, and shouldn’t be, statisticians.” Management “We need to align our demand plan with our sales and operations plan.” “ I really need to focus on adding business insights to the forecast.” Demand planner “The system should pick the best statistical model.” “I need more powerful statistical tools.” Data scientist © 2015 SAP SE or an SAP affiliate company. All rights reserved. For internal SAP and partner use only 7 Simplification – what we are doing Role-based UI concept Local demand planner Regional/global demand planner Data analyst and scientist Simplified UI concepts Use of Microsoft Excel as the planning front end Mobile-ready HTML5 UI Use of SAP Fiori user experience (UX) concepts for user interaction © 2015 SAP SE or an SAP affiliate company. All rights reserved. For internal SAP and partner use only 8 SAP Fiori app for demand sensing issue Investigate strong deviations between sensed demand and consensus demand © 2015 SAP SE or an SAP affiliate company. All rights reserved. For internal SAP and partner use only 9 Excel UI Leverage the Excel UI to add additional or one’s own key figures for further analysis © 2015 SAP SE or an SAP affiliate company. All rights reserved. For internal SAP and partner use only 10 Scope for IBP for Demand 5.0 Pre-Processing algorithm: Demand Sensing algorithms (short term forecasting) Statistical Methods (mid- / long-term forecasting) Pre-Processing algorithms Time series algorithms Regression based methods Demand specific analytics, e.g. Demand Sensing Issues Integration with ERP and APO Substitute missing values Outlier correction with interquartile range test and variance test Time series algorithms: Simple moving average Weighted moving average Single exponential smoothing Double exponential smoothing Triple exponential smoothing Automated triple exponential smoothing with parameter optimization 1st order exponential smoothing with adaptive alpha Croston’s method for intermittent demand Regression based algorithms: Multiple linear regression (MLR) Exception management Fiori Apps and Excel as a planning front-end Combination of these algorithms (similar to composite forecasting in APO) or Pick the best © 2015 SAP SE or an SAP affiliate company. All rights reserved. For internal SAP and partner use only 11 Supported process flow with IBP 5.0 Step 1: Create master data types in IBP for Demand Step 2: Initialization: Upload historical forecasts and sales orders into IBP for Demand Step 3: • Daily/Weekly: Upload via HCI (HANA Cloud Interface) consensus mid-long term forecast from APO Demand Planning • Daily: Upload new sales orders from ERP • Step 4: Daily: Run Demand Sensing with IBP for Demand (incl. optional results evaluation / adjustments) • Step 5: Daily: Transfer short term forecasts via HCI into APO Supply Network Planning APO Demand Planning Forecast IBP for Demand Short-Term Forecast (focus Demand Sensing) APO SNP Master Data, Sales orders ERP © 2015 SAP SE or an SAP affiliate company. All rights reserved. For internal SAP and partner use only 12 PoC 1: demand sensing weekly forecast accuracy improvement Global health and beauty care consumer products company Lag Demand planning wMAPE Demand sensing wMAPE Absolute difference % Change Week 1 61% 31% 30% 49% Week 2 62% 46% 16% 26% Week 4 65% 52% 13% 20% Week 6 68% 56% 12% 18% Compelling results seen in the PoC for internal signals 18%–49% reduction in forecast error across 6 weeks 6%–8% reduction in forecast bias across 6 weeks Driven by significant over-forecasting bias, 7–8 day average order lead times, and complexity from large number of SKU location combinations wMAPE = Weighted mean absolute percentage error © 2015 SAP SE or an SAP affiliate company. All rights reserved. For internal SAP and partner use only 13 PoC 2: demand sensing weekly forecast accuracy improvement Global confectionary products company Demand planning wMAPE Lag Demand sensing wMAPE Absolute difference % Change 1 53.1 % 15.0 % 38% 72% 2 49.7 % 34.2 % 15% 31% 3 53.0 % 39.4 % 14% 26% 4 55.9 % 42.9 % 13% 23% 5 59.1 % 46.6 % 13% 21% 6 59.9 % 49.0 % 11% 18% © 2015 SAP SE or an SAP affiliate company. All rights reserved. Lag 1 For internal SAP and partner use only 14 Thank you Contact information: Dr. Stephan Kreipl Chief Product Owner Demand Management stephan.kreipl@sap.com © 2015 SAP SE or an SAP affiliate company. All rights reserved.
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