The BW and APO Business Solution Succeeding in BW and APO Demand Planning Integration.
APO Demand planning
-
Upload
gabriel-sylar-hussain -
Category
Documents
-
view
231 -
download
0
Transcript of APO Demand planning
8/10/2019 APO Demand planning
http://slidepdf.com/reader/full/apo-demand-planning 1/18
Forecasting Enhancements
in APO Demand Planning
Solution Management SCM
SAP AGSeptember 2008
8/10/2019 APO Demand planning
http://slidepdf.com/reader/full/apo-demand-planning 2/18
8/10/2019 APO Demand planning
http://slidepdf.com/reader/full/apo-demand-planning 3/18
© SAP 2008 / Page 3
Using POS in Demand Planning & Forecasting
Motivation and Business Benefits
Motivation
Leveraging POS data in supply chain processes is becoming more important than ever to increase revenue,decrease costs and increase efficiency.
Consumer Products and Hi Tech companies continue to struggle with demand latency – „in both segments it takesmore than 2 weeks to sense channel sales.“ (Lora Cecere, AMR Research, May 2007)
Increasingly more CP companies are receiving consistent daily or weekly point-of-sale data directly from ansignificant number of retailers.
Demand Signal Repository (DSR) applications have reached a maturity level that allows POS data to be cleansedand mapped with the high level quality that is necessary for supply chain applications and processes.
Business Benefits
Improve demand visibility and response
typically shipment history includes effects such as logistic rounding, shipment scheduling, productsubstitution and availability effects and does not reflect the original demand.
including POS data in forecasting counters the effects of historical inaccuracies and is closer to a forwardlooking view of demand patterns to come
Higher forecast accuracy
the reduced demand latency and improved demand visibility leads to a higher forecast accuracy in the theshort to medium term forecast horizon, typically 2-8 weeks.
including POS data in forecasting dampens the short term variability („bull whip effect“) in all stages of the
supply chain – and makes supply chain planning more stable – even down to the production schedule.
8/10/2019 APO Demand planning
http://slidepdf.com/reader/full/apo-demand-planning 4/18
© SAP 2008 / Page 4
A Quick Look at Different Approaches to Use
POS in Forecasting
Different approaches to using POS in forecasting:
„exact“ method
using detailed POS data (e.g. by store/sku/day) to create a store level POS forecast and include
replenishment parameters to calculate impact on manufacturer supply chain highest benefit potential, but . . .time & resource-intensive – cpu & user
„qualtitative“ methods
including POS data in existing processes with no or marginal process extension
benefit potential maybe not as high as exact methods, but . . .faster and easier to implement – „low
hanging fruit“
Approach is often a question of cost-benefit trade-off and strategic importance to company andrelationship to customer/retailer)
8/10/2019 APO Demand planning
http://slidepdf.com/reader/full/apo-demand-planning 5/18
8/10/2019 APO Demand planning
http://slidepdf.com/reader/full/apo-demand-planning 6/18
© SAP 2008 / Page 6
Highlights of the new MLR Forecasting Method
„MLR with POS“
combines statistical forecasting with causal
analysis
calculates fluctuation due to inventory in the
retail supply chain
(which is not visible to the manufacturer)
works with Life Cycle Planning
has its own alerts
diagnosis group using MAPE
new MLR with POS alert types in Alert Monitor
8/10/2019 APO Demand planning
http://slidepdf.com/reader/full/apo-demand-planning 7/18
© SAP 2008 / Page 7
MLR With POS - Algorithm in a Nutshell
Several sets of MLR iterations
1st regression run: dependent variable: week-to-week changes in (shipment) history
independent variables: lagged week-to-week changesin shipment history and POS with different lags
calculated coefficients: inventory fluctuation
2nd regression run inventory fluctuation and statistical forecast used to calculate
flux-adjusted forecast
dependent variable: shipment history independent variables: expost forecast, inventory fluctuations
Check whether the data is sufficient to run the algorithm min. 52 weeks or 364 days
for seasonal: 2 seasons
otherwise error
Statistical forecasting methods: constant (min. 25 periods)
seasonal
no trend
8/10/2019 APO Demand planning
http://slidepdf.com/reader/full/apo-demand-planning 8/18
© SAP 2008 / Page 8
MLR Forecasting Using POS
Enhancements in SCM 7.0
The scope of the new development includes:
New forecasting algorithm for POS data
incorporation
UI adjustment for the end user to set up the
forecasting method as well as changing parameters in
interactive planning
New alerts for forecasting errors
8/10/2019 APO Demand planning
http://slidepdf.com/reader/full/apo-demand-planning 9/18
© SAP 2008 / Page 9
MLR Profile Enhancements for MLR
Forecasting Using POS Data
Current MLR Profile used
Additional subscreen to select MLR method
New settings:
History key figure and version
POS key figure and version
Forecast models: constant or seasonal
Other parameters:
– Save flux to key figure
– Ignore leading zeros
– Diagnosis group for interactive alerts
8/10/2019 APO Demand planning
http://slidepdf.com/reader/full/apo-demand-planning 10/18
© SAP 2008 / Page 10
Forecast in Interactive Planning
Run & adjust forecast in
interactive planning
Key figures:
Forecast
Shipment history
POS history
Statistical forecast
Inventory flux
Adjust forecast parameters in
interactive planning on the fly:
Forecast / history horizon or
number of periods
Statistical forecast model &
smoothing factors
Key Figure & version
Other parameters
View messages
8/10/2019 APO Demand planning
http://slidepdf.com/reader/full/apo-demand-planning 11/18
© SAP 2008 / Page 11
New Alert Types
Alerts
Insufficient data to execute algorithm
MAPE upper limit exceeded
Calculated MAPE greater than statistical forecast MAPE
8/10/2019 APO Demand planning
http://slidepdf.com/reader/full/apo-demand-planning 12/18
© SAP 2008 / Page 12
1. Forecasting Enhancement “MLR with POS”New Forecasting Algorithm to Incorporate Aggregated POS Data in
Forecasting
2. Statistical Forecast EnhancementReinitialization and trend dampening
Agenda
8/10/2019 APO Demand planning
http://slidepdf.com/reader/full/apo-demand-planning 13/18
© SAP 2008 / Page 13
Motivation and Business Benefits
Motivation
Companies struggle with dynamic market conditions leading to changes in sales history basis used for statisticalforecasting e.g.
changing numbers of ship-to destinations by changing customer locations fast growth by entering new markets
In such an environment traditional forecast methods can lead to over forecast by not considering dampening trends
Improvements in competitive, state-of-the-art and scientific algorithms should be reflected as enhancements in DPforecasting
Business Benefits
Improve forecasting accuracy in a dynamic supply chain environment
Shift planner’s time to more value added analytical tasks
Less over forecasting in an automated system forecast
8/10/2019 APO Demand planning
http://slidepdf.com/reader/full/apo-demand-planning 14/18
8/10/2019 APO Demand planning
http://slidepdf.com/reader/full/apo-demand-planning 15/18
8/10/2019 APO Demand planning
http://slidepdf.com/reader/full/apo-demand-planning 16/18
© SAP 2008 / Page 16
Structural Change in History
© SAP 2008 / Page 16
Structural change
8/10/2019 APO Demand planning
http://slidepdf.com/reader/full/apo-demand-planning 17/18
© SAP 2008 / Page 17
SAP APO DP Forecast Improvements (3)
Offer new BAdI method for additional white noise test calculation
New white noise test is based on the Box-Pierce test
Use BADI method /SAPAPO/SCM_FCSTPARA -> WHITE_NOISE_TEST to activate the
BADI implementation containing the new white noise test
8/10/2019 APO Demand planning
http://slidepdf.com/reader/full/apo-demand-planning 18/18