APO Demand planning

8/10/2019 APO Demand planning

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Forecasting Enhancements

in APO Demand Planning

Solution Management SCM

SAP AGSeptember 2008



© SAP 2008 /

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.



© SAP 2008 /

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)



© SAP 2008 /

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



© SAP 2008 /

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



© SAP 2008 /

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



© SAP 2008 /

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



© SAP 2008 /

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



© SAP 2008 /

New Alert Types

Alerts

Insufficient data to execute algorithm

MAPE upper limit exceeded

Calculated MAPE greater than statistical forecast MAPE



© SAP 2008 /

1. Forecasting Enhancement “MLR with POS”New Forecasting Algorithm to Incorporate Aggregated POS Data in

Forecasting

2. Statistical Forecast EnhancementReinitialization and trend dampening

Agenda



© SAP 2008 /

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



© SAP 2008 /

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

APO Demand planning

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