Dorman’s Journey towards Integrated Demand Planning leveraging SAP APO DP and HANA

Dorman’s Journey towards

Integrated Demand Planning

Leveraging SAP APO-DP and HANA

Mitesh Verma Dorman Products

Alex Pierroutsakos Bristlecone Inc. 1

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Agenda

Leveraging

HANA Benefits Q&A

WHY ARE WE HERE? WHO ARE WE? DEMAND PLANNING

PROCESS IMPROVEMENTS

• About Dorman Products

• About Bristlecone Inc.

• Pre-APO DP & HANA System Landscape • Historical Order Adjustment • Dynamic Forecast Model Selection • Automated Realignment • Real time integration of Sales Panning

and Budgeting via Excel with APO DP • Lead time Based Forecast Accuracy

LEVERAGING HANA BENEFITS Q&A

• Integration and Reporting Landscape overview

• Traditional BW vs HANA Reporting

• Metrics, Reports and Dashboards

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Agenda

Leveraging

HANA Benefits Q&A









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and Budgeting via Excel with APO DP • Lead time Based Forecast Accuracy • Process Improvements

Why Are We Here

Share our experience implementing SAP Advanced Planning and Optimization

(APO) Demand Planning leveraging HANA as our enterprise data warehouse.

We will discuss various demand planning process improvements we implemented

as a part of this project.

Share how using HANA as our enterprise data warehouse helped us reduce the

demand planning cycle time.

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HANA

Agenda

Leveraging

HANA Benefits Q&A









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Who Are We

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Dorman Products Overview

Who Are We

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Bristlecone Inc Overview

Who Are We

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Who Are We

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Agenda

Leveraging

HANA Benefits Q&A









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Demand Planning Process Improvements

Demantra (implemented in 2002 and never upgraded) was used for Demand Planning.

SAP ECC and multiple Access and SQL databases were feeding data in Demantra.

Demantra was like a black box with no expertise within Dorman to make any tweaks or

configuration changes.

For reporting data was extracted from Demantra into SQL databases and then fed into BO

Universe for reporting. Users were manually combining Plan and Actual data.

There was no consensus planning process and both sales and demand planning team used to

plan independently on separate datasets.

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Pre-APO DP & HANA System Landscape

Demand Planning Process Improvements

Historical Order

Adjustments

Dynamic Forecast

Model Selection

Automated

Realignment

Integration of

Sales Panning and

Budgeting via DP

Excel- Add in

Lead time Based

Forecast Accuracy

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Historical Order Adjustments

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Typical : Manual Adjustment based on planner

OUR APPROACH:

• Identify specific issues in actual ordering pattern causing incorrect statistical forecast

• Identify limitations with standard outlier corrections

• Have logical and system driven historical adjustments , followed by manual corrections if needed

WHAT WE DID:

• If an order was non fulfilled due to some issues and was reordered, system would reduce the ordered qty used for statistical forecasting.

• System outlier correction was based on interquartile mean based of last 12 months of history

Historical Order Adjustments

Inflated forecast

** Even though the pattern looks good it is inflated

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Dynamic Statistical Forecast Model and Parameter Selection

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Typical : Use Auto model(56) for statistical forecast

OUR APPROACH:

• Segment and Classify products based on volume value, forecastability, pattern and age of product.

• Dynamically identify the changes in pattern and change statistical models

WHAT WE DID:

• System calculate COV(XYZ) and ABC (Value and Volume) – Monthly

• Base statistical run to identify and update pattern – Monthly

• Dynamic runs based on ABC, XYZ , pattern , age – Monthly

Dynamically Updated Models

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Parts with direct shipments to

customer or OE parts ?

Forecast Profile Model

ZMP_6M_MOVING_AVG New Parts introduced within 12 months?


ZMP_2M_MOVING_AVG New Parts introduced

within 13-24 months?

A, B, C

Classified Product?

D, E, F

Classified Product?


ZMP_AUTO_MODEL_OUTLIER

Yes

Yes

Yes

No

No

No

No


ZMP_6M_MOVING_AVG

Forecastability D, N

Forecastability E, M, Blank


ZMP_COMPOSITE_SEASONAL


ZMP_AUTO_MODEL_N13


ZMP_COMPOSITE_SEASONAL


ZMP_COMPOSITE_TREND

Yes

Seasonal

Constant

Trend

Seasonal Trend

Automated Realignment

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Typical : Manual Realignment

OUR APPROACH:

• Identify the change drivers for product and customer attributes

• Record the change and use them for realignment

WHAT WE DID:

• Execute a program in ECC based on change drivers to record changes to a table

• Update the realignment table with these changes and use standard realignment functionality

• When new historical data comes it automatically realigns using the same data

Integration of Sales and Annual Planning with Demand Planning

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Typical : Offline / Sales CRM

OUR APPROACH:

• Integrate Sales and Demand Planning in APO-DP forecast

• Simple excel interface for sales

WHAT WE DID:

• Used APO Excel Add-in as an excel front end for sales

• Common APO DP data used by both Demand and Sales Planning

• Mapping at appropriate levels/hierarchy between Demand and Sales Planning

Sales Planning integrated APO –DP Realtime

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Lead time Based Forecast Accuracy

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Typical : Forecast Accuracy based on Lag? (Lag1 or Lag2)

OUR APPROACH:

• Capture the forecast lag in a key figure called “Lead time Lag” based on the lead time of the part

• Calculate Forecast Accuracy based on this lead time lag.

WHAT WE DID:

• Used a macro to identify the lead time of the part and then capture the lead time lag in a separate key figure.

• Extract the Lead time lag in HANA and then feed it to Qlik for forecast accuracy dashboard

Process Improvements

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Sales and Demand Planning using same base data

Seamless integration of units and dollars

Heavy lifting done by system

Reduced demand planning and SIOP cycle time

Planners focus on exceptions and analysis not data gathering

C level looking at the same forecast numbers

All adjustments in the system

Reduced manual effort and intervention

Agenda

Leveraging

HANA Benefits Q&A









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Landscape Overview

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SAP HANA

SAP ECC SAP APO

Non-SAP System

Master Data and Transaction Data

‐ Demand Plan ‐ Sales Plan ‐ SNP Plan ‐ APO Specific

Master Data BODS

BODS

SLT

Live Cache Replication and SLT

‐ Material Master ‐ Sold-to Master ‐ CIR ‐ Order History ‐ PGI History ‐ Invoice History ‐ Credits ‐ POS ‐ NPP ‐ NBP ‐ Forecastability ‐ Ranking ‐ ASP

Traditional BW vs HANA

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Typical Data Flow: ECC – BW – APO Dorman Data Flow: ECC – HANA – APO

PSAs Staging Layer DSOs

Activate Data in DSOs

APO Sales History Cube

Load Data in Planning Book

SAP APO SAP APO

PSAs Staging Layer 1 DSOs


Staging Layer 2

DSOs/Cubes


SAP BW SAP HANA

Standard Tables

Custom Tables

Standard Tables

Custom Tables

SAP ECC SAP ECC

Extractors (Standard & Custom)

(Export) DataSources

PSAs

APO Sales

History Cube

Load Data in

Planning Book

Replicated

Tables

Information

Views

SLT – Real time Data

SAP BODS

Reporting Data Flow

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Typical Data Flow: APO – BW Dorman Data Flow: APO – HANA

SAP BW SAP HANA

SAP APO SAP APO

Planning Area DataSources Planning Area DataSources

PSAs InfoCubes MultiProvider

Planning Book Planning Book

Replicated Table

Snapshot Table

Information Views

ANALYTICAL TOOLS – BO/QLIKSENSE ANALYTICAL TOOLS – BO/QLIKSENSE

• Replication Model allows for much faster data extraction of live cache than planning area based extractor: ─ Extraction takes 5-6 hours for the entire horizon via Planning area DataSource whereas Replication model takes close to 2

hours only – more than 30 million records

• Dorman uses SAP Business Objects and QlikSense for analytical needs

Key Analytics

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Key Analytics

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Key Analytics

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Key Analytics

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Agenda

Leveraging

HANA Benefits Q&A









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Benefits

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Minimal Data Movement No Additional Data Persistence or Staging – helps minimize points of load failures

All calculations pushed in HANA – calculations happening at document-item level

Lesser development objects to monitor and manage

Real time data via SLT No need to create extractors on any tables in ECC – they can simply be replicated via SLT – completely eliminating the development time of an extractor

Dynamic selections – no hard coding, leveraging TVARVC and procedures for date range selections

Faster load times – reduction from 6 hours to 1 hour

Agenda

Leveraging

HANA Benefits Q&A









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and Budgeting via Excel with APO DP • Lead time Based Forecast Accuracy • Benefits

Q&A

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For questions, contact me at: [email protected] [email protected]

Don’t forget to fill out the Session Evaluation on the Mobile App!

Dorman’s Journey towards Integrated Demand Planning Leveraging SAP APO-DP and SAP HANA Mitesh Verma – Dorman Products

Alex Pierroutsakos – Bristlecone Inc. 38

mailto:[email protected]

mailto:[email protected]

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