implementing apo demand planning at a small company 1051

8/3/2019 Implementing Apo Demand Planning at a Small Company 1051

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2003 Supply Chain Management Forum

Implementing APO DemandPlanning at a small company

Session Code: 710

Presenters: Mike Beller, Entegris

Chandra Mohan, Bristlecone

Joan Ellison, Entegris


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Who is Entegris?

Core competency: Materials Integrity

Management Core markets: Semiconductor, Data Storage,

Life Sciences, Fuel Cell Core products: Silicon wafer carriers, wafer

shippers, disk shippers, fluid handling

Fiscal year 2003 revenue approx. $245M


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G e o g r a p h i c a l Su p p l y M a p : I n d i c a t i v e

Manufacturing Locations DCs Customers


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SAP landscape

Went live on SAP R/3 4.0B in August 1998

(MM,PP,FI,CO,SD,HR) Upgraded to 4.6C in August 2001

R/3 currently implemented at all major facilities

outside of Japan

Licensed mySAP suite in 2002

Outsourced hardware maintenance for SAP systemsin spring 2003.


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Importance of Planning

Semiconductor has traditionally been Entegris

largest market. Semiconductor market has historically been subject to

rapid and significant changes in demand.

Failure to anticipate upturns leads to high level ofexpedites, long lead times, and loss of market share

Failure to anticipate downturns leads to excessinventory and capacity


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Supply chain planning at Entegris:

the old days Planning/forecasting processes ranged from

inconsistent to nonexistent Planning responsibilities divided into four groups: Sales: Generated a high-level dollar-based forecast on a

quarterly basis

Product management: Refused to provide forecasts ourproducts are too unique to forecast.

Operations: No unit forecast available, planned at a high

level based on the dollar forecast

Logistics: Received forecasts from certain key customers,data not visible to other groups


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Early attempts to improve

In February 2002, implemented a custom forecasting

system in R/3 for selected product lines Bare-bones functionality

Low participation

Ongoing programming support

No experience with R/3 Sales & Operations Planning

functionality or budget for investigating it

No business push for consistent practices and tools


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Business emphasis on supply chain

In early 2002, internal reorganization placed

greater emphasis on supply chain planning Supply chain functions consolidated under one

senior VP

Creation of a demand management group

responsible for forecasting

Executive push for a project to address supplychain planning processes and tools


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Project goals

Define one global forecasting/demand planning

process based on industry best practices to apply forall Entegris facilities/product lines.

Implement tools within the SAP environment to

enable the realization of the global demand planningprocess.

Match inventory to customer demand

Reduce lead times by reducing schedule disruption


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Planning tool selection

For cost reasons, decided to make use of

existing and available functionality withinSAP for demand planning did not consider

third-party supply chain planning systems.

Considered R/3 Sales & Operations Planning

APO Demand Planning selected:

SAPs long-term direction

Collaborative capabilities


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Consulting partner evaluation

Selected three candidate consulting partners

based on past experience/other contacts Each candidate received a detailed

requirements document, and conducted a series

of fact-finding interviews to gather further data Each candidate then presented:

Their proposed demand planning business process A demo of how this process would be

implemented within APO-Demand Planning


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Why we selected Bristlecone

High level of knowledge and experience with

demand planning business practices Significant experience with APO-Demand

Planning implementation Close ties to SAP

Local resource


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Business case prep

Project budget approved, but difficulty in

quantifying projected benefits/ROI. Before implementation, build a detailed

business case to quantify benefits and justifythe project


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Chandra Mohan

Bristlecone

B i l C S i i


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Bristlecones Company Statistics

Areas ofAreas of

ExpertiseExpertise

KeyKey

IndustriesIndustries

Number ofNumber of

EmployeesEmployees

HeadqrtrsHeadqrtrs

FoundedFounded

OwnershipOwnership

GlobalGlobalPresencePresence

BusinessBusiness

OfficesOffices

United States, Germany, India

Santa Clara, California; Atlanta, Georgia; Cleveland,Ohio; Houston, Texas; Richmond, Virginia.

Supply Chain Management, Product LifecycleManagement, Enterprise Resource Planning

Process Manufacturing, Consumer Packaged Goods,Apparel/Footwear, High Tech, Industrial, Oil & Gas

1998

Private

125

San Jose, CA


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SCM

ERPSMBPLM

SCM

ERPSMBPLM

ConsultingConsultingServicesServices

SC Opportunity Assessment

SC Network Strategy SC Process benchmarking

SCOR framework SC Business Process Improvement SAP APO Implementation Meta-app development

SCM-PLM SCM-CRM

ManagedManaged

ServicesServices APO Help Desk Planning Expert

On Call

PackagedPackaged

SolutionsSolutions plannerDATM

SAP APO Value AcceleratorsModel ValidatorPlanning Results Analyzer

Bristlecone Services

B i tl S l Ch i E i


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SAP APO Implementation

ITT Night Vision

GaAsTek

Nestle USA

Millennium Chemicals

Motorola HP

Dow Corning

Palm

Pennzoil Owens Corning

Eastman Chemicals

Entegris

Hasbro

Baker Hughes

SC Strategy

Nike

Pennzoil

Shell

SC Process

Nike

LSI Logic

Entegris

VWR

Bristlecones Supply Chain Experience

SC system evaluation/selection

Celestica

Ferro Corporation

Highest number ofsuccessful APO

implementations

Supply Chain domain

expertise that spansseveral industryverticals


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Objective, Scope and Deliverables

Identify business benefits:

Identifying Supply Chain Opportunities for Realization Benchmark against other companies

Estimating the realization value

Scope for the assessment exercise Demand Planning

Supply Planning


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Objective, Scope and Deliverables

Benefit Scope

Inventory Reduction

Order to Delivery Cycle Time Reductions Drivers:

Demand Plan Accuracy

Improved Supply Planning Capabilities Where,When, How Much to produce

Deliverables:

Value matrix


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Approach using, Supply Chain

Analysis (SCAn)

Primary Data GatheringSecondary DataGathering

Data Analysis

Benchmarking & GapAnalysis (PMG)

Estimation ofRealization Value


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Understanding of Entegris Business

Revenue forecast was the only prevalent forecast

Unit level forecasting was introduced for a small

subset of products recently

Lack of a formalized consensus demand plan building

process (Product Management, Sales, Production and

Procurement function in isolation)

Forecast accuracy was difficult to estimate as the interim

forecasting system had been operational for a couple of

months only.


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The benchmarks are based on Fiscal

2001 Entegris data

Estimated Entegris forecastaccuracy

rrying Cost

0%

10%

20%30%

40%

50%

60%

70%

80%

90%

MAPE%

Best In Class Median Entegris

Forecast Accuracy

2002 2001 2002 2001 2002 200

ished Goods 19.17 24.3 2.13 2.7 21.3 2P 1.17 11.34 0.13 1.26 1.3 12.

3.35 3.8 3.35 3.8 6.7 7.

2002 2002

ished Goods 19.17 2.13

P 1.17 0.13

3.35 3.35

Inventory Carrying Cost

7

5

-

20

40

60

80

100

DaysofSupply

Best In Class Median Entegris

Total Inventory

Finished Goods Inventory Reduction


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Finished Goods Inventory Reduction

APO EnablerDemand Planning

Supply Planning

Root Cause1. Inaccurate forecast

2. Lack of Formal Demand

Planning & Supply Planning

Processes3. Lack of Planning System

Financial Value ($ mil)

1. One time Inventory Reduction = $X Mil Savings in $ per year

X * Cost of Capital % = $Y Mil

Opportunity

1. One time Inventory Reduction Current days of supply = A

Target days of supply(#) = B

# Calculated based on assuming customer servicerequirements, an improvement in forecast accuracy andreduction in supply variability all for made to stock products

R M t i l I t R d ti


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Raw Materials Inventory Reduction

APO EnablerDemand Planning

Supply Planning

Root Cause1. Inaccurate forecast

2. Lack of Formal Demand

Planning & Supply Planning

Processes3. Lack of Planning System

Financial Value ($ mil)1. One time Inventory Reduction = $W Mil

Savings in $ per year

W* Cost of Capital % = $Z Mil

Opportunity1. One time Inventory Reduction

Current days of supply = C

Target days of supply = D

Other Benefits (not quantified)


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Other Benefits (not quantified)

Reduction in obsolescence costs Potential increase in revenue due to better customer

service

Reduction in expediting costs (Transportation costs)

Better plant throughput

Reduction in warehousing costs

Visibility of RM demand to suppliers

Reduced RM delivery variability

Negotiate better terms with suppliers


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Benefit: Total Quantified Value

Annual $ benefit per year, resulting from inventory

reduction due to better Demand and Supply planning.

This was based on the following assumptions: Improvement of forecast accuracy

Support customer service requirements (Line Fill Rate) of

up to 95%

Future Potential (based on further improvement in

forecast accuracy, reduction in manufacturing batch

sizes and reduction in planning cycle times)


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Joan Ellison

Project Leader and Sr. Business Analyst, ITEntegris, Inc


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APO-DP Implementation details

Phase 1 Nov-02 till May-03This is the development of the initial forecast process for

products that are identified as High Revenue and make-to-

stock strategy. Go-Live March 3rd

2003.This includes moving the system hardware & basis support to our

Hosting Vendor. Go-Live mid May.

Project Team = core team + extended teamIT Project Lead/Business Analyst ( 1 )

Demand Management Business Project Lead ( 1 )

Bristlecone Consultants ( 2 )IT Business Warehouse Analyst ( 2 )

Extended Team from the Business ( 6 )


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Initial fears and concerns Getting the business to own the process, not the IT department.

Resistance to forecasting from the business as a whole. We

cant forecast our products, its not possible First use of BW in a productive environment any prior BW

efforts were only in a development project.

Entegris knew very little about APO & BW and theirdependencies. We were relying heavily on our consultingpartner, Bristlecone, to educate us.

Decisions and timing of the hosting move impacted hardware

size, performance & client availability. Initial system was in-house and only a Dev to QA landscape. APO and BW shared abox.

F P


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Forecast Process

Blueprinting determined we needed todevelop two forecasting processes in

APO.Monthly Forecast - by Product Managers

System generated forecast that is adjusted by the

Product Manager. The Adjusted PM Forecast isreviewed in a Monthly Consensus Meeting andfinalized. The Consensus Forecast is then moved to

R/3 as Independent Requirements.

F P


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Forecast Process

Blueprinting determined we needed todevelop two forecasting processes in

APO.Weekly Forecast by Logistics Managers

Customer forecast is what drives this process. The

Logistics Manager gets forecasts from a select groupof customers who we have established contracts with.This Customer Forecast can be adjusted by the

Logistics Manager. The Adjusted Logistics Forecastis moved to R/3.

F P


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Forecast Process Blueprinting determined we needed a way to identify the

products that were to be forecasted in APO

Developed the Forecast Tier / Manufacturing Strategy

relationship based on revenue.Tier 1 = 80% revenue + Tier 2 = 95% revenue + Tier 3 = 100%

Make-to-stk Assemble-to-order Build-to-OrdTier 1 350 products

Tier 2

Tier 3

M thl f t


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Monthly forecast processMonthly Product Manager Forecast ProcessUser roles :

End User display only ( 4 users ) Supervisors & Managers

End User change capability ( 18 users ) Product Managers

Location of users - US based in Minnesota and Colorado

Forecast timing first week of the fiscal month is the duration of thisforecast cycle.

Forecast horizon 12 months in the future with current month asunchangeable.

Forecast includes only Tier 1010 Tier 1 / Make-to-stock approx 350products.

Forecast profile Univariate profile #14 Weighted Moving Average

M thl f t


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Monthly forecast processTimeline for Monthly Forecast ProcessDay 0

Extract sales order history and master data elements from R/3 into BW.

Transfer data from BW to APO.

In APO, run clean up jobs to prepare for the forecast.

In APO, run the forecast jobs and alerts.

Day 1 Day 3

Distribute the Forecast vs. Actual report from BW.

Product Managers and Logistics Managers make their adjustments in their planningbooks.

Day 4

Run jobs in APO and reports in BW for use in the Consensus meetings.

Day 5

Finalize the Consensus Demand Plan.

Transfer to R/3.

W kl L i ti f t


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Weekly Logistics forecast processWeekly Logistics Forecast ProcessUser roles :

End User display only ( 1 users ) Manager / Director

End User change capability ( 4 users ) Logistics ManagersLocation of users Global - based in Germany, Singapore, Portland Or.

Forecast timing Monday & Tuesday of each week is the duration of thisforecast cycle. The first week of every fiscal month the Logistics

Weekly forecast cycle overlaps the Monthly Product Managers forecastcycle.

Forecast horizon 17 weeks in the future with current week asunchangeable.

Forecast products Tier 1010 in Logistics Distribution Plants.Forecast profile Univariate profile - #56 Forecast with automatic model

selection.

W kl L i ti f t


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Weekly Logistics forecast process

Timeline for Weekly Forecast ProcessDay 0

Extract sales order history and master data elements from R/3 into BW.

Transfer data from BW to APO. In APO, run clean up jobs to prepare for the forecast.

In APO, run the forecast jobs and alerts.

Day 1 Day 2

Distribute the Forecast vs. Actual report from BW.

Logistics Managers make their adjustments in their planning books.

Finalize the Adjusted Logistics Forecast.

Day 2 Transfer to R/3.

APO R/3 BW landscape


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APO - R/3 BW landscape

BW

DevelopmentBW

Development

BW

ProductionBW

Production

APO

DevelopmentAPO

Development

R/3

DevelopmentR/3

Development

APO

ProductionAPO

Production

R/3

ProductionR/3

Production

APO

QAAPO

QA

R/3

QAR/3

QA

Transports

Data Flow

6

5

4

3

2

1

7

9

8

R/3

Sand BoxR/3

Sand Box

10

11

How we use the system


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technical aspectsSingle Planning Area

Storage bucket periodicity week & fiscal monthSingle Master Planning Object Structure

Characteristics = 15

Key Figures = 21

Characteristic combinations = 27,014

Planning Book = 40Macros in use = approx 40



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Planning Books / Data Views :

A planning book was created for each Product Manager and

Logistics Manager. (quantity 25) Configuration assigns aplanning book to a user id and restricts what they can get into.

This allows us to easily separate and identify each product

manager and their selections. It allows us to define alerts

differently for each product manager. It makes adding or

changing an existing macro more difficult to roll out. Time

will tell if this is a good decision.

We have planning books / data views (quantity 15) that we use inprocessing macros to prepare the data.



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Monthly forecast processCharacteristicsProduct number

Market Segment number

Product Family number

Product Manager number

Profit Center

Forecast Tier / Mfg Strategy

Ship to number

Sold to number

Customer Market Segment

Country

Region

Sales District

Ship from location

Key FiguresSales order history quantity

Invoiced salesPrevious consensus forecast

Actual sales order bookings

Batch statistical forecast

Product Manager adjustments

Adjusted Product Manager forecast

Sales forecast

Adjusted Logistics forecastConsensus forecast

Product Managers monthly data view


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Product Managers monthly data view



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Weekly Logistics forecast processCharacteristicsProduct number

Market Segment number

Product Family number

Product Manager number

Profit Center

Forecast Tier / Mfg Strategy

Ship to number

Sold to number

Customer Market Segment

Country

Region

Sales District

Ship from location

Key FiguresSales order history quantity

Previous Logistics forecastActual sales order bookings

Adjusted Product Manager forecast

Batch statistical forecast

Customer Forecast

Logistics adjustments

Adjusted Logistics forecast

Logistics weekly data view


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g w y a a v w



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Business Warehouse and APO

dependency complexity

reporting using BEX learning curve



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APO DP Implementation details

Phase 1 accomplishments

Established two forecast processes

Established a data flow from R/3 to BW to APO to BW

Setup the APO-DP tool and generated forecasts by the Go-live

date of March 3rd.

Trained 25 end users on both the process and the system

Transferred the forecast to R/3

Moved the BW and APO systems to the Hosting vendor with

minimal downtime and impact to the business.



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APO DP Implementation details Mid project fears and concerns Resistance to forecasting from the business as a whole. Management says

we will do it, but PM level is still resisting.

First use of BW in a productive environment any prior BW efforts were

only in a development project. Building real cubes with real data andensuring accuracy of the data was challenging.

Entegris knew very little about APO & BW and their dependencies.Moving data from system to system gets easier to understand, but timing

and dependencies continue to challenge us. Very small team keeps internal knowledge base concentrated in a few

individuals.

Decisions and timing of the hosting move impacted hardware size,

performance & client availability. Initial system was in-house and only aDev to QA landscape. Hardware was undersized and performance waspoor. We went live on the APO-QA system and BW-Dev system.



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Phase 2 May-03 till July-03Forecast for Fiscal Year Budget Planning, including Capacity Evaluation in

the R/3 sandbox system. Develop forecast in APO-DP for all productsusing a separate planning area and multiple versions.

Project TeamIT Project Lead/Business Analyst ( 1 )

Demand Management Business Project Lead ( 1 )

Bristlecone Consultants ( 1 )

IT Business Warehouse Analyst ( 1 )

Phase 2 accomplishments created another planning area used to generate theforecast for all non-tier 1010 products. Added the values from tier 1010 togenerate a comprehensive forecast of all products. Transferred the forecastto a R/3 sandbox system and performed rough capacity evaluation for 18months.

APO

BW

Historical Revenue & Units (BUoM)APOODSSF

Invoiced History


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Planning

Area (APO)

(ZFV_PA)

Sales Changes

Profit Center changes

(ZFV_PC)

Units ConversionHist Revenue

Hist inv. Sales (BUoM)

(ZV_EA)

Planning Area

(Conversion)(ZFV_EA)

APO Info

Cube

Sales history

(Z_FV_IC)

Forecast Versions

Sales History

Version Units

(APOFVER)

Version Units

Historical Revenue & Units (EA)

Flat file based on

Sales Excel sheet

Master DataMaster Data

A

Converted Units

Hist Revenue


Hist inv. Sales (EA)

(ZFV_BW_EA)

APO Forecast

Versions

Version Units

(ZV_BW)

Converted UnitsHist Revenue


Hist inv. Sales (EA)

(APOASPFV)

ASP (EA)(MATNRASP5)

APOODSIN

Sales Order Hist.

y

Sales Org 1000

Reports

Planning Area

(APO)

(ZQ_PA)

Tier 1010



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Phase 3 June-03 till Sept-03Incorporate sales units from the newly developed Sales Forecast

Process to the existing Monthly Forecast Process.

Project Team

IT Project Lead/Business Analyst ( 1.5 )

Demand Management Business Project Lead ( .5 )

Bristlecone Consultants ( 1 )

IT Business Warehouse Analyst ( 1 )Phase 3 accomplishments the Sales Forecast values are transferred

from the front-end system to BW, then to APO resulted inintegrating the Sales & Unit processes. The Sales forecast valuesappear in the planning book where there was previously blankspaces. Changes to the characteristics were also made.

APO

BW

BWHistorical Revenue

A


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Planning

Area (APO)

(ZQ_PA)

Frontend

Data

Sales Unit Forecast

(ZQ_FRONT)

BW

Revenue

Revenue BW

Revenue History

(ZQ_SALES)

Planning Area

(Sales)

(ZQ_MSALES)

APO Info

Cube

Sales history

(ZQ_IC)

BW Structures

Sales History

Consensus Revenue

Final Revenue

Consensus Units

Final Revenue (Adjusted)

Consensus Revenue (Consensus Units * ASP + Other Revenue)

Frontend data via BW

Master Data

Master Data

A

A

APO

Revenue

Revenue APO

(ZQ_APO_SA)

APO Units

Consensus Units

+

+

(ZQ_BW_F)

Sales Unit Forecast (FE)



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O p e e tat o deta s

Long term project fears and concerns Resistance to forecasting from the business. We still have

varying degrees of participation and support. Management is

pushing use of the process but we still have several ProductManagers who barely participate.

Entegris knew very little about APO & BW and their

dependencies. Moving data between systems is stable. Needmore development of BW-BEX reporting skills outside of the

BW team.

Very small team keeps internal knowledge base concentratedin a few individuals.

Six months later


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Unified global forecasting process is established and

adhered to

APO tool is performing well, although still requires

significant technical support

Some inventory reductions have been achieved,although still far from the two-year goals

Forecast accuracy has improved, but not as much as

we would like

Future direction


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Stabilize APO-BW data transfer

Continue to work towards targeted inventory and leadtime reductions

Investigate/prepare for APO Supply Network

Planning


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2003 Supply Chain Management Forum

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following this session.

Session Code: 710

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implementing apo demand planning at a small company 1051

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