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2003 Supply Chain Management Forum
Implementing APO Demand Planning at a small company
Session Code: 710Presenters: Mike Beller, Entegris
Chandra Mohan, BristleconeJoan Ellison, Entegris
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
Geographical Supply Map: Indicative
Manufacturing Locations DCs Customers
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 systems
in spring 2003.
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 of expedites, long lead times, and loss of market share
• Failure to anticipate downturns leads to excess inventory and capacity
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 – “our
products 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
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
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 supply
chain planning processes and tools
Project goals
• Define one global forecasting/demand planning process based on industry best practices to apply for all Entegris facilities/product lines.
• Implement tools within the SAP environment to enable the realization of the global demand planning process.
• Match inventory to customer demand• Reduce lead times by reducing schedule disruption
Planning tool selection
• For cost reasons, decided to make use of existing and available functionality within SAP for demand planning – did not consider third-party supply chain planning systems.
• Considered R/3 Sales & Operations Planning• APO Demand Planning selected:
– SAP’s long-term direction– Collaborative capabilities
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
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
Business case prep
• Project budget approved, but difficulty in quantifying projected benefits/ROI.
• Before implementation, build a detailed business case to quantify benefits and justify the project
Chandra MohanBristlecone
Bristlecone’s Company Statistics
Areas ofAreas ofExpertiseExpertise
KeyKey IndustriesIndustries
Number of Number of EmployeesEmployees
HeadqrtrsHeadqrtrs
FoundedFounded
OwnershipOwnership
GlobalGlobal PresencePresence
BusinessBusiness OfficesOffices
United States, Germany, India
Santa Clara, California; Atlanta, Georgia; Cleveland, Ohio; Houston, Texas; Richmond, Virginia.
Supply Chain Management, Product Lifecycle Management, Enterprise Resource Planning
Process Manufacturing, Consumer Packaged Goods, Apparel/Footwear, High Tech, Industrial, Oil & Gas
1998
Private
125
San Jose, CA
SCMERPSMBPLM
SCMERPSMBPLM
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
Managed Managed ServicesServices
• APO Help Desk• “Planning Expert
On Call”
PackagedPackagedSolutionsSolutions• plannerDATM
• SAP APO Value Accelerators•Model Validator•Planning Results Analyzer
Bristlecone Services
SAP APO ImplementationITT Night VisionGaAsTekNestle USAMillennium ChemicalsMotorolaHPDow CorningPalmPennzoilOwens CorningEastman ChemicalsEntegrisHasbroBaker Hughes
SC StrategyNikePennzoilShell
SC ProcessNikeLSI LogicEntegrisVWR
Bristlecone’s Supply Chain Experience
SC system evaluation/selectionCelesticaFerro Corporation
• Highest number of successful APO implementations
• Supply Chain domain expertise that spans several industry verticals
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
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
Approach using, Supply Chain Analysis (SCAn)
Primary Data GatheringSecondary Data Gathering
Data Analysis
Benchmarking & GapAnalysis (PMG)
Estimation of Realization Value
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.
Understanding of Entegris Business• No demand visibility upstream in manufacturing and
procurement– ‘Expedites’ was all pervasive– Manual changes to the production schedules were common
• Capacity planning was done once a year• Three manufacturing strategies were prevalent,
though not formally used:– 50% Made-to-Stock– 35% Made-to-Order– 15% Assemble-to-Order
Understanding of Entegris Business
The benchmarks are based on Fiscal 2001 Entegris data
Estimated Entegris forecast accuracy
arrying Cost
0%10%20%30%40%50%60%70%80%90%
MAP
E %
Best In Class Median Entegris
Forecast Accuracy
2002 2001 2002 2001 2002 2001nished Goods 19.17 24.3 2.13 2.7 21.3 27P 1.17 11.34 0.13 1.26 1.3 12.6
M 3.35 3.8 3.35 3.8 6.7 7.6
2002 2002nished Goods 19.17 2.13P 1.17 0.13
M 3.35 3.35
Inventory Carrying Cost7 5
-
20
40
60
80
100
Days
of S
uppl
y
Best In Class Median Entegris
Total Inventory
Finished Goods Inventory Reduction
APO EnablerDemand PlanningSupply Planning
Root Cause1. Inaccurate forecast2. Lack of Formal Demand
Planning & Supply Planning Processes
3. Lack of Planning System
Financial Value ($ mil)1. One time Inventory Reduction = $X Mil
Savings in $ per yearX * Cost of Capital % = $Y Mil
Opportunity1. One time Inventory Reduction
Current days of supply = ATarget days of supply(#) = B
# Calculated based on assuming customer service requirements, an improvement in forecast accuracy and reduction in supply variability – all for made to stock products
Raw Materials Inventory Reduction
APO EnablerDemand PlanningSupply Planning
Root Cause1. Inaccurate forecast2. Lack of Formal Demand
Planning & Supply Planning Processes
3. Lack of Planning System
Financial Value ($ mil)1. One time Inventory Reduction = $W Mil
Savings in $ per yearW* Cost of Capital % = $Z Mil
Opportunity1. One time Inventory Reduction
Current days of supply = CTarget days of supply = D
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
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)
Joan EllisonProject Leader and Sr. Business Analyst, IT
Entegris, Inc
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 )
APO-DP Implementation details …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
can’t forecast our products, it’s 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 their
dependencies. We were relying heavily on our consulting partner, 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 a box.
Forecast Process …Blueprinting determined we needed to
develop two forecasting processes in APO.
Monthly Forecast - by Product Managers• System generated forecast that is adjusted by the
Product Manager. The Adjusted PM Forecast is reviewed in a Monthly Consensus Meeting and finalized. The Consensus Forecast is then moved to R/3 as Independent Requirements.
Forecast Process …Blueprinting determined we needed to
develop 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 group of customers who we have established contracts with. This Customer Forecast can be adjusted by the Logistics Manager. The Adjusted Logistics Forecast is moved to R/3.
Forecast Process …Blueprinting determined we needed a way to identify the
products that were to be forecasted in APODeveloped 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 productsTier 2Tier 3
Monthly forecast processMonthly Product Manager Forecast ProcessUser roles :End User – display only ( 4 users ) Supervisors & ManagersEnd User – change capability ( 18 users ) Product ManagersLocation of users - US based in Minnesota and ColoradoForecast timing – first week of the fiscal month is the duration of this
forecast cycle.Forecast horizon – 12 months in the future with current month as
unchangeable.Forecast includes only Tier 1010 – Tier 1 / Make-to-stock approx 350
products.Forecast profile – Univariate profile – #14 Weighted Moving Average
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 planning
books. 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.
Weekly Logistics forecast processWeekly Logistics Forecast ProcessUser roles :End User – display only ( 1 users ) Manager / DirectorEnd 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 this
forecast cycle. The first week of every fiscal month the Logistics Weekly forecast cycle overlaps the Monthly Product Managers forecast cycle.
Forecast horizon – 17 weeks in the future with current week as unchangeable.
Forecast products – Tier 1010 in Logistics Distribution Plants.Forecast profile – Univariate profile - #56 Forecast with automatic model
selection.
Weekly Logistics forecast processTimeline 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
BWDevelopment
BWDevelopment
BWProduction
BWProduction
APODevelopment
APODevelopment
R/3Development
R/3Development
APOProduction
APOProduction
R/3Production
R/3Production
APOQA
APOQA
R/3QA
R/3QA
Transports
Data Flow
6
5
4
3
2
1
7
9
8
R/3Sand Box
R/3Sand Box
10
11
How we use the system … technical aspects
Single Planning Area Storage bucket periodicity – week & fiscal monthSingle Master Planning Object Structure Characteristics = 15Key Figures = 21Characteristic combinations = 27,014Planning Book = 40Macros in use = approx 40
How we use the system …Planning Books / Data Views :A planning book was created for each Product Manager and
Logistics Manager. (quantity 25) Configuration assigns a planning 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 in processing macros to prepare the data.
How we use the system … Monthly forecast process
CharacteristicsProduct numberMarket Segment numberProduct Family numberProduct Manager numberProfit CenterForecast Tier / Mfg StrategyShip to numberSold to numberCustomer Market SegmentCountryRegionSales DistrictShip from location
Key FiguresSales order history quantityInvoiced salesPrevious consensus forecastActual sales order bookingsBatch statistical forecastProduct Manager adjustmentsAdjusted Product Manager forecastSales forecastAdjusted Logistics forecastConsensus forecast
Product Managers monthly data view
How we use the system … Weekly Logistics forecast process
CharacteristicsProduct numberMarket Segment numberProduct Family numberProduct Manager numberProfit CenterForecast Tier / Mfg StrategyShip to numberSold to numberCustomer Market SegmentCountryRegionSales DistrictShip from location
Key FiguresSales order history quantityPrevious Logistics forecastActual sales order bookingsAdjusted Product Manager forecastBatch statistical forecastCustomer ForecastLogistics adjustmentsAdjusted Logistics forecast
Logistics weekly data view
APO-DP Implementation details …
Business Warehouse and APO• dependency• complexity• reporting using BEX• learning curve
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.
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 and ensuring 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 a Dev to QA landscape. Hardware was undersized and performance was poor. We went live on the APO-QA system and BW-Dev system.
APO-DP Implementation details …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 products using a separate planning area and multiple versions.
Project Team IT 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 the
forecast for all non-tier 1010 products. Added the values from tier 1010 to generate a comprehensive forecast of all products. Transferred the forecast to a R/3 sandbox system and performed rough capacity evaluation for 18 months.
Planning Area (APO)
(ZFV_PA)
APO
Sales ChangesProfit 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)
BW
Forecast Versions
Sales HistoryVersion Units(APOFVER)
Version Units
Historical Revenue & Units (EA)
Historical Revenue & Units (BUoM)
Flat file based on Sales Excel sheet
Master DataMaster Data
A
Converted UnitsHist Revenue
Hist inv. Sales (BUoM)Hist inv. Sales (EA)
(ZFV_BW_EA)
APO Forecast VersionsVersion Units
(ZV_BW)
Converted UnitsHist Revenue
Hist inv. Sales (BUoM)Hist inv. Sales (EA)
(APOASPFV)
ASP (EA)(MATNRASP5)
APOODSINSales Order Hist.
APOODSSFInvoiced History Sales Org 1000
ReportsPlanning Area
(APO)(ZQ_PA)Tier 1010
APO-DP Implementation details …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 in integrating the Sales & Unit processes. The Sales forecast values appear in the planning book where there was previously blank spaces. Changes to the characteristics were also made.
Planning Area (APO)
(ZQ_PA)
APO
Frontend Data
Sales Unit Forecast(ZQ_FRONT)
BW Revenue
Revenue BWRevenue History(ZQ_SALES)
Planning Area (Sales)
(ZQ_MSALES)
APO Info Cube
Sales history(ZQ_IC)
BW
BW Structures
Sales HistoryConsensus RevenueFinal Revenue
Consensus Units
Final Revenue (Adjusted)
Consensus Revenue (Consensus Units * ASP + Other Revenue)
Historical Revenue
Frontend data via BW
Master DataMaster Data A
A
A
APO Revenue
Revenue APO(ZQ_APO_SA)
APO UnitsConsensus Units++(ZQ_BW_F)
Sales Unit Forecast (FE)
APO-DP Implementation details …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 Product Managers who barely participate.
• Entegris knew very little about APO & BW and their dependencies. Moving data between systems is stable. Need more development of BW-BEX reporting skills outside of the BW team.
• Very small team keeps internal knowledge base concentrated in a few individuals.
Six months later…
• 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
• Stabilize APO-BW data transfer• Continue to work towards targeted inventory and lead
time reductions• Investigate/prepare for APO Supply Network
Planning
2003 Supply Chain Management Forum
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Session Code: 710
