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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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Understanding of Entegris Business
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Understanding of Entegris Business
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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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
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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How we use the system
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
How we use the system
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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 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.
How we use the system
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How we use the system
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
How we use the system
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How we use the system
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
APO-DP Implementation details
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APO-DP Implementation details
Business Warehouse and APO
dependency complexity
reporting using BEX learning curve
APO-DP Implementation details
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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.
APO-DP Implementation details
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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.
APO-DP Implementation details
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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 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 (BUoM)
Hist inv. Sales (EA)
(ZFV_BW_EA)
APO Forecast
Versions
Version Units
(ZV_BW)
Converted UnitsHist Revenue
Hist inv. Sales (BUoM)
Hist inv. Sales (EA)
(APOASPFV)
ASP (EA)(MATNRASP5)
APOODSIN
Sales Order Hist.
y
Sales Org 1000
Reports
Planning Area
(APO)
(ZQ_PA)
Tier 1010
APO-DP Implementation details
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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 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)
APO-DP Implementation details
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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
Thank You For Attending!
Please remember to completeand return your evaluation form
following this session.
Session Code: 710
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