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Everything that needs to be known to record and track the forecast accuracy
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CREATING TOMORROW'S SOLUTIONS
Stephen P. Crane, CSCP, Director Strategic Supply Chain Management
Institute of Business Forecasting & Planning Conference
Phoenix, AZ February 22-24, 2009
A ROADMAP TO WORLD CLASS FORECASTING ACCURACYSix Keys to Improving Forecast Accuracy
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 2
WACKER Company Overview
Why Forecast?
Forecasting Background and Challenges
Six Keys to Improving Forecast Accuracy
– Process, People, & Tools
– Statistical Forecasting
– Forecasting Segmentation
– Data Aggregation
– Sales Adjustments to Statistical Forecast
– Measurement & Exception Reporting
Forecasting Accuracy Results
Conclusions
CONTENT
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 3
CONTENT
WACKER Company Overview
Why Forecast?
Forecasting Background and Challenges
Six Keys to Improving Forecast Accuracy
– Process, People, & Tools
– Statistical Forecasting
– Forecasting Segmentation
– Data Aggregation
– Sales Adjustments to Statistical Forecast
– Measurement & Exception Reporting
Forecasting Accuracy Results
Conclusions
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 4
Wacker Chemie AG
WACKER Group (2007)
OVER 90 YEARS OF SUCCESS
• Founded in 1914 by Dr. Alexander Wacker • Headquartered in Munich
• Sales: €3.78 billion
• EBITDA: €1.00 billion
• Net income: €422 million
• Net cash flow: €644 million
• R&D: €153 million
• Capital expenditures: €699 million
• Employees: 15,044
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 5
WE ARE COMMITTED TO BENCHMARK-QUALITY PRODUCTS DESIGNED FOR OUR FOCUS INDUSTRIES
Industries• Adhesives• Automotive and transport• Basic chemicals • Construction chemicals • Gumbase• Industrial coatings and printing inks• Paper and ceramics
Products: • Polymer powders and dispersions for the
construction industry• Polyvinyl acetate solid resins, polyvinyl alcohol
solutions, polyvinyl butyral and vinyl chloride co-
and terpolymers
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 6
CONTENT
WACKER Company Overview
Why Forecast?
Forecasting Background and Challenges
Six Keys to Improving Forecast Accuracy
– Process, People, & Tools
– Statistical Forecasting
– Forecasting Segmentation
– Data Aggregation
– Sales Adjustments to Statistical Forecast
– Measurement & Exception Reporting
Forecasting Accuracy Results
Conclusions
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 7
FORECAST TYPES
Most companies use three types of forecasts
Sales or channel forecast
Corporate planning forecasts
Supplier forecasts
These forecasts are very different in their use, frequency, and definition
Need consistent demand signal across these three forecasting processes, but most companies don’t know how to align them
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 8
WHY FORECAST?
The forecast drives supply planning, production planning, inventory planning, raw material planning, and financial forecasting
Companies that are best at demand forecasting average;
– 15% less inventory
– 17% higher perfect order fulfillment
– 35% shorter cash-to-cash cycle times
– 1/10 the stockouts of their peers
1% improvement in forecast accuracy can yield 2% improvement in perfect order fulfillment
3% increase in forecast accuracy increases profit margin 2%Source: AMR Research 2008
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 9
INVENTORY LEVEL VS. FORECAST ACCURACY
Source: Aberdeen Group 2008
0
20
40
60
80
100
120
20% 30% 40% 50% 60% 70% 80% 90% 100%
Forecast Accuracy at SKU Location
Inven
tory
Days o
f S
ale
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 10
World class forecasting accuracy performance
– 95% currency by product line
– 90% by product line
– 85% product mix level
WHAT IS A GOOD FORECAST?
Source: Buker Management Consulting
Goal
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 11
CONTENT
WACKER Company Overview
Why Forecast?
Forecasting Background and Challenges
Six Keys to Improving Forecast Accuracy
– Process, People, & Tools
– Statistical Forecasting
– Forecasting Segmentation
– Data Aggregation
– Sales Adjustments to Statistical Forecast
– Measurement & Exception Reporting
Forecasting Accuracy Results
Conclusions
Presentation TitleFirst Name Last Name, Organizational Unit, Date of Presentation, Slide 12
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 13
FORECASTING BACKGROUND & CHALLENGES
Typical forecasting process involves historical demand data loaded into a database using software to generate statistical forecasts
Statistical software is rarely allowed to operate on its own
Management usually overrides the statistical forecast before agreeing to final forecast
Forecasting is often difficult and thankless endeavor with high inaccuracies
Companies react to inaccuracies with investments in technology
New investments do not guarantee any better forecasts
There are often fundamental issues that need to be addressed before improvements can be achieved
Forecasting Process
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 14
FORECASTING BACKGROUND & CHALLENGES
When businesses know their sales for next week, next month, and next year, they only invest in the facilities, equipment, materials, and staffing they need
There are huge opportunities to minimize costs and maximize profits if we know what tomorrow will bring – but we don’t.
Therefore we forecast!
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 15
CONTENT
WACKER Company Overview
Why Forecast?
Forecasting Background and Challenges
Six Keys to Improving Forecast Accuracy
– Process, People, & Tools
– Statistical Forecasting
– Forecasting Segmentation
– Data Aggregation
– Sales Adjustments to Statistical Forecast
– Measurement & Exception Reporting
Forecasting Accuracy Results
Conclusions
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 16
WORLD CLASS FORECASTING ACCURACY REQUIRES MAKING THE RIGHT DECISIONS
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 17
Step 1: Defining the Process, People, & Tools
SIX KEYS TO IMPROVING FORECAST ACCURACY
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 18
STEP 1:DEFINING THE PROCESS, PEOPLE & TOOLS
Process
Forecast accuracy improvement occurs with the proper blending of process, people, and IT tools
Overemphasis on any one leads to an imbalance that can defeat the desired result
The process should be defined first, then followed by roles and responsibilities, and then IT applications
The more people that touch a forecast, the greater the bias and the greater the forecast error
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 19
SALES & OPERATIONS PLANNING PROCESSSupply Networking Planning WD 1- 6
Submit Supply Plan with
Documented Options
Approve Supply Plan
Sequential Business Unit
Planning[WD 1-5]
Finalize & Approve
Supply Plan [WD 6]
Upload WD 1 Inventory
Develop Updated Supply Plan Proposal
Review Supply Shortage &
Capacity Overload Alerts
Resolve Supply Alerts
Adjust Target Stock Levels
Fix SNP Planned orders
Enter Adjusted Demand
Supply Planning WD 13 -End
Adjust Supply Planning
Constraints
Supply Network Planning Preparation Phase
Analyze Draft Supply /
Capacity Plan
Update Supply Change
Summary
Submit Off System Demand Figures
Update Loc Shift Table For Future Source
Changes
Release FC to R3 For MRP
Update Master Data & Upload
Inventory
Refresh Inactive Version /
Release DP to SNP
Review Supply Planning Metrics
Preferred Sources
Assigned to Demand
Forecasting & Demand Planning WD 0 -16
Add New Business
Entries in R3
Upload New Sales History &
Business Combos
Apply Future Demand Changes
Create Statistical Forecast
DP Passed to CO for
Financial Forecast
Demand Planning Data Preparation Phase [WD 0-7]
Review Historical Sales
Data
Create Unconstrained Demand Plan [WD 8-16]
Review Final Sales Manager
Figures
Review Demand Metrics
Review FC Adjustments with Sales Managers
BT/BU Review & Approve
Unconstrained Demand Plan
Update Demand Change
Summary
Apply Planning Type
Assignments
Apply Historical Sales Data
Adjustments
APO Data Passed to BW
Publish Unconstrained Demand Plan
Approve Supply Chain Plans WD 7-8
Agree & Communicate
Approved Plans
Communicate Implications to
Financial & Sales Plans
Partnership Meeting[WD 7]
Executive S&OP [WD 8]
Review Action Items from Last
Month
Review Revenue
Projections
Review Unconstrained Demand Plan
Exceptions
Review Supply Options & Cost
Projections
Review Performance
Metrics
Summarize Supply Chain
Plans
Review Financial Plan
Key Business Issues &
Resolution
Review Supply Chain Plans
Review Performance
Metrics
Review Action Items from Last
Month
Automated Jobs
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 20
People
People make organizations and are critical to the forecasting process and how it’s used within the organization
They need to understand how their role fits with the work process and how to make improvements
Position descriptions need to be defined with clear responsibilities that are accepted by the organization
Full time positions are essential
Limit number of people making decisions about final forecast
STEP 1:DEFINING THE PROCESS, PEOPLE & TOOLS
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 21
IT Tools
Forecasting software is sometimes “sold” as the answer to forecasting issues
Software does not solve forecasting problems. Processes and people solve problems
Implementing forecasting software should not be considered an IT project, but a business process improvement project
Forecasting applications can eliminate much of the manual work associated with forecasting if configured properly
STEP 1:DEFINING THE PROCESS, PEOPLE & TOOLS
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 22
Step 2: Establish Statistical Forecasting Capability
SIX KEYS TO IMPROVING FORECAST ACCURACY
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 23
Many supply chains are too complex to manually generate forecasts for all products and customers
Forecasting engines are widely used to improve forecast accuracy by generating statistical forecasts
Statistical forecasting uses sales history to predict the future by identifying trends and patterns with the data to develop a forecast
Need to decide at what level the forecasting should be done
– Product family
– Individual product
– Product/customer
– Product/customer ship-to
– Plant/product/customer ship-to
STEP 2:STATISTICAL FORECASTING CAPABILITY
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 24
Need to decide how often to forecast
– Quarterly
– Monthly
– Weekly
– Daily
Determine how much sales history is required for meaningful statistical forecast (minimum 2 years)
Sales history master data must be correct especially when migrating from legacy systems. Very difficult to do correctly
Analyze the forecast error associated with using available forecasting algorithms to optimize accuracy of forecast
STEP 2:STATISTICAL FORECASTING CAPABILITY
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 25
Typical statistical forecasting methods include
– Multiple regression analysis
– Trend analysis
– Seasonal
– Simple moving average
– Weighted moving average
– Exponential smoothing
– Automatic selection Recommended
STEP 2:STATISTICAL FORECASTING CAPABILITY
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 26
It is critical to make appropriate sales history adjustments to generate an accurate statistical forecast
Adjustments Necessary to History
– Data errors
– Lost product volume
– Lost customers
– One time customer outages
– Discontinued products
– Non-optimal sourcing
STEP 2:STATISTICAL FORECASTING CAPABILITY
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 27
Step 3: Forecasting Segmentation - 80/20 Analysis
Exceptions Collaboration
Business IntelligenceData Aggregation
SIX KEYS TO IMPROVING FORECAST ACCURACY
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 28
STEP 3:FORECASTING SEGMENTATION - 80/20 ANALYSIS
It is crucial to distinguish the “high-value” items for special attention while automating the “not-as-valuable” items
COV (Coefficient of Variation) = STD Deviation/Ave. Demand
Notes
High
Low
StatisticalForecastability(measured by
1/COV)
HighSales Volume/Impact
Low
Manage by Exception using Exception Reports
Collaborate with Customer (if possible)
High Impact ItemsNon-High Impact Items
Use Data Aggregation in Statistical Model for all
Non-HI Items
Gather Business Intelligence for all HI
items
~ 80% Total Volume
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 29
STEP 3:FORECASTING SEGMENTATION EXAMPLE
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 30
Step 4: Data Aggregation
SIX KEYS TO IMPROVING FORECAST ACCURACY
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 31
SAP APO 4.1 used for forecasting and demand planning
Forecasting done at product/customer ship-to level
Thousands of unique customer / product combinations exist to manage
Too much data for Planners to review monthly
Sales history for many combinations (~80%) was sporadic and difficult to forecast, i.e., high forecasting errors
So how do you get a good forecast for sporadic combinations?
– Forecast at a more aggregate level
STEP 4: DATA AGGREGATION
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 32
Getting a good forecast for sporadic combinations….
Compile non-high impact items from segmentation analysis
Program forecast model to aggregate non-high impact items to logical planning source (plant, warehouse, prod. unit, etc.)
Generate statistical forecast at aggregate planning source
Disaggregate statistical forecast to lowest forecast level in model based on past history (plant/product/customer ship-to)
Aggregation to the plant/product level reduced number of combinations to review by 80%
Aggregation produces a more accurate forecast for sporadic items allowing more time to focus on high impact items
STEP 4: DATA AGGREGATION
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 33
Step 5: Sales Adjustments to Statistical Forecast
SIX KEYS TO IMPROVING FORECAST ACCURACY
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 34
STEP 5:SALES ADJUSTMENTS TO STATISTICAL FORECAST
Accurate forecasting is not just getting a forecast from the customer. The customer isn’t always right!
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 35
Since statistical forecasting is based on previous sales, it is necessary to take into account elements that can affect sales
Seasonality of the business
State of the economy
Competition and market position
Product trends
It’s also important to ask customers the right sales questions to validate forecast assumptions
Where does this project rank today?
When are you looking to make a purchasing decision?
When will you be implementing?
What would you like to see happen as a next step?
STEP 5:SALES ADJUSTMENTS TO STATISTICAL FORECAST
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 36
Adjustments Made to Forecast Pre-buying Promotional impacts Upside and downside volumes Customer plants expected to be down New business New customers
Adjustments Made to History Data errors Lost product volume One time customer outages Packaging changes Discontinued products
Decide where adjustments should be made
STEP 5:SALES ADJUSTMENTS TO STATISTICAL FORECAST
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 37
Step 6: Measurement & Exception Reporting
SIX KEYS TO IMPROVING FORECAST ACCURACY
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 38
STEP 6:MEASUREMENT & EXCEPTION REPORTING
If you can not effectively measure forecasting performance, you can not identify whether changes to the process are improving forecast accuracy
Effective measures should evaluate accuracy at different levels of aggregation (plant, warehouse, sales region, etc.)
Measuring and reporting forecast accuracy helps to build confidence in the forecasting process
Once people realize that sources of error are being eliminated, the organization will begin to use the forecast to drive business operations
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 39
A variety of analyses and exception-based measurements are needed to understand where the biggest sources of forecast error are
– Identifying High Impact exceptions for Sales review
– Accuracy of adjustments made to statistical forecast
– Identifying non-High Impact exceptions
– Current month variances (Forecast – Actual)
– Forecast but no sales
– Sales but no forecast
– No sales in last 12 months
STEP 6:MEASUREMENT & EXCEPTION REPORTING
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 40
Identify High Impact “Exceptions” to focus forecast review
– Decreasing or increasing sales rates
– Aggressive statistical forecast based on historical run rates
HIGH IMPACT EXCEPTIONS FOR SALES REVIEW
Sold To Loc Product MAY 2006 JUN 2006 JUL 2006 JUL 2006 AUG 2006 SEP 2006Customer A MO Product 1 142,800 80,948 101,251 88,863 80,000 80,000 Customer B GA Product 2 - 20,276 - - 10,000 10,000 Customer C TX Product 3 101,577 101,378 81,284 82,009 86,990 87,060 Customer D AZ Product 4 60,899 81,166 39,208 60,000 60,000 60,000 Customer E TN Product 5 60,954 20,348 81,247 53,012 60,520 60,582 Customer F TX Product 6 141,131 121,971 141,321 120,000 140,000 120,000 Customer G NY Product 7 101,496 81,438 82,019 81,210 87,681 87,738 Customer H FL Product 8 100,761 120,302 100,788 103,993 104,148 104,239 Customer I GA Product 9 121,753 142,392 121,817 116,505 117,554 117,554 Customer J NJ Product 10 163,003 142,709 61,217 155,800 155,800 155,800
History S&OP Forecast
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 41
(Units in KGS)
Statistical Forecast Final S&OP Forecast (Includes Forecast Adjustments)Impact +/-
ACCURACY OF FORECASTADJUSTMENTS
Ship-to CustomerJun06 Stat
FcstStat FcstAbs Error
Jun06 S&OP Final
S&OP Abs Error Impact +/-
Customer A 2,167,904 1,465,968 701,936 1,691,155 476,749 225,187Customer B 286,650 494,531 207,881 281,859 4,791 203,090Customer C 316,315 454,387 138,072 313,809 2,506 135,566Customer D 743,619 906,002 162,383 680,000 63,619 98,764Customer E 20,266 0 20,266 50,000 29,734 (9,467)Customer F 244,023 370,466 126,443 205,698 38,325 88,117Customer G 332,211 252,729 79,482 330,000 2,211 77,271Customer H 20,312 40,547 20,236 113,400 93,088 (72,853)Customer I 50,000 130,416 80,416 80,000 30,000 50,416Customer J 301,221 111,502 189,719 159,757 141,464 48,255Customer K 40,479 56,471 15,992 102,000 61,521 (45,529)Customer L 142,709 84,879 57,830 155,800 13,091 44,739Customer M 163,592 237,196 73,604 193,000 29,408 44,196Customer N 0 1,603 1,603 40,000 40,000 (38,397)Customer O 40,615 0 40,615 37,800 2,815 37,800Customer P 0 52,893 52,893 16,000 16,000 36,893Customer Q 152,434 194,153 41,719 140,000 12,434 29,285
Jun06 Act
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 42
Accurate customer intelligence provided by Sales can significantly improve forecast accuracy
(Units in KGS)
ACCURACY OF FORECAST ADJUSTMENTS
If adjustments to the statistical forecast do not improve forecast accuracy, why make them
MonthActual
DemandStat
Forecast
Stat Forecast
ErrorS&OP
Forecast
S&OP Forecast
Error Impact
Jan06 6,091,955 7,593,962 5,196,370 5,918,751 1,886,262 54.3%Feb06 9,147,987 8,661,173 4,497,213 9,061,139 3,013,993 16.2%Mar06 11,570,962 11,932,441 3,992,114 12,448,817 2,885,691 9.6%Apr06 10,625,650 12,512,901 5,348,524 13,477,086 4,550,418 7.5%May06 10,815,034 9,840,902 3,791,501 11,297,905 3,059,782 6.8%Jun06 8,817,693 9,041,067 3,697,356 9,311,634 2,569,887 12.8%
Last 6 Month Ave. 57,069,281 59,582,446 26,523,078 61,515,332 17,966,033 15.0%
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 43
These items have the biggest overall impact to forecast accuracy results
Determine the source of the variances
CURRENT MONTH VARIANCE ANALYSIS
Product Customer JULY Actual JULY Forecast JULY VarianceJULY Fcst Accuracy
Product 1 Customer A 2,134,314 KG 1,611,990 KG 522,324 KG 67.6%Product 2 Customer B 1,230,867 KG 900,000 KG 330,867 KG 63.2%Product 3 Customer C 433,674 KG 147,542 KG 286,132 KG -93.9%Product 4 Customer D 0 KG 240,000 KG 240,000 KG 0.0%Product 5 Customer E 61,117 KG 300,000 KG 238,883 KG 0.0%Product 6 Customer F 165,799 KG 330,000 KG 164,201 KG 50.2%Product 7 Customer G 431,901 KG 268,665 KG 163,236 KG 39.2%Product 8 Customer H 424,536 KG 262,575 KG 161,961 KG 38.3%Product 9 Customer I 334,660 KG 174,096 KG 160,565 KG 7.8%Product 10 Customer J 20,040 KG 175,167 KG 155,127 KG 11.4%Product 11 Customer K 490,342 KG 355,456 KG 134,886 KG 62.1%Product 12 Customer L 40,642 KG 171,132 KG 130,490 KG 23.7%Product 13 Customer M 264,235 KG 143,679 KG 120,557 KG 16.1%Product 14 Customer N 181,589 KG 77,946 KG 103,644 KG -33.0%
Forecast Variances -- By Ship-to Customer JULY 2006
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 44
Zero out the history for these customers in the forecast model
FORECAST BUT NO SALES
Last 3 Mo. Stat Fcst Final Fcst Final Fcst Final Fcst
Product Customer Ship-to
Average Monthly Demand
Ave Fcst Next 3 Mo.
09/2006 10/2006Ave Fcst Next 3
Mo.
Product 1 Customer A 0 0 125,000 125,000 125,000Product 2 Customer B 0 15,694 80,000 123,000 101,333Product 3 Customer C 0 0 87,000 87,000 87,000Product 4 Customer D 0 0 72,000 72,000 72,000Product 5 Customer E 0 34,619 34,485 34,676 34,619Product 6 Customer F 0 0 1 20,000 20,000Product 7 Customer G 0 18,047 18,047 18,047 18,047Product 8 Customer H 0 17,293 17,282 17,298 17,293Product 9 Customer I 0 10,654 10,624 10,667 10,654Product 10 Customer J 0 9,763 9,744 9,772 9,763Product 11 Customer K 0 8,223 8,223 8,223 8,223
APP N. America -- 18 Month Forecast - All Markets
0
2
1
Date
KG
#REF!
#REF!
#REF!
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 45
Add forecasts for these customers
SALES BUT NO FORECAST
Act Sales Act Sales Act Sales Last 3 Mo. Stat Fcst Stat Fcst Final Fcst Final Fcst
Product Customer Ship-to
05/2006 06/2006 07/2006Average Monthly Demand
09/2006 10/2006 09/2006 10/2006
Product 1 Customer A 0 0 84,550 28,183 0 0 0 0Product 2 Customer B 0 0 61,117 20,372 0 0 0 0Product 3 Customer C 29,402 20,330 0 16,577 0 0 0 0Product 4 Customer D 13,789 12,093 20,339 15,407 0 0 0 0Product 5 Customer E 0 0 40,760 13,587 0 0 0 0Product 6 Customer F 0 0 40,587 13,529 0 0 0 0Product 7 Customer G 5,700 11,400 22,800 13,300 0 0 0 0Product 8 Customer H 0 0 39,635 13,212 0 0 0 0Product 9 Customer I 0 0 39,608 13,203 0 0 0 0Product 10 Customer J 0 0 35,150 11,717 0 0 0 0Product 11 Customer K 654 0 34,382 11,679 0 0 0 0
APP N. America -- 18 Month Forecast - All Markets
0
2
1
Date
KG
#REF!
#REF!
#REF!
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 46
CONTENT
WACKER Company Overview
Why Forecast?
Forecasting Background and Challenges
Six Keys to Improving Forecast Accuracy
– Process, People, & Tools
– Statistical Forecasting
– Forecasting Segmentation
– Data Aggregation
– Sales Adjustments to Statistical Forecast
– Measurement & Exception Reporting
Forecasting Accuracy Results
Conclusions
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 47
FORECAST ACCURACY IMPROVEMENT
40
50
60
70
80
90
1004
Q0
3
1Q
04
2Q
04
3Q
04
4Q
04
1Q
05
2Q
05
3Q
05
4Q
05
1Q
06
2Q
06
3Q
06
% F
ore
cast
Acc
ura
cy
(Product Mix Level)
Process, People,Statistical Forecasting
Exception AnalysisForecast Adjustments
Forecast SegmentationData Aggregation
World Class
+ 6% + 15%+ 7%
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 48
50
60
70
80
90
100
Pro
du
cti
on
Pla
n A
dh
ere
nc
e (
%)
38% Improvement
2004 2007
PRODUCTION PLAN ADHERENCESUPPLY PLANNING ACCURACY
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 49
30
40
50
60
70
80
90
100
Dir
ec
t P
rofi
t A
cc
ura
cy
(%
)
21% Improvement
2004 2007
FINANCIAL FORECAST ACCURACY
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 50
ACTUAL FORECAST DEVIATION TREND
Forecast deviation at product level APO Go-Live
Target
.0
.1
.2
.3
.4
.5
Jul-
07
Au
g-0
7
Sep
-07
Oct
-07
No
v-07
Dec
-07
Jan
-08
Feb
-08
Mar
-08
Ap
r-08
May
-08
Jun
-08
Jul-
08
Au
g-0
8
Sep
-08
Oct
-08
No
v-08
Dec
-08
Jan
-09
Fo
rec
as
t D
ev
iati
on
AP WACKER
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 51
CONTENT
WACKER Company Overview
Why Forecast?
Forecasting Background and Challenges
Six Keys to Improving Forecast Accuracy
– Process, People, & Tools
– Statistical Forecasting
– Forecasting Segmentation
– Data Aggregation
– Sales Adjustments to Statistical Forecast
– Measurement & Exception Reporting
Forecasting Accuracy Results
Conclusions
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 52
If you follow the Roadmap to improve your
companies sales forecasting practices, you will
experience reductions in costs and increases in
customer and employee satisfaction. Costs will
decline in inventory levels, raw materials, production,
and logistics. But the first step a company must take
before realizing these kind of benefits, is to recognize
the importance of sales forecasting as a management
function, and be willing to commit the necessary
resources to becoming world class.
CONCLUSIONS
Roadmap To World Class Forecasting Accuracy, Januray 25, 2009Stephen Crane, Page 53
THANK YOU FOR YOUR ATTENTION
The Wacker Group
Stephen P. Crane
Director Strategic Supply Chain Management
CREATING TOMORROW'S SOLUTIONS