How to Incorporate Market Intelligence into Statistical Forecasting

Air Products and Chemicals, Inc.

Stephen P. Crane, CSCPDirector Supply Chain

How to Incorporate Market Intelligence into Statistical

Forecasting

Orlando, FloridaOctober 25-27

A Key to Improving Forecast Accuracy

“How to Incorporate Market Intelligence into Statistical Forecasting”

APCI 2006, The Value of Business Intelligence to Forecast Accuracy, S. P. Crane 2

Content

Air Products in Brief

Improving Forecast Accuracy

Forecasting Segmentation

Market Intelligence

Measuring Forecast Value Added

Pilot Study

Pilot Results

Conclusions

Who is Air Products?

3




Global supplier of gases, chemicals, equipment, and health care services

FY06 revenue ~$8.6 billion

Serving customers in technology, energy, industrial and healthcare markets

One of the safest large-scale chemical companies

Operations in more than 30 countries



Air Products ERP Platform

SAP R/3 Single Instance

– Initial Go-Live July 2002 (Release 1- Germany)

– Releases 2, 3, 4, 5 (2003 – 2006)

– Global Release strategy developed through 2007

APO (Advanced Planner & Optimizer) v3.0

– Initial scope included deploying Demand Planning (DP) and Supply Network Planning (SNP) modules

– Standardized Demand Planning and Supply Planning processes for all businesses

– First APO forecast generated in October 2003

– APO v4.1 functionality upgrade scheduled for October 2006

Global Work Processes

MetricsMetricsFollow the Money!Follow the Money!



Supply Chain KPIs

Supply chain planning• Forecast Accuracy %

– Forecast Value Added • Production Plan Adherence • Inventory DOS• Inventory Accuracy %• Master Data Accuracy (APO,

production, customer)

Operational Performance• On-stream• Efficiency• Non-prime inventory• Off-grade

Financial• Financial Forecasting

Accuracy• Cash-to-Cash cycle time

Purchasing/Fulfillment• % Orders Received on Time• On-Time delivery• % Perfect Order Fulfillment

Customer• % Complaints closed by

target date

Top 5 Corporate KPIs



Content




Market Intelligence


FVA Pilot Study

Pilot Results

Conclusions



Utilizing Forecasting Segmentation

Gathering Market Intelligence

Reducing Forecast Variance

Resulted in Improved:

– Inventory Accuracy

– Production/Supply Accuracy

– Order Fulfillment Performance

– Financial Accuracy




High Forecasting Accuracy Yields Tangible Benefits in Supply Chain Performance

Companies with better demand forecast accuracy have:

– 15% less inventory

– 17% better perfect order performance

– 35% shorter cash-to-cash cycle times

than their peers

Source: AMR Research Report “The Hierarchy of Supply Chain Metrics: Diagnosing Your Supply Chain Health,” (February 2004)



Content




Market Intelligence


FVA Pilot Study

Pilot Results

Conclusions



Demand planning needs to be based on statistical forecasting and selected market intelligence to increase the accuracy of the forecast. Forecasting segmentation should be the key analysis for prioritizing your forecasting resources.



COV (Coefficient of Variation) = STD Deviation/Average Demand

Notes

Source: Accenture

High

Low

StatisticalForecastability(measured by

1/COV)

High

Sales Volume/Impact

Low

Rationalize/Consolidate Collaboration

Rationalize SKUs, consolidate stocking locations, make to order

Customer CollaborationGather Majority of Market Intelligence

Statistical ForecastingStatistical Forecasting

Use statistical forecasting at an aggregate level, minimize safety stock levels

Selected Account Review

Q1

Q3 Q2

Q4



0 20,000 40,000 60,000 80,000 100,000 120,000 140,000-0.50

0.50

1.50

2.50

3.50

4.50

Air Products PolymersForecasting Segmentation by Product

Sta

tis

tic

al F

ore

ca

sta

bili

ty

Low

High

Average Monthly DemandLow High

Q1

Q2Q3

Q4

1.0

(July 2004)



0 20,000 40,000 60,000 80,000 100,000 120,000 140,000-0.50

0.50

1.50

2.50

3.50

4.50

Air Products PolymersForecasting Segmentation by Product

Sta

tis

tic

al F

ore

ca

sta

bili

ty

Low

High

Average Monthly DemandLow High

Q2Q3

1.0

(March 2005)

> 98% Demand

74% Reduction

Q1Q4



Content




Market Intelligence


FVA Pilot Study

Pilot Results

Conclusions



What is Market Intelligence?

Market Intelligence is a formal process for the collection and integration of information and data about customers, products, and market segments, into an existing demand planning process, which is not typically reflected in sales order history

Market Intelligence helps organizations to achieve better visibility of operational aspects of their business with improved business information resulting in optimized inventory levels and improved financial accuracy



Should you be focusing on Market Intelligence?

Is your forecast accuracy as high as you would like it to be?

Are you able to reliability supply products to your customers at the right place, the right time?

Are you getting feedback from customers on changes in demand that you integrate into your demand planning process?

Are you getting all the feedback you need from customers to properly manage their demand?

Do you know which SKUs are the most important ones to focus on in managing demand?



Types of Market Intelligence

Changes in customer demand which are not taken into account by statistical forecasting

– Future expected events

– Non-repeatable past events

– Product introduction & rationalization

– Sales account plans

– Changes in regulatory and environmental laws

– Changes in customer plant operations

– Promotions, sales, etc.



Specific Examples of Market Intelligence

New or discontinued products

New or lost customers

Container changes

Customer ship-to-location changes

Unplanned customer plant outages

Customer plants which are expected to be down

Pre-buying, dock strikes, hurricanes, etc.

Non-optimal sourcing, e.g., due to supply shortages, currency fluctuations, lack of resources, etc.

Sales upside accounts



Why is Market Intelligence Needed?

Forecasting process has two primary goals:

– Make the forecast more accurate (reduce forecast error)

– Make the forecast less biased

Organizations fail to realize the benefits from their forecasting process for many reasons;

– Existing processes and measures are designed to only measure the final forecasting results

– Most processes fail to capture the effectiveness or “value added” by the overall forecasting effort




Not uncommon for many activities in a business forecasting process to fail to add value

Internal politics, personal agendas, financial performance targets often skew the process, and actually make the forecast worse

These non-value added activities need to be identified, improved, or just eliminated

Businesses need a way to measure the effectiveness of the management judgment or Market Intelligence applied to statistical forecasts



So the fundamental problem is:

– Many businesses don’t know whether the efforts to apply Market Intelligence to statistical forecasts are making the forecast better or worse

– Many Forecast Accuracy KPIs only measure the final results of the forecasting process

– Forecast accuracy or variance does not tell you whether your efforts made the forecast better or worse than just using the statistical forecast alone




Content




Market Intelligence


FVA Pilot Study

Pilot Results

Conclusions



Measuring Value of Market Intelligence

Forecast Value Added (FVA) is designed to measure effectiveness of the Market Intelligence process

Forecast Value Added (FVA) is simply the change in forecast accuracy that occurs after all adjustments have been made to the statistical forecast

FVA is measured by comparing the final forecast accuracy to the statistical forecast accuracy

If forecast accuracy is no better after applying Market Intelligence than without it, the Market Intelligence is not adding value and can be eliminated




By applying the measure to each material/ship-to combination:

– You can identify which Market Intelligence is adding value and which isn’t and from whom

– You can identify non-value added adjustments that can be eliminated to improve the accuracy of forecasting process

FVA is designed to compare the actual demand to both the statistically generated forecast and to management’s overrides of the statistical forecast



Market Intelligence Process

Gather and Submit Market Intelligence Information DAY –30 to 0

Information from Customer Contact personnel (Sales,

CSO, etc)

Information Received Directly From Customers

Information Regarding New

Products

Gather Customer Data

Create Demand Change

Notification(s)

Gather Marketing Data

Information Regarding

Existing Products, Markets,

Segments

Create Demand Change Summary

Create Demand Change

Notification(s)

Review Demand Change

Notification(s)

Assess Impact of Demand Change

Notifications

Summarize Agreed Demand

Change Notifications in

Demand Change Summary



Content




Market Intelligence


FVA Pilot

Pilot Results

Conclusions

World-Wide Leader in Vinyl Acetate Ethylene Co-Polymer Dispersion Technology, Serving Adhesives, Nonwovens, Coatings, and PSA markets

28

Air Products Polymers, LP



Air Products Polymers Fast Facts

$600 million global business

Diversified geographies

– 65% of sales in N. America

– 30% Europe, 5% Asia

6 Plants (4 NA, 1 Europe, 1 Asia)

230 Products

1,800 Ship-to Customer Locations

3,500 Planning Combinations (Material-Ship-to-Primary Source Plant)



FVA Pilot Dimensions

NA region selected for pilot

Pilot conducted by NA Demand Manager

Market Intelligence measured at the level where forecast adjustments were made

FVA measured at material/ship-to level manually in APO

Pilot duration 6 months, Jan-June 2006



Content




Market Intelligence


Pilot Study

Pilot Results

Conclusions



Pilot - FVA Results

(Units in KGS)

Ship-to Customer Jun06 ActJun06 Stat

FcstFcst Abs

ErrorJun06 S&OP

FinalS&OP Abs

Error FVA ImpactCustomer 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

Statistical Forecast

Final S&OP Forecast (Includes Market Intelligence)

FVA Impact



Pilot - FVA Results

Forecast Value Added Metric

– Measures the effectiveness of Market Intelligence

The market intelligence provided by sales & marketing is having a significant impact on forecasting accuracy

Month

Actual Demand

Stat Forecast

Stat Forecast

ErrorS&OP

Forecast

S&OP Forecast

Error FVA

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%

(Units in KGS)



Forecast Accuracy 3 Stages of Improvement

40

50

60

70

80

90

1004

Q0

3

1Q

04

2Q

04

3Q

04

4Q

04

1Q

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2Q

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3Q

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4Q

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1Q

06

2Q

06

3Q

06

% F

ore

cast

Acc

ura

cy

Air Products Polymers, LP

DataClean-up

Market IntelligenceData

Aggregation

WorldClass

+ 6% + 15%+ 7%



Financial Forecast Accuracy

30

40

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60

70

80

90

100D

ec-0

4

Jan

-05

Feb

-05

Mar-

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-05

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Dec-0

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-06

Feb

-06

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Dir

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(%

)

21% Improvement



Next Steps

Deploy FVA KPI to Europe

APO statistical forecast to be passed to BW

FVA KPI to be automated using BW in Demand Planning cube

– FVA by Business

– FVA by Primary Source Plant

– FVA by Material/Customer Ship-to Location

FVA will then be available to all business units



Conclusions

Market Intelligence has a significant impact on improving forecast accuracy and effectiveness of overall forecasting process

Measuring FVA from Market Intelligence provides feedback mechanism to Sales, Marketing, and customers, which improves the quality and accuracy of the Market Intelligence being collected

Measuring FVA causes an organization to ask more questions about what is really going on with customers

Improvement in forecast accuracy from Market Intelligence provides additional downstream supply chain benefits



Through the effective collection of Market Intelligence, a clear picture of “what will be

needed, where it will be needed, and how much will be needed” for customers, can be provided

by marketing/sales organizations along with collaboration from customers and other

business intelligence sources.

Conclusions

Thank you!

Stephen P. Crane, CSCP

Director Supply Chain

Air Products and Chemicals

[email protected]

How to Incorporate Market Intelligence into Statistical Forecasting

Business

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