power of metrics in achieving supply chain excellence ibf

Fostering Demand Planning, Forecasting, S&OP for 30+ Years!

1

The Power of Metrics in Achieving Supply Chain Excellence

(Firmenich Case Study)

IBF Supply Chain Planning & Forecasting Scottsdale, AZFebruary 24-26, 2013

Stephen Crane, CSCPDirector S&OP Flavors NAFirmenich


2

Agenda

Who is Firmenich

Evolution of S&OP

S&OP Process Hierarchy

Role of Performance Metrics

Eight Steps to Success

KPI Scorecard

Forecast Accuracy & Bias

Benefit Examples


3

Over 117 Years of Success

Firmenich• Founded by Charles Firmenich in 1895 • Largest Privately Owned Company in Fragrance &

Flavor Industry• Headquartered in Geneva Switzerland• Divisions: Perfumery, Flavors, Ingredients

Company Foundation 1895 in Geneva, Switzerland

Global Sales 2.78 Billion Swiss Francs

Average Growth per year: 7%

Number of Employees: 6,000

Global presence: Over 64 Countries

Manufacturing Worldwide: 22 Plant Sites

R&D Centers: Geneva, Princeton, Shanghai

R&D Awards: 35 awards including a Nobel Prize

Patents: 1,400

Annual Investment in R&D Around 10%


4

Firmenich ERP Platform

SAP R/3 Single Instance

– Initial NA Go-Live December 2009 (Princeton)

– Global Implementation Completed in December 2011

APO (Advanced Planner & Optimizer) v4.1

– Deployed Demand Planning (DP) and Supply Network Planning (SNP) modules

– Implemented standardized processes for S&OP, Demand Planning, Supply Planning

Performance Management (KPI Scorecard)

– S&OP implementation started in 2008

– Standardized KPI Metrics for plant sites, regions, and global business


5

Firmenich Roadmap To S&OP Excellence

The Fundamentals

• Demand• Supply• Inventory• Service• Improve Planning• KPI Scorecards

Execute the Plan

• Executive S&OP• Align all Functions to One

Plan • Integrate NPI into S&OP• Regional Independence• Align Demand w/Finance• Identify Plan Gaps & Actions• Link Planning w/Execution

Profitable Growth

• Focus on Costs & Productivity

• Cost-To-Serve• Customer Segmentation• Product Portfolio

Management• Complexity Reduction• Continuous Improvement

S&OP Excellence

• Competitive Advantage• Scenario Planning• Network Optimization• Supply Chain Automation• Benchmark Performance

Creating a Competitive Advantage for Firmenich and our Customers


6

Enterprise Process BlueprintLevel 1 Process

S

S&OP (Planning)

Purchasing OperationsCustomer

Care

Quality

Innovation

Sales

Competency Center

Business Strategy & Portfolios

Supply Chain

Cu

sto

me

rs

KPIsTo Measure

Improvement

HR Finance HS&E Technology MasterData

http://www.canstockphoto.com/file_view.php?id=4659034


7

S&OP Monthly ProcessLevel 2 Process

Demand Planning (How much could we sell?)

Supply Planning(How much can we supply?

Balance Demand & Supply(Do they match?)

Issue Supply Chain Plans(Can we execute plans?)

Sales & Operations Planning

FinancialProjections

Procurement Planning

Production Planning

S&OP provides outputs to, and needs Input from many other processes

Logistics Planning

Inventory Planning


8

Definitions: Metrics, KPIs & Measurement

Metric – “a standard of measurement”– Inches, feet, meters

– Value of inventory

• Key Performance Indicator – a metric that is important to understanding past, present, or future business performance

– Braking Distance

– Inventory Turns

• Measurement – the value derived from applying the metric– Braking Distance of a car traveling 50 MPH is 82 feet

– Inventory turns 6 times per year



9

What Drives Functional Metrics?

If it moves, measure it

• If it is easy to measure, report it

• My boss decided it was a good thing to measure

• If it went wrong once years ago, we still measure it

• Just in case we ever get asked

• This way we know everything


10

Performance Measurement & Strategic Alignment


11

Role of Performance Metrics

To provide a quantified definition of what is important and how the organization is performing.

To ensure alignment between strategic, tactical, and operational goals & objectives.

To facilitate a cross-functional view of relative importance among individual, team, and functional goals & objectives.

To motivate the organization towards continuous improvement.

To provide a means to link individual and organization performance to reward systems.

“You cannot improve what you do not measure”


12

Eight Steps to Measurement Success

Plan: Create a development plan for metrics with timeframes and milestones

Scope: Focus on a few metrics that really matter providing the most balanced view of end-to-end supply chain performance

Process: Define clear ownership, roles, responsibilities, and structure

Culture: Proactively address organizational resistance

Communication: Create a balanced scorecard to track historical data and for easy organizational communication

Tools: Invest in the tools that will make it reliable and repeatable

Turn Data into Action: Hold weekly/monthly scorecard reviews to monitor progress, prioritize corrective actions, and engage the organization

Power Through Process: Institutionalize your measurement program through a business process to “make it sticky”



13

12 Key Metrics To Maximize Supply Chain Performance

Forecast Accuracy (Demand Planning)

Supply Plan Accuracy (Supply Planning)

On Time Shipping Rate

Fill Rate

Perfect Order Fulfillment

Capacity Utilization

Inventory Days of Supply

Freight Bill Accuracy

Freight Cost per Unit

Cash to Cash Cycle Time

COGS % of Revenue

Revenue Forecast Accuracy

Largest Profit Drivers

“Without a yardstick, there is no measurement. And without

measurement, there is no control.”


14

Metric ProfileDevelop a Detailed KPI Profile For Each Metric

Balanced Scorecard Perspective: Select the perspective – Financial, Customer, Growth, or Internal

Frequency: How often is this metric reviewed? Hourly, Daily, Monthly, Quarterly, etc.

Business Process Category: What business process should be measured ? Customer Service, Inventory, Forecasting, Production, or Procurement

Level of Detail: What level of information is available? By product, business, country, customer, customer segment, etc.

Owner: Who is accountable for the result of this KPI? Document name and function

Calculation: Document the formula and any special components – average count, mean absolute deviation, percentages, differences, etc.

Data Coordinator: Who is responsible for gathering data for KPI? Document name and function

Baseline Data: What year, month, or other time period was used as the baseline?

Data Source: Where does the information come from? BW, SAP, Financial systems. What is measured? What are primary components? Fit into one sentence

Tolerance: What is the allowable error to target?

Data Availability: When is the data available for updating?

Purpose: Why do we measure this KPI? What is the desired outcome?

Comments: Include possibility of automating data collection, issues around gathering the data, potential resources for the information. Include any other information that tells the user more about the KPI


15

S&OP Scorecard ExampleIncreases Visibility of Performance to Organization

Owner Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun

Planning Forecast Accuracy (%) S&OP

Supply Plan Adherence (%) S&OP

Sales Total Sales ($ K) Finance

Volume Sold (MT) Finance

Revenue Forecast Accuracy (%) Finance

Manufacturing Total Expenses ($K) Man.

Capacity Utilization (%) Man.

Production Volume (MT) Man.

Service On Time Shipping Logistics

On Time Delivery Logistics

Inventory Inventory ($ USD, in Millions) IM

MOH (Inventory Months on Hand) IM

Inventory Record Accuracy (%) IM

Quality Service Index QC

Product Index QC

Safety TRC HS&E

LTC HS&E

Customer Care Billing Accuracy CC

Lead Time Compliance CC

MOQ Compliance CC

Logistics % Carrier Compliance Logistics

Expedited Freight Costs Logistics

Flavors North America Operations Scorecard

Status

Flavors NA Operations KPIs

Average FY-12 vs.

Average FY-11

FY-12 Target

Tolerance

FY12

Separate Tabs For Each Plant



16

KPI Benefits Over Last 2 YearsCommunicate Successes Along the Way

Forecast Accuracy increased by 250%

Forecast Bias reduced by 80%

Supply Planning Accuracy increased by 30%

Customer On Time Delivery increased by 26%

Inventory MOH decreased by 53%

Supply Chain Costs decreased by 15%

Order MOQ Compliance increased by 8%

Supplier On Time Delivery increased by 20%

Total Recordable Cases (TRC) decreased by 46%

Lost Time Cases decreased by 87%

http://www.forbes.com/sites/tanyaprive/2012/11/13/navigating-the-sea-of-crowdfunding-how-to-pick-the-right-platform-for-your-brand/


17

Firmenich NA Business Dimensions

6 Plants

3250 Products

916 Customers

3,715 Forecasting Records (APO DP)

98% of customer/product combinations not statistically forecastable


18

Forecast Accuracy44% Annual Rate of Improvement

Nov-09

Jan-1

0

Mar-10

May-10

Jul-1

0

Sep-10

Nov-10

Jan-1

1

Mar-11

May-11

Jul-1

1

Sep-11

Nov-11

Jan-1

2

Mar-12

May-12

Jul-1

2

Sep-12

0%

10%

20%

30%

40%

50%

60%

NA - 1 Month Linear (NA - 1 Month)

Fo

rec

as

t A

cc

ura

cy

%

Target

http://driveyoursuccess.typepad.com/.a/6a0120a80567b0970b01539301d8d2970b-pi


19

Forecast Bias (Aggregate)80% Reduction Over Last 2 Years

-20%

-10%

0%

10%

20%

30%

40%

24%

19%

25%

30%

9%

12% 11%

1%

16%

-2%

16%

-5%

2%

10%

4%

-3%

7%

19%

1%

4%

-10%

Fo

reca

st B

ias

%


20

Forecast Bias (Aggregate)Last 12 Months

Jan-12 Feb-12 Mar-12 Apr-12 May-12 Jun-12 Jul-12 Aug-12 Sep-12 Oct-12-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

-5%

2%

10%

4%

-3%

7%

19%

1%4%

-10%

NA - Bias

Bias


21

Forecast Bias Over/Under Bias at Product Level

Jan-12 Feb-12 Mar-12 Apr-12 May-12 Jun-12 Jul-12 Aug-12 Sep-12 Oct-12-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

-700,000

-600,000

-500,000

-400,000

-300,000

-200,000

-100,000

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

438,746

592,292 601,562

509,082 487,472

576,909 559,478

485,457 481,769

350,101

-467,497

-569,743

-487,658-459,021

-520,568 -494,550

-362,705

-474,414-435,461

-474,499

-5%

2%

10%

4%

-3%

7%

19%

1%4%

-10%

NA - Total Over Forecasted NA - Total Under Forecasted NA - Bias

Bias

Ov

er/

Un

de

r in

Th

ou

sa

nd

s

Excess Inventory

Supply Risk

27% Reduction


22

Forecast Bias by PlantHelps to See Different Trends and Issues

Nov-1

1

Dec-1

1

Jan-

12

Feb-1

2

Mar

-12

Apr-1

2

May

-12

Jun-

12

Jul-1

2

Aug-1

2

Sep-1

2

Oct

-12

-5%

0%

5%

10%

15%

20%

Princeton

Nov-1

1

Dec-1

1

Jan-

12

Feb-1

2

Mar

-12

Apr-1

2

May

-12

Jun-

12

Jul-1

2

Aug-1

2

Sep-1

2

Oct

-12

-30%

-20%

-10%

0%

10%

20%

30%

40%

Newark


23

Forecast Accuracy ResultsProvides Feedback to Account Managers (ACM)

RANK ACCOUNTMANAGER May-12 Jun-12 Jul-12 Aug-12 Sep-12 Oct-12 3 Mth Avg.1 JENS 28% 36% 32% 49% 37% 52% 46%2 KEW 46% 32% 31% 55% 52% 25% 44%3 CEC 50% 26% 43% 41% 39% 38% 39%4 LBC 58% 21% 34% 59% 53% 0% 37%5 AGAR 56% 47% 0% 57% 49% 3% 36%6 AGIA 31% 44% 28% 28% 31% 47% 35%7 PKR 18% 20% 31% 28% 32% 45% 35%8 YVD 4% 0% 8% 30% 34% 28% 31%9 THM 42% 7% 0% 33% 17% 39% 30%10 SAST 35% 15% 38% 29%11 BCAR 0% 0% 0% 27% 39% 19% 28%12 JBD 28% 10% 0% 5% 14% 65% 28%13 JACK 25% 5% 24% 38% 34% 12% 28%14 CAC 28% 43% 44% 35% 6% 30% 24%15 MRY 25% 0% 12% 13% 36% 14% 21%16 JRBD 51% 39% 17% 3% 30% 24% 19%17 BMG 0% 0% 36% 26% 26% 0% 17%18 WSIM 23% 5% 1% 33% 0% 19% 17%19 JOHG 0% 0% 0% 28% 0% 20% 16%20 RBE 20% 13% 43% 45% 0% 0% 15%


24

Calendar Mth/Yr 10/2012ACM (All)Class (All)

ValuesMaterial Cust Sum of ACM Fcst Sum of Sales ACM Dev Final Dev Over/Under ACM Accur869240 2CBUM 276814 2,187 43,364 41,177 7,711 UNDER 5%050001 AP50102 112557 9,434 29,921 20,486 14,961 UNDER 32%

125340 956 0 956 0 OVER 0%241108 0 8,727 8,727 8,727 UNDER 0%290272 3,378 4,156 777 4,156 UNDER 81%

868928 CB 736440 12,383 39,917 27,534 19,958 UNDER 31%868560 CB 109207 0 31,752 31,752 0 UNDER 0%868894 CB 109207 14,869 26,127 11,259 9,072 UNDER 57%059432 TBHAP0551 707592 9,798 19,596 9,798 0 UNDER 50%050999 560091T 366341 7,986 19,160 11,174 0 UNDER 42%534104 TP0186 106614 10,835 18,625 7,790 4,673 UNDER 58%868664 CB 102553 27,500 18,500 9,000 9,000 OVER 51%571101 119002 13,553 17,481 3,928 1,605 UNDER 78%

Don’t focus on product level accuracy

Forecast Accuracy Pivot TableView by ACM, Month, Product Class, Deviation, Over/Under Bias


25

Forecast Deviation ComparisonFocus on Deviations Not Accuracy

Sales Forecast Accuracy Deviation

1000 kg 500 kg 50% 500 kg

10,000 kg 5,000 kg 50% 5,000 kg

Don’t focus on product level accuracy


26

Forward Looking Forecasting AnalysisFocus on Forecast Not Sold & Unforecasted Demand

Jul-12 Aug-12 Sep-12 Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 Mar-13 Apr-13 -

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

120.0%

42.5% 38.1% 40.7% 28.7% 35.9% 45.6%76.7%

95.4% 98.8% 98.5%

Actual Consumed Forecast Forecast Not Sold (Volume) % of Forecast not Sold

Jul-12 Aug-12 Sep-12 Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 Mar-13 Apr-13 -

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

28.8% 36.3% 37.0% 34.8% 37.4%

21.9%

25.3%

18.4%0.0% 0.0%

Actual Forecasted Demand (Vol) Unforecasted Demand (Volume


27

Top Over & Under Forecasted ItemsJanuary 2013 Analysis Updated Weekly

Demand Forecast Demand ForecastACM Customer # Item Description Jan-13 Jan-13 ACM Customer # Item Description Jan-13 Jan-13KHAR 114521 APPLE FLAVOR 4,805 - JENS 115048 APPLE FLAVOR - 45,000 MRY 104732 WHITE CAKE FLAVOR 4,536 1,275 YVD 102553 CHEESE SAUCE FLAVOR - 19,792 MRY 104732 SWEET MIX FLAVOR 2,268 - CEC 109207 CHICKEN AND SALT ENH FLAVOR 20,866 38,556 JOHG 961345 BUTTER DURAROME FLAVOR 6,000 4,000 KMCC 287580 ORANGE AROMA OR50100 FLAVOR - 15,967 CRY 112211 COCOA FLAVOR 2,265 593 BCAR 112557 APPLE FLAVOR - 14,960 KMCC 241108 APPLE FLAVOR 1,662 - CEC 119002 SAUTEED ONION FLAVOR 1,700 14,824 TOND 120877 APPLE FLAVOR 1,452 - SRY 276814 CHICKEN GARLIC HERB FLAVOR - 12,025 CRY 266063 ORANGE CREAMSICLE FLAVOR 1,588 202 AGAR 352385 SWEETNESS MOUTHFEEL 2X FLAVOR - 8,165 SRY 123100 HONEY VANILLA DURAROME FLAVOR 1,200 - CEC 119002 VANILLA FLAVOR 2,177 9,299 SRY 123100 CHERRY FLAVOR 1,200 - PKR 120877 CHICKEN FLAVOR - 6,769 PKR 502418 RASPBERRY FLAVOR 1,815 907 KMCC 112486 ORANGE FLAVOR - 6,360 JOHG 244118 FLAVOR ENHANCER 907 - CEC 614433 MOLASSES FLAVOR - 6,124 JRBD 564642 VANILLA CREAM FLAVOR 2,722 1,814 AGAR 352385 LEMON FLAVOR - 6,033

North AmericaPotential Under Forecasting Potential Over Forecasting

Top Over Forecasted Products

Top Under Forecasted Products

Customer Care to talk to customers about the gaps


28

Forecast Bias Action Summary

Analysis is the key. Focus should always be on forecast deviation not forecast accuracy

Hold structured Demand Reviews with Sales and Customer Care focused on Top deviations

Investigate Top Over Forecast and Under Forecast Deviations

Track Forecast Bias by plant, investigate Top deviations

Measure and report forecast accuracy by ACM

Measure and act on forecast not sold & unforecasted demand, increasing collaboration with customers


29

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

Increasing Forecast Accuracy from 40% to 60% would reduce NA inventory by ~$30 million

Benefits of Accurate Volume ForecastsIncreasing Forecast Accuracy Decreases Inventory


30

Questions?

Thank you for participating!

Stephen P. Crane, CSCP

Director S&OP Flavors NA

Firmenich

[email protected]