Power of metrics in achieving supply chain excellence ibf

The Power of Metrics in Achieving
Supply Chain Excellence
(Firmenich Case Study)

IBF Supply Chain Planning & Forecasting
Scottsdale, AZ
February 24-26, 2013
Stephen Crane, CSCP
Director S&OP Flavors NA
Firmenich

1
Fostering Demand Planning, Forecasting, S&OP for 30+ Years!

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

2

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%

3

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
4

Firmenich Roadmap To S&OP Excellence
S&OP Excellence

• Competitive Advantage
Profitable Growth • Scenario Planning
• Network Optimization
• Supply Chain Automation
• Focus on Costs &
• Benchmark Performance
Execute the Plan Productivity
• Cost-To-Serve
• Customer Segmentation
• Executive S&OP • Product Portfolio
The Fundamentals • Align all Functions to One Management
Plan • Complexity Reduction
• Integrate NPI into S&OP • Continuous Improvement
• Demand • Regional Independence
• Supply • Align Demand w/Finance
• Inventory • Identify Plan Gaps & Actions
• Service • Link Planning w/Execution
• Improve Planning
• KPI Scorecards

Creating a Competitive Advantage
for Firmenich and our Customers

5

Enterprise Process Blueprint
Level 1 Process

Business
Strategy & Supply Chain
Portfolios Competency Center

S&OP (Planning)
Sales
KPIs
To MeasureS

Customers
Customer
Innovation PurchasingImprovement
Operations
Care

Quality

Master
HR Finance HS&E Technology
Data
6

S&OP Monthly Process
Level 2 Process

Production
Planning

Sales & Operations Planning
Procurement
Planning
Demand Planning
(How much could we
sell?)

Balance Demand
Issue Supply
Chain Plans Inventory
& Supply
(Do they match?) (Can we execute Planning
plans?)

Supply Planning
(How much can we
supply? Logistics
Planning

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

7

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

8

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

9

Performance Measurement & Strategic
Alignment

10

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”

11

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”

12

12 Key Metrics To Maximize
Supply Chain Performance
 Forecast Accuracy (Demand Planning)
Largest Profit Drivers
 Supply Plan Accuracy (Supply Planning)
 On Time Shipping Rate
 Fill Rate
 Perfect Order Fulfillment
 Capacity Utilization
 Inventory Days of Supply “Without a yardstick, there is no
 Freight Bill Accuracy measurement. And without
measurement, there is no control.”
 Freight Cost per Unit
 Cash to Cash Cycle Time
 COGS % of Revenue
 Revenue Forecast Accuracy

13

Metric Profile
Develop a Detailed KPI Profile For Each Metric

Balanced Scorecard Perspective: Select the Frequency: How often is this metric reviewed?
perspective – Financial, Customer, Growth, or Internal Hourly, Daily, Monthly, Quarterly, etc.

Business Process Category: What business process Level of Detail: What level of information is available?
should be measured ? Customer Service, Inventory, By product, business, country, customer, customer
Forecasting, Production, or Procurement segment, etc.
Owner: Who is accountable for the result of this KPI? Calculation: Document the formula and any special
Document name and function components – average count, mean absolute deviation,
percentages, differences, etc.
Data Coordinator: Who is responsible for gathering Baseline Data: What year, month, or other time period
data for KPI? Document name and function was used as the baseline?

Data Source: Where does the information come from? Tolerance: What is the allowable error to target?
BW, SAP, Financial systems. What is measured? Data Availability: When is the data available for
What are primary components? Fit into one sentence updating?
Purpose: Why do we measure this KPI? What is the Comments: Include possibility of automating data
desired outcome? collection, issues around gathering the data, potential
resources for the information. Include any other
information that tells the user more about the KPI

14

S&OP Scorecard Example
Increases Visibility of Performance to Organization

Flavors North America Operations Scorecard

FY12 Average FY-
12 vs. FY-12
Tolerance Status
Average FY- Target
11
Flavors NA Operations KPIs 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

15

KPI Benefits Over Last 2 Years
Communicate 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%

16

Firmenich NA Business Dimensions

 6 Plants

 3250 Products

 916 Customers

 3,715 Forecasting Records (APO DP)

 98% of customer/product combinations not statistically
forecastable

17

Forecast Accuracy
44% Annual Rate of Improvement

60% Target
Forecast Accuracy %

50%

40%

30%

20%

10%

0%

NA - 1 Month Linear (NA - 1 Month)
18

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

40%

30% 30%

24% 25%
Forecast Bias %

20% 19% 19%
16% 16%
12% 11%
10% 9% 10%
7%
4% 4%
1% 2% 1%
0%
-2% -3%
-5%

-10% -10%

-20%

19

Forecast Bias (Aggregate)
Last 12 Months

40%

30%
19%
20%
10%
10% 7%
B 2%
4% 4%
1%
i -3%
0% -5%
a
-10%
s -10%

-20%

-30%

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

NA - Bias

20

Forecast Bias Excess Inventory

Supply Risk
Over/Under Bias at Product Level

27% Reduction

40% 700
592 602 577 559 600
509 487 485
30% 482
439 500
19% 350 400

Over/Under in Thousands
20%
300
10%
10% 7% 200
B 4% 4%
2% 1% 100
i
0% -3% 0
a -5%
s -10% -100
-10% -200
-300
-20%
-363 -400
-459 -435 -500
-30% -467 -488 -495 -474 -474
-521
-570 -600
-40% -700
Jan-12 Feb-12 Mar-12 Apr-12 May-12 Jun-12 Jul-12 Aug-12 Sep-12 Oct-12

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

21

Forecast Bias by Plant
Helps to See Different Trends and Issues

Princeton Newark
20% 40%
30%
15%
20%
10% 10%
5% 0%
-10%
0%
-20%
-5% -30%

22

Forecast Accuracy Results
Provides 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%
23

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

Don’t focus on product
Calendar Mth/Yr 10/2012
level accuracy
ACM (All)
Class (All)

Values
Material Cust Sum of ACM Fcst Sum of Sales ACM Dev Final Dev Over/Under ACM Accur
869240 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%

24

Forecast Deviation Comparison
Focus on Deviations Not Accuracy

Don’t focus on product
level accuracy

Sales Forecast Accuracy Deviation

1000 kg 500 kg 50% 500 kg

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

25

Forward Looking Forecasting Analysis
Focus on Forecast Not Sold & Unforecasted Demand

1,400,000 120.0%
1,200,000 100.0%
76.7%
1,000,000 38.1% 28.7% 35.9% 45.6% 95.4% 98.8% 98.5%
42.5% 40.7% 80.0%
800,000
60.0%
600,000
40.0%
400,000
200,000 20.0%

- 0.0%
Jul-12 Aug-12 Sep-12 Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 Mar-13 Apr-13
Actual Consumed Forecast Forecast Not Sold (Volume) % of Forecast not Sold

1,400,000 40.0%
1,200,000 35.0%

1,000,000 30.0%
36.3% 37.0% 34.8% 37.4% 25.0%
800,000 28.8%

21.9%
20.0%
600,000
15.0%
400,000 10.0%
25.3%
200,000 0.0% 5.0%
18.4% 0.0%
- 0.0%
Jul-12 Aug-12 Sep-12 Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 Mar-13 Apr-13
Actual Forecasted Demand (Vol) Unforecasted Demand (Volume

26

Top Over & Under Forecasted Items
January 2013 Analysis Updated Weekly

Top Under Top Over Forecasted
Forecasted Products Products

North America
Potential Under Forecasting Demand Forecast Potential Over Forecasting Demand Forecast
ACM Customer # Item Description Jan-13 Jan-13 ACM Customer # Item Description Jan-13 Jan-13
KHAR 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

Customer Care to talk to customers about the gaps

27

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

28

Benefits of Accurate Volume Forecasts
Increasing Forecast Accuracy Decreases Inventory

Increasing Forecast
120 Accuracy from 40% to
60% would reduce NA
inventory by ~$30 million
Inventory Days of Sale

100

80

60

40

20

0
20% 30% 40% 50% 60% 70% 80% 90% 100%

Forecast Accuracy at SKU Location
Source: Aberdeen Group 2008

29

Questions?
Thank you for participating!

Stephen P. Crane, CSCP
Director S&OP Flavors NA
Firmenich

Stephen.crane@firmenich.com

30

Power of metrics in achieving supply chain excellence ibf

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