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Planning for Complexity &
the Predictability of Demand
Sridhar Bashyam
Director, Supply Chain
Frito-Lay North America

Ben YoKell
Principal, Demand Planning Intelligence Consortium
Chainalytics

Supply Chain Planning & Forecasting Conference
February 23-25, 2014 | Scottsdale, AZ
1
Agenda

Company Overview

Demand Planning at Frito-Lay North America

Demand Planning Benchmarking

2
Agenda

Company Overview

Demand Planning at Frito-Lay North America

Demand Planning Benchmarking

3
PepsiCo Overview

4
Who We Are
PepsiCo is a global food and beverage powerhouse. Our broad range of
more than 3,000 delicious products offers consumers convenient, nutritious
and affordable options in nearly every country around the world.

Global Beverages

Global Snacks

Global Nutrition

Performance

Brands

Scale

People

more than

22

>200

~300,000

billion-dollar
brands

countries
& territories

employees

$65 billion
revenue
5
Our History

6
From Seed to Shelf
Frito-Lay owns the entire supply chain, so there are
significant optimization opportunities.

Move Information Before Inventory
Raw Materials
& Purchased
Finished Goods

Plant

Warehouse

Distribution

Sales OPS

Route Sales

Retail Store

R

Product Flow
Visibility to company wide information, enabling optimization and increasing the
velocity through each function, while reducing the effort and improving productivity.
7
Our Integrated Supply Chain

Statistically Calculated Safety Stock

Multi-Echelon Inventory
Optimization
by JDA Software

Production Planning & Scheduling

Daily Production Planner
by Frito-Lay
North America
Statistical Forecasting Models

Execute
& Deliver

Base Forecast
New Products

+
Promo Lift

+
Event Lift

Cannibalized Products
Discontinued Products

Replenishment Planning

Transaction Data

SAP

8

Supply Chain Planner
by JDA Software
Agenda

Company Overview

Demand Planning at Frito-Lay North America

Demand Planning Benchmarking

9
The Whole Truth

Every week we send
a 100% accurate forecast
through the system…

…Sales just sells
the wrong amount.
10
Demand Signal Threading in a DSD
System

3+ Weeks Out

11

Week Of
FLNA Forecasting Facts

Number of Regions

13

Avg. Number of SKUs

~800

Lowest Level Intersections

~8.5 Million

Historical Sales & Promotion Data

3 Years

Forecast Bucket

Weekly

Forecast Horizon

1 Year

Forecast Frequency

Once a Week

Duration of Forecasting Process

~60 Hours
Sales forecasted into the store

R

Plant
12

Distribution

DC

Route Sales

Retail Store
Frito Lay DP SAS Solution

Historical Sales & Causal Data

Causal Data


Current Functionality

Promotion enrollments by account
–
–
–

SAS Promo Lift


SAS Holiday Lift
SAS Base Forecast

Statistical Forecast

Planner Adjust using DP UI
Final Forecast
Supply Chain Planning
13

Price point
Duration
Days in week

Events: Holidays, quarter close, seasonal
activities
Delivering the Final Forecast
is a Collaborative Effort
Product Supply

Sales
 Input of accurate and timely
promo enrollments

 Owns the signal

 Notification of non-price
point driven activity

 Capacity and volume push

 Execution

Region Finance

Sales Ops
 Forecast feedback via
weekly calls with
replenishment planners

 Aligning DP forecast to the
organization’s financial
forecast

Demand Planning

Plants
 Forecast feedback from
plant-based replenishment
planners
14

 Hold Key Account Managers
accountable for accurate
and timely promo
enrollments
Demand Planning Benchmarking
Benefits of Benchmarking
 Accuracy and bias benchmarking
vs. relevant demand
 Use of demand segmentation and
forecastability concepts
– Region accuracy target setting
– Assessing opportunities for
accuracy improvements

“How am I doing overall?”
“How difficult is my portfolio
to plan to for?”
“How well should I be doing for
my level of portfolio difficulty?”
“How well could I be doing for
my level of portfolio difficulty?”

“Where should I focus
my energy and attention?”

15
Agenda

Company Overview

Demand Planning at Frito-Lay North America

Demand Planning Benchmarking

16
Demand Planning Intelligence
Consortium
Conventional
Benchmarks

Demand Planning
Intelligence Consortium

Questionnaire-based

Data and analytics-based

 Participants self-report forecast accuracy
however they measure it, which varies
 Reports are typically annual
 Level of aggregation is very high
 Limited comparative analysis
 Best practices ID’d without accounting
for differences in portfolio forecastability

49%
33%

PERSONAL CARE

13%

HOME CARE

5%

17

FOOD & BEVERAGE

PET CARE

VS.

 Standardized transactional data collection and
computational approach by unbiased 3rd party
 Reports are periodic and repeating
 Level of benchmarking is highly granular
 Provides understanding of drivers and causes
 Best practices ID’d accounting for differences
in portfolio forecastability

DPIC Statistics





Industry: Consumer Packaged Goods
Geography: North America and Europe
Members: ~ 40 Businesses
Item-Locations: ~ 500,000
Segmented Benchmarking &
Analytics

Nearly 1,250
Forecast Accuracy
and Bias
Benchmarks
18
How difficult is my environment to
forecast?
Segmentation provides an understanding of each portfolio, which drives
demand planning performance potential as much (or more) as complexity.

19
How am I doing?
Forecastability-Based Intelligence

Forecast Accuracy

Forecast Accuracy

Conventional

DPIC Forecastability Index™

Forecastability yields a very different conclusion!
20
How well could I be doing?
Targets can be set with more realism by customizing for the specific
demand portfolio, and should be differentiated: “One Size Does Not Fit All.”

21
Some Frito-Lay DPIC Findings
 Frito-Lay is a top performer, both overall and in segmented
analytics results
 This is in contrast to assumptions around high complexity
 Top performance results stems from good process, great
execution, and relatively high demand forecastability
Forecastability + Process Design & Execution > Complexity

 Room still exists for improvement; even a top overall
performer has segments of the business with opportunity

22
What’s Next for Frito-Lay Demand
Planning
 Continue our membership in the consortium
– Work on opportunity areas presented by survey results

 Add new causals to our SAS models
 Leverage point of sale data
 Continue to explore new technologies such as demand
sensing
 Grow our modeling team into a Forecasting Center of
Excellence (COE)

23
Thank you!

Additional Information?
Email
DPIC@chainalytics.com

Visit
http://bit.ly/DPIC-info
Connect
http://bit.ly/ChainLI

24

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Planning for Complexity and the Predictability of Demand | 2014 IBF Supply Chain Planning Conference

  • 1. Planning for Complexity & the Predictability of Demand Sridhar Bashyam Director, Supply Chain Frito-Lay North America Ben YoKell Principal, Demand Planning Intelligence Consortium Chainalytics Supply Chain Planning & Forecasting Conference February 23-25, 2014 | Scottsdale, AZ 1
  • 2. Agenda Company Overview Demand Planning at Frito-Lay North America Demand Planning Benchmarking 2
  • 3. Agenda Company Overview Demand Planning at Frito-Lay North America Demand Planning Benchmarking 3
  • 5. Who We Are PepsiCo is a global food and beverage powerhouse. Our broad range of more than 3,000 delicious products offers consumers convenient, nutritious and affordable options in nearly every country around the world. Global Beverages Global Snacks Global Nutrition Performance Brands Scale People more than 22 >200 ~300,000 billion-dollar brands countries & territories employees $65 billion revenue 5
  • 7. From Seed to Shelf Frito-Lay owns the entire supply chain, so there are significant optimization opportunities. Move Information Before Inventory Raw Materials & Purchased Finished Goods Plant Warehouse Distribution Sales OPS Route Sales Retail Store R Product Flow Visibility to company wide information, enabling optimization and increasing the velocity through each function, while reducing the effort and improving productivity. 7
  • 8. Our Integrated Supply Chain Statistically Calculated Safety Stock Multi-Echelon Inventory Optimization by JDA Software Production Planning & Scheduling Daily Production Planner by Frito-Lay North America Statistical Forecasting Models Execute & Deliver Base Forecast New Products + Promo Lift + Event Lift Cannibalized Products Discontinued Products Replenishment Planning Transaction Data SAP 8 Supply Chain Planner by JDA Software
  • 9. Agenda Company Overview Demand Planning at Frito-Lay North America Demand Planning Benchmarking 9
  • 10. The Whole Truth Every week we send a 100% accurate forecast through the system… …Sales just sells the wrong amount. 10
  • 11. Demand Signal Threading in a DSD System 3+ Weeks Out 11 Week Of
  • 12. FLNA Forecasting Facts Number of Regions 13 Avg. Number of SKUs ~800 Lowest Level Intersections ~8.5 Million Historical Sales & Promotion Data 3 Years Forecast Bucket Weekly Forecast Horizon 1 Year Forecast Frequency Once a Week Duration of Forecasting Process ~60 Hours Sales forecasted into the store R Plant 12 Distribution DC Route Sales Retail Store
  • 13. Frito Lay DP SAS Solution Historical Sales & Causal Data Causal Data  Current Functionality Promotion enrollments by account – – – SAS Promo Lift  SAS Holiday Lift SAS Base Forecast Statistical Forecast Planner Adjust using DP UI Final Forecast Supply Chain Planning 13 Price point Duration Days in week Events: Holidays, quarter close, seasonal activities
  • 14. Delivering the Final Forecast is a Collaborative Effort Product Supply Sales  Input of accurate and timely promo enrollments  Owns the signal  Notification of non-price point driven activity  Capacity and volume push  Execution Region Finance Sales Ops  Forecast feedback via weekly calls with replenishment planners  Aligning DP forecast to the organization’s financial forecast Demand Planning Plants  Forecast feedback from plant-based replenishment planners 14  Hold Key Account Managers accountable for accurate and timely promo enrollments
  • 15. Demand Planning Benchmarking Benefits of Benchmarking  Accuracy and bias benchmarking vs. relevant demand  Use of demand segmentation and forecastability concepts – Region accuracy target setting – Assessing opportunities for accuracy improvements “How am I doing overall?” “How difficult is my portfolio to plan to for?” “How well should I be doing for my level of portfolio difficulty?” “How well could I be doing for my level of portfolio difficulty?” “Where should I focus my energy and attention?” 15
  • 16. Agenda Company Overview Demand Planning at Frito-Lay North America Demand Planning Benchmarking 16
  • 17. Demand Planning Intelligence Consortium Conventional Benchmarks Demand Planning Intelligence Consortium Questionnaire-based Data and analytics-based  Participants self-report forecast accuracy however they measure it, which varies  Reports are typically annual  Level of aggregation is very high  Limited comparative analysis  Best practices ID’d without accounting for differences in portfolio forecastability 49% 33% PERSONAL CARE 13% HOME CARE 5% 17 FOOD & BEVERAGE PET CARE VS.  Standardized transactional data collection and computational approach by unbiased 3rd party  Reports are periodic and repeating  Level of benchmarking is highly granular  Provides understanding of drivers and causes  Best practices ID’d accounting for differences in portfolio forecastability DPIC Statistics     Industry: Consumer Packaged Goods Geography: North America and Europe Members: ~ 40 Businesses Item-Locations: ~ 500,000
  • 18. Segmented Benchmarking & Analytics Nearly 1,250 Forecast Accuracy and Bias Benchmarks 18
  • 19. How difficult is my environment to forecast? Segmentation provides an understanding of each portfolio, which drives demand planning performance potential as much (or more) as complexity. 19
  • 20. How am I doing? Forecastability-Based Intelligence Forecast Accuracy Forecast Accuracy Conventional DPIC Forecastability Index™ Forecastability yields a very different conclusion! 20
  • 21. How well could I be doing? Targets can be set with more realism by customizing for the specific demand portfolio, and should be differentiated: “One Size Does Not Fit All.” 21
  • 22. Some Frito-Lay DPIC Findings  Frito-Lay is a top performer, both overall and in segmented analytics results  This is in contrast to assumptions around high complexity  Top performance results stems from good process, great execution, and relatively high demand forecastability Forecastability + Process Design & Execution > Complexity  Room still exists for improvement; even a top overall performer has segments of the business with opportunity 22
  • 23. What’s Next for Frito-Lay Demand Planning  Continue our membership in the consortium – Work on opportunity areas presented by survey results  Add new causals to our SAS models  Leverage point of sale data  Continue to explore new technologies such as demand sensing  Grow our modeling team into a Forecasting Center of Excellence (COE) 23