PepsiCo’s Practical Application of Supply Chain Resilience Strategies and Inventory Optimization

Tim Rowell, CPIM

PepsiCo, Supply Chain Sr. Manager

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Irma

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Agenda

Overview / Background

Supply Chain Resilience

Inventory Deep Dive

Case Studies

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OVERVIEW / BACKGROUND

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PepsiCo Overview

Global Beverages

Global Snacks

Global Nutrition

Brands

22billion-dollar

brands

Performance

More than $63 billion

revenue

Scale

>200 countries

& territories

People

More than 250,000

employees

PepsiCo is a global food and beverage powerhouse.

Our broad range of delicious products offers consumers convenient,

nutritious and affordable options in nearly every country around the world

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PepsiCo Overview

Good For You

Better For You

Fun For you

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Specialized Supply Chain

As PepsiCo proactively adapts to emerging market trends for healthy &

nutritious beverages, our supply chain becomes more global and complex

Global Nutrition Segment

Good for You Products

Chilled Supply Chain

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Case Study - International Supply

Natural, Functional Beverage

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PepsiCo Coconut Water Supply Chain

• Copackers in Southeast Asia

• Packaging Materials from Germany,

Pakistan, Serbia, Singapore

• Warehouses near East/West ports

• Redistributed to regional DCs

• Backup US Copackers

Southeast Asia

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Supply Chain Starts Here

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SUPPLY CHAIN RESILIENCE

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Supply Chain Resilience Concepts

Presented at 2013 APICS International Conference

(Orlando)

Michigan State PHD, Steven Melnyk

Supply Chain Resilience – The ability of a supply chain to anticipate, create plans to

avoid or mitigate, and/or to recover from disruptions to supply chain functionality

APICS Dictionary, 14th Edition, ©2013

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Supply Chain Resilience Strategies

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Several strategies for SC Resilience: a) early warning, b) buffers, c)

supply chain configuration d) brand equity…etc.

Relevant strategies applied to managing our coconut water supply chain

a) Early Warning1) The earlier you are aware of the disruption, the sooner you can start working on the recovery

2) Improve frequency of communications and data quality

b) Buffers – 3 kinds of buffers

1) Stock Buffers

- Pre-build prior to high risk SE Asia supply timeframe and prior to peak seasonal demand

in US

- Optimize inventory targets to balance fill rate and expiration risk

2) Capacity Buffers

- SE Asia: how much more can we get above the contractual commitments when needed

- US copack: how much can we get from US copackers when needed

3) Lead Time Buffers

- SE Asia – flexibility to change short term schedule; dual sourcing where possible (500ml

Pure)

- US copackers – stage long lead time materials for quicker response when SE Asia supply

disrupted (or demand spikes)

Application to Coconut Water Supply Chain

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Seasonal Inventory DOH

Targets

Supply & Demand

SeasonalityLow Sales Moderate Sales Peak Sales Moderate Sales Low Sales

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

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

Typhoons Moderate Risk Low Risk Moderate Risk Peak Risk

Holidays HolidaysChinese

NY

Holy

WeekRamadan

Understand and Address Seasonal Risks

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Production Lock Hold Transit, Customs, Port Dist

Line Time, Ingredients Hold Dist

8 2 8 2

8 2 2

FG lead time from SE Asia

is 20 weeks

2nd SE Asia copacker *

improves reliability**, but

same 20 week lead time

US contingency lead time is

8 weeks (less if line time &

ingredients available)

Notified of

Supply

Disruption

Order More from

SE Asia Copack

#1 or #2

Schedule US

Contingency

Supply

Production Lock Hold Transit, Customs, Port Dist

8 2 8 2

Arrives 8

Weeks Sooner

vs SE Asia

Example Supply Scenario and Lead Times

(weeks)

* Dual SE Asia sourcing currently applies only to 500ml Pure. For others, scheduler will increase plan at

Copack #1 in subsequent months after disruption has subsided.

** It is important to quantify (and optimize) both average (m) and standard deviation (s) of supply (lead time and quantity).

For example, transit and port clearance times continually change; monitor and update as needed.

Decompose Lead Times and Optimize

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Certification

Supply – stable, on shelf, fresh

Promotion – new channels, drive awareness

Brand Equity and Reputational Resilience

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INVENTORY DEEP DIVE

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Inventory Policy is Part of SC Resilience

– Our work on applying these Supply Chain Resilience strategies within

PepsiCo’s global supply chain is leading to groundbreaking research,

particularly in the area of inventory buffers.

– International supply has long lead times and high variability. Inventory

required to meet customer service targets begins to introduce risk of

expiration, especially with organic products with shorter shelf lifes.

– We have derived formulas in conjunction with industry experts and

academia (and validated with historical data) to predict both the percentage

of stockouts as well as the percentage of expired product, and implemented

the solution that strikes the optimal balance.

– In doing so for the coconut water business, we found opportunities to

reapply this to other emerging product categories with supply

challenges such as probiotic beverages. These probiotic beverages have

the unique challenge of very short shelf life compared to other items in the

portfolio of premium juice beverages.

– Adapting the formulas for the product-specific characteristics of probiotic

beverages (supply & demand variability, lead time and shelf life) allowed us

to find the optimal balance for that product group as well and implement the

results to improve our supply

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The SawtoothInventory vs Time

Place order and the reorder quantity (ROQ) arrives one lead time

later. Safety stock buffers against uncertainty of supply and

demand.

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Widely Documented: Correlate Inventory Level with Percent Backorders/Cuts

Inventory vs Time

backorders/cuts ( ) across all replenishment

cycles throughout the year given variable

demand and supply

divide by annual demand to get percent backorders/cuts

fill rate % = 1 – percent backordered/cut

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Emerging Area of Research: Correlate Inventory Level with Percentage Expired

amount leftover at the end of the

replenishment cycles ( ) throughout the

year

divide by annual demand to get percent expired

Expected inventory

(safety stock) prior

to receiving next

replenishment

Expected amount of

each production lot

leftover at the end

of its shelf life

Inventory vs Time

* “Shelf Life” in this context is viewed from internal perspective and has subtracted the “remaining shelf life” guaranteed to external customers upon receipt. It is otherwise known as “Warehouse Life”.

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Consider the Boundary Cases

ROQ½

ROQ Zero

-½ ROQ RO

Q

Scenarios of expected inventory at the end of product shelf life

assuming no demand or supply variability

100% expired

0% backordered/cut

50% expired

0% backordered/cut

0% expired

0% backordered/cut

0% expired

50% backordered/cut

0% expired

100% backordered/cut

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Illustration of the Symmetric Relationship

-ROQ -½ROQ Zero ½ROQ ROQ

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Layer in Variability

-ROQ -½ROQ Zero ½ROQ ROQ

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Definition of TermsSafety Stock (SS) = Expected inventory position when reorder arrives

Lead Time (LT) = Time it takes from the point an order is placed until it arrives and is available to sell to customers. Lead Time

Demand (LTD) is the demand during lead time

Lock = Time required by manufacturer that production schedule be locked and no more changes accepted

Make = Time allotted for manufacturer to produce the scheduled quantity and product to clear incubation (if required)

Dist = Time it takes to distribute product from manufacturer to demand points

ROQt = Run cycle (or time between replenishments); the timeframe that the reorder quantity (ROQ) is expected to cover

Shelf Life* = Total internal shelf life from the time produced until the last date it can be sold to customer (consumer shelf life minus

time guaranteed to customers upon receipt).

eRSL* = Expected inventory remaining at the end of the internal shelf life

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Method DerivationArranging Terms

X* = Lock + Make + Shelf Life – ROQt

Y* = SS + LTD – eRSL*

Where: LTD = Lock + Make + Dist

Y* = Lock + Make + Dist + SS – eRSL*

Algebraic Reduction

Set X* = Y*

Lock + Make + Shelf Life – ROQt = Lock + Make + Dist + SS –

eRSL*

Lock + Make + Shelf Life – ROQt = Lock + Make + Dist + SS –

eRSL*

Shelf Life – ROQt = Dist + SS – eRSL*

SS = Shelf Life – ROQt - Dist + eRSL*

Procedure

• Specify eRSL*

• Calculate SS

• Calculate mean (m) and standard deviation (s) of LTD

• Based on m, s, q (ROQ) and SS: calculate % backorders/cuts

(A)

- Iterate with conventional formula: SS=ks, k=f[L(z)=Aq/s]

- Or calculate explicitly per Zipkin: A=(s/q)[F1(zr)-F1(zr+q)]

• Calculate m* and s* of demand over X*

• Based on m*, s*, q and eRSL*: calculate % backorders/cuts*

• Determine estimated percentage of expired inventory based on

symmetric relationship with percentage backorders/cuts

(see calculation mechanics on next slide)

* “Shelf Life” in this context is viewed from internal perspective and has subtracted the “remaining shelf life” guaranteed to external customers upon receipt. It is otherwise known as “Warehouse Life”.

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Calculation MechanicsStart at midpoint (MP), where eRSL=0

• Calculate SSMP

• Calculate % backorders/cuts at SSMP given m, s, q

• Calculate % backorders/cuts at eRSLMP given m*, s*, q

Ascending

• Calculate % backorders/cuts at SSMP+2 given m, s, q

• Calculate % backorders/cuts at eRSLMP+2 given m*, s*, q

• Set % expired at = eRSLMP-2 = % backorders/cuts at eRSLMP+2

• Calculate % backorders/cuts at SSMP+4 given m, s, q

• Calculate % backorders/cuts at eRSLMP+4 given m*, s*, q

• Set % expired at = eRSLMP-4 = % backorders/cuts at eRSLMP+4

• Continue for MP+6, MP+6, etc (or increments other than 2)

Descending

• Calculate % backorders/cuts at SSMP-2 given m, s, q

• Calculate % backorders/cuts at eRSLMP-2 given m*, s*, q

• Set % expired at = eRSLMP+2 = % backorders/cuts at eRSLMP-2

• Calculate % backorders/cuts at SSMP-4 given m, s, q

• Calculate % backorders/cuts at eRSLMP-4 given m*, s*, q

• Set % expired at = eRSLMP+4 = % backorders/cuts at eRSLMP-4

• Continue for MP-6, MP-8, etc (or increments other than 2)

eRSL

(wks)

SS

(wks)

q

(wks)

Avg Cycle

Stock

Target

Inv

Avg Wkly

Demand s m m* s*Cuts %

for SS

Cuts %

for SS*

Expired

%

-10 -2 4 2 0 10,000 37,500 200,000 240,000 41,458 49.7% 81.4% 0.0%

-8 0 4 2 2 10,000 37,500 200,000 240,000 41,458 31.1% 67.1% 0.1%

-6 2 4 2 4 10,000 37,500 200,000 240,000 41,458 16.5% 49.8% 0.4%

-4 4 4 2 6 10,000 37,500 200,000 240,000 41,458 7.3% 32.6% 1.3%

-2 6 4 2 8 10,000 37,500 200,000 240,000 41,458 2.7% 18.5% 3.8%

Midpoint 0 8 4 2 10 10,000 37,500 200,000 240,000 41,458 0.8% 9.0% 9.0%

2 10 4 2 12 10,000 37,500 200,000 240,000 41,458 0.2% 3.8% 18.5%

4 12 4 2 14 10,000 37,500 200,000 240,000 41,458 0.0% 1.3% 32.6%

6 14 4 2 16 10,000 37,500 200,000 240,000 41,458 0.0% 0.4% 49.8%

8 16 4 2 18 10,000 37,500 200,000 240,000 41,458 0.0% 0.1% 67.1%

10 18 4 2 20 10,000 37,500 200,000 240,000 41,458 0.0% 0.0% 81.4%

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Model Correlates Well with Historical Data

SL=40d, Low/Med s SL=40d, Med/High s

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Deriving Formulas from Scratch

𝑠 − 𝑥 𝑔(𝑥)𝑑𝑥

∞‒

S

S = the order up to quantity in a periodic review systemp = production lead time (order to produce)w = warehouse life

f(x) = pdf for weekly demand xx ~ N (m, s)

g(x) = pdf for demand x over period p+w

x ~ N (m(p+w), s (p + w))h(x) = pdf for standard normal distribution

For any given S

E (cases expiring) =

Let z = (x-m’)/s’ → x = zs’ + m’ where m’ = m(p+w)

k = (S-m’)/s’ → S = ks’ + m’ where s’ = s (p + w)

∞‒

k

E (cases expiring) = s’(k-z)h(z)dz

∞‒

k

= s (p + w) (k-z)h(z)dz

Where: h(z) = 𝑒−½𝑧

2

2(standard normal dist: m=0, s=1)

Solve with Numerical Integration

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Other References1. Opher Baron, Managing Perishable Inventory, Rotman

School of Management, University of Toronto, Toronto,

Ontario, Canada. Wiley Encyclopedia of Operations

Research and Management Science, edited by James J.

Cochran, Copyright © 2010 John Wiley & Sons, Inc.

2. Karaesmen, I. Z., Scheller-Wolf, A., and Deniz, B.

Managing Perishable and Aging Inventories: Review and

Future Research Directions. In Kempf, K. G., Keskinocak,

P., and Uzsoy, R., editors, Planning Production and

Inventories in the Extended Enterprise, volume 151 of

International Series in Operations Research &

Management Science, chapter 15, pages 393-436.

Springer US, Boston, MA. 2011.

3. Nahmias, S. Perishable Inventory Systems, volume 160

of International Series in Operations Research &

Management Science. Springer US, Boston, MA. 2011.

4. Baron O, Berman O, Perry D. Stochastic (τ, S) models

for managing inventory of perishable items. Working paper.

Rotman School of Management, University of Toronto;

2008.

5. Brown GW, Lu JY, Wolfson RJ. Dynamic modeling of

inventories subject to obsolescence. Manag Sci

1964;11(1):51–63.

6. Nahmias S, Pierskalla W. Optimal ordering policies for a

product that perishes in two periods subject to stochastic

demand. Nav Res Logist Q 1973;20:207–229.

7. Broadheim E, Derman C, Prastacos GP. On the

evaluation of a class of inventory policies for perishable

products such as blood. Manag Sci 1975;22:1320–1325.

9. Lian Z, Liu L. A discrete-time model for perishable inventory

systems. Ann Oper Res 1999;87:103–116.

10. Weiss H. Optimal ordering policies for continuous review

perishable inventory models. Oper Res 1980;28:365–374.

11. Graves S. The application of queuing theory to continuous

perishable inventory system. Manag Sci 1982;28:400–406.

12. Nahmias S, Perry D, Stadje W. Actuarial valuation of

perishable inventory systems. Probab Eng Infor Sci 2004;18:219–

232.

13. Nahmias S. Perishable inventory theory: a review. Oper Res

1982;30(4):680–708.

14. Prastacos GP. Allocation of a perishable product inventory.

Oper Res 1981;29:95–107

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CASE STUDIES

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Case Study: Coconut WaterLong lead time, long shelf life products

9 month shelf life* 6 month shelf life***

* “Shelf Life” in this context is viewed from internal perspective and has subtracted the “remaining shelf life” guaranteed to external customers upon receipt. It is otherwise known as “Warehouse Life”.** Target inventory includes Safety Stock + Avg Cycle Stock + In Transit

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Case Study: Coconut WaterLong lead time, long shelf life products

**

* “Shelf Life” in this context is viewed from internal perspective and has subtracted the “remaining shelf life” guaranteed to external customers upon receipt. It is otherwise known as “Warehouse Life”.** Target inventory includes Safety Stock + Avg Cycle Stock + In Transit

Zoomed In View

9 month shelf life* 6 month shelf life*

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Case Study: Naked Juice SmoothiesShort lead time, short shelf life products

* “Shelf Life” in this context is viewed from internal perspective and has subtracted the “remaining shelf life” guaranteed to external customers upon receipt. It is otherwise known as “Warehouse Life”.** Target inventory includes Safety Stock + Avg Cycle Stock + In Transit

40 day shelf life* 20 day shelf life*

**

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Rules of ThumbFor Example: Max Stock Policy = ½ Shelf Life*

For the 4 examples studied, this policy will result in less than 10% expired ( , )

* “Shelf Life” in this context is viewed from internal perspective and has subtracted the “remaining shelf life” guaranteed to external customers upon receipt. It is otherwise known as “Warehouse Life”.** Target inventory includes Safety Stock + Avg Cycle Stock + In Transit

** **

Coconut Water Smoothies

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For More Info on Inventory Optimization

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2011 Inventory Optimization excerpt 1

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2011 Inventory Optimization excerpt 2

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Today’s presenter

Fun facts and APICS highlights

Tim Rowell- Supply Chain Sr. Manager

- 19 years at PepsiCo/Tropicana

- APICS CPIM since 2003

Contact Info:1001 13th Avenue EastBradenton, FL 34208941-742-3676tim.rowell@pepsico.comlinkedin.com/pub/tim-rowell/0/281/275•

1991 Bachelors of Science in Mechanical Engineering, University of Florida

1991 Started at Westinghouse in Nuclear Safety Analysis

1997 MBA (MSIA), Carnegie Mellon Graduate School of Industrial Administration

1997 Started at Tropicana, a division of Seagram's

1999 PepsiCo acquired Tropicana

1999 Began work on Tropicana inventory model with Dr. Sridhar Bashyam (Frito Lay US)

2000 Consulted with Dr. Steven Nahmias on periodic review and continuous review formulas

2000 Established inventory model based on Fill Rate for Tropicana business

2000 PepsiCo acquired Quaker/Gatorade

2001 QTG synergy

2003 APICS: became Certified in Production and Inventory Management (CPIM)

2003 Presented at APICS International Conference: Tropicana Inventory Planning Methodology

2005 Began work on Project OneUp including Multi-Echelon Inventory Optimization for QTG

2006 Applied Dr. Paul Zipkin (Duke/Fuqua) models for Continuous Review and Periodic Review

2007 PepsiCo acquired Naked Juice

2008 Mark Entsminger documented/circulated derivation for quantity-based supply variability

2009 PepsiCo implemented Project OneUp Release 4

2009 PepsiCo reacquired PBG

2010 LSS inventory optimization for ingredients (chilled supply chain)

2011 Presented at APICS International Conference: Essential Inventory Truths

2012 PepsiCo acquired 100% of ONE

2013 APICS International Conference: Michigan State PHD Steven Melnyk presented on SC Resilience

2013 Implemented APICS/MS Supply Chain Resilience strategies for coconut water

2014 PepsiCo NAB APICS Pilot

2015 Performed deep dive optimization and segmentation of Naked Juice business

2015 Developed model to correlate % backorders/cuts and % expired as function of target inventory

2016 Vetted model with academia and industry, including Terra Technology / E2open

2016 PepsiCo Inventory Optimization organization alignment

2016 Presented at APICS International Conference: Practical Application of SC Resilience Strategies

Luke 10:27

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THANK YOU