Confidential

Enablers for Maturing Your S&OP Processes

Click to edit Master title style

Demand(customer)

Transportation Distribution &Inventory

Supply & Manufacturing

How much demand will we

generate? At what service level

can we profitably

satisfy demand?

At what point in my supply chain

should I decouple push vs. pull?

What is the best flow path?

How should we transport

product through the supply

chain?

What activities should we outsource?

How much and where should inventory be

positioned in the supply chain?

When should we buy or make

product to make best use of capacity?

What infrastructure is

required for manufacturing &

distribution?

2

We Support Value-Driven Supply Chain Decisions

Click to edit Master title styleSome of Our Consulting Clients

3

FOOD AND BEVERAGE

RETAIL

HOME/OFFICE DURABLES

HEALTHCARE

HOME/OFFICE NON DURABLES

OTHER INDUSTRIES

SERVED

• 62 of the Fortune 500• 9 of the Top 15 US Retailers

• 13 of AMR’s Top 25 Supply Chains• 5 of the Top 20 Global Forest and Paper Companies

• 8 of the World’s 25 Largest Food & Beverage Mfgs• 9 of the Top 10 Consumer Goods Supply Chains., SC Digest

Packaging

LSPChemical/ProcessAuto/IndustrialUtilities/

Telecomm/Media

Click to edit Master title style

4

Selected S&OP Objectives

Increase Profits

Free Up Capacity

Focus Resources

Increase Flexibility

Improve Forecast

Manage Complexity

Business Strategy

• Competitive Differentiation

• Geo Strategy• Financial Targets

Financial Planning

• Revenue Forecast• Budgeting• Capital Plans• Cost Control

Market Planning

• Prod Forecast• Promo Plans• Brand/Channel Strategy & Pricing

R & DCategory Mgmt

• New Prod Into• Prod Lifecycle Plan• Prod Mix/ Pricing / Placement

Sales Planning

• Sales Forecasts• Customer Business Policies/Plans

Demand Planning

• Historical Demand• Stat Forecasts

SUPPLY

DEMAND

CORPORATE

Supply Planning

• Prod Forecast• Promo Plans• Brand/Channel Strategy & Pricing

Demand Management

• Product Allocation

Operations Planning

• Sales Forecasts• Customer Business Plans

• Product Allocation

Logistics Planning

• Historical Demand• Stat Forecasts

Click to edit Master title style

5

Selected S&OP ObjectivesAnalytical Enablers

Increase Profits

Free Up Capacity

Focus Resources

Increase Flexibility

Improve Forecast

Manage Complexity

Cost-To-Serve Models

Product & Customer Portfolio Management

Segmentation/ Tailored SC Networks

Network Design and Analysis

Inventory Deployment and Policy Optimization

Click to edit Master title styleReasons Companies Initiate Network StudiesCost Reduction Clear Leader and Growing

Source: 184 Chainalytics’ employee project experiences

1995-1999 2000-2004 2005-2009

Never Done Thought It Was Time 11% 2% 0%

Develop Internal Compentancy 4% 4% 8%

New Markets 9% 9% 2%

New Management 0% 6% 11%

Excess/Insufficient Capacity 20% 9% 8%

Merger/Acquisition/Divestiture 9% 15% 12%

Cost Reduction 35% 38% 46%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Cost Reduction41%

Merger, Acquisition, Divestiture

12%

Excess/Insufficient Capacity

11%

New Management6%

New Markets6%

Develop Internal Compentancy

6%

Never Done Thought It Was Time

3%

Process Re-engineering3%

Annual Planning Process3%

Politcal/Regulatory2%

New Product Introductions2% Sourcing

Change2%

Assess 3PL Outsourcing2%

Increase Service1%

Trend Last 10 Years

Trend Last 5 Years

6

Click to edit Master title style

Customer/Channel Segmentation Flow Path Service Level Strategy Service Territory Alignment Inventory Deployment Mode Usage Supply Chain Risk Assessment Master/Tactical Planning Social Responsibility

Recent Focus Areas in Supply Chain Network DesignBest Use of Current Networks

Network Optimization Inventory Optimization Simulation Transportation Modeling Total Cost to Serve Portfolio Management

Types of Analysis Modeling Technologies

7

Click to edit Master title style

8

Periodic versus Continuous Analysis ApproachKeeps Network In Tune, Ability to React

EF

FO

RT

NE

TW

OR

K C

OS

TS

LostOpportunityActual

$

Optimal $

Typically 24+ Months

TIME

• Require months of concentrated, cross-functional effort•Do not support answering tactical or ad-hoc questions with holistic, fact-based analysis in the interval between major studies

•Require resources to “re-learn” the model (and perhaps the business)•Lose potential opportunities by allowing the network to atrophy during the typical 12-24 month gap between major studies

Periodic

EF

FO

RT

NE

TW

OR

K C

OS

TS

TIME

Initial Study

Actual $

Optimal $

•Does not completely eliminate spikes in effort, but reduces their effort & duration

•Supports ad-hoc questions with holistic, fact-based analysis• Changing costs, demand, customers & requirements, and product mix

•Potential M&A activity•Support freight, labor, and procurement negotiations•Ensures the network remains optimal:• Plant-DC-Customer assignments, Manufacturing line configuration•Allows resources to remain constant ,maintain expertise in model & business

Continuous

Click to edit Master title styleDesign Projects Supported by Analytics and Optimization Results Typically Contrary to Conventional Wisdom

81.8%

73.2%

83.6% 83.4% 83.6%

95%

100% 100% 100% 100%

85%

62.1%

55.0%

61.4% 61.4% 61.4%

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

BASELINE 1 Best Use ofExisting

2 OptimalMachine

Deployment

3a CloseGarland

3b Close St Joe

% M

ak

e

Company planned to outsource these products. Using Activity Based Costing in the study showed they should

maintain or increase amount made in plants.

$0

$10

$20

$30

$40

$50

$60

$70

$80

Millions

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

Company had always built a significant amount of inventory in non-peak season (Sep-Nov). Studied

demonstrated ability to not build during this timeframe.

9

138.3

129.7129.2

127.2

124.8124.1

121.2120.6

116.6

135.2

127.0

122.2

117.9

124.3

126.7

130.7

Achievable St Joe Total Costs In Play Targeted St Joe Total Costs In Play

Company planned to invest significantly in existing Plant 1. Greatest savings came from closing down Plant 1.

Click to edit Master title styleDesign Projects Supported by Analytics and Optimization Results Typically Contrary to Conventional Wisdom

% o

f SKU

s

Base 0% 0% 1% 18% 80%

2% Strategy 20% 6% 9% 9% 57%

12% Strategy 58% 8% 6% 9% 20%

18% Strategy 69% 7% 5% 4% 14%

1 2 3 4 5

Company believed that vast majority of their SKU’s (80%) should be stocked at ALL locations. Optimal deployment

strategy indicated 70% of SKU’s should only be stocked at ONE location.

$-

$10

$20

$30

$40

$50

$60

$70

$80

$90

Network Devices Displays Printers & OfficeEquipment

Supplies & Media Security Devices

Millions

Inventory Value Inventory Value - SMO

17%

8%

17%11%

10%

Company believed that all SKU’s within a Group needed to have the same service level. By optimizing the service level

of each sku to maximize profit, while retaining the overall service level for the Group, inventory was reduced by 14%

10

Click to edit Master title style

11

Simple Strategies and Policies Typically Do Not Provide Best ResultsBlackjack Strategies

 

Betting:Hit/Stick:Split/Double:House Advantage:

Typical Player

Same Base Unit/RandomHit only if cant bust

Split Pairs/Double on 10/111.5 to 5%

8 decks, H17, DAS, No Surrender, Peek

Estimated casino edge for these rules: 0.69 %

Dealer Upcard Your

Hand

2 3 4 5 6 7 8 9 10 A

5 H H H H H H H H H H6 H H H H H H H H H H7 H H H H H H H H H H8 H H H H H H H H H H9 H D D D D H H H H H10 D D D D D D D D H H11 D D D D D D D D D D12 H H S S S H H H H H13 S S S S S H H H H H14 S S S S S H H H H H15 S S S S S H H H H H16 S S S S S H H H H H17 S S S S S S S S S S

A,2 H H H D D H H H H HA,3 H H H D D H H H H HA,4 H H D D D H H H H HA,5 H H D D D H H H H HA,6 H D D D D H H H H HA,7 DS DS DS DS DS S S H H HA,8 S S S S DS S S S S SA,9 S S S S S S S S S S2,2 P P P P P P H H H H3,3 P P P P P P H H H H4,4 H H H P P H H H H H5,5 D D D D D D D D H H6,6 P P P P P H H H H H7,7 P P P P P P H H H H8,8 P P P P P P P P P P9,9 P P P P P S P P S ST,T S S S S S S S S S SA,A P P P P P P P P P PDlr 2 3 4 5 6 7 8 9 10 A

Key:

H = Hit S = Stand P = Split

D = Double (Hit if not allowed)

DS = Double (Stand if not allowed)

Basic Strategy

Same Base Unit/StructuredPlayer/Dealer CardsPlayer/Dealer Cards

0.5%

Card Counter

Base Unit Multiplier via Remaining Card Favorability (Running Count)

Basic altered by FavorabilityBasic altered by Favorability

-2% (Hit and run ~ -4%)

Click to edit Master title style

12

Designing Tailored Supply Chain NetworksDemand Characteristics Drive Inventory Deployment

Item-Locations 194 1%COGS $250,900,000 34%

Item-Locations 3,395 20%COGS $428,000,000 59%

Item-Locations 13,791 79%COGS $51,100,000 7%

Item-Locations 11,283 65% Item-Locations 4,001 23% Item-Locations 2,096 12%COGS 41% COGS 23% COGS 35%

Fast: >1,000/Week

Demand Variability

Dem

and

Velo

city

High: >1.5

$302,900,000

Medium: 0.6 - 1.5

$170,200,000

Low: < 0.6

$256,900,000

Slow: < 25 Units/Week

Medium: > 25 Units and <1,000/Week

COV (Std Dev Demand / Mean Demand)

0

10

20

30

40

50

60

70

80

BEAUTY HOME CARE NUTRITION PERSONAL CARE

Item-Locs(H's): 100.29, 57.7% COGS: $(M's) 22.9, 3.1%

0

10

20

30

40

50

60

70

80

BEAUTY HOME CARE NUTRITION PERSONAL CARE

Item-Locs(H's): 11.63, 6.7% COGS: $(M's) 159.9, 21.9%

0

10

20

30

40

50

60

70

80

BEAUTY HOME CARE NUTRITION PERSONAL CARE

Item-Locs(H's): 0.91, 0.5% COGS: $(M's) 120.1, 16.5%

0

10

20

30

40

50

60

70

80

BEAUTY HOME CARE NUTRITION PERSONAL CARE

Item-Locs(H's): 30.5, 17.5% COGS: $(M's) 18.9, 2.6%

0

10

20

30

40

50

60

70

80

BEAUTY HOME CARE NUTRITION PERSONAL CARE

Item-Locs(H's): 9.22, 5.3% COGS: $(M's) 120.1, 16.5%

0

10

20

30

40

50

60

70

80

BEAUTY HOME CARE NUTRITION PERSONAL CARE

Item-Locs(H's): 0.29, 0.2% COGS: $(M's) 31.2, 4.3%

0

10

20

30

40

50

60

70

80

BEAUTY HOME CARE NUTRITION PERSONAL CARE

Item-Locs(H's): 7.12, 4.1% COGS: $(M's) 9.3, 1.3%

0

10

20

30

40

50

60

70

80

BEAUTY HOME CARE NUTRITION PERSONAL CARE

Item-Locs(H's): 13.1, 7.5% COGS: $(M's) 148, 20.3%

0

10

20

30

40

50

60

70

80

BEAUTY HOME CARE NUTRITION PERSONAL CARE

Item-Locs(H's): 0.74, 0.4% COGS: $(M's) 99.6, 13.6%

Candidates for Centralization

Candidates for Full Stocking or

Direct Ship

Click to edit Master title styleReducing Stocking Locations Increases Product Velocity and Reduces Demand Variability

0%

200%

400%

600%

800%

1000%

1200%

1400%

1600%

1800%

0.0 50.0 100.0 150.0 200.0 250.0

Mean Days Between Ships

CO

V D

aily

Dem

and

0%

200%

400%

600%

800%

1000%

1200%

1400%

1600%

1800%

0.0 50.0 100.0 150.0 200.0 250.0

Mean Days Between Ships

CO

V D

aily

Dem

and

Product Stocked at All Locations

Product Stocked at Single Location

0

1

2

3

4

5

6

7

8

1 2 3 4 5

Stocking Locations

Day

s

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Dem

and

Var

iab

ilit

y

Avg Mean Days Between Shipments Avg Demand Variability

13

Click to edit Master title style

14

Key Attributes

Determine Bulk & Direct to Store Articles

Determine Stored vs. Not Stored

Determine Cross Dock vs. Flow Through

59,619 total Items

56,199 Items through the future DCs

44,687 Items either Flow Through or Cross

Determine Flow Through Manual & Automation

16,770 Items Flow Through

• 994 Bulk Items

• 2,426 Direct to Store Items

• 11,512 total Items Stored

• 8,114 Items Seasonally stored

• 27,917 Items Cross Docked

• 14,112 Items Flow Through Automation

• 2,658 Items Flow Through Manual

The process that mapped Items to Flow Channels is outlined

here.

Key Items attributes and costs drivers were

considered in assigning Articles to

Flow Channels.

Designing Tailored Supply Chain NetworksProduct Characteristics Drive Flow Paths

Attributes

Live Goods

Hazmat

Remote Vendor/Store

Imported

Seasonal

High Demand Variability

Long Lead Time

Short Lead Time

High Product Value

Small Cube/Carton Sizes

Low Pick Density

Conveyable

Long Lead Time

Low Product Value

Not Small Cube/Carton Sizes

High Pick Density

Conveyable=Automation

NonConveyable=Manual

Dire

ct to

Sto

reSt

orag

eCr

oss

Doc

kFl

ow T

hrou

gh

Click to edit Master title style

15

Customer & Product Portfolio ManagementWho and What is Driving our Profitability

Number of Products

• A products (22%) account for 80% of revenue and 81% of contribution margin, and 10% of the space

• While C products (46%) account for 5% of revenue and 4% of margin contribution, and 60% of storage space

There may be opportunities to reduce complexity by addressing the portfolio size and business practices associated with the large tail of low

revenue-generating PRODUCTS22%

24%46%

8%

80%

15%

5%0%

81%

15%

4%0%10%

18%

60%

12%

ABCD

ItemsRevenue

Margin

Storage Space

Click to edit Master title style

6%8%

85%

1%

80%

15%

5%0%

71%

14%

15%0%

5%8%

80%

7%

ABCD

16

Number of Customers

There may be opportunities to reduce complexity by addressing the portfolio size and business

practices associated with the very large tail of low revenue-generating CUSTOMERS

Customer & Product Portfolio ManagementWho and What is Driving our Profitability

Customers

Revenue

Margin

Storage Space

• A customers (6%) account for 80% of revenue and 71% of contribution margin, and 5% of the space

• While C Customers (85%) account for 5% of revenue and 15% of margin contribution, and 80% of storage space

Click to edit Master title style

StageI

Reacting

IIAnticipating

IIICollaborating

IVOrchestrating

Balance:

S&OP

Goal Development of an

operational plan

Demand and supply

matching

Profitability Demand sensing, and

conscious tradeoffs for

demand shaping to drive an

optimized demand- response

Ownership S = Sales

OP = Factory

capabilities

S = Sales and Marketing

Plans

OP = Planning and factory

capabilities

S = Go to Market Plans

OP = Design of demand

driven plan, make & deliver

processes

S = Go to Market Strategies and

Solutions

OP = Translation of demand into

plan, make, deliver, source and

service strategies, with connection

to execution

Metrics Order fill rate, asset

utilization, inventory

levels

Order fill rate, forecast

error, inventory turns,

functional costs

Demand error, customer

service, working capital,

total costs

Demand risk, customer service,

cash flow, market share and profit

Techniques/

Technology

Excel spread sheets,

ERP Supply chain

capabilities

Excel, demand forecasting,

inventory management,

general supply chain

planning tools, inventory

optimization

what if analysis for

demand shaping, what if

analysis for reconciliation

with financial plans, cost

to serve,

Analytics to find risk- value trade

offs, risk management techniques,

price optimization, complex

simulation

17

Four Stages of S&OP Maturity Companies Stuck in Early Stages

Source : AMR Research/ 2009 S&OP Study of 182 Companies

S

OP

S

OP

SOP S OP

20%

47%

19% 14%

20%

47%

19% 14%

20%

47%

19% 14%

20%

47%

19% 14%

SherTrackDemand-Driven Predictive Manufacturing