CSCMP India 2012 Conference 1-2 June Mumbai, India ITC ...€¦ · inventory management, general supply chain planning tools, inventory optimization what if analysis for demand shaping,

CSCMP India 2012 Conference

1-2 June

Mumbai, India

ITC Maratha – MUMBAI

Creative Approaches to Supply Chain Profitability:

A Global Perspective from India

Sales, Inventory, and Operations Planning:

Fueling Your Company’s Growth

Jeff Metersky Vice President, Sales, Inventory & Operations Planning

Chainalytics

Agenda

Who is Chainalytics?

Asia Market Growth and Implications

Sales & Operations Planning Introduction

Analytical Enablers for Effective S&OP


4

TODAY

Over 80 FTEs Worldwide

Our Clients

More Than 180 Unique Clients

14 of AMR’s Top 25 Supply Chains

57 Fortune 500 Companies

5 of Top 10 Retailers

7 of Top 10 Food & Beverage Manufacturers

5 of Top 10 CPG Companies

6 of Top 10 Forest, Paper and Packaging Companies

Our Experience

More Than 375 Engagements

• 1st Named to “100 Great Supply Chain Partners” List by SupplyChainBrain; Recognized for 8 Years Running

• Launch of Freight Market Intelligence Consortium (FMIC)

2001

2002

2003

2004

2005

2006

2007

2009

2010

2011

2008

Our Genesis

• Market Lacked Proven, Focused Supply Chain Analytics Competence

• “Best Analytical Minds in Supply Chain”

Empowering Fact-Based Decisions

Across Your Supply Chain

• Launch of Sales & Operations Variability Consortium (S&OVC)

• Mike Kilgore named a “Pro to Know” by Supply & Demand Chain Executive; Steve Ellet, Gary Girotti, Irv Grossman, Jeff Metersky, Matt Harding, and Kevin Zweier also named Pros to Know

• Established Chainalytics India Private Limited in Bangalore

2012

Strategic Growth via Mergers & Acquisitions

• Supply Chain Operations (Chainnovations)

• Packaging Optimization (Adalis Packaging Solutions Group)

• FMIC named “Top Supply Chain Innovation” by Supply & Demand Chain Executive

• Named to ARC Advisory’s “10 Coolest Supply Chain Boutiques”

Years

Quarters

Months

Weeks

Planning

Horizon

Value-Driven Supply Chain Decisions

5

At what service

level can we

profitably satisfy

demand?

How should

we transport

product through

the supply

chain?

How much and

where should

inventory be

positioned in the

supply chain?

Can we reduce our

transport and

logistics costs by

improving cube

utilization?

Should our

warehousing

and material

operations be

insourced

or outsourced?

When should

we buy or make

product to make

the best use of

our capacity?

What is the

best flowpath? How well do

our current

operations

mitigate repair

and warranty

costs?

How can we

increase

visibility to

stakeholders?

Transportation

Service Supply Chain

Logistics Operations

Sales, Inventory & Operations Planning

Packaging Optimization

Supply Chain Design

Agenda





3%

24%

2%

16%

0%

5%

10%

15%

20%

25%

1975 2010 % W

orl

dw

ide

Mid

dle

Cla

ss

Asian Population Asian Purchasing Power

• Asia's middle class

is expected to

continue it’s growth

trajectory to reach

2 Billion by 2020.

(More than double

current figure.)

• By 2030, middle

class Asians will be

the next global

consumers and

assume the role of

US & European

middle classes

1 Historical Data Excludes Japan

Source: “Rise of middle classes in India, China key to growth in Asia,” The Economic Times, April 4, 2012

1 1

Rapid Rise in Asian Middle Class Population

160

267

0

50

100

150

200

250

300

2011 2016

Mill

ion

s

Indian Middle Class Population

Rapid Rise in Indian Middle Class Population

• India’s middle class

is projected to reach

267 Million by 2016.

Source: “India's middle class population to touch 267 million in 5 yrs,” Hindustan Times, February 6, 2011

Significant Expenditure on Food in Emerging Markets

Food Share of Household Expenditures

33%CHINA

NIGERIA

40%

RUSSIA

28%

MEXICO

24%

PAKISTAN

46%

INDIA

35%

ITALY

14%

EGYPT

38%

BRAZIL

25%

JAPAN14%

KENYA

45%

INDONESIA

43%

CHILE

23%FRANCE

14%

SPAINAUSTRALIA13%

11%GERMANY

11%

CANADA9% U.S.

7%

UK9%

Source: Nielson Emerging Markets Global Forces White Paper March 2012

New Product Introductions

• From 1992 – 2009 in the USA

there were only 10 times were

new product introduction

declined for CPG products (1)

• 2009 – India ranked 6th Globally

for Personal Care product

introductions (2)

• 2010 – FMCG Companies

introduced 10,000+ New SKUs

in India (3)

Sources: (1) Datamonitor; (2) “Personal Care in India” Research and Markets May 2010; (3) “Eat, pray, love innovations”

Business Today India June 12, 2011

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

1992 1994 1996 1998 2000 2002 2004 2006 2008

New

Pro

duct

Intr

oduc

tion

sFood and Beverage Nonfood

United States

Boundaries

Supply Economy Traditional

Media

Brick & Mortar Set Pricing

FIXED MODEL FLEXIBLE MODEL

Varied Distribution Points + Traditional and New Media

$

@

Negotiated Pricing Boundless

+ Demand Economy

New Economy Based on Information Age

Source: Nielson Emerging Markets Global Forces White Paper March 2012

Challenge for Asian FMCG Companies and Retailers Build and Maintain Brand Loyalty Under…

• Rapid Pace of New Product Introduction

• Associated SKU Proliferation

• Demand Uncertainty and Volatility

• Expanded Supply Chains with Global

Coordination and Local Execution

• Multi-Channel Fulfillment Designed for:

Flexibility, Responsiveness, Adaptability

Agenda





Sales & Operations Planning is Crucial for Success

14

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


Plans


Logistics Planning


• Stat Forecasts

Selected S&OP Objectives

15

Increase Profits

Free Up Capacity

Focus Resources

Increase Flexibility

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


Policies/Plans

Demand Planning


• Stat Forecasts

SUPPLY

DEMAND

CORPORATE

Supply Planning

• Prod Forecast

• Promo Plans

• Brand/Channel

Strategy & Pricing

Demand Management


Operations Planning

• Sales Forecasts


Plans


Logistics Planning


• Stat Forecasts

Effective S&OP Enhances Company Performance

Performance Drivers

Sales

Price

COGS

SG&A

Working

Capital

Fixed Assets

Revenue

Growth

Cost

Reduction

Asset

Utilization

Return on

Capital

Employed

Value Chain Solutions

• Forecast accuracy and fill rates

• Improved new product introduction

• Market segmentation and target pricing

• Margin management and dynamic pricing

• Product Portfolio Management

• Sourcing strategies

• Supply Chain efficiency

• B2B transaction cost management

• Demand supply matching

• Inventory level / placement optimization

• Network optimization

• Capacity Utilization Improvement

Company

Performance

Stage I

Reacting

II Anticipating

III Collaborating

IV Orchestrating

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

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

S

OP

S

OP S

OP S OP

20%

47%

19% 14%

20%

47%

19% 14%

20%

47%

19% 14%

20%

47%

19% 14%

Source: Gartner (AMR Research) 2009 S&OP Study of 182 Companies

Agenda





Integrated

Financial Planning

Scenario

Planning


19

Increase Profits

Free Up Capacity

Increase Flexibility

Manage Complexity

Cost-to-Serve Models

Product & Customer Portfolio Management

Segmentation / Tailored SC Networks

Network Design and Analysis

Inventory Deployment and Policy Optimization

Total Cost-to-Serve Perspective

Resource Pool

Cost o

Object n Cost

object 2 Cost

Object 1

Cost Object

Resources Price Leakages

Volume Discounts

Payment Terms

Freight Allowance

Promotions Bonuses

PROFIT

Pocket Price Waterfall

Gross Sales Price

Net Sales Price (Pocket Price)

Mfg Distribution Transport Account

Mgmt

Sales Rep

Visits COG

Acquired

Cost-to-Serve Waterfall

To drive consensus, objectivity is needed – driven by a comprehensive understanding of the cost and revenue elements associated with customer/product portfolios.

Activity

Supporting Activity

Marketing & Sales Supply Chain

Decisions are Influenced by Total Cost-to-Serve

Before Cost-to-Serve Analysis After Cost-to-Serve Analysis

Product A - $20K Profit Product A - $20K Loss

Revenue 230,000$ Revenue 230,000$

COGS 140,000$ COGS 140,000$

GM 90,000$ GM 90,000$ Attributed Costs

Allocation 70,000$ Cost-to-Serve 110,000$ Orders 30,000$

Net 20,000$ Net (20,000)$ Cust. Srv. 25,000$

Vendor Mgt. 25,000$

Channel Mgt. 30,000$

Proper allocation of costs can be an important differentiator between effective and ineffective profitability management. Before Cost-to-Serve Analysis After Cost-to-Serve Analysis



COGS 140,000$ COGS 140,000$



Net 20,000$ Net (20,000)$ Cust. Srv. 25,000$

Vendor Mgt. 25,000$


Before Cost-to-Serve Analysis After Cost-to-Serve Analysis



COGS 140,000$ COGS 140,000$



Net 20,000$ Net (20,000)$ Cust. Srv. 25,000$

Vendor Mgt. 25,000$


Customer & Product Portfolio Management Who is driving our profitability?

Number of Products

A Products (22%) account for 80%

of revenue, 81% of contribution margin,

and 10% of the space

C Products (46%) account for 5%

of revenue, 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 PRODUCTS. 22%

24%46%

8%

80%

15%

5%0%

81%

15%

4%0%10%

18%

60%

12%

ABCD

Items

Revenue

Margin

Storage Space

6%8%

85%

1%

80%

15%

5%0%

71%

14%

15%0%

5%

8%

80%

7%

A

B

C

D

Customer & Product Portfolio Management What is driving our profitability?

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.

Customers

Revenue

Margin

Storage Space

A Customers (6%) account for 80% of revenue,

71% of contribution margin, and 5% of the space

C Customers (85%) account for 5% of revenue,

15% of margin contribution, and 80% of storage

space

Number of Customers

Designing Tailored Supply Chain Networks Demand Patterns Drive Design and Policy

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

Phase out 43.8% 11.9% 10.2% 10.7% 7.4% 5.6% 0.8% 0.8% 1.3% 0.3% 0.1% 0.9% 0.2% 0.1% 0.4% 0.3% 0.8% 2.4% 2.0%

Stable - Phase out 7.2% 6.8% 6.6% 9.2% 7.8% 8.7% 6.4% 6.9% 8.2% 5.6% 6.2% 5.0% 5.4% 3.4% 2.5% 1.5% 1.2% 0.9% 0.6%

Stable 5.3% 4.5% 4.6% 5.9% 4.9% 6.1% 4.8% 4.9% 5.8% 4.5% 4.8% 5.2% 6.6% 4.9% 6.4% 4.9% 4.8% 6.2% 4.9%

Stable 2

Phase in - Stable

Phase in 1.2% 0.2% 0.7% 1.1% 0.1% 1.6% 0.6% 0.1% 0.1% 0.2% 0.7% 2.0% 5.3% 6.5% 14.7% 11.3% 12.3% 21.7% 19.6%

Seasonal 1.2% 1.3% 0.9% 1.0% 0.7% 13.2% 8.3% 5.9% 13.1% 5.4% 5.8% 7.8% 11.6% 6.0% 5.7% 2.2% 2.8% 3.4% 3.9%

0%

10%

20%

30%

40%

50%

60%

Pe

rce

nt

of

De

man

d

Phase out, 18%

Stable -Phase out,

18%

Stable, 35%

Phase in, 19%

Seasonal, 11%

Items

Phase out, 1%Stable -

Phase out,

19%

Stable, 69%

Phase in, 9%

Seasonal, 1%

Demand

46% 42% 3% 1%

6% 2% 1% 2%

34% 19% 12% 3%

33% 2% 7% 24%

19% 3% 8% 8%

61% 0% 6% 54%

% Item Locations 65% 23% 12%

% Demand 5% 15% 80%

High

Con Agra

Low Med

Dem

and

Var

iab

ility

High

Med

Low

Demand Velocity

Company F

36% 29% 5% 2%

7% 2% 2% 4%

38% 19% 13% 5%

23% 3% 6% 13%

27% 4% 12% 11%

70% 1% 7% 63%


% Demand 5% 15% 80%

Del Monte

De

man

d V

aria

bili

ty

Med

High

Med High

Low

Demand Velocity

Low

Company D

24% 22% 2% 0%

4% 1% 1% 2%

41% 27% 11% 4%

26% 3% 6% 17%

34% 10% 12% 13%

70% 1% 8% 61%


% Demand 5% 15% 80%

Med

Church & Dwight

Dem

and

Var

iab

ility

High

High

Med

Low

Low

Demand Velocity

Company G

55% 46% 8% 1%

10% 3% 5% 3%

34% 11% 13% 10%

48% 2% 9% 38%

11% 2% 2% 7%

42% 0% 2% 40%


% Demand 5% 15% 80%

Med High

Dem

and

Var

iab

ility

High

Med

Low

Low

Henkel

Demand Velocity

Company H

24% 23% 1% 0%

2% 1% 0% 0%

36% 25% 9% 3%

18% 3% 6% 9%

40% 9% 13% 18%

81% 1% 9% 70%


% Demand 5% 15% 80%

De

man

d V

aria

bili

ty

High

Med

Low

Low Med High

Demand Velocity

Nestle -Beverage Company C

57% 36% 16% 5%

19% 3% 8% 8%

35% 12% 12% 11%

52% 1% 6% 44%

8% 1% 3% 4%

29% 0% 1% 27%


% Demand 5% 15% 80%

Nestle - Confectionary

Dem

and

Var

iab

ility

High

Med

Low

Low Med High

Demand Velocity

Company E

32% 30% 1% 0%

3% 2% 1% 1%

35% 20% 10% 5%

23% 2% 6% 14%

34% 7% 10% 16%

74% 1% 8% 65%


% Demand 5% 15% 80%

Demand Velocity

Nestle - Prepared Foods

De

man

d V

aria

bili

ty

High

Med

Low

Low Med High

Company B

11% 11% 0% 0%

1% 0% 0% 0%

19% 16% 3% 0%

4% 2% 1% 0%

70% 13% 22% 35%

95% 3% 13% 79%


% Demand 5% 15% 80%

Nestle - Infant Nutrition

De

man

d V

aria

bili

ty

High

Med

Low

Low Med High

Demand Velocity

Company A

Highest FCA Most Low Variability Most High Velocity

Lowest FCA 2 nd Most High

Variability

Designing Tailored Supply Chain Networks Demand Characteristics Drive Demand Planning Strategies

Designing Tailored Supply Chain Networks Demand Characteristics Drive Inventory Deployment

26

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



0

10

20

30

40

50

60

70

80



0

10

20

30

40

50

60

70

80



0

10

20

30

40

50

60

70

80



0

10

20

30

40

50

60

70

80



0

10

20

30

40

50

60

70

80



0

10

20

30

40

50

60

70

80


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

0

10

20

30

40

50

60

70

80



Candidates for Centralization

Candidates for Full Stocking or

Direct Ship

Designing Tailored Supply Chain Networks Product Characteristics Drive Flow Paths

27

59,619 Total Items

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.

Determine Bulk & Direct to Store Articles

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

Determine Stored vs. Not Stored

Determine Cross Dock vs. Flow Through

• 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

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

Dir

ect

to

Sto

re

Sto

rag

e

Cro

ss D

ock

Flo

w T

hro

ug

h

Focus Areas in Supply Chain Network Design

• Customer/Channel Segmentation

• Flow Path

• Service Level Strategy

• Service Territory Alignment

• Inventory Deployment

• Mode Usage

• Supply Chain Risk Assessment

• Master/Tactical Planning

• Social Responsibility

• Network Optimization

• Inventory Optimization

• Simulation

• Transportation Modeling

• Total Cost-to-Serve

• Portfolio Management

Types of Analysis Modeling Technologies

http://www.chainalytics.com/partners/solutionPartners.asp


http://www.smartops.com/

http://www.oco-inc.com/index.htm

http://www.barloworldscs.com/bwlogistics/content/en/supply-chain-software-home

http://www.riverlogic.com/contactus


Reasons US Companies Initiate Network Studies Cost Reduction Clear Leader and Growing

29

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

Change

2%

Assess 3PL Outsourcing2%

Increase Service1%

Trend Last

10 Years

Trend Last

5 Years

Source: 184 Chainalytics’ Employee Project Experiences

Periodic vs. Continuous Analysis Approach Keeps Network In Tune, Ability to React

30

EF

FO

RT

N

ET

WO

RK

CO

ST

S

Lost

Opportunity Actual $

Optimal $

Typically 24+ Months

TIME

Periodic

EF

FO

RT

N

ET

WO

RK

CO

ST

S TIM

E

Initial

Study

Actual $

Optimal $

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

business

• 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

Continuous

Design Projects Supported by Analytics and Optimization Results Typically Contrary to Conventional Wisdom

31

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 of

Existing

2 Optimal

Machine

Deployment

3a Close

Garland

3b Close St Joe

% M

ake

Commodity Wire M/B Filler M/B Pocket Only M/B Sewn Book M/B

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.

138.3

129.7129.2

127.2

124.8

124.1

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.

Inventory Projects Supported by Analytics and Optimization Results Typically Contrary to Conventional Wisdom

32

% o

f S

KU

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 & Office

Equipment

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%.

Inventory Deployment Strategy: Consolidation of Locations Increases Product Velocity and Reduces Demand Variability

33

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

Inventory Deployment Strategy: Postponement

What is postponement? • Economical delay of committing materials

and product for as long as possible

• Holding of inventory in a less finished state.

• Pushing the point of product differentiation

closer to the customer

What are the benefits of postponement? • Gain agility in supply chain

– More responsive to market opportunities

– Offer wider range of customization options

– Decreased reliance on finished good forecasting in times of economic uncertainty and shorter product lifecycles

• Economic Benefits – Allows companies to take advantage of low labor markets

for building standard/component parts while achieving

– Reductions of 30-40% reductions in inventory(1) • Decrease lead times and lead time variability for finished

goods

• Inventory held in less finished state – lower carrying costs

– Increased Revenue - higher fill rates to customer orders

• Manage constrained capacity more effectively – Don’t build what you may not need

– Increased importance as economy recovers

Where to hold? What form to hold in?

Postponement Opportunities

High

Product

Variety

High

Demand

Uncertainty

Short

Product

Lifecycles

Global

Supply Chain

Customization

not Costly

High Value

Add at End

of Chain

Postponement

Candidates

Postponement Example Current State

Procure Finished Products

Including Retail Configurations

Forward Deploy Finished

Products at DCs

Consolidate Shipments

Deconsolidate Shipments

Ship To Customers

Postponement Example Delay Retail Display Configurations in Asia


Stock Finished Product


Flow Thru DCs To Customers

Assemble to Order Retail

Display Configurations

Postponement Example Delay Retail Display Configurations in U.S.


Forward Deploy Finished

Products at DCs



Ship To Customers

Assemble to Order Retail

Display Configurations

Postponement Example Delay Final Assembly of Finished Goods in U.S.

Procure Components

Ship Components to

Plants



Ship To Customers

Assemble to Order Finished Products and

Retail Configurations

S&OP Analytical Enablers Roadmap Continue S&OP

Maturity Journey

•AS is Assessments

•Maturity Models•BIC Practices

•Metrics Performance

II An

ticip

atin

gIII

Col

labo

ratin

gIV

Orc

hest

ratin

g

Integrate with Financial Planning

Establish Product Portfolio Management

Create Total Cost-to-Serve Models

Establish Customer Segmentation

Overtly Inject Risk into Planning

Design/Execute Tailored Supply Chain Networks

Incorporate Scenario Planning

•Invite Finance to table•Synchronize financial and

operational plans

•Consensus plan Budget

impact

•Supply Chain Cost•Sales/Mkt/Admin Cost

•Commercial Items

•Profit Focused•Strategic Time Frame

•Cross-Functional Team

•Product Lifecycle Plan

•Prof it Focused

•Strategic/Tactical Time

Frame

•Cross-Functional Team

• Policy/Customer Service

Adjustments

•Enterprise-wide Risk

Management Framework

•Uncertainty & Complexity

Templates & Tactics

•Predictive Analytics using

Probabilistic Methods

•Risk Response Plans

•Supply Chain Design

• Network, Inventory, and

Transportation Opt

•On-going analysis

Questions & Answers

CSCMP India 2012 Conference 1-2 June Mumbai, India ITC ...€¦ · inventory management, general supply chain planning tools, inventory optimization what if analysis for demand shaping,

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