cscmp india 2012 conference 1-2 june mumbai, india itc ...€¦ · inventory management, general...
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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
Who is Chainalytics?
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
Who is Chainalytics?
Asia Market Growth and Implications
Sales & Operations Planning Introduction
Analytical Enablers for Effective S&OP
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
Who is Chainalytics?
Asia Market Growth and Implications
Sales & Operations Planning Introduction
Analytical Enablers for Effective S&OP
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
• Customer Business
Plans
• Product Allocation
Logistics Planning
• Historical Demand
• 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
• 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
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
Who is Chainalytics?
Asia Market Growth and Implications
Sales & Operations Planning Introduction
Analytical Enablers for Effective S&OP
Integrated
Financial Planning
Scenario
Planning
Analytical Enablers for Effective S&OP
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
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$
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$
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%
% Item Locations 52% 30% 18%
% 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%
% Item Locations 59% 24% 17%
% 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%
% Item Locations 59% 23% 19%
% 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%
% Item Locations 57% 22% 21%
% 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%
% Item Locations 49% 31% 20%
% 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%
% Item Locations 57% 22% 22%
% 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%
% Item Locations 39% 26% 35%
% 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
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
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
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
Procure Finished Products
Stock Finished Product
Deconsolidate Shipments
Flow Thru DCs To Customers
Assemble to Order Retail
Display Configurations
Postponement Example Delay Retail Display Configurations in U.S.
Procure Finished Products
Forward Deploy Finished
Products at DCs
Consolidate Shipments
Deconsolidate Shipments
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
Consolidate Shipments
Deconsolidate Shipments
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