Product Portfolio Management at HP
A Case Study in Information Management
ISM 158: Business Information Strategy
April 13, 2010
Outline
• The benefits and challenges of product variety
• Analytics for variety management
• Implementation and impact at HP
2
Product variety at HP today
Over 2,000 laser
printers
3
Over 20,000
enterprise server
& storage SKUs
Over 8,000,000
possible desktop
& notebook PC
configurations
Why offer product variety?
• Expand market reach – offer something for everyone
–Many geographies
–Many customer types (consumer, small-to-medium business,
enterprise)
–Many industries (healthcare, technology, energy, government…)
• Be a “one stop shop” – offer comprehensive solutions
• Increase brand visibility
• Win marketshare
4
Challenges of product variety
Company
Suppliers
Customers
5
• Product design costs
• Sales & marketing costs
costs
• Administrative costs
• Sales Productivity costs
• Inventory-driven costs
•
•
•
•
Availability / stockouts
Delivery time predictability
Order cycle time
Confusion
• Forecast inaccuracies
• Inventory-driven
• Obsolescence costs
Challenges of variety:
illustration of inventory driven costs
Two similar laptop models:
•
•
•
•
The two laptop models have independent, identically
distributed random demand D1, D2 in each week.
Variance of D1, D2 is 2.
“Safety stock” inventory of each product is typically k
where k is a constant related to the desired service
level.
Total safety stock: 2k
Pool into a single laptop model:
•
•
•
6
Assume no loss in demand (total random demand for
single product is D=D1+D2.)
Variance of D=D1+D2 is 22.
If we apply the same service level objective, then
required safety stock for the pooled product is (2)k.
By pooling demand from two independent products with equal volumes, the
required safety stock and associated inventory-driven costs is reduced by (22)/2 = 29%.
Variance of D1+D2
Var(D1+D2)
= E[(D1+D2 – E(D1+D2))2]
= E[(D1+D2 – 2)2] where  = E[D1] =E[D2]
= E[((D1 – ) + (D2 – ))2]
= E[(D1 – ) 2] + E[(D2 – )2]
+ 2E[(D1 – )(D2 – )]
= Var[D1] + Var[D2] + 2Cov[D1,D2]
Since D1,D2 are independent, then:
Var(D1+D2) = Var[D1] + Var[D2] = 22
7
The organizational divide
Marketing
Marketing
8
Supply Chain
More platforms
More skus
More features
Better forecasting
Precise buffer stocks
Less inventory
More market
share
More choices
Lower cost
Shorter order cycle
Reliable deliveries
Product Variety Management Lifecycle
Pre-launch Variety Management
• Before bringing a product to
market, estimate its Return On
Investment (ROI)
• Explicitly consider the costs of
variety in this ROI analysis
9
Post-launch Variety Management
• After products have been
launched, use sales data to
maximize value from the
existing portfolio
Outline
• The benefits and challenges of product variety
• Analytics for variety management
• Implementation and impact at HP
10
Post-launch variety management
• Use order history to understand products’ relative
importance
– Evaluate unimportant products for discontinuance
– Improve operational focus on key products. For example:
• Divert limited resources toward forecasting & managing key products
• Allocate inventory budget toward key products to improve availability
• How to evaluate products relative importance from order
history?
– Rank by revenue
– Rank by units shipped
– ….
Limitations of simple product
rankings
• Ignores interdependencies among products
Order coverage
• A customer order is covered by a product portfolio if all
of its products are included in the portfolio
covered order
non-covered
order
A product portfolio
• Order, revenue or margin coverage of a portfolio is the
number, revenue or margin of historical orders that can
be completely fulfilled from the portfolio
13
Designing a product portfolio to
maximize coverage
• Problem statement: Given a portfolio size n, find the
portfolio of n products that maximizes revenue coverage
relative to a given set of recent orders
A diversion: a brief introduction to
linear programming
Decision variables x = x1
x2
ct x
Maximize
Subject to:
Ax  b
x0
xn
Linear objective function c t x
c t = (c1, c2, …, cn) is an n-vector of
objective coefficients
Linear constraints A x  b, x  0
A =
a11 a12 … a1n
a21 a22 … a2n
…
Solution technique:
the Simplex Method
(George Dantzig, 1947)
am1 am2 … amn
b=
b1
b2
bm
is an mvector
of resources
is an m x n matrix of
constraint coefficients
A diversion: integer linear
programming
Maximize ct x
Subject to:
Ax  b
Integer-valued Decision variables x
=
x1
x2
xn
Linear objective function c t x
c t = (c1, c2, …, cn) is an n-vector of
objective coefficients
xi 0,1,2,…., i =1,…,n
Linear constraints A x  b
A =
Solution technique:
Branch-and-Bound
and variations
a11 a12 … a1n
a21 a22 … a2n
…
am1 am2 … amn
b=
b1
b2
bm
is an mvector
of resources
is an m x n matrix of
constraint coefficients
Designing a product portfolio to
maximize coverage
• Problem statement: Given a portfolio size n, find the
portfolio of n products that maximizes revenue coverage
relative to a given set of recent orders
• An integer programming formulation:
Maximize o Ro yo
Objective
function
Subject to:
yo  xp for each (o,p)
where product p is in order o
p xp  n
xp ,yo 0,1 Decision variables
Constraints
Notation
xp=1 if product p is included
yo=1 if order o is covered
Ro revenue of order o
Revenue Coverage Optimization Tool
(RCO)
• Rank products according to
their importance to revenue
coverage
• RCO ranking corresponds to
efficient frontier of revenue
coverage and portfolio size
• Use RCO ranking to identify:
– Core Portfolio
– Extended Portfolio
– Possible candidates for
discontinuance
% of revenue covered
100
80
60
40
20
0
0
18
300
600
900
# of products
1200
Evolution of RCO formulation
Integer
Program
IP(n)
IP(n): Find product set of size
n that maximizes total revenue
of orders covered.
Notation:
Maximize o Ro yo
xp=1 if product p is included
Subject to:
yo  xp if product p is in order o
p xp  n
xp ,yo 0,1
yo=1 if order o is covered
Ro revenue of order o
Evolution of RCO formulation
Integer
Program
IP(n)
Lagrangian
Relaxation
LR()
IP(n): Find product set of size
n that maximizes total revenue
of orders covered.
LR(): Maximize revenue of
covered orders minus lambda
times portfolio size.
Maximize o Ro yo
Maximize o Ro yo - (p xp)
Subject to:
yo  xp if product p is in order o
p xp  n
xp ,yo 0,1
Subject to:
yo  xp if p is in order o
0  x p, y o  1
“Selection problem”
Evolution of RCO formulation
Integer
Program
IP(n)
Lagrangian
Relaxation
LR()
LR(): Maximize revenue of
covered orders minus lambda
times portfolio size.
Min s-t cut is an optimal solution to
selection problem (Balinsky 1970)
•
Maximize o Ro yo - (p xp)
Subject to:
yo  xp if p is in order o
0  x p, y o  1
“Selection problem”
Parametric
Bipartite Max
Flow Problem

orders
product


s

min cut
R1
s

.
.
.
t


.
.
.
Max flow  min cut (FordFulkerson)
Rn
Performance evolution
Integer
Program
IP(n)
CPLEX
days +
memory
limitations
Prior algorithm for HPLabs
Lagrangian
bipartite
SPMF arc
Relaxation
parametric
balancing
LR()
max flow
CPLEX
hours
+
memory
limitations
C++
20 minutes
for many 
values
HPLabs
SPMF vertex
balancing
C++
C++
2 minutes
for all 
10 seconds
for all 
Computation times on Personal Systems Group’s typical
worldwide 3 month order data
22
Comparison to traditional ranking
• RCO
• Revenue impact
• Maximum order revenue
• Units shipped
• Revenue generated
23
Outline
• The benefits and challenges of product variety
• Analytics for variety management
• Implementation and impact at HP
24
Product discontinuance decisions
• Take aim at products in the tail of the ranking
• These products don’t generate much revenue of their own,
nor do they enable sales of other high-revenue products
• This analysis enabled fact-based discussions between
marketing and sales organizations
• It led to discontinuance of over 3000 products since 2004
% of revenue covered
100
80
60
40
20
0
0
300
600
# of products
25
900
1200
The Recommended Offering program
• Define Recommended Offering: the top ranked products
covering 80% of revenue
• Shift inventory investment to Recommended Offering
products
• Offer customers quick delivery time on orders that are
completely within the Recommended Offering
• Significantly improved order cycle time & competitiveness
% of revenue covered
100
80
60
40
20
0
0
26
300
600
# of products
900
1200
Summary of business impact
• Over $500M in savings and $180M in ongoing annual savings
• Significant order fulfillment improvements
• Thousands of SKUs eliminated
Marketing
Analytics
Supply Chain
Fact-based discussions
Data-driven decisions
Power of analytics
Our customers are the real OR winners!
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Takeaways
• The benefits and challenges of product variety
• Perspectives of different organizations within a firm on
product variety
• Metrics to understand product importance from order
history
• How effective use of analytics can bridge the
organizational divide and bring about operational
efficiencies and competitive advantage
28
Thank you