product portfolio management at hp a case study in information management ism 158: business...
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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
3
Over 2,000 laser printers
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
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5
Challenges of product variety
Company
Customers
Suppliers
• Product design costs • Forecast inaccuracies
• Sales & marketing costs • Inventory-driven costs
• Administrative costs • Obsolescence costs
• Sales Productivity costs
• Availability / stockouts• Delivery time predictability • Order cycle time• Confusion
• Inventory-driven 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:• Assume no loss in demand (total random
demand for single product is D=D1+D2.)
• Variance of D=D1+D2 is 22.
• If we apply the same service level objective, then required safety stock for the pooled product is (2)k.
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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] = 22
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The organizational divide
Supply Chain
Better forecastingPrecise buffer stocksLess inventory
Lower costShorter order cycle Reliable deliveries
Marketing
More platformsMore skusMore features
More market shareMore choicesHappier customers
Marketing
Post-launch Variety Management
Product Variety Management Lifecycle
• Before bringing a product to market, estimate its Return On Investment (ROI)
• Explicitly consider the costs of variety in this ROI analysis
• After products have been launched, use sales data to maximize value from the existing portfolio
9
Pre-launch Variety Management
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
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Order coverage
• A customer order is covered by a product portfolio if all of its products are included in the 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
A product portfolio
covered order
non-covered order
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
Maximize ct x Subject to:
A x b x 0
Solution technique: the Simplex Method (George Dantzig, 1947)
a11 a12 … a1n
a21 a22 … a2n
…
am1 am2 … amn
b1 b2
bm
is an m-vector of resources
b =
x1 x2
xn
Decision variables x =
Linear objective function c t x c t = (c1, c2, …, cn) is an n-vector of objective coefficients
Linear constraints A x b, x 0A = is an m x n matrix of
constraint coefficients
A diversion: integer linear programming
Maximize ct x Subject to:
A x b
Solution technique: Branch-and-Bound and variations
a11 a12 … a1n
a21 a22 … a2n
…
am1 am2 … amn
b1 b2
bm
is an m-vector of resources
b =
x1 x2
xn
Integer-valued Decision variables x =
Linear objective function c t x c t = (c1, c2, …, cn) is an n-vector of objective coefficients
Linear constraints A x bA = is an m x n matrix of
constraint coefficients
xi 0,1,2,…., i =1,…,n
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
Subject to: yo xp for each (o,p)
where product p is in order op xp n xp ,yo 0,1
Notation
xp=1 if product p is
included
yo=1 if order o is covered
Ro revenue of order o
Objective function
Decision variables
Constraints
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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 products
% o
f re
venu
e
covere
d
0 300 600 900 12000
20
40
60
80
100
Evolution of RCO formulation
IP(n): Find product set of size
n that maximizes total revenue of orders covered.Maximize o Ro yo
Subject to: yo xp if product p is in order op xp n xp ,yo 0,1
Integer Program
IP(n)
Notation:
xp=1 if product p is
included
yo=1 if order o is covered
Ro revenue of order o
Evolution of RCO formulation
LR(): Maximize revenue of covered orders minus lambda times portfolio size.
Maximize o Ro yo - (p xp)
Subject to: yo xp if p is in order o0 xp, yo 1
“Selection problem”
Lagrangian Relaxation
LR()
Integer Program
IP(n)
IP(n): Find product set of size
n that maximizes total revenue of orders covered.Maximize o Ro yo
Subject to: yo xp if product p is in order op xp n xp ,yo 0,1
• Min s-t cut is an optimal solution to selection problem (Balinsky 1970)
Max flow min cut (Ford-Fulkerson)
Evolution of RCO formulation
LR(): Maximize revenue of covered orders minus lambda times portfolio size.
Maximize o Ro yo - (p xp)
Subject to: yo xp if p is in order o0 xp, yo 1
“Selection problem”
.
.
.
.
.
.
products
orders
t
R1
Rn
Parametric Bipartite Max Flow Problem
s
Lagrangian Relaxation
LR()
Integer Program
IP(n)
min cut
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Performance evolution
Integer Program
IP(n)
Lagrangian Relaxation
LR()
Prior algorithm for bipartiteparametric max flow
days +memory
limitations
hours+
memory limitations
20 minutes
for many values
Computation times on Personal Systems Group’s typical worldwide 3 month order data
CPLEX C++CPLEX
HPLabs SPMF arc balancing
2 minutes for all
C++
HPLabs SPMF vertex
balancing
10 seconds for all
C++
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Comparison to traditional ranking
• RCO
• Revenue impact
• Maximum order revenue
• Units shipped
• Revenue generated
Outline
• The benefits and challenges of product variety
• Analytics for variety management
• Implementation and impact at HP
24
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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
% o
f re
venu
e
covere
d
0 300 600 900 12000
20
40
60
80
100
# of products
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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 products
% o
f re
venu
e c
overe
d
0 300 600 900 12000
20
40
60
80
100
27
Summary of business impact
• Over $500M in savings and $180M in ongoing annual savings
• Significant order fulfillment improvements• Thousands of SKUs eliminated
Our customers are the real OR winners!
Marketing
Supply Chain
Fact-based discussionsData-driven decisionsPower of analytics
Analytics
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
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