Product Portfolio Management at HP A Case Study in Information Management ISM 158: Business...

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

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Product variety at HP today

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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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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

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Pre-launch Variety Management

Outline




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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


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)


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


LR()

Integer Program

IP(n)

min cut

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Performance evolution

Integer Program

IP(n)


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




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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

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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

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Thank you

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