HP Product Variety Management

KEL571 Revised March 14, 2011

©2010 by the Kellogg School of Management at Northwestern University. This case was prepared by Ilya Kolesov ’09 and Professors Gad Allon and Jan A. Van Mieghem. The authors gratefully acknowledge the assistance of HP’s Kathy Chou, Deborah-Anna Reznek, and Julie Ward, and of Kellogg PhD candidate Seyedmorteza Emadi. Cases are developed solely as the basis for class discussion. Cases are not intended to serve as endorsements, sources of primary data, or illustrations of effective or ineffective management. To order copies or request permission to reproduce materials, call 800-545-7685 (or 617-783-7600 outside the United States or Canada) or e-mail custserv@hbsp.harvard.edu. No part of this publication may be reproduced, stored in a retrieval system, used in a spreadsheet, or transmitted in any form or by any means—electronic, mechanical, photocopying, recording, or otherwise—without the permission of the Kellogg School of Management.


HP Product Variety Management

Deborah-Anna Reznek, analytical business consultant in the strategic planning and modeling group, had joined Hewlett-Packard (HP) in 2008 after graduating from the MBA program at the Kellogg School of Management at Northwestern University. She had been working on issues related to product variety management for the last several months. The growth in HP’s product variety had caused significant organizational complexity, created major operational and performance challenges, and threatened to cause HP to fall behind its competitors in a number of areas. Julie Ward, a 1995 PhD graduate from Stanford University and principal scientist in the business optimization lab at HP, was helping Reznek with her work. Kathy Chou, vice president of sales at HP, asked Reznek to come up with some tangible results by the end of next week to figure out issues related to product variety management.

Reznek had been fortunate in her work to gain exposure to various departments at HP. She had become very familiar with how HP normally tackled product variety management, collaborating with the marketing team, which supplied the demand forecast data to operations to derive yearly product order quantities.

HP was the world’s largest technology company by 2009, serving more than 1 billion customers in more than 170 countries on six continents. It was composed of three business units: the Personal Systems Group, the Imaging and Printing Group, and the Technology Services Group. HP produced twenty distinct product lines, including enterprise storage and servers, personal systems, and imaging and printing, along with software services. Variety in the product portfolio enabled HP to meet the needs of diverse customers, but by 2008 there were signs that the gains from additional variety might be diminishing due to the inability to manage so much product variety. While there was a high demand for many of HP’s products, other products appeared only infrequently in customer orders or were of little strategic importance to the business.

HP’s Personal Systems Group (PSG) was a $35 billion business in 2009. This business included HP’s commercial and consumer desktop and notebook PC divisions, as well as those for workstations, handheld computing, and digital entertainment product lines. As the product portfolio grew within PSG, product variety led to increased complexity within the group. PSG needed a systematic and data-driven approach to product differentiation.

This document is authorized for use only in Angela Montgomery’s MSPM 6130 Budgeting and management of Operations course at Laureate Education – Baltimore, from June 2017 to July 2018.



HP Expands Its Product Offering

HP was founded in January 1939 by Bill Hewlett and Dave Packard, classmates at Stanford University. The company’s first product was a resistance-capacitance audio oscillator (HP 200A), an electronic instrument used to test sound equipment. The Walt Disney Studios, one of HP’s first customers, purchased eight oscillators to develop and test the sound system for the movie Fantasia.

HP was formally incorporated on August 18, 1947. Dave Packard was appointed president and Bill Hewlett, vice president. After the successful acceptance of its product and surging demand from the U.S. government for electronic equipment on account of World War II, the company moved from a garage behind Packard’s house to a building in Palo Alto.

The company announced its initial public offering in 1957 at $16 per share. In 1958 HP acquired F. L. Moseley Company of Pasadena, California, a producer of high-quality graphic recorders. F. L. Moseley was HP’s first acquisition and became the forerunner to HP’s printer business. HP went global by establishing a manufacturing plant in Boeblingen, Germany, and marketing operations in Geneva, Switzerland, in 1959.

During its journey to become the world’s largest technology company, HP greatly expanded its offering to meet a growing variety of customer needs. With 2009 revenues of $114 billion and 304,000 employees, HP was a global market leader in PCs, printers, and servers. In 2009 HP shipped 48 million PCs annually and more than 1 million printers weekly. Customers often placed orders for several products. The variety at HP was prevalent not only in desktop PCs, but in nearly every offering, comprising more than 2,000 different laser printer stock-keeping units (SKUs), more than 15,000 server and storage SKUs, and more than 8 million possible configure- to-order combinations in its notebook and desktop product lines. Managing the high number of possible SKUs and assembling them into orders had become an increasingly complex and slow process, resulting in falling customer service. These problems were referred to as “the product variety management challenge” within the company.

The Variety Challenge


One challenge contributing to the variety problem was the “configure-to-order” (CTO) option. CTO represented the ability for a user to define the component configuration of a product at the very moment of ordering that product, and a vendor to subsequently build that configuration dynamically upon receipt of the order. HP’s CTO system allowed millions of possible product combinations from seven major desktop models offered at the time. The shortage of a single component could delay the shipment of many orders, leading to poor order fulfillment performance and ultimately loss of business to competitors. The operations group built the PC and shipped it to the customer, ideally within ten days order-to-delivery (OTD) time. As of 2009, HP could configure-to-order more than 8 million notebook and desktop configurations alone. PSG faced the challenges presented by the CTO system as well.

This document is authorized for use only in Angela Montgomery’s MSPM 6130 Budgeting and management of Operations course at Laureate Education – Baltimore, from June 2017 to July 2018.



Costs Versus Benefits of Variety

The real challenge in managing product variety was constructing a true measure of the costs and benefits of product variety. Costs of product line complexity were not captured in standard accounting systems and thus were difficult to measure systematically. HP had several procedures for deciding whether to remove a product from the portfolio. Often the decisions were made based on a product’s individual profit margin or aggregate revenues. However, this approach ignored key elements of the product’s importance—a low-revenue product could be complementary to many of the high-revenue orders.

HP’s PSG faced comparatively low per-SKU costs, but high costs for simultaneously managing inventory and availability on the large number of underlying parts. Due to challenges associated with maintaining adequate availability across its vast product line, PSG’s average OTD was not always competitive. Therefore, resolution of the variety management challenge was becoming a key priority at PSG.

The lack of a clear approach to making these decisions stemmed from the difficulty in understanding variety-driven costs. For example, outside of PSG, high-end imaging and printing products and business-critical servers faced variety-driven costs associated with creation, development, testing, and launching new SKUs. However, standard accounting systems were not designed to track these costs or to tie them to variety. By contrast, marketing teams were proficient at tracking and understanding the benefits of product variety in terms of adding customers. HP needed a systematic process for assessing both costs and benefits before approving new SKU introductions.

Another challenge of product variety management was that there were inconsistencies and redundancies across product line-ups in various geographies, producing a doubling or tripling effect on numbers of SKUs between the Americas, Europe, and Asia.

Current Approaches to Addressing Product Variety Challenges

In an effort to address product variety challenges, HP adopted a “divide and conquer” approach. The company divided the problem into two parts according to the stage of the product, i.e., it had to devise a strategy to determine whether to launch a product (pre-launch) and a strategy for already existing products (post-launch).

Strategy During the Pre-Launch Stage

During the pre-launch stage, HP screened new product proposals before introduction. It evaluated ROI for each proposed product prior to product launch after analyzing upfront and ongoing cost impacts. Products that did not meet a threshold ROI level were excluded before introduction.

To accurately assess ROI, HP identified the major cost drivers and how they were impacted by product variety. Evaluating cost elements and cost structure had been one of the main challenges for HP during this phase. What constituted a majority of the complexity cost? HP tried to balance costs and benefits from adding variety to a product portfolio. First, HP considered

This document is authorized for use only in Angela Montgomery’s MSPM 6130 Budgeting and management of Operations course at Laureate Education – Baltimore, from June 2017 to July 2018.



costs across the complete life cycle, from conception through post-life support. Second, it analyzed the entire business cost structure, including fixed and variable costs, with respect to volume. Variable complexity costs are those associated with having lower part volumes per SKU. These costs include material costs (volume discounts), variability-driven costs (financing, storage depreciation, obsolescence, fire sales) and shortage costs (material price premiums, expediting, lost sales because of shortages). In contrast, fixed complexity costs are those associated with having a larger number of SKUs. These consist of resource costs (R&D, testing, product management), external cash outlays (tooling, costs to contract manufacturer), and indirect impacts of variety (manufacturing switching costs, warranty program expenses, quality impacts, return costs).

HP balanced the complexity costs against its projected marketing and sales benefits. It screened out low-value products, which were not necessarily the same as low-volume products. Screening products by volume overlooked the significant differences in complexity cost among different product types. Volume thresholds and rules of thumb could be useful but only if they adjusted for cannibalization effects and complexity cost differences between different SKU types.

This strategy of product variety evaluation during the pre-launch stage allowed PSG to eliminate low-ROI products before launching them into the portfolio. By 2009, these programs had generated more than $100 million in margin improvements and continued to generate more than $40 million per year for PSG.

Strategy During the Post-Launch Stage

HP decided to initiate a global product offering program in order to make a set of products available worldwide to its largest global customers. Each customer had a preferred set of standard products and wanted those products offered worldwide with a consistent price and components. HP needed an approach to designing a global product offering to replace the existing “best guess” process.

Kathy Chou asked Deborah-Anna Reznek and Julie Ward to focus on product variety management of existing products. Reznek and Ward had to develop a method to understand the tradeoffs in managing product variety in the product portfolio when a history of customer order data was available. During the post-launch stage, the costs of variety became sunk. In the future, HP might focus more on profitability than on revenue optimization during this stage. However, the current main objective for HP was to maximize revenue from the active portfolio: How could HP plan its product availability to maximize order revenue? (See Exhibit 1.)

To address this, Reznek and Ward needed to develop a new metric of product importance that captured the interrelationships among products through orders.

Reznek’s meeting with Chou is approaching and she needs your help.

This document is authorized for use only in Angela Montgomery’s MSPM 6130 Budgeting and management of Operations course at Laureate Education – Baltimore, from June 2017 to July 2018.




Review current product portfolio management at HP and suggest a systematic and data-driven method to support a global product offering program. To be specific, consider the data in Exhibit 1 to address the following:

a. Which SKUs are candidates for a “global core” product offering? For an extended offering? For elimination?

b. How would your portfolio perform in terms of revenue generated vs. number of SKUs included, assuming that an order won’t be captured if one of the SKUs is missing?

This document is authorized for use only in Angela Montgomery’s MSPM 6130 Budgeting and management of Operations course at Laureate Education – Baltimore, from June 2017 to July 2018.



Exhibit 1: Order Data This exhibit shows how revenue varies across orders placed by customers. The orders are placed

within one period. Each row has the following information for each order-product pair:

 Order number—orders are enumerated from 1 to 27

 Product number—products are enumerated from 1 to 41

 Revenue—revenue generated by this product in this specific order

 Units—number of units of the specific product in this order

 Revenue per unit—revenue per unit generated by this product in this specific order

Note that an order may contain several products and thus may span several products.

Order Number

Product Number

Revenue ($) Units

Revenue Per Unit ($)

1 1 6,500 1 6,500 2 2 5,000 10 500 3 3 81,300 10 8,130 4 4 19,500 10 1,950 5 5 44,500 10 4,450 6 6 9,600 3 3,200 7 6 9,600 3 3,200 8 6 9,600 3 3,200 9 6 9,600 3 3,200

10 6 9,600 3 3,200 11 7 25,320 8 3,165 12 5 44,500 10 4,450 13 5 44,500 10 4,450 14 4 15,600 8 1,950 15 4 19,500 10 1,950 16 4 15,600 8 1,950 17 4 11,700 6 1,950 18 8 45.1 22 2.05 18 9 13,405 22 609 18 10 144,348 22 6,561 18 11 3,320 22 151 18 12 35,245 22 1,602 18 13 33,710 44 766 18 14 28,645 22 1,302 19 15 2,380 2 1,190 20 16 0 1 0 20 17 0 1 0 20 18 7,695 1 7,696 20 19 0 1 0 20 20 11,193 1 11,194 20 21 15,148 1 15,148 20 22 0 1 0

. . . . . .

This document is authorized for use only in Angela Montgomery’s MSPM 6130 Budgeting and management of Operations course at Laureate Education – Baltimore, from June 2017 to July 2018.



Exhibit 1 (cont’d) Order

Number Product Number

Revenue ($) Units

Revenue Per Unit ($)

21 16 0 1 0 21 17 0 1 0 21 18 7,696 1 7,696 21 19 0 1 0 21 20 11,194 1 11,194 21 21 15,148 1 15,148 21 22 0 1 0 22 16 0 1 0 22 17 0 1 0 22 18 7,696 1 7,696 22 19 0 1 0 22 20 11,194 1 11,194 22 21 15,148 1 15,148 22 22 0 1 0 23 23 13 1 13 24 24 6,989 4 1,747 25 25 1,004 2 502 26 26 2,880 4 720 26 27 384 1 384 26 28 26,189 8 3,274 26 29 534 2 267 26 30 123 1 123 26 31 31,680 2 15,840 26 32 1,193 2 597 26 33 1,267 2 634 26 34 19,796 2 9,898 26 35 6,758 1 6,758 26 36 350 1 350 26 37 5,915 1 5,915 26 38 84 2 42 26 39 194 1 194 26 40 83,324 1 83,324 26 41 0 1 0 27 37 5,370 1 5,370

This document is authorized for use only in Angela Montgomery’s MSPM 6130 Budgeting and management of Operations course at Laureate Education – Baltimore, from June 2017 to July 2018.

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