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68 Part I • Decision Making and Analytics: An Overview

2.1 Opening Vignette: Decision Modeling at Hp Using
Spreadsheets

HP is a major manufacturer of computers, printers, and many industrial products. Its vast
product line leads to many decision problems. Olavson and Fry (2008) have worked on
many spreadsheet models for assisting decision makers at HP and have identified several
lessons from both their successes and their failures when it comes to constructing and
applying spreadsheet-based tools. They define a tool as “a reusable, analytical solution
designed to be handed off to nontechnical end users to assist them in solving a repeated
business problem.”
When trying to solve a problem, HP developers consider the three phases in devel-
oping a model. The first phase is problem framing, where they consider the following
questions in order to develop the best solution for the problem:

• Will analytics solve the problem?
• Can an existing solution be leveraged?
• Is a tool needed?
The first question is important because the problem may not be of an analytic nature,
and therefore, a spreadsheet tool may not be of much help in the long run without fixing
the nonanalytical part of the problem first. For example, many inventory-related issues
arise because of the inherent differences between the goals of marketing and supply
chain groups. Marketing likes to have the maximum variety in the product line, whereas
supply chain management focuses on reducing the inventory costs. This difference is par-
tially outside the scope of any model. Coming up with nonmodeling solutions is impor-
tant as well. If the problem arises due to “misalignment” of incentives or unclear lines
of authority or plans, no model can help. Thus, it is important to identify the root issue.
The second question is important because sometimes an existing tool may solve a
problem that then saves time and money. Sometimes modifying an existing tool may solve
the problem, again saving some time and money, but sometimes a custom tool is neces-
sary to solve the problem. This is clearly worthwhile to explore.
The third question is important because sometimes a new computer-based system
is not required to solve the problem. The developers have found that they often use
analytically derived decision guidelines instead of a tool. This solution requires less time
for development and training, has lower maintenance requirements, and also provides
simpler and more intuitive results. That is, after they have explored the problem deeper,
the developers may determine that it is better to present decision rules that can be eas-
ily implemented as guidelines for decision making rather than asking the managers to
run some type of a computer model. This results in easier training, better understanding
of the rules being proposed, and increased acceptance. It also typically leads to lower
development costs and reduced time for deployment.
If a model has to be built, the developers move on to the second phase—the actual
design and development of the tools. Adhering to five guidelines tends to increase the
probability that the new tool will be successful. The first guideline is to develop a proto-
type as quickly as possible. This allows the developers to test the designs, demonstrate
various features and ideas for the new tools, get early feedback from the end users to
see what works for them and what needs to be changed, and test adoption. Developing
a prototype also prevents the developers from overbuilding the tool and yet allows them
to construct more scalable and standardized software applications later. Additionally, by
developing a prototype, developers can stop the process once the tool is “good enough,”
rather than building a standardized solution that would take longer to build and be more
expensive.

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