An ROI Model for Time-to-Market
for the Use of EDA tools
by Tets Maniwa
One of the overwhelming issues facing the EDA community is
the need and desire to increase total sales. One of the greatest
hurdles in the ongoing chase to get more seats is the inability
to convert the design software budget dollars into new seat
licenses. Although most large companies have more than adequate
dollars budgeted for software, less than a quarter of the
dollars represent new tool acquisitions. The balance of the
funds are for maintenance, training, and management functions
like parceling out the limited number of seats available.
The inherent value of EDA tools is to provide more automation
to the design task, thereby increasing the individual engineer's
productivity. As an example of the value of a tool, design
for test tools reduce the time for test development and are
able to improve fault coverage over manual methods in the
test to over 90 percent of all faults. The tool leads to better
test coverage of the design resulting in a higher probability
of catching the rare or random errors that make the system
fail. So the tools simultaneously reduce engineering time
and improve test quality by enhancing internal node observability
and controllability. As an added benefit, the window to the
internal nodes makes the system debug and integration much
easier, due to the availability of the internal state data
at the time of failure. So here an additional tool not only
improves the risk-performance equation in its intended department,
but also aids another group in performing the debugging work.
The problems with standard financial models for return on
investment (ROI), however, include the lack of a sense of
time (ROI equals the average return divided by average investment)
and the total lack of connection with the issues that most
concern the engineering managers. The managers are most concerned
with risk reduction, overall productivity, and net increases
in total dollar sales, whereas the standard ROI measures only
look at changes in the direct outputs from the investment.
The greatest problem in approaching the issue from an investment
perspective is the need to quantify the results from a change
before the fact.
Standard financial models display the effects of delays in
product release on costs and revenues, but suffers in this
regard, because they require the quantification of risk factors
and clear estimates of productivity changes. These are exactly
the values that people want to measure, but are also the most
difficult values to determine.
In addition, the direct outputs for new tool acquisitions
are changes in productivity, a metric that the engineering
community abhors because it implies the design task is a quantifiable,
fixed process and not the exercise in creativity and skill
in design that the engineers say it is. Therefore, the attempts
to assign weighting values in the financial analysis to adjust
the productivity creates a conflict for the person who will
be reporting the numbers. A dramatic increase in productivity
implies a large part of what the engineer does can be replaced
by a piece of software. A small increase or a decrease in
productivity implies the tool is not of great value. Neither
of these results is desirable for the EDA community or for
the engineer reporting the numbers.
One reason that financial models break down in the ASIC world
if that the return on investment depends on more than just
the engineering department's efforts. External factors like
market position, pricing, profitability, and product features
are all part of the return portion of the equation, but these
factors are not in the control of the EDA tool purchase decision
maker. The overall history of ASICs has been, unfortunately,
that although over 90 percent of all ASICs pass customer specifications
on the first pass, less than half go into production. If a
new product doesn't go into production, the return on investment
becomes a negative value that has no real relation to the
measurement parameters of productivity.
Another reason that the basic financial models break down
is the need to factor in some adjustment for risk. The relative
productivity changes, as difficult as they are to measure,
are much easier to quantify than risk reduction, because the
level of risk may have no correlation to any dollar amounts.
The addition of a tool may increase the risk due to the down
time to learn the tool, or may cause a large enough change
in the overall design methodology to expose other missing
links in the tool chain. On the other hand, an incremental
tool change can reduce the risk by enabling a more complete
exploration of the design space, thereby ensuring a successful
product design. The risk reduction and productivity improvement
are probably the most difficult parameters to quantify in
assessing the value of a new tool, and the traditional financial
analyses only point out the inability to predict a virtually
unmeasurable future result.
Time-to-Market Model
As an attempt to address some of the other issues in the valuation
of tools, here is a simplified model that combines the traditional
financial items like return on investment with some concepts
from time to market analyses. The traditional inputs for ROI
are the costs for the tools and the savings (in time and money)
as a result of the tools. The new model also incorporates
the estimated reduction in end-item unit volume and ASP for
every month the product release is delayed from the best case
schedule. Despite the statement that productivity and risk
are hard to quantify, the model generates an ROI number as
well as provides a means to evaluate a number of scenarios
to bound the relative risk.
The model is in an Excel workbook with three worksheets. The
assumptions and variables are entered into the first table
called "Inputs". This passes the data to another
worksheet for cost, ROI, and productivity analysis. The final
sheet shows the time-to-market effects of the tools purchase,
in terms of total design costs, size of market, and product
sales. The effects of new tool purchases shows up in the "Impacts"
worksheet, where relatively small changes in product development
time have significant affect on the company's sales numbers.
The number of variables for contributions to the bottom line
are too complex for a general analysis, but are easily available
for more detailed analysis within the company doing the design.
All of the inputs for the analysis are available on the first
page, and are the details you will need to get from the customer.
The values are linked into the following sheets as variables
in fairly simple equations. The pages are protected only to
keep the formula intact. If you find a better algorithm for
the cost/benefit evaluation, please feel free to modify the
spreadsheet, by turning protection off and making your changes.
Note the "Costs " page shows fairly small changes
in productivity and a negative ROI for most cases. This is
the problem with the traditional measurements, one can't always
find much in the way of good news in productivity or ROI for
a standard analysis. If a new tool makes a sufficiently large
change in productivity, the ROI eventually goes positive.
By combining the costs data and the effects on the total product
life revenues, the model provides a means of identifying the
total influence a tool purchase makes on the company's revenues.
In the "Impacts" worksheet, we observe the effects
of tool purchases on the release of the target IC. By adjusting
costs and delays, a user can also get an estimate for the
end-of-life function, which is the cross-over point in a late
introduction where revenue goes below some threshold value.
For some scenarios, this cross-over point is before the design
is completed, and therefore is a useful early indicator that
a design program should be stopped early, rather than expending
resources on a money-losing proposition. If the EDA tool can
help a company recover from this situation, then the tool
truly is of much higher value to the user than just the change
in productivity or some ROI. The value of the tool might be
the salvation of a company.
Tets Maniwa is executive editor for Integrated System Design
magazine. He has also worked in engineering and ASIC marketing.
ROI Model
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