Return on investment of decreasing the time to market.



Return on investment of decreasing the time to market.


ROI calculations are a popular way of motivating investments. In the engineering world, these are mainly focused on cost savings though. But what about the monetary value of reducing the time to market? Novator Solutions try to put a number of the true value of investments aimed at reducing development time, using our ROI Estimator. By adding the cost savings of reducing the development cost, and the increased earnings of extending the product lifetime – which sooner or later gets surpassed by other technology, maybe even yor own – a strong case is built for investing in tools that help shorten your development time.


ROI calculation: A holy grail

During a workshop around hardware-in-the-loop (HIL) testing we ran into an interesting problem statement. The question concerned the return on investment (ROI) when spending resources on decreasing the time to market. By leveraging the likes of HIL testing, bugs in embedded software may be detected earlier in the product development cycle. Finding bugs early makes fixing them faster and less painful, and more time can be spent on innovating. This shortens the development cycle and time to market.

Most people would agree that decreasing the time to market is highly profitable. Few could put a number on just how big an impact an improvement of, say, two weeks would have though. Even fewer would be confident in their estimate. What we started to do was to go through the exercise of building a model of this with the purpose of getting a ballpark figure of the impact a change in development time has on the profitability of a product.

This blog post walks you through the thought process and presents the model we created. The model is of course a crude approximation of reality and should be used as such, but if you are able to accept our assumptions, you will be able to use our model to estimate the magnitude of savings your organization can achieve by a particular effort that shortens the development time, all other factors staying the same.


Developing the model

To start, we needed to make some assumptions on how the revenue that a product produces is distributed across the product life cycle. Also, we had to define some parameters for our estimates. We introduced the product lifetime as the time before a competing product, or disruptive technology has completely taken over the market. We also set the expected number of units sold during this time, if the time plan would be perfectly executed.

Finally, we assumed that the revenue per time unit follows a gaussian distribution and this curve can be normalized using the parameters we defined. The rationale was that when a product is introduced to the market, it starts slow, but then sales is picking up and increases as more customers adopts the product and the word spreads. As the product matures, the growth is slowing down and, at some point, the revenue rate decreases towards zero as competition (external or internal) displaces the product and the product eventually becomes obsolete. As for the validity of the gaussian assumption, it is more likely to assume steeper inclines on both edges. This would only result in an even higher impact to the revenue and profit of the time to market.

Approximation of a products revenue per day from its release

Figure 1: Approximation of a products revenue per day from its release.

Increased revenue

We then wanted to introduce a delay in the release date. This should have no impact of the later part of the product lifecycle as the eventual obsolescence of the product is assumed to be depending on external factors and not decided by when it was released. We therefore decided to lock the EOL date and scale the curve appropriately. This gives us an estimate on how a changed release date impacts the overall revenue over the product lifetime.

The revenue difference is equal to the area difference of the scheduled and actual curves in figure 2a) and 2b). We call this number the missed opportunity revenue and it is a positive number when we have a delay. If the product is a released ahead of schedule, this number becomes negative. This means a net gain in the revenue because of the increased sales in the early stage of the life cycle.


Decreased development cost

When a product release is delayed, the development team needs to spend more time working with the product. The cost of this increased time is in general significant. It accumulates all the salaries and costs for the development team that needs to be allocated to continue the process and release the product. Using the same argument, we assume that releasing the product early frees up these resources, i.e. cost is reduced.

Naturally, there are a whole lot of expenses of different origin associated with the product. This includes e.g. manufacturing, marketing, sales, support, and distribution. These costs can be assumed to be the same regardless of the time to market. In our context we thus consider them a per-unit cost. Therefor they are covered in the model by the operating profit margin. This includes all the expenses mentioned above and all other costs associated with the product, except for the development cost of the product which is treated explicitly.


Impact on profitability

We now have all the pieces needed to make a quantitative estimate of what a delay would cost in terms of missed profit. Or equally well; how much would the profitability be improved if the development time was reduced? The profit from the product is the integrated revenue curve, scaled by our operating profit margin, from which the cost of development must be subtracted. The result is illustrated in figure 3.

As seen from the figure, a delay of one month already has a significant impact on the product’s profitability. The point where the curve crosses the x-axis is called the break-even time. This signifies the time when the product has paid back the development investment. As would be expected, a delay in the release significantly postpones the break-even point.

If you are considering an investment aimed at decreasing your time to market, this is the number you should divide the cost of that invesment with to see your true ROI. Feel free to try our model out on your own product using our ROI Estimator, developed in the LabVIEW NXG Web Module.

Do you want to shorten your time to market, by making your embedded software testing more efficient? Please give our sales manager Johan Hillergren a call on +46-702 148 203.

Get your measurement data displayed in graphs directly in your web browserRevenue lost due to delayed release

Figure 2a): Assuming that the products end-of-life-date is decided by external factors, an approximation of the loss caused by a delayed release date can be calculated.

Approximate increase in revenue due to earlier than expected release

Figure 2b): Likewise, releasing the product earlier than expected causes an increased earning over the lifetime of the product.

Profit loss from missed opportunity and increased development cost

Figure 3: A delay impacts the profit even harder than the revenue. It adds both the missed opportunity and the increased development cost.



Do you want to shorten your time to market, by making your embedded software testing more efficient? Please contact Jonas Mäki for more information:

Tel: +46 – 76 610 13 99


We are looking forwar to hearing from you!