Cisco, Are You Spending the Right Amount on R&D?

Technology companies all face similar issues when it comes to their product portfolio:

  • Relatively short product life-cycles
  • Need to continually innovate
  • Uncertainty of outcomes
  • Build or Buy decisions

We took an outsiders view of Cisco, and its major product lines, excluding its services business which is becoming more important, but will not require major resource investment decisions.

Product information we considered while developing the model included:

  • Size of market
  • Point in product life-cycle (when it was released)
  • Typical life-cycle length for the type of product
  • Investment required to develop new product with similar profile
  • Latest date when a product would still be viable
  • Acquisition cost of companies/products in the market

The data is not exact, but strategy development does not require precise forecasts to give you useful directional guidance.

If we look at the existing portfolio and new product development (NPD) by R&D (or acquisition cost) required (on horizontal axis), and the risk-adjusted Net Present Value (NPV) of the cash flows for each opportunity, it looks like this:


The potential products would be pre-vetted with the usual process to see that they fit within the overall company strategy, that the company has or can acquire the necessary organizational capabilities to develop and deliver the product, and uncertainty has been considered.  In other words, all of the considered projects would be done if resources were available. Also note that the values are risk-adjusted, with at least three cases made for each forecast.

The big provisional note there was, “…if resources were available.

There are no companies with infinite resources, and if there was one, it would have acquired all the others and owned the entire market.  Even a company as large as Cisco has to control its spending on new products. In the most recent quarter, it spent $5.6B on R&D, which was 15% of its product revenue of $37B.

I wanted to see if they were spending the right amount, if they wished to achieve near and long-term growth goals.  Using npv10’s Portfolio Optimizer, it was simple to set constraints on R&D spend as a percentage of the resulting cash flows.  We ran seven different cases, with R&D as a percent of product revenue ranging from 0% to 16%. Once data was in, it took a little over an hour to run this set of analyses.

Each scenario selected a different set of new products with different start times (within the time-constraints allowed).  Sometimes the scenario decided an acquisition would be more appropriate, and other times not. An acquisition may speed things up, but they also come at a higher cost because much of the development has been de-risked.

The results were interesting.  Obviously cutting off all R&D spending helped near-term cash flow, but also dramatically impacted the future as existing products reached the end of their competitive life-cycle.  Spending the most on R&D constrained cash flow over the first few years, but then dramatically increased as all of the new products entered their markets and generated revenue (which in turn provided more cash flow for future R&D).   It also showed how almost all of the cases resulted in the same cash flows 2 ½ years out, but had vastly different numbers leading up to that time and afterwards.

We did not consider additional goals (such as growing revenue, paying dividends, improving margins, etc.) or constraints (headcount resources below some level, number of simultaneous product launches, etc.) but those can rapidly be layered onto the analysis.

In the end, your company needs to decide what your corporate goals are, and what’s holding you back from achieving them.  It’s up to us to help you quickly and optimally determine various paths forward and the trade-offs among them.

“Momsplaining” value engineering and Quanta AI

On a recent visit with my mom, I received the usual question: “What is it that you do exactly?” I typically use a chip replacement example with mobile phones to illustrate value engineering. My mom studied anthropology. She still struggles with the difference between emails and texts on her phone. Nevertheless, as I’ve been working in tech, I trudged on with the illustration.

For example: the phone we bought two years ago came with a chip—let’s say the chip cost $10 then. Today, because of technology innovations (Moore’s Law), that same chip may cost only $5. If you ship the same phone with the lower cost chip and sell it at the same price, you made money. Say they sell 10 million phones, they just saved $50 million. $50 million here, $50 million there, pretty soon you’re talking real money. Value engineering positively impacts profitability. 

She said, “oh, I think I get it.” “It’s sort of like my George Foreman electric bar-b-que. I just replaced it and the new one has a plastic grease trap whereas the old one had a metal one.” Mom’s a quick study. One of the more successful value engineering projects I worked on in Silicon Valley was the replacement of a metal fan tray with a plastic one—it saved millions.

“Exactly”, I replied. “George’s engineers had to run the specs on the plastic trap” (can’t have it melt or catch fire). “The savings on the trap reduced the cost of goods sold—which positively impacts George’s gross margins. George only charges $79.99 for your bar-b-que, he needs to keep his margins as high as possible. George has 60 or so products, it’s likely there are value engineering efforts on most, if not all of them. Each dollar saved impacts George’s profitability.”

What we do is bundle individual value engineering projects into an investment portfolio. We use our proprietary software, Quanta AI, to maximize the investments. Quanta AI ensures the proper combination of value engineering investments aligns with margin management strategy. Value engineering ensures companies meet profitability targets and keep their investors happy.

She glossed over the AI part, but George’s bar-b-que helped illustrate value engineering and what we do. Her next question was inevitable: “So, when are you going to work with George?”

Why AI and Goal-Based Planning?

Artificial Intelligence (AI) and goal-based planning go together like peanut butter and chocolate.  It really is an unbeatable combination.

When we first applied our goal-based planning process to a Value Engineering (VE) fund, we increased the returns by an additional 20% and we showed our client that they could either make that much more, by selecting different projects, or reduce their capital outlay by almost that much, and continue making the same overall savings.  

But for many companies, the main problem is not selecting from among a large number of VE opportunities.  It’s coming up with the cost-reduction opportunities in the first place.

Enter Artificial Intelligence.  Our Quanta AI seeks out the opportunities from among your many products or components, based on some obvious information (number of units you expect to sell after reducing the cost, types of components used, etc.) and some not-so-obvious ones, which for now remain proprietary.  We’ll take in whatever data you have though, and find opportunities.

But now you may have too many opportunities, and do not have the budget or people resources to implement them.  Enter goal-based planning.

We take all of the value engineering projects you have available, including some you may have started, add in those that Quanta AI has identified, mix in your available resources, set your targeted goals for savings, and out comes a scheduled list of projects.  If you include uncertainty in your forecasts for one or more of the projects (maybe sales will be below/above target, or cost savings will be below/above estimated, or the project takes longer than expected), we show you the uncertainty across your entire portfolio of VE projects.

Now that you know which projects to pursue and what their total  risk-adjusted outcome should be, you can start working on addressing some of the constraints that are keeping you from saving even more.  Perhaps you add more capital to your internal VE fund, hire additional people to implement the projects, or we can help you identify outside resources for the implementation phase, while your internal development teams focus on new products.

Once we help get your fund set up, and Quanta AI scouring your product and components list for opportunities, we know you’ll agree that AI and Goal-based Planning belong together.  #QuantaAI