Introducing Quanta AI

Technology firms suffer constant margin pressure on products already on the market (in the sustaining life-cycle phase). Value engineering reduces product cost most commonly through component replacements, material changes and part eliminations. The value extracted does not impact core function; in fact, it often improves it. Value engineering projects have the most impact on a firm’s gross margins when bundled into a portfolio. Over time, portfolio returns are amplified as multi-year investments come to fruition. This is a key margin management lever.

Mark Streich and I met at the Thunderbird School of Global Management ten years ago. We recently reconnected at npv10 to champion Quanta AI in the technology sector. After finishing the MBA program, I spent five years as a value engineering fund manager for a Fortune 500 Silicon Valley company, and my work intrigued Mark, a serial entrepreneur and trained computer scientist in the Silicon Valley. He spent the past few years developing portfolio optimization AI for the oil and gas industry. In a single-click, executives can model hundreds of wells with differing production, investment and revenue streams to support decision making with data. He also tested his platform in real estate with top-notch results. A manually managed portfolio can be optimized by over 20 percent using this tool and approach to help guide strategy with data.

This proprietary platform is the bedrock for Quanta AI. We are targeting value engineering as it dovetails with our Silicon Valley experience. Additionally, Quanta AI can also support resource allocation for new product introduction, as well as research and development. If there’s a portfolio with multiple complexities and a myriad of variables, Quanta AI can optimize it. Astoundingly, large firms still rely on program managers using Excel to crunch large data sets. This approach is cumbersome, time consuming and futile as a method to capture the most valuable combination of projects.

Quanta AI incorporates all potential variables and provides optimum outcomes based on goals the investor wishes to model. Risk, predictive analytics and forecast variability are all incorporated into a single repository. The algorithms generate forward-looking data and metrics to determine if future investments are aligned with goals and resources. Savvy investors can decide—with a click—which investments to buy, sell or hold relative to a choice of financial targets (e.g. NPV, IRR, cash flow, debt to equity, payback period, etc).

In my value engineering portfolio management experience, we needed Quanta AI. Managing hundreds of investments with various return profiles manually is outdated, unnecessary and inefficient. As Mark says, “That’s what computers are for.” And that’s what Quanta AI is for.

Big Data

Big Data Not Required for Making Big Decisions

“Big Data” has been all the rage for a while.  Everyone is trying to figure out what to do with all that data that your business is collecting about customers and operations.

I want to propose the “Big Data Decision Value Theorem™” :  The value of a decision is inversely proportional to the amount of data you have available to make it.

In other words, if you have billions of data points, any individual decision made is probably not worth a lot.  Collectively, all those tiny value decisions may add up to be worth a significant amount, which is why companies like Amazon, Google, and Netflix collect so much information about each individual using their services.  The value of a single ad delivered to a single user isn’t worth much to Google, but scaling it to billions of ads served to a billion users, and we’re talking real money.

Conversely, making large strategic decisions requires less data than you would imagine.

Okay, so you probably wouldn’t take this to the extreme, and say you only need 1 (or no!) piece of data to make a “bet your company” decision, but the idea of managing the businesses in your company does not require a billion pieces of data.  You probably have a good idea of how a business is doing by looking at a few charts that show its current state, past trends, and forecasts.  Reviewing the competitive landscape rounds out the information you need to make decent decisions about a single business.

But what if you have 30+ businesses or 100+ properties and are trying to view them as a cohesive whole?

While you can’t begin to understand the interactions of all the businesses by looking at all their separate charts, computers can aid you.  They can notice that 7 businesses generate excess cash flow during times when 14 other businesses need cash to grow.  Or that adjusting when new opportunities are executed can keep you from needing additional capital.

These are the kinds of tools that npv10 is building.  They aid your analysis, and help make decisions about what to do, and when.  They only require the same data you probably see in reviewing each business separately.  And then you can start thinking strategically about what your company’s overall goals are, and the tools will help you figure out how to get there.

We will be glad to do a quick analysis for you, using what data you have, to show you the value your company can create by using our products.  As the tools are not yet released, we can do some number of analyses free of charge.