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Economic Modeling

I recently asked the CEO of a startup, one who is thoroughly an effectual thinker, what he thought of building an economic model similar to that found in "Developing Products in Half the Time." The answer was that he would not believe the model. I then asked a product manager of a company servicing a mature market the same question, and the answer was that not only do they build models, but they drive the model from data from past projects and industry analysis, and the CEO hammers every corner of the model until they believe it represents reality. Only then is a project approved. This CEO is a hard core causal thinker.

I was not at all surprised. The effectual thinker knows that data and assumptions are suspect and constant feedback is more reliable. The causal thinker knows that if the data is good, they can make better tradeoffs. Each has its place.

Regardless of ones thinking tendencies, a lot can be learned by experimenting with a model. By playing with assumptions and their effect on the economics, one can get a feel for which assumptions need better testing and how they relate to development tradeoffs.

I'll walk through and example and see what we can  learn. Here is the story: we are a capital equipment supplier selling manufacturing equipment to electronics manufacturers in the US and Asia. The market size is $100M per year, our product is priced at $200K, and the market grows at 8% per year. The product is targeted at a newly developing market segment.

Let's get started on a model. The first thing we need to do is model revenue. Because we are targeting a new segment, we will model the market and sales separately. The market model has two components: the available market and the diffusion of the new product. The available market is:

Available = Market Size X Quality X Awareness X Buy.

The Quality factor is used to model the effect of entering the market and then improving quality. This is a simple way of accounting for learning through market experience. Awareness is one of the A's in the classic ATAR model. Buy models the fact that there are substitutable solutions and this is the portion of sales that can be expected.

The diffusion effect is the standard model that uses a Trial Rate and a Copy Rate.

Let's build out a spreadsheet for this and talk about its assumptions:

 

Market size is in units sold with an 8% growth rate. The market will only buy half as many systems in the first year and four years later quality does not matter. Awareness starts out at 25% and ends at 100% in year 7. The assumption is that there are many small customers all over the world that are hard to find. 60% of the market will buy this type of solution and 40% will by substitute solutions or adapt what they already use. The diffusion model uses a trial rate of 10% and a copy rate of 20% The adopter column displays the number of systems per year that the market will buy. The penetration column shows the total systems sold. At year ten 350 systems will be in use.

The important assumption is that there are multiple competitors entering the market at the same time, and this represents how the addressable market will play out as a whole. We now have to model how much of this market we can take:

This model says that we can make 80% of the addressable market aware of our product, but initially we can only support sales to 30% of the available market. By year 4 we can support 100% of the available market. Our sales conversion rate is 30%, which is a way of modeling market share. The model says we can take 30% of the market we can reach. The Quantity column represents repeat business on average for this type of product. Sales ramps up over the first few years as customers gain experience with the product in production and purchase risk decreases. The end result is a number of units sold.

Multiply these models together and with sales prices and we have a revenue model for the base case:

 

 

The goal is to model four sensitivities: COGS, Development Cost, Performance, and Delay to Market. We can model performance reduction and market delay in the revenue model. Performance is modeled by reducing the Buy column of the market model by 10%. If the performance is lowered, people will turn to substitutes. We could also model this in the sales conversion rate, but using Buy simplifies the model. Market delay is modeled by delaying availability one year and lowering the sales conversion rate to reflect a loss of market share. The assumption is that a one year delay will cause a 33% reduction in market share. If you play around with the spreadsheet, it becomes clear that it is loss of market share that causes the largest loss. If this is not the driving factor in a new product, you can model the delay in other ways. For example, if your competitor can lock up the input value chain you can model higher COGS. Here are all three models side by side:

The left graph is 10% performance reduction, the middle graph is the base case, and the right graph is the one year delay to market. Clearly in this case delay to market has the bigger effect.

Let's now model revenue and look at the other to sensitivities:

COGS is set to 55%, thus a 45% margin, which is a very conservative number. We model higher production costs by raising COGS 10%. Our SG&A is 25%. Development cost is $1M, and there is a $100K yearly development cost associated with product improvements. A cost overrun is modeled as a 10% increase in development cost. We use a 30% tax rate and make an approximate cash flow projection. We then calculate a IRR of 44%, a NPV, etc.

A note on the model: the base values are setup in a table so we can tweak the base assumptions.

The sensitivities are handled with switches:

Each sensitivity is tested using the switches and the result is graphed:

A one year delay to market has the greatest effect followed by manufacturing cost. Performance impact is much smaller and development cost is very much smaller! However, what gets a lot of attention in the heat of development? Development cost, especially if you are in a start up. Sometimes you just don't have the cash and you have no choice but to starve development. But, if your product development looks like this, your decisions should reflect it. This is the basis of product development in all Reinertsen's books on Lean Product Development.

How realistic is the model? Like most models the market model is the most difficult, and market share the hardest number to estimate. If you have developed similar products, you can use analogies. Performance requires some guess work, but development cost and manufacturing cost are usually fairly accurate, and when they are not, you can improve the numbers as the project develops. We can deal with the market delay estimation by testing, but guess what? You have to at least have a prototype product to do that? You can do better by concept testing before engineering development. There is no other choice than to do the best you can and refine the model as you go along.

Much of the value of economic modeling is in the thinking process. Are you a fast follower? Does the late entry effect market share? Does it effect development cost? Is this a winner take all market? How does performance affect sales? Even if the model is not perfect, you will probably gain a first order approximation of what is driving cost and what should be managed. The point is that your intuition may be inaccurate, so using models will test it. If you can build the model as a cross functional team, you will have a much better model. Modeling as a team forces you to justify assumptions and reach common agreement on how to manage the cost of product development so that everyone is aligned and not working at cross purposes and different assumptions on cost.

So, if this were your model, what would you do with it? What tradeoffs would you make? How would it affect your process and decision making?

Note: if you want a copy of the Numbers spreadsheet used in this post, send me an e-mail. See the contact page of the Website.

Product Development in the Wild

Standard product development wisdom says use a Phase Gate process, but is that always the best practice? Reinertsen is appropriately suspicious of methods and best practices, and offers principles in his Lean Product Development. In general I side with Reinersten, but are there circumstances where even Lean Product Development principles dare not go? Let's take a look at a special case of product development: products created by entrepreneurs.

According to Saras D. Sarasvathy at University of Washington, entrepreneurs use effectual reasoning as opposed to causal reasoning:

Causal rationality begins with a pre-determined goal and a given set of means, and seeks to identify the optimal - fastest, cheapest, most efficient, etc - alternative to achieve the goal.

Effectual reasoning... begins with a given set of means and allows goals to emerge contingently over time from the varied imagination and diverse aspirations of the founders and the people they interact with.

The contrast between the two forms of reasoning is shown in the following diagram from Saravathy:

 

The causal model starts with markets, and ends with segmentation. This approach is very similar to Phase Gate approaches to product development. The standard PDMA stages are:

  1. Opportunity Identification
  2. Concept Generation
  3. Concept Evaluation
  4. Development
  5. Launch

Opportunity Identification selects markets and segments. At the end of Concept Evaluation the business case is complete and it is time to execute the Development and Launch. The Phase Gate framework assumes causal thinking.

Lean principles begin with an economic model that quantifies four economic objectives:

  1. Cycle Time
  2. Product Cost
  3. Product Value
  4. Development Expense

The goal is to be able to trade off each objective to maximize economic gains. The remaining principles are about maximizing economic outcomes. This works well with causal thinking, because causal thinking is about goals and how to reach them. Lean is about the goal of maximization of economic results by achieving flow.

Effectual thinking turns causal thinking on its head. Rather than plan then execute, one executes and then plans. Start with the customer, even selling what you don't yet have, then figure it out as you go. One is never sure what market they will end up in. Phase Gate is irrelevant in this context, because you Launch first, develop the product, then define your market as you go.

With Lean one can sell an undeveloped product, develop an economic model, and then apply Lean principles. However, in general effectual thinkers are in playing with new products in new markets. Saravathy describes the situation using the term Suicide Quadrant. It is very difficult to build an economic model in Suicide Quadrant.

 

Nonetheless, other Lean principles apply. According to Saravathy effectual thinking operates on three principles:

  1. Effectual reasoning emphasizes affordable loss
  2. Effectual reasoning is built upon strategic partnerships
  3. Effectual reasoning stresses the leveraging of contingencies

Lean has similar principles:

  1. Reduce loss from bad outcomes via fast feedback
  2. Exploit unplanned economic opportunities
  3. Use fast feedback to make learning faster and more efficient

Effectual reasoning and Lean both rely on feedback. This commonality makes Lean Product Development a better match for effectual thinkers than Phase Gate. Most entrepreneurs would reject Phase Gate simply because it represents causal thinking, which they reject outright. Saravathy points out that companies tend to shift from effectual thinking to causal thinking as they mature. Applying a limited form of Lean Product Development during the early life of an entrepreneurial company should allow a smooth transition to causal thinking. As the shift occurs Lean principles can be applied as appropriate until the foundational economic model becomes practical.

Phase Gate is more appropriate for a very mature company that wants to minimize risk at the expense of cycle time. Trying to force Phase Gate on an entrepreneur will result in an immediate immune response. So if you find yourself working for an entrepreneur and want to put some process in place, I suggest avoiding Phase Gate and take a look a Lean Product Development. Look up Reinertsen on Amazon and buy a couple of books or give me a jingle.

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