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All things about product strategy, platform strategy, and product line strategy
Dealing with Assumptions is the time consuming aspect of Reverse Financials, covered in my previous post. Managing assumptions is really about dealing with uncertainty and risk. Let's start by making a distinction between uncertainty and risk.
Risk is uncertainty with a downside.
Uncertainty is your friend. It gives you the opportunity for an upside and a way to beat your competitors. Risk is your enemy. It creates the possibility of loosing accumulated capital. The goal is to turn risk into uncertainty.
Before we dig into the Reverse Financials, let's take a look at uncertainty management in general. The framework I use comes from Hugh Courtney's book 20/20 Foresight. You can read the book if you want a deep dive.
Uncertainty can be categorized into 4 levels.
Level 1 is complete certainty. This does not mean you know everything, but it means everything can be known with enough certainty that you can make decisions without considering uncertainty. In this world, to the extent it still exists, you can use discounted cash flow, Porters, SWAT, and all the traditional tools to make decisions. Life is like a chess game. Whoever is best at reading the board and making strategy wins.
Level 2 consists of a set of mutually exclusive collectively exhaustive (MECE) outcomes. This represents standards wars, regulatory changes, strategy moves in some more stable industries, etc.
Level 3 is bounded outcomes, a range of outcomes. Market share falls into this category.
Level 4 amounts to unbounded outcomes.
Assumptions in Reverse Financials
I want to draw attention to a couple things. First, most of the uncertainty we will deal with in Reverse Financials will be Level 2 and Level 3. Second, product strategy will effect uncertainty. For example, in a disruptive innovation, you probably have more time than in a sustaining innovation. Most companies are scared of disruptive innovations and will watch from the sidelines and will be a fast follower, or a me too. With a sustaining innovation, preemption is far safer, so delaying can have drastic consequences.
The first step in dealing with assumptions is to categorize each assumption by the level of uncertainty.
Let's go through each item in the Uncertainty column from bottom to top.
The first item is General Overhead with Level 1 uncertainty. Management has determined the overhead % and is not going to change. Therefore, this is an assumption that can be ignored.
The next item is a burden rate, probably covers manufacturing. The spreadsheet shows a range of 20% to 30%, but it is categorized Level 1. The implication might be the number can be determined and the range removed, or there is simply a mistake in the analysis so far. It is best to do the research and find out which is the case.
The next item is raw materials we a Level 2. Engineering has proposed several product architectures. One of them includes creating a new platform, several of them propose using an existing platform along with several choices for reusing components from previous product designs. These choices affect cost, development time, and performance. If you are a product manager, this should keep you up at night. The Level 2 uncertainty from engineering affects product value and go to market strategy, implying that the uncertainty of revenue depends on choices made by engineering. (Everyone knows this intuitively, so there should be no surprise here.)
Moving along we get to sales support and cost of warrantee, etc. These items are just not very predictable and nobody has any way to make the uncertainty Level 2, so they are level 3. They are bounded by experience.
We now get to cost of sales and marketing. This is Level 2 because there are some basic choices around using internal sales, reps, and other channels of distribution. However, each channel is reasonably well understood, so this is not Level 3.
Finally, we get to revenue, which is Level 3 uncertainty, but it has some Level 2 characteristics due to Level 2 uncertainty within engineering. As is usually the case, this is the hardest uncertainty to deal with, as information is so much harder to get compared to internal affairs.
Dealing With the Uncertainties
Courtney categorizes strategies into three questions:
- Shape or Adapt
- Now or Later
- Focus or Diversify
I will ignore the third question as my concerns here are more tactical and "focus or diversify" applies more to overall strategy and portfolio management. However, one could make a strong argument against this claim, so feel free to do so :-)
Let's consider the overall question whether to shape or adapt with respect to uncertainty. Because we are developing a product, we must consider uncertainties that are external to the organization differently than those internal. Internal uncertainties are easier to shape than external ones, but not always. There are no rules that say you have to answer the same for both internal and external uncertainties.
Now or later does not have so much independence. If your strategy for dealing with external uncertainty is "now", it is pretty hard to apply a "later" strategy with internal uncertainties because they are bounded by the external timing. There is a similar boundary in a "later" external strategy. Waiting carries the risk of preemption. A competitor can always be first, and there is no way to undo your delay.
Because of the asymmetries, let's start with external uncertainty. We have a Level 3 uncertainty, which means we have a range of possible outcomes. The overall worst case scenario in the spreadsheet says we can have a return on sales of - 5%. The first thing we might do is a sensitivity analysis to get some feel for where it hurts most.
|Tech sales Support
|Install, Warr, Training
|Sales and Marketing
These values show the resulting change from a 10% change in each parameter. Because revenue and raw material costs are interrelated as discussed above, we can address those first. Let's start with revenue.
The initial post said this was a disruptive innovation. The first question is: can we convert this into a Level 2 risk and use a shaping strategy? Possibilities might include patents, industry standards, tying up a critical resource, or a network externality. If this is possible, it is probably better to shape than adapt. A scenario analysis would prepare for each Level 2 outcome and uncover ways to shape the outcome.
If there is no way to convert to Level 2, it still might be best to shape, but it also might be better to delay decisions using real options techniques. A real option creates a option to execute in the future when there is better data. For example, a critical technology might be developed, but the decision to develop a product might be delayed until the environment is ready. A product launch might be delayed to time the market. Multiple options might be created so that with more data, one option may be chosen and the other discarded.
Part of the analysis is deciding on whether you are making a big bet, or managing downside. Also, you must know if your organization is capable of the strategy. Can your organization support a big bet? Do you have to make a big bet because you are a startup and don't have the cash required to finance multiple options? Are your managers flexible enough to adapt to an unfamiliar strategy?
Let's look at the raw materials uncertainty. It was specified as Level 2. In this case the designers have a finite number of technology and architecture choices and they affect the cost structure. The same issues apply. A scenario analysis can uncover assumptions and issues with each choice. It might be possible to create options by developing prototypes of several architectures in parallel (set based design), then evaluate them, considering how it relates to the revenue risk management strategy.
We must also consider how the design strategy relates to the market strategy. If the market strategy is to shape through an industry standard, the design team has to architect around standard. The design team may want to prototype a couple of options based on guesses as to the final form of the spec. Then wait and see how it plays out. Once the standard is near acceptance, execute on the prototype closest to the standard.
The main point is that all assumptions are uncertainties. Uncertainties should be analyzed to uncover their characteristic, and strategies should be formed to manage it. You have to decide whether to shape or adapt, and whether move forward now or later. You must apply the proper tools for the uncertainty level. And you must align the strategies where they interact.
Other Ideas on Assumption Management
My main point of reference is Discover Driven Growth. McGrath proposes managing assumptions using an options approach. Information is produced by learning, which lowers downside. Checkpoints define strategic places to stop and evaluate, which is a decision point where you stop, pivot, or purchase a new option.
The fundamental concern I have with a process tuned to real options is that it inhibits big bets, and tends to emphasize adaption over shaping. While this might work in many situations, any process that is focused on one approach for managing uncertainty has the downside of misapplication. Like all tools, context matters. So my take away is simply this, use reverse financials and assumptions, but manage assumptions with a rich risk management toolkit and don't commit your process to any one tool or strategy.
However, if you must have simplicity, an options approach is probably best, because most market risk is Level 3. Another assumption to manage ;-)
Causal financial models lend themselves situations where inputs to the model are well known. Sustaining innovation falls into this category. But what kind of models should one use for disruptive innovations? This post will demonstrate risk based models intended for disruptive innovations where model inputs are not well defined.
Jose Briones uses the following Project Categorization in his Beyond Stage-Gate presentation:
Jose then categorizes the financial analysis into three levels going from the top right corner to the bottom left:
- Level 1 - Reverse Income Statement/Real Options
- Level 2 - Probabilistic Decision Analysis
- Level 3 - NPV/DCF
The message is clear, the more certain your innovation, the more you can use traditional tools. Larry McKeough addresses Level 3 in his Rocky Mountain Product Camp 2010 Presentation. However, Larry's DCF spreadsheet tool considers assumptions, so even his tool recognizes the presence of risk in low uncertainty innovations.
Risk Caused By Disruptive Innovation
Christensen uses the following model of disruptive innovations as shown in my Rocky Mountain Product Camp 2010 Presentation:
There are two risky places to innovate. The first is the low-end or new-market disruption. Both of these have considerable market and technical uncertainty, as shown by the circles below. Each of these strategies involve new value networks, new customers, and new definitions of value.
The second place is when sustaining innovation pushes performance beyond user needs and the supply chain begins to reconfigure itself to supply flexibility and speed to the market. When platforms disaggregate and margins shift between players, look out!
DCF is not well suited for these situations, especially the low-end and new-market disruptions. A better tool is reverse income statement and assumption management. The remainder of this post will walk through a reverse financials statement. A follow on post will address assumption and risk management.
Let's proceed by building out a spreadsheet step by step. I will roughly follow McGrath's example from his book Discovery Driven Growth. Assume the product is a $100K machine used in manufacturing lines. First, we will frame and scope, then work on deliverables, and finish with the reverse financials.
Framing and Scoping
Management has stipulated $1M in operating profits with a 17% return on sales (ROS) and a 20% return on assets (ROA). This results in a $5M allowance for assets, and requires sales to be approximately $6M. Given a $100K selling price, this implies selling 5 systems a month. Two questions follow:
- Can manufacturing build 5 systems a month?
- Can ROS/ROA be maintained?
The benchmark range of return on sales is 25% to 3%. The data came from the public financials of companies in the same product category. This should immediately send shivers down your spine! You should start asking yourself questions like: how do we insure our product lands in the good side of the range?
Notice what has happened so far. Instead of creating a bottom up plan that results in a ROA/ROS figure, we start with a required ROA/ROS and ask, what does this imply? It implies a sales level of sales of 5 systems per month. It implies assumptions about ROA/ROS that may not be real. Not only does the benchmark data have a wide range, but if this is a disruptive innovation, benchmarks may not even apply.
Now have to take the next step, which is the deliverables specification. We have to start breaking down the overall assumptions into smaller assumptions we can manage.
Starting with assumption F10/A3, the sales team has predicted the cost of sales and marketing is 15%, with a range of 13% to 17%. The left side (F10) says that to meet the original ROS/ROA, we require 15%. The right side (A3) says that the possible range is 13% to 17%. Therefore, we have to manage the assumptions on the right side to either meet the requirements on the left, kill the project, or improve some other assumption to compensate.
Using the data from the deliverables, a reverse income statement and reverse balance sheet are created. The left side shows a return on sales (F40) of 25% and a return on assets (F47) of 44%. This is better than the original 17% and 20% requirement. Therefore, if the left side of the spreadsheet holds, we have a project. But as my father used to say, IF is the biggest word in the dictionary.
Look at the worst case on the right side. return on sales is -5% and return on assets is -6%. Assumptions matter!
If this were a sustaining innovation, the assumptions would be reasonably accurate with small ranges, and DCF would be a great tool. Diffusion curves could be used to estimate revenues. An IRR could be generated and compared to a weighted cost of capital. Small uncertainties could be accounted for by running a Monte Carlo analysis on the DCF.
We basically framed/scoped management requirements for ROS and ROA. We then worked backwards and created financial deliverables that meet those numbers, and we treated the deliverable components as assumptions with ranges of values. Finally, we created reverse financials that show the best and worst case ROS/ROA.
- Don't use the wrong tools. Your classic MBA tools don't always work.
- Work backwards from goal to assumptions.
- Manage assumptions.
Where do we go from here?
The next post will discuss basic risk management concepts and address how to deal with the assumptions of reverse financial statements.
I was in Tokyo this week. It is always interesting to see how context affects products. For example, look at this parking lot:
The building is over ten stories tall, and lifts the car up into the building. When you back out, a round disk on the ground rotates the card so you are pointing towards the street. As a side benefit, you don't have to worry about theft, as long as you trust the operator.
The product is a perfect match to a city with very high land prices due to lack of space. This would never work in my home town in Colorado. Land is cheap; cars are big. Another example is this gas pump. The station is very small, and getting in and out of the street dangerous. The pull down nozzle can reach the tank from any direction the car parks. This prevents turning around to get the car in the right position. Gas is pumped for you by the attendant, so you don't have to worry about managing the hose.
More subtle tradeoffs like this exist. For example, a company I am on the board of directors of sells capital equipment. Some customers care about overall economic benefit. Because the equipment is connected to another machine that costs 4 times as much, it economically better to make our equipment faster, even if if the costs goes up, because it lowers the over all cost of the combination. However, some countries in Asia are very sensitive to initial capital outlay, and willing to accept a lower initial cost, even if it is not a economic maximum due to lower performance. Knowing the market allows us tune the product and business model to match.
You have built a $100M company from the ground up, taken if from an ad hoc wildcatter to disciplined product development machine. Your phase gate system weeds out bad concepts, lackluster products, and has generally been successful. However, more and more your products are finishing development only to discover the market has shifted and launch performance has underperformed predictions. No matter how much more upfront work you do, nothing improves. By the time you finish development and gain market feedback, 80% of the investment is sunk, and there is no recovery.
Your developers talk you into trying Agile Product Development, and you agree. The first few products come out well and sales are solid. As the team gains confidence and independence it starts to feel out of control. The team begins to fail when it chases markets that are too small and some projects never seem to finish. Your phase gate controls have all broken down and are not managing risk.
What do you do?
Jim Highsmith (Agile Project Management), has a chapter on governance that deals directly with this problem. Jim points out that in the old phase gate systems, governance and operation (product development) are tightly coupled. Solving your problem requires "separation of governance from operations and then loosely coupling them." Governance is a linear process for managing investments and risk, operating much like real options. Agile works on the principle of iteration, experience, and feedback.
The crux of the solution is in redefining the stages and gates to align with Agile. Traditional phase gate has 5 stages: Opportunity Identification, Concept Generation, Concept Validation, Development, Launch. This clashes with Agile which makes less assumptions and depends on exploration and feedback. Highsmith suggest using 4 phases and gates: Concept, Expansion, Extension, Deploy. Each phase mitigates risk. During Concept the core product ideas are worked out and a few critical iterations are completed. During Expansion high value features are built and as much risk as possible is is driven out. Extension completes the product with minimal risk and better foresight into costs and market acceptance. Deployment puts the product into the market.
The core difference is the Agile team is building a deployable product in every iteration all through all the phases. This reduces the time from requirements to feedback so that the process can respond to market dynamics, rather than the traditional phase gate that builds a deployable product at the end after most of the investment is made. At each gate, executives evaluate the project as an option on future investment. Do we make the investment and buy the next option? Do we cancel the project? Do we wait and see?
The alternative phase gate system allows the executive team or portfolio manager to make linear investment decisions while the team runs an iterative process. This combination will also speed up gate decisions. A classic phase gate requires all work to line up for a moment in time to product documents and presentations for a gate meeting. This interruption causes value flow to stop. Agile iterations are time boxed with deployable product at the end of each time box, so the team is already lined up in time. Evaluation is simplified because the team can demonstrate a product to customers and the executive team. So not only does Agile speed up delivery of value and responsiveness, it actually speeds up getting through the gates, as long as the executives get in alignment with the heartbeat of the project.
Much of the traditional phase gate system can be salvaged, so this is not a start over from scratch. Opportunity Identification and Concept Generation can be placed in the new Concept Phase. Much of the Concept Testing can be placed in the Expansion phase, however a deployable product is created each iteration, so Concept Evaluation, Prototyping, and Market Testing all become the same thing. The key is to redefine the phases and gates to complement Agile.
Now that I have proposed a Strategy Pattern for Agile, it is time to pick it apart a bit. After all, it is only a model, and a stereotypical one at that.
Jim Highsmith hits the balance issue on the head in Agile Project Development:
Agile Teams can place too much emphasis on adaptation or evolution and too little on anticipation (in planning, architecture, design, requirements definition). Failure to take advantage of knowable information leads to sloppy planning, reactive thinking, excessive rework and delay. Remember: Agility is the art of balancing.
This suggests that one cannot ignore the upper quadrants of the strategy diagram. From a company culture point of view, how do you pull that off? I suggest that what one does not want are tribes within an organization subscribing to one of the four strategies trying to balance each other, say executives operating from Planning and developers operating from Adaptive. This creates a us vs. them mentality. The idea would be for stakeholders to know how (behaviorally) to operate from all quadrants, but as a minimum, from one on the predicability side and one on the non-predictability side. This flexibility allows teams to draw from both.
I imagine balance as development loops taking paths through quadrants like a chaotic attractor that bifurcates as needed to include whatever quadrant is required given the reality the company faces. The role of executive leadership is to gently push the pattern into the shape required whenever it is stuck in a maladaptive pattern. It gets stuck whenever someone lacks flexibility or foresight to recognize that the current pattern is maladaptive.
How do you create such an organization? Leadership, proper selection employees, coaching, education, example, collaboration, etc. Lot of soft skill stuff. There is a natural tendency to use control, but I think this is one place where control is helpless. Creating balance is more like guiding emergence than putting rockets into space.
This post looks at Agile Project Management as a Strategy Pattern and the transition from predictive strategies.
Agile Project Management is like Lean Product development with its emphasis on feedback and learning. Traditional Waterfall project development works much like Phase Gate: Specify, Design, Implement, Test, Deliver. One of the problems with the traditional method is that feedback comes late in the process. This means that mistakes made early in the process are revealed late where they are more costly to fix. Agile slices the development into features, iterations, and releases. A set of features are taken all the way to a releasable product, and then feedback is obtained. Even if the product is not officially deployed, an iteration is deployment ready. Feedback allows prioritization of features, learning, and the ability to track a moving target.
The Agile life cycle is:
The names are chosen to suggest flexibility. Iterations can include Speculate, Explore, Launch, or Speculate, Explore, etc. Loops are created where they are needed on a project by project basis. Speculate, Explore, and Launch form the core of adaptability. A pure Agile culture might look like the following Strategy Pattern:
At the highest level the pattern is about beginning with a Visionary Strategy and then moving to an Adaptive Strategy. The loop in Visionary accounts for what Jim Highsmith (Agile Project Management) calls Iteration 0. Iteration 0 is the minimal amount of work required to build a feature set capable of generating feedback. This is where initial platform and architecture development occur. The loop in Adaptive reflects iterating with the customer in the loop.
Much of the difficultly Agile teams face stems from executive management operating in the Planning quadrant. Managers want predictability of schedules, cost, value delivered, and all the things found in a typical business case. Agile teams tend to disconnect from the Planning quadrant. Highsmith makes a strong argument for balance: that structure and flexibility must coexist in a healthy way, much like bones and muscles.
Where does this pattern apply best? Malleable Technologies. The most obvious is software development, which has near infinite malleability. Where does it apply least? Perhaps building an oil tanker. One cannot implement a subset of an oil tanker and expect a customer to test it. However, what if you can take a rigid technology and make it malleable? Modeling and Simulation are tools that make a technology malleable, and so are prototyping tools. One can make a 3D model and construct a prototype with lasers and plastic powder that a end user can play with it. Tools can be an enabler of this Strategy Pattern.
Whether tools enable this shift or not, the real challenge is the cultural shift which requires a flexible mindset and balancing it with a prediction based mindset. Perhaps the best advice I can give about making this transition is take baby steps, say product enhancements followed by simple products. Work your way up to larger projects learning at each step along the way. But if you can make the transition, you can gain an competitive edge over your competition that is hard to copy. It is much harder to copy cultural changes than technologies.
In new product companies, strategy and product development process can be misaligned, resulting in low company performance. Imagine using a Phase Gate process for a fast paced Web 2.0 company, or using a highly iterative process to design a nuclear power plant. The Web 2.0 company would slow to a crawl and die a quick death and most consumers would rather not experimented upon by nuclear plant designers. In this post I will propose a strategy framework that will form a basis for future posts about alignment with product development process. The strategy framework will consider strategy a dynamic process and look at what I call Strategy Patterns. For those exposed to the GOF design patterns, the analogy is intentional.
The basic framework uses a two dimensional grid with emphasis on prediction along the vertical and emphasis on control along the horizontal. This is not my creation, you can read the original work if you want to see how the framework was created (Strategic Management Journal 27: What to do next: the case for non-predictive strategy). Each quadrant represents a strategy orientation or mindset.
Planning assumes that markets already exist out there in the world, and one can study them well enough that one can make predictions about its behavior. Analyze markets, pick one with the characteristics you are looking for, select products for development, test market them, predict your market share, and launch.
Visionary assumes one can construct a new market based on their imagination and ability to control through prediction. It is not about discovery of markets and products, it is about creating them, and not only that, but one can still write a business case and predict market share, profit, etc.
Adaptive assumes that the market already exists, but there is no way to predict what it wants, so one must constantly adapt. This is the land of feedback and incrementalism.
Transformative is where entrepreneurs live. If one can't predict, and one wants to construct a market, one is in this space. The effectual logic of Sarasvathy works here (see my previous blog on Effectuation). Start with who and what you know, build relationships, make a deal, and work with it.
Keep in mind that the model is not reality. There are few pure companies that fit into a quadrant, and companies move around in them. CEOs and senior VPs also have personal preferences. assumptions, and mindsets that cause them to act in ways consistent with one of these quadrants. In many cases executive management are not all acting from the same strategy orientation.
Successful companies move around this model in patterns. Let's look at a couple of example patterns:
Web 2.0 Startup
In this pattern a couple of recent college grads come up with what they think is the next generation of social networking. Perhaps with some help from Tech Stars they create a prototype and simple business model. They give a demo at the Denver Boulder Entrepreneur Meetup, get some feedback and launch. At this stage they are visionaries. With a little luck they get a 100 or so customers and then plateau.
After talking to some customers they realize the website is not satisfying their needs. They collect ideas from their customers and modify the website. This continues and eventually they gain customers. With more customers come more ideas, and more changes. The feedback loop with customers puts them in a tight develop, feedback, develop loop. They are now adaptive.
An angel investor becomes interested and makes an investment that allows them to increase marketing and the number of customers grow until a VC takes notice. The VC invests and the company grows even larger. Eventually the VC wants to scale up and go public or sell the company and cash out. The VC hires a professional CEO and boots out the founders. The new CEO accepts the new market as given and starts positioning add on service levels and products based on research and business cases. The company is being pushed into planning.
The company becomes the next Google before it can get stuck in the planning mindset and decides to create new markets with its enormous resources. Looks like we are back to visionary, but with other mindsets available. Perhaps the company can now balance adaptive with visionary or oscillate between them. Create new markets, then evolve them through adaptation, while leveraging some amount of planning.
Private Equity Driven Restaurant Startup
A PE firm decides to reinvent hamburgers. They do an analysis of their competitors, their operations, real estate choices, menus, profit margins, etc. They develop a combination of changes that result in higher efficiency by using strip malls, limiting the menus, using smaller movable tables, and using time motion studies to change cooking techniques. They develop a menu for upscale burgers that can maintain higher prices. A business model is created that contains a diffusion model of market penetration using adapter and copy cat rates, with accurate cost estimates and profit predictions. Demographics are studied. A few restaurants are built and test marketed in the Denver area. The model is improved and the company starts expanding, and improving the model so that they can predict profits more accurately. In addition to improving their ability to predict, lessons are learned along the way and improvements are made. Once the model is stable, a franchise system is put into place to accelerate growth. Models for franchise growth are made... rock solid planning with no desire to get out of the box. This company builds predictable business to satisfy its risk adverse investors.
Capital Equipment Startup
This company designs and sells low volume high priced manufacturing equipment. The founders are industry veterans who think they know a better way to build equipment. They set off with a vision knowing that no customer will talk to them without something to show. They design the first system. They are visionaries. As soon as they start selling, they discover many unmet needs and requirements that prevent customers from buying, so they start adapting until they finally have a product that results in sales. Meanwhile, an executive with an entrepreneurial mindset uses his industry contacts to build a web of relationships out of which he builds channels and partners. Engineering/Marketing is still in the adaptive mode, but the executive is in transformative mode constructing new channels. One customer says they will buy a system if it is modified, and the executive being an entrepreneur, takes a order before the system is modified. Engineering/Marketing is now thrown into a transformative mode. The system is delivered, and it generates new ideas, so engineering starts modifying the product. Back to adaptive mode...
The company grows until a major competitor takes notice. An acquisition occurs and eventually the founders exit. The parent company researches the market and determines it can add modules to the product, upgrade its look and feel, and enter new markets. Business cases are built for several new markets and NPV models are used to pick the market with the highest IRR. The startup is being drug into the planning mode.
If strategy changes, can the development process remain constant? In many cases, no. For example, a Phase Gate process might work well for Planning, but would not survive a hard core effectual thinking operating in Transformative mode. A true Visionary may operate completely out of intuition, which would be deadly in a mature market where Planning is the better strategy. I'll discuss product development as it is related to strategy in future posts, but for now I'll stake my claim that strategy and product development process are interrelated, therefore development process must change with strategy.