Steve Elliot, the AgileCraft CEO, and I recently conducted a webinar on Value Engineering – if you missed it, please take a look here. This practice is new to many, and yet is truly lean way of maximizing your innovation portfolio, products and investments. We had such an incredible response to the webinar, that we decided to jointly publish a white paper which goes into much more detail about how to implement Value Engineering in your organization, along with some real success stories. This white paper can be downloaded for free in the AgileCraft resource library.
To give you a taste of what to expect, let’s start with a simple definition: Value Engineering is an outcome-based approach to product strategy and development that favors learning via rapid experimentation, and using the information you gather to inform further product investment decisions. It is a system of developing hypotheses, setting and committing to a disciplined set of success criteria and performing quick and cheap experiments to glean information that immediately informs the next product development decision. And above all, it is a mindset shift at all levels that favors safe-to-fail experimentation, learning fast, and early from customers over sticking to a plan, budget or scope that may be out of date by the time the work is completed.
The traditional way of looking at product delivery requires a huge up-front planning and analysis to de-risk the investment of a massive long-term plan. With Value Engineering, leadership provide clarity of the key outcome we aim to achieve, then encourage teams to figure out the fastest, most effective way of achieving that outcome by investing in small, quick and cheap experiments to get there. New information is the result of the spend, and that is used to make decisions that direct spending to value rather than remaining in mediocre or low value areas.
When it comes to investments in new innovations, most organizations expect to get a “thing”, a functional end product that – hopefully – meets customer needs and generates revenue. With Value Engineering, the new “thing” is new information and knowledge. We invest in experiments to gather information to inform our approach, effectively paying for information in order to allow us to make better decisions about where to invest or not invest – which means killing efforts that aren’t returning the value we expect. Those decisions might mean that entire products may be shut down, or pivot entirely away from the original idea to a related but different path, and that decision seen as success.
The important point is to stop doing things that don’t provide the value we desire. This doesn’t just mean stopping the things that provide absolute lack of value – equally if not more important is to stop doing things that are simply mediocre, or just good enough. The idea is to learn enough about your product plans’ end value, so that you are constantly redirecting investment into only the high value items. To achieve this, work in progress needs to be limited. A staunch focus needs to be undertaken on end to end cycle time for completion of work to achieve a final result, and then use the information gained will inform our next set of actions.
When limiting WIP, predefined success criteria and a strong understanding of risks come into play. It’s important to bracket the size of the investment we are making relative to the riskiness of the experiment you plan to tackle. When uncertainty is high, we need to make lots of small investments in order to make it safe-to-fail and generate results that provide new information for our next bet.
This may seem counterintuitive, because in most traditional enterprises, ideas are usually generated from business leaders thinking in isolation, then handing out lists of features to teams for execution. Budgeting and funding reviews typically takes place once a year, requiring big batches of business cases, and corresponding pitches for funding, teams, and resources. The measures of success are staying under budget, delivering all the features in the business case, and being completed by a certain day and time. All of these are outputs – execution metrics.
As a result, ideas become too big, take too long to deliver, and are too slow to provide feedback. When we take a step back and think about it, uncertainty exists in all ideas. So, if we make bets by funding small, inexpensive actions that reduce uncertainty, we can quickly and cheaply discover if the hypothesis is valid or not. The important point is to move away from making big bets, and to move toward making many small bets so that we’re paying for information in smaller increments. We can then adapt our approach based on the information we learn from our bets.
If you want to learn more, I again encourage you to watch our webinar, download our white paper, listen to the mini-podcast, and follow the AgileCraft Value Engineering showcase page on LinkedIn. If you’re ready to jump in and get started, I’m available to speak or do a training and readiness assessment at your organization – find out more at https://barryoreilly.com/. I also encourage you to check out my books Lean Enterprise and Unlearn.
If you’re looking for a software solution to help you start, manage and maintain your Value Engineering experiment portfolio, AgileCraft is the only tool in the industry with this built-in, self-serve capability. Get started today at https://agilecraft.com/.
Thanks for reading!