Month: March 2020

With the vast majority of white-collar staff in companies currently working from home, the normal ways of managing people are disrupted quite fundamentally. Working closely with people in such a way that you can tell them what to do is much more difficult when you’re not physically in the same place.

Similarly, many organizations rely on meetings to align and coordinate work that crosses team and function boundaries. Because virtually all meetings take place online, their effectiveness is even lower than usual. I’ve already talked to people who are literally stuck behind their computers for back-to-back online meetings for ten hours in a row.

Rather than complaining about the situation and the inefficiency of working in this way, I’d like to outline an alternative approach: move to cross-functional teams that get tangible, quantitative outcomes as their target and that are otherwise left to their own devices on how to accomplish these outcomes.

Although it’s obvious that in almost all contexts, this is a better approach, few, especially traditional, companies are adopting it. The reasons are many, but some of the primary ones include: a lack of clear quantitative goals for most individuals and teams – in [last week’s post](https://bits-chips.nl/artikel/why-you-dont-define-desired-outcome/), I wrote about the reasons for this; a need for control by management, as in my experience, especially in more traditional companies, the culture tends to lean towards hierarchy; the Taylorian mindset of dividing every end-to-end task into multiple slices and giving each slice to a department, function or team and assuming a waterfall-style handover process; the belief that most work is repetitive and can be optimized by asking individuals and teams to specialize on their narrow slice of the work.

These reasons may have been justifiable at some point in the past, but this is most certainly no longer the case. All work that’s repetitive these days is automated and if it isn’t, you better automate it soon. This means that the only work left for humans is the work that we’re best at: complex, unique tasks that require creativity and a variety of skills to resolve.

Cross-functional teams are uniquely suited for taking on this type of work. By establishing the team around the skills expected to be required for the task, people with different skills can together work on addressing the challenge. As coordination cost within teams is orders of magnitude lower than coordination across teams, functions and departments, the efficiency of work is much higher.

The second aspect of the approach is that rather than telling teams what to do and how to work, they receive tangible outcome targets. It’s then up to them to figure out how to achieve these targets. This may require experimentation with customers and products, prototyping, and so on.

As we describe in our work on value modeling, the outcome targets should be part of a hierarchical value model that links the top-level KPIs for the business with the mid-level and team-level targets. So, all teams have targets on which to focus but also guardrail metrics that they’re not allowed to affect negatively.

With most of the people in industry working from home, perhaps the time has come to reinvent your organization along these principles and instead of suffering through the disruption, you can use it to lift your organization to the next level. Focus on setting outcome targets, not on telling people how to get there.

During multiple meetings this week (online, obviously), the same challenge came up: companies and their customers are extremely poor at precisely defining what the desired outcome is that they’re looking to accomplish. At first blush, every person that I meet claims to know exactly what he or she is looking to achieve, but when push comes to shove and the individual is asked to define this more precisely, the lack of specificity rapidly starts to become apparent.

This is, of course, far from the first time that I’m exposed to this and the interesting thing is that there’s a variety of words that companies will use to cover up the lack of specificity. Words like customer value, quality, speed, reliable and robust are often used as generic terms used to look good but that prove to be void of any real meaning when investigated in more depth.

The challenge is that not being clear on your desired outcome causes all kinds of problems for organizations. One is that prioritization in the company tends to be driven by the loudest customer. Customers tend to create a lot of noise about something that they happen to care about at the moment, especially if it’s not working well. The consequence is that they contact the company with messages of deteriorating quality. If you haven’t defined what you mean with quality and you don’t have metrics to follow up what the current level is, it’s very easy to simply go along with the customer opinion and allocate significant resources to addressing this potential quality problem. The opportunity cost of this is enormous as the proclaimed problem might be a minor thing and it might well be that using the resources elsewhere would have been much better for the company.

A lack of precise definition of desired outcomes creates several issues inside the organization as well. First, it may easily lead to teams whose efforts are mutually conflicting due to different interpretations of what success looks like. For instance, teams looking to improve customer value by developing a simpler, more intuitive user interface will run into conflict with those that seek to improve customer value by developing additional features or more configuration and variants of existing features. The latter will need UI real estate to expose the user to the additional functionality and options that will easily undo the efforts of the first.

Another aspect, which I’ve written about before, is the inefficiency of development that’s not grounded in precisely, or rather quantitatively, defined customer value. For a company allocating 5 percent of its revenue to R&D and an average FTE cost of 120 k€/year, an agile team of 8 people working in 3-week sprints costs around 60 k€ per sprint but has to generate 1.2 M€ (60 k€/0.05) in business value per sprint. Vague statements about quality, speed, reliability and value do not help organizations to accomplish this outcome.

Concluding, many organizations fall short in precisely defining the desired outcome for initiatives, projects, products, and so on. Resources are allocated because of loud customers, historical patterns, ingrained behaviors or internal politics, not because these resources are providing the highest RoI for the company. In our research, we work with companies on value modeling to overcome these challenges and help them get on the way of precisely and quantitatively defining desired outcomes. Contact me if you want to know more.

All of Europe, as well as the rest of the world, is in the throes of the coronavirus and some people I meet are comparing it to the start of one of those apocalyptic movies where the world is going to hell in a handbasket. Of course, nobody can ignore the human suffering that comes from getting sick and potentially even dying from COVID-19. It is terrible and my heart goes out to those that are affected.

The current actions, including avoiding large gatherings, quarantining geographical areas, closing schools and recommendations concerning washing your hands, limiting travel and so on all make sense. However, these actions will only slow the rate of infection in order to decrease the overloading of the healthcare systems (even more) by spreading the cases out over time.

For the companies I work with, the impact has been unprecedented. All travel that’s not absolutely business critical has been suspended. Visitors (and researchers) are no longer welcome. Employees are asked to work from home wherever possible.

The dark COVID-19 clouds can have a silver lining for companies. The disruption of normal work practices offers a great opportunity to review and evaluate all processes and activities in the company and between the company and its ecosystem partners. This allows us to determine which processes that earlier required physical interaction can also be accomplished by virtual means. Similarly, we can review all activities and processes that were performed by humans by virtual means but that can be automated and be achieved without any human involvement. Finally, we may even identify activities that are obsolete and that were only conducted for historical reasons and we can shut those down.

The second benefit that companies can seek to exploit is a step function change in the adoption of digital practices. In many cases, we either figure out how to do things remotely and virtually using digital means or it doesn’t get done at all. This includes holding meetings, managing daily standups for agile teams and running the operations of systems. In fact, I could easily see the efficiency of companies significantly improve due to the forcing function provided by the current situation.

Third, many organizations are built on the principle of aligning all the gears in the machinery as closely as possible in order to optimize operational efficiency. The consequence tends to be very brittle systems that are unable to withstand adverse circumstances. Situations as we’re experiencing now force us to rethink things in terms of risk management and should encourage you and your organization to think in Taleb’s antifragility principle where systems get stronger in the face of detrimental developments.

Fourth, although the short-term economic damage is significant, the removal of business travel and working from home does provide some breathing room in the lives of many professionals. This space can be used to think strategic rather than tactical, to reflect and introspect and to find smarter ways to accomplish the same if not better outcomes.

Concluding, COVID-19 is terrible from a humanitarian, as well as from an economic perspective. Although the short-term impact on revenue and margins is undeniably bad for most companies, we can use these circumstances to realize at least some benefits that may help our companies going forward. This is important as this is not the end. In the same way as the Spanish flu in the early 1900s, SARS in 2002 and swine flu in 2009 weren’t the end. As the poet Rumi wrote centuries ago, this too shall pass.

One of the well-known struggles of every company I work with is to combine innovation with efficiency-oriented operations. This is the classic problem of ambidexterity: the company needs to deliver on today’s revenue and margins while securing its future. The problem is not that companies aren’t aware of the challenge but that they lack the tools or mechanisms to achieve the balance in a good way. This is in some ways surprising as many reams of paper have been written about the challenge, ranging from books to research papers and from blog posts to company presentations.

The key challenge is that the two systems of operation are fundamentally different. In operations, we use the classical assembly-line way of thinking by chopping up the work in chunks, giving it to different people and functions and having handovers between them. The focus is on how each step in the process can be executed at the lowest cost. On the sales side, the focus is on tactics to make customers that have a known and established need for the product to choose us over competitors and to buy now rather than later. This leads to waterfall processes, hierarchical organizations, transactional business models and a relentless focus on efficiency. Resource allocation is based on operational needs and investments sustaining innovation typically have a predictable RoI that follows a Gaussian probability curve.

Innovation, on the other hand, is concerned with finding new opportunities. This means experimenting with new concepts to existing customers and, potentially, selling existing concepts to new customers. As we look to test as many concepts as possible against the lowest cost per experiment and, at the same time, the concept has to cover all relevant aspects including technology, engineering, business model and support, the nature of innovation requires empowered cross-functional teams that engage in continuous relationships with potential customers. The RoI of this type of innovation tends to follow a power function, meaning that the RoI of the most successful innovation is higher than the return of all other innovation initiatives combined. By its very nature, innovation is a high-risk, high-return game.

Many companies go wrong in clearly separating the two operating systems, resulting in a host of issues around innovation. As the majority of resources are on the operations side of the business, innovation efforts tend to be evaluated based on criteria driven by an operations mindset. This tends to lead to two extremes. The first is where any innovative idea is killed before it has had any chance to prove itself. As innovation is, by definition, breaking the existing rules of the game, it’s obvious that the opinions inside the company will seek to kill the idea unless it’s protected. At the other end of the spectrum, there are situations where an innovative concept needs to be fully developed and ready for production before we’re even willing to show it to a customer. This leads to the [minimal viable elephant](https://bits-chips.nl/artikel/are-you-building-a-minimal-viable-elephant/) I wrote about earlier.

Combining innovation and operation is hard. In my experience, there are at least two tools that are useful in this context, ie the three horizons model and unstructured time. The three horizons model (attributed to McKinsey) divides the business of the company into three categories. Horizon 1 are the mature cash cow products that generate the majority of the revenue. The model says that this horizon should receive 70 percent of all resources in the company but also that individual products in this category should receive a resource allocation that’s 5-10 percent below the growth rate. That means that if the product grows at 5 percent per year, it may still mean that the available resources are flat or shrink every year.

Horizon 2 is concerned with the proven, rapidly growing products that aspire to become future H1 products. This part of the business should receive 20 percent of the resources and resource allocation to individual products can and likely should grow faster than revenue growth in order to accelerate things.

Horizon 3 is concerned with new innovation concepts that are unproven. This part of the business should receive 10 percent of the resources and these resources are allocated to running experiments with customers with the intent of finding viable options. In [earlier columns](https://janbosch.com/blog/index.php/2017/02/05/innovation-is-hard-work/), I’ve described how, in my experience, this should be organized.

The important part is to realize that any organization needs to allocate at least 10 percent of the total resources to innovation. One way to allocate those resources is by offering employees unstructured time for innovation. So, everyone who wants to can use 10 percent of their time for innovation efforts without any approval required from any manager. These hours can be used to build teams of people that all use their 10 percent to explore a particular innovation. In [this article](https://janbosch.com/blog/index.php/2017/10/13/the-end-of-innovation-as-we-know-it/), I provide more details.

Concluding, combining innovation and operation is difficult but also essential to ensure the longevity of the company. We need to survive today and ensure a viable future. Although everyone understands this conceptually, my experience is that in most companies, these two systems of operation are conflicting, with the investment in innovation often being the victim. So, think about this: for all the talk about innovation, does your organization really conduct innovation, protect it and grow new businesses out of its innovation efforts? Or is it just ‘feel good’ talk?

As part of the research in Software Center, I work with dozens of companies in the software-intensive embedded systems space on a variety of topics. One of these topics is the development of new products. Having worked with online companies, as well as startups, I’ve become indoctrinated with Steve Blank’s ideas and the “lean startup” concepts. One of the key tenets is that you validate with customers every step of the way. In fact, you seek to minimize the amount of R&D investment between customer proof points. The second tenet is to only rely on what customers say when you absolutely need to, but whenever possible rely on measuring their behavior.

In the embedded systems industry, for some reason, companies are extremely reticent to validate with customers before the entire product has been built. Over the years, I’ve heard a great variety of reasons as to why this is. The main ones include: “We can’t show customers anything but a complete product”, “We’ll damage the company brand if we show an early prototype”, “This idea is so good that they’ll buy it if we only build it”, “Experimentation with customers makes us look like idiots because it looks like we don’t know”, “This stuff is secret and we don’t want to tip off the competition”, “It’s so hard to organize this across the company as I need to coordinate with everyone.”

The consequence of this is that companies tend to build, as one of my colleagues quipped, minimal viable elephants (MVEs) instead of minimal viable products (MVPs). When I confront people with this and we get past the ‘excuses’, it seems to me that there are at least three fundamental causes to this phenomenon.

First, most of the companies established themselves and became successful by building mechanical and electronic products. Because of a variety of reasons, not the least the need to establish manufacturing facilities, the design processes for atom-based products have traditionally been very waterfall and specification based. With the advent of additive manufacturing and rapid prototyping hardware facilities, this is, of course, changing, but the traditional approaches are still widespread. It’s important to realize that building innovative digital offerings requires a fundamentally different process than building physical products. In fact, using the traditional process is a recipe for disaster as you’re flying blind and base your decisions on the beliefs of you and the others at your company – beliefs that are almost certainly wrong.

Second, especially in new product development, complicated internal political processes and games tend to be part of the equation. As a consequence, the attention shifts from the customer and the business ecosystem to the internal political landscape. The folks involved in the development of the new product often have to compromise with various forces in the company. This causes functionality to be added or changed independently of actual customer feedback but based on the opinions of HIPPOs. In the worst case, this results in new products that, for all practical means, are a Swiss army knife that can do many small things but doesn’t solve the one key problem that initiated the product development in the first place.

Third, many think of new product development as a one-shot opportunity that we have to get right on the first try. This, of course, is driven by the difficulty of changing mechanical and electronic products after the start of manufacturing. However, digital offerings are akin to a conversation: constant updates are cheap, allow you to measure how customers respond and, in fact, are expected by many customers.

Building digital offerings that include mechanical and electronic components as well requires a different view on the priorities and process. First, establish problem/solution fit by simply spending ample time with intended customers, well before any R&D effort. Second, establish product/market fit by presenting the intended solution concept to customers and validate the fit, as well as their willingness to pay. Third, build the scrappiest, fastest, smallest realization of the product that allows for small-scale validation. Here you transition from measuring what customers say to what they actually do. Fourth, build a slightly more advanced prototype of the product that can be validated on a larger scale.

Of course, each of these steps is conducted iteratively and you only proceed to the next step when the feedback that you received from customers justifies it. In practice, it often means that the mechanical and electronic parts of the product are over-dimensioned in terms of specifications in order to allow for a larger ‘design space’ of opportunities for the digital part.

Concluding, innovating digital offerings is hard for embedded systems companies and the result often is a ‘minimal viable elephant’ that sees no customer feedback until after the start of manufacturing. Instead, focus extensively on collecting customer feedback throughout the entire innovation process and on customer behavior wherever possible. In the end, it’s the customer who decides whether you’ll be successful.