A-SPICE has nothing to do with spices. It is the acronym for Automotive  Software Process Improvement and Capability dEtermination. It receives some bad press recently and is often called guilty to slow development down. The thing is: it is not a process. It is not intrinsically bad. It just depends on the context and what you make out of it. No bashing of anything in this article. Here ideas in forward direction. You can replace “automotive” with any industry you like. I am 100% certain the concept is transferable.

My personal view:

Automotive processes do not need to be slow and overloaded. It depends on how you build them. Facts:

• Nobody ever said, automotive processes need to contain tools. Tools need to support what you are doing. Tools support methods. Nothing else is relevant.
• There is a natural food chain. Processes <– methods <– tools. Processes are supported by methods. Methods are supported by tools.
• Processes need to describe the outcome of the development process. Period.
• Nobody ever said, automotive processes need to contain methods. Methods describe ways to execute tasks that lead to outcome.
• Note: Output is not evil in itself. Sometimes outputs make sense for the execution of your method of choice. Metering output however is not a measure of success in general. In cases like Tayloristic workflows, it is, but it is not a global truth.

In consequence:

1. Separate the process from the methods that are used to deliver the process outcome. The process defines the succession of outcomes. Methods help us to generate the outcome.
2. Multiple different methods thus can lead to one process outcome. No more discussion of an “agile process” or “classical process”. The process can be the SAME, as the process defines outcomes, not outputs.
3. The process points to potentially different methods, leading to the same result. The process is method-agnostic.
4. The method now can be of “classical” or “agile” character. It actually does not matter at all.
5. Each of the different methods employed, uses tools to support the methods. Alternative tools are possible. The methods need to be tool-agnostic.
6. It is possible that two teams use different tools to deliver similar outcomes in the same process.

Yes, this is describing a tree. The process-method-tools landscape does not really need to be a simple graph, as one tool could support two different methods, but you get the picture. I do not want to overcomplicate things.

Therefore, if a tool is exchanged, the process does not need to change. Why should it? I can describe the process through the outcomes constructing the process. The output belongs to the method. i.e. it is method-internal. The same applies to the methods themselves. Why should the process change when I am using a new method to generate the same outcome? Makes no sense.

Think API-first in the context of the process.

You are thinking I am nuts? Maybe yes, but trust me, a process landscape like this is possible.

Use a general rule: No process step shall be more than three sentences long. English sentences, not German ones. It needs to be simple to read and understand. That is enough. Short and concise descriptors are the source of the API strategy. If I get tired reading the process, is it digestible for the developer? I do not think so. Put a bit of design thinking into the process development. Who exactly is the user of the process? What is the target? What does the user need right now? … Keep it simple and modular.

More points:

• Method descriptions shall be method descriptions and typically contain trainings or link to trainings. Sorry, if your developers do not know how to generate a process outcome, train them or better, give them access to training so they can learn what they need. It is not the job of the process to deliver knowledge and skill. Educating people is what line management (which also in agile setup exists, even if it is realized via self-managed teams) needs to do.
• Methods are supported by tools.
• You can model your process in a tool. Fine. It shall then lead the developer and project manager or product owner through the steps of the process. Understand that the process shall support product quality. It is not a flow chart for the activities to be executed by the developer or any other project member. The flow chart of the activities is call “method”. See above: not part of the process.
• Another word on tooling: The lifetime of a tool typically is short. Exchanging a tool is a big pain, unless your tool supports a standard interface. If there is a standard interface, I can exchange the tools. Anything else is a vendor lock-in strategy. “No problem, I will adapt my tool to your process.” I have my opinion on that. You might want to look at Data Centric structures.

If you let all of this marinade a bit, you will discover how to iteratively develop and improve a process including associated methods and supportive tooling.

A-SPICE is not evil. It is simply not always used in a smart way.

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Let us assume the main challenge were to deliver products with high quality, (defect free, lifetime of the product matching the expectations of the customer, always being close the state of the art, never outdated, …) with short turn around time, while requirements or actually feature lists a frequently updated.

These targets are probably agreeable by many people.

Taylorism is bad and agility is good. Or not? Is this the contrasting pair? Maybe yes, maybe no. It does not matter. Let us have a look at the rules of the game.

Taylorism

The basic rules in Taylorism are that the boss knows best and success can be measured precisely. In the last couple of years, Taylorism is widely regarded as out-of-time. As everything, it needs to be seen in its context. The challenge was to produce goods with relatively unskilled workers that work for money, not for purpose. This is the environment that was present at the time. Read Taylor in the original version to get some exposure. The writing style is just the style of the time, so have patience. What Taylor basically did was to take the division of labor in to small units as proposed by Charles Babbage and add a cost function to the equation. The division of labor simply meant that not all work in a production process requires the same skill level. Less skilled worker could take over the less skilled parts while more skilled worker would have execute the more complicated tasks. Equating in the salaries of the workers, you can see an optimization strategy.

Cost = time (worker_1)*hourly_cost(worker_1) + time (worker_2)*hourly_cost(worker_2) + … + time (worker_n)*hourly_cost(worker_n) + overhead costs

There is some overhead to be computed in for the handover and storage and maybe some inefficiency and whatever. Then you can optimize operations. You need to understand EXACTLY what you are doing, then you can optimize HOW you are doing it. This necessary precise knowledge coined the term “scientific management” as Taylor called it. If you are in a situation, where you can understand your processes well enough, you can use this method. Lean manufacturing is maybe the gold standard for scientific management. In lean, there are the overhead costs that need to be looked at carefully. Zero line breaks, no rework for electronics, target of single piece flow, … At first lean manufacturing does not appear to lead to cheaper production at impressive quality standards, but it is the holistic treatment of the entire value generation that does the magic. Single components are simply not enough and the introduction of a single action may destroy the entire party.

Now you can connect Taylor to Lean Manufacturing and Machine Learning. All the same thing: the cost function. Henry Ford added something more into it: you may want to not only include the wages of your workers in, but also the training and education costs. He widened the field of view if you like. Total cost of employment needs to be considered. At some point he realized that it makes a lot of sense to pay enough salary to make your people stay. Money is not everything, but it at least it helps. Sometimes. Legend goes that Taylor and Ford never met or never collaborated. I don’t know. The world is small and who knows what exactly happened around 100 yrs ago?

I made the connection to Lean Manufacturing already. What is now the difference between Taichi Ohno’s manufacturing system and the Tayloristic system? The workers and their culture that is ASSÙMED by the two people. In both cases, the laborer are pretty much untrained. The difference is their assumed interest in the work they are asked to execute. Why “assumed”? Well, who knows what really motivates people and who can look into somebody elses mind? You have to make an assumption on you “average” worker and his or her “average” interest in the work they are asked to do. For sure the assumption is wrong, but we only need to be right on average. And not even that. Taylorism assumes little interest of the worker in the result, Ohno assumes people want to deliver the best result possible. Thus Taylor controls, Ohno involves. It is that simple. If you submit Taylor’s clientele to Ohno’s process you will run into quality issues. The other way around will lead to boredom and attrition. – In simple terms. As always: the context matters a lot.

In addition, there is an extra twist with Henry Ford: He played with the production system. At one point he envisioned the production line at Highland Park, where the work was brought (by gravity) to the worker. Starting with the raw material at the top of the building and having the finished Model-T’s driving out in the bottom. In the end he reduced the production time of a car from more than 12 hrs to about 90 mins. If you dig into the details, you will find that the improvement process itself was not straight forward and it took a couple of years to see this huge improvement in production time. In addition he fought attrition rate by increasing the hourly salary to more than twice the salary paid by his competitors and lowered the working hours. Overall this improved quality and costs.

Anyhow, we now understand where Taylorism excels: In the well known environment. Only if I understand the cost function, I can optimize the system. If my understanding of the cost function is incomplete, there is no way I can find the optimum in terms of cost in a predictable manner. Intrinsically, the cost function can only be found iteratively. I make a proposal, use it and check I get the correct result. If not, I need to improve the cost function. The improved cost function then needs to be checked again. The old and nicely working PDCA cycle by Shewhart and Deming. In total, the scientific management method. That does not mean I can only start scientific management when I have 100% knowlege about my system. I can get started on incomplete information. If and when I go through the PDCA-cycle, my knowledge will automatically improve. Actually there is always something that can be improved, so you are never done with scientific management. Production is an environment, where scientific management excels. Clear data, clear cost functions, clear return on invest, short cycles, clear measured cost for inputs consumend and clear measured numbers for outputs generated are our parameter used for optimization. We do not need to be too picky. A few approximations here and there are acceptable.

As a consequence we now understand that accounting is key to generate the correct cost function. For production typically, the costs are more easily found and attributed to the correct production step. In development, the costs sometimes are not so well documented, i.e. the total costs are typically correct, but the documentation with respect to the individual steps sometimes has improvement potential. In this respect, Taylor, Ford, and Ohno follow a similar concept. Taylor apparently focused on manufacturing alone. Ford considered training and re-training of his workforce and non-conformance costs. Ohno tried to optimize the time needed to fulfill the order from the end customer. The time from the point the order was signed until the money was in the bank. In a similar way, Lean Manufacturing optimizes the money spent for the entire production process, leading to the reduction of stocks on input and output side.

The advantage of the Tayloristic approach and everything that may be inspired by it, is that it is a controlled, predictable process for repetitive activities. There is not much variance allowed within the process. In the Toyota Production System, just as in lean production and management, the worker are encouraged to propose and implement improvements. These process improvements from bottom up direction will optimize all production steps. Therefore workers are heard and will contribute to the improvement of the production steps. Not so much into the improvement of the product. It is often said that Tayloristic management limits the engagement of the worker in the process and reduced their identification with the product.

Henry Ford is not F. W. Taylor and both are not Taichi Ohno, nor W. Edwards Deming. Each of them propose valid, useful, and superb methods. All of them were developing for their specific context. Understand the context, know your context and choose the elements that make sense for your situation. One more thing: There is no best practice. There are only good practices and good ideas.

Agile Approaches

The basic rule in agile approaches is to understand that we are in a situation, where the well known Tayloristic approach does not work. We may know what to achieve, but cannot write a detailed plan. We need to question the paradigms to see what to do differently. Sometime it is just that we cannot optimize because we do not have a precise cost function. What is the added value of base development? The value generated by development can be calculated after we sold the last product. Nice, but too late. Return on invest is not a smart indicator to optimize the development. I cannot use this number to optimize the development process. What is the value of a line of code a developer wrote? Sometimes a smart idea reduces the number of lines of code and is very valuable. How does this enter the equation? If I am traveling in a well developed environment with roads and highways and maps and navigation systems, it is easy to measure the progress I am going to make towards a target destination. When we are traveling in the wild, maybe a jungle or a semi-desert and have no satellite data exploration of our route, it is very difficult to make a prediction. Well even with a lot of data available, predicting the arrival at a certain location is difficult. Here scientific management will fail. Iterative methods will flourish.

The first thing is that we need to know when following agile or iterative/adaptive principles is what we want to achieve in terms of customer experience. I we do not know the outcome as we think it should be experienced, we shall stop all activities. That is simple. If we know what to achieve, and know exactly what to do, stop thinking about agile and write a plan and execute the plan. Still it helps to communicate strategy, environment, plan, ideas, etc. If you cannot write a plan, do not know HOW to achieve your target, communicate strategy, environment, plan, ideas, etc. Listen to your team to get feedback and embark on the voyage. Respect your team and invite them to contribute with ideas and feedback. Together with the team, you need to develop a plan. We do not need to know exactly what to do. We simply ask “Who might have an idea?” This is the motto of the game.

As a consequence, we need to acknowledge that as a manager of such an undertaking, nobody can tell the developer/worker WHAT to DO. We need skilled and knowledgable people that if and when we tell them what to achieve will find a way to reach the objective. We are changing the role of the manager into the role of a leader that gives guidance. Guidance on what to achieve. In case we have the “right” people on the team, our job is to develop the team to keep the work efficient. We need to refrain from telling people what to do and need to develop trust into what they are doing. This is not a laissez-faire approach to leadership. As leader we still are responsible for the outcome of the activity. In the setup in the Tayloristic approach, the manager checks the fulfillment of the individual activities and needs to see the progress report in terms of activities executed. If all the activities are executed, success is guaranteed. In the agile approach the execution of the tasks anticipated is not a guarantee for success. Trust is generated by the delivery of outcomes, i.e. intermediate results leading the greater target. Team and leader agree on intermediate outcomes. These outcomes are tracked. Due to the large uncertainty in such a project, if an intermediate objective is not met, the team shall not be blamed automatically. What is to be done is to investigate the situation and act accordingly.

Working in the agile mode generates more overhead on the planing and alignment side of the undertaking. Instead of one person absorbed in planning activities, we have the entire team involved. In addition, we need alignment in the team. Depending on the challenge to be solved, one or the other method is the right choice. It might also be smart to run the activity in some hybrid mode, where some functionality is developed in a Tayloristic framework, while the rest is done in an agile setup.

And never trust your consultants too much. I used the picture of the voyage in the wild. Remember Henry Morton Stanley with the quote “Mr. Livingston, I suppose?”. The story is cool, but do you really believe you travel to Africa in 1871 to search a single man and actually find him just like that? The story told is a bit over the top, but entertaining. Agile works, Taylor works, but both need to be taken with some grains of salt.

Let us Compare

Back to serious content. The context drives the choice of the methodology, nothing else. These are aspects of the two different approaches. Depending on context, each of these aspects may be regarded as positive or negative. It just depends on what I need now.

Tayloristic approaches and agile approaches in consequence are solution paths to two different challenges. How to find out which is the right choice for the current challenge? Very simple: If you inspect the challenge and are asking yourself “What do we need to do now?” With high probability, the Tayloristic approach is likely good. If you are asking yourself “Who might have an idea?” Then the agile solution is likely the better choice. Maybe the person you are asking has a clear idea already, so you could get an easy fix. However, it is better not to randomly trust people because you like their assessment. “In God we trust, all others bring data.” is attributed to W. Edwards Deming. Do not get the impression that Deming and Taylor were proposing similar management techniques. Remember, they differ dramatically based on the role and a competency assumption for the workers in the line. Also here the context is essential. Tayloistic Style is made for workers that do not identify with product quality. Deming and Ohno are focusing on the situation, where worker identify with the product and product quality. The best thing is to understand method and context and not to copy anything, but take what makes sense in your context. Adapt. It is hard to imagine in current times that we still find value creators that are not interested in the quality of their output. If there are some in your context – the proven method is clear. If you have engaged value creators, some aspects of scientific management still make a lot of sense.

A more formal and very interesting discussion is found in the works by Gerhard Wohland (unfortunately not translated to English language – yet).

Gerhard, please do not be upset. I know your concept has many more details, what is reproduced here this is all I need right now. In addition, I think, the world is a little bit more complex. Instead of “dead” and “alive” I prefer “formal value creation” and “adaptive value creation”. Let us define terms first.

Formal value creation refers to classical Tayloristic value creation. The boss kows best, defines the processes and orders the worker/developer what to do. The structure is going to deliver high value and quality if

• rules are defined and followed.
• methods are developed and trained.
• clear processes are defined and followed.
• the organization is steered based on data.
• we define a plan and follow the plan.
• we have clear targets to be reached.

The condition for this to be successful is that we have a slowly evolving market situation. We have enough time to adapt to the changing environment and situation. We do respond to changes of the environment, i.e. we are in principle adaptive, but there is enough time to adapt the methods, processes and workflows to the changing outside world by the modification of the guidelines for the value creators.

In contrast, we will use adaptive value creation when we do not have the time to adapt the workflows and methods on management level. (Basically this refers to adaptations with a process release of every 6 months or less frequent.) In the case of the need for higher responsiveness, what we need to communicate and define to be successful is

• common principles and rules. Decision are made locally, but aligned via common principles.
• habits and values are developed and trained.
• the target outcomes are defined and guardrails are respected.
• the organization is lead based on information, i.e. correlated data.
• a tactic is followed, based on an agreed strategy. A plan exists and is adapted to new learning.
• An outcome as target is defined, but options to get there are respected.

In consequence, the formal value creation clearly has advantages when the change in the environment is slow enough that the leadership has time to analyze the situation, adapt the processes in the organization, and roll them out. When the market is changing faster than the organization can adapt, then the adaptive value creation has advantages. However, people need to behave differently in both types of value creation. Executives, leader, developer, and worker need to show different talents and skills. In the shop-floor environment, adaptive value creation is close to craftsmanship or even the Toyota Production System. Still differences sure exist, but the overall atmosphere is similar. In the adaptive value creation mode, the value creators need skill and knowledge to create the best workflow. In the realm of formal value creation, diligence is what brings us forward. Adaptive value creation is prototyped by agile ways of working, but not limited to agile practices.

What to Do …

In very simple terms:

• if management and the steering part of the organization has enough time to analyze new customer requests, and can respond, Taylor and the like are a very good choice. We can analyze the new features, break them down to requirements, define an architecture and simply write a plan and deliver. This is working with internal references like documents and specifications.
• in all the cases, where the is not enough time for the steering part of the organization to analyze new customer requests, we need to allow the value provider to take decisions and deliver independently. The central part of the organization then needs to issue rules and guidelines to steer the organization. Otherwise we will end up in a pretty disordered setting. You can envision this as working with an external reference since you will need to ask the customer if you are on the right path.

Here again, context rules. Working in adaptive mode without getting feedback is not going to lead to satisfied customers. The same way, following a clear plan and schedule and asking for a review will iterate the customer. You should be knowing what to do. Don’t ask.

O.K. so far? That just directly tells us that the good old Taylor with scientific management is still the a very good approach under the premise that we exactly know how to do the job we are asked to do. No research, no REAL development. With REAL development I am targeting challenges where in the beginning I do not know for sure how the solution will look like. There are development tasks, were I just need to adjust some parameter and I am done. In this case, yes I will generate something new, but the learning is very limited. In challenges, where I will be learning a lot and new knowledge is generated, the Taylor approach is not so effective. Whenever learning is involved, i.e. I have a plan, I am executing the plan and check the result, a.k.a. I am following the scientific method with the option to fail and actually generate new knowledge, the Tayloristic approach is not smart. Then agile or other adaptive methods are of advantage.

Reflecting these two aspects, it is clear that the adaptive approach on a higher level is always the best solution. If Taylor is the best choice, I will use Taylor. If agile is the best choice, I will use agile. In addition, we always can blend in some Toyota Kaizen: even in Tayloristic settings, the worker or developer have direct exposure to the work. They know how to improve the workflow. Kaizen is thus somewhat in the middle between Taylor and agile. Somewhat. It really does not matter much.

Thus, there never should be a discussion or dispute V-cycle or agile. If you really are agile, you will use the V-cycle or a real Tayloristic setup whenever it leads to faster and cheaper and more reliable results. When it is not faster or generates issues, you will use agile approaches.

To turn the screw one more time: Tayloristic approaches, Lean ideas, Toyota Production System, Agile mindset/methods are actually solutions to challenges. They all have their right of existence – that is granted anyways – and they all have their environment, where they flourish. At this point we need to look at reference points. This is something from the old greek philosophy: Without a reference point you do nothing. Archimedes a bit far fetched.

After we stopped this useless discussion through the understanding when to do what, we shall go to something important: architecture of things.

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Good. Let us get started. Two questions are to be answered first:

1. What is a product?
2. What is a viable product?

First things first: What is a product?

For me a product is something that one person creates and some other person uses. It could be something physical that you can touch. It could be something immaterial, such as a service. An idea that I have and somebody else uses, does not count as a product. There are strange situations possible, where one person creates something and nobody is actually using it. O.K. there was an intended user. So more complete would be “something the somebody creates for the intended use by somebody else. That should be good enough. There is no need to be mathematically correct and complete in the definition. The definition is simple and very close to what is found in wikipedia.

The second point is about a “viable product”. What makes a product a viable product? In the biological sense, something viable is able to live and to develop. This is the definition I am going to use. That means, the product needs to have a lifecycle. It needs to be able to develop, change its features and adapt to changes in the environment. It needs to respond to different needs. Worst case, it will perish in case it cannot adapt enough to the changing market requirements. The definition of the death of a product then is related to its use. In case the product is still there, but nobody is actually using it, it needs to be considered “dead”. The mere existence of a product does not make it alive.

Based on these considerations, we have some boundary conditions for our products:

• products are never static. Products adapt to the changing needs of the user or stake holder.
• they fulfill at least a subset of desires expressed by the user. They fulfill at least one job that needs to be delivered by the user or that pleases the user.
• maybe more…

This is an abstract definition of the entity product, however it excludes built-to-order deliverables. Built-to-order jobs in some cases are built on top of a product platform or are just simple configurations of a platform. The trivial example is computer hardware that is “built-to-order”, where you can select RAM size and mass storage space, and maybe the color of the housing. This is “built-to-order” in many cases just to reduce the amount of material in the storage. There might be a yacht that you order, that is built according to your specifications or large truck. Both products are products in a sense that They are built on top of a certain platform, but very customer specific. In case of the yacht, we may enter a pretty unique job at the higher end of the price range.

In most cases, things developed based on a specific order are built on top of something that had been pre-developed and are then adapted. The adaptations may be larger or smaller. that does not matter in principle. The delivered units however do not have a lifecycle. They do not have versions after they went into production. They will not be phased out and replaced with a new version.

Now it should be clear what we discuss when we talk about a “product”. If what you are doing, falls into the category of “built-to-order” items – no matter how big your item is – many things what we are discussing will not directly address your topic, but: Aspects of nearly everything we discuss can be of good use to you, so please stay with us. In an extreme case, your product might be a harbor. It will be built to order. It will be very unique, but it has a lifecycle. You very likely will not build it once and then forget it. It will be extended (hopefully for the owner), maintained, adapted, changed, modified, re-purposed, … . It for sure has a lifecycle like this. In consequence many of the practices and treats I am introducing here will be perfectly useable.

”Stop scaling agility” is the title of this post. Why should I use agility and by the way, what is “agility” at all? And why should I scale it?

Definition of terms continued: “agility”. Many people have tried to define it. There is no definition for a good reason. I believe the best thing is: Follow the agile manifesto. If you do that, then the next topic is also solved. Then there is no need for “scaling agility”. That then makes no sense. Logically. You may need to scale scrum for example. That is correct, but scrum is just a single method. Before now hell breaks loose, check the web page on the agile manifesto and read about the history. There are just principles, no recipes. There is nothing that needs scaling. Period.

Agile methods now mainly reflect on the “New New Product Development Game” article by Takeuchi and Nonaka. No discussion, there the teams need to be small, just like scrum teams for example. To build a large product in reasonable time, you need to have a large team. Here the work of Melvin Conway kicks in. –> with little interaction between the constituents of the system, you can develop a team setup that consists of units with little interaction. Scaling topic solved.

Scaling agility is a big topic and big business nowadays. Agility in its original form is used in teams to collaborate. One of the key elements is communication. Thus in consequence, the team size is limited. To the lower end at doubt three to four individuals. At this size or in even smaller teams communication and alignment is automatically build in. For larger teams, the upper limit may be discussed but lies somewhere between ten and 20 team members. If we want to organize work, delivered by coordinated actions of more people, we embark on so-called agile scaling concepts. These concepts come with more or less formal overhead.

There are commercial and non-commercial solutions available. All have one thing in common: they are more or less complex and involve coordination of different activities. Discussing complexity with system engineers shows that there are elaborated methods to manage complexity. No matter what, all of these methods slow down the value generation. In consequence complexity kills innovation and productivity. The most natural reaction then is to reduce complexity and make things simple: Always strive for simplicity to be fast. Complexity is killing speed.

This does not imply to the keep the problem we are tackling simple. Problems we are solving may be very complex. The solutions however need to be simple for the teams to develop them with high quality and little distraction.

How can we develop something complex and keep it simple for the teams? The answer is in the product, not in the way we orchestrate the project execution.

Focus on

• Architecture
• Loose coupling
• API first principles
• Data orientation

These are the big topics I will be addressing. In this order.

As Hors’d Oeuvre let me focus on the obvious thing: Do we always need agile methods (if you now come with “agile is a mindset, not a method”, please read here.)? My clear answer to that is: NO. And this is the most agile answer you can give. Do what ever gives the best results in the given context to satisfies the customer (and yourself).

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Bureaucracy as a service: Bureaucracy has few friends, but a dominant position in most organizations. What is the necessary core? What can be done to counter the power of bureaucracy?

Organizing for people means making organizations a good working environment. At the same time, you will gain in effectiveness and flexibility.

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Empirical management means drawing conclusions from the past about the future, i.e. systematically evaluating and using the available information.

Empirical management requires the management of knowledge, systems and artifacts and the active participation of employees as knowledge carriers in order to collect verifiable information. It also means following a process such as Lean or Scrum that systematically and continuously integrates these insights.

This improves the quality of decision-making by utilizing practical experience and treating people with respect.

Keep your options open

There is another compelling reason for managers to grasp the insights behind real options. While option-pricing models are indeed a superior valuation tool—the usual use of the theory—we believe that real options can also provide a systematic framework serving as a strategic tool and that the real power of real options lies in this strategic application. This article seeks to provide such a framework.

The real power of real options. Mc Kinsey & Company, June 2000[1]

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The Last Responsible Moment

It is usually a good idea to never leave a decision to the last responsible moment. Keeping your options open until the LRM – when the cost of not deciding is higher than the cost of deciding – increases your flexibility and the empirical basis, i.e. the quality of the decision.

A decision-making process for maximum options

• For each decision, the available options must be determined.
• Determine the latest point in time at which a decision can be made, i.e. the conditions that must be met in order to enter into a commitment. Decision time = deadline – option implementation time. The first decision is made before the first option expires.
• Until this deadline expires, the search for new options continues and existing options are refined or expanded
• Identify options for each contingency and know in advance which option to exercise under a given condition.
• Try to delay the decision point. Often this is free or has a very low cost. To do this, we must be able to implement the option as quickly as possible. During the slack period, work on how to speed up the process.
• Be aware that optimizing costs does not equal optimizing returns or minimizing risk. Sometimes it pays to invest in more than one option, even if it costs a little more. After all, options have value.
• Wait to decide… and wait… and wait… until the conditions are right.
• When you need to commit and act, do so as quickly as possible. And you can move forward with confidence, knowing you’ve made the best decision possible.

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One of the most striking statements on motivation is Daniel Pink’s Youtube video about the three most important factors of motivation:

• Autonomy (independence)
• Mastery (control over a subject)
• Purpose (objective)

What makes a good working environment?

Translated to the company side, this results in some necessary conditions for employees to be motivated, committed and effective:

An environment of trust and respect

For trust and respect to develop, it needs

• a safe environment in which one can act without fear
• trustworthy and authentic leaders who guarantee a large part of this safe environment

decentralized decisions

An environment in which autonomy and responsibility can arise requires decentralized decision-making authority.

Leveraging diversity

Diversity helps to adapt to new developments. A diversity of perspectives leads to the ability to deliver better solutions and more flexible work in diverse working groups.

What else

This is a selection of factors that we consider to be central to an organization. Other factors, some of which are almost synonyms for the ones above, are

• meritocracy – the fair recognition of performance
• openness – it’s hard to imagine trust without openness
• community – working and setting goals together
• ownership – having authority over one’s own area
• a culture of discipline – the requirement to also justify the trust placed in you

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Bureaucracy relies on rules and procedures, many of which have a life of their own, regardless of the value they have for the end users of the organization’s products and services. When such procedures become overly complex, the only way to break them is to price them – create an internal market system that allows users to decide for themselves whether to use the services they offer want to take.

Enjoyment of work instead of a depersonalized system

Bureaucracy is a depersonalized system – work is done by roles, not people. When this organizational approach is put into practice, the workplace becomes sterile and daunting, and people tend to do what is asked of them and nothing more. In such situations, the way forward is to emphasize community: to highlight the opportunities and the joy of working in a socially vibrant environment.

What are the two biggest problems with bureaucracy (Birkinshaw)

Guard rails, governance and self-determination

‘Guard rails’ of possible action are the systemic, organizational boundary conditions within which autonomy and personal responsibility can be developed: ideally, clear delimitations of which actions are appropriate and which are to be negotiated bilaterally in individual cases.

Just as there is nothing bad about hierarchy and lines, so little about guard rails. Knowing the scope of action helps to recognize in self-organization and turbulence where rather undefined zones emerge and negotiation is indicated.

As a term in English, the ‘guard rails’ are circumscribed as governance : rules that define what is possible and what is not under any circumstances. The gray areas of discretionary decisions in fuzzy situations are the areas in which management can take action and make decisions even when there is uncertainty.

Helpful governance also gives indications of what happens when actions or experiments fail and do not produce the expected effect: what learning, what consequences then happen?

MVB – minimum viable bureaucracy

Stefan Knecht based on an idea by Henrik Kniberg: culture, corporate culture, stands between chaos and bureaucracy. On top of that there is a small, agile person.

Organize bureaucracy as a service

The big question: which of the rules should be codified as part of the operating system – and which are part of the internal market mechanisms?

One of the hairy problems is the accumulated power of the bureaucracy. Market-like mechanisms are an antidote to complex and slow internal processes. Community-based mechanisms build a bridge to interesting and motivating work environments.

Examples

The prime example of bureaucracy as a service is the Chinese appliances company Haier, which has organized everything around a network of micro-enterprises.

Zappos was a famous proponent of Happiness at work and then Holacracy, but now the company has started implementing internal market-like contracts between its operating units.

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A look at the prevailing management models serves as a source of the best examples:

• Ford 1920: assembly line. Ford’s management model became the most influential at the beginning of the 20th century. It took advantage of the opportunities offered by the assembly line.
• General Motors 1930: multi-division company
• Toyota 1960: Lean. What is remarkable about lean is that, in addition to a different way of thinking about optimization, a different view of people emerges that sees employees not only as resources
• Spotify 2010: A prime example of an agile company (we’ll leave out the details of the “Spotify model” and the question of whether it even exists as such)
• Haier today: Rendanheyi

Haier? Rendanheyi? Haier is a Chinese conglomerate specializing in household appliances and the inventor of a radical management model based on micro-enterprises and dynamic clusters thereof. The example of Haier is certainly not easily generalized, but it does question many of the conventional practices and can therefore serve as a spotlight.

Source: Corporate Rebels

Rendanheyi: The Haier Business Modell

Some essential prerequisites and practices shape the tangible reality of the management model:

• ensuring customer relations
• Decision-making structures and models
• How are learning, cooperation and knowledge sharing institutionalized?
• Psychological safety

Customer orientation

One of the key elements of a management model is the way in which customers are involved. The example of Haier shows how much is decided by this definition:

• Conventional brands (e.g. Mercedes) involve the customer in a single transaction.
• Platform brands (e.g. Amazon) generate additional value through a continuous stream of interactions.
• Haier focuses on creating an “ecosystem brand” that gains its competitiveness through lifelong users, e.g. by constantly interacting with users to discover small user wishes that could become a need for all other users as well.

Decisions

Decision-making in the organization has many aspects

• What types of decisions: You can differentiate between business decisions, policy decisions, personnel decisions, and compensation decisions and formulate separate rules for each.
• How does the context (e.g. its complexity) influence decision-making (see here)?
• Who makes decisions: is decision-making centralized or decentralized? In adaptive organizations, it can be said that decentralized decision-making generally speeds up the process. In SAFe, this means “decisions as low as possible but not lower.” At Haier, this maxim leads to radically decentralized decision-making, following the general logic of the management model.
• How is conflict resolution organized: the principle of consent is an example of a non-classical method: decisions should be made in such a way that no one has “serious objections.” This is used in the various variants of sociocracy and holacracy.

Learning, cooperation, knowledge transfer

Learning in organizations starts in the team. Beyond that, an organization needs a strong focus on providing the climate and structures for a learning environment.

Psychological Safety

Psychological safety is the individual security of being able to do and say things that put oneself at personal risk and thus make oneself vulnerable. Voice behavior is a better term: being able to express criticism and dissent without succumbing to group think, group pressure, sanctions or disadvantages. This makes psychological safety the key factor for the effectiveness and performance of teams and the decisive condition for organizations to avoid or correct mistakes.

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