Complexity kills speed. In classical systems we often need to manage variants and versions of components. The combination of both could be called a configuration. 

• Variant means “coexistence of two solutions at the same time”. 
• Version means “temporal succession of implementations”. 

In a cyber-physical system with embedded components, both aspects have to be managed in the classical world. If I want to manage the problem, I need to know all possible implementations beforehand, that is I need to know all configurations of the system and need to validate each of them. At least the ones I want to “allow”. Or limit the number of possible configurations to those that “make sense” (for me) and show the desired functionality and at least validate this subset. I think we can all agree that configuration management is a complex task with a lot of dependencies. 

If managing something is complex and tedious, maybe there is a different solution. (This is a reference to my old atomic physics professor, observing us calculating quantum theory wave functions for an exercise on end-less printer paper. “Are you sure you are on the right path? There must be a simple solution. This is not elegant.” He was right. There was a simple one. I learned later.) Remember: There is always an elegant version of a brute force solution. 

What is the solution when classical management is becoming too slow and complex? Bingo. 

Selfmanagement. Trust the experts/developers/worker a.k.a. value creators. Provide the right objective and guidance, then they are going to figure out the rest. Objective and guidance, a.k.a. a reference frame requires commitment and boldness.

What if we reverse the problem of configuration management and stop managing? Instead of pushing solutions to a cyber-physical system, we let the system PULL the valid configuration? Not used configurations do not need to be validated and tested. The system knows how it is built and can do the job of the “system integrator”. This is simple and fast. It comes at a price tag, but suddenly a new configuration does not add to the bill. Where is break-even?

Not possible and all phantasy? I do not think so. I believe it can be done and I am convinced it is implemented somewhere already. In the automotive world. All you need is an automatic test and release system built into your cyber-physical whatever and a suitable description of the components and their relations. Heard of a digital twin before? I used to think of it as a digital simulation of the system of interest. Possible, but more powerful is “just” a description file, describing your system of interest possibly as a graph.
No guarantee though that is really exists. I am just reading tea leaves here and there. Correlating random information. I would bet on it.

Oh, and one more thing: in mathematics, elegant proofs sometimes are visible when you watch the system from the outside. 

Cheers.

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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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In a second it struck me that there is something dreadfully wrong in this statement. I just could not nail it down.

Some years ago around the same time. Enter podium discussion at a tiny conference close to Detroit. Some agile “what-ever”. I was on stage with two big names. No idea why I was asked to participate on stage. The topic was agile scaling or rather how to run big projects in agile. At the time, I was very much into micro services and reversing Conway’s law and architecture centricity and topics around it. We were discussing the standards like big room planning, program boards, minimum viable products, walking skeletons, whatever… At one point I had an idea. “We need to manage the work such that there are as few as possible dependencies on the board. Best case scenario is NO dependencies.” That one touched a nerve and I was disqualified as not understanding systems at all. Easy bait. Lesson learned: Do not be creative on stage. Does not work.

But: Still, the idea was attractive to me. It leads to simple solutions with very little dependencies and dependencies is where I get a headache.

Fast forward to some project management conference. Quite a few system engineers were presenting. I have seen a few people proudly showing off their skills in management of complex dependencies.

My mantra became a lot clearer: “Keep it simple, stupid.” There are many variants to the idea and it dates back a couple of hundred years. Just search the web for some trivia. Anyhow, the recommendation to the executives by the consulting company should have been:

“You need to reduce complexity.”

How to reduce complexity? Make it complicated for few and simple for many. This involves

• Unterstanding that architects are in a leadership role. They shape business and products. Cannot be separated. Both go hand in hand.
• Understanding that organizations that develop systems are systems themselves and that these are social systems of people, developing for people. (In the end there most of the time there is still a human interacting with your product. The product should be there to serve a purpose.)
• Understanding that the world is changing at a certain pace. To manage solutions, you need to be faster than the changing environment. Elements of self-management might be a good idea to maintain speed.
• Understanding that “divide and conquer” does not necessarily mean micro-management.

Why should we reduce complexity at all?

• Simple solutions are easy to build.
• Simple solutions are easy to adapt and to modify.
• Simple solutions are cheap.
• Simple solution can be delivered very fast.

Complexity kills speed. I want to move

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Reading through the HBR article, one of the great successes presented is the Shimano Coasting idea. Ever heard of it? Me not and I am a lot into bikes. It is about opening a new market to the biking industry. It failed. Is it due to design thinking? No, I do not think so. There is some 2010 analysis of the failure of the marketing campaign by Yanni Groth.

What caused Shimano’s Coasting-program to fail ? Interesting reading as well. Marketing…

As a small side joke: After describing the Shimano Coasting idea, the sub-headline is “Taking a Systems View”. Well, yes. That systems thing might have lead to a different solution.

This might be a bit what they were after. Not sure how successful it is commercially, but: Impressive to me and it lasts for some time, survived the COVID pandemic, so it probably is robust: Swapfiets based in the Netherlands (expanding). Get people on the bike. No. The people have a strong need for local transportation. A hassle-free bike is an answer top the need. You simply pay a small “transportation fee”, a.k.a. you rent the bike full service and you are all set. Bikes come in different flavors. There where I have seen them (the city of Groningen), the main options were color. Multiple things go together here:

• user need – simply forget your car in Groningen. Makes no sense.
• marketing see below…
• simplicity of the process to get a bike – use internet, generate an account, make an appointment, take the bike and ride away.
• low risk – it comes with two locks. If stolen, there is limited liability.
• corporate identity – they come with a blue front tire and are low-tech.
• infrastructure – there apparently a lot of infrastructure in the background to keep the city clean of abandoned and stolen bikes, get the bikes returned. The main feature is that you always have a ride. It never breaks. Well, it does occasionally, but you can swap it.
• maybe some secrets I did not discover first glance.

The success boils down to focus on customer experience. Bikes as a service.

More competent analysis on the marketing side by Jeroen Coelen: The hidden reason of Swapfiets’​ success. Marketing is one thing. Here another “random” marketing analysis Discover Swapfiets’ Inbound Marketing Success | Topkee.

You ask me: Marketing is one thing. I would bet that proven service and dedication is what keeps the business going.

Hey guys at Swapfiets, would be cool if you would correct, comment, extend, whatever! Thanks.

One last thing: When I was in one of the joints, I overheard a conversation: “Oh, when you are new to biking, you should take this one. It only has the rear brake. More simple to ride. The one with two brakes is more advanced.” And: This is not Shimano bashing or anything. Just something to learn from.

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