Central Planning

The problem with central planning and outdated organisational myths

I am a project manager by profession, so in a way I should be the biggest fan of central planning. But I am not; in fact it is time to debunk a lot of the myths around central planning, five-year plans, and other outdated organisational myths.

Central planning, monitoring and control

Central planning as a concept emerged at the beginning of the 20th century in Socialist and Communist states. It implies that there is an executive committee that makes all the decisions centrally and uniformly, without any consideration of the local context. Everyone needs to fall in line and execute the plan irrespective of their circumstances. Of course, historically, this has created all kinds of inefficiencies and led to the collapse of most socialist and communist governments across the world.

Yet, in many private and public organisations, we still use five-year strategic plans and Gantt Charts that span from one side of the room to the other. Unconsciously, we employ concepts that not only have been proven wrong but rely on an outdated model of the world. This might have been relevant more than a century ago for the factory floor but it couldn’t be more irrelevant now in the complex world we are living in.

It might be surprising to some, but both the concepts of central planning and the five-year economic plans, were heavily influenced by the father of “scientific” management, Frederick Taylor, and his close friend and disciple, Henry Gantt.

The scientific management that is not very scientific

Frederick Winslow Taylor, born in 1856, was an American mechanical engineer and one of the first ever management consultants. Although he was from a wealthy background, he declined the offer to study in Harvard. Instead he decided to become an apprentice machinist. During the 1880s he worked mainly as a labourer in the Steel industry, while later in the 1890’s as a general manager and consultant in different industries.

Taylor’s famous experiment

In 1898 he arrived at the Bethlehem Steel plant in Pennsylvania to solve a machine shop capacity problem. He was obsessed in finding the one best way to perform a task through rigorous measurement and analysis. Taylor believed that workmen were not putting as much effort as they could. He believed that he could create an incentive system, where they would be paid per output instead of hourly wages. In his view, this “cooperation” between management (reduced costs) and labour (increased wages) could dramatically improve productivity.

His experiment concerned a group of iron-pig handlers, whom their only task was to load iron onto wagons.

First, Taylor started with selecting workers that would take part through a “scientific” recruitment method. His goal was to hire high-priced, first-class, pig-iron handlers, based on their character, history, and skills. However, despite his effort, he only managed to hire only a handful of them.

Taylor refers particularly to Schmidt, who was the first scientifically trained pig-iron loader. In his words, Schmidt was a great fit as:

a) he was eager to earn extra cash, and

b) he was very stupid, as no-one would an active mind would want to do such a repetitive and meaningless job.

Taylor told Schmidt that he should obey every instruction without talking, and no breaks unless he is given one. The talking was for the managers to do. The agreement was to pay him by the tonne instead of a minimum daily wage.

Schmidt was probably the only person who succeeded fully through all of the trials. Eventually, Taylor’s project in Bethlehem Steel came to an end in less than 2 years, and the company scrapped all related programmes.

Taylor’s vision

The new kind of organisation Taylor envisioned had two basic functions, “Planning” and “Doing”. As such, he divided everyone in two classes, one of purely physical workers, who would do all the work and no talk, and a second one of purely managers, who would dictate every single task, and direct and measure every move. In essence, he proposed a new managerial elite that would do all the thinking and talking.

Taylor put the freak in control freak

Matthew Stewart – “The Management Myth”

There is no hiding from it, Taylor’s approach dehumanised labour. Workers were for him cogs in a big industrial machine that needed detailed monitoring and controlling every minute. Furthermore, by rewarding individual performance he prevented all collective action.

Frederick Winslow Taylor: Hero of Scientific Management | QAD Blog
Frederic Taylor

It took around 10 years before Taylor published, The Principles of Scientific Management, although the term scientific management didn’t come from him. Due to his extended social network and through his well-connected friends Taylor’s work made it to the front pages of national newspapers. His ideas of management and control found many excited followers. Due to the level of publicity, all the major universities showed interest in Taylor’s work and asked him to teach. The rest is history.

The issues with “Scientific Management”

As mentioned earlier, Taylor was one of the first ever management consultants, and this was not by accident. He was a brilliant storyteller, using even theatrics to make his point, and fascinate his audiences. Although his experiment affected only a handful of manual labourers in the yard of Bethlehem Steel plant, this didn’t stop Taylor from extrapolating his results to all types of work and industries.

Unfortunately, there is evidence that his story is full of major inaccuracies and made up data. Moreover, Taylor never supplied his data to independent observers in order to replicate his experiment and verify his results. Yet, in Taylor’s mind, using stopwatches, and writing down numbers was “scientific”, although no-one ever validated his experiments.

Nevertheless some elements of his work, like using analytical methods in an engineering setting, have applicability. But, it is obvious that management is more than time and motion studies, and certainly it is not about treating workers like cogs in a machine.

Scientific management follows an extremely rationalist approach with significant influence from positivist and reductionist thinking, which were dominant in the early 20th century. Everything had to be measured in the field and analysed rationally or it didn’t exist. I think you can easily see the limitations of this approach. Moreover, the reductionist assumption that everything operates like a complicated machine is a fallacy that unfortunately even today still haunts us.

Despite this, Taylor’s work was extremely influential globally. Perhaps it comes to no surprise that the most enthusiastic supporters of it were Lenin and Stalin in Soviet Union!

Central planning and the five-year plan

One of the most popular questions in job interviews is “Where do you see yourself in five years?”. I always wondered why everyone has such an obsession with five year plans. Similarly in business and government, how many times haven’t we’ve heard about five-year plans? There seems to be some kind of mystical attraction to number five. Apparently we all need to have five-year plans to guide us or else what are we going to do?

What if I told you that when we are talking about five-year plans we are quoting Joseph Stalin? Or that we are applying practices from the central planning model of Soviet Union?

The historical fact is that Joseph Stalin implemented the first ever five-year plan in 1928. Its purpose was to centrally plan economic growth in Soviet Union by developing heavy industry and collectivising agriculture across the largest land mass in the world.

However, this was an unmitigated disaster that led to extensive famine, and caused the death of millions of people. In addition, subsequent five-year plans undid whatever positive was achieved with the first ones. Despite this, the Soviet five-year plans continued for decades. Other Communist and Socialist states, such as China and East Germany, also adopted them extensively.

The five-year planning process took its inspiration directly from one of Taylor’s most successful disciples, Henry Gantt. The hidden assumption was that someone could safely predict and control the future, by planning large-scale interventions. However, the biggest assumption was that there were no unknowns, complexities, or asymmetries.

Today we know that complex things, like the economy, markets, governments, societies, organisations, are all considered complex adaptive systems. As such, there are unpredictable emergent behaviours and other complex phenomena that central planning simply cannot predict years before.

Megaprojects – Taking central planning to the next level

Like many others, Stalin, as part of his five-year plans, promoted megaprojects that could “transform” the USSR. Unfortunately, the outcome of his ambition was devastating for both peoples’ lives and the Soviet economy. The most classic example of how central planning can go terribly wrong was the Dead Road project. This was essentially a railway project that was going to connect the remote polar regions of Russia. It is still unknown what was the intention behind this project.

The construction of the railway began in 1947 and most of the workers (up to 100,000 according to some estimates) comprised prisoners from Gulag labour camps. In winter, bitter cold; in the summer, clouds of mosquitoes, a lack of equipment and food, slave labour, primitive technology, violence, tyranny, death… These were the conditions that prevailed on this insane building project that had been personally ordered by Stalin. In the post-war period, it was clear to almost everyone in the leadership of the USSR that prisoners’ slave labour in the corrupt Gulag system was wasteful and desperately inefficient. Only Stalin failed to realise this and he was obsessed by similar construction projects. To this day, it is still not completely clear – even to Russian historians – what made him want to link the uninhabited and hostile environment of Siberia’s Polar regions by railway.

The history of the Dead Road – Gulag Online

Although this project was never completed, it cost thousands of lives that perished for a road leading to nowhere. And it all started with an over simplistic and overoptimistic central plan put together by people with no knowledge of the risks and realities on the ground.

Megaprojects and Failure

Although a totalitarian regime, USSR was not the exception. The truth is that, globally and historically, most megaprojects have always faced similar problems. Bent Flyvbjerg, one of the most prominent scholars in the field of megaprojects and Chair of Major Programme Management in Oxford University has shown that failure is the rule in megaprojects, rather than the exception.

Specifically he found that of a sample of 3,022 major projects, from across the world, 27% were on budget or better, 2.8% were on budget and on time, while only 0.2% of projects were on budget, on time and on benefits. In other words, in the vast majority of projects there was an underestimation of time and cost with an overestimation of benefits and dividends.

An illusion of forecast and control

The famous psychologist and Nobel laureate Daniel Kahneman used the term “Planning Fallacy” to describe our tendency to be overly optimistic about how fast we can complete future tasks, while ignoring our experience of how long it took to complete similar tasks in the past.

It is unfortunate but even today, when it comes to major initiatives, we still use the same up-front, long-term, central planning approach. Despite its disastrous track record. It seems as if blindly, we keep repeating the same mistakes over and over again. Treating a complex reality as a monolith is a fundamental error of judgment. Instead of seeing reality as it is. Decentralised, uncertain, spontaneous, and full of unknowns. We treat it as a predictable, repeatable process, straight from Taylor’s playbook.

In one of his latest publications, Professor Flyvbjerg suggests that infrastructure projects shouldn’t be treated like a monolith. Instead, we should use a modular approach. First, start with an initial design for a small module. Then implement and validate the design, and then apply the same module iteratively while learning and improving the design. His smart scale-up approach, similar to using Lego Blocks, can apply from start-up businesses to infrastructure projects and public services.

As Eric Ries suggested in “The Lean Startup”, when developing a product or a service, you should aim to create a Minimum Viable Product (MVP) in less than a year. This will allow you to validate it in the market and pivot if your assumptions are wrong. This is much smarter than spending years to plan, design and develop a product for a big-bang launch, only to realise that the product is no longer viable in the market. Most companies now have adopted the Lean Startup approach.

The two laws of forecasting

Tetlock and Gardner, through decades of research, found that we can plan complex events, such as economic policies or business cycles, only for a relative short time horizon (up to 1 year). It seems that after the 1st year our forecast accuracy starts declining rapidly, and when it comes to 5 years it is purely random. To make this point clear, Tetlock compared the accuracy of the human forecasters with the accuracy of the forecast of dart-throwing-chimpanzee.

First Law of Forecasting

You have relative certainty for the first year of a forecast, and you can forget about knowing much about anything beyond three to five years.

Bent Flyvbjerg

The First law of forecasting explains why five-year plans are a utopia. In essence, it says that the longer into the future you plan for an outcome, the more variance, uncertainty and risk there will be. Also, depending on the complexity of what we forecast the time horizons for accuracy will be different. This means, that it is preferable to plan for small time-horizons so as to minimise risk and variances, and then replan in frequent intervals. The idea is to be agile and keep adapting to a changing reality as you go. Compared to complicated, multi-year plans this is a more realistic, accurate, and sustainable approach with much less overhead.

Second Law of Forecasting

You should only attempt to forecast that which is actually forecastable, and never pretend something is forecastable that is not.

Bent Flyvbjerg

The second law of forecasting says that we cannot forecast everything, as there are phenomena that are chaotic. In that case we shouldn’t pretend that we can forecast. A good example of this is the financial crisis of 2008. Who could have forecasted that?

Time to stop trying to predict and control the future

Do we still see people as cogs in a machine? Do we still believe that there should be managers who do all the thinking while workers do all the muscle work? Also, do we really believe that we can predict how the market will be in 3 to 5 years from now, locking ourselves in static plans that resist any adaptation? Academics have already proven that any long-term forecast is completely random. So why waste our time in heavy, up-front planning exercises? Also, what does a central committee know or understand compared to thousands of teams of people with immense collective knowledge and potential? Why are we so hooked into practices that use 19th century philosophies and come from a lost world?

Contrary to the commonly used practice, creating multi-year, complicated, central plans is a fool’s errand. I really struggle to understand how the five-year planning process has come to dominate businesses or how individuals plan their careers based on it. Perhaps there is something magical with the number five that attracts people. Maybe the idea of having an all-encompassing central plan is too attractive to let go. However, I think that at the heart of these outdated approaches are some key assumptions that are invisible to us. This is where our discussion should be focused. What are our values? What is our model of the world? And what our businesses need?

In my following article I will examine the hierarchical bias that still negatively impacts organisations and the need for more decentralised and localised decision making. I will also make the case why it is time to forever abandon the Gantt Chart.

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