What’s old is new in today’s workplace revolution. A historical perspective on the era of data-driven work flows and process improvement in Oil & Gas.


Modern day process improvement has its roots in the early days of the Industrial Revolution, an era where the implementation of radical theories like ‘scientific management’ sparked not only rapid performance gains in areas such as manufacturing, but also heated debate on what it meant to be a worker.

Frederick Taylor wasn’t the most popular fellow in this field of thought, known for pioneering a management approach that saw the position of a worker reduced to that of a cog in a machine; something (rather than someone) that can be managed and optimised like any other resource to eliminate wastage and improve the bottom line.

According to Taylorism, as Frederick Taylor’s theories are now known, unnecessary worker initiatives should be axed in favour of enhancing business outcomes, especially through the standardisation of workplace operations that saw employees doing only a small subset of tasks that best aligned with their perceived skillset. Mass production through specialised repetition, what many would call the birth of the industrial revolution in 1911. (see the seminal paper here)

Workers being viewed and treated as less than human wasn’t an ideology that aligned with the highly influential union movement at the time, though, leading to widespread criticism that eventually gave way to other styles of management and process improvement. Or so we thought? (Charlie Chaplin’s performance in the movie “Modern Times” (1936) is a great reference here!)

Digital Taylorism

Let’s fast forward a hundred years to the present day, where digital technology is as prolific as a pen in the modern-day workplace, or in many cases it has superseded it. A familiar rebellion is now unfolding, where unions and academics claim ‘Digital Taylorism’ is taking root and threatening the fundamental benefits of the ongoing ‘digital transformation’.

An evolution of the early model, ‘Digital Taylorism’ refers to the vast array of digital equipment being used to oversee workers and the business operations they are partaking in. Process improvement, in this form, is about monitoring and collecting as much data as possible, to better inform decisions relating to specific end goals.

This could be as simple as surveillance cameras being used to oversee business activities, or it could be more purely digital in the sense of monitoring what devices are being used by workers, how often and for what purpose. Text, image and video analysis is also providing a deeper understanding of tasks happening in this context. This is happening right now, and the end result is a surveillance workplace where optimisation is sought by looking at what human workers are doing, how fast they are doing it and whether or not they are deviating from a preconceived plan.

The ethical nature of corporate surveillance isn’t the debate here, but instead it could be argued that the idea of employees being so finely monitored and controlled using technology is in fact threatening the fundamental point of digital transformation in the workplace. Free from heavy burden and restriction, technology was supposed to grant freedom, flexibility and the space for creativity to flourish – to the benefit of the company, but at odds with the very early ideas of Taylorism.

Without a doubt a tension is brewing between this formal process of optimising operations and the benefits derived from a degree of employee autonomy, exacerbated by management’s potential to monitor and restrict individuals like never before thanks to technology. Consequently, are we seeing the status of employees regressing back to that of a cog in a machine, all in the name of aggressive performance enhancement, reducing costs and increasing competition?

Drilling Optimisation in Oil & Gas

The very nature of drilling in Oil & Gas requires us to be very mechanistic in terms of the processes that have to be followed. You could say, in a high-risk environment it’s not wise to stray too far from the basics. On the other hand, though, it’s an area where unknown circumstances are faced; where human improvisation and sometimes creativity are relied upon to overcome engineering difficulties.

In this context the way I look at process improvement is slightly different to others in the field. If we look at what is happening on today’s rigs, where we are looking at real time data, with sensors recording measurements every second, we are then trying to fit human behaviour into that rigid framework. In this sense the human workers are very much part of the drilling rig ‘machine’ and are being instructed to execute specific tasks at precise moments, exactly what Taylorism preached early on (see here).

In theory this should work like clockwork, but some activities on an oil rig require more improvisation, and unanticipated problems should be expected. The flexibility to respond based on previous lessons learnt, organisational best practices, the driller’s broader past experience, records of past performance etc., enables employees to perform more efficiently than they would have if they were doing things, uninformed, for the first time. Of course, data and IT knowledge systems are at work here, but data should be one of the tools of the employee, verses the employee acting as a tool to execute the wishes of the data.

We need to be aware of these two fundamentally different approaches, often intertwined, in dealing with process improvement. On one hand we have the more mechanistic ‘worker as a machine’, and on the other we have the empirical approach based on past data and experience, as well as a degree of autonomy to fully understand a role, be motivated to fulfil it and decide on the best course of action to achieve it.


When it comes to drilling, or any field of work for that matter, there’s two very important aspects that managers are aiming to satisfy: overall job satisfaction (assuming that ‘everyone who works should be happy’) and that employees are achieving the generally accepted quality in their work.
To achieve both of these would be the best outcome, setting an organisation up for long term success on the back of positive employees doing great work. Ideas like Total Quality Management are useful here, where we can look at unifying both trains of thought when it comes to process improvement.

From the mechanistic, ‘scientific management’ point of view it is certainly helpful to look at employees as one part of “the machine”; as one resource in the overall operation, and providing them with structure and a set tools to fulfil an end goal in most cases would be considered good business practice.

Through the lens of Digital Taylorism we can view monitoring and offering guidance with technology as ensuring employees are kept ‘on track’ towards our end goals, but thinking every possibility for performance improvement, in this context, can be reduced down to bits of data would be a mistake, and goes against the fundamental nature of human traits that the ‘digital transformation’ was supposed to liberate.

The freedom, flexibility and creativity that has been the foundation of best practices, born out of difficult circumstances, for decades would be lost, as would the benefit of new employees jumping right in and thriving off the know-how of their predecessors.

It’s this historical perspective and actually understanding the conditions and scenarios that led employees to make decisions in the past that will prevent or effectively alleviate unexpected future problems. It’s in this context that employees are free to carry out their work, utilising technology as a tool to bring together all this past experience in an efficient way that only the recent digital transformation can enable.

Ultimately finding a balance between the different perspectives is key. As they say, ‘You can lead a horse to water but you can’t force it to drink.’ The rapidly advancing technologies in our business environments are only as powerful as those that are hired and trained to wield them, after all.

It’s fair to say that today’s data scientists and young professionals might not be very familiar with the experience of Taylorism 100 years ago. While we have only scratched the surface here, it’s amazing to see the parallels between the industrial revolution of the early 1900s and the ‘digital revolution’ of today. Quite often history repeats itself in new clothes, and in all cases there’s consequences for those who choose not to learn from the past.

By Dr. Carlos Damski & Michael Brown