This week I was invited to give a guest lecture on data science for a group of change managers. We discussed the social effects of predictive maintenance on the workforce and how to deal with the implementation of this concept from a change management perspective. At the end of the lecture I came to realize that it's the data scientist who should know about change management rather than the other way around. This might make the implementation of the improvements found by data scientists much more efficient since this tackles behavioral change: One of the biggest hurdles in implementation of our ideas and realizing our goals. Change management may be seen as a domain opposed to data science.
Industry 4.0 is disrupting manufacturing on multiple fronts – from production throughput, predictive maintenance and quality, to supply chain and inventory management. While this wave of innovation is being greeted with much enthusiasm by a traditionally conservative industry, a clear strategy for deployment and ongoing management is required to successfully adopt Industry 4.0 technologies. Total Productive Maintenance (TPM) is a lean manufacturing approach developed in Japan in 1971. The approach includes a number of methodologies still widely used today such as the Six Big Losses, and is well suited to the smart factory and its IoT use cases. In a nutshell, Total Productive Maintenance is a system for optimizing maintenance and reaching a state of perfect efficiency in production.
Industries with distributed fixed assets--be they telecommunication broadband or railway networks, wind turbines or drilling facilities, elevators and escalators or washing machines--share specific challenges when it comes to maintenance. As the assets are distributed throughout a region, there is usually no dedicated maintenance team per asset. To the contrary, maintenance workers cover whole areas, travel to the assets' various locations, and bring the appropriate instructions, spare parts, and tools. Maintenance costs typically range between 20–60 percent of opex spend, depending on industry, asset type, and capex spend--an opportunity that has only been a minor priority over the past couple of years. At the same time, ensuring high levels of asset availability and system reliability is a key priority for operations leaders. Often, regulations severely penalize shortfalls (eg, of power transmission and distribution), breakdowns incur high revenue losses (eg, for wind turbines), or breakdowns result in high safety and environmental dangers (eg, in drilling facilities).
The convergence of artificial intelligence (AI), big data, automation and the internet of things (IoT) already has widespread implications on the way we design, make and maintain things. These transformative technologies collectively are the drivers of a "fourth industrial revolution". Previous seismic shifts in industrialisation were brought about by the advent of steam, electricity and digital technology. Today, it is data-driven, autonomous and self-learning technologies which are driving the rapid changes we are seeing across many sectors of business and industry. Of course, information has always been the lifeblood of engineering and manufacturing.
This blog post has been written with the collaboration of Juan Olloniego and Germán Hoffman. Even if machines have done a big part of the heavy lifting for us since the industrial revolution, they still depend on us for their maintenance. As they have that annoying tendency to break from time to time, their conservation becomes essential to keep up with our daily activities. Now, with the industry 4.0, the internet of things, and the artificial intelligence advent, we are letting a new kind of machines take care of their older counterparts. We make these new transistor-based machines look after their ancestors.