Much of the new technology that is the foundation of Digital Transformation, such as the Internet of Things (IoT), Big Data, Cloud, and Mobility have either their roots or widespread proliferation within the consumer sector which drives volume adoption. It is the consumer market size, which accelerates the evolution of the technology as well as the lowering of the price point. In most cases, the consumer-driven adoption of technology has been spurred on by the technologists within the IT department. For many manufacturers, the value of these new technologies is best realized on the shop floor.
This particularly pertains to B2B manufacturers and asset-intensive industries that produce raw materials such as the mining sector or paper manufacturers.
Historically, the application of new technology has been piloted by individuals or small teams that see the value of something before the technology becomes widely accepted as robust and secure enough for the shop floor. These experimenters often rely on “guerrilla” techniques to trial the technology because it is not yet supported officially by the IT organization or because the funding process for a more widely acceptable solution is too drawn out. They want to try something to test a hypothesis or solve an immediate problem.
In some cases, these guerrilla technologists are from the IT function who see a technology they believe has applicability in operations while in other cases it is operations technologists who have a problem and sees that they can prove the solution works using low-cost consumer tech. This leads to the question: Does IT or Operations do Guerrilla Tech better?
Why Guerrilla Tech Exists
Ever since the advent of low cost microprocessor-based computers in the late 1970’s, engineers have been applying hobbyist and commercial technology to the plant floor. While industrial grade digital technology was available, it was generally expensive and since it was in its infancy; unproven. By using low-cost hobbyist or commercial technology, engineers were able to demonstrate the feasibility of process improvements and justify the higher cost industrial-grade technology.
In the last 40+ years, commercial technology has evolved to the point that it is often rugged and reliable enough that it is suitable for the plant environment. Similarly, software (both operating system level and programming languages) that was divided between real-time and scientific type environments (like FORTRAN) at one time, and business programming languages (like COBOL) has evolved to such that both languages and operating environments are robust enough yet scalable enough for the industrial and business sectors. So, applications such as Predictive Analytics have become accessible to the “experimenter.”
The OT Approach
Frequently, engineering and maintenance/technical staff either see a production problem that they believe a new innovative technology can solve or are tasked with a specific process improvement objective and seek out technologies that might help them accomplish their goal. Their approach is typically driven by the business need first, and they view technology as the tool to help them. In some cases, they know of commercial off-the-shelf (COTS) solutions from traditional suppliers that can address their requirements, but have been unable to build an adequate business case for purchase. By looking to an unconventional supplier, either of consumer grade technology or a smaller vendor with a boutique solution, they can test out their theory, build a business case and many times, opt for the COTS solution in the long-term.
An example scenario one might see in this OT driven approach is where operations complain that downtime on a specific piece of equipment is causing production problems and they ask a maintenance engineer to find out why the equipment fails so often. A simplistic, but illustrative solution would be to buy a simple plug-in sensor and software for an iPhone or Android device for under $25 to test out the theory that vibration analysis might be used to predict impending failure on the equipment in question. Once the theory was validated, you could then build the business case for buying the $1000 plus industrial grade instrument to use to regularly monitor that equipment.
The IT Approach
In contrast, often those in the IT organization see a new technology they find interesting and innovative and then become the technology advocates seeking a business problem that they see the technology capable of addressing. Sometimes this even comes from top level management in the form of vague directives, such as the CEO telling the CIO “we need an IoT strategy – come up with something.” In other cases, it may be an individual or team who becomes inspired by examples they may hear at a users’ group event or a trade show.
An example of this might be where the IT organization has a Predictive Analytics tool they use to monitor the performance of their IT infrastructure to improve reliability and performance. Furthermore, someone in IT realizes that with the IIoT, they can get data from actual production equipment and perform the same type of analysis using that generalized Big Data Predictive Analytics engine. Of course, there are COTS solutions available today from traditional/classic APM providers that do exactly that, but they have traditionally cost more than was justifiable by operations from an ROI perspective. But, with the already bought and paid for existing technology, a few weeks of time and some networking/interfacing a significant portion of the value can be obtained.
So, both these approaches, the IT and the Operations led initiatives, are leveraging commercial grade technology to solve industrial grade problems. So, who does guerrilla tech better?
Not IT vs. OT
The bottom line is that it is not about IT vs. OT. The LNS view is that every time you try and reduce the discussion to a “versus” discussion you end up strengthening the dysfunction that often exists in manufacturing between the operations focused staff and those that are the information technologists. While the technology itself is converging, the differing functional requirements of the roles, the differences in background and education of the practitioners and the very nature of real-time manufacturing as contrasted against the methodical records keeping the role of the compliance and financial functions dictates differences in how problems will be addressed.
Smart organizations will recognize the differences and leverage each aspect to achieve maximum results. It should not be IT vs. OT, but instead IT and Operations working together. Find the problems, develop the most cost-effective solution, and make it part of the Digital Transformation journey.
Tags: Industrial Transformation / Digital Transformation, Information Technology, Industrial Internet of Things (IIoT), Big Data, Mobile / Mobility, Predictive Maintenance (PM), Asset Performance Management (APM), Cloud