Thirty years ago, Siemens ran a series of ads promoting computer-based systems for industrial applications using posters by a then-famous German cartoonist that made fun of corporations and how they performed industrial tasks like mechanical drafting, manufacturing measurement, and constraint and control without the aid of computing equipment and software. One cartoon depicted a sleepless proletarian attempting to get some shut-eye with his legs dangling woefully from the end of a grossly undersized bed. “There are some enterprises,” the poster copy read, “which do their planning without Computer Aided Industry.”
It may be significant to note that this 30-year-old recommendation about the use of computers went out several years before Siemens’ formal acquisition of Nixdorf Computer (1991) — and at a time when the use of IBM-compatible personal computers was still largely a market experiment. Even then, the German industrial giant was lobbying for a position on what will be known as the Industrial Internet of Things (IIoT).
Today’s vision of the Industrial IoT bypasses the selection of microcontrollers, the monitoring and control of sensors, the accumulation of terabytes of data for analyzation, and the modular implementations of operating systems. Siemens’ vision, like many of its industrial competitors, focuses on the advantages of digitization at a higher level of organization and abstraction — what do you do with the massive amounts of data we’ve already accumulated?
The blueprint for the next Industrial revolution — what Siemens calls a “manufacturing makeover” and what others call Industry 4.0 — has already been formulated and presented to the German government. The key word describing the revolution is “digitization.” Siemens’ advisories to business leaders — particularly Americans — suggests that the revolution has occurred, the data is in place, so “use it.”
Internet autonomy is the ultimate goal. Developers of Industrial IoT systems perceive that its future cyber-physical systems will be so intelligent and so autonomous that they can make a decision to cannibalize a co-worker. Source: Siemens.
What Siemens’ success means to you
It is a bit hard to tell whether the company is gloating in the way that it communicates with American entrepreneurs: In a whitepaper series of “use cases” for the IIoT implementations — no longer a series of poster jabs — the company emphasizes the footprint that it is building in the U.S.
The Siemens advisory to American entrepreneurs focuses on five areas for development. The vision includes, first and foremost, a revitalization of manufacturing. Then:
- Intelligent infrastructure
- Reliable transportation
- Sustainable energy
- Advanced manufacturing
Key to the manufacturing makeover, Siemens insists, is the acceptance and use (certainly, the exploitation of) the new digital environment. On the surface, this includes familiar IoT components (sensors, controllers, data converters, etc.). On the larger level, it forces factory automation to embrace a higher level of abstraction. This is more than the re-programming of programmable logic controllers (PLCs) or the Supervisory Control and Data Acquisition (SCADA) systems that we are still challenged to deploy. It is the ability to imagine autonomous machines that are capable of gathering inputs and making their own decisions. Operational Technology (OT) will require smarter data, says Siemens.
Siemens is one of the primary authors of the German Industry 4.0 specification, which identifies intelligent factory technologies and the data exchanges required for manufacturing. In as much as the Industry 4.0 specification is constructed at the behest of the German government, it is the German industrial giants — like Siemens AG — that will be responsible for its definition and implementation.
This “smart factory” description actually treats the IIoT as a subsystem, with cloud computing, cyber-physical systems, and cognitive computing taken as components. Within a modular structure, cyber-physical systems monitor physical processes and create a virtual copy of the physical world. The smart factory is thought to be autonomous and capable of making its own decisions. Over the IIoT, cyber-physical systems communicate in real time.
In the deployment of Industry 4.0, the cyber-physical link serves as a superstructure for IIoT. Where it operates autonomously, it is capable of linking legacy systems — applying an overarching authority. Source: https://en.wikipedia.org/wiki/Industry_4.0.
While the Industry 4.0 specification is intended to provide a roadmap for factory automation, the specification offers assumptions for the operation of computers and machinery in terms of how they communicate. There are four design requirements embedded in the spec:
- Interoperability: the ability of machines, devices, sensors — and people — to connect and communicate with each other real time via the IIoT.
- Information transparency: the ability of information systems to create a virtual copy of the physical world by reinforcing digital plant models with sensor data.
- Technical assistance and support for humans (annotated CAD drawings, for example).
- Decentralized decision making: Siemens may be out front by insisting that cyber-physical systems must perform their tasks as autonomously as possible.
The rise of smart production — a roadmap
Accenture Consulting has been tracking IIoT deployments. “The rise of smart production,” their experts say, “requires making the most of the Industrial Internet of Things in manufacturing.” Accenture’s advice actually begins on the IoT level (not yet Industrial IoT), recommending what engineers would recognize as an assessment of resources. Accenture identifies six decision factors in which the next-generation factory will be implemented: These include assessments of the available equipment, the workforce, and the materials supply chain. Also worth looking at and assessing are the accepted business process, the company’s facilities, and work environment.
Accenture calls the industry-wide realignment of Operational Technology (OT) and Information Technology (IT) as part of a solid business case, along with the ability to harness technology change and retrain its workforce.
Aided by the convergence of OT and IT, Accenture sees industrial automation processing in four distinct phases (Note: Some of these recommendations may seem more synergistic with consumer markets than industrial production):
- Improvements in operational efficiency, including better asset utilization, with cost reductions and greater work productivity.
- The introduction of new products and services. These will need to show greater reliance on software services and data monetization.
- Outcome-based markets, like pay-per-view media or pay-per-result tasking, will tighten connections to the internet.
- Eventually, we’ll evolve an autonomous “pull economy.” This will utilize continuous demand sensing, with end-to-end automation.
The goal is to keep manufacturing in a tightly closed loop in which the IIoT machinery senses a demand and, spontaneously, without additional instruction, designs and manufactures the product to satisfy that need.
The Industry 4.0 specification provides a communications hierarchy for computers and computing equipment on the IIoT. On the cognitive level, the system operates autonomously. Source: https://en.wikipedia.org/wiki/Cyber-physical_system.
The industrial internet now
Implementations of Industry 4.0 have helped the industrial IoT move from proselytization to factory-floor applications — not just in manufacturing, but also information and communication technologies. There remains a need to connect legacy devices to the IIoT — for no other reason than to utilize the internet data already collected.
Legacy data-acquisition systems lack the connectivity of newer pieces of equipment, suggests Industrial Internet Now. Legacy sensors and controllers may offer several more years of useful service, if they can be linked to the cloud.
Historically, before there was an IoT support system, data-acquisition systems using PLCs or SCADA interfaces were already capturing operational data. What is needed now is a harvest of data from those systems.
The remaining challenge lies in recognizing innovation and integrating it into business. Industrial Internet Now reported that, when it comes to “digital transformation” — using all of that data — only a small percentage of survey respondents believed that their company had BOTH “vision” and “execution” in place.