By guest author Roland Thomas, CEO of OFS*
There is significant chatter about Industry 4.0 or the fourth industrial revolution. All the analysis bodies – such as Capgemini, Fujitsu, Gartner and PWC – appear to have been researching and reporting on what Industry 4.0 is and what prize awaits us if we join the revolution.
They have loosely defined Industry 4.0 as the creation of smart factories and a bunch of enabling technologies, concluding that adopting technology such as AI, advanced robotics, condition-based maintenance, augmented reality and digitising supply chain activities is the way forward.
Here’s a manufacturer’s practical guide to the technologies driving Industry 4.0 and see here for how quality data can literally drive production in a smart factory.
Summarising further they found that most manufacturers either have, or are working on, an Industry 4.0 strategy, but less than 10%-20% were actually happy with their level of accomplishment.
There are several reasons why that would be, and the truth is likely a combination. There is certainly cynicism that Industry 4.0 is shrouded in hype, that there is a lot of relabelling and not much that is truly new. There is also concern that it is simply too big, too complex and too much technology to absorb.
There are questions about timescales from an industry that traditionally invests in capital equipment that may last decades, clashing with a tech industry that may reinvent itself every 3-5 years. Hence programs that last 3-5 years could be obsolete by the time they are complete.
Several of the technology trends can be combined to reflect the requisites for smart factories, such as aligning the physical and digital world, digital twin, AI foundation, intelligent apps and analytics, intelligent things, cloud to the edge, and immersive experience.
Unfortunately, there is a considerable gap between the growing expectations of these technologies and what they are currently able to deliver. Some technology, such as AI, has been evolving for 60 or 70 years, while other technologies still need a few years before primetime. This is causing confusion and dissatisfaction with Industry 4.0 strategy and plans. It looks daunting if you take a technology-first approach, but far less so if you take a people-and-solutions-based approach. This means you identify an outcome that you desire and align the technology to deliver that outcome rather than the other way around.
In addition to taking a solutions-based approach, some trends are based on technology that is ready for prime time.
Conversational platforms: This is about recognising the power of incorporating and managing the conversations that turn information into actions, which ultimately drives change. People will define the intent, but translating intent from conversations will shift towards the computer. Conversational platforms already understand basic language and simple user intent, but there is still work to be done to allow users to communicate in a fully natural unstructured way.
Even-driven solutions trend: Today it is common that some IT processes, e.g. the scheduling system, might be manually coupled with operational technologies (OT) such as your OEE system. The trend going forward will see them linked as one process, in real time, designed to adjust co-operatively as events occur. Business events could be anything that is noted digitally, reflecting the discovery of notable states or state changes, for example, the start or end of a production run or 15 minutes before the end of a run so transport can be ready etc..
You can find out more about OEE here.
Technology allows events to be detected and analysed, but technology alone – without cultural and leadership change – does not deliver the full value. Digital business drives the need for IT leaders, planners and architects to embrace event thinking … which brings us to the first “people” question.
The ‘who’ in your Industry 4.0 strategy?
Look around at the people in your business. There are concepts that require new knowledge. Who in your business understands the purpose of a support vector machine for your classification or regression challenges in the machine learning subset of AI? Who knows the ins and outs of NoSQL databases such as MongoDB or how they might be used to avoid massive data-transformation projects or when you need to consider a big data platform, like Hadoop, based on data size? Who has the data science skills required to find patterns capable of being used for predictive actions, or the access control and cyber-security skills to avoid data breaches or isolate control systems. If they are not in your business now, where will you find them?
Spare a thought for the universities and tech colleges who scrambled to find experts to define and conduct courses that ensure their graduates will be suitably armed for this challenge. New Data Science and Cyber Security degrees have been established, but the flow-on will take time. Many of such courses are only just starting to have graduating classes and these graduates are snapped up quickly.
Having said that, the more progressive organisations who recognised this challenge early have been recruiting for such skills, so we are seeing a smattering of people with “Industry 4.0” appearing in their title.
Too big, too complex?
When discussing Industry 4.0 initiatives, people are sometimes presented with complex – perhaps incomprehensible – architecture diagrams. The architecture considers all sensor and control data on old and new equipment (having retrofitted old equipment) as well as the digitisation of all the paper forms.
There are operational applications, such as Down Time Reporting, Quality or Warehousing Systems, and front-office applications such as ERP systems whose data requirements must be considered. The data needs a home, so it might be stored in a data warehouse or big data platform, and made accessible through web services.
To avoid mishaps, it will need some complex cyber security and access control system. A single broad project to implement such a system is complex, costly and time consuming. It will be disruptive, and, when complete, your business will be at the start of the Industry 4.0 journey as it has only digitised your current systems rather than adding something new.
An alternative approach that balances the desire to begin Industry 4.0 initiatives yet control the investment, requires your best current people. The overarching architecture serves to identify many the requirements to avoid identified pitfalls – yet it is not necessary to implement it all at once.
By being driven by solving specific valuable problems, best-of-breed technology solutions can be combined together to only implement what is needed to deliver the business value in an Industry 4.0 strategy. Such a list might be expressed as:
- Eliminate 3,000 hours a year in processing invoices, or eliminate all paper forms from the factory floor
- Eliminate 5% unplanned downtime or 20% of setup time on bottling line 7
- Detect possible fraud in purchasing, using machine learning (to achieve this, collect just the required IIoT information to support the machine learning objective — find out more about IIoT here)
- Recover 5% factory space and 2% efficiency gain from robots that can operate safely without cages doing repetitive task “x”( Check out how next-generation robots are transforming manufacturing)
- Reduce by 20% number of maintenance call outs using model-based reliability analysis to enable real CBM (FRACAS)
- We want to create digital trust with a customer-facing artefact providing trusted provenance that this manufacturing process is compliant (read here how one manufacturer created trust)
This thinking was one of the drivers behind the OFS and Matthews relationship to reduce the pervasive “data silo” issue and to work towards building richer and immutable information into the labelling.
This is a people job, to inform and direct the architects, so that they can create a design to solve the specific problem while having one eye looking more broadly in an attempt to future proof the solution.
People, policy and politics
Consider all the people and groups who will experience change and disruption during the roll out of a broad strategy: operators, schedulers, quality, order processors, maintenance engineers, fork-lift drivers and more – not to mention your IT team and management. Not only will the technical nature of their job change, their established social relationships within the organisation will change. The change itself may create disruption if it devalues or marginalises their job, but it can also disenfranchise them if it is done in such a way that doesn’t value their contribution.
Turn your mind to all of the policy touch-points such as IT security policies controlling who can see or access what information; policies for when IIoT data is exposed to the cloud; and safety policies for when sensors are used in control systems and PLCs on the control network share data with other IT systems, because this could expose them to accidental or malicious risks resulting in loss or injury. Cybersecurity policy and tools can limit the impact of fast-growing threats – such as ransomware – and ensure their business remains operational at all times.
These changes to work practice and policy will add work to people who already have a busy job. It has the potential to become a political quagmire. Imagine how much easier it would be if you take a “people first” approach to demonstrate that many of those people are lynchpins to your data strategy in the first place.
Cyborgs and centaurs
Blending human engagement with machines is an OFS principle, and it is imperative to specifically incorporate this capability into your I.40 strategy and data plan. Operators, in particular, are a rich source of contextual information and, as long as the operators know they are being heard, can become prolific contributors. The pressure actually mounts on management to act on the information.
Paraphrasing futurist Kevin Kelley in 1997, WATSON’S precursor, IBM’s Deep Blue, beat the reigning chess grand master Garry Kasparov in a famous man-versus-machine match. This was followed by more victories until humans largely lost interest in such contests. But Kasparov realised that it would only be fair if he’d had the same access to a database of all previous chess moves that Deep Blue had. He then pioneered the concept of man-plus-machine matches, in which AI augmented human chess players rather than competed against them.
In such “freestyle chess matches”, you can include your unassisted human self, or move the pieces for your chess computer or, as Kasparov preferred, you can play as a “centaur”, which is the human/AI cyborg. A centaur player will listen to the moves whispered by the AI but will occasionally override them. Today, the best chess player alive is a centaur.
Surprisingly, the advent of AI didn’t diminish the performance of purely human chess players. Cheap, chess programs inspired more people than ever to play chess, and the players got better than ever. There are more than twice as many grand masters now as there were in 1997.
If AI can make really smart chess players even better, it doesn’t take much imagination to believe its best use in the factory will be to create empowered super operators, maintenance engineers, and even operations managers. It’s time to consider your Industry 4.0 strategy.
* About the author:
Roland Thomas is the CEO of OFS and has spent 35-plus years providing software solutions for faster, better, cheaper manufacturing. Much of this time was focused on simulating injection moulding with a company called Moldflow, which in Industry 4.0 speak, developed facets of the “digital twin”. Beyond OFS and Moldflow, Roland works with PHM technology, specialising in model-based failure safety and reliability of engineering systems, which is a prerequisite to condition-based maintenance. He has also previously worked with a service creating applications for commercial data aggregation using NoSQL databases; data analytics and machine learning.
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