From experience, I used to make two kinds of engineering error. The first was to use the wrong efficiency factor. The answer was often close enough to pass scrutiny, but was still wrong. The second was to get the decimal wrong. The answer had the right digits, but was out by an order of magnitude. When I got involved in IT, I soon realised that even the smallest error was enough to bring down an entire system. It is the same when trying to predict IT trends, you need to get both the order of magnitude as well as the details right, otherwise you are going to misjudge altogether.
In the field of process automation, engineers work to automate plants in order to free people from repetitive tasks. However, as we move into the age of intelligent machines these automation boundaries between human and machine are changing. The reality is that many more of the tasks currently only performed by humans, will in future be delegated to machines.
Artificial intelligence (AI) and cognitive technology
Cognitive technology refers to the broad category of technologies that can perform tasks that can otherwise only be done by humans. Two simple examples are voice and image recognition. Cognitive technology is still in its relative infancy, but is expected to mature rapidly and become part of mainstream IT. While consumerisation of this technology is already happening outside the enterprise space, for example personal assistants on mobile phones, developments in this space will eventually have a profound impact on the manufacturing enterprise.
Behind the scenes, AI and cognitive technologies are already infused into everyday experiences and are in widespread use in context aware applications like personal assistants, online map services, cross selling (like Netflix or Amazon recommendations) and so on. This has the effect of setting new consumer expectations that are hard for ordinary business to fulfil, while also opening up a range of new untapped opportunities to exploit for competitive advantage.
Machine learning allows natural language to be interpreted and processed to deduce patterns and correlations. When done on scale, for example by a machine processing all the published research on a subject, these correlations can be used to make accurate predictions and augment human decision making in ways that were previously impossible. A good example is the machine diagnosis of medical conditions that guide a doctor to explore all probable likely alternative conditions before settling on a diagnosis.
AI in manufacturing
In the next few years, AI and cognitive technology will impact on every aspect of manufacturing. Companies will in future need to go about defining, developing and implementing solutions that will rely on cognitive technology in some form. This is new territory for engineers and IT professionals alike.
In the field of process automation, AI techniques are not new. Back in 1998, I remember investigating the use of neural networks to model the behaviour of an aerobic fermenter based on measured variables such as pH, temperature, historical cell growth rates and so on. However, at the time this was a very specialised field. Nowadays deep neural networks are a mainstream technique in IT and are becoming more prevalent than ever before.
Mainstream IT is driving much of the innovation in machine learning
One trend to watch closely is the entry of mainstream software vendors like Microsoft and IBM into this space. These vendors have the resources to leverage vast cloud platforms and databases of structured and unstructured information to perform data processing and analysis at scale. They can do this on powerful hardware that can easily be allocated according to real-time demand. It is significant that the established mainstream IT vendors are now making their cognitive technology available through web service APIs for everyone to access and utilise. Readily available bot frameworks and cognitive APIs for speech and image recognition can be accessed from your office by simply calling a web service.
Another area to watch is the evolution of innovative database techniques for unstructured data. This information can be stored in database constructs confusingly called ‘graphs’ that make it possible for unrelated information to be correlated to detect patterns that can be utilised in ways not possible previously.
Companies are starting to use cognitive technology across the whole value chain in industries as diverse as oil and gas to automotive, and in areas as diverse as R&D, plant operations, supply chain, marketing and customer service.
Where to start?
There are several ways in which AI and cognitive technology might be used in manufacturing, for example:
1. Enhance and augment products with new features/services. For example, you could recognise patterns in individual customers that are used to make personalised recommendations on better use of the products.
2. Develop operational insights. For example, by monitoring key process variables and using pattern recognition techniques across all your data sources you could determine when there is an elevated risk of safety incidents on site, process upsets, likely logistical problems or equipment breakdown.
3. Further automate and optimise your business processes. Information trapped in your ERP system can be combined with MES data and unstructured information held in a graph database to provide better real time insights into the business and manufacturing processes, with the resulting call to action served to the responsible person through a mobile device. These proactive alerts can help prioritise interventions where necessary ensuring a quick response to upset conditions.
Process control specialists and IT professionals will in future need to be well informed on AI and cognitive technology. The business case for these solutions will in general be motivated by the increased value as opposed to cost reduction. This will require a business mindset as well as a deep technical knowledge. Managers need also to recognise the future impact these technologies will have on the way systems are integrated and the skills needed by your company in this era of thinking machines.
Gavin Halse
Gavin Halse is a chemical process engineer who has been involved in the manufacturing sector since mid-1980. He founded a software business in 1999 which grew to develop specialised applications for mining, energy and process manufacturing in several countries. Gavin is most interested in the effective use of IT in industrial environments and now consults part time to manufacturing and software companies around the effective use of IT to achieve business results.
For more information contact Gavin Halse, Absolute Perspectives, +27 (0)83 274 7180, [email protected], www.absoluteperspectives.com
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