Process Intelligence in the Fourth Industrial Age: PI
This is not about AI, but may involve it. PI, Process Intelligence involves seeing an organization as a machine: In terms of function, operation, information, technology. This could be a closed system, composed of functioning parts and definitive relations.
While the theory goes that industry 4.0 creates what has been called a ‘smart factory’, part of the industry 4.0 agenda, ‘cyber-physical systems’ remain — to put it simply — obscure to many. When we place a GPS transmitter on a vehicle that tells us where it is going and sends it to a server on the net, that is the Internet of Things or IoT working for us.
Process automation WHAT·HOW·WHEN
We can look back 3 years to appreciate the real nature of accelerating change. 3 years ago we only really started talking about the impact of the internet of things — or IoT. IoT involves the use of devices that plug-in to how people, businesses, manufacturers, service providers all go about doing what they do. IoT devices used in sector automation impacts on processes. IoT devices collect vast quantities of data — that other than the context that defined it — to know where something is going in the example — remains valueless in potentially more valuable contexts yet to be discovered.
Such IoT-based data can be used to tell organizations what to define and implement. In other words, the days of ‘strategizing’ are coming to an end as meaningful and functional change — as a result of new possibilities resulting from new micro-tech takes over. This is the new 4.0 context for organizations today. This is also predominantly a question concerning how can we put all that data — not ‘big’ data, but all the small data produced by our IoT capable devices — to some very good use.
- Industry 4.0 aspects include:
- 4.0 interoperability: The ability of machines, devices, sensors, and people to connect and communicate with each other via the Internet of Things (IoT) or the Internet of People (IoP).
- 4.0 information transparency: The ability of information systems to create a virtual copy of the physical world by enriching digital plant models with sensor data. This requires the aggregation of raw sensor data to higher-value context information.
- 4.0 process assistance: The ability of assistance systems to support humans by aggregating and visualising information comprehensibly for making informed decisions and solving urgent problems on short notice.
- 4.0 process augmentation: The ability of cyber physical systems to physically support humans by conducting a range of tasks that are repetitive, overly routine or even unsafe for human co-workers.
The Machine optic involves processes, methods, data capture, data-information processing and product development. disrupting using IoT and cyber-physical systems. (Morgan 2006) aspects are not included.
We can understand a ’machine’ mindset by considering AI, or Artificial Intelligence. The development of intelligent machines has been going on for a very long time. And while advancements in Artificial Neuron Networks (ANN) for example, will become a part of everyday language, with automated processes by machine learning, self-learning systems, deep learning and ‘machine driven innovation’– all may be far removed from the day-to-day of most people, and to a certain extent, also irrelevant. Unless that is, that expertise itself becomes an algorithm or becomes replaced by apps that tell us how by best practice. This is what Steve Jobs called using ‘(by)productivity apps using the network, tying people together. We’re going to win.’
The difference in 2017 is, we have machines taking over that process of tying together to be productive. So while time will tell the extent to which tasks are solved by machines, the impacts of new-tech are all around us and have only just begun.
The question is, are we ready for what is already happening and is coming our way?
Process Intelligence — PI
For most, the most direct influences of new-tech won’t be expertise apps, but streaming through the internet of Things (IoT) and the Industrial-Internet-Of-Things (IIoT), meaning we gather data from intelligent wireless-connected gadgets across very large networks. Think of GPS tracking for example. The ‘streaming new-tech’ is the one major reason why many companies today have seen that the future is only attainable, if they can offer value above and beyond the product. The net result is an increasing switch to service, from product orientation. The machine optic herewith places new-tech in light of what offerings are required and when, if such offerings face a danger of being replaced by other offerings more tune in to needs or patterns of use, and what it means in terms, perhaps even defining a whole new ball park. This is more than hype, and is the reason why so many large companies today, already speak the same language of ‘canabalization’ — of eating up the old when transforming into the new.
Most of this is ‘pure data’ — the items recorded and stored in memory by devices, systems. A bank statement is really nothing more than data — what is bought, when, at what time. It is when the data stored in one application, that is used by another, that things start to get interesting, making disruptions much more than just a word: It is the effect of what happens, when data is put into motion using new tech producing data from patterns in motion, what products do, why they do it, when and how. We can also understand value+ from the point of view of machine-derived data used to create value elsewhere, by sharing that data and cross-breeding it with other data. However, none of this can happen without people.