Digital Techno Bytes

Digital Transformation …. Keeping it Simple.

Open any technical journal or a magazine or even on social media like LinkedIn the most trending topic is all about Digital Transformation , Industry 4.0 ,Industrial Internet , IoT , Machine Learning , Cloud Computing , AI or AR  and how they are helping the industries and to achieve their Business Outcomes. All these jargons mean only one thing being connected and using data to take informed decisions . Decisions that transform the way we run our businesses.

 To understand all this we need to know what is Industry 4.0 is all about  First revolution came when Steam engines were invented and steam was used to power the first machines that mechanized some of the work our ancestors did. Next revolution was when electricity was discovered, this was used to power up the assembly lines which gave birth to mass production.  The third revolution  of industry came about with the advent of computers and the beginnings of automation, when robots and machines began to replace human workers on those same assembly lines.

And now we enter Industry 4.0, in which computers and automation are integrating together in an entirely new way building an ecosystem  with Robotics, Artificial Intelligence (AI) and Augmented Reality (AR) along with machine learning algorithms that can self learn and control the smart devices and machinery to achieve business outcome with very little or no manual interventions. All this is possible over remote networks and getting connected to computer systems on the plant shop floor.

Industry 4.0 introduces what has been called the “smart factory,” in which cyber-physical systems monitor the physical processes of the factory and make decentralized decisions. The physical systems become Internet of Things, communicating and cooperating both with each other and with humans in real time via the wireless web.

For a factory or system to be considered Industry 4.0 ready, it must include:

·         Big Data – Huge amounts of data from disparate islands of information, machines , manufacturing systems , ERP etc.

·         Interoperability — machines, devices, sensors and people that connect and communicate with one another.

·         Information transparency — the systems create a virtual copy or what we call Digital Twin of the physical/ real  world through sensor data in order to Contextualize information.

·         Technical assistance — both the ability of the systems to support humans in making decisions and solving problems and the ability to assist humans with tasks that are too difficult or unsafe for humans.

·         Decentralized decision-making — the ability of Digital systems to make simple decisions on their own and become as autonomous as possible.

Simply put to roll out Industry 4.0 or a Digital Road Map for any organization is a 3 step process to be implemented in phases . These are Capture , Contextualize and Control. Each phase is important in its own respect and any organization will be in any of states of readiness. Some ahead of others with Integrated Plants and some in very nascent stage of getting their plant floors connected for data capture. To elaborate these 3 phases.

Capture – This is the very basic and 1^st stage where data has to captured from all know sources of information be it from machines , manual operated work stations , Quality labs systems , Utilities , Supply chain and Business Systems .The data coming from these systems is huge may be in Tera Bytes and are generated at different velocity and in different varieties. Conforming to the 3 V’s of Big Data – Volume , Velocity and Variety.

The data being Industrial in nature majority of it is related to Process e.g. Temperature , pressure , Quality eg. Volume , density , viscosity and some in the form of transactional data which is manually entered. All these need to stored in a different way than the traditional Relational databases. To effectively store and recall you will need a database which can store for long periods of time (years together) and in huge volumes and yet be able to be retrieved at a click of a mouse without latency. This database can be on premise or now with advance technology in the Cloud . The cloud offer the user to reduce his overall cost of digitization by offer him the chance to pay per use. Also the system administration and IT setup cost is taken out of the equation.

The 2^nd phase is the important phase , that of Contextualize. Contextualize is to get meaningful insights from all the data being captured. This gives the person looking at the data an additional perspective to help in quick decision making . One level above the standard data that is visualized in form of the traditional SCADA screens or Plant Dash Boards. Insights catering to reasons of line stoppages , reasons of unplanned downtime , reduced efficiencies of workforce to effectively and proactively plan the production schedules. Also insights on which products to be run on which lines to maximize productivity . Here the key is to get both Real time information for As is Information as well as Historical data to analyze best times and best operating condition so as to be operational consistency which I turn will lead to increased production and reduce cost of operation.

The 3^rd and last stage is Control or what we call the Optimization stage. After analyzing the data both real time and historical you can understand the gaps in the current processes and find areas of improvements. It could be as simple as Digitization of SoP’s for smoother task management and improve the workforce efficiency. Or insights of Energy Consumption leading to implementation projects for better utilization of utilities in terms of use steam and water. Or decision on whether to switch between cheap Grid power to costly Captive Power using diesel or furnace oil as fuel. Understand reasons of bad quality during material processing and identifying golden parameter to achieve consistency to get best quality products at all times.

This today is being achieve by Machine learning algorithms which mine the Big Data to come out with process rules to help in Optimization either automatically by way of an Advance Process Control or Semi automatic decision support systems or Intelligent Alarming.

Another example is to use machine learning for assets management by way of Predictive and Prescriptive Analytics what we know as APM or Asset Performance Management. Take the instance of a tube leakage or a exceptional bearing temperature rise in a critical  component of a Turbine . If this alert is given to  a Combined Cycle Power plant Operations & Maintenance team say about 30-40 days before it is going to happen it will lead a huge savings 1stt in terms of  no unplanned downtime and 2^nd in terms of preventing unnecessary opening up of capital equipments leading to increased time of shut downs and 3^rd reduces the cost of huge spares inventories . This in turn reduces the cost of generation and increases uptime which in turn affects the bottom line of the Power generation business.

But as with any major shift, there are challenges inherent in adopting an Industry 4.0 model:

·         Data security issues are greatly increased by integrating new systems and more access to those systems. Additionally, proprietary production knowledge becomes an IT security problem as well.

·         A high degree of reliability and stability are needed for successful interfacing between plant equipment and the Cloud as inter communication can be difficult to achieve and maintain.

·         Maintaining the integrity of the production process with less human oversight could become a barrier.

·         Loss of high-paying human jobs is always a concern when new automations are introduced.

·         And avoiding technical problems that could cause expensive production outages is always a concern.

Additionally, there is a lack of experience and manpower to create and implement such systems — not to mention a general reluctance from stakeholders and investors to invest heavily in newer technologies.

But the benefits of an Industry 4.0 model could outweigh the concerns for many production facilities. In very dangerous working environments, the health and safety of human workers could be improved dramatically.  Supply chains could be more readily controlled when there is data at every level of the manufacturing and delivery process. Computer control could produce much more reliable and consistent productivity and output.  And the results for many businesses could be increased revenues, market share, and profits.

Reports have even suggested that emerging markets like India could benefit tremendously from Industry 4.0 practices, and the city of Cincinnati, Ohio has  declared itself an “Industry 4.0 demonstration city” to encourage investment and innovation in the manufacturing sector there.

The question, then, is not if Industry 4.0 is coming, but how quickly. As with big data and other business trends, I suspect that the early adopters will be rewarded for their courage jumping into this new technology, and those who avoid change risk becoming obsolete and redundant.

So to put this into simple perspective Industry 4.0 is nothing but smart way to Capture Contextualize and Control data / processes leading to reduces cost and improving efficiency and achieving Business Outcomes.

As they say Being Simple is so Difficult but then Simple is always Beautiful…