Often seen these days that non techie professionals are either mesmerized or confused with the volley of technical jargons thrown on them by the “technical colleagues”, among this prevalent these days are Artificial Intelligence (AI), Machine Learning (ML), Data Analytics, Industry 4.0, Cloud Computing, Edge compute, vision analytics, Native Language Processing (NLP), Industrial Internet of Things (IIoT) etc., I have heard people spelling out AI, ML, Data Analytics under the same breath.
With this brief write up, I would like the reader to join me on a quick journey of demystifying and understanding how it becomes essential to adopt the right cut of technologies to derive the maximum from your capital investment.
Information technology has seen a quantum leap in last 5 years, metaphorically defying the Moore’s law and its adoption accelerated during 2020 Covid19 Pandemic.
Shifting our focus back to the point of discussion, traditionally we have seen MES systems and SCADA system in manufacturing were kept segregated from the Enterprise IT system by way of separate networking (LAN) and not connecting it with WAN, so were the precision Lab testing equipment, some ran over obsolete and unsupported Operating Systems due to hard coded software.
Whereas most enterprise were cautious to interconnect their ERP systems with external applications. Suddenly over the span of last 5 years, there was paradigm shift in business demand and IT Managers’ execution strategies on field or on shop floor.
Talking about Manufacturing sector where the key to success is OEE (Overall Operational Efficiency) which is directly proportional to A(Availability), P(Productivity) & Q (Quality) on the shop floor, Digital transformation is helping the sector by efficient adoption of technologies.
Availability, is the time during which the production machine was available during a given sample time. This is enhanced by implementation of sensors for basic parameters on the production machine, for continuously measuring pressure, temperature, vibrations & current, this is processed through IIoT System, and the data is filtered via Edge devices to feed it into Data Analytics algorithms that constructs a model for statistical regression or other algorithms. In the industrial processes it is prudent to have machine learning algorithms that train itself over a period of time basis events and its linked interdependencies to later provide an accurate anomaly in the function.
Coupled with Condition Based Monitoring (CBM) the model is able to provide not only descriptive but Predictive and Prescriptive analytics to forecast the faults or automatically trigger the corrective actions like releasing a service Purchase Order from ERP to the agreed contractor for conducting specified service on the equipment.
For the Productivity enhancement, count of total components manufactured Vs planned production and cycle time are captured and by integration with the ERP production is automatically punched, while the demand is automatically fetched from ERP via MRP (Material Requirement Planning) run as per customer requirement.
Quality being the most complex component of OEE, as the measuring or inspection automation has a broad spectrum of variation due to variety of components and CTQs (Critical to Quality) parameters. It may be Vision Camera based inspection which feeds the results to Algorithms or it can be simple sensor based feedback, gauges or meters etc which qualifies a component as OK or NG. Post capturing of data there are predictive and prescriptive analytics that is done via the algorithms.
The point to ponder that challenges the set paradigm that has always put the ERP at the core of the Information System, but now it is shifting as the interfaces and compute is happening in multiple systems which are interfaced with the ERP system.
An important aspect that has come up recently, is that interconnect and seamless data flow between MES (Manufacturing Execution System), ERP, SCM (Supply Chain Management), CRM (Customer Relationship Management), Customer based EDIs, Order portal over secure web, DMS (Distributor Management System), Procurement Systems.
With this we see an amalgamation of Enterprise IT Security and IOT security which are two different verticals and work in tandem.
I dare say, we live in the most exciting times where we have seen the advent of quantum computing, cryptocurrency, Artificial Intelligence, Exploration of Deep space, Interstellar space, Asteroid mining and other unprecedented calamities & pandemics which are new challenges to control with the right mix of human determination and use of technology. The code of conduct and ethics & morality in Artificial Intelligence is soon going to be a major field of research.
To conclude, I would urge the investors and stakeholders to come forward with open mind to invest in the technology of future and the innovators and engineers to keep innovating with to find the right mix of technologies to derive maximum benefits, as there are no silver bullets.
The author is CIO at Rockman Industries limited (Hero Group)