Manasi Menon

For decades, manufacturing and production operations created masses of data from sensors, control strategies, diagnostics, procedures and other applications used to safely control the process. Operational analytics have continued to advance to turn this mass of data into actionable information.

Emerson’s Manasi Menon joins me in this 23-minute operational analytics podcast to discuss how organizations are putting these analytics into action to help improve performance in safety, reliability, energy & emissions, and production.

We hope you’ll enjoy this episode and will consider subscribing to the whole Emerson Automation Experts podcast series on your Apple iOS or Google Android mobile device.

Transcript

Jim: Hi, everyone. This is Jim Cahill, and welcome to another Emerson Automation Experts podcast. Today, I’m joined by Manasi Menon. Manasi has a Bachelor of Technology degree in electronics and instrumentation and also an MBA. She’s worked for several automation suppliers and has been with us at Emerson for the past eight years. Manasi currently leads our machine learning and analytics marketing efforts. Welcome, Manasi.

Manasi: Thank you, Jim.

Jim: Well, let’s get started. I gave a little bit of some of your background, but can you give us a little bit more and your path through working with the automation suppliers and Emerson up to your current role with our analytics?

Manasi: Sure, Jim. As you mentioned in the introduction, I am an electronics engineer who’s worked very closely with some of our customers in this process automation industry. There is a lot of interest and there is a lot of talking around analytics today, but our customers have always been using analytics as an opportunity to improve their processes. So, my introduction to analytics was back in the days as a process engineer where I was working for end users who were using conventional advanced process control systems to improve their process, whether it is optimizing their process or monitoring the process. So, right from that time frame, after that, doing my MBA, I ended up in a particular position where I was working as an analyst monitoring the support functions of the organization. So, with that process engineering background and an interest and passion for analytics, I thought this role would be really good where I could contribute to as well as learn from, especially articulating the value add of our analytics in our industry.

Jim: Well, that’s interesting that so many more of our customers are looking at digital transformation and doing something with this wealth of data that they’ve always been collecting. So, I imagine there’s a lot of people on our customers’ side coming into this area of having more responsibilities for analytics. So, I guess with all that, for someone new into the consideration process for additional analytics as part of their automation architecture, what are some ways that they can get started?

Manasi: So, as part of my role, I talk to a lot of customers, some of them who have advanced quite a bit in the stage of deploying analytics in their organization and some of them who are still new. One of the most important things that I have seen works really well when thinking about deploying analytics in their organization is to have a clear goal, a strategy or a clear business goal in mind as to why they want to deploy analytics. Of course, what is also important is once you’ve identified the goal, understand what kind of sponsorship do they have within their organization, to adopt that goal and replicate it. So, that is first step. Then the second step is to identify a clear use case. And it depends on where you are in the organization. It’s very important to collaborate with folks within your organization. And when I say collaborate, what that means is work with plant managers, with the operators, with the reliability engineers because these are the people who are going through those problems, which that analytical solution can resolve.

So, once you talk to them, interview them, you would understand what use case you would like to deploy the analytics to. Then mostly these use cases are very easily replicable or customizable or easily you could deploy it in multiple facets of your organization. So, that’s one consideration to choose that use case. Then when you do that, then you would think about, okay, what does the technology infrastructure that I need to make this possible? Whether it is data, whether it is network or the software application itself that you would use. That’s the typical path that we have seen a successful organization, or a successful customer would do to deploy analytics.

Jim: Can you tell us a little bit more about the different types of analytics that can be applied?

Manasi: So Jim, analytics is extremely broad. Per Clean Energy Smart Manufacturing Institute, there are about over 600 or so analytical vendors out there that offer some types of analytics, whether it is artificial intelligence or machine learning or optical character recognition. So, there are just so much, so much out there. Now, within that, we feel that that space can be divided into two key areas. One is business analytics, focusing on HR, supply chain, finance, and CRM and one is operational analytics, which is I talked about is what Emerson is focusing on, which is the day-to-day operations of the plant. Now, within the operational analytics, there are again two different kinds of analytics depending on the problem that you’re trying to solve. So, if you’re trying to solve the problem of a very well-known asset or a very well-known use case where there is enough subject matter expertise around it, then it’s called principles-driven analytics. If you’re trying to solve a problem that you do not know why it is happening and it may be happening because of the way multiple parameters or multiple things are correlating and interacting with themselves, then we call it data-driven analytics. So, within this operational realm itself, there are two different kinds of analytics.

Now, Emerson has a very, very, very strong background and history of analytics. We’ve had a lot of analytical offerings from 40 years ago, like with AMS Device Manager where we have analytics embedded in our diagnostics devices, to analytics which is embedded within our automation system such as DeltaV, whether it is machinery-specific analytics such as AMS Machinery Manager, or that principles-driven analytics that I talk about, such as Plantweb Insight, which is helping our customers to get started really easy with some of their asset-specific analytics, which is monitoring their health and performance of the asset, to data-driven analytics where you’re trying to solve a problem that you do not know why it’s happening. So, we have Emerson’s KNet that offers advanced analytics techniques in combination with some of these principles-driven analytics to give you a more holistic approach as to why a problem is happening. So, Emerson has a very strong and a variety of offerings to cater to each