Executive Interview: Robert Joseph, Director, Industry Strategy for Industry 4.0, Stanley Black & Decker
Implementing IIoT at a 175-year old company requires a broad range of skills, and a concentration on IT as well as AI
Robert Joseph, Director, Industry Strategy for Industry 4.0, Stanley Black & Decker, is a data scientist working on implementing the Industrial Internet of Things (IIoT) at Black & Decker. His career spans experience at AT&T Bell Laboratories, US West, Sony, Freshwater Software, and for school districts across the country. Also, as a university professor for 10+ years, he has taught over 3,000 students in all levels of computer science and mathematics. He holds a Pd.D. in Computer Science from Carnegie Mellon University, and a BS and an MS from MIT in electrical engineering. He recently spent a few minutes to talk with AI Trends Editor John P. Desmond.
AI Trends: Thank you, Robert, for talking to us today. Could you describe your responsibilities at Black and Decker and how AI fits into those?
Robert Joseph: Thank you John. I am happy to speak with you. Please note at the outset that I am expressing my viewpoint from working in the industry, and in no way am I representing the viewpoint of any company or organization. At Black & Decker, I am Director of Industrial Strategy or Industry 4.0. My responsibility as a data scientist on the team for data analytics, is to look at machine data coming into our data area and help to analyze that data. We work to make the machines more efficient and to figure out if something is broken or is soon going to break, so we can do preventive maintenance.
How long has Black & Decker been using AI?
I joined Black & Decker about two and a half years ago as a data scientist in what was called the digital accelerator. In that role, I was doing data science in the company as a whole. That division was two years old, so AI has been practiced for at least five years. Stanley Black & Decker made a concerted effort to create a hub of data excellence in the Atlanta area, five or six years ago.
Your background includes or combines computer science, electrical engineering, and teaching. How does this prepare you for the role you’re in at Black & Decker today?
That’s interesting, because I’m in Industry 4.0, also known as Industrial IoT (IIoT) which combines the Internet of Things. That entails understanding machines and how they work, and understanding sensors and how they work. In this role, I need to be able to put all that together to understand also the computer science behind it. Coupled with that is change management. I have always said that bringing in a new technology is as much about change management as it is about the technology itself.
So as a teacher, as well as a practitioner, I understand the need to bring everybody on board to learn what this technology can do and how it might be different from other more familiar technologies. The combination of electrical engineering, computer science and teaching, makes it ideal to be in the world of Industry 4.0.
How was the AI playing into the business strategy at Black & Decker?
Many companies are like Stanley Black & Decker in that they’re trying to figure out exactly how to use AI. Every department is trying to understand which AI technologies would be good to try, and which ones allow them to get the biggest bang for the buck.
With that, you’re starting to see people using AI in unique ways. On the finance level, they are looking at the best ways to spend money, how to optimize where people stay when they travel, what cars they rent, those types of things. In the factories, we are working on improving the factory output. In the distribution centers, we are working on taking advantage of some new techniques in robotics and packing for example. AI is being distributed throughout the company in many areas.
Many companies are applying advances in AI to solve specific problems in machine vision or audio understanding or natural language processing. So AI is being pushed not just inside companies, but in everybody’s life in a number of different ways.
Could you talk about what’s going on with IIoT data analytics at Black & Decker?
Sure. We are a 175-year-old manufacturing company. When I go to conferences and listen to what other people are doing and listen to the challenges that they have, it’s a similar challenge throughout. How do you get data? How do you get sensor data from some of these machines? Many of our machines are older machines that don’t have PLCs [programmable logic controllers], that don’t have any sensors already hooked up to them, then some of them are the more modern machines. We have to plan how we get the right sensor data coming out, and once we get the data, how we make sense of it.
One school of thought is you take all the data and you push it up to the cloud. But I think people are realizing more and more that in some situations you can’t do that. You sometimes need a box on prem, meaning you need a machine on the premises of the factory to do some of the computation there. People call this edge computing.
So with our system, as with most manufacturing systems, we are using edge computing to do some of the more complex kinds of processing, such as for machine vision to look for defects, or doing vibration work to try to predict equipment failure. The slower stuff, such as temperature data, we might push to the cloud and do some processing there.
Ultimately, as in most companies, once you get that information and you start to understand more about it, you’ve got to give it back to the people that could do something about it. So the operators and manufacturing engineers need to get information. Some information is real time, in that something needs to be done now in order to fix a machine before more scrap is produced. And some information is indicating that a ball bearing or motor is likely to fail in the next week, so it should be put into a preventive maintenance rotation.
What are the challenges you face in trying to roll out AI at Black & Decker?
I have found that, as with most things, it looks great on paper and it makes a lot of sense. But when the rubber meets the road, that’s when IIoT is the same as everything else. The challenges fall into three categories. The first is people. There are always multiple opinions, even with just one person. So multiply that by a few more people and then you’ve got a whole boatload of opinions. And there are different perspectives too.
As I watch this whole IIoT movement going along in manufacturing, I see the different perspectives come into play. Change management isn’t just one way: me telling you how your life is going to change. It’s actually two-way, in that I need to understand what you’re doing now and what your perspective is, and then incorporate that into the change management.
Another challenge is the hardware. It takes time and it takes money to buy sensors, and then start to collect the data and then make sure that the sensors are working properly. If you have a lot of sensors, then one of them is going to break, just because of percentages. Even if it’s 0.0001% of the sensor breaking, you have enough of them, then you’re going to have to deal with all of that. So there’s the physical hardware challenge.
Then the third challenge is management buy-in. Companies that don’t have management buy-in and are trying to do AI initiatives have a hard time, because that’s not where the focus is, and that’s not where the money is, and that’s not where the resources go. So those are the three big areas of challenge that I see. I see that we have two of the three at Black & Decker; we have management buy-in here.
Could you describe your development platform for AI applications the key tools and technologies in use? Are there any important software and service supplier partnerships that you’d care to mention?
When you’re a large company, you use a lot of tools and have many camps. In terms of cloud, the two big cloud areas are AWS and Azure, and then Google is the third one. The primary tools of the data scientists are Python and R, and those come in different versions. For some edge computing work we have done, we have worked with Foghorn; in my opinion, they have one of the best edge computing tools out there. It’s flexible, fast and versatile. I feel comfortable saying as a vendor, they are rock solid. [Learn more at Foghorn Systems.]
It’s helpful if AI developers can think in terms of what business function they are trying to deliver. The skills and tools will change but many of the functions we need to support stay the same.
Could you describe the deployment platform for the AI applications in use? And what sorts of considerations do you need to make for deployment platforms?
That’s an age-old question. As a data scientist, I’m more focused on the prototyping platform, where we are developing the system. That’s different from being responsible for the system being up 24/7 and getting the call at 1 am if it goes down. We are working on an architecture and a process to support deployment. It’s a work in progress; it takes time.
I am hearing large companies refer to Applied AI as being a focus on the deployment of AI, requiring people with a different skill set that is difficult to find right now.
It’s a weird combination of skills you need to have. It’s not your typical IT. The technology in AI is changing quickly. The systems are usually built by somebody who is not a software developer in the traditional sense. You need to take something done by a non-programmer and make it bulletproof.
Do you ever decide that there are some projects that are not appropriate for AI?
Yes, projects that don’t have the data available to support what you’re trying to predict. And some projects can be solved more simply, maybe without a neural network, such as with a graph that shows something is out of whack.
Has AI had an impact on the organization structure at Black & Decker? Are there some new titles?
We have new titles in data science. All companies are trying to figure out how to structure the company to support AI, and structure IT to support AI. The IT skills needed to keep up with SAP or Salesforce or whatever standard business software is in use are different from the skills needed to create an environment for data scientists to explore. When they stop exploring, they need to push the application out to production. So it is a different org chart that everybody’s trying to figure out.
So that is also a work in progress?
I think it’s always going to be a work in progress. That’s why companies reorg so often, because the world is constantly changing. New opportunities and new threats arise. Many companies are starting to embrace the digital analytics way of looking at the world and trying to figure out what that means. That is reflected in how they organize themselves.
Are you able to find and hire the talent you need to execute on your AI initiatives?
We’re trying to find people in a limited pool. We need to find people with the right technical skillset as well as the right people skills.
Do you have any advice for undergraduate students interested in a career in AI for what they should study or early career professionals and how they should focus to pursue a career in AI?
The biggest thing I would say is be an active learner. What I mean by that is that, the professor or the teacher will give you their perspective on the world and will teach you what they think are the key things you need to know, but you should always be thinking about, “Well, what is it that I need to know for my career? And what is it that I need to be proficient at?”
So, definitely use other sources besides the sources that are put in front of you. Be active about what are the things that you need, be active about what are the subjects that you need to study, and be active about going and talking with people that are doing the jobs and understanding more about what they do.
The other advice I would give, especially for people in school, is to do an internship. That internship can be a paid internship or an unpaid internship, but you want to get some experience of what corporate life is like, because it’s very different than school life. It’s more ambiguous, it doesn’t have all the answers to everything and you’re more left to be accountable to yourself. So, be an active learner and get some experience.
See Robert Joseph’s LinkedIn page.