Learn How Bell and Howell Transforms the Business of Service
Bell & Howell uses data to support remote service calls and trouble shoot before sending technicians out to customers. This practice dramatically boosts the effectiveness and efficiency of service calls, as measured by FTFR. In this episode, Chris MacDonald speaks with Haroon Abbu, Vice President, Digital, Data, and Analytics about how Bell & Howell use data to support remote service calls and trouble shoot before sending technicians out to customers.
Learn more and check out other episodes here: http://ptc.co/A0ZC50PzzlM
Welcome to Speaking of Service, the podcast that uncovers practical ways to grow service revenue, control costs, and improve customer satisfaction. If you're looking to innovate, gain a competitive edge, or just learn about the latest service trends, you've come to the right place. In today's episode, Chris McDonald, head of AI and analytics sits down with Harun a. Vice President digital data and analytics about how Bell and Howell use data to support remote service calls and troubleshoot before sending technicians out to customers. Welcome to the show. Today we're gonna talk about predictive maintenance strategies. Leaders when embarking on the strategy often think, How do I know when a critical asset or device is going to fail? And while that is a important question, it is one, not only the only question. It needs to consider the fact that there are many different ways of asking questions depending on the data you have available or could have available, and is it actionable? Ultimately, predictive maintenance is a much broader strategy that involves showing up at the right time to the customer with the right materials and with the right instructions on how to actually resolve that. That strategy is no better exemplified than with our partner Bell and how, Um, and I'm very excited to welcome to the show today, Harun Abu, who leads the data, digital data and analytics group at Bell, and how Bell and how has delivered. Critical equipment that their customers depend on and has brought next level SLAs and service to their customers. To their customers and their offerings. They partner with us and what I really admire about their strategy is how data is at the heart of it, and they have leaned into the notion that applying advanced analytics is important for them. On behalf of their customers to enable that strategy, to enable that level of sla. So Harun, please take a minute to introduce yourself and welcome to the show. Thanks. Uh, my name is Harun Abu. I'm the, uh, vice president of, uh, digital data and analytics here at Ball Howell. I've been here, uh, with the company for about 12 years. Um, our, my, my main goal is to monetize. To improve the efficiency of our field service organization through I digital and, uh, some of the advanced analytics tools that we have built over the years. Uh, if you don't know Bellen Howell, um, again, Bellen Howell is headquartered here in uh, Bar Rai. We have about 850 field service engineers across North America. We manufacture industrial automation, equip. Uh, recently, uh, click and collect equipment, which is for our grocery and, uh, retail customers. When you order something online, go pick up in store. We have these, um, in store pickup, robotic towers, robotic, um, uh, you know, delivery mechanisms, uh, that can dispense, um, your general merchandise, the ordered or the grocery, or even pharmacy prescriptions. So glad to be part of this convers. No thank you for being here. Running a service business with revenues tied to uptime and those SLA commitments to customers while dealing with a variety of assets and a variety of customer settings, and how to ensure the efficiency of executing on that service. Um, how do you do it and how do you think about? Remote service, um, in order to enable being maximally effective on, on sort of both ends of the spectrum, ensuring those revenue commitments and optimizing your service strategy. Yeah, so we are a services first company. As I mentioned, we are one of the largest field service. Organizations in North America. So when we transition from manufacturing into Services first company where we were not only servicing our equipment that we have sold over the years, but also servicing other OEM equipment, as you mentioned. You know, we service number of, um, other equipment, especially the um, especially the companies who are coming from Europe and other places, robotic companies. Uh, we. Uh, you know, number of OEMs. So we needed a solution to remotely monitor and remotely fix, uh, these equipment. So three to four years ago, we put together Remote 360 Solution, which is a comprehensive collection of remote monitoring, diagnostics and repair services that is powered by Internet of Things, uh, which is actually PDCs thing, work solution to remotely monitor and. Customer equipment and to maximize availability. The solution combines advanced data analytics with cloud technologies and the power of visualization to transmit machine data into insights that help our decision making process for our technicians as well as for our customers. And it empowers the field service technician by providing that contextual information. Right. So remote 360. Uh, is unique in the way it extracts data, transmits it to the cloud, and process and builds algorithmic models, right? What does that mean for some of our grocery customers where we, our equipment is used to dispense the groceries or general merchandise, or even prescription that you just ordered, right? For them? What's critical is that we maintain the tempera. On the grocery lockers or on the, you know, the grocery, uh, automated retrieval systems. So temperature is, uh, key so that, you know, the ice cream that you order doesn't melt, et cetera. So we monitor temperatures in real time, every few seconds, and we give them the audit trail if the consumer left the door. You know, we, we would know, we would get an alert and then we would make sure that the door gets closed. So those are some of the simple examples. Um, for our industrial and warehouse equipment providers, we provide real time data to maximize productivity, saving the company, uh, and to, you know, improve their operations as well. Mm-hmm., So the Remote 360 solution has helped us internally. That's what we call intrinsic benefits to improve the efficiency of our own service organization. Field service engineers as well as extrinsically to our cons to our customers as well, to improve their operations, which, you know, I'll, I'll talk a little bit more about it later as well, and how we are using data to improve customer operations. Now it's, it's a great segue to, to my next question. It sounds like you. Your organization, you've developed a strategy where you understand the importance of data that describes a critical process. Literally the, you know, the center of your business, how you are serving an asset that a customer's operation is dependent on, right? Maybe a different context, but you're understanding how to contextualize that data, how to leverage that data for the intrinsic value. The extrinsic value. And I think one thing that um, can often be, You know, difficult is service call planning, right? You have lots of field engineers, lots of equipments, different contexts. So that service call planning has a level of uncertainty to it. Have you experienced, um, how that data strategy you have, um, you know, has helped there? I. Yeah, I mean, it is, it is a great question. Uh, for us, you know, given that the company has a long history, 120 years history, um, you know, going through multiple types of equipment and now we are, you know, on the cutting edge of technologies servicing retail customers help them in their digital journeys, right? So digital has been strategically intentional for us in the last few years. So when I say digital, I mean, digital is, you know, it's, uh, d with a big digital D uh, a big D, which is a subset of data analytics, machine learning, AI information and communication technologies, et cetera. So we put in, um, digital backbone for remote monitoring. Et cetera. So, you know, when you look at the, the journey of our digital and data analytics, the first thing that we tried to do was operational efficiencies, which as I mentioned, you know, um, we had to remotely fix these machines, which we are doing right now. About 96% of our remote calls, uh, are fixed remotely on, um, uh, on click and collect type of, so, and, and that's actually helping our technicians. The next one is, um, advanced operational efficiencies, which is basically using the data that we collect from the machines in the customer's environment. Right now, as we place these machines in customer environment, we get data on how customer is using that machine. How, why would the customer leave a door open or under what situ. A machine that's located in, um, colder region, react to a machine, you know, the machine in Boston versus Mission in California. What are the implications? So we are learning all that data in real time and then modeling it so that we can use that input to improve our own design of the equipment. That's called op, you know, advanced operational Efficiency. And we are doing. You know that with the help of analytics. And then the third thing is actually to create some kind of a data analytics value chain, where we are now sharing that data, the data about the machine, how it is used by the consumers, as well as, um, you know, how it can be used to improve the customer's operations. Give an example that, um, you know, in a retail, uh, setup, in a retail grocery setup, Um, you know, you place an order for buying groceries and then you know, your appointment time to go pick up groceries is, let'ssay it's that, uh, 11:
30 AM Right? So, so the store associates have to pick up your grocery or general merchandise items before 1130 and make sure that they're inducted or they're loaded into this robotic machine so that you get a message saying that your grocer is ready for pickup. But many times what we've observed, especially. You know, during times of labor shortage, your groceries is not loaded by the time your apartment is up. So 1130 appointment is still not loaded, right? It's 1145. So customer is getting disappointed. Uh, it impacts your customer satisfaction. So what we have done is we have built a real time dashboard to let the store know, you know, this is, you know, you have X number of orders at 11. And it's not loaded yet. So how well you're performing mm-hmm. and what particular time slot is problematic. So we can pinpoint and tell them that, hey, Saturday morning or Sunday morning at eight 30 appointment for pickups, you're, you know, 20% on time. Your, your late 80% of the. That has really helped them to improve their operations as well, whether it is staffing decision, whether you know, how they can improve their operations for measuring their own, uh, employees. And also to build some labor standards, How long it'll take to pick up, how long it'll take up to load the machine with groceries, et cetera, et cetera. So what we have done is using the data that we collect from the. In the customer's environment and how it is being used, we are able to provide that contextual information, not only to our technician, but also not, not only to our technicians and our engineering team to improve the design of the product, which is intrinsic. We are also expanded it to our value chains. Now telling the cons customer saying, You can improve your operations using the data that we collect in real time to improve your own operations, improve your own labor productivity. So, so, so we have built that value chain and, and, and that has helped us and our customers tremendously as well. A lot of, lot of powerful insights in there and I, I certainly appreciate the way that. Describe digital, sort of with that big D It's, I think a lot of listeners and customers, particularly business executives, they think about this, you know, what does it mean to be digital? They have this, this understanding of, okay, it's obviously around some data. It's about the physical world, you know, et cetera. But really it's a, it's a backbone. Just, you know, in one way that requires connectivity. You know, uh, data, you know, persistence and storage and analytics, but it's also a business strategy. And you kind of, you touched on this, right? There's a, there's internal business use cases and there's external business use cases. And if you think about data as a strategic corporate asset, right in the center of these business use cases, you know it, et cetera, right? The same dataset, some of the same technologies can. Uh, not just intrinsic value, but a customer, and you can follow that customer in time of need and say, How can I leverage this data? I'm. Managing right to add additional value to a customer, say in a time of a pandemic to make sure the groceries are on the shelf right. How does that, what have you found effective, um, in terms of bellon house's, long term planning and partnering with the business? I mean, I think any company has the experience of being sometimes in silos and, and not aligning to the strategy. But it sounds like there's something, um, organizationally that, that you guys have gotten right. Um, in terms of, of serving business needs and aligning data.. Yeah. I think as, as you said, you know, for a company that has been around for that many years mm-hmm., um, data has to be what I call strategically, intentionally. How business review uses that word, you know, strategically intentional for us, uh, because it's very easy to say that, Oh, data analytics is, I need that one particular report without really understanding the value of data. And we had to, you know, we had these learning experiences as well because when we first started, You know, or monitoring using PTCs Thingworks, that was required for us because we had landed a great deal with one of the largest retailer, um, retailer customer in the world. And we had to go that path. But we encountered resistance saying that, Hey, it's never going to work. Right. So we took, you know, one part of, we took that particular machine class and then with PDCs, Professional Services help, we. Uh, Remote 360. Well, at that time it was called Remote 360, but you know, we, we designed, uh, a remote solution mm-hmm. and then when it started working right, because we don't have to travel to a retail location throughout the country, we were able to, at that point, we were resolving about 70% of our, um, issues remotely was still dispatching 30% of the times, but they saw the benefit, right? We were saving labor time. Because you know, we were able to remotely and diagnosed. Within 20 minutes versus rolling the truck travel. That is on an average about three to four hours. So they saw that benefit. Plus they were also able to see the information, which is very important because for a technician in the remote monitoring who is working now day shift, he's able to see what happened the night before by a different technic. What else he did, right? So that service order information is right there in front of him. You also can see, you know, what kind of parts were replaced, et cetera. So providing that contextual visualization information really helped. So as the word spread out that Okay, remote monitoring can be in. Right. Um, so many other techniques started using it too. Mm-hmm.. And then interestingly, our own product design team, they would write machine logs as an engineer. You know, they would write machine logs. Mm-hmm. with no idea that the logs that they read would be for some purpose. For by somebody , because before the extent was that somebody, some technician ever in mm-hmm. once, maybe they would just take all those machine log files, put in a thumb drive, and then maybe analyze it one day., right. If there is an issue. So what we did is we took that machine data in real time and process it using Thingworks modeling and all that. And then we are using that to contextualize information. Now engineers. Thinking about it and they are, they now developed a, you know, kind of common method to write the machine logs thinking that hey, analytics team is going to use it, right? So, and then they also saw the benefit because we now started to visualize that log information, right? What are my top 10 logs created and what are the, what are the causes for that? So they can see a predator chart in real time saying that, Hey, these are my top issues on the machine. So they also saw the. Right. So created that ecosystem of users internally, they started to see the benefits. So, and then on top of all that, um, you know, we established Analyst Center of Excellence. Um, our ceo, Larry Blue, is very passionate about it. And because for us it's really a competitive advantage. Many of our competitors in this space who. Uh, other equipment do not necessarily have the capability to the extent that we have. I came off of, um, field service conference and, you know, we did present our, um, remote solution. And, uh, I think the level that we have, the sophistication that we have built over the last four years has really given us a distinctive advantage. And, uh, recently we won an award for Remote 360 Solution. For is S I P, which is, uh, International Society for Service Innovation Professionals. Uh, that's a good cognition. Um, so we are in with big good companies, so I think, you know, we have done something right, as you say, and uh, but essentially it took us, you know, we are in the fourth, fourth year now of um, uh, Remote 360 and making sure that it's helping us internally. and then we are now to the point of creating value chains out of it to help the customers with that. Absolutely. And I think that, you know, for one, you've made progress along the way in those four years, but the fact that you continue to evolve and grow takes an organizational conviction that is a, you know, fundamentally from a, a culture perspective exists within your company and is so critical. And I love the, the fact that you talked. You know, the engineering data and the data logs, right? It's a perfect example of how you see this. It starts as let's do remote service. The service organizations thinking about it, they may not believe it, right? Uh, but they see the value. They see how it can. Right. So suddenly they start to adopt and engineering here is, wait, wait a minute, what's going on here? And they see the data that they're using as part of a service context, and suddenly people contribute better to the data they bring. They start to care about bringing data into this strategic asset, this data both to serve, engineering and service. It becomes a team sports suddenly, uh, you know, organically and naturally. Absolutely. So, yeah, that's exactly what's happening. I would love to, you know, as a, as I close things up, um, I would love to know, if you look back on those four years, right? And you could tell our listeners, you know, just a few things that if you would've known that or could have done it differently, um, or could have gotten the organization aligned in this way. What are one or two of those things? I think, um, um, doing the experiments because, you know, we, we spend some. Do prototyping, experimenting, and there's a saying that in a lot of things get, there are lot, there are a lot of pilots, but nothing becomes outta depression, right? So we went through this pilot S for first fu durations, but then we said, Okay, we are going. No, no longer do pilots anymore. We are going to go right into production. So we'll pick a small project, make sure that it's successful, right? And then go on to the next one. Because in many comes struggle with making something many companies struggle to deploy. Uh, a lot of these, um, iot projects, they never come out of the lab, right? That's what we want to do is, you know, as a small, we don't have. You know, resources as a, you know, medium size company, we wanna make sure that we get good use of, um, iot technologies. Uh, we've done good progress on remote monitoring, but a lot of other things we still need to prog make progress on. For example, you know, the use of augmented reality, the use of, um, um, you know, some of yours like remote collection, including shock. We have not. The progress that we would expected to. Right. So again, you know, we want to go faster. Mm-hmm., uh, and then make sure that our technicians, um, adopt those things at a faster rate. So, you know, how do, what is, what is the solution for that is basically, To make sure we have organizational buy-in. Yeah. And there are some, there are, um, the spheres of influence. There are certain people within the service organization who can be the champions, who can adopt these technologies and make sure that everybody in the organi. The organization can follow it and, uh, make sure that those are successful. And I think when you buy-in and the agile and the agile muscle that you can try to build as you get that buy-in to, as you said, you know, don't just, it's not a science experiment. We don't wanna do one off. We wanna test how to scale this. Yeah. Right. Right. And make sure that it, it is ultimately it's people. People are the real key for digital transformation. You can have best of the best technologies, but if people do not adopt it, then successful. Um, so that's one of the important lessons is to involve people early on, make sure that you are solving their problem, augmenting their work, not creating additional paperwork. Mm-hmm. additional form to. Making sure you know, it really, really reduces their workload and they can see it. Whether it's giving them that contextual information on what happened in the last X number of days, creating a visualization for problematic machine. You know, show me five machines out of these thousand machines that were problematic in the last three. tell me what happened or what can I do for those three machines? For those x number of machines that you say are problematic, right? You know, these have lot of door. Getting jammed, right? Right. These are the top three in the entire country and these is what done in next number of days. This is when you replace the parts so he can proactively look at it and proactively touch. Now that saves him, saves the technician time. So he, you know, technician doesn't have to research it. The algorithms and the visualization have done the work for them. Right. So those are the, some of the things that we have to make sure that it's helping Technic. To reduce their workload, right? It's not just enabling a, a business initiative, it's enabling a human to do their jobs better, which then enables a business kpi. It's that, that human-centered digital transformation. And I think, uh, absolutely a lot of what we've, uh, what you've covered and let me say, I really appreciate you joining us, um, and taking the time. I think there's no better example of Bell and how, in terms of understanding, Even organically, what it means to have a human-centric digital transformation strategy that looks at data as a strategic organizational asset that must provide value, um, in the given prioritized use case and context. Whether it's meeting your customers, where they are, serving them where you need, while also optimizing your business, extending that value out to engineering, getting them to not just realize value but contribute to that data. Really well by focusing on that human centered way of what do our service technicians need? How do I meet them where they are? Right? That's, that's a practical application. Applying digital as a backbone into real world's contextualized use cases. That is incredibly powerful. I appreciate the insights you provided. Thank you very much for joining the showin. Thank you. Thanks for listening to the Speaking of Service podcast, brought to you by ptc. If you enjoyed this episode, please subscribe wherever you get your podcasts and leave a rating or review. And be sure to check out other episodes to hear new perspectives on improving life for aftermarket professionals, service teams, and the customers they support. If you have a topic of interest or want to provide feedback, email us at speaking of service at PTC dot. Or visit us at ptc.com/speaking of service.