Reference no: EM133599246

Questions

1. How might Hermitage codify and manage the knowledge of experienced maintenance technicians into the new rules to be written for handling maintenance responses under the upcoming deluge of big data from the Internet of Things? Explain.

2. How might big data analytics actually be more costly rather than less costly with respect to maintaining escalators?

3. What specific value might come from analyzing data on past maintenance calls? What specific data do you think would have value? Why?

Hermitage Escalator Company

Hermitage Escalator Company is an independent division of a large international manufacturer that sells and provides maintenance of elevators and escalators. Hermitage was started by an entrepreneur living in Hermitage, Tennessee in 1954, just as the demand for the "magic staircase" began to take off. In 1989 Hermitage was acquired by an elevator company that wanted to provide a full line of lift devices. Hermitage was able to maintain substantial autonomy as a separate division and to retain its brand name. Hermitage sells over 2,000 escalators each year. Principal areas of escalator sales include department stores, shopping malls, airports, transit system stations, convention centers, hotels, arenas, stadiums, office buildings, and government buildings. At the extreme, an escalator may carry people up multiple stories or hundreds of feet horizontally along corridors such as in an airport concourse.

How Escalators Work

In its simplest form, an escalator is like a conveyor belt with a pair of long chains, one on each side, looped around gears at each end. An electric motor turns the drive gear at the top, which pulls and rotates the chain loops, similar to the chain mechanism on a bicycle. A typical escalator uses a 100-horsepower motor to rotate the gears. The motor and chain system are housed inside a metal structure extending between the two floors. The chain loops move a series of steps. As the chains

move, the steps stay level. At the top and bottom of the escalator, the steps collapse on each other, creating a flat platform. This makes it easier for riders to get on and off the escalator.

Each step in the escalator has two sets of wheels that roll along on two separate tracks. The set of wheels near the top of each step are connected to the rotating chains, and hence are pulled by the drive gear from the top of the escalator. The other set of wheels at the bottom of the step simply glide along its track. The tracks are placed so that each step always remains level. At the top and bottom of the escalator, tracks level off to a horizontal position, flat-tening the stairway. Each step has a series of grooves so it will fit together with the step behind it and in front of it during the flattening.

The electric motor in an escalator also moves the handrail. The handrail is simply a rubber conveyor belt that is looped around a series of wheels. This belt is precisely configured so that it moves at the same speed as the steps to give riders some stability.

An escalator is much better than an elevator for mov-ing people a short distance because of the escalator's high loading rate. Escalator speeds vary from about 90 feet per minute to 180 feet per minute. An escalator moving 145 feet per minute can carry more than 10,000 people an hour, many more people than a standard elevator can move.

The Need for Maintenance

Although escalators are simple in concept, like all machin-ery there are many parts involved that may break down at any time. Safety mechanisms include inlet guards, an oper-ating panel, safety switches, and step switches. The top and bottom operating units include a control panel, drive unit, main gear, drive chain, sprocket, and safety switches. The truss between floors includes the main track, trailing track, moving handrail drive unit, sprocket, moving handrail drive chain, and inlet guard. Other elements include the moving handrail, its interior panel, deck board, and skirt guard. The steps include the tread, riser, step demarcation line, driving rollers, and step chain.

Escalator maintenance is important for customer sat-isfaction because nobody likes to climb a broken escalator. Maintenance can also be a serious issue. There are horror stories of people falling into broken escalators that opened up when people were climbing up the stairs. Moreover, escalators have collapsed sending people tumbling to the bottom. Escalators have caught fire, such as in 1987 when an escalator in a London Underground station actually exploded, sending flames into the ticketing office, killing 31 people. The cause was the accumulation of pounds of tiny bits of paper and lint that had collected in the inner workings and undercarriage of the machine.

The IoT

Approximately 28,000 new escalators and elevators are installed each year in the United States. Hermitage, like all manufacturing companies, was facing an industrial revo-lution, popularly known as Industry 4.0 or the industrial Internet of Things, as manufacturing converged with the digital economy, specifically with emerging big data col-lection systems and analytics. There is much discussion of how to create profitable business models using big data analytics. The best approach may be to identify bottle-necks and other problems, determine what real-time data is needed to overcome the problem, and then apply appro-priate sensors, wireless connections, and analytics to col-lect, transfer and analyze this data. Hermitage must also decide what action should be taken in response to a prob-lem, such as how to provide data or a data visualization to decision-makers, or even how to create decision algorithms that would automatically decide what action should be taken in response to a problem.

Concerns at Hermitage

Mardell Anderson, maintenance superintendent at Hermitage, had a number of concerns about digitalizing the new escalators as part of the industrial Internet of Things. According to Anderson, "Fixing an escalator the first time, every time, is crucial to maintain customers and reduce labor costs. I would really like to see our ability to fix a problem before it happens. Prediction and prevention, as opposed to reaction, is where the industry is headed. We are driving toward preventative and predictive maintenance out of necessity. If we can install sensors and Internet of Things components, we could dispatch the right people to fix the right problem at the right time. Or better yet, prevent the problem from occurring at all." Low-rise escalator equipment can be maintained with competent technical know-how. Competent maintenance workers make about $50 per hour and can earn over $100,000 a year with overtime. An IoT package is cost-effective on new escalators but expensive to retrofit on legacy escalators. "It will take several years to replace leg-acy escalators," Anderson said. "In the meantime, we are learning the weak spots in our escalator systems and where sensors would have the best payoff. It would be great if we had information coming from the equipment to tell us which mechanics to send and when to send them but that is several years away on our installed maintenance base. It could be 10 years before we have all of our installed esca-lators talking and communicating in a meaningful way."

Anderson wanted to be creative in his approach to maintenance during the 10-year interim. "Is it possible to find another way to achieve predictive maintenance? We have a lot of repair and callback history that we could use to do big data-type effort, even before escalators have sensors and are connected to the IoT. Knowing that a large percentage of our callbacks are related to electric motors and chains, for example, is important. I want us to get bet-ter at using the data we already have as we move toward

big data analytics." "A really good mechanic can troubleshoot an esca-lator over the phone by just asking questions," Anderson says. "There are not millions of things that can go wrong. There is a finite number of questions for every escalator that would lead them down the right path. That kind of knowledge needs to be part of our predictive maintenance system."

Anderson continued, "Our challenge is selecting and positioning the sensors and cameras within the units and then selecting the right things to measure in order to arrive at predictive capability. Measuring the right things using well-placed hardware and connecting it to the cloud would not achieve our goal without clear rules. The rules of maintenance are not derived from a rulebook, but from the accumulated knowledge and expertise of hundreds of maintenance and repair mechanics over decades of service. As part of the remote monitoring program, our rules for data collection and remote monitoring will allow the first generation of rules to be written that help dispatch the right person to the right problem."

"Now we are taking this knowledge to the next level to do predictive analytics. We want to capture and trans-fer knowledge that we have in our most talented techni-cians down to the next generation that will lead us into our future service model. With seemingly unlimited IoT data on the horizon from sensor technology, we will use this information to rewrite the rules on how we dispatch and who we send on service for maintenance calls." "Predictive maintenance analytics will not be easy to put into practice. For example, with new data coming in, we can expect to see 'false positives' - meaning indicators that something was wrong when nothing was wrong. With matters of safety and satisfaction, how could a technician know if the alarm was a false one? False positives have the potential to increase our costs, not decrease them. Then there is the issue of natural variability in our operating equipment. How can we set parameters on distinguishing what we call "noise" from a problem that requires action? In addition, once it is determined that an action has to be taken, what should that action be? Could a certain mainte-nance action be bundled together with other maintenance items and then addressed as part of the next regularly scheduled maintenance service?"

"When you move to a predictive model, you can eas-ily drive up costs. If you take every single thing that was sensed and automatically send a technician to check it out, you spend money for each visit with no benefit. That would cost a lot. If there is a safety issue or an error that could cause a shutdown, then dispatch the technician right away. But, if we are being smart, some issues detected can be bundled into our next maintenance visit to the location." "So, my thinking is that when we write our rules for

maintenance and maintenance calls, we should start with analyzing the callbacks and repairs in our history, because that is where we can influence costs and outcomes. The existing data could help us tremendously. We for sure know on which type of equipment each callback or repair occurred."

At Hermitage, all new installations will have an IoT sensor package installed. A big question is how the Inter-net of Things can be used to increase revenue and decrease rather than increase costs, all the while providing bet-ter customer service. How can Hermitage capture all the promise of IoT to deliver more value to the customer and to itself?