Case Study

How Intelligent Systems Work for KONE Elevators and Escalators Company

KONE is a global industrial company (based in Finland) that manufactures mostly elevators and escalators and also services over 1.1 million elevators, escalators, and related equipment in several countries. The company employs over 50,000 people.

THE PROBLEM

Over 1 billion people use the elevators and escalators manufactured and serviced by KONE every day. If equipment does not work properly, people may be late to work, can-not get home in time, and may miss important meetings and events. So, KONEs objective is to minimize the downtime and users suffering. The company has over 20,000 technicians who are dispatched to deal with the elevators anytime a problem occurs. As buildings are getting higher (the trend in many places), more people are using elevators, and there is more pressure on elevators to handle the growing amount of traffic. KONE faced the responsibility to serve users smoothly and safely.

THE SOLUTION

KONE decided to use IBM Watson IoT Cloud platform. IBM installed cognitive abilities in buildings that make it possible to recognize situations and behavior of both people and equipment. The Internet of Things (IoT), is a platform that can connect millions of things together and to a central command that can manipulate the connected things. Also, the IoT connects sensors that are attached to KONEs elevators and escalators. The sensors collect information and data about the elevators (such as noise level) and other equipment in real time. Then, the IoT transfers to information centers via the collected data cloud. There, analytic systems (IBM Advanced Analytic Engine) and AI process the collected data and predict things such as potential failures. The systems also identify the likely causes of problems and suggest potential remedies. Note the predictive power of IBM Watson Analytics (using machine learning, an AI technology) for finding problems before they occur. The KONE system collects a significant amount of data that are analyzed for other purposes so that future design of equipment can be improved. This is because Watson Analytics offers a convenient environment for communication of and collaboration around the data. In addition, the analysis suggests how to optimize buildings and equipment operations. Finally, KONE and its customers can get insights regarding the financial aspects of managing the elevators. KONE also integrates the Watson capabilities with Salesforces service tools (Service Cloud Lightning and Field Service Lightning). This combination helps KONE to immediately respond to emergencies or soon-to-occur failures as quickly as possible, dispatching some of its 20,000 technicians to the problems sites. Salesforce also provides superb customer relationship management (CRM). The peoplemachine communication, query, and collaboration in the system are in a natural language (an AI capability of Watson Analytics). Note that IBM Watson analytics includes two types of analytics: predictive, which predicts when failures may occur, and prescriptive, which recommends actions (e.g., preventive maintenance).

THE RESULTS

KONE has minimized downtime and shortened the repair time. Obviously, elevators/ escalators users are much happier if they do not have problems because of equipment downtime, so they enjoy trouble-free rides. The prediction of soon-to-happen can save many problems for the equipment owners. The owners can also optimize the schedule of their own employees (e.g., cleaners and maintenance workers). All in all, the decision makers at both KONE and the buildings can make informed and better decisions. Some day in the future, robots may perform maintenance and repairs of elevators and escalators.

WHAT CAN WE LEARN FROM THIS VIGNETTE?

Today, intelligent technologies can embark on large-scale complex projects when they include AI combined with IoT. The capabilities of integrated intelligent platforms, such as IBM Watson, make it possible to solve problems that were economically and technologically unsolvable just a few years ago. The case introduces the reader to several of the technologies, including advanced analytics, sensors, IoT, and AI. The case also points to the use of cloud. The cloud is used to centrally process large amounts of information using analytics and AI algorithms, involving things in different locations. This case also introduces us to two major types of analytics: predictive analytics and prescriptive analytics. Several AI technologies are discussed: machine learning, natural language processing, computer vision, and prescriptive analysis. The case is an example of augmented intelligence in which people and machines work together. The case illustrates the benefits to the vendor, the implementing companies, and their employees and to the users of the elevators and escalators. Note: This case is a sample of IBM Watsons success using its cognitive buildings capability. To learn more, we suggest you view the following You Tube videos: (1) you. tube.com/watch?v=6UPJHyiJft0 (1:31 min.) (2017); (2) you. tube.com/watch?v=EVbd3ejEXus (2:49 min.) (2017). Sources: Compiled from J. Fernandez. (2017, April). A Billion People a Day. Millions of Elevators. No Room for Downtime. IBM developer Works Blog. developer.ibm.com/dwblog/2017/kone-watson-video/ (accessed September 2018); H. Srikanthan. KONE Improves People Flow in 1.1 Million Elevators with IBM Watson IoT. Generis. https://generisgp.com/2018/01/08/ibm-case-study-kone-corp/ (accessed September 2018); L. Slowey. (2017, February 16). Look Whos Talking: KONE Makes Elevator Services Truly Intelligent with Watson IoT. IBM Internet of Things Blog. ibm.com/blogs/internet-of-things/kone/ (accessed September 2018).

QUESTIONS FOR THE OPENING VIGNETTE 1.

What is the synopsis of the case?

2. It is said that KONE is embedding intelligence across its supply chain and enables smarter buildings. Explain.

3. Describe the role of IoT in this case.

4. What makes IBM Watson a necessity in this case?

5. Check IBM Advanced Analytics. What tools were included that relate to this case?

6. Check IBM cognitive buildings. How do they relate to this case?