Schindler Elevator Corporation

This case provides the opportunity for a comprehensive analysis in a service operations strategy course. It touches on subjects including the customer value proposition, profitability, employee management, customer management, robust people versus robust process, queue management, and staffing models.This case can be used as a basis for discussing the "Four Truths" from Francis Frei and Anne Morriss, Uncommon Service: How to Win by Putting Customers at the Core of Your Business, 6th ed. (Boston: Harvard Business Press, 2012). The Four Truths are (1) you can't be good at everything, (2) someone has to pay for it, (3) you must manage your employees, and (4) you must manage your customers. Students are asked to decide the necessary service strategy and operational changes as Schindler Elevator Corporation decides how to use current trends in the internet of things. Excerpt UVA-OM-1593 Rev. Jul. 30, 2018 Schindler Elevator Corporation What Floor, Please? In 2017, the US elevator industry was struggling with the transformative potential (and new competitive challenges) of big data and the internet of things (IoT). Across industries, new ways of using data were emerging that had not been widely anticipated before the combination of big data analytics and wired (or wireless) sensor network technology. Industries were presented with new ways to monitor and exploit the predictive and integrative value of measuring different things. How could the experience of riding a motorcycle be enhanced by networking together sensors on the motorcycle and sensors in the clothing of the rider? How could “smart homes” get even smarter by networking together information from an occupant's wearable device (e.g., heart rate, or body temperature) with the occupant's home HVAC thermostat or a sensor measuring mold content? And what happened when those networks were smart—meaning that they could make decisions and take useful action? Some of the largest barriers to a truly smart sensor network were amassing enough data to be meaningful, and figuring out how to distinguish noise from actionable data. Once collected and determined to be useful, the data still had to be acted upon. That required businesses to make decisions about how to use the data that created value from the customer's perspective. Broadly speaking, to a customer, what often created the most value was some form of prediction—in other words, a sensor network knowing (and adjusting to) the customer's needs before a problem occurred. . . .