Data Sharing and Analytics Drive Success With IoT

Creating Business Value With the Internet of Things

by: Stephanie Jernigan, Sam Ransbotham, and David Kiron
Already a member?
Not a member?
Sign up today
Member
Free

5 Free Articles per month, $6.95/article thereafter. Free newsletter.

Subscribe
$75/Year

Unlimited digital content, quaterly magazine, free newsletter, entire archive.

Sign me up

Commercial laundry facilities are a fact of life in apartment buildings and college campuses around the world. Until recently, managing such laundry facilities has been rather straightforward for building managers and college administrators: Approximate how many machines are needed, collect the quarters when the coin boxes get full, and fix the machines when they break. Residents and students are mostly satisfied if machines are available when they need them, machines don’t eat quarters or socks, and magazines left by previous customers aren’t too crumpled or dated.



Coin-operated laundries, a $5 billion-a-year industry,1 are changing rapidly thanks in large part to the advance of digital technology. Consider WASH Multifamily Laundry Systems, an El Segundo, California-based laundry facilities management service provider that processes 1.7 billion quarters a year for 75,000 locations in the U.S. and Canada.2 Its extensive network of hundreds of thousands of interconnected washer, dryers, vending machines, and payment systems serves roughly 7 million residents.3 These devices generate more than a stream of quarters — they generate a continuous stream of data that is being used to create several distinct types of business value for WASH, its customers, and its suppliers.

Working closely with manufacturers, WASH uses machine data to anticipate maintenance before downtime occurs. Working with payment processors, WASH Laundry provides launderers with an array of payment, coupon, and loyalty programs. And the possibilities aren’t restricted to improving operations. Working with apartment-building owners, WASH uses data from its large device network to model and test managerial intuition about questions such as whether it is cost-effective to switch from cash to payment cards before committing to widespread changes. What’s more, alternative pricing options become possible with device data: Colleges are working with WASH to adjust pricing at peak periods to spread demand, reduce congestion, and improve student experience.

These new possibilities enable different and deeper relationships with WASH’s ecosystem of suppliers and customers.

Read the Full Article

References

1. “About the Industry: Laundry Facts,” n.d., www.coinlaundry.org.

2. "Fact Sheet: About WASH," n.d., http://www.washlaundry.com/wp-content/uploads/2013/12/about-wash.pdf; S. Ransbotham, “Making Data Experiments Powerful,” MIT Sloan Management Review, July 19, 2016, https://sloanreview.mit.edu.

3. S. Ransbotham, “Making Data Experiments Powerful,” MIT Sloan Management Review, July 19, 2016, https://sloanreview.mit.edu.

4. L. Winig, “GE’s Big Bet on Data and Analytics,” MIT Sloan Management Review, February 18, 2016, https://sloanreview.mit.edu.

5. M. Fitzgerald, “Data-Driven City Management: A Close Look at Amsterdam’s Smart City Initiative,” MIT Sloan Management Review, May 19, 2016, https://sloanreview.mit.edu.

6. “Take Control of Your Energy Costs With EnerNOC’s Energy Intelligence Software” (brochure), March 2015. https://www.enernoc.com/.../Take_Control_of_Energy_Costs_with_EIS.pdf

7. N. Statt, “Nest Says It May Offer ‘Compensation’ to Revolv Users for Disabling Smart Home Hub,” The Verge, April 5, 2016, http://www.theverge.com.

8. N. Bilton, “Nest Thermostat Glitch Leaves Users in the Cold,” New York Times, January 13, 2016.

9. B. Schneier, “The Internet of Things Is Wildly Insecure — and Often Unpatchable,” Wired, January 6, 2014, http://www.wired.com.

10. C. Pettey, “Gearing Up for the Internet of Things,” Smarter With Gartner, April 28, 2016, http://www.gartner.com/smarterwithgartner.

11. G. Press, “Transform or Die: IDC’s Top Technology Predictions for 2016,” ContentLoop, November 11, 2015, http://www.content-loop.com.

12. S. Ransbotham, D. Kiron, and P.K. Prentice, “Beyond the Hype: The Hard Work Behind Analytics Success,” MIT Sloan Management Review, March 8, 2016, https://sloanreview.mit.edu.

13. L. Winig, “GE’s Big Bet on Data and Analytics,” MIT Sloan Management Review, February 18, 2016, https://sloanreview.mit.edu.

Reprint #:

58181

More Like This

Add a comment

You must to post a comment.

First time here? Sign up for a free account: Comment on articles and get access to many more articles.