RC TOM Challenge 2018

November 13, 2018

Read The Full Prompt

The TOM Challenge provides an opportunity for you to continue exploring organizational learning and innovation through the lens of process improvement and/or product development, the focus of RC TOM’s second module. In this challenge, you will investigate how an organization is grappling with machine learning, additive manufacturing, or open innovation. These megatrends are likely to significantly affect how organizations manage process improvement and product development in the coming years of your career. The TOM Challenge requires you to (1) conduct research and write an essay that examines how one organization is facing a particular aspect of one of these megatrends, and (2) write six comments that share your reflections on some of your section mates’ essays.

Your essay should address four questions in the context of the organization you choose:

  1. Why do you think the megatrend you selected is important to your organization’s management of process improvement and/or product development?
  2. What is the organization’s management doing to address this issue in the short term (the next two years) and the medium term (two to ten years out)?
  3. What other steps do you recommend the organization’s management take to address this issue in the short and medium terms?
  4. In the context of this organization, what are one or two important open questions related to this issue that you are unsure about that merit comments from your classmates?

Your essay should convey facts, analysis, and your recommendations. It should focus on a single organization (e.g., a single company, non-profit organization, or government agency) and a concern related to one megatrend. It is fine if the concern you choose relates to other megatrends that the organization is facing, but that’s not required. Roughly a third of your essay should be dedicated to each of the first three questions, with just a few sentences dedicated to the fourth question. Your essay should be at least 700 words but no more than 800 words, and must conclude with a word count in parentheses (such as 778 words).

When posting your essay to Open Knowledge, be sure to enter “Machine Learning”, “Additive Manufacturing”, or “Isolationism” in the Topics field.

More details on research, sourcing, deadlines, and other matters are provided in the RC TOM Challenge: 2018 noteFor assistance with the Open Knowledge platform during business hours (9:00 am – 5:00 pm M-F), email openknowledge@hbs.edu. A short video with instructions on how to post an essay to this platform is available at https://rctom.hbs.org/how-to/.

Responses (927)

Can machine learning provide a solution to CNP frauds?
Posted on November 13, 2018 at 8:15 pm
FICO Machine Learning Algorithms Improve Card-Not-Present Fraud Detection by 30% – About a year back, FICO, a software firm announced that its new Falcon consortium models for payment card fraud detection include machine learning innovations that improve card-not-present (CNP) fraud [...]
Waymo: The future of (not) driving
Posted on November 13, 2018 at 9:49 pm
Waymo is attempting to improve transportation using autonomous vehicles. As it is preparing to launch a ride-hailing service with these cars, there are several challenges it faces - from perceived safety issues to long-term transportation policy around the world.
Comcast: Don’t cut the cord
Posted on November 13, 2018 at 8:28 pm
As the largest cable TV company and largest home Internet service provider in the United States, Comcast boasted a customer base of over 22.5 million TV subscribers and 25.1 million broadband subscriptions in 2017 (Kafka). With this enormous scale comes vast amounts [...]
SenseTime and Public Safety
Toni Campbell
Last modified on November 14, 2018 at 9:38 pm
Cities and countries already have begun to deploy AI and ML technologies for public safety and security. On one hand machine learning applications in image and video recognition can help law enforcement officials in detecting criminal activities and efficiently prevent [...]