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 note.  For 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://aiinstitute.hbs.edu/platform-rctom/how-to/.

Submitted (926)

Machine Learning for Machines
Akash Section H
Posted on November 13, 2018 at 7:21 pm
Kebotix is a startup in Boston combining machine learning and robotics to accelerate the discovery of advanced materials.
Wayfair is using data and pictures to furnish your home, as only you can.
jrod
Last modified on November 13, 2018 at 8:04 pm
The home furnishing market has long been dominated by brick and mortar stores. Wayfair is using machine-learning and artificial intelligence to change your buying experience and ensure that you buy furniture that uniquely fits your style and personality, at a [...]
Medtronic – missing out on a personalized medicine opportunity, or appropriately cautious?
tf
Posted on November 14, 2018 at 10:31 am
Medtronic states they won't be using additive manufacturing to create personalized medical devices any time soon - are they missing out on an opportunity, or do they realize that the opportunity is overstated?
Unlocking the power of STATS
Significantly Correlated
Posted on November 13, 2018 at 4:12 pm
In an arena where any slight edge could mean the difference between winning and losing, machine learning has never been more important in sports. As the industry becomes more and more inundated with data, sifting through information to create meaningful [...]
Facebook’s Negotiating Chatbots – Deal or No Deal?
LW
Last modified on November 12, 2018 at 7:56 pm
With machine learning, Facebook has built robots that can negotiate and even lie; what are the business and societal implications for Facebook and its users?
2018: A Space Odyssey – How NASA uses Machine Learning for Space Exploration
Cherish Weiler
Last modified on November 15, 2018 at 1:45 am
Neural network machine learning algorithms are revolutionizing the classifications of galaxies and giving us a deeper understanding of the origins and evolution of the universe.
Is machine learning the new wingman?
Kay
Posted on November 13, 2018 at 9:46 pm
Machine Learning is Taking-Over the Online Dating Industry
Print-a-Part: How 3D Printing is transforming Medical Device Manufacturing
Prineeta Kulkarni
Last modified on November 13, 2018 at 8:17 pm
Win-Win Innovation Fueled by the trend of precision medicine, the use of new technologies such as additive manufacturing is seeing an uptick. Additive manufacturing, specifically 3D printing, allows rapid and flexible manufacturing of medical devices that are customized to patient [...]
Redefining the oil and gas industry through machine learning
Electric Sheep
Last modified on November 13, 2018 at 8:12 am
Exploring machine learning developments in the oil and gas industry
Open for Business: Harnessing Open Innovation at the Massachusetts Bay Transportation Authority
Danny Noonan
Posted on November 13, 2018 at 7:30 pm
Still recovering from record-setting snowfall in the winter of 2015 that crippled public transit in greater Boston, the Massachusetts Bay Transportation Authority (MBTA) faced a projected $335 million operating budget deficit and a $7 billion maintenance backlog. Searching for innovative [...]
Is machine learning in education the new textbook?
Mandy Z
Posted on November 13, 2018 at 1:55 pm
Education technology company Knewton strives to deliver personalized learning experience for college students using machine learning technology. Can Knewton's product really replace textbooks as the company aspires to?
American Express: Machine learning for customer churn prediction and more effective customer retention
CS squared and B cubed
Posted on November 13, 2018 at 7:59 pm
The financial services industry is especially challenged in customer retention. American Express has used machine learning to predict churn for its own customers, and have transformed that capability as a product for its merchants.
Modern Meadow: Using Additive Manufacturing to Reimagine Fashion and Food
Diversification Magic
Posted on November 13, 2018 at 1:23 am
Biofabrication has the potential to radically change how we obtain, process, and transform animal products for food and fashion.
Man or Machine? Does AI have a place in Venture Capital?
sashafierce
Posted on November 14, 2018 at 10:28 am
This post explores that evolution of predictive analytics and how VC's can leverage machine learning to invest in the next big thing.
Can machines replace lawyers?
Counselor
Posted on November 13, 2018 at 5:09 pm
With industry experts claiming that AI is a game-changer in the legal industry, what are law firms doing to take advantage of faster document processing provided by machine learning softwares?
OMG: How Texting Grammar Could Impact Your Creditworthiness
N. Fleming
Last modified on November 12, 2018 at 7:12 pm
BankMobile looks to machine learning and alternative data sources to boost the bottom line. Can consumers and regulators be convinced?
Do or Die? Walmart’s foray into Machine-Learning and the implications for Its competitiveness amidst the Amazonian squeeze
Don Johnson
Posted on November 13, 2018 at 11:24 pm
Will machine-learning be the tie-breaker in this new dawn of consumer retail?
Machine Learning at Airbnb
Ashima_Singh
Posted on November 13, 2018 at 3:22 pm
Airbnb is a two-sided rental marketplace, where market dynamics play a key role in matching guests with hosts1. For Airbnb, supply and demand vary drastically across different geographies, different customer preferences and different check-in dates.2 Hence, optimizing matches between hosts [...]
Open Innovation at Nestle – Establishing an extended innovation ecosystem
Yaping
Last modified on November 12, 2018 at 5:31 pm
In a world of distributed knowledge and expertise, it's clear that open innovation has clear advantage across the value chain. This essay discussed the challenges of traditional in-house innovation model and why it is important for Nestle to use open [...]
How McKinsey is Dealing with the Machine Learning Challenge
NCB
Posted on November 13, 2018 at 6:29 pm
All industries are facing a great challenge regarding how to take advantage of all the data they have available to steer their business. How can strategy consulting firms, known for their "generalist approach", help its clients in a topic that [...]
The Unilever Foundry – bringing innovation to 400 brands, under one roof
M
Posted on November 13, 2018 at 1:08 pm
Can a 90-year old company learn to behave like a start-up? Slow growth, ever-changing consumer tastes and the rise of digitally native upstarts are threatening the traditional consumer-packaged goods industry. Unilever’s significant investment in open innovation intends to challenge this [...]
Tala: Providing loans for those without a financial identity
Gabriel Araujo
Posted on November 13, 2018 at 4:58 pm
2.5 billion people in the world don't have access to financial services. Tala is a startup trying to access this market by evaluating user's credit worthiness using only their smartphone data.
Artificial Intelligence Taking Off for Airbus
IM_HBS
Last modified on November 13, 2018 at 8:01 pm
In an increasingly competitive and innovative landscape, can Airbus use machine learning to improve efficiency while not sacrificing on safety?
Google Duplex: Does it Pass the Turing Test?
William Knightly
Posted on November 13, 2018 at 7:58 pm
Google Assistant's new feature can make a real phone call to make a reservation on your behalf.
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