Jane Harvard

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Walmart Plays Catch-Up with Amazon Using Machine Learning
Jane Harvard
Last modified on November 14, 2018 at 7:25 pm
Walmart uses machine learning to remain competitive in the retail industry, through personalization and process improvement at check-out.

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On November 15, 2018, Jane Harvard commented on Eli Lilly: Improving R&D Through Open Innovation :

I agree with “Greatest of All TOM”, that Eli Lilly should implement a revenue sharing component to its program. The entire open innovation approach in this context is to get better access to scientific talent, ideas and theories. With financial incentives, Eli Lilly will be able to attract more quality talent, and ultimately deliver new medicines and solutions to the market faster. However, I would also recommend that Eli Lilly implements measures to evaluate the effectiveness of the program. As it invests in attracting ideas and talent, it will need to consider how it will measure ROI.

On November 14, 2018, Jane Harvard commented on Additive Manufacturing in Construction :

The construction industry feels like a natural fit for additive technology. However, I’m particularly interested in how additive manufacturing in construction will affect labor. You noted that the automation of the technology can decrease accidents and safety hazards. However, since 3D printers don’t need to eat or sleep, I wonder what impact this will have on labor demand in the industry. While the technology will likely need careful human supervision – at least for the foreseeable future – labor content will decrease. While this may decrease the number of jobs in some geographies, it could address a skills shortage in construction in many high-income countries [1].

[1] [1] de Laubier, Romain. “Will 3D printing remodel the construction industry?” BCG, January 23, 2018.

On November 14, 2018, Jane Harvard commented on Machine Learning at Airbnb :

AirBnB is in a particularly interesting position to leverage machine learning, because of the inherent amount of data a customer provides to transact on its platform. In any business, machine learning is most effective when the company has a lot of data. In retail, for example, it would be priceless for retailers to know every relevant data point on their customers (e.g. size, color preferences, location, gender, age, etc.). However, due to privacy concerns or simply not wanting to spend the time, customers are often reluctant to provide this information.

AirBnB has the luxury of collecting information on customers’ budget, contact information, and various data points on style, size and location of preferred accommodations. I am interested to know how they will apply this technology beyond customer search and pricing going forward.

On November 14, 2018, Jane Harvard commented on BMW & The Ultimate Printing Machine: How Additive Manufacturing is Revolutionizing the Auto Assembly Line :

Your article does a fantastic job at highlighting the benefits of additive manufacturing on manufacturing flexibility, time and labor. I’m particularly interested in how reducing lead time will affect the auto industry. Inherently, as lead times are reduced, market responsiveness increases [1]. As new models are in market more quickly, will this change customer expectations regarding technology and safety? I wonder if automobile companies will feel pressure to innovate and release newer models more quickly. Either way, it seems like additive manufacturing may enable these companies to keep up.

[1] Giffi, Craig., Gangula, Bharath. and Illinda, Pandarinath. “A Deloitte Series on Additive Manufacturing.” Deloitte University Press, 2018.

On November 14, 2018, Jane Harvard commented on Great Scott! What’s next for open innovation at LEGO? :

LEGO’s application of open innovation is a fantastic example of using external brainpower to improve product development. I like the parallel that you drew to IDEO – this type of idea generation encouragement allows the company to truly identify what the customer wants, and ultimately both LEGO and the customer win.

I agree that the target customer group going forward should be older, adult hobbyist who will appreciate the innovation. A full community of adult LEGO enthusiasts exists, and encouraging this community to be a part of the innovation process will help LEGO to build personal relationships with these people and have dialog with its fans. As loyalty is fostered and the community expands, the creative opportunities in innovation will be endless.

On November 14, 2018, Jane Harvard commented on Anticipatory shipping—retail’s crystal ball? :

This is a very interesting application of machine learning. In retail, with the multitude of applications of data-driven technology, companies need to carefully consider how each will impact the business. For instance, if machine learning is being used to forecast inventory, the benefit is seen in cost reduction, but is not consumer-facing. If machine learning is used to refine individual customer product recommendations, for example, the customer gains from a better experience, and the company gains from increased conversion.

In this case, anticipatory shipping may significantly reduce shipping costs for Amazon, but perhaps more importantly, will improve the customer experience through convenience and “delight.[1]” Everyone loves to receive packages, and the delight factor typically increases if the customer is not anticipating its receipt. It will be interesting to monitor the customer response to this initiative, if it is properly executed.

[1] “Why Amazon’s Anticipatory Shipping Is Pure Genius.” Forbes, Forbes Magazine, 28 Jan. 2014, http://www.forbes.com/sites/onmarketing/2014/01/28/why-amazons-anticipatory-shipping-is-pure-genius/#78f75a2a4605.