Thank you for this article! It is great news that 3D printing is used to manufacture replacement parts that are not being produced anymore due to high setup times and fixed costs. I am also very much intrigued by the application of 3D printing to build novel parts with hollow parts to improve performance. This could result in significant performance and efficiency enhancements and serve as a strong competitive advantage for Porsche in the high-end high-performance car market.
In the case of Porsche, I disagree with the idea that Porsche should share their .cad files with individuals or garages. In addition to the quality trust point raised by commenter EL, vintage car appraisers and collectors are adamant that only original parts are used. As a premium brand, Porsches will only retain their value if all parts are original and replaced by an official dealer. That being said, I do think your idea has a lot of potential in the mass car market.
I think there is a high likelihood that over the medium- to long-term, 3D printing will enable great customization and make production a more local endeavor. However, again I see the most potential in the mass car market, rather than in the premium luxury market, as customers typically place a lot of value on the manufacturing location. For example, a Porsche in Germany is also referred to a “car from Zuffenhausen”.
Thank you for sharing this interesting application of 3D printing. Utilizing the Cocojet chocolate printer as a B2C marketing tool to drive awareness and customer loyalty in the challenged chocolate market makes total sense to me. However, it is disappointing that actual 3D chocolate printing at this stage is not feasible and that THC will revert to using it predominantly for 2D designs.
Given the unique properties of chocolate that limit the 3D printer application in B2C, it is not immediately clear to me how you expect THC to overcome the same issues in the B2B market, unless there are meaningful advances in 3D chocolate printing. A B2B application will require significant production scale, even for the specialty chocolates use case. For example, HBS alone produces ~5,000 cookies (arguably not chocolate – but similar) per day for consumption on the HBS campus. At half an hour per chocolate, that would equate to 2,500 hours using one machine.
Due to the low production capacity and limited use cases of 3D chocolate printing outside of B2C marketing, it appears too early for THC to invest in a new distribution channel. I believe that the company should continue to monitor process improvements in the technology and re-evaluate their distribution strategy when 3D chocolate printing becomes more viable.
With the rise of digital entertainment, it makes total sense to me that Lego is one of the first firms to turn to content co-creation to drive customer awareness and engagement. In my view physical entertainment has a future as I expect customers to pivot back to toys like Lego once the drawbacks of the current overexposure to digital entertainment become more prevalent.
With regards to the sustainability of Lego’s innovation platform, I would bifurcate between the innovation pipeline and customer engagement. The funnel appears very big and only few ideas will make it through all the approval phases. As a consequence, I believe that consumers will initially engage with the platform and then lose interest over time. Further, the strong economics will attract professional or hobby designers who will not necessarily be brand advocators or drive revenues. As a consequence, I believe that the innovation pipeline is sustainable but the customer engagement is not.
Thank you for this article and pointing out that product innovation in the CPG space really is all about non-product specific innovation (e.g. re-packaging of products or changing the consumption experience). This really opened my eyes!
The Easy Star Bottle Opener and temperature-sensing inks are interesting innovations but more evolutionary than revolutionary and thus are likely going to remain niche and will not significantly impact Heineken’s beer sales. However, the combination of IoT bottles and machine learning to enhance just-in-time delivery and reap supply chain efficiencies appears very promising. I am looking forward to hear more about this.
I disagree with your recommendation that Heineken should crowdsource the selection to collaborators as I believe that the “dreamers” shouldn’t act as “critics”. Innovation selection is driven by what product changes are actually economically viable and in line with the product and marketing strategy for the product. Consumers are unlikely to be able to assess this appropriately. Consequently, I am concerned that Heineken would commit to innovation that would damage the beverage’s positioning or product experience.
With regards to your second question, I would not recommend them using the Innovators Brewhouse platform for their 300 smaller brands. The innovation resulting from those smaller beers will be very similar to the Heineken brand bottle innovation because it is non-product specific as you have mentioned earlier (Easy Star Bottle Opener and temperature-sensing inks). Instead, I would recommend driving further awareness of the Heineken Innovators Brewhouse platform as a marketing tool to enhance Heineken beer sales. Any strong innovation coming out of this could then be adopted across its 300 beer brands.
Thank you for sharing this piece on an interesting application of machine-learning! Your approach to profiling users into “how” they make purchases rather than “what” they purchase resonates a lot with me as particularly millennials are thought to place significant value on experiences. As a consequence, the shopping experience is gaining more and more importance relative to the materialistic value of the purchase.
That being said, I believe that there is still some value in Pinterest’s original segmentation into interests, frequency of action and sequence of actions, particularly as these “hard” segmentation parameters can be tracked reliably while assessing “price-consciousness” and “brand awareness” is much more challenging. I would be curious what segmentation accuracy level you believe machine learning could achieve and in what time span. With that in mind, rather than just inferring the softer from the harder segmentation, what would you think about a matrix segmentation that combines both segmentation approaches but retains their respective information value?
The threat posed by a price comparison tool will depend on the quality of the user segmentation, which is why I believe a matrix segmentation will be critical. If the segmentation quality is high, retailers will be able to employ near perfect discrimination and receive significant benefits from Pinterest’s value add. This will in turn drive revenue per user and hence valuation ahead of the company’s IPO.
Thank you for sharing an interesting story of how a major logistics player is already leveraging machine learning to forecast and identify transportation delays and capacity constraints before they even occur. The application of machine learning to customs clearance sounds fascinating, particularly as border protection processes are ripe for innovation.
I believe that supply chain management and logistics are two fields where major value can be created from machine learning and where the current applications are only the tip of the iceberg.
With regards to your question, I would recommend DHL apply their machine learning algorithms to forecast and then optimize their mix of ocean freight, air freight and land transportation to achieve cost savings. Moreover, the insights derived from machine learning should improve their strategic supply chain decision-making – for example, where they should set up new distributions centers.
On your last question, I don’t believe DHL needs to or should share the sources of their information with customers or suppliers. That is, unless there is a “need to know” basis in case of e.g. security-related questions.