Machine learning can not only improve production process, but can also assist production and inventory planning. Building up such capability requires gathering data from the demand side. For example, what is demand in each plant correlated to? Any other factors (such as age of local buildings, population growth) that could affect order quantity? Machine learning could use these data to predict the demand for the next quarter and hence improve cash flow generation of the business.
Very interested topic. Machine learning relies on data. Salesforce is in a good position to gather business data since their products are embedded in many businesses across different industries. However, the concern is whether Salesforce’s customers are open to share their data which most likely will contain sensitive information. I guess one way to circumvent the problem is to gather data by industry verticals (but not individual firms) and share those data as industry market intelligence with their customers. This initiative will also help Salesforce’s attract more customers since they will be interested in knowing the real time trend in their industry too.
Vehicles require high structural strength to reduce harm during accidents. 3D printed structures or components may still lack such physical endurance. As a result, production could focus on the components that do not need to operate under stress such as plastic trims.
Another database could be created with all the cases of the bugs that the hackers have helped identified. This database could be sold to major corporations under a subscription model which could generate additional revenue for HackerOne to support their other cyber-security initiatives (or to reduce the 20% fees)
In addition to prototyping, 3D printing can facilitate mass production if the production process can be effectively modularized. Components of different specifications can be produced using 3D printers quickly and then are assembled into finished products. The more granular Nike can divide their products into highly standardized modules, the higher the efficiency 3D printers can run.
Eli Lilly could take the initiatives to invite other pharmaceutical companies to join their OIDD platform to encourage open knowledge. Thomson Reuters has a separate medical/pharmaceutical patent database that is highly sophisticated but charges high fees to subscribe (this patent database business was carved out and acquired by Baring Private Equity Asia). The enlarged OIDD could become an alternative knowledge sharing platform to Thomson Reuters for both smaller scale innovators and academic institutions.