Blockchain for Supply Chain Requires These 5 Lessons
90% of all supply chain blockchain pilots will be proof of concept trials through 2020. Few recent digital trends receive more attention than blockchain. But according to Gartner principal research analyst Alex Pradhan, supply chain leaders should not bow to hype and pursue the technology for its own sake.
Industry 4.0 and its profound effect on supply contracts
Disruptive technology is having a profound effect on the diversified industrial supply chain – and this is only the beginning. It’s predicted that we will see more disruption in the industrial space in the next five years than we have in the last 20*, making it harder for the bigger players to quickly adapt their business models – leaving once unchartered territory open for SMEs.
Digitization makes the Supply Chain agile and customer-related
Manufacturers around the world are preparing for the new industrial revolution, Industry 4.0, however, is it just the manufacturers who need to prepare for Industry 4.0? How does it impact the supply chains of industries globally? Industry 4.0 can be viewed as the convergence towards digitisation of global supply chains, which includes in its umbrella the entire business process of all industry segments. Its impact depends on the maturity and level of automation in the specific industry segment, but it will be omnipresent in all industries and applicable throughout the supply chain.
How Chief Data Officers Can Get Their Companies to Collect Clean Data
In analytics, nothing matters more than data quality. The practical way to control data quality is to do it at the point where the data is created. Cleaning up data downstream is expensive and not scalable, because data is a byproduct of business processes and operations like marketing, sales, plant operations, and so on. But controlling data quality at the point of creation requires a change in the behaviors of those creating the data and the IT tools they use.
Data preparation is often considered a necessary precursor to the “real” work found in visualizing or analyzing data, but this framing sells data prep short. The ways in which we cleanse and shape data for downstream use have significant bearing on our final analytic output, and cutting corners on data prep can run up a huge cost for companies.