Captain Obvious has been working overtime in supply chain. His natural leadership has inspired many who are now following the same path. Yet, underneath the uniform, there is little to be found but a great deal of confusion.
Many domains are complex, irredemiably so, and supply chain is certainly one of them. So what could an itemized list of tricks help you to achieve?
A single smartphone could technically run a predictive inventory allocation for a very large retail network, yet why do so many companies have such slow systems in place?
I would like to share some insights into my motivations, as a CEO, for undertaking such a series of supply chain lectures. It boils down to the four key challenges faced by Lokad.
Lokad’s supply chain practice is to refresh all the data pipelines - forecasts included - at least once a day. Why is that?
Wading through the miasma of supply chain technologies remains a challenge. What can help to guarantee success?
Lokad have come at the 6th position in the M5 Forecasting competition out of 909 competing teams. It’s an impressive feat.
Module-by-module comparison between classic APS (Advance Planning and Scheduling) systems and the quantitative supply chain as implemented by Lokad.
Envision, the domain-specific language (DSL) of Lokad, was engineered to address challenges where Python will never deliver cost-effective solutions.
When we started our Youtube channel just over a year and a half ago, we never imagined we'd reach this milestone.
What are the importance of feedback loops and how do we use them in our forecasts
What solutions can Quantitative Supply Chain provide for large manufacturing companies?
