Supply Chain Science
Back to the blog ›
Fall in love with the problem, not the solution
The most common strategy (tragedy) for software solutions remains replicating behavior that's essentially "human".
Humans in modern supply chains
"Machine should work; People should think." vs "Built for people not perfection". Two different visions.
A numerical take on DDMRP
How much novelty does "Demand Driven Material Requirements Planning" really bring to the table for supply chains?
Lean scalable processing for supply chains
Are large scale calculations through cloud computing worth the cost?
The limited applicability of backtesting
Backtesting is simple and elegant, but is it really a silver bullet?
Improving a forecasting technology
Learn more about how Lokad continues to deliver superior forms of supply chain optimization
Book: The Quantitative Supply Chain
Discover why your supply chain deserves what machine learning and big data have to offer
Entropy analysis for supply chain IT system discovery
The problem is not data-processing capabilities. The real challenge is to make sense of all your fields.
The Supply Chain Scientist
Discover the role of the Supply Chain Scientist, the keystone of a Quantitative Supply Chain initiative.
The test of supply chain performance
Test your supply chain performance through 12 questions. Where do you stand?
2017, year of quantitative supply chain
Read our new supply chain manifesto and learn how this approach can transform your supply chain.
Preparing enterprise data takes 6 months
Learn the importance of data preparation and the typical length of a proper set-up phase.