The previous articles mostly examined how individual workers executed Envision scripts. However, both for resilience and for performance, Envision is actually executed across a cluster of machines.
During execution, thunks read input data and write output data, often in large quantities. How to preserve this data from the moment it is created and until it is used (part of the answer is on NVMe drives spread over several machines), and how to minimize the amount of data that goes through channels slower than RAM (network and persistent storage).
Like most other parallel execution systems, Envision produces a directed acyclic graph (DAG) where each node represents an operation that needs to be performed, and each edge represents a data dependency where the downstream node needs the output of the upstream node in order to run.
A Supply Chain Optimization pipeline covers a wide range of data processing needs':' data ingestion and augmentation, feature extraction, probabilistic forecasting, producing optimal decisions under constraints, data exports, analytics, and dashboard creation.
When all you have is a hammer, everything looks like a nail. The hammer long favored by the supply chain community has been time-series and, as a result, in supply chain circles all problems look like time-series forecasts. The hammering temptation is compounded by the extensive literature that exists on time-series forecasting beyond supply chain use cases.
Incrementalism in supply chain includes improving the forecasting accuracy, improving the service level, reducing the stock level, reducing the lead time. Despite its prevalence among large companies, this approach rarely yields any tangible benefit.
In terms of predictive optimization, most supply chains are stuck in the early 1990s. As we started to address the root cause of predictive optimization failures, Supply Chain as a Service emerged as our business model.
The emergence of a terminology is, at best, a haphazard process. Supply chain is no exception and, in hindsight, a sizeable portion of supply chain terminology is inadequate. Confusing terminology hurts newcomers and seasoned practitioners alike. Newcomers struggle more than they should with accidental complexity. Practitioners may not realize that the premise of their field ismore shaky than it appears.
Lokad employs a team of around 30 supply chain scientists. The scientist is responsible for the data pipeline, the forecasts, the economic modelling and the end-game decisions. But how many SKUs do they manage?
18 months ago, LokadTV had reached 1000 subscribers. Since that time, our audience has now tripled reaching 3000 subscribers. We remain committed to bringing topics of genuine interest to the supply chain community.
Most web apps feature web APIs styled as REST, yet Lokad features FTPS and SFTP, which may appear surprising. However, this choice is intentional, why did Lokad choose to go this route?
My first professional supply chain experience happened back in 2004. At the time, I was a computer science student at the Ecole Normale Supérieure (ENS), a university in Paris. My interests covered a wide range of wholly theoretical subjects, yet, I was also intrigued by the idea of testing out those theories “in the wild”.
