The broad field of machine learning (ML) provides a wide array of techniques and methods that cover numerous situations. Supply chain, however, comes with its own specific set of data challenges, and sometimes aspects that might be deemed “basic” by supply chain practitioners do not benefit from satisfying ML instruments – at least according to our standards.
Supply chain data present themselves almost exclusively as relational data such as orders, clients, suppliers, products, etc. Those data are collected through the business systems - the ERP, the CRM, the WMS - that are used to operate the company.
The stochastic gradient descent (SGD) is one of the most successful techniques ever devised for both machine learning and mathematical optimization. Lokad has been extensively exploiting the SGD for years for supply chain purposes, mostly through differentiable programming. Most of our clients have a least one SGD somewhere in their data pipeline.
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.
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?
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?
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.
