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Jan 22, 2024

Sales Analysis through Envision - Workshop #2

This second Envision Workshop offers students and supply chain specialists guided training on analyzing retail customers from Lokad's probabilistic, risk management perspective.

Jan 8, 2024

Selective Path Automatic Differentiation: Beyond Uniform Distribution on Backpropagation Dropout

The Selective Path Automatic Differentiation (SPAD) approach enhances Stochastic Gradient Descent (SGD) by adopting a sub-data-point perspective. This technique, implemented at the compiler level, trades gradient quality for gradient quantity, supplementing traditional SGD methods with a more nuanced view.

Dec 19, 2023

An opinionated review of Deep Inventory Management

A team at Amazon has published Deep Inventory Management (DIM) late 2022. This paper presents an DIM inventory optimization technique that features both reinforcement learning and deep learning. As Lokad went through similar path in the past, its CEO and founder Joannes Vermorel provides his critical assessment of the suggested technique.

Nov 20, 2023

Differentiable programming to optimize over large scale relational data

Paul Peseux's PhD research on differentiating relational queries - another under-researched area of supply chain - introduced TOTAL JOIN operator, Polystar and a mini-language ADSL to differentiate relational queries, all of which Lokad integrated into its DSL Envision as part of autodiff for optimizing daily inventory decision-making.

Aug 21, 2023

Supplier Analysis through Envision - Workshop #1

Lokad launches its first Envision Workshop, teaching students (and supply chain specialists) how to analyze retail suppliers using Lokad's probabilistic, risk management perspective.

Jun 26, 2023

Inventory management under the constraint of multi-reference minimal order quantities

Gaetan Delétoille's PhD research on MOQs - a surprisingly under-researched area of supply chain - introduced the w-policy, something Lokad integrated into its solution for daily inventory decision-making.

Jun 19, 2023

Classification algorithms distributed on the cloud

Matthieu Durut, second employee at Lokad, defended his PhD back in 2012 for his research work done at Lokad. This PhD paved the way for the transition of Lokad toward cloud-native distributed computing architectures, nowadays critical to deal with large-scale supply chains.

Jun 12, 2023

Large scale learning: a contribution to distributed asynchronous clustering algorithms

Benoit Patra, first employee at Lokad, defended his PhD back in 2012 for his research done at Lokad. This PhD brought radically novel elements to the supply chain theory, and set the stage for the future development of Lokad's probabilistic forecasting approach.

Feb 6, 2023

Stochastic gradient descent with gradient estimator for categorical features

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.

Sep 21, 2022

Differentiating Relational Queries

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.

Sep 6, 2022

Reproducible Parallel Stochastic Gradient Descent

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.

Dec 6, 2021

Envision VM (part 4), Distributed Execution

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.