In many, probably most, large companies operating a supply chain, the IT department has years of backlog. The backlog is made of a myriad of glitches, inconsistencies, or fragilities that could be fixed but aren’t. Besides creating vast bureaucratic overheads, this endless series of pointless annoyances demotivates everyone.
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.
Almost exactly 18 months ago, I wrote a post to celebrate passing 3,000 subscribers on LokadTV. That post, in fact, was written almost 18 months after we celebrated passing 1,000 subscribers. Curiously enough, we passed 1000 subscribers about 18 months after we began actively promoting the channel.
The state of supply chain communication is dismal. By way of anecdotal evidence, for the last couple of months, my daily morning routine consists of attempting to find one post – just one – that qualifies as _worthy of interest_ to an audience of supply chain minded people – in order to have it shared on news.lokad.com. This seemingly simple exercise proves exceedingly difficult. Most days, I do not succeed.
The last couple of years haven’t been kind to supply chains. In fact, the only thing that hasn’t been in short supply appears to be forces of disruption':' lockdowns, wars, inflation, etc. As a result, a newfound interest in ‘resilient’ supply chains has emerged.
Aeronautics companies are fundamentally risk adverse – for very good reasons – and this reflects in the management of the companies’ activity in every area. However, this mentality can be a hindrance when it comes to supply chain, as avoiding risk and not integrating the very concept of risk at the heart of the supply chain means losing sight of what you are optimizing.
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 ambition of news.lokad.com is to create a vibrant online community. No terms to accept, no forced login, no ads, no monetization plan, just a plain discussion text-based board, with a voting system to let the most interesting contributions surface.
Transcript of the talk given by Joannes Vermorel at the Ecole polytechnique in Palaiseau (France) on Friday, June 3rd for the symposium 'Artificial Intelligence, Digital and Climate Change'.
The price tag of a piece of software ranges from nothing, as it happens with open source, to quite a lot - enterprise software leaning heavily towards the latter. However, operating a piece of software always involves some degree of overhead. The notion of TCO (Total Cost of Ownership) precisely tries to capture the cost in full, taking into account both direct and indirect costs.
Over the years, it has become increasingly frustrating to witness that most companies seeking to improve their supply chain performance are setting themselves up for failure through their own RFP (request for proposals) and RFQ (request for quotes) processes.
