Paradoxically, data is the most under-valued and de-glamorised aspect of AI.

Lack of focus on the data plumbing spells the doom of most supply chain initiatives. While this article wasn't written with supply chain use-cases in mind, it's clearly relevant to the supply chain field. Data plumbing being "glamorous" means that it's difficult to gather resources for stuff that don't really have any kind of visible payback. Yet, data engineering is a highly capitalistic undertaking.

Ha ha. It's the pending $1 billion question that haven't cracked yet at Lokad.

Unfortunately, this article gives away very little about what Google may or may not do for supply chains. This article is written like a marketing piece. Nowadays, pretty much every single software company is using at least two cloud computing platforms to some extent. Thus, it's should be a surprise if a Software Company X that happens to do produce supply chain software runs on Cloud Platform Y. Yet, it this doesn't say why this cloud platform is better suited than its competitors.

The article puts forward Vertex AI, a recent ML offering of Google. However, as per the documentation of Google, it's state Pre-trained APIs for vision, video, natural language, and more which gives strong-vibes of being absolutely not the right quite of ML for supply chain. Furthermore, the AutoML (featured by Vertex AI) is also the sort of technology I would strongly recommend not using for supply chain purposes. It adds massive friction (compute costs, development times) for no clear upside in production.

Also, https://killedbygoogle.com/

Even with the Amazon.com behemoth dominating the e-commerce landscape, there’s room for smaller, scrappier rivals, Giovannelli says. In fact, many independent service providers are under contract to Amazon and other e-tailers to fill out the growing need for delivery vehicles.

In short, Amazon supply chain is a massive success, and just like the iPhone of Apple, it proves that there is a vast market waiting for alternatives. When considering large markets, even dominant players like Apple struggle to reach 25% market share. This is gives a lot of room for other actors. This is what those private equity investors are looking for.

Within the Lokad TV channel, this episode about DDMRP is probably the one, so far, that generated the most polarized feedback. On one hand, we got a lot of support. On the other hand, we got heavy criticized by the DDMRP evangelists. The most frustrating part of me is that the critics have been, so far, have only been shallow dismissals, or authority arguments.

A longer written discussion was produced at:
https://www.lokad.com/demand-driven-material-requirements-planning-ddmrp

Year's later, DDMRP community has still not addressed my top three objections:

• Incorrect baseline: half-backed MRPs can't be a baseline for DDMRP.
• Limited formalism: BOMs are literally applied graph theory, which DDRMP ignores.
• Real-world complexity dismissal: economics are absent from DDMRP.

It will be on September 14th at 15h00 (CET).

Watch the live at:
https://www.youtube.com/watch?v=3uqezVCMhSE

Most supply chain initiatives fail, and yet, the vast majority of supply chain case studies speak only of successes. Only the epic-scale failures become visible to outsiders. Even insiders frequently don't realize that most of the past initiatives of their own company have failed. Large companies aren't necessarily very capable when it comes to their institutional memory of past debacles.

The case of Target Canada was discussed in my supply chain lecture:
https://tv.lokad.com/journal/2021/2/3/supply-chain-personae/