Inventory Optimization Models and Simulations (with Nicolas Vandeput)

00:08 Introduction
00:28 Nicolas, what have you been up to since we last saw you on the show?
00:57 Joannes, today we are going to discuss Nicolas’ book ““Inventory Optimization: Models and Simulations””? What is so different about this book?
03:46 Who is this book intended for?
05:13 Programming has historically been reserved for IT departments. What is changing now?
08:25 Why is Python the language of choice for this book? What does it provide that other programming languages do not?
11:42 Can we do better than Python?
16:18 Nicolas, your book mentions a few sources of confusions in inventory optimization. What are the common confusions that we should be aware of in the industry?
18:33 Would you say this confusion is why people are so happy to pay so much for enterprise software?
22:31 Nicolas, your book clearly explains many inventory optimization models and their limitations. Would you say that some of these limitations can be overcome? How?


Historically, programming has often been seen as an intimidating task reserved for computer whizzes and IT practitioners. Nowadays, however, with the rise of tools such as Python, programming has become that much more accessible. For this episode of LokadTV, we’re joined, once more, by Nicolas Vandeput to discuss just how simple programming can actually be.

Since the last time Nicolas was on the show, he has published a new book: Inventory Optimization Models and Simulations (buy on Amazon). This publication puts forward what we believe to be a cornerstone of modern supply chain optimization, which is the need for programmatic expressiveness.

This programmatic expressiveness is the only way to be able to successfully deal with real world supply chains and the real world situations that accompany them. Instead of focusing on idealized supply chains, with mathematical frameworks that fall apart when confronted with the real word, Nicolas shows how simple and straightforward numerical recipes with Python can be used to address such real world situations.

Although programming has been around for quite a while, for a long time, companies were struggling to simply achieve an accurate digital counterpart of their supply chain, i.e. an ERP or WMS set-up to manage stock levels. This took some time, with the first ERPs being deployed at the end of the 70’s, so we have four decades under our belts nowadays and are able to start thinking differently.

Most modern supply chains are run by Excel, and Python could be seen as a natural continuation of this, permitting more programmatic expressiveness than what Excel allows. There are many other alternative programming languages, like JavaScript or Java, but Python has multiple advantages, such as an extensive library of resources, which means that it’s very difficult to get stuck. But maybe its biggest benefit is its simplicity.

However, although Python may be the most adapted programming language for supply chain, it definitely has its limitations.

To conclude, we talk more about common confusions within supply chains, such as the safety stock formula and why it’s so often incorrectly used, and what supply chain can do to attract more brilliant minds.