Blackboxing and Whiteboxing

00:08 Introduction
00:30 What is “Blackboxing” about?
03:20 How widespread is this problem? Is this something we frequently observe in the industry?
05:11 Why is this white box approach occurring?
06:04 Do you have a real life example of how these black box issues have affected companies in the real world?
09:01 Why the “Whiteboxing” approach is so different?
11:26 How does it actually work in practice?
14:20 What is it about economic drivers that are so useful?
15:18 What about the Lokad approach that makes “Whiteboxing” so possible?
17:36 Is there anything I can do to improve my understanding of what is going on?
21:07 Why is “Whiteboxing” so important?


Any nontrivial demand forecasting model becomes a black box for supply chain practitioners, that is, an opaque subsystem that produces numbers that are difficult to understand and to challenge. Whiteboxing, as part of the Supply Chain Management practice, is the answer to this problem. Practitioners don’t need to understand the how but need to understand the why.

In computer science, a black box refers to a system or an object that can be viewed both in terms of its inputs and outputs without any knowledge of its internal workings, which can evidently be highly problematic.

This phenomenon is something that is being observed more and more frequently, particularly with the use of non-trivial numerical recipes and the rapid growth in popularity of Artificial Intelligence technology.

In this episode, we learn how blackboxing can come about in supply chains and why to a certain extent it is exhibited in every single ERP with the computation of safety stock.

Often, this can be harmful for companies as decisions are made without a full and sufficiently broad comprehension of what is happening within. Supply Chains are already such complex systems, involving so many products and people, that they naturally have their own opacities. Therefore, adding even more layers of opacity obviously can’t bring any benefits.

Frequently, companies respond to blackboxing issues by requesting more information, KPIs for example, which just adds more complexity, more opacity and so the vicious circle continues.

So just what is the solution? Here, we investigate the antithesis, something that is known as ‘Whiteboxing’. We understand how by building an environment that supports checking, whiteboxing can provide a way of verifying that the results you are producing are sane. We discuss how this works in practice and how at Lokad we use Envision, our in-house created programming language, to defeat opacity, therefore ensuring that our clients can easily see that all operations are on show.