Getting started with a quantitative supply chain initiative - Lecture 7.1

00:00 Introduction
02:31 Failure root causes in the wild
07:20 Deliverable: a numerical recipe 12
09:31 Deliverable: a numerical recipe 22
13:01 The story so far
14:57 Getting things done today
15:59 Timeline of the initiative
21:48 Scope: applicative landscape 12
24:24 Scope: applicative landscape 22
27:12 Scope: system effects 12
29:21 Scope: system effects 22
32:12 Roles: 12
37:31 Roles: 22
41:50 Data pipeline - How
44:13 A word on transactional systems
49:13 A word on data lake
52:59 A word on analytical systems
57:56 Data health: low-level
01:02:23 Data health: high-level
01:06:24 Data inspectors
01:08:53 Conclusion
01:10:32 7.1 Crafting a vision, a scope, and a data pipeline - Questions ?


Conducting a successful predictive optimization of a supply chain is a mix of soft and hard problems Unfortunately, it is not possible to take those aspects apart. The soft and hard facets are deeply entangled. Usually, this entanglement collides frontally with the division of work as defined by the organigram of the company. We observe that, when supply chain initiatives fail, the root causes of the failure are usually mistakes made at the earliest stages of the project. Furthermore, early mistakes tend to shape the entire initiative, making them near impossible to fix ex post. We present our key findings to avoid those mistakes.