The Evolution of Supply Chain Education (with Paul Jan) - Ep 150

[@gmplopes]

Thanks for another interesting conversation on teaching supply chain management (I loved the analogy of the cow represented by a sphere 🤣). As you know, Porto Polytechnic School of Engineering, in the Master's programme in Supply Chain Engineering and Management, as University of Toronto, is trying to implement the paradigm shift advocated by LOKAD in teaching supply chain management. So, it was very interesting to listen to Paul Jan experience.

[@Lokad]

Thanks, Manuel! We look forward to implementing a collaboration with Porto Polytechnic School of Engineering. For now, the first two (free) Envision workshops discussed in the interview are available here: docs.lokad.com/gallery/workshop-supplier-analysis/ docs.lokad.com/gallery/workshop-sales-analysis/

[@tamojitmaiti]

Johannes, you make a good point about the spherical cow assumption in core engineering not ending up in field calculations, but the same not being true in supply chain. Very astute observation. For someone who transitioned into “data science” from mechanical engineering and then operations research, in my limited experience, I can propose a reason as to why this is the case. In engineering, we rarely had managers who weren’t engineers themselves first. So, everyone sort of spoke the same language of math and physics and simulations and it was easier to have conversations regarding these. However in supply chain, what I’ve found is, there is, by default organisational structure, a split between the planning side (scientists versed with stats) and the implementation side (planning managers who rely on “experience”). And somehow, the implementation side calls all the shots, so more often than not, the technical solution that is chosen by the company depends on the level of technical expertise of the most technically challenged planning manager.

[@Lokad]

Astutely observed. Indeed. Cheers, Joannes

[@tobymillerFPA]

Which one would you or the guest recommend R or python? I prefer R as it is specifically built for statistics and its functional language is more approachable to someone with a math foundation than OOP of python. However most job listings require python for data science and such.

[@Lokad]

Python is more versatile option than R, and over the last decade, it has grown into a "classic" general purpose programming language, much like Java or C#. I would recommend Python, but more importantly, I would recommend learning about software and software engineering in general. Mastering the programming syntax is one of the easiest parts of software. Best regards, Joannes