A good watch, but longer…
This WSJ documentary offers a GREAT explanation on the supply chain. https://lnkd.in/giuVkH-c. Or, if you are really bored (from me):
Recent observations & career advice (videos/podcasts):
https://lnkd.in/gJK8xBZe
https://lnkd.in/g-CanjDx
https://lnkd.in/gvPiGTt5
What is SCM?…
https://lnkd.in/g2BC9YHn
https://lnkd.in/gfchx-9d
https://lnkd.in/gn-97v_B
Sourcing Strategy:
https://lnkd.in/g2YuH4Ty
https://lnkd.in/g5Gppj_5
https://lnkd.in/gtKnNdkz
https://lnkd.in/gD5sY6AH
https://lnkd.in/gxNpCsim
Resume Advice:
https://lnkd.in/eWen96qj
https://lnkd.in/e7utipBi
https://lnkd.in/eVEHgvk6
Grad school advice:
https://lnkd.in/g4tvSi9g
https://lnkd.in/gM4GSR45
GDP:
https://lnkd.in/g6A6tkv9
https://lnkd.in/gngM96n9
Also, trendy things we are doing in our supply chain program to prepare our students for these issues…1. Getting hit w/ a price increase? We will be looking at raw material market data from multiple sources, we will visualize & analyze historical pricing scenarios, & do some simulating on planned purchases & what-if scenarios against forward price curves. Managers love this cloud platform.
https://lnkd.in/gMuhMNf6
https://lnkd.in/gQZ7HfWb
Business Data Analytics:
*Advanced Excel (power query & power pivot) & macros
*Data visualization (Tableau, Power BI and python with seaborn & matplotlib)
*Data mining, machine learning & data science
*Python & Jupyter notebook (data analytics & statistical libraries such as pandas, numpy)
*Relational data models (Excel data model)
*Graphic & statistical libraries (Seaborn, Matplotlib, Pandas,& Plotly)