Supply Chain Analytics (and a little Data Engineering):  Coursera Edition

Bottom Line Up Front (BLUF):  

If you are interested in learning some basics of Data Engineering, auditing a class in Coursera may be a reasonable place to start your learning.  However, when it comes to Supply Chain Analytics, you (might) get what you pay for. 

Where and When:

Over the the week of Thanksgiving 2023, I audited seven Coursera courses

Supply Chain Analytics Specialization 

  • Supply Chain Analytics Essentials
  • Business Intelligence and Competitive Analysis
  • Demand Analytics
  • Inventory Analytics
  • Supply Chain Analytics
  • Sourcing Analytics

Data Engineering

  •  ETL and Data Pipelines with Shell, Airflow and Kafka

Who Might be Interested in the Courses:

Supply Chain Analytics Specialization 

  • Discussion about “Resilience” has been increasing in popularity within post-COVID industry. While “resilience” may often seem like just a buzzword, it is a complex topic; and there are different ways to quantify and study the resiliency of a business, healthcare system, or other complex organization.   A primary focus of resilience discussions is the supply chain. The ability to track shipments, predict demand, and measure risk are all essential to supply chain analysis.  But where do you begin if you need the basics and love to code? Coursera training might offer a more hands-on experience than a literature review, but a focus on supply chain analytics ensures you are applying the theory as you go.  

ETL and Data Pipelines 

  • Interested in a refresher on data pipeline basics? Do you want to learn more about usage of directed graphs in data engineering? Just want to try out some Apache products?  The Coursera “ETL and Data Pipelines with Shell, Airflow and Kafka” course would be suitable.

Who Created / Taught the Courses:

Supply Chain Analytics Specialization

  • The course was created by IBM and taught by 4 instructors from both IBM and Skill-Up Technologies.

ETL and Data Pipelines 

  • The courses were created and taught by a professor at Rutgers University.  While never explicitly mentioned, the primary analytic platform used in the course, SCDATA, is likely a project or collaboration of the instructor.  

What I Found Useful:

Supply Chain Analytics Specialization 

  • Supply Chain Analytics Essentials – Course did a great job covering the various key terms and stages in a supply chain.  Notably, there was a discussion of what can go wrong at each stage.
  • Business Intelligence and Competitive Analysis – Good summary of supply chain KPIs.  Introduced the SCDATA tool, which allows users to create different visualizations from the GUI. The platform provides data from 33,000+ enterprises publicly traded on the world’s twenty one largest stock exchanges across five continents. 
  • Demand Analytics – Provided a basic, but useful method of studying product demand with linear regression.
  • Inventory Analytics – Introduced ABC analysis, which I did not have much experience with prior to the course.  Interesting way to break down products by boh utility and profit
  • Supply Chain Analytics – Provided a real world case study of “push” and “pull” business models.  It was interesting to see how product type impacts whether it should be sold primarily online or in stores.  
  • Sourcing Analytics – Focused primarily on calculating leverage between buyers and suppliers.

ETL and Data Pipelines

  • The first module provided a solid foundational knowledge of  Extract, Transform, and Load ( ETL) processes.  The next three modules each introduced a relevant technology (in this case, pipeline routines with Bash scripts, pipelines with Apache Airflow, and streaming pipelines with Apache Kafka).
  • At the end of each module was a lab that walked me through each technology and then provided an independent practice exercise. The last module was a final project that combined knowledge from the labs.  All of the assignments were easy to follow, great for applying learning, and hosted in an easy to use environment.
  • Even though I only audited the course, I could still access all of the labs and other materials.


Supply Chain Analytics Specialization

  • I could have done with a little more technical complexity, or a better merging of data science tools and techniques with supply chain basics.  The only tools used in the class were Excel and SCDATA, which is more of an advanced widget than a flexible GUI.  I had hoped the last couple classes would cover available software packages for a popular programming language, such as Python or R.  At the very least, I would have been interested to do some more complex problems in Excel.

  • The introductory class brought up many ideas about supply chain analytics… but then never brought them up again in following lectures. A lot of time in each course was spent repeating an “introduction to SCDATA”,” which may have been needed for taking one-off courses but seemed like a waste of time for those doing the specialization.

ETL and Data Pipelines

  • The quiz questions were a little confusing to follow, even if you were paying close attention to the course.

  • More detailed documentation or a resource list for each of the technologies discussed would have been helpful

Overall Thoughts:

I took these courses to gain more experience with data engineering tradecraft and do a deep-dive into supply chain analytics.  Both topics are relevant to my current work, but approach things from a slightly different point of view (or in the case of data engineering, a different tool set).  Even if the Supply Chain Analytics Specialization was more an advertisement of SCDATA than a full course, I still took away some ideas of how to reframe problems in my current work.

Some Cool Stuff  I Found Along the Way