Why won’t the supply chain just adopt AI & data analytics software? AI helps manage the supply chain. So what’s the delay? Great, great read. They keep quoting this Curkovic guy.


OK, I am no sage, but notice the common themes with sages such as Mr. Jim Tomkins:

“There has also been a lack of urgency in embracing digital supply networks, which is the only way to deal with perpetual disruption. End-to-end digital supply networks use artificial intelligence, machine learning and data analytics to bring visibility and actionability to every enterprise in a supply chain from raw material to finished product. This offers true optionality.”

“The supply chain task at hand is not an enterprise problem; it is an end-to-end network problem involving multiple enterprises. The solution does not lie with fixing one link in the chain but in devising an ecosystem where all the links of the chain work together with a real-time single version of the truth, using artificial intelligence and machine learning capabilities to beget an autonomous digital supply network.”
I wish more high rollers were doing this (& then made it cheap & easy to use for all the mom & pops):
Bosch & AWS enter into collaboration to digitalize logistics
I recently attended the Tableau Conference, where I indulged my nerdiness for four days. As a self-described data science evangelist, I was thrilled to see autoML, natural language generation, and other advanced automation features be added to Tableau, one of the world’s leading data visualization and business intelligence platforms.

While there, I met people who have also bought into the data revolution and are seeing immediate impacts on their businesses’ productivity and performance. As a tech worker, exposure to data science and analytics is an everyday part of the job, but it is certainly not in the industry I research: the supply chain. So I continue to ask myself, “Why won’t the supply chain just adopt artificial intelligence and data analytics software?”

In this article, I will break down the cultural and logistical barriers the supply chain faces in meeting the data analysis demands the pandemic brought on. I’ll also talk about steps that are being taken to address this and how G2 helps businesses create a modernized supply chain.


Third-Party Logistics Providers Need Data Analytics to Save Money. Solution? Hire WMU Supply Chain majors that minor in Business Data Analytics:
1. Advanced Excel (power query & pivot) & macros;
2. Data visualization (Tableau, Power BI & python w/ seaborn & matplotlib);
3. Data mining/RapidMiner, machine learning & data science;
4. Python & Jupyter notebook (data analytics & statistical libraries such as pandas, numpy);
5. Relational data models (Excel data model); and,
6. Graphic & statistical libraries (Seaborn, Matplotlib, Pandas, & Plotly).

3PLs and 4PLS: The WMU ISM supply chain career fairs have the largest presence of 3PLs & 4PLs that I have ever see. This is great news!!! This is a $1 Trillion industry and job market!

Supply Chain students & managers, 3PL vs 4PL-

Dr. Sime (Sheema) Curkovic, Ph.D., Professor, Operations/Supply Chain

Lee Honors College Faculty Fellow

Associate Director, Supply Chain Center

Western Michigan University

Haworth College of Business

Schneider 3246, Kalamazoo, MI 49008–5429
269.267.3093 | sime.curkovic@wmich.edu
The Western Way: “Better, faster, cheaper” www.wmich.edu/supplychain

WMU: #1 in MI for earnings by graduates
Sample Lectures: What is SCM?


WMU…One of nation’s best undergrad SCM programs (Gartner); 2nd in technology (SoftwareAdvice); 2nd in top global talent (SCM World)

Join: https://www.simecurkovic.com/

Vitae: https://wmich.edu/supplychain/directory/curkovic

Connect: https://www.linkedin.com/in/sime-curkovic-61617a115/

Follow me at: https://medium.com/@sime.curkovic

#supplychain #supplychainmanagement #dataanalytics #saas #datavisualization #tableau #powerbi #python #datascience #bigdata #digitization #industry4 #logistics

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