Sime Curkovic, Supply Chain Professor, Western Michigan University

Sime Curkovic is a Professor of Supply Chain Management at Western Michigan University. He received his undergraduate degree in Management Systems from GMI Engineering & Management Institute (now known as Kettering University). He received his Ph.D. degree from Michigan State University.  Dr. Curkovic has taught several courses in sourcing, operations, logistics, and multinational management.  His research interests include environmentally responsible manufacturing, total quality management, supply chain management, and integrated global strategic sourcing. Dr. Curkovic’s publications have appeared and/or are under review in the International Journal of Purchasing and Materials Management, the IEEE Transactions on Engineering Management, the Decision Sciences Journal, the Journal of Operations Management, International Journal of Production Research, and the Journal of Quality Management. Dr. Curkovic’s previous work experiences were with General Motors in the Midwest, Mexico, and Germany. Dr. Curkovic is a born U.S. citizen, and his name is of Croatian descent.


Our students are going into a job market inundated with outdated processes. Manual and labor-intensive operations will force them to spend hours every week doing repetitive tasks that could be automated for much greater efficiency and accuracy, allowing them to focus on more fulfilling work.  The majority of their time will be spent gathering data, while much less will be spent analyzing and providing insights to support strategic decision-making.

For example, during these inflationary times, it is time for business faculty to start teaching the lost art of Price Analysis and Strategic Cost Management (in very different ways). I have asked a lot of business managers how they “prepare” to negotiate price increase requests from their suppliers. In particular, I was curious about how and where they get their data from (i.e., Chicago Board of Trade (CBOT), Chicago Mercantile Exchange (CME), Commodity Exchange (COMEX), London Metals Exchange (LME), New York Mercantile Exchange (NYMEX), etc.).  Many said their suppliers provide that information. I am not convinced that using data from your suppliers is a form of “Preparation” for the negotiation process.

Surprised by price increases? As much as 70% of current contracts have price increase provisions! In the WMU supply chain management program we teach our students to track commodity forward price curves.  We now look at raw material market data from multiple sources, visualize and analyze historical pricing scenarios, and simulate planned purchases and what-if scenarios against forward price curves.

How do we better prepare our business students to be job ready day one? Procurement organizations need processes and “tools” to mitigate and negotiate on these price increase requests in a strategic, data-driven manner (and academics need to do a better job of teaching it).  Traditionally, we have worked very hard to help our students develop very sophisticated data analytics skill sets to manage these very large and complicated forms of information.

Employers place a premium on these business analytics skill sets and would include:

  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);
    6. Graphic & statistical libraries (Seaborn, Matplotlib, Pandas, & Plotly).

See our Business Analytics minor at:

However, I would recommend complimenting the above skill sets with bringing in some cloud-based software and technology that gets us beyond manually updating and coding giant colored Excel spreadsheets.  For example, I have been collaborating with N-Alpha and they have a cloud platform called materialx that I am bringing into the classroom ( It gets us away from manually updating spreadsheets.  Further, these technology software companies tend to be very supportive in helping faculty and students as these students will eventually become the future business professionals that actually use the technology (win-win-win, right?).  And the technology is out there!

We will soon have some white papers from our WMU SCM program based on the following price analysis & strategic cost management research.  Some of our alumni are already testing and / or implementing these cloud-based services that allows procurement organizations, finance, and all other organizations that are exposed to raw material pricing changes, to stay on top of market pricing from multiple sources and proactively assess its impact on future raw material purchases.

These technologies are also now serving as the basis for addressing price indexing implementations (formulas, alerts, etc.).  It can all be done automatically and updated daily, saving hours from manually updating spreadsheets. These tools are quick and easy to use to prepare for price negotiations with suppliers, and often returns its investment (which is minimal to begin with) rapidly. These technologies also allow organizations to replace multiple spreadsheets and email threads with one tool that tracks pricing, facilitates collaborative decisions, revisiting past decisions and what led to them, and capturing organizational knowledge.

I have asked many of my former students what are the most prevalent technologies used in your supply chain and business role.  The two most common answers are Excel spreadsheets and email.  It is 2023 now and we need to move further along.  You could make a strong case that using antiquated business tools was a major source of supply chain disruptions the last few years.

My former students keep telling me they are the “Excel Spreadsheet” generation.  Excel works and they have very advanced spreadsheet skills.  They also tell me that it gives them a competitive advantage in the workplace (i.e., people depend on their monthly report outs per se and very important people read them).  The older managers have very weak Excel skills and even the younger graduates coming in have to play catch up to the people that are in their 30s and 40s (that have very advanced Excel skills).  My former students also feel very comfortable with Excel.  Many of these spreadsheets are their own creation and they find it empowering.  In general, it works, it works well, it gets the job done, they feel comfortable with this version of technology while most others do not feel comfortable with it, the technology itself is cheap, and it gives them job security.  Some said it took years to build up these spreadsheets, and now they are up and running.  However, as I teach my current students, there are alternatives rooted in technology that will allow you to do things better, faster, and cheaper.

Final Thought 

In talking with a colleague, we both agreed that many hiring managers do not have a full understanding of the Artificial Intelligence (AI) skill sets associated with our graduating students. Our business students told us many times that their hiring managers valued only the traditional Excel capabilities (i.e., lookup functions, pivot tables, etc. – however, that is NOT AI).  Managers also greatly overlook the opportunities from other analytical solutions (skill sets that our students have). This makes it a bit difficult to sell the analytical techniques taught in classes that go beyond our Advances Excel and Predictive Analytics courses.  For example, our data mining class is essentially a machine learning class for business, which is the core of AI. The course is designed to solve the problems that Excel falls short on.

Hopefully we do a better job of training our students to “sell” the AI skills and managers become more open to embracing the benefits (which might require a culture change).  Embracing and trying new technologies requires leadership that is willing to try new things.  Otherwise, we keep using spreadsheets and email to manage very large and complicated data sets.

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