27 Nov Challenges for supply chain analysts in 2020
What are the challenges for supply chain analysts in 2020? What is on their agenda? In our previous post in this series we presented the challenges of supply chain leadership
and closely related to these issues the team of supply chain analysts is working on key topics. In this post we present the top-8 challenges for supply chain analysts and the related required skill-set. .
The top issues in analytics in the supply chain can be broken down into roughly 4 categories: Costs, Service, Volatility and Supply Chain Planning Issues related to structure. At the top of the ranking we find the analysis of customer performance, footprint & alignment questions and optimization of production and sourcing. All in line with key issues on the agenda of supply chain leadership. What is remarkable, is the importance of root cause analysis. Digging into the data to find the exact cause of a specific problem.
Data driven forensics
With the enormous volatility in the market and problems popping up, this type of analysis is of critical importance. Data driven searching for the root cause of a problem is what we refer to as data driven forensics. This capability will be essential. Next to the rising importance of root cause analysis the importance of cost to serve is remarkable. Getting a grip on the costs per customer and the cost to serve will be very important.
Skills set of the modern supply chain analyst
In order to support supply chain leadership and answer the top issues in supply chain management, a specific skill set is required. Supply chain analysts need to able to deal with more and more complexity in the organization and environment, they will need to understand how to conduct experiments, test hypotheses and make causal inferences from the data at their disposal. More broadly, they need to be able to think creatively. Translate decision makers’ business-related questions into useful questions about data.
Data Management, Querying & Analysis (1)
The number one skill that analysts need is the ability to handle large data sets and use tools that are capable of doing this. Cleansing, validate and handling these ever growing data sets is a critical skill.
Statistics and programming (2)
Secondly, learning statistical programming with Python and R is ranked critical and will be even more important in 2020. These two skills are the tools used by a supply chain analyst to get the job done. Strong quantitative skills are thus an essential part of a data analyst’s toolkit. Although different jobs may require different levels of mathematical understanding. At a minimum, professionals in this field should have a solid grasp of basic statistics.
Basics Excel (3)
The third skill is perhaps an odd one. That is enhancing the skills in Excel and Access. Although they are thought to loose a bit of their importance, it is still essential to be a master in Excel. Already in 2011 LaValle, et al. (2011) mentioned that a company should add, and don’t detract. Keep existing capabilities (like Excel) and add new ones is an important remark. In this time transition, new tools (Hadoop, Hive, Pig, Impala, Python, R, etc.) should supplement the ones that are still availabe. These sophisticated modeling and visualization tools will soon provide greater business value than ever before. And still at the same time, many companies will rely heavily on spreadsheets, PowerPoints and charts.
Machine Learning and AI (4)
Machine Learning and AI are both way beyond the hype. They are certainly important techniques to be mastered by supply chain analyst, as they will become common in the supply chain. More and more successful applications are reported in the supply chain realm. With theses advances more and more analytics tasks will be delegated to intelligent systems that not only will detect patterns in data, but also learn with experience and improve their own performance. This trend will change the supply chain analyst’s role in many ways. More and more, analysts need to understand how to apply AI tools and approaches to real-world problems, while machines take over the more routine or repetitive aspects of data analysis.
Visualization and Presenting (5)
Finally the ability to present and visualize is essential. To help decision-makers, data analysts need to be able to tell stories with data and convey their results in an accessible, informative way. They need the ability to create effective graphs, diagrams and dashboards. A critical task that may require programming or business intelligence tools.
Coming up next: The top 10 supply chain related technology to invest in.
As a starting point we took the research done by the Mahajan&Saha (2017), LaValle et al. (2018), Butner, K, (2018), Furr&Shipilov (2019), and Klickman (2018) and build upon it through our interviews with supply chain analysts we trained in the last few years. I’m fully aware that this is not a scientific approach by definition, but it does provide us with a very good indication of the topics that are on the agenda.
Glickman, R, (2018), 7 Key Data Analyst Skills for the Future, Treasure Data Blog
Furr, N., Shipilov, A., (2019), Digital Doesn’t Have to Be Disruptive, The best results can com from adaptation rather than reinvention, Harvard Business Review, July-August, 2019.
Butner, K, (2018), New Rules for a New Decade, a vision for smarter supply chain management, IBM Institute for Business Value, ibm.com/iibv.
LaValle, S., E.Lesser, R. Shockley, M.S.Hopkins, N.Kruschwitz, (2011), Big Data, Analytics and the Path From Insights to Value, MIT SLoan Management Review, Winter 201, Vol.52, No.2.
Mahajan, S., S.Saha, A.Macias, (2017), Analytics: Laying the foundation for supply chain digital transformation, The Hacket Group.
Gartner, (2019), Gartner Identifies Top 10 Data and Analytics Technology Trends for 2019.