The Modern Data Analyst
What challenges can supply chain analysts expect in 2024? What is on their agenda for next year? In this contribution we take a closer look at the Top-8 Challenges for supply chain analysts and the skills they need to tackle them,
So what is on the agenda?
At the top of that list we see that the analysis of Customer Performance, Footprint & Alignment and Production & Sourcing Optimization. When dealing with these issues we see that Root Cause Analysis (RCA) is of critical importance. A powerful tool aimed at identifying the (root) cause of the problem. Data-driven.
It is safe to say that with the enormous volatility of the market and the associated issues, this type of problem analysis is becoming increasingly important. Data-driven research into the root cause of a problem is also called Data Driven Forensics. This is withou a doubt an essential skill for analysts. In addition to the increased importance of such Root Cause Analysis, we see a notable increase in Cost to Serve analyses. Ensuring insight and control over all costs incurred to manufacture, distribute, sell and market a product or service.
Skills of the modern supply chain analyst
To support Supply Chain Leadership and provide answers to the top issues in supply chain management, a certain set of skills is necessary. They will have to be able to experiment, test hypotheses and make connections between the available data. In general, this means that they have to learn to be creative. They must learn to translate different business questions into clear and practical questions.
The following skills are required:
Data Management, Querying & Analysis (1)
First and foremost, supply chain analysts must be able to work with large data sets and have the right tools to manipulate these data sets. Being able to clean, validate and effectively edit these rapidly growing data sets are essential skills.
Statistics and programming (2)
In second place is the ability to program with Python and R. This will become even more important in 2024. A supply chain analyst must master these tools to be able to conduct good analyses. Having a strong quantitative approach is part of the basic skillset, They will need a specific level of mathematical insight.
Basics Excel (3)
A third talent that may sound a bit weird. It is about expanding skills in Excel and Access. Although both programs seemed to become less and less relevant, it is still essential to be a master in Excel. Many organizations still use these programs to store and process their data, which means that you also need to maintain your basic knowledge in this area in order to guide and help the transition towards a more modern data environment.
Machine Learning and AI (4)
Machine Learning and Artificial Intelligence (AI) have long gone beyond the hype and are now among the techniques that must be mastered as a supply chain analyst. We are witnessing more and more successful applications in the supply chain field. The advantage is that more and more analytical tasks can be ‘delegated’ to these intelligent systems, which can not only easily recognize patterns in data, but also learn from their experiences and thus improve their own performance.
Visualization and Presenting (5)
Finally, it is crucial to present and visualize the (data) story well. A data analyst will always have to be able to tell a clear story based on data and convey research results to the (internal) client in an informative and understandable way. They will have to make effective use of graphics, diagrams and dashboards. A skill in which probably programming or business intelligence tools are used.
Tools for Data Analysts
Beyond these skills, data analysts need to master a range of specific technologies in their work. Such tools may vary with their specific roles and the needs of the business. Examples include:
SAS and other statistical analysis software
Excel (for working with relatively small datasets)
MySQL and other relational database systems
NoSQL database systems such as MongoDB
Visualization tools such as Tableau
Big data tools such as Hadoop, Hive, Pig, and Impala
ML tools such as TensorFlow, Caffe, MxNet, Torch
In addition, data analysts may need tools specific to their industry or business function. For data analysts working in marketing, for instance, customer data platforms can streamline the process of gathering, processing and organizing data from a variety of sources.
One final remark
In any supply chain organization it should be about ‘adding skills‘ instead of ‘subtract skills‘. A supply chain analyst should make sure to maintain their current skills (such as with Excel) and at the same time, the will have to keep adding new tools (Hadoop, Hive, Pig, Impala, Python, R, etc.) to their skills list, because these advanced modeling and visualization tools are becoming increasingly valuable to supply chain analysts. But, at the same time, one has to keep in mind that many organizations still use spreadsheets, PowerPoints and Charts!
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