Five stages of achieving Supply Chain analytics maturity

Note: As a standard practice, I will include a plagiarism detection link in all the articles on my blog site. You can use the online tool below to evaluate any article published on the web for plagiarized content. All you have to do is to paste the url of the article there and it will go through every sentence in the article and flag any plagiarism. I believe as a follower of “writer’s integrity” I should include this tool in all my articles:


Developing in-house Supply Chain analytics expertise does not happen overnight

We hear about “Smart” Supply Chains and “Self driving” Supply Chains very frequently these days. Talking to some vendors and “experts” may make you feel that developing these capabilities is a one time exercise where you need to invest in technology, flip a switch and you will have a “Smart” Supply Chain.

Unfortunately, that is not the case. Organizations need to develop a roadmap to attain Supply Chain analytics expertise.

Organizations must follow a journey to build world class Supply Chain analytics maturity. There are no shortcuts.

In my perspective, a typical journey to attaining Supply Chain analytics competency maturity should consist of five stages, that we will discuss below.

The Supply Chain Analytics Maturity Pyramid

At a high level, there are four stages that an organization needs to take to reach the pinnacle of Supply Chain analytics maturity (fifth stage). Using this phased approach also allows organizations to build foundation for subsequent stages. Trying to tackle more than one stage at a time is something I strictly advise against.

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Often, some consultants hired to build your analytics capabilities jump right into doing analytics to find “value” with whatever datasets they can find. This is an extremely dangerous approach because if you have not developed your data quality and integrity (Stage 1), the output of the analysis will be dicey. Will you bet your career and millions of Dollars of your organization on results that were generated using questionable data?

All modeling and Analytics is susceptible to GIGO (Garbage in Garbage out) principle. Without ensuring that your input data going into a model or analytics exercise is good, you are just looking at an output that is giving you a picture that may be horribly skewed from reality/feasibility.

Next time an Analytics consultant hired by you to build internal analytics competency promises to do initial “quick win”analytics to identify “low hanging fruits”, without ensuring data integrity and consistency first- you should question their competency.

In the graphics below, we will explore each of these stages, in terms of people, processes and technology. These graphics obviously are not comprehensive but still provide key insights into the competencies that you need to develop in the three key areas (people, processes and technology) for each stage.

Follow the stages in a phased manner to build strong and true competency

Stage 1: The most critical and foundational stage


Stage 2: Start becoming a data driven enterprise


Stage 3: Start extracting insights from the data


Stage 4: Start predicting and prescribing


Stage 5: The “Self running” Supply Chain



Note: Views are my own

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