Artificial intelligence has been making steady progress over the last few decades. However, while self-driving cars might be just around the corner, we are still decades away from having software smart enough to devise a supply chain strategy. Yet, at the same time, it would be incorrect to conclude that supply chain as a whole is still decades away from being positively impacted by machine learning algorithms.
Lokad’s supply chain science competency was born out of the observation that while algorithms alone were insufficient, they actually became formidable enablers in the hands of capable supply chain experts. Machine learning offers the possibility to achieve unprecedented levels of supply chain performance by taking care of all the extensive but otherwise clerical micro-decisions that your supply chain requires: when to order a product, when to move a unit of stock, when to produce more items, etc.
The Supply Chain Scientist is a mix between a data scientist and a supply chain expert. This person is responsible for the proper data preparation and the proper quantitative modelling of your supply chain. Indeed, it takes human supply chain insights to realize that some relevant data may be missing from a project and to align the optimization parameters with the supply chain strategy of the company.
Too often, supply chain initiatives come with fragmented responsibilities:
- Data preparation is owned by the IT team
- Statistics and reporting is owned by the BI (business intelligence) team
- Supply chain execution is owned by the supply chain team
The traditional S&OP answer to this issue is the creation of collective ownership through monthly meetings between many stakeholders, ideally having the whole thing owned by the CEO. However, while we are certainly not opposed to the principle of collective ownership, our experience indicates that things tend to move forward rather slowly when it comes to traditional S&OP.
In contrast to the collective ownership established through scheduled meetings, the Supply Chain Scientist holds the vital role of taking on the end-to-end ownership of all the quantitative aspects of a supply chain initiative.
This focused ownership is critical in order to avoid too common pitfalls associated with traditional supply chain organizations which are:
- Data is incorrectly extracted and prepared, primarily because the IT team has limited insights in relation to the use of the data.
- Statistics and reporting misrepresent the business; they provide less-than-useful insights and suffer from less-than-perfect data inputs.
- Execution rely heavily on ad-hoc Excel sheets in order to try to mitigate the two problems described above, while creating an entire category of new problems.
When we begin a quantitative supply chain initiative with a client company, we start by making sure that a Supply Chain Scientist is available to execute the initiative.
Learn more about supply chain scientists