Note: As a standard practice, I will include a plagarism 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 plagarized 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 plagarism. I believe as a follower of “writer’s integrity” I should include this tool in all my articles:
To start with, here is a disclaimer: “linkage Algorithm” is not a standard terminology. Actually, the usage of AI algorithms that I am sharing in this article is something that is rarely discussed so existence of any standard terminology is out of question.
Thinking beyond the obvious
We think about AI algorithms as replacing existing Supply Chain planning systems. In my mind, there are three distinct ways AI algorithms should be leveraged in Supply Chain landscapes. In this post, I will focus on a key aspect, category of AI algorithms, that I believe we are failing to focus on, is their ability to actually help link existing Supply Chain planning & transactional systems at different hierarchies in an intelligent way.
These algorithms, that we can call “Linkage” algorithms will help us strike the balance between centralized and decentralized decision making in Supply Chains. And what is the best part about these algorithms ? They will primarily be heuristics based- essentially meaning that you do not need advanced AI algorithms like Neural Networks to implement them. Just some fundamental heuristics, classification, clustering and common sense.
The Centralized vs Decentralized decision making in Supply Chains
Centralized decision making is needed to realize efficiencies stemming from integration. Decentralized decision making is needed for rapid, detailed execution of operations.
How can Linkage Algorithms help?
Ex: A manager uses a tactical optimization modeling system to determine short term production targets for each plant. The Linkage algorithm disaggregates the data to a level and transforms it so that it can be fed to a production planning optimization modeling system.
It then takes the output from this sytem and “calculates” the master schedule and optimal capacity level, which it then feeds into production scheduling and MRP systems. These “Linkages” will be key to developing the capability of builsing your Supply Chain systems, analytics and planning ecosystem as a platform, as suggested in many of my posts. The attractive aspect of such an architecture is that you can keep you existing portfolio of planning systems (if you believe they are suitable for your planning needs), and yet infuse AI capabilities in the architecture.
An illustration of Systems inteconnectivity in Supply Chains
What does the linkages even mean ? What is the inteconnectivity between systems that you are talking about ? If these are the questions that are going through your mind, the illustration below is an example of the type of Linkage architecture that you can establish, by designing a “Linkage Algorithm”.
Again, as is my habit, I have simplified the illustration for the purposes of “Executive understanding” but the key task of such an algorithm will be to “intelligently” perform the Aggregation and Disaggregation indicated in the illustration.
Views expressed are my own.