The article results from a series of swarm optimization research papers I read a few months ago. One of the papers was focused on using a grasshopper optimization algorithm for structural design. You can read an overview of the grasshopper algorithm and find links to some of the research papers in this article. A theme that emerged was that this algorithm could be leveraged to create the optimal design of structures and be used to evaluate the existing design.

Since the goal of the research I was doing was to understand how these algorithms can be extrapolated to the supply chain world, a thought that came to my mind was- Can we leverage this, and similar algorithms, to evaluate the current state of supply chains?

There is this whole other topic of designing the supply chain. I am no longer a fan of network optimization models, even though I once had a career building such models. The entire network optimization approach is obsolete compared to the business environment in which supply chains today operate. And that should not be surprising, considering that in another decade or so, network optimization modeling will be around for almost half a century. We will focus on the design element in tomorrow’s episode of “Think About It”. For now, let us focus on the evaluation of the current state.

Mapping the current state of supply chains is a critical step for most supply chain transformation initiatives. The driver is that the current state mapping and analysis will highlight the gaps that exist in the current state. How do we generally perform the current state analysis, though? Data.

Data from every nook and corner of your supply chain is collected, transformed, and interweaved to build a picture of the current state of the end-to-end supply chain (or any sub-function). For some sub-functions, I have often postulated that it may not be the best approach, but for now, we focus on the current process of gaining a picture of the current state with data.

An algorithm is trained to analyze and reconcile the data with the optimal to evaluate whether a structure is designed optimally. Why can’t we leverage the same approach for supply chains? The current state of analysis, i.e., assessing data and reconciling with what is considered optimal or standard, is a manual task, banking upon the expert knowledge of humans. But is that something that is actually so complex that it can not be automated?

Though you don’t have to publicly acknowledge it, the fact is that most current state gap identifications, when looking at functions from a strategic level, are pretty much evident when it comes to analysis. Costs, volume, flow lanes-discrepancies are not very challenging to identify. As they say, visualizing your supply chain’s flows can help you identify non-optimal flows. The gist is it is not difficult to build an “expert knowledge” repository of “non-optimal” that an algorithm can use to reconcile.

In the second part of the article, we will explore an approach to strategize about designing such an algorithm.