To begin with, let me define the word “co-creation” in the context of this article. For decades, supply chain collaboration with external partners has been a strategic imperative for companies. But very few have succeeded. Co-creation is when a supply chain network creates plans considering every node and flow, internal and external. As the name suggests, it is made in collaboration with all the internal and external nodes in the supply chain.

But we know the collaboration and data sharing needed to generate actual co-created plans is difficult (if not non-existent).

If you were to answer the question below honestly, what would be your answer?

“What are the key bottlenecks for building an integrated view of any supply chain (including all external partners)?”

My answer will be a lack of trust and confidentiality concerns. And whatever reason comes across your mind, if you keep peeling the layers, it will eventually, boil down to these two.

This article will not cover how organizations can build trust with external supply chain partners and address confidentiality concerns. But another question you need to ask is, “Is the data required to co-create plans with partners sensitive?

Except for a few industries, most of the time, the data points needed are not sensitive. In our context, that means that the data points are not information that provides a competitive advantage and can be leveraged against the organization. If you believe most of your data points are sensitive, you need to revisit the roadmap of identifying data points required for co-creation.

The challenge is to share data points with external partners embedded with tons of other data. And with organizations struggling to wrap their hands around internal systems and datasets, who has the time to understand which data points to exclude from specific datasets, how to do that etc.?

So the workarounds are that we keep ourselves constrained to primary data-sharing methodologies like EDI. Or we just bring up the issue of confidentiality and bail out altogether?

It is not that attempts have not been made to address this trust issue. In 2015, I read an article from an operations consulting company highlighting the concept of “clean rooms.” If I remember correctly, these cleanrooms were third-party managed data repositories, allowing the sharing of sensitive data (consumer demand, product cost breakdown, etc.) in a legal and secure data environment.

I have not seen an explosive growth in this area. Many supply chain visibility platform companies fill gaps in partial ways by sharing visibility data across partners. But this data is not what will help co-create optimized plans.

A few approaches can be taken to build “cleanrooms.” Prominent technology vendors are often suitable to create these cleanrooms because they can access almost all enterprise data. But let us now jump into how AI on edge can help share the data needed for co-creation.

AI on the edge helps address one of the challenges I highlighted earlier in the article- the pain of identifying non-sensitive data and setting up flows and architectures to share the same.

Most AIoT devices and AI-enabled edge devices generate (or will develop) a limited set of data points as outputs. But these are important data points from a supply chain planning perspective. The advantage of these devices is that you can set up sharing architecture relatively quickly when implementing/installing
these devices.

This means that when the edge device captures and streams data, your partners can also access the same. By identifying the edge devices you want to share, you can eliminate the pain of going through datasets with hundreds of columns (most unused and not helpful).

Consider an example. If widgets from your supplier go through a smart vision-enabled conveyor belt, your supplier can get the number of defective widgets in real-time, with precise data and pictures. The minor logic aspect here is to ensure that the dataset gets shared (sent) only to the supplier whose parts are being scanned. But you do not have to build workflows to exclude, include, clean, and harmonize large repositories.

While this may seem like just one data point, these edge devices will collectively generate a ton of data soon. And this presents an opportunity to share the data strategically with suppliers to co-create. Because as supply chains across companies and industries become more and more streamlined, strategic moves like co-creation will become differentiating capabilities.