AI and Data, the Future of Supply Chain Management

Seth Earley

Because enterprises are like organisms in an economic ecosystem, the principles that enable a healthy biological ecosystem are, from a physical, chemical and informational perspective, identical to those that enable a healthy business ecosystem and that ensure the survival of members of that business ecosystem.

Value is created by solving problems through the application of information and creativity. By speeding the information flows and reducing inefficiencies, we are equipping our part of the bigger picture to operate effectively, adapt quickly and evolve to meet competitive threats and exploit opportunities in the environment.

Supply chains are a crucial and complex part of the information flowing in this ecosystem. They are an intricately structured and variable system that is highly sensitive, with many possible outcomes based on even minor changes in the initial conditions or components. Supply chains feature a large collection of interacting components that are difficult to understand or examine due to their design and operations. And they represent a system in process, changing and developing over time.

It’s critical to think holistically about the information ecosystem as you prepare the digital representation of various stages of product design and development. Even a product designed in isolation from other systems and groups - whether in a specialized department or in a separate contracting organization - is still part of an information ecosystem. Information that may be inconsequential to the group that is creating the product, such as an obscure material specification that has no immediate value, will likely have value either downstream (perhaps to a distributor or engineering group) or upstream (perhaps to a procurement manager or supply chain manager).

Too often, these unseen dependencies and information relationships are neglected, and the impact of this neglect can be significant. If a piece of data that will be needed when assembling or distributing a future product is not captured, is lost or is incorrectly represented, the cost of remediation is orders of magnitude larger than that of addressing the data need at the source.

Of course, it is difficult to know what will be important in the future without mapping out the information supply chain. Today’s manufacturers and product designers do not simply design and manufacture physical goods. They design and manufacture data streams and data specifications that are as important as the good itself. But this requirement is not always well considered at the time of design. A marketer may need a piece of data that resides in engineering. Getting that data after design teams have moved on or personnel have shifted priorities is difficult and costly.

It is not feasible to capture every piece of data that could potentially be useful for an unknown downstream purpose. Instead, you need to map the data flows that correspond with the physical and manufacturing flow and collaborate with downstream consumers of the data to understand and anticipate needs. Then capture and manage that data provenance in the right structure and application and in compliance with data quality standards.

Design, manufacturing and marketing groups need to be aware of downstream processes. Each department and group must understand how the data exhaust produced by their processes is going to inform both upstream and downstream systems. Your data exhaust is someone else’s data fuel.

For example, in life sciences research, antibodies are manufactured through certain processes and the data associated with those processes is critical to end users. But even more important are the ways that fellow researchers use a particular antibody in experiments that have been written up in peer-reviewed journals. How do other researchers use the associated reagents? How well did they perform under certain protocols? What were the upstream manufacturing processes? What are the downstream applications? Where did they not perform?

For your enterprise, there are similar questions. How do your processes fit in with the larger business objectives, marketing strategy, customer education and organizational processes? What information is important to customers, competitors and suppliers? What are their roles in the information ecosystem? Mapping out and understanding these dependencies is critical to optimizing information flows beyond the immediate needs of the process at hand. Understanding and planning for these needs will help your organization differentiate based on a deeper understanding of the data. This is how your organization turns hidden data flows into a competitive advantage.