The future of supply chain management is AI and Data
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.
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