The Future Implications of Community Intelligence in the Supply Chain
Community intelligence works on the collective wisdom of various disciplines, point of view, and experiences.
FREMONT, CA: Developing resources and systems have often required collaboration with various sources outside the organizations. But there are a few disadvantages of this approach such as the time needed to extract information, planning on how to communicate, sharing, and analyzing it. Community intelligence works on a similar principle that works on the collective wisdom of various disciplines, point of view, and experiences which dynamically pervades the same community that endangered it, finding patterns and solving problems beyond the complexities humans can handle. But how does it help the supply chain?
Within the supply chain, the discussion can be divided into two areas:
• Machine learning (ML) can be used for predictive analytics within the supply chain. ML, a specific subset of AI, enables a machine to procure deep insights from patterns and behaviors. In the supply chain, it references models to predict the future or the effect of random actions in the estimation of future demand, demand shaping, demand segmentation, and the impact of actions such as trends or promotions in social media.
• Implicit support of the everyday operational activities of the planner within his influence, such as tracking of actual orders against forecast and tracking the actual factory output against projected and lead time.
There are some critical shortcomings in the ongoing journey toward intelligent supply chain. First, the asynchronous data silos hinder the ongoing adoption as it allows the decisions that are loosely coupled relying on asynchronous communication. The supply planning model needs to adopt an aggregate statement of capacity, followed by tool planning.
Second, single point estimation of demand fed by purchase lead times from inventory planners, demand planning, and business preferences from executives implies the information is stored in one location even if the detail data and algorithms belong to various silos.
These limitations are also the principal components of community intelligence. In short, the critical factor for each success is real-time analytics and a memory engine. Hopefully, the core principles of community intelligence will be considered to leverage the value of AI in the coming few years.
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