Читать книгу Artificial Intelligent Techniques for Wireless Communication and Networking - Группа авторов - Страница 82

As well as the wealth of differentiated data system silos in most businesses, IoT-enabled physical sensors across supply chains now provide a golden mine of knowledge for tracking and manipulating supply chain planning processes as well. Analysis of this golden pot manually using billions of sensors and software will result in massive waste of operating resources and delayed production cycles. Intelligent AI-driven analytics have tremendous value in the supply chain and logistics. As supply chain components become the principal nodes for placing data and algorithms for driving machines, innovative efficiencies can be achieved.

AI systems are typically cloud based and need a constant server bandwidth. Often operators need specialized hardware to achieve this AI capability, and a significant initial investment is required for several supply chain partners in this intelligence hardware. The problem here is that most AI and cloud-based systems are very flexible and need to be more successful in the real opening users/systems stage. Since every AI system is unique and distinct, it has to be addressed in detail with its supply chain partners. As any other approach to digital technology, training is another factor that takes considerable time and money commitment. Business efficiency will be impacted because supply chain suppliers will need to work with AI supplier to create a training solution which is cost-effective and effective during the integration procedure.