An AI-powered augmented analytics solution provides end-to-end exception management and provides operating teams with almost real-time prescriptive recommendations on refining network architecture and making it adaptable to existing scenarios.
FREMONT, CA: Logistics network design is one of the most critical and fundamental facets of the enterprise's logistics strategy. Manufacturing companies are spending a large percentage of their financial and human capabilities in introducing the network architecture and ensuring better adaptability as the logistics needs evolve in the future. New technologies, product releases, consumer markets, competitor behavior, commodity prices, mergers and acquisitions, sustainability shifts, and the current political, economic and regulatory environment are among the established factors that contribute to inefficiencies. They also add to obsolescence and lack of agility in incumbent design and demand continuous tracking and optimization to reach the target.
AI-based Augmented Analytics Brings Competence and Business Steadiness to Logistics Networks
The coronavirus pandemic has put a lot of burden on the global supply chain network. Worldwide demand and supply imbalances have threatened the reliability and productivity of the current network design for many multinational manufacturing firms. There are four distinct types of threats affecting manufacturing and e-commerce organizations right now; demand, supply, process, and financial risk. Unfortunately, existing corporate continuity strategies are not adequate to reduce these operating challenges in the sense of the current crisis.
Consumers now demand versatility in omnichannel efficiency, and suppliers are striving for global inventory consistency such that the entire network—whether it is a large distribution center or a store—will both be allowed as possible inventory fulfillment points. Today, it is no longer a matter of being able to ship from any network asset; the goal is to be ready to ship any item, any format, from almost anywhere. The obstacles to doing so can be physical—linked to inventory, training, and supplies—or technical, related to Warehouse Management Software (WMS).
To address these risks and obstacles, more and more companies are engaging in Machine Learning and AI-based platforms to test network alternatives and use methods such as Optimization, Simulation, and Heuristics to identify the most cost-effective at chosen strategic service levels. An AI-powered augmented analytics solution provides end-to-end exception management and provides operating teams with almost real-time prescriptive recommendations on refining network architecture and making it adaptable to existing scenarios.
The COVID-19 crisis increased the penetration of e-commerce worldwide and generated tremendous opportunities through supply chain digitization, digital twins, and AI-powered augmented analytics. It helped develop near-real-time organizational insights for sourcing, scheduling, warehousing, and transportation teams to mitigate the risks associated with demand, process, supply, and finance.