Infor Nexus Predictive ETA
BROCHURE
Supply Chain Visibility
Accurately projecting the arrival of inventory is critical to downstream supply chain operations. This information is used to promise orders to customers, coordinate receiving resources, and allocate inventory to maximize revenue and margins. Uncertainty and inaccuracy in arrival dates leads to poor customer service and costly behaviors such as: expediting, reordering, and holding excess buffer inventory that ties up working capital.
The challenge in predicting product availability
In global supply chains, it’s necessary to link your shipments, orders, and inventory in transit with projections, updates, and inputs coming in from multiple parties and external systems in order to build a coherent view of active and future product flows. Individual truck or ocean vessel arrival estimates simply aren’t sufficient to answer the critical business question: When will critical inventory originating across oceans actually be received at my distribution center or warehouse and be ready to fulfill customer orders?
Business impact
Poor intelligence about future inventory availability has wide-ranging impacts. When lead times extend or transport exceptions cause delays, the resulting late deliveries and stockouts result in lost revenue and poor customer experience. When transit times shrink, shipments arrive “early,” and excess inventory builds up.
Throughout the year, supply chain teams scramble to make sense of conflicting, inaccurate, incomplete, and constantly shifting arrival information.
Traditional approaches fall short
Many companies establish high-level transit time estimates for a given lane in their ERP or transportation systems. These estimates are often heavily padded for uncertainty to ensure inventory is unlikely to be delivered too late, but they don’t accurately reflect real-world cycle times. Your future inventory will move across trucks and ocean vessels or air freight, through customs, and then rail and more trucks in complex, multi-leg, multi-modal product journeys. More sophisticated approaches involve creating static models for each shipping lane with journey milestones to check against expected progress and update time-to-arrival. Setting up and maintaining these models for thousands of lanes is a highly labor-intensive effort that tends to quickly fall behind today’s dynamic supply chain performance needs.
A solution: Infor Nexus Predictive ETA for inventory
Network + big data + machine learning = better ETAs
Infor Nexus™ leverages the latest AI technologies to generate more useful and dynamic projections of near-future product availability, and with far less overhead and effort than alternate methods. Our machine learning algorithms tap into the dense shipping and supply chain data flowing through the Infor® Nexus global network to generate sophisticated predictive models. These models are continuously updated with real-time network transactions and activity to dynamically recalculate inventory arrival projections with the latest information available. Once active, models continue to develop more precision based on new data as it’s processed.
Network data insights
The Nexus network is unique in its multi-enterprise scope and global supply chain coverage, with activity data from leading global brands, suppliers, and shippers along with international 3PLs, customs brokers, and more. This includes detailed transactional and milestone performance details not available from other sources. Our single-instance, multi-tenant network data covers global transit times by lane, individual port dwell times, overland transport legs, and contributing factors such as customs clearance. Extensive ecosystem connections and platform intelligence are combined with powerful data cleansing and harmonizing capabilities to release the power of big data.
Customer value
Gain more confidence and precision in planning and execution, accelerate supply chain processes, reduce waste, better manage inventory, and more accurately promise orders to customers. Infor Nexus Predictive ETA provides a significant improvement in accuracy over complex, manually-created static models. When compared to other legacy approaches, the increase in prediction accuracy can be exponential. The solution provides intelligent and dynamic projections for inventory arrival to final destinations across extended global supply chains—expanding your opportunities to drive speed and efficiency in supply chain planning, execution, and decision-making.
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