Overview

Manhattan Active IQ is a computational intelligence solution designed to create optimal outcomes for businesses. Leveraging over three decades of experience, it integrates adaptive systems, decision science, and data science to solve complex problems and enhance operational efficiency.

Features

Omnichannel Commerce Intelligence

See how machine learning and algorithms contribute to our applied intelligence for Omnichannel Commerce solutions. Adaptive Systems in Order Management  Fulfillment sourcing optimization uses adaptive algorithms to continuously prioritize fulfillment selection, minimize split shipments, maximize distressed inventory, and deliver the lowest total cost fulfillment plan for each order. Data Science in Order Management  Machine learning improves predictive promising models for ship and delivery date estimation that considers backordered, on-hand and inbound inventories, protection levels, labor capacity, number of shipments, day of week, time of day, size of order, backlog in store/location, service level, carrier, and in-transit merge routes. Optimization in Promotions and Store Systems  Numerous optimization algorithms and mathematical models enable promotional recommendations, in-store task assignment matching, and more.

Supply Chain Planning Intelligence

Below are examples of applied intelligence across Manhattan inventory solutions like allocation, demand forecasting, replenishment, and planning. Adaptive Systems in Demand Forecasting  Continuous demand sensing, demand forecasting, and automated policy tuning leverages data science techniques and adaptive algorithms to reliably anticipate and respond to changes in demand, making it easier to place inventory in the correct place. Decision Science in Inventory Planning  Evaluates numerous inventory optimization policies to research, experiment, and test network system impact before committing to a safety stock, order frequency, or forecast policy. Data Science in Demand Forecasting  Machine learning and advanced statistical models identify the impact of business events on demand to optimize forecast models.

Supply Chain Execution Intelligence

Below are examples of applied intelligence across Manhattan supply chain execution solutions like warehouse management, labor management, transportation management, and more. Data Science in Labor Management  Machine learning delivers more accurate task time estimations based upon continuous analyzed results by resource, instead of static averaged standards. Optimization in Transportation Management  Calculates a multitude of advanced algorithms and models for shipment planning optimization, procurement optimization, fleet optimization, and more. Adaptive Systems in Warehouse Management  Adaptive work release algorithms that continuously re-evaluate and prioritize orders and allocations in response to real-time demand and predicted downstream impact.