The fulfillment of orders has never been so complex or important

Rising consumer demand for fast and accurate delivery is driving retailers to expand and strengthen their omnichannel offerings including using stores for delivery. At the same time, ever-increasing financial pressures are spurring retailers to look for innovative ways to decrease inventory costs and improve margins. Today’s order management systems must excel at helping retailers balance customer promises and business profitability.

Omnichannel fulfillment optimization is the solution

Leading omnichannel retailers optimize order management by making instantaneous and intelligent decisions about the most profitable way to meet a customer promise. Which store is the best option to ensure delivery of a next-day promise? Where should the inventory be pulled to avoid costly markdowns? How can the customer promise be kept, and a costly split shipment avoided? The decisions are endless, the data is monumental, and everything must be executed seamlessly within core order management processes for optimal performance.

Manhattan Order Management uses Adaptive Network Fulfillment (ANF) technology to help retailers intelligently manage order fulfillment across a retailer’s complete omnichannel network. ANF applies advanced machine learning algorithms that adjust and readjust to a retailer’s network and dynamically optimize fulfillment decisions in real time. ANF is part of Manhattan Order Management, so there are no integration complications and overall efficiency and performance is maximized.

With the ability to evaluate large numbers of parameters across distribution centers, transportation, stores, and customers, ANF optimizes order routing in real time to reduce costs while ensuring customer promises are always kept. Manhattan Order Management delivers all the capabilities retailers need to get the maximum value from its inventory: global inventory visibility, dynamic inventory availability, and optimized sourcing with ANF.

Features + Functions
Optimization
  • Define optimization strategies based on service levels, free shipping, clearance items, and customer classifications
  • Evaluate multiple fulfillment considerations holistically
  • Convert each consideration to a cost of fulfillment, including shipping/handling, capacity, rejection rate, inventory levels, days of supply, selling price, and proximity to customer
  • Balance fulfillment workload across facilities
  • Address surplus units and protect last units in-store 
  • Leverage historical rejection rates, accuracy, and workload
  • Use incentives or deterrents based on real-time data, such as surplus inventory or maximum fulfillment capacity
  • Prioritize fulfillment from facilities that need volume, such as distribution centers and larger footprint stores
Understanding and Adapting
  • View every fulfillment decision, with overall cost breakdown, and decision parameters
  • Evaluate single-order detail or real-time global network performance
Fulfillment
  • Route orders and track status in real time to a distributed network of DCs, stores, and suppliers
  • Support complex merge-in-transit fulfillment flows for a single delivery to the customer
  • Utilize vendor drop-ship order fulfillment
  • Handle fulfillment outages, capacity constraints, and inventory protection