Optimized Fulfillment Sourcing

Improve Order Fulfillment Performance

Order Management

Balancing customer order fulfillment expectations while controlling fulfillment costs is a challenge all omnichannel retailers face. Modern shoppers expect fast delivery executed reliably for their online purchases. Consistently meeting these expectations builds customer loyalty and grows revenue. However, meeting these expectations comes at a high cost, with omnichannel fulfillment costs consuming an increasingly significant percentage of revenue. Intelligent, real-time fulfillment decisions are needed in today's fluid omnichannel retail world to ensure customer order promises are profitably met. Such decisions are only possible when a retailer's order management system can dynamically source and orchestrate order fulfillment by assessing the increasingly vast factors and variables that affect fulfillment performance and profitability. Manhattan Active Order Management applies real-time, intelligent fulfillment sourcing optimization to boost each order's fulfillment performance and profitability.

Fulfillment Sourcing Optimization Using Machine Learning

Manhattan Active Order Management dynamically determines how to best fulfill each order by leveraging the Manhattan Active Platform's Computational Intelligence technology. Using machine learning with adaptive algorithms, it assesses vast numbers of parameters in real-time across distribution centers, stores, transportation options, and customers to determine the fulfillment source for each order that meets the customer fulfillment promise (e.g., committed delivery date) profitably. Parameters such as delivery method and promise, shipping and handling costs, facility capacity, rejection rates, inventory levels, days of supply, proximity to the customer, selling price, seasonality, inventory disposition, safety stock levels, and more are assessed to determine a fulfillment source selection. Retailers can define sourcing optimization strategies based on service levels, free shipping, clearance items, and customer classifications.

Analysis tools help retailers to understand and improve fulfillment sourcing optimization. Visibility into every fulfillment decision, including cost breakdown and decision parameters, enables retailers to improve profitability decisions. Delivery performance improvements are supported by real-time analysis of performance for each order and across the global fulfillment network in real-time.

Improving Profitability

Manhattan Active Order Management's intelligent fulfillment sourcing optimization considers various cost factors to improve fulfillment profitability. For example, the sourcing of an order can be switched automatically from a distribution center to a store to avoid costly inventory markdowns. Optimization considers current and forecasted price markdowns and shipping and handling costs to determine if store fulfillment is a more profitable alternative.

Manhattan Active Order Management's optimization capabilities will assess all orders to a customer destination to avoid costly split shipments while ensuring the customer's order promises are met. Merge-in-transit fulfillment optimization dynamically determines combining multiple orders into a single, less costly shipment for delivery to the customer.

Ensuring Promises

Manhattan Active Order Management continually evaluates multiple performance-focused fulfillment parameters, including facility historical rejection rates, accuracy, and workload, to determine the best fulfillment route to meet customer order fulfillment promises. For example, fulfillment prioritization can be given to facilities specifically designed for high volumes during peak seasons, such as distribution centers and larger stores. With Manhattan Active Order Management, keeping customer promises and improving fulfillment profitability are always balanced to ensure customer satisfaction.

• Continuously optimizes order fulfillment execution to determine the least costly way to keep an order fulfillment promise.
• Advanced adaptive algorithms continuously evaluate vast numbers of parameters to arrive at an optimal fulfillment source for each order.
• Historical fulfillment performance data and current workload information are evaluated to ensure the selected fulfillment route can keep the order fulfillment promise.
• Manhattan's intelligent order fulfillment optimization enables retailers to dynamically expand their fulfillment networks, such as using store fulfillment, to uncover new ways of controlling fulfillment costs while keeping order fulfillment promises.