Ipventure, Inc.

Other

June Ray Limited

Kaiser Foundation Hospitals

Peter Ham

Boris Klots

William H Waddington

Patricia C Grewell

Ajit Ramachandra Mayya

Joyo Wijaya

Louis H Borders

Sunil Bhargava

Margaret Sue Lloyd

Gray Andrew Clossman

Arvind Peter Relan

Avrind Peter Relan

Gerry Perham

Gautam Bhargava

Franklin R Koenig

Radhakrishna Hari

Susan L Griese

A technique for effecting electronic commerce using a data network is described. The data network includes a plurality of subsystems which, together, form an integrated system for receiving customer orders for selected items via a data network, fulfilling the customer orders, and delivering the ordered products to the customers. Moreover, according to a specific embodiment, the integrated nature of the system architecture of the present invention allows the on-line merchant to provide a guarantee to the customer that the ordered items will be available to be delivered to the customer at the specified delivery date, time, and location.

Online store product availability

Integrated online store

Order allocation techniques pertaining to stops that a container makes in the process of fulfilling a customer order is disclosed. In one embodiment, this is accomplished by first identifying a pod that stocks the largest number of different items in a customer order. Then, a second pod is identified that stocks the largest number of remaining items in the customer order. The collection of pods defines a container path through the distribution center.

Method and system for order fulfillment in a distribution center

Webstore supporting multiple merchants

Order allocation techniques that reduce the number of stops that a container makes in the process of fulfilling a customer order is disclosed. In one embodiment, this is accomplished by first identifying a pod that stocks the largest number of different items in a customer order. Then, a second pod is identified that stocks the largest number of remaining items in the customer order. Ultimately, all items in the customer order are assigned a pod. The collection of pods defines a container path through the distribution center.

Order allocation techniques that reduce the number of stops that a container makes in the process of fulfilling a customer order is disclosed. In one embodiment, this is accomplished by first identifying a pod that stocks the largest number of different items in a customer order. Then, a second pod is identified that stocks the largest number of remaining items in the customer order. Ultimately, all items in the customer order are assigned a pod. The collection of pods defines a container path through a distribution center. In one embodiment, the techniques include retrieving an item identified. with the retrieval being dependent on an expiration time associated with the item. In another embodiment. the techniques include using information from a handheld computing device via a wireless interface regarding movement of items in the distribution center.

A load balancing technology segregates various inventory types (e.g., potatoes vs. milk, vs. pretzels, vs. tissue paper, etc.) based upon how frequently they are ordered in a distribution center. Inventory types that are ordered at the slowest rate are not 'replicated' over multiple pods in the distribution center. Rather, they are constrained to reside at a single pod within the distribution center. Items that are ordered somewhat more frequently than those in the slowest group are replicated in multiple pods across the distribution center. In other words, these items are separately stocked at locations on more than one pod in the distribution center. This means that a container passing through the distribution center can obtain each of the items in the second group of item types at multiple pods in the distribution center. Thus, these items do not create a bottleneck in the order fulfillment process. Inventory types in a third group, the fastest movers, are segregated from items in the first two groups. They are stored in a separate type of pod that fulfills orders even faster than the other type of pods.

Inventory replication based upon order fulfillment rates

Integrated system for ordering, fulfillment, and delivery of consumer products using a data network

An order allocation algorithm maximizes throughput by reducing the number of stops that a container makes in the process of fulfilling a customer order. This is accomplished by first identifying a pod that stocks the largest number of different items in a customer order. Then, a second pod is identified that stocks the largest number of remaining items in the customer order. Ultimately, all items in the customer order are assigned a pod. The collection of pods defines a container path through the distribution center.

Order allocation to minimize container stops in a distribution center

A warehouse management system that maximizes throughput and reduces carrying costs by reducing the number of stops that a container makes in the process of fulfilling a customer order. This is accomplished by allocating inventory to orders using the following series of considerations. First, the system selects a pod in order to maximize throughput. If there are multiple locations within the pod that stock the same inventory item, then the method chooses one of those locations based upon the expiration date. Finally, if multiple of these locations have units that expire within the same expiration period, the location with the fewest units is chosen.

Order allocation to select from inventory locations stocking few units of inventory

Growing community of inventors

Palo Alto, CA, United States of America

Peter Ham