275 results about "Order fulfillment" patented technology

Systems and methods can enhance efficiencies of order fulfillment processes. For example, this document describes systems and methods for efficiently processing direct-to-customer shipments at a last mile delivery (“LMD”) facility. The systems and methods are focused on rapid loading, unloading and cycling of LMD delivery vehicles through the operation in a space-efficient manner. In some embodiments, the delivery vehicles are cycled through the LMD facility in organized batches to allow high throughput with fast cycle time.

Use of a language model to automatically perform visual assessment of quality of an item being fulfilled by a picker. The online system receives an image of the item and identifies a set of potential problems associated with the item. The online system generates a plurality of prompts for input into the language model including the image and one or more questions each corresponding to a respective potential problem of the set potential problems. The online system requests the language model to generate, based on the plurality of prompts, a feedback response for each potential problem. The online system generates an aggregated output by aggregating the feedback response for each potential problem, and based on the aggregated output, a second message that identifies one or more relevant problems associated with the item. The online system causes a device of the picker to display the second message.

The invention relates to the technical field of commodity trade management, and discloses a commodity trade business management system based on a block chain. A block chain data acquisition module of the system obtains commodity basic information, trade enterprise qualification information and logistics node information in real time through a preset data interface, and synchronizes the information to a block chain distributed account book; the trade risk quantitative analysis module generates a trade link real-time risk coefficient set based on account book real-time data in combination with a trade compliance rule base, historical order fulfillment data and current logistics state data; a risk control strategy collaborative analysis module generates a plurality of candidate risk control strategy schemes when the risk coefficient exceeds a preset threshold value, calculates a collaborative execution cost factor when the enterprise qualification state is changed in combination with logistics node information, and obtains an interference influence value set of each scheme on the trade process; and a risk control strategy execution feedback module updates a block chain smart contract execution instruction according to the optimal risk control strategy scheme, and facilitates efficient management and control of commodity trade business.

The invention discloses a multi-objective optimization-based multi-commodity order fulfillment method and device, equipment and a medium, and relates to the technical field of supply chain management. The multi-commodity order fulfillment method comprises the following steps: acquiring order and inventory information including a current order set, a commodity set and a warehouse inventory set; and modeling an order fulfillment problem based on an actual demand of fulfillment of a multi-commodity order in the order and inventory information. The established model is a multi-objective optimization problem containing three objectives. Generating an initial population by adopting a heuristic method based on priority and inventory feasibility; and carrying out iterative optimization on the initial population by adopting a rapid non-dominated sorting genetic algorithm with elitism based on a double-layer elitism strategy to obtain a Pareto non-dominated solution set. And selecting a final order fulfillment scheme from the Pareto non-dominated solution set according to the preference of a decision maker. According to the method, three core operation indexes are synchronously improved under the condition of meeting feasibility constraints such as inventory, processing capability and demand.

A system having a process for order fulfillment bottleneck mitigation that includes receiving, at a picking station, a first tote specific to a first order with the first tote containing items specific to the first order and a plurality of other orders. A first quantity of items required by the first order are retrieved along with a second quantity of items that is the sum of the items required by the other orders. The second quantity of items are stored in a storage at the picking station. A second tote is received at the first picking station that has no further orders pending. A third quantity of items required by a second order is retrieved from the storage and placed into the second tote.

The invention provides a supply chain toughness and efficiency collaborative improvement method and system and electronic equipment, and the method comprises the steps: obtaining various supply chain management variable data of a supply chain, such as an inventory level, a supplier attribute, a customer order, transportation time, cost, a risk impact resistance coefficient, a recovery coefficient and the like; then, on the basis of the supply chain management variable data, various to-be-evaluated supply chain strategies meeting one or more constraint conditions of a preset continuous supply constraint condition, a preset factory production capacity constraint condition and a preset order fulfillment constraint condition are screened out, and a supply chain comprehensive target value is obtained through a calculation formula of the supply chain comprehensive target value. And outputting the optimal supply chain strategy with the maximum supply chain comprehensive target value. According to the embodiment of the invention, the optimal supply chain strategy which can guarantee the efficiency of the supply chain, can resist risk impact and can be quickly recovered can be determined, the comprehensive performance of the supply chain is further improved, and the sustainable development of an enterprise is maintained.

A transfer device for unloading a transport loading aid with stacked article carriers and for loading a target loading aid with the carriers includes a base frame, loading tongues independently movable by first drives between retracted initial and extended receiving positions, clamping jaws for gripping at least one carrier relatively movable by a second drive between retracted opened and abutting clamping positions, and a lifting frame movable by a third drive. The jaws are mounted on the lifting frame and are movable between lower and upper height positions such that for unloading the transport loading aid the at least one carrier is liftable by the jaws placed against the at least one carrier, and at least one tongue is at least partially slidable under the lifted at least one carrier to receive it in the receiving position. Further, an order fulfillment facility for picking carriers includes this transfer device.

Some embodiments provide order packaging control systems, comprising: a product packaging (PP) control circuit communicatively couple with an automated custom package fabrication (CPF) system configured to fabricate on demand custom packages, an automated product retrieval (PR) system configured to autonomously retrieve one or more products, pick stations, and a set of sensor systems. The PP control circuit can be configured to receive orders; prioritize the orders and define an order fulfillment sequence, track states of the PR system and the CPF system; determine product retrieval timing, and correspondingly determine fabrication timing of when the CPF system is to fabricate corresponding packaging; and control the PR system and the CPF system in controlling synchronous operation of the PR system and the CPF system to synchronize an availability of products of an order at a pick station and an availability of a custom package at the pick station.

Systems, methods, and / or articles of manufacture that utilize omnichannel Artificial Intelligence (AI) restaurant management modeling to accurately compute and update order fulfillment times in an omnichannel ordering environment.

The invention relates to the technical field of enterprise operation management, in particular to a retail enterprise inventory collaborative management system based on intelligent order fulfillment and cross-channel demand prediction, which comprises an order management module, an intelligent order fulfillment module, a demand prediction module, an inventory collaborative management module, a data analysis module and a user management module. Compared with a simple scheme in the prior art that the historical logistics cycle is directly adopted as the predicted arrival time, and the scheme cannot accurately reflect the current logistics progress and the actual situation, the scheme of the invention adopts a more refined prediction formula; the logistics average period T, the current logistics progress P and the coefficients beta and gamma corresponding to the logistics average period T and the current logistics progress P are comprehensively considered, so that the arrival time can be predicted more accurately, more reliable logistics information is provided for customers, and the customer experience and credibility are improved.

The embodiment of the invention provides an order implementation method, system and device based on distributed transactions, equipment and a storage medium, and relates to the technical field of computer application. Receiving an order request sent by user equipment, and sending a TRY request to each micro-service in the plurality of micro-services in parallel; after it is determined that the data reservation operation of each micro-service is successful, CONFIRM requests are sent to each micro-service in parallel; and after determining that the data reservation operation of any micro-service fails or receiving a cancel request for any micro-service, respectively sending a CANCEL request to each micro-service, so that each micro-service cancels the data reservation operation of the micro-service after receiving the CANCEL request sent by the order service. According to the invention, the method achieves the control of a plurality of micro-services, improves the business processing efficiency while guaranteeing the consistency and atomicity of distributed transactions, and further improves the business throughput.

A trained model is used to generate a post-delivery effort-based tip increase recommendation for a user of an online system. The online system applies the computer model to predict, based on information about an original order placed by the user and data describing an effort required to fulfill the order, a tip amount that is likely to lead to satisfaction of a picker associated with the online system who fulfilled the order. Responsive to information about a sentiment of the user in relation to the fulfillment process, the online system generates, based on the predicted tip amount and an original tip amount provided by the user before the fulfillment process for the order was completed, a tip adjustment amount. The online system causes a user interface of a device associated with the user to display the tip adjustment amount prompting the user to adjust the original tip amount.

Aspects of the present application relate to an order fulfillment system that may include one or more product induction regions. A given product induction region may include a plurality of product storage apparatuses, each holding a plurality of products, and a product transfer apparatus operable to transfer a product, among the plurality of products, into a shipping container. The order fulfillment system may include autonomous mobile robots (AMRs). An AMR may move to a shipping container delivery system to receive a shipping container and then move the shipping container to the product transfer apparatus to receive the product. The AMR may then move to a further location for further processing of the shipping container. The order fulfillment system may be combined with a production system. The product storage apparatuses may be universal crates, which may be returned to production system when empty to be refilled with products.

The invention belongs to the technical field of logistics management, and particularly relates to a monitoring management system for e-commerce logistics, which comprises a parameter acquisition module, a parameter preprocessing module, a parameter processing module, a dynamic scheduling module, an early warning execution module and a strategy optimization module. On the basis of dynamic matching of delivery urgency, cost coefficient and vehicle bearing capacity, transportation resource allocation is optimized, the no-load rate and resource waste are reduced, and the logistics resource utilization rate is remarkably increased; a three-level early warning mechanism and an elastic scheduling strategy quickly cope with load fluctuation, overload and load reduction / low-load capacity expansion, the logistics delay risk is reduced, and the order performance timeliness is guaranteed; through data-driven priority delivery coefficient calculation and simulation optimization threshold adjustment, manual dependence is reduced, and the scientificity and foresight of scheduling decisions are improved; the strategy optimization module constructs a self-adaptive logistics management system through historical data backtracking and bottleneck diagnosis, a dynamic iteration algorithm model and threshold parameters, and improves industry competitiveness for a long time.

In some implementations, an autonomous vehicle may receive information associated with an order including multiple items. The autonomous vehicle may, after traveling to a first pickup location and obtaining a subset of the items in the order, identify one or more alternative pickup locations having respective inventories including one or more items that are unavailable at the first pickup location. The autonomous vehicle may generate one or more virtual credentials linked to a primary credential associated with a user that submitted the order and use the one or more virtual credentials to obtain, at each alternative pickup location, one or more items that were unavailable at the first pickup location. The autonomous vehicle may travel to a final destination to deliver the subset of the multiple items obtained from the first pickup location and the one or more items obtained from each of the one or more alternative pickup locations.

An online system receives orders including items from customers and allocates the orders to pickers. A picker obtains items included in an order from a customer and delivers the items to the customer to fulfill the order. To provide encouragement for pickers fulfilling orders, the online system generates a highlight reel of accomplishments of a picker fulfilling orders. The online system generates prompts for a generative model, such as a large language model, based on stored information describing order fulfillment by a picker. Content, such as text, generated in response to a prompt by the generative model includes one or more portions of the stored information describing order fulfillment is included in the highlight reel. A template for the highlight reel is selected for the picker, with the template identifying content displayed to the picker and an order in which the content is displayed.

A system is configured to train a customer understanding model to generate a preference score for substitution items. The customer understanding model generates a preference score for each of a plurality of related substitution items based on order data including data indicative of at least one item ordered and location data indicating a location of a first store. The customer understanding model ranks each of the substitution items based on the preference score. Order data is transmitted including substitution data identifying each of the substitution items and corresponding rank. Performance data associated with a set of operations implemented based on the order data and the substitution data is obtained. An updated customer understanding model is trained based on the performance data and iteratively modified based on the updated training dataset and updated performance metrics generated from second performance data.

A beverage dispenser and method for operating a beverage dispenser may include receiving a handshake request from a mobile device of a user. A communications link may be established with the mobile device. Responsive to receiving control data associated with a beverage order from the mobile device, a physical function may be caused to be performed by the beverage dispenser.

A real-time order fulfillment and delivery optimization system that includes the following: an order processing module configured to dynamically receive and process customer orders using an AI-based prioritization algorithm; an inventory management module configured to allocate stock in real time based on stock availability, demand forecasting, and proximity to the warehouse; a route optimization module that uses artificial intelligence to determine the most efficient delivery routes by analyzing traffic, weather conditions and road restrictions in real time; a module for managing last-mile delivery that is configured to dynamically assign delivery agents, redirect deliveries in real time, and minimize failed delivery attempts; a customer interaction module that enables real-time tracking, automatic notifications, and dynamic rescheduling of deliveries based on user preferences; and a predictive analytics module that uses machine learning to improve order processing efficiency and optimize future logistics processes.

The disclosed technology provides for incentivizing a user to pick up an order at an fulfillment location. A method can include receiving, at a mobile device, information for picking up an order at a fulfillment location and and incentization information from a server system, where the incentivization information includes information for an incentive associated with picking up the order at the fulfillment location during an incentivization window. The method can include changing a display of the computing device to display information for picking up the order during the incentivization window, receiving a second communication from the computing device indicating that the computing device has arrived at the fulfillment location, and changing the display of the computing device to display a confirmation of the incentivization information based on whether the particular timepoint at which the computing device arrived at the fulfillment location is within the incentivization window.

The invention belongs to the technical field of order fulfillment process monitoring, and discloses an order fulfillment process delay early warning method and system based on multi-node state acquisition, and the method comprises the steps: collecting the dynamic state data of multiple nodes in a fulfillment process in real time; matching judgment is carried out through the node rule base, primary early warning is triggered, and an early warning context data set is constructed; inputting the data set into a pre-trained delay risk prediction model, and outputting an overall risk probability and a key influence node identifier; issuing a grading early warning signal according to the risk probability; carrying out interpretable root cause positioning through a root cause analysis rule chain based on directed acyclic graph representation; incremental learning is carried out on the prediction model based on early warning feedback data to form an optimized closed loop; the system correspondingly comprises a data acquisition module, a rule judgment module, a risk prediction module, an early warning release module, a root cause positioning module, a feedback learning module and the like. According to the invention, the early-stage, accurate and automatic early warning and root cause analysis of the full-process risk of order fulfillment are realized.

The invention relates to the technical field of artificial intelligence engineering, and provides a supply chain management-oriented multi-modal AI large model fusion software platform construction method, which adopts the following technical scheme that the supply chain management-oriented multi-modal AI large model fusion software platform construction method comprises the following steps of: collecting multi-source heterogeneous data in a supply chain, comprising real-time inventory change data, historical demand fluctuation characteristics, emergency semantic information, transportation mode multi-modal information, supplier risk scores and public opinion analysis results, and non-structured documents and numerical indexes of cross-enterprise business processes, a multi-modal AI large model fusing texts, images and time sequence information is constructed based on the collected data, and the multi-modal AI large model is used for analyzing the data. And performing semantic feature extraction, numerical index fusion modeling and risk prediction, outputting based on a multi-modal AI model, dynamically regulating and controlling an inventory water level, adaptively regulating and controlling a purchase plan, intelligently regulating and controlling a logistics scheduling strategy, foresight regulating and controlling a supply chain cooperation strategy, and regulating and controlling an order fulfillment progress in real time.

Based on orders fulfilled by shoppers of an online concierge system, the online concierge system identifies items in an order that are difficult to find in a warehouse in which the order is fulfilled. When a shopper obtains a difficult to find item from the warehouse, the online concierge system prompts the shopper to provide information for finding the difficult to find item in the warehouse. The online concierge system stores the information for finding the difficult to find item from the shopper in association with the difficult to find item and with the warehouse. Subsequently, when a different shopper is fulfilling an order from the warehouse including the difficult to find item, the online concierge system displays the information for finding the difficult to find item in the warehouse to the different shopper.

Order fulfillment system comprises an order sortation system and an article storage and retrieval system positioned directly above the order sortation system. A controller is configured to: receive an order; direct a retrieval mechanism to move a storage bin containing a first article required for the order to an output station; determine a first destination receptacle among a plurality of destination receptacles to deliver, with a computer controlled vehicle, the first article based on a destination determined for the first article; direct collecting, at the output station, of the first article from the storage bin; direct the computer controlled vehicle to transport the first article received at the output station to the first receptacle.

The invention provides an order fulfillment method and system supporting delayed delivery after cut-off, and relates to the technical field of warehouse management, and the method comprises the steps: obtaining fulfillment orders of a plurality of delayed delivery requests; carrying out delayed delivery qualification verification on each fulfillment order; inquiring the real-time state of a warehouse-out notification list associated with each verified performance order in the warehouse; according to the real-time state, classifying and intercepting the performance orders passing the verification to obtain a plurality of first target performance orders which are successfully intercepted; performing performance order delivery date modification processing on each first target performance order to generate a plurality of second target performance orders; under the condition that each second target fulfillment order reaches the delivery day cut-off time, generating a finished goods supply order, an ex-warehouse notification order and a delivery carriage acceptance order of the second target fulfillment order, and synchronously executing management operations of inventory freezing, inventory release and inventory supplementary occupation; and otherwise, waiting for the second target fulfillment order to the delivery day cut-off time.

A storage, retrieval and processing system is disclosed for processing objects. The storage, retrieval and processing system includes a plurality of bins including objects to be distributed, the plurality of bins being provided on a conveyance system, a programmable motion device that includes an end effector for grasping and moving any of the objects, the programmable motion device being capable of reaching any of the objects within at least one of the plurality of bins in an input area of the conveyance system, a perception system for providing perception data regarding a selected object that is presented to the perception system by the programmable motion device, and a routing conveyance system including a track-mounted shuttle for receiving the selected object, and for moving the selected object in each of horizontal and vertical directions toward a destination container responsive to the perception data.

The invention belongs to the related technical field of vehicle control and scheduling, and particularly relates to an unmanned vehicle scheduling system and method based on strategy driving, and the system employs a three-layer architecture, and comprises a framework layer which comprises a visual human-computer interaction interface, a scheduling service system and an automatic driving system, data management, work order management, scheduling task execution and unmanned vehicle state feedback functions are provided, and the layer is kept unchanged when a service scene is newly added; the strategy layer supports three elements of dynamic configuration and strategy management and provides a customized interface of scheduling rules and parameters, and the three elements of the strategy comprise a scheduling algorithm plug-in program, a parameter configuration page and a new work order page; and the scheduling layer is used for instantiating and operating scheduling logic, associating a specific strategy, calling the function of the framework layer and the scheduling capability defined by the strategy layer to command the unmanned vehicle to complete scheduling operation, and realizing business work order fulfillment. The system and the method have the advantages of rapid scene adaptation, high system compatibility, low development cost, flexible service configuration and the like.

Described are vending and storage systems and methods for consumables, such as consumer packaged goods, and, in particular, to systems and methods that provide one or more of user verification, optimized storage conditions and monitoring of the consumer packaged goods, dispensing mechanisms, and virtual assistance to the user. Described are also systems and methods for automating and optimizing order fulfillment of products by tracking current inventory and analyzing inventory velocity for production planning.

A mobile robot for travelling within an order fulfillment center including a deck and one or more vertical storage racks, a vertical storage rack of the one or more storage racks including a plurality of horizontal rails, spaced apart from each other on respective levels of the vertical storage rack, and a pair of vertical tracks, spaced apart from each other and extending between the plurality of levels of the vertical storage rack, the mobile robot including a motor, a shaft configured to be driven by the motor, first and second pairs of wheels and a pair of drive gears.

A system includes a plurality of stocked items arranged throughout a store, wherein each of the stocked items includes an item identifier (ID) code. A computing system receives an electronic customer order that includes a plurality of ordered items. A mobile scanning device is carried by a user and includes a mobile scanning device ID. The mobile scanning device wirelessly receives the electronic customer order. The mobile scanning device scans a first item ID code on a first stocked item and wirelessly transmits the mobile scanning device ID to the computing system along with the first item ID code indicating that the first stocked item has been picked and scanned. The computing system generates association data that associates the mobile scanning device ID with the first item ID code. The association data indicates that the first stocked item is moving throughout the store with the mobile scanning device.