394 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.

Systems and methods for distributed order fulfillment are disclosed. Real-time fulfillment data indicating unavailability of a first item identified in an order is received from a first computing device. The real-time fulfillment data is generated in response to an input received by the first computing device. Order update data indicating unavailability of the first item and one or more substitute items is transmitted to a second computing device. The order update data causes the second computing device to generate an interface including the substitute items and a portion of the order update data. A substitute response from the second computing device. In response to receiving the substitute response, The selected substitute item is substituted in place of the first item during fulfillment when the substitute response indicates acceptance the substitute item and the first item is removed from the order when the substitute response indicates rejection of the substitute items.

The invention relates to the technical field of information, in particular to an omni-channel order management system based on a block chain. Comprising an order management and digital twinning module, an inventory management and cloud network cooperation module, a payment and intelligent contract module, a logistics and edge computing module, a customer service and after-sales management module, an authority management and security module, a data analysis and prediction module and a compliance and audit module. The order management and digital twinning module creates a digital model for an order through a digital twinning technology, and displays the change of an order state in real time; and the inventory management and cloud network cooperation module synchronizes inventory information of each channel in real time and dynamically allocates resources by using a cloud network fusion technology. Through digital twinning, an enterprise can dynamically monitor each link of an order in real time, when a complex supply chain is processed, problems can be predicted in advance, delay or order loss is avoided, and more efficient and more transparent order fulfillment is realized.

A customer application renders a customer graphical user interface including a plurality of items for sale at a store and generates a customer order in response to input received from the customer. The customer order includes a plurality of ordered items for picking in the store. The customer application is configured to generate an order modification interface configured to receive at least one of addition modifications, deletion modifications, and substitution modifications. A computing system is configured to determine whether the customer order is eligible to be modified by the customer based on satisfaction of one or more modification eligibility conditions and send an eligibility determination to the customer device that indicates whether the customer order is eligible to be modified. The customer application is configured to receive customer input in the order modification interface that modifies the customer order when the customer order is eligible to be modified.

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.

A material handling system for retrieving, transporting, and delivering inventory totes for order fulfillment activities within a material handling facility, includes unit sortation fields, a storage area, a shuttle system with shuttles, and a control system. The unit sortation fields are arranged within the material handling facility, each for sorting inventory items for order fulfillment, with each inventory item required by an order. The storage area stores inventory totes, each containing inventory items. Each of the shuttles retrieves an inventory tote from the storage area for delivery to a selected unit sortation field of the unit sortation fields. The control system controls the order fulfillment activities within the material handling facility. The control system selects a unit sortation field from the unit sortation fields based upon the order fulfillment operations underway within ones of the unit sortation fields.

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.

In a method for storing and picking stackable article carriers in an order fulfillment facility, in which transport loading aids are transported between a loading station with an automatically operated loading device, a storage zone and a removal station with an automatically operated unloading device by autonomously moveable, driverless transport vehicles, the transport loading aids are loaded with article carrier stacks in the loading station and the article carrier stacks are discharged from the transport loading aids in the unloading station. Subsequently, the article carriers for a picking order are reloaded onto one or multiple target loading aids. Further, an order fulfillment facility stores and stackable article carriers.

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.

A computer program product for prioritizing order fulfillment at a retail sales facility receives purchase request information at a retail sales computing device that includes identifying information and order information for the first customer, receives location information for the first customer, processes the received location information for the first customer together with location information of the retail sales facility to predict an estimated time of arrival (ETA) for the first customer at the retail sales facility, schedules a time interval for acquiring updated location information for the first customer, and places the identifying information, order information and ETA for the first customer in an order fulfillment queue for all current customers. Entries in the order fulfillment queue are ordered in increasing ETA order. ETA is continuously updated for each customer, and thereby the order for fulfilling customer orders in the order fulfillment queue. For example, if a customer's distance from the retail sales facility increases (for example, due to a mistaken turn), that customer's order will drop to a later-served position in the queue. The location information for the first customer includes GPS-based location information of the first customer that is captured by a GPS device associated with a mobile computing device of the first customer. The location information for the retail sales facility includes GPS-based location information of the retail sales facility. The ETA prediction is performed using the GPS-based location information of the first customer and the GPS-based location information of the retail sales facility.

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.