KR102575630B1 - Computer-implemented systems and methods for artificial intelligence (ai)-based inbound plan generation - Google Patents

Abstract

Systems and methods for AI-based inbound plan creation are provided. The systems and methods use the model to predict available storage associated with a fulfillment center; compute a constraint capacity associated with a fulfillment center; predict demand capacity associated with fulfillment centers; determine an inbound constraint based on the predicted available storage and the calculated constrained capacity, using the predicted available storage, the calculated constrained capacity, and the predicted demand capacity; Calculate the difference between the predicted demand capacity and the determined inbound constraint; and performs preprocessing planning by outputting excess demand capacity or remaining available capacity based on the calculated difference; and generating an inbound plan for the fulfillment center based on the output of the preprocessing plan performed.

Description

Computer-Implemented Systems and Methods for Artificial Intelligence (AI) Based Inbound Plan Generation

This disclosure relates generally to computerized systems and methods for AI-based inbound plan generation. In particular, embodiments of the present disclosure relate to inventive and non-traditional systems that involve using models to predict future inventory and available storage to create inbound plans for fulfillment centers.

Fulfillment Centers (FCs) encounter millions of products every day as they operate to fulfill consumer orders as soon as they are placed and enable shippers to pick up shipments. Tasks for inventory management within the FC may include receiving merchandise from sellers, storing received merchandise for easy picking access, packing items, confirming orders, and delivering packages. Currently, existing FCs and systems for inventory management of FCs are configured to handle large volumes of incoming and outgoing goods, but when FCs receive more orders than they can handle, storage space is required for FCs to receive all of these orders. A common problem arises from not properly allocating the FCs, resulting in products not being properly distributed among the FCs. For example, a seller associated with FC may order a large quantity of products from a supplier during peak season, but the FC may not have sufficient resources to receive the ordered products in a timely manner. This leads to a large backlog problem in FC by slowing down all receiving processes, which eventually accumulates into future problems. Backlog problems can lead to lost sales because they prevent sellers from generating revenue by distributing products.

Traditional FC management systems typically manually allocate storage space based on orders received to alleviate such problems. However, these traditional systems do not efficiently solve the backlog problem because they rely only on data related to orders as they are received. Moreover, these traditional systems can only rely on data associated with a single FC instead of multiple FCs, preventing optimization of FC storage allocation for incoming orders and reducing productivity.

Accordingly, improved methods and systems are needed for creating inbound plans to optimize storage allocation between FCs.

Registered Patent Publication No. 10-2264625 (2021.06.14)

One aspect of the present disclosure relates to a computer-generated system for AI-based inbound plan generation, the system including a memory storing instructions and at least one processor. The at least one processor uses the model to predict available storage associated with the fulfillment center, compute constrained capacity associated with the fulfillment center, predict demand capacity associated with the fulfillment center, and use the predicted available storage, calculated determine an inbound constraint based on the predicted available storage and the calculated constraint capacity, using the constraint capacity and the predicted demand capacity, and calculate a difference between the predicted demand capacity and the determined inbound constraint; and executing a command to perform a preprocessing plan by outputting an excess demand capacity or remaining available capacity based on the calculated difference, and to generate an inbound plan for the fulfillment center based on the output of the performed preprocessing plan. can be configured to

Another aspect of the present disclosure relates to a computer-implemented method for AI-based inbound plan generation, the method using a model to predict available storage associated with a fulfillment center, calculate a constrained capacity associated with a fulfillment center, Forecast the demand capacity associated with the fulfillment center, use the predicted available storage, calculated constrained capacity, and predicted demand capacity to determine inbound constraints based on the predicted available storage and calculated constrained capacity, and forecasted demand calculate the difference between the capability and the determined inbound constraint; and performing a preprocessing plan by outputting excess demand capacity or remaining available capacity based on the calculated difference, and generating an inbound plan for the fulfillment center based on the output of the performed preprocessing plan. .

Another aspect of the disclosure relates to a computer-generated system for AI-based inbound plan generation, the system including a memory storing instructions and at least one processor. The at least one processor uses the model to predict available storage associated with the fulfillment center, compute constrained capacity associated with the fulfillment center, predict demand capacity associated with the fulfillment center, and use the predicted available storage, calculated determine an inbound constraint based on the predicted available storage and the calculated constraint capacity, using the constraint capacity and the predicted demand capacity, and calculate a difference between the predicted demand capacity and the determined inbound constraint; and performing a preprocessing plan by outputting excess demand capacity or remaining available capacity based on the calculated difference, generating an inbound plan for the fulfillment center based on the output of the performed preprocessing plan, and predicting availability Calculate an average absolute percentage error between storage and actual available storage; if it determines that the calculated average absolute percentage error is above the threshold, select a new model to predict available storage; and by creating a new inbound plan for the fulfillment center based on the new model, executing instructions to perform verification tests on the model.

Other systems, methods and computer-readable media are also discussed herein.

1A is a schematic block diagram illustrating an exemplary embodiment of a network including a computer system for communications enabling delivery, transportation, and logistics operations, in accordance with disclosed embodiments.
FIG. 1B is a diagram illustrating a sample of a Search Results Page (SRP) that includes one or more search results that satisfy a search request in accordance with an interactive user interface element, in accordance with a disclosed embodiment.
1C is a diagram illustrating a sample of a Single Detail Page (SDP) containing a product and information about the product along with interactive user interface elements, in accordance with a disclosed embodiment.
1D is a sample diagram of a shopping cart page that includes items in a virtual shopping cart according to interactive user interface elements, in accordance with disclosed embodiments.
1E is a diagram illustrating a sample of an order page including items along with information regarding purchase and delivery from a virtual shopping cart, in accordance with interactive user interface elements, in accordance with disclosed embodiments.
2 is a schematic diagram of an exemplary fulfillment center configured to utilize the disclosed computer system, in accordance with a disclosed embodiment.
3 is an exemplary network of devices and systems for AI-based inbound plan creation, in accordance with a disclosed embodiment.
4 depicts an exemplary sub-process for AI-based inbound plan creation, in accordance with a disclosed embodiment.
5 depicts an example sub-process for AI-based inbound plan creation, in accordance with a disclosed embodiment.
6 illustrates an example sub-process for AI-based inbound plan creation, in accordance with a disclosed embodiment.
7 depicts an example process for AI-based inbound plan creation, in accordance with a disclosed embodiment.

Next, it will be described in detail with reference to the accompanying drawings. Wherever possible, like reference numerals are used in the drawings to refer to the same or like parts in the following description. While several exemplary embodiments are described herein, modifications, adjustments, and other implementations are possible. For example, substitutions, additions, or changes may be made to components and steps in the drawings, and the exemplary methods described herein may be changed by replacing, reordering, removing, or adding steps to the disclosed methods. Accordingly, the detailed description that follows is not limited to the disclosed embodiments and examples. Instead, the proper scope of the invention is defined by the claims.

Embodiments of the present disclosure relate to systems and methods configured for AI-based inbound plan generation. In some embodiments, the system may use the model to predict the available storage capacity (ASC) associated with the FC. In some embodiments, the system may use the model to predict ASC for a plurality of selected Expected Delivery Dates (EDDs) (eg, in a specific time period, such as two weeks). In some embodiments, the system may use the model to predict ASC for multiple FCs. In some embodiments, the system may use the same or different combinations of different models to make predictions.

In some embodiments, the system may use the model to predict ASC for specific storage types. For example, storage types may include pallets and bins. In some embodiments, the system may use the model to predict ASC for specific competency types. For example, competency types may include totable, non-totable, and grande. In some embodiments, the system may be a combination of storage type and capability type (e.g., bin-type toteble product, bin-type non-totable product, bin-type grande product, pallet-type toteble product, pallet-type The model can be used to predict ASC for type non-tortable product or pallet-type grande product).

In some embodiments, stock-keeping units (SKUs) may be sorted by method of final delivery to a customer. In some embodiments, one or more product classes may correspond to different final delivery methods to a customer. For example, certain SKUs, such as low-value, non-perishable SKUs, may be shipped in a bag (i.e., “tongable”). High-value or perishable SKUs may need to be shipped in a box in a different packaging (i.e. “non-tortable”). Other SKUs may be shipped in bulk or in original packaging from the manufacturer or supplier (i.e. “Grande”). For example, a box of toilet paper may be classified as grande, as consumers often purchase large amounts of toilet paper, and the box may be shipped from the destination to the consumer in the original packaging provided by the toilet paper manufacturer. Thus, some storage space in a destination may be tied to a SKU type, such as being allocated to tortable items, non-totable items, and grande items. For example, grande items can be stored in an area where there are few obstacles where a forklift can move, and tortable items can be stored on a shelf.

In some embodiments, SKUs may be further classified based on the rate of processing the SKUs. For example, faster processing SKUs may be stored in FC's pallets, while slower processing SKUs may not be stored in pallets, but instead stored in bins. Thus, a combination of Storage Type and Capacity Type can be Bin-Type Totable Product, Bin-Type Non-Tortable Product, Bin-Type Grande Product, Pallet-Type Totable Product, Pallet-Type Non-Tortable Product, or Pallet-Type Non-Tortable Product. Can include type grande products.

The system may use the predicted ASC, calculated constrained capacity, and predicted demand capacity to perform pretreatment planning. Based on the output of the preprocessing plan performed, the system can generate an inbound plan for the FC. For example, when the predicted demand capacity exceeds the determined minimum capacity (eg, during a peak period), the system allocates excess demand capacity between one or more additional FCs based on the inbound plan generated for each additional FC. can be distributed In some embodiments, the system may optimize the distribution of excess demand capacity based on the type of storage or capacity of the additional FCs. In some embodiments, the system may optimize any distribution of expected demand capacity (eg, but not limited to, excess demand capacity) based on the priority associated with each SKU.

Referring to FIG. 1A , a schematic block diagram 100 illustrating an embodiment of an example system that includes a computer system for communications enabling delivery, transportation, and logistics operations is shown. As shown in FIG. 1A , system 100 may include a variety of systems, each of which may be interconnected through one or more networks. The systems may be connected to each other via a direct connection (eg using cables). The illustrated system includes a Shipment Authority Technology (SAT) system 101, an external front-end system 103, an internal front-end system 105, a transportation system 107, and a mobile device 107A, 107B, 107C. , merchant portal 109, shipment and order tracking (SOT) system 111, fulfillment optimization (FO) system 113, fulfillment messaging gateway (FMG) ( 115), supply chain management (SCM) system 117, warehouse management system 119, mobile devices 119A, 119B, 119C (located inside the fulfillment center (FC) 200) ), a third party fulfillment system (121A, 121B, 121C), a fulfillment center authorization system (FC Auth) 123, and a labor management system (LMS) 125 ).

In some embodiments, SAT system 101 may be implemented as a computer system that monitors order status and delivery status. For example, the SAT system 101 can determine if an order has passed its Promised Delivery Date (PDD), initiate a new order, reship items in undelivered orders, and deliver undelivered items. take appropriate action, including canceling unsolicited orders and initiating contact with the ordering customer. The SAT system 101 may also monitor other data, including outputs (such as the number of packages shipped over a specified period of time), and inputs (such as the number of empty cardboard boxes received for use in delivery). . The SAT system 101 also enables communication (e.g., using store-and-forward or other techniques) between devices such as external front-end systems 103 and FO systems 113. It can act as a gateway between different devices in the system 100 that allows

In some embodiments, external front end system 103 may be implemented as a computer system that allows external users to interact with one or more systems within system 100 . For example, in an embodiment where system 100 enables presentation of the system so that a user can place an order for an item, external front end system 103 receives the search request, presents the item page, and , can be implemented as a web server requesting payment information. For example, external front-end system 103 may be implemented as a computer or computers running software such as Apache HTTP Server, Microsoft Internet Information Services (IIS), NGINX, and the like. In another embodiment, external front-end system 103 receives and processes requests from external devices (e.g., mobile device 102A or computer 102B) and, based on these requests, stores databases and other data. It may run custom web server software designed to obtain information from and provide responses to received requests based on the obtained information.

In some embodiments, external front end system 103 may include one or more of a web caching system, a database, a search system, or a payment system. In one aspect, external front-end system 103 may include one or more of these systems, while in another aspect external front-end system 103 may have an interface (e.g., a server to server, database-to-database, or other network connection).

An exemplary set of steps represented by FIGS. 1B, 1C, 1D and 1E will help explain some operation of the external front end system 103. External front end system 103 may receive information from systems or devices within system 100 for presentation and/or display. For example, the external front-end system 103 may include a Search Result Page (SRP) (eg, FIG. 1B), a Single Detail Page (SDP) (eg, FIG. 1C), One or more web pages may be hosted or provided, including a cart page (eg, FIG. 1D), or an order page (eg, FIG. 1E). A user device (eg, using mobile device 102A or computer 102B) can request a search by going to external front end system 103 and entering information into the search box. External front end system 103 can request information from one or more systems within system 100 . For example, the external front-end system 103 may request information satisfying a search request from the FO system 113. External front-end system 103 may also request and receive (from FO system 113) a Promised Delivery Date or "PDD" for each product included in the search results. In some embodiments, the PDD is an estimate of when a package containing the product will arrive at the user's desired location if ordered within a specified period of time, for example by the end of the day (11:59 PM), or the product will arrive at the user's desired location. (PDD is discussed further below with respect to FO system 113).

The external front-end system 103 may prepare an SRP (eg, FIG. 1B) based on the information. The SRP may contain information that satisfies the search request. For example, this may include photos of products that satisfy the search request. The SRP may also include information about each price for each product, or enhanced delivery options for each product, PDD, weight, size, offers, discounts, and the like. The external front-end system 103 may send the SRP to the requesting user device (eg, over a network).

The user device may select a product from the SRP by clicking or tapping the user interface, for example, or using another input device to select a product represented in the SRP. The user device may make a request for information about the selected product and send it to the external front end system 103 . In response, the external front end system 103 may request information about the selected product. For example, the information may include additional information beyond what is presented for the product on each SRP. This may include, for example, expiration date, country of origin, weight, size, number of items in the package, handling instructions, or other information about the product. Information may also include recommendations for similar products (eg, based on big data and/or machine learning analysis of customers who have purchased this product and at least one other product), answers to frequently asked questions, customer reviews, May include manufacturer information, photos, etc.

The external front-end system 103 may prepare a Single Detail Page (SDP) (eg, FIG. 1C ) based on the received product information. The SDP may also include other interactive elements, such as a "Buy Now" button, "Add to Cart" button, quantity field, item picture, and the like. The SDP may include a list of vendors offering products. The list may be ordered based on the price offered by each seller, such that the seller offering the product at the lowest price is at the top of the list. This list may also be ordered based on seller ranking, such that the highest ranked seller is at the top of the list. The seller ranking may be made based on a plurality of factors, including, for example, the seller's past track record of meeting promised PPDs. The external front-end system 103 may forward the SDP to the requesting user device (eg, over the network).

The requesting user device may receive an SDP listing product information. Upon receiving the SDP, the user device can interoperate with the SDP. For example, the user of the requesting user device may click on or interact with the "Place in Cart" button of the SDP. This adds the product to the shopping cart associated with the user. The user device may send this request to the external front end system 103 to add the product to the shopping cart.

The external front-end system 103 may generate the shopping cart page (eg, FIG. 1D). In some embodiments, the shopping cart page lists products that the user has added to a virtual “shopping cart”. The user device may request the shopping cart page by clicking or interacting with the icon of the SRP, SDP, or other page. In some embodiments, the shopping cart page includes all products that the user has added to the shopping cart, as well as the quantity of each product, the price per item of each product, the price of each product based on the associated quantity, information about the PDD, delivery method, shipping cost, User interface elements for modifying products in the shopping cart (e.g., deleting or modifying quantities), options for ordering other products or setting regular delivery of products, options for setting up interest payments, purchasing You can list information about the products in the shopping cart, such as user interface elements to proceed. A user of a user device may click on or interact with a user interface element (eg, a button that says "Buy Now") to initiate a purchase of a product in a shopping cart. If so, the user device may send this request to the external front end system 103 to initiate the purchase.

External front end system 103 may generate an order page (eg, FIG. 1E ) in response to receiving a request to initiate a purchase. In some embodiments, the order page re-lists items from the shopping cart and requests entry of payment and shipping information. For example, the order page may include information about the purchaser of the items in the shopping cart (eg name, address, email address, phone number), information about the recipient (eg name, address, phone number, delivery information) , shipping information (eg, speed/method of delivery and/or pickup), payment information (eg, credit card, bank transfer, check, stored credit), user interface elements requesting a cash receipt (eg, tax purposes), etc. The external front end system 103 may send the order page to the user device.

The user device can enter information on the order page and click or interact with user interface elements that send the information to the external front-end system 103 . From there, external front end system 103 transmits information to other systems within system 100 to create and process new orders with products in the shopping cart.

In some embodiments, external front-end system 103 may be further configured to allow merchants to send and receive information related to orders.

In some embodiments, internal front-end system 105 allows internal users (eg, employees of an organization that owns, operates, or leases system 100) to interact with one or more systems within system 100. It can be implemented as a computer system. For example, in an embodiment where system 100 enables presentation of the system to allow a user to place an order for an item, internal front end system 105 may allow an internal user to view diagnostic and statistical information about an order. It can be implemented as a web server that allows you to view, edit item information, or review statistics about an order. For example, internal front-end system 105 may be implemented as a computer or computers running software such as Apache HTTP Server, Microsoft Internet Information Services (IIS), NGINX, and the like. In another embodiment, the internal front-end system 105 receives and processes requests from systems or devices represented within system 100 (as well as other devices not shown) and, based on those requests, stores databases and other data. It can obtain information from (running custom web server software designed) to provide a response to the received request based on the obtained information.

In some embodiments, internal front end system 105 may include one or more of a web caching system, database, search system, payment system, analytics system, order monitoring system, and the like. In one aspect, internal front-end system 105 may include one or more of these systems, while in another aspect internal front-end system 105 may have interfaces (e.g., server-to-server interfaces) connected to one or more of these systems. server, database-to-database, or other network connection).

In some embodiments, transportation system 107 may be implemented as a computer system that enables communication between systems or devices within system 100 and mobile devices 107A-107C. In some embodiments, transport system 107 may receive information from one or more mobile devices 107A-107C (eg, cell phones, smart phones, PDAs, etc.). For example, in some embodiments, mobile devices 107A-107C may include devices operated by deliverymen. A delivery person, which may be full-time, temporary or shift work, may use the mobile device 107A-107C to deliver a package containing products ordered by the user. For example, to deliver a package, the delivery man may receive a notification on the mobile device indicating the package to be delivered and the location to deliver. Upon arrival at the delivery location, the delivery man can leave the package (eg, in the back of a truck or in a crate of packages) and, using a mobile device, data associated with the identifier on the package (eg, a barcode, image, text string, RFID tags, etc.) can be scanned, captured, and packages can be delivered (eg, by placing on a front door, leaving a security guard, handing them off to a recipient, etc.). In some embodiments, the delivery man may use the mobile device to take picture(s) of the package and/or obtain a signature. The mobile device may transmit information to transportation system 107 including information about the delivery including, for example, time, date, GPS location, photo(s), identifier associated with the delivery person, identifier associated with the mobile device, and the like. can Transportation system 107 may store this information in a database (not shown) for access by other systems within system 100 . In some embodiments, transportation system 107 may use this information to prepare and transmit tracking data indicating the location of a particular package to other systems.

In some embodiments, a particular user may use one type of mobile device (eg, a full-time employee may use a professional PDA with custom hardware such as a barcode scanner, stylus, and other devices) while other users may use different types of mobile devices (eg, temporary or shift workers may use off-the-shelf cell phones and/or smart phones).

In some embodiments, transportation system 107 may associate a user with each device. For example, the transportation system 107 may associate a user (e.g., represented by a user identifier, employee identifier, or phone number) and a mobile device (e.g., International Mobile Equipment Identity (IMEI), International Mobile Subscription Identifier (e.g., IMSI), phone number, Universal Unique Identifier (UUID), or Globally Unique Identifier (GUID)). The transportation system 107 may use these associations in conjunction with data received upon delivery to analyze the data stored in the database to determine, among other things, the location of the operator, the efficiency of the operator, or the speed of the operator.

In some embodiments, merchant portal 109 may be implemented as a computer system that allows a merchant or other external entity to communicate electronically with one or more systems within system 100 . For example, a seller may use a computer system (not shown) to upload or provide product information, order information, contact information, etc. for a product to be sold through the system 100 using the seller portal 109. there is.

In some embodiments, delivery and order tracking system 111 receives, stores, and receives information about the location of a package containing products ordered by a customer (eg, a user using devices 102A-102B). It can be implemented as a forwarding computer system. In some embodiments, the delivery and order tracking system 111 may request or store information from a web server (not shown) operated by a delivery company that delivers packages containing products ordered by customers.

In some embodiments, delivery and order tracking system 111 may request and store information from systems represented in system 100 . For example, shipping and order tracking system 111 may request information from transportation system 107 . As noted above, transportation system 107 may be configured to provide one or more mobile devices 107A-107C (eg, a mobile phone) associated with one or more of a user (eg, delivery man) or vehicle (eg, delivery truck). , smart phone, PDA, etc.) can receive information. In some embodiments, shipment and order tracking system 111 may also use a warehouse management system (WMS) 119 to determine the location of individual products within a fulfillment center (eg, fulfillment center 200). You can request information from Shipping and order tracking system 111 requests data from one or more of transportation system 107 or WMS 119, processes it, and provides it to a device (eg, user device 102A, 102B) upon request. can do.

In some embodiments, fulfillment optimization (FO) system 113 may send information about customer orders from other systems (eg, external front-end system 103 and/or shipping and order tracking system 111). It can be implemented as a computer system that stores. The FO system 113 may also store information indicating where certain items are maintained or stored. For example, certain items may be stored in only one fulfillment center, while certain other items may be stored in multiple fulfillment centers. In another embodiment, a particular fulfillment center may be configured to store only a particular set of items (eg, fresh produce or frozen products). The FO system 113 stores this information as well as related information (eg, quantity, size, date received, expiration date, etc.).

The FO system 113 may also calculate a corresponding Promised Delivery Date (PDD) for each product. In some embodiments, PDD may be based on one or more elements. For example, the FO system 113 may determine past demand for a product (eg, how many customers have ordered that product over a period of time), forecasted demand for the product (eg, how many customers will have a product in an upcoming period) expected to be ordered), historical demand across the network indicating how many products were ordered over a period of time, forecasted demand across the network indicating how many products are expected to be ordered during the upcoming period, and storing each product The PDD for a product may be calculated based on the number of one or more products stored in each fulfillment center 200, expectations or current orders for that product, and the like.

In some embodiments, FO system 113 periodically (eg, hourly) determines the PDD for each product and retrieves or retrieves it from another system (eg, external front-end system 103, SAT system ( 101), and store it in a database for transmission to the shipping and order tracking system 111). In another embodiment, FO system 113 receives electronic requests from one or more systems (eg, external front end system 103, SAT system 101, shipping and order tracking system 111) and responds to the request. PDD can be calculated according to

In some embodiments, fulfillment messaging gateway (FMG) 115 receives a request or response in one format or protocol from one or more systems in system 100, such as FO system 113, and transmits it in another format or protocol. , and forwards the request or response in the converted format or protocol to another system such as the WMS 119 or the third party fulfillment system 121A, 121B, or 121C, and vice versa. can be implemented

In some embodiments, supply chain management (SCM) system 117 may be implemented as a computer system that performs predictive functions. For example, the SCM system 117 may store, for example, past demand for a product, predicted demand for the product, network-wide past demand, network-wide predicted demand, and stored in each fulfillment center 200. Based on the number of products, expectations or current orders for each product, the level of demand for a particular product can be predicted. In response to these predicted levels and the quantity of each product across all fulfillment centers, the SCM system 117 creates one or more purchase orders to purchase and stock an amount sufficient to satisfy the predicted demand for a particular product. can do.

In some embodiments, warehouse management system (WMS) 119 may be implemented as a computer system that monitors workflow. For example, WMS 119 may receive event data representing individual events from individual devices (eg, devices 107A-107C or 119A-119C). For example, WMS 119 may receive event data indicating the use of one of these devices to scan a package. As discussed below with respect to fulfillment center 200 and FIG. 2 , during the fulfillment process, the package identifier (eg, barcode or RFID tag data) is transferred to a particular stage of the machine (eg, automated or handheld). barcode scanner, RFID reader, high-speed camera, tablet 119A, mobile device/PDA 119B, computer 119C, etc.). WMS 119 may store each event representing a scan or read of a package identifier in a corresponding database (not shown) along with the package identifier, time, date, location, user identifier, or other information, and transfer this information to other systems. (eg, delivery and order tracking system 111).

In some embodiments, WMS 119 may store information associating one or more devices (eg, devices 107A-107C or 119A-119C) with one or more users associated with system 100 . For example, in some situations, a user (such as a part-time or full-time employee) may be associated with a mobile device in that it owns it (eg, the mobile device is a smartphone). In another situation, a user temporarily stores a mobile device (e.g., a user who rents a mobile device from the start of the day, uses it for the day, and returns it at the end of the day), may be related to

In some embodiments, WMS 119 may maintain an activity log for each user associated with system 100. For example, WMS 119 may perform any assigned process (e.g., unloading a truck, picking up an item at a pickup area, working a rebin wall, packing an item), user identifier, location ( For example, the floor or area of fulfillment center 200), the number of units moved through the system by an employee (eg, number of items picked up, the number of packed items), the number of devices (eg, number of items packed), , identifiers associated with devices 119A-119C), and the like. In some embodiments, WMS 119 may receive check-in and check-out information from a timekeeping system, such as a timekeeping system operating on devices 119A-119C.

In some embodiments, third party fulfillment (3PL) systems 121A-121C represent computer systems associated with third party providers of logistics and products. For example, some products may be stored at fulfillment center 200 (as described below with respect to FIG. 2) while other products may be stored off-site or on demand. It can be produced according to, and cannot be stored in the fulfillment center 200 otherwise. 3PL systems 121A-121C may be configured to receive orders from FO system 113 (eg, via FMG 115) and deliver products and/or services (eg, delivery or installation) can be provided. In some implementations, one or more 3PL systems 121A-121C may be part of system 100, while in other implementations, one or more 3PL systems 121A-121C may be external to system 100 ( eg, owned or operated by a third party provider).

In some embodiments, the fulfillment center authentication system (FC Auth) 123 may be implemented as a computer system with various functions. For example, in some embodiments, FC Auth 123 may act as a single-sign on (SSO) service to one or more other systems within system 100 . For example, FC Auth 123 allows the user to log in via the internal front end system 105, determines that the user has similar permissions to access resources in the shipping and order tracking system 111, and Allows users to access those privileges without requiring a second login process. In another embodiment, FC Auth 123 allows users (eg, employees) to associate themselves with specific tasks. For example, some employees may not have an electronic device (such as devices 119A-119C) and may instead move between jobs and between areas within fulfillment center 200 during the day. FC Auth 123 can be configured to indicate what these employees are doing at different times and which zones they belong to.

In some embodiments, labor management system (LMS) 125 may be implemented as a computer system that stores attendance and overtime information for employees (including full-time and part-time employees). For example, LMS 125 may receive information from FC Auth 123, WMS 119, devices 119A-119C, transport system 107, and/or devices 107A-107C.

The specific configuration shown in FIG. 1A is by way of example only. For example, while FIG. 1A shows FC Auth system 123 coupled to FO system 113, not all embodiments require this particular configuration. Indeed, in some embodiments, the systems within system 100 can be used to connect to the Internet, intranets, wide-area networks (WANs), metropolitan-area networks (MANs), wireless networks conforming to IEEE 802.11a/b/g/n standards, leased They may be connected to each other through one or more public or private networks including lines and the like. In some embodiments, one or more of the systems in system 100 may be implemented as one or more virtual servers implemented in a data center, server farm, or the like.

2 shows a fulfillment center 200 . Fulfillment center 200 is an example of a physical location that stores items for delivery to customers upon order. The fulfillment center (FC) 200 can be divided into a number of zones, each of which is shown in FIG. 2 . In some embodiments, these "zones" can be thought of as virtual divisions between different stages of the process of receiving an item, storing an item, retrieving an item, and shipping the item. Thus, while “zones” are shown in FIG. 2, in some embodiments other divisions of zones are possible, and the zones of FIG. 2 may be omitted, duplicated, or modified.

The inbound zone 203 represents an area of the FC 200 where items are received from vendors who wish to sell products using the system 100 of FIG. 1A. For example, a vendor may use a truck 201 to deliver items 202A and 202B. Item 202A may represent a single item large enough to occupy its own shipping pallet, and item 202B may represent a set of items stacked together on the same pallet to save space.

An operator may receive an item in the inbound area 203 and optionally check the item for damage and correctness using a computer system (not shown). For example, an operator may use the computer system to compare the quantity of items 202A, 202B to the ordered quantity of the item. If the quantities do not match, the operator may reject one or more of the items 202A and 202B. If the quantities match, the operator can transport the items (eg, by dolly, handtruck, forklift, or manually) to the buffer area 205 . Buffer zone 205 may be a temporary storage area for items that are not currently needed in a pickup zone, for example because there are sufficient quantities of those items in the pickup zone to meet predicted demand. In some embodiments, forklift 206 operates to transport items around buffer zone 205 and between inbound zone 203 and drop zone 207 . If items 202A, 202B are needed at the pick-up area (eg, due to predicted demand), the forklift may transport the items 202A, 202B to the drop area 207 .

The drop zone 207 may be an area of the FC 200 where items are stored prior to being transported to the pickup zone 209 . An operator assigned to a pickup operation ("picker") accesses an item 202A, 202B in the pickup area, scans a barcode for the pickup area, and retrieves a mobile device (e.g., device 119B). can be used to scan barcodes associated with items 202A and 202B. The picker may then take the item to the pick-up area 209 (eg, by placing on a cart or transporting).

Pickup area 209 may be an area of FC 200 where item 208 is stored in storage unit 210 . In some embodiments, storage unit 210 may include one or more of a physical shelf, bookshelf, box, tote, refrigerator, freezer, cold storage, or the like. In some embodiments, pickup area 209 may be organized into multiple floors. In some embodiments, a worker or machine may pick up the item in a variety of ways, including, for example, a forklift, elevator, conveyor belt, cart, hand truck, wheelbarrow, automated robot or device, or manual operation to pick up area 209. can be transported to For example, a picker can drop items 202A and 202B on a hand truck or cart in drop zone 207 and take items 202A and 202B to pickup zone 209 .

A picker may receive a command to place (or “stow”) an item in a particular spot in the pick-up area 209 , such as a particular space on the storage unit 210 . For example, the picker can scan item 202A using a mobile device (eg, device 119B). The device may indicate where the item 202A should be loaded, for example, using aisles, shelves, and position indicating systems. The device can then have the picker scan the barcode at that location before loading item 202A at that location. The device may transmit data (e.g., over a wireless network) to a computer system, such as WMS 119 of FIG. 1A, indicating that item 202A has been loaded into its location by a user using device 119B. .

Once the user places an order, the picker may receive instructions to device 119B to retrieve one or more items 208 from storage unit 210 . The picker may retrieve the item 208, scan the barcode on the item 208, and place it on the vehicle 214. In some embodiments, transport device 214 is represented as a slide, however, transport device may be implemented as one or more of a conveyor belt, elevator, cart, forklift, hand truck, wheelbarrow, and the like. Item 208 may then arrive at packing area 211 .

Packing area 211 may be the area of FC 200 where items are received from pick-up area 209 and packed into boxes or bags for final shipment to customers. At the packing area 211, a worker assigned to receive the item ("rebin worker") will receive the item 208 from the pickup area 209 and determine which order it corresponds to. For example, a refining operator may use a device such as computer 119C to scan a barcode on item 208 . Computer 119C may visually indicate which order item 208 is associated with. This may include, for example, spaces or "cells" on the wall 216 corresponding to orders. Once the order is complete (eg, because the cell contains all of the items in the order), the refining operator can notify the packer (or "packer") that the order is complete. A packer can retrieve items from the cell and place them in a box or bag for shipping. The packer may then direct the boxes or bags to the hub area 213 via, for example, a forklift, cart, wheelbarrow, hand truck, conveyor belt, hand or other method.

The hub area 213 may be an area of the FC 200 that receives all boxes or bags (“packages”) from the packing area 211 . Workers and/or machines in the hub area 213 can retrieve the packages 218, determine which part of the delivery area each package is to be delivered to, and direct the packages to the appropriate camp area 215. For example, if a delivery area has two small sub-areas, the package will be sent to one of the two camp areas 215. In some embodiments, an operator or machine may scan the package (eg, using one of devices 119A-119C) to determine its final destination. Sending the package to the camp area 215 may include, for example, determining the portion of the geographic area the package is destined for (based on zip code) and determining the camp area 215 associated with the portion of the geographic area. can

In some embodiments, camp zone 215 may include one or more buildings, one or more physical spaces, or one or more areas from which packages are received from hub zone 213 for classification into routes and/or sub-routes. . In some embodiments, camp area 215 is physically separate from FC 200 , while in other embodiments camp area 215 may form part of FC 200 .

Workers and/or machines in the camp area 215 may, for example, compare destinations with existing routes and/or sub-routes, calculate workload for each route and/or sub-route, time of day, delivery Which route and/or sub-route the package 220 should be associated with may be determined based on the method, the cost to ship the package 220, the PDD associated with the item in the package 220, and the like. In some embodiments, an operator or machine may scan the package (eg, using one of devices 119A-119C) to determine its final destination. Once a package 220 is assigned to a particular route and/or sub-route, operators and/or machines can transport the package 220 to be shipped. In exemplary FIG. 2 , camp area 215 includes a truck 222 , a car 226 , and couriers 224A and 224B. In some embodiments, courier 224A may drive truck 222, where courier 224A is a full-time employee who delivers packages for FC 200, and the truck owns FC 200. , is owned, leased, or operated by the same company that leases or operates it. In some embodiments, courier 224B may drive car 226, where courier 224B is a "flex" or emergency worker who makes deliveries on an as-needed (eg, seasonal) basis. am. Car 226 may be owned, leased or operated by delivery person 224B.

Referring to FIG. 3 , an exemplary network of devices and systems for AI-based inbound plan creation is shown. As shown in FIG. 3 , system 300 may include an inbound constraint capability system 330 , an inbound demand forecasting system 340 , and a preprocessing planning system 350 , each of which may include network 310 . Through this, it is possible to communicate with the user device 320 associated with the user 320A. In some embodiments, the inbound constraint capability system 330, the inbound demand forecasting system 340, and the pretreatment planning system 350 may be connected to other components of the system 300 via a direct connection, for example using cables. can communicate with In some other embodiments, system 300 may be part of system 100 of FIG. 1A and communicate with other components of system 100 via network 310 or via a direct connection, eg, using cables. can communicate Inbound constraint capability system 330, inbound demand forecasting system 340, and preprocessing planning system 350 may each include a single computer or multiple computers interoperating to perform one or more processes and functions associated with the disclosed examples. Each may be composed of a distributed computer system including In other embodiments, functionality associated with systems 330, 340, and 350 may be implemented by fewer systems than shown in FIG.

As shown in FIG. 3 , inbound constrained capability system 330 may include a processor 332 , a memory 334 , and a database 336 . The inbound demand forecasting system 340 may include a processor 342 , a memory 344 , and a database 346 . The preprocessing planning system 350 may include a processor 352 , a memory 354 , and a database 356 . The processor 332, 342 or 352 may include one or more known processing devices, such as the Pentium ^™ family of microprocessors manufactured by Intel ^™ or the Turion ^™ family manufactured by AMD ^™ . Processor 332, 342 or 352 may constitute a multi-core or single-core processor that simultaneously executes parallel processes. For example, processor 332, 342, or 352 may concurrently execute and control multiple processes using a logical processor. The processor 332, 342 or 352 may implement virtual machine technology or other known technology to provide the ability to execute, control, run, manipulate, store, etc. multiple software processes, applications, programs, etc. In another example, the processors 332, 342, or 352 are configured to provide parallel processing capabilities so that the inbound constraint capability system 330, the inbound demand forecasting system 340, and the preprocessing planning system 350 can run multiple processes concurrently. It may include a configured multi-core processor device. Those skilled in the art will understand that other types of processor devices may be implemented that provide the capabilities disclosed herein.

Memory 334, 344 or 354 may store one or more operating systems that perform known operating system functions when executed by processor 332, 342 or 352, respectively. By way of example, the operating system may include Microsoft Windows, Unix, Linux, Android, Mac OS, iOS or other types of operating systems. Thus, examples of the disclosed subject matter may operate and function as computer systems running any type of operating system. Memory 334, 344 or 354 may be a volatile or nonvolatile, magnetic, semiconductor, tape, optical, removable, non-removable, or other tangible storage device or tangible computer readable medium.

Database 336, 346 or 356 may include, for example, an Oracle ^™ database, a Sybase ^™ database, or other relational database or a non-relational database, such as Hadoop ^™ sequence files, HBase ^™ or Cassandra ^™ . . The database 336, 346 or 356 is a computing component configured to receive and process requests for data stored in the memory devices of the database(s) and provide data from the database(s) (e.g., a database management system, database server, etc.). The database 336, 346 or 356 may include a NoSQL database such as HBase, MongoDB ^™ or Cassandra ^™ . Alternatively, databases 336, 346 or 356 may include relational databases such as Oracle, MySQL and Microsoft SQL Server. In some embodiments, database 336, 346 or 356 may take the form of a server, general purpose computer, mainframe computer, or any combination of these components.

Databases 336, 346, or 356 may store data that may be used by processors 332, 342, or 352, respectively, to perform methods and processes associated with the disclosed examples. The databases 336, 346 or 356 may be located within the inbound constraint capacity system 330, the inbound demand forecasting system 340 or the preprocessing planning system 350, respectively, as shown in FIG. 3, or they may be located in the inbound It may reside within a storage device located external to the constraint capability system 330 , the inbound demand forecasting system 340 , or the preprocessing planning system 350 . Data stored in 332, 342 or 352 may include any suitable data associated with the FC (e.g. inventory data, product data, product order data, past expected delivery date, expected delivery date, past actual delivery date, actual delivery date, current and historical inventory data, predicted available storage capacity, actual available storage capacity, model data, storage type, capacity type, FC profile, historical and actual demand data, generated inbound plans, validation data, SKU data, etc.) .

User device 320 may be a tablet, mobile device, computer, or the like. The user device 320 may include a display. The display may include, for example, a liquid crystal display (LCD), a light emitting diode screen (LED), an organic light emitting diode screen (OLED), a touch screen, and other known display devices. The display may show various information to the user. For example, it may display options for creating an inbound plan, inbound plan created, data associated with creating an inbound plan, FC data, and the like. User device 320 may include one or more input/output (I/O) devices. I/O devices may include one or more devices that enable user device 320 to send and receive information from user 320A or other devices. I/O devices may include various input/output devices, cameras, microphones, keyboards, mouse-type devices, gesture sensors, motion sensors, physical buttons, voice input, and the like. The I/O device may also include an inbound constraint capability system 330, an inbound demand forecasting system 340, or a preprocessing planning system 350, for example, by establishing a wired or wireless connection between the user device 320 and the network 310. ) may include one or more communication modules (not shown) for transmitting and receiving information from.

In some embodiments, user 320A may be an internal user (eg, an employee of an organization that owns, operates, or leases system 100 or 300). Internal front end system 105 may be implemented as a computer system that allows user 320A to interact with system 300 using user device 320 . For example, an embodiment where the system 100 or 300 enables a presentation that allows a user to display options for creating an inbound plan, the generated inbound plan, data associated with creating the inbound plan, FC data, etc. .

In some embodiments, inbound constrained capacity system 330 may use the model to predict available storage capacity (ASC) associated with FC (eg, quantity of product, percentage of total capacity, etc.). For example, current and historical inventory data (eg, finished inventory data, inbound data, outbound data, etc.) for each FC can be used to predict the ASC associated with the FC. In some embodiments, system 330 may predict the ASC by predicting the outbound quantity of the product. For example, system 330 may use the model to predict the outbound quantity of a product for a selected expected delivery date (EDD) using the following formula:

Outbound Quantity of Product (EDD)

= ending stock of product (EDD - 1)

- End of Inventory of Product (EDD)

+ Inbound Quantity of Product (EDD)

Here, "EDD - 1" represents one day before EDD. That is, the outbound quantity of products for the selected EDD can be calculated by subtracting the ending stock of products in the EDD from the ending stock of products the day before the EDD, and adding the inbound quantity of products in the EDD.

System 330 may use the model to calculate the EDD's finished inventory of products using the formula:

End of Inventory of Product (EDD)

= ending stock of product (EDD - 1)

+ Inbound inventory of product (EDD) - Outbound inventory of product (EDD)

That is, system 330 may calculate the ending stock of products in the EDD by adding the inbound inventory of products in the EDD to the ending stock of products the day before the EDD, and subtracting the outbound inventory of products in the EDD.

In some embodiments, system 330 may use the model to predict ASC for a plurality of selected EDDs (eg, in a specific time period, such as two weeks). In some embodiments, system 330 may use the model to predict ASC for multiple FCs. In some embodiments, system 330 may use the same or different combinations of different models to make predictions. In some embodiments, one or more models may predict ASC based on inventory in one or more FCs or cube availability in one or more FCs (eg, 1,000 m ³ , space for 3,000 units, etc.).

In some embodiments, system 330 may use the model to predict ASC for a particular storage type. For example, storage types may include pallets and bins. In some embodiments, system 330 may use the model to predict ASC for specific competency types. For example, competency types may include totable, non-totable, and grande. Toteable competency types can contain products in totes, whereas non-toteable competency types cannot contain products in totes. Grande competency types can lie somewhere between toteable and non-toteable, with products being able to be carried in totes. In some embodiments, system 330 may include a combination of storage type and capability type (e.g., bin-type tortable product, bin-type non-totable product, bin-type grande product, pallet-type tortable product). , pallet-type non-tortable products or pallet-type grande products) to predict the ASC.

In some embodiments, system 330 may calculate a constrained capability associated with FC. For example, system 330 may calculate a constrained capability (eg, quantity of product, percentage of total capability, etc.) based on the inbound infrastructure capability associated with the FC. For example, inbound infrastructure capabilities include the FC's staffing, the staffing for each station in the FC, the FC's driver, the FC's forklift driver, the number of products that can be received based on the FC's available workforce, etc. (For example, the constrained capacity of an FC can be calculated based on the fact that there are 100 stations in the FC, but only 80 workers can work in the FC). In some embodiments, pharmaceutical capabilities may relate to stock-keeping units (SKUs).

In some embodiments, system 330 may calculate a constrained capacity for a particular storage type. For example, storage types can include pallets and bins. In some embodiments, system 330 may calculate a constrained competency for a particular competency type. For example, competency types may include Torable, Non-Tortable, and Grande. In some embodiments, system 330 may include a combination of storage type and capability type (e.g., bin-type tortable product, bin-type non-totable product, bin-type grande product, pallet-type tortable product). , pallet-type non-tortable products or pallet-type grande products) can be calculated.

In some embodiments, SKUs may be categorized by method of final delivery to the customer. For example, certain SKUs, such as low-value, non-perishable SKUs, may be shipped in a bag (i.e., “tongable”). High-value or perishable SKUs may need to be shipped in a box in a different packaging (i.e. “non-tortable”). Other SKUs may be shipped in bulk or in original packaging from the manufacturer or supplier (i.e. “Grande”). For example, a box of toilet paper may be classified as grande, as consumers often purchase large quantities of toilet paper, and the box may be shipped from the destination to the consumer in the original packaging provided by the toilet paper manufacturer. there is. Thus, some storage space in a destination may be tied to a SKU type, such as being allocated to total items, non-totable items, and grande items. For example, grande items can be stored in an area where there are few obstacles where a forklift can move, and tortable items can be stored on a shelf.

In some embodiments, SKUs may be further classified based on the rate of processing the SKUs. For example, faster processing SKUs may be stored in FC's pallets, while slower processing SKUs may not be stored in pallets, but instead stored in bins. Thus, a combination of Storage Type and Capacity Type can be Bin-Type Totable Product, Bin-Type Non-Tortable Product, Bin-Type Grande Product, Pallet-Type Totable Product, Pallet-Type Non-Tortable Product, or Pallet-Type Non-Tortable Product. Can include type grande products.

In some embodiments, system 330 may determine an inbound constraint for one or more FCs by determining a minimum value among the computed ASC and the computed constrained capabilities of the FC. In some embodiments, system 330 may calculate the ASC and constraint capabilities of each FC and determine inbound constraints for each day within the time period. For example, system 330 may determine inbound constraints for one or more FCs for each day of the week. System 330 may calculate an average daily inbound restriction by dividing the total inbound restriction for the week by the number of days in the week. System 330 can generate an inbound plan that takes into account fluctuations within a time period by calculating an average daily inbound constraint, thereby creating a more robust inbound plan.

In some embodiments, inbound demand forecasting system 340 may use financial or inventory data to predict demand capacity associated with FCs. For example, system 340 may forecast national demand for products for the selected EDD. In some embodiments, system 340 may forecast national demand for a product for multiple EDDs within a time period. System 340 may use historical inventory data, supplier lead-time data, supplier delivery cycle data, and the like to predict national demand. In some embodiments, nationwide demand data may be per SKU. In some embodiments, the demand capability may be a forecast capability required to store multiple predicted demand products. System 340 may forecast demand capacity by predicting an outbound target for a product based on financial or inventory data and forecasting inbound inventory required to meet the outbound target. System 340 may forecast demand capacity by maximizing storage among one or more FCs capable of receiving inbound inventory required to meet outbound targets. In some embodiments, system 340 may calculate an average daily demand capacity by dividing the total demand capacity over a period of time (eg, 2 weeks) by the number of days in the week (eg, 14 days). System 340 may generate an inbound plan that takes into account fluctuations within a time period by calculating average daily demand capacity, thereby creating a more robust inbound plan.

In some embodiments, system 340 may predict demand capacity for a particular storage type. For example, storage types can include pallets and bins. In some embodiments, system 340 may calculate demand competencies for specific competency types. For example, competency types may include Torable, Non-Tortable, and Grande. In some embodiments, system 340 may include a combination of storage type and capability type (e.g., bin-type tortable product, bin-type non-totable product, bin-type grande product, pallet-type tortable product). , pallet-type non-tortable products or pallet-type grande products) can be predicted.

In some embodiments, SKUs may be categorized by method of final delivery to the customer. For example, certain SKUs, such as low-value, non-perishable SKUs, may be shipped in a bag (i.e., “tongable”). High-value or perishable SKUs may need to be shipped in a box in a different packaging (i.e. “non-tortable”). Other SKUs may be shipped in bulk or in original packaging from the manufacturer or supplier (i.e. “Grande”). For example, a box of toilet paper may be classified as grande, as consumers often purchase large amounts of toilet paper, and the box may be shipped from the destination to the consumer in the original packaging provided by the toilet paper manufacturer. Thus, some storage space in a destination may be tied to a SKU type, such as being allocated to total items, non-totable items, and grande items. For example, grande items can be stored in an area where there are few obstacles where a forklift can move, and tortable items can be stored on a shelf.

In some embodiments, SKUs may be further classified based on the rate of processing the SKUs. For example, faster processing SKUs may be stored in FC's pallets, while slower processing SKUs may not be stored in pallets, but instead stored in bins. Thus, a combination of Storage Type and Capacity Type can be Bin-Type Totable Product, Bin-Type Non-Tortable Product, Bin-Type Grande Product, Pallet-Type Totable Product, Pallet-Type Non-Tortable Product, or Pallet-Type Non-Tortable Product. Can include type grande products.

In some embodiments, preprocessing planning system 350 may use the predicted ASC from system 330, the calculated constrained capacity from system 330, and the predicted demand capacity from system 340 to perform preprocessing planning. there is. For example, system 350 may include storage type, capability type, or combination of storage type and capability type (e.g., bin-type tortable product, bin-type non-totable product, bin-type grande product, A pretreatment plan can be performed by selecting a pallet-type tortable product, a pallet-type non-totable product, or a pallet-type grande product). For selection, system 350 may retrieve the determined inbound constraints from system 330. System 350 may determine whether the determined inbound constraint is less than or equal to the predicted demand capacity.

If the determined inbound constraint is less than or equal to the predicted demand capacity, system 350 may calculate the difference between the predicted demand capacity and the determined inbound constraint. Since the calculated difference is negative, system 350 may output excess demand capacity. If the determined inbound constraint is less than or equal to the predicted demand capacity, system 350 may calculate the difference between the predicted demand capacity and the determined inbound constraint. Since the calculated difference is positive, system 350 can output the remaining available capacity. Based on the output of the preprocessing plan performed, system 300 may generate an inbound plan for one or more FCs. For example, if the determined inbound constraint exceeds the predicted demand capacity, the system 300 may generate an inbound plan since one or more FCs may store the predicted demand capacity. In some embodiments, the generated inbound plan is a bin-type toteble product, a bin-type non-totable product, a bin-type grande product, a pallet-type toteble product, a pallet-type non-totable product, or a pallet-type product. It may include capacity allocation for at least one of the type grande products, where the predicted demand capacity may be fully allocated or distributed among different capacity storage types.

In some embodiments, when the predicted demand capacity exceeds the determined inbound constraint (eg, during a peak period), the system 350 intersects one or more additional FCs based on the inbound plan generated for each additional FC. Excess demand capacity can be allocated. For example, the system 300 may add storage or capacity types of additional FCs (e.g., bin-type tortable products, bin-type non-totable products, bin-type grande products, pallet-type tortable products, The distribution of excess demand capacity may be optimized based on pallet-type non-tortable products or pallet-type grande products, etc.). In some embodiments, system 300 may optimize any distribution of expected demand capacity (eg, but not limited to, excess demand capacity) based on the priority associated with each SKU.

In some embodiments, one or more systems or components of system 300 may perform various simulations by adjusting one or more variables discussed above. System 300 may generate an inbound plan for one or more FCs based on an optimization in which the demand capacity that can be stored in the FC is maximized (eg, the available storage capacity in one or more FCs is maximized).

In some embodiments, system 300 may perform verification tests on one or more generated inbound plans (eg, after generating the inbound plan, after each simulation of the inbound plan, etc.). For example, system 300 can perform a validation test on a model used to predict ASC by calculating an average absolute percentage error for predicted ASC and actual ASC. Upon determining that the calculated average absolute percentage error is greater than or equal to a threshold value (eg, 20%), system 300 selects a new model to predict ASC and generates a new inbound plan for FC based on the new model. can do.

For example, system 300 can calculate Mean Absolute Percentage Error (MAPE) using the following formula:

where n can be the number of EDDs (eg within a period), t can be the EDD, A _t can be the actual ASC, and F _t can be the predicted (or forecasted) ASC .

In some embodiments, system 300 may calculate Symmetric Mean Absolute Percentage Error (SMAPE) using the following formula:

where n can be the number of EDDs (eg, within a period), t can be the EDD, A _t can be the actual ASC, and F _t can be the predicted (or predicted) ASC.

In some embodiments, system 300 may perform a verification test by calculating the model's tracking signal (TS) using the following formula:

where n can be the number of EDDs (eg within a period), t can be the EDD, A _t can be the actual ASC, F _t can be the predicted (or predicted) ASC, and MAD can be It can be the mean absolute deviation. System 300 may calculate MAD using the following formula.

In some embodiments, if TS is outside a certain range (eg, -4 to 4), system 300 selects a new model to predict ASC and based on the new model a new inbound plan for FC. can create

Referring to FIG. 4 , a process 400 for AI-based inbound plan creation is shown. In some embodiments, system 330 may perform several of the steps described herein, although other implementations are possible. For example, any of the systems and components described and illustrated herein (eg, system 100, system 340, system 350, etc.) can perform the steps described in this disclosure.

At step 401, system 330 may determine whether the selected EDD is before the current date. Then, if YES, system 330 may iterate one day at a time until the selected EDD is on or after the current date. If NO, system 330 may then proceed to step 403 .

At step 403, system 330 may calculate an ASC for the selected EDD. For example, system 330 may use the model to predict an ASC associated with an FC. For example, system 330 may use current and historical inventory data (eg, finished inventory data, inbound data, outbound data, etc.) for each FC to predict the ASC associated with the FC. In some embodiments, system 330 may predict ASC (eg, quantity of product, percentage of total capacity, etc.) by predicting outbound quantity of product. For example, system 330 may use the model to predict the outbound quantity of a product for a selected expected delivery date (EDD) using the following formula:

Outbound Quantity of Product (EDD)

= End stock of product (EDD-1)

- End of Inventory of Product (EDD)

+ Inbound Quantity of Product (EDD)

At step 405, system 330 may determine whether the EDD is the last date in the period. Then, if no, system 330 can return to step 401 and repeat the process described above. If yes, system 330 may then proceed to step 407 . That is, in some embodiments, system 330 may use the model to predict ASC for a plurality of selected EDDs in a particular time period (eg, two weeks). In some embodiments, one or more models may predict ASC based on inventory in one or more FCs or cube availability in one or more FCs (eg, 1,000 m ³ , space for 3,000 units, etc.).

In some embodiments, system 330 may use the model to predict ASC for a particular storage type. For example, storage types can include pallets and bins. In some embodiments, system 330 may use the model to predict ASC for specific competency types. For example, competency types may include Torable, Non-Tortable, and Grande. In some embodiments, system 330 may include a combination of storage type and capability type (e.g., bin-type tortable product, bin-type non-totable product, bin-type grande product, pallet-type tortable product). , pallet-type non-tortable products or pallet-type grande products) to predict the ASC.

In some embodiments, SKUs may be categorized by method of final delivery to the customer. For example, certain SKUs, such as low-value, non-perishable SKUs, may be shipped in a bag (i.e., “tongable”). High-value or perishable SKUs may need to be shipped in a box in a different packaging (i.e. “non-tortable”). Other SKUs may be shipped in bulk or in original packaging from the manufacturer or supplier (i.e. “Grande”). For example, a box of toilet paper may be classified as grande, as consumers often purchase large amounts of toilet paper, and the box may be shipped from the destination to the consumer in the original packaging provided by the toilet paper manufacturer. Thus, some storage space in a destination may be tied to a SKU type, such as being allocated to total items, non-totable items, and grande items. For example, grande items can be stored in an area where there are few obstacles where a forklift can move, and tortable items can be stored on a shelf.

In some embodiments, SKUs may be further classified based on the rate of processing the SKUs. For example, faster processing SKUs may be stored in FC's pallets, while slower processing SKUs may not be stored in pallets, but instead stored in bins. Thus, a combination of Storage Type and Capacity Type can be Bin-Type Totable Product, Bin-Type Non-Tortable Product, Bin-Type Grande Product, Pallet-Type Totable Product, Pallet-Type Non-Tortable Product, or Pallet-Type Non-Tortable Product. Can include type grande products.

At step 407, system 330 may determine whether the selected EDD is before the current date. Then, if yes, system 330 may iterate one day at a time until the selected EDD is on or after the current date. If no, system 330 may then proceed to step 409 .

At step 409, system 330 may calculate a constrained capability associated with the FC. For example, system 330 may calculate the constrained capacity by multiplying the constrained capacity (eg, quantity of product, percentage of total capacity, etc.) for the period based on the inbound infrastructure capacity associated with the FC. For example, the inbound infrastructure capability may include the FC's workforce, the FC's workforce for each station, the FC's driver, the FC's forklift driver, the number of products that can be received based on the FC's available workforce, etc. (For example, the constrained capacity of an FC can be calculated based on the fact that there are 100 stations in the FC, but only 80 workers can work in the FC). In some embodiments, pharmaceutical capabilities may relate to stock keeping units (SKUs).

In some embodiments, system 330 may calculate a constrained capacity for a particular storage type. For example, storage types can include pallets and bins. In some embodiments, system 330 may calculate a constrained competency for a particular competency type. For example, competency types may include Torable, Non-Tortable, and Grande. For example, system 330 can calculate a constraint capacity for each storage type and each constraint capacity. In some embodiments, system 330 may include a combination of storage type and capability type (e.g., bin-type tortable product, bin-type non-totable product, bin-type grande product, pallet-type tortable product). , pallet-type non-tortable products or pallet-type grande products) can be calculated.

At step 411, system 330 may determine whether the EDD is the last date in the period. Then, if no, system 330 can return to step 407 and repeat the process described above. That is, in some embodiments, system 330 may use the model to predict constrained capabilities for a plurality of selected EDDs in a particular period of time (eg, two weeks). If yes, system 330 may then proceed to step 413 .

At step 413 , system 330 may determine whether all FCs of a particular number of FCs (eg, all FCs) have gone through process 400 . For example, in some embodiments, multiple FCs may need to go through process 400 . Then, if no, system 330 may return to step 401 to repeat the process described above. If yes, system 330 can then complete process 400 .

In some embodiments, system 330 may use the model to predict ASC for multiple FCs. In some embodiments, system 330 may use the same or different combinations of different models to make predictions. In some embodiments, system 330 may calculate constrained capabilities for multiple FCs. In some embodiments, system 330 may determine an inbound constraint for one or more FCs by determining a minimum value among the computed ASC and the computed constrained capabilities of the FC. In some embodiments, system 330 may calculate each FC's ASC and constraint capabilities and determine inbound constraints for each day within a given period. For example, system 330 may determine inbound constraints for one or more FCs for each day of the week. System 330 may calculate an average daily inbound restriction by dividing the total inbound restriction for the week by the number of days in the week. System 330 can generate an inbound plan that takes into account fluctuations within a time period by calculating an average daily inbound constraint, thereby creating a more robust inbound plan.

Referring to FIG. 5 , a process 500 for AI-based inbound plan creation is shown. In some embodiments, system 340 may perform several of the steps described herein, although other implementations are possible. For example, any of the systems and components described and illustrated herein (eg, system 100, system 330, system 350, etc.) may perform the steps described in this disclosure. .

At step 501 (eg, after step 413 of FIG. 4 ), system 340 may forecast regional (eg, national) demand for products for the storage type or capability type for the selected EDD. In some embodiments, system 340 may predict national demand for storage types or capability types (eg, products associated with storage or capability types) for multiple EDDs within a given time period. System 340 may use historical inventory data, supplier lead-time data, supplier delivery cycle data, and the like to predict national demand. In some embodiments, system 340 may use financial or inventory data to predict demand capacity associated with FCs. In some embodiments, nationwide demand data may be per SKU. In some embodiments, the demand capability may be a forecast capability required to store multiple predicted demand products.

At step 503, system 340 may forecast demand capacity by predicting an outbound target for a product based on financial or inventory data and predicting inbound inventory required to meet the outbound target. System 340 may forecast demand capacity by maximizing storage among one or more FCs capable of receiving inbound inventory required to meet outbound targets. In some embodiments, system 340 may calculate an average daily demand capacity by dividing the total demand capacity over a period of time (eg, 2 weeks) by the number of days in the week (eg, 14 days). System 340 may generate an inbound plan that takes into account fluctuations within a time period by calculating average daily demand capacity, thereby creating a more robust inbound plan.

In some embodiments, system 340 may predict demand capacity for a particular storage type. For example, storage types can include pallets and bins. In some embodiments, system 340 may calculate demand competencies for specific competency types. For example, competency types may include Torable, Non-Tortable, and Grande. In some embodiments, system 340 may include a combination of storage type and capability type (e.g., bin-type tortable product, bin-type non-totable product, bin-type grande product, pallet-type tortable product). , pallet-type non-tortable products or pallet-type grande products) can be predicted.

In some embodiments, SKUs may be categorized by method of final delivery to the customer. For example, certain SKUs, such as low-value, non-perishable SKUs, may be shipped in a bag (i.e., “tongable”). High-value or perishable SKUs may need to be shipped in a box in a different packaging (i.e. “non-tortable”). Other SKUs may be shipped in bulk or in original packaging from the manufacturer or supplier (i.e. “Grande”). For example, a box of toilet paper may be classified as grande, as consumers often purchase large amounts of toilet paper, and the box may be shipped from the destination to the consumer in the original packaging provided by the toilet paper manufacturer. Thus, some storage space in a destination may be tied to a SKU type, such as being allocated to total items, non-totable items, and grande items. For example, grande items can be stored in an area where there are few obstacles where a forklift can move, and tortable items can be stored on a shelf.

In some embodiments, SKUs may be further classified based on the rate of processing the SKUs. For example, faster processing SKUs may be stored in FC's pallets, while slower processing SKUs may not be stored in pallets, but instead stored in bins. Thus, a combination of Storage Type and Capacity Type can be Bin-Type Totable Product, Bin-Type Non-Tortable Product, Bin-Type Grande Product, Pallet-Type Totable Product, Pallet-Type Non-Tortable Product, or Pallet-Type Non-Tortable Product. Can include type grande products.

At step 505 , system 340 may determine whether all FCs of a particular number of FCs (eg, all FCs) have gone through process 500 . For example, in some embodiments, multiple FCs may need to go through process 500 . Then, if no, system 340 may return to step 501 to repeat the process described above. If yes, system 340 can then complete process 500 .

Referring to FIG. 6 , a process 600 for AI-based inbound plan creation is shown. In some embodiments, system 350 may perform several of the steps described herein, although other implementations are possible. For example, any of the systems and components described and illustrated herein (eg, system 100, system 330, system 350, etc.) may perform the steps described in this disclosure. .

At step 601 (e.g., after step 413 in FIG. 4 and after step 505 in FIG. 5), system 350 uses the ASC predicted from system 330 to perform a preprocessing plan (e.g., after step 405 in FIG. 5). )), the calculated constrained capacity from system 330 (eg, process 400), and the predicted demand capacity from system 340 (eg, process 500). For example, system 350 may include storage type, capability type, or combination of storage type and capability type (e.g., bin-type tortable product, bin-type non-totable product, bin-type grande product, A pretreatment plan can be performed by selecting a pallet-type tortable product, a pallet-type non-totable product, or a pallet-type grande product). For selection, system 350 may retrieve the determined inbound constraints from system 330.

At step 603, system 350 may determine whether the determined inbound constraint is less than or equal to the predicted demand capacity. If the determined inbound constraint is less than or equal to the predicted demand capacity, system 350 may proceed to step 605 .

At step 605, system 350 may calculate the difference between the predicted demand capacity and the determined inbound constraint. Since the calculated difference is negative, system 350 can determine that the calculated difference is excess demand capacity.

Returning to step 603, if the determined inbound constraint is less than or equal to the predicted demand capacity, system 350 may proceed to step 607.

At step 607, system 350 may calculate the difference between the predicted demand capacity and the determined inbound constraint. Since the calculated difference is positive, system 350 can determine that the calculated difference is the remaining available capacity.

Based on the output of the preprocessing plan performed, system 300 may generate an inbound plan for one or more FCs. For example, if the determined inbound constraint exceeds the predicted demand capacity, the system 300 may generate an inbound plan since one or more FCs may store the predicted demand capacity. In some embodiments, the generated inbound plan is a bin-type toteble product, a bin-type non-totable product, a bin-type grande product, a pallet-type toteble product, a pallet-type non-totable product, or a pallet-type product. It may include capacity allocation for at least one of the type grande products. Forecasted demand capabilities can be fully allocated or distributed between different capability storage types.

Referring to FIG. 7 , a process 700 for AI-based inbound plan creation is shown. Although in some embodiments, components of system 300 (eg, systems 330, 340, 350, etc.) may perform various steps described herein, other implementations are possible. For example, any of the systems and components described and illustrated herein (eg, system 100, etc.) may perform the steps described in this disclosure.

At step 701 , the system 330 may use the model to predict the ASC (eg, quantity of product, percentage of total capacity, etc.) associated with the FC. For example, current and historical inventory data (eg, finished inventory data, inbound data, outbound data, etc.) for each FC can be used to predict the ASC associated with the FC. In some embodiments, system 330 may predict the ASC by predicting the outbound quantity of the product. For example, system 330 may use the model to predict outbound quantities of products for the selected EDD.

In some embodiments, system 330 may use the model to predict ASC for a plurality of selected EDDs (eg, in a specific time period, such as two weeks). In some embodiments, system 330 may use the model to predict ASC for multiple FCs. In some embodiments, system 330 may use the same or different combinations of different models to make predictions. In some embodiments, one or more models may predict ASC based on inventory in one or more FCs or cube availability in one or more FCs (eg, 1,000 m ³ , space for 3,000 units, etc.).

In some embodiments, system 330 may use the model to predict ASC for a particular storage type. For example, storage types can include pallets and bins. In some embodiments, system 330 may use the model to predict ASC for a particular storage type. For example, competency types may include Torable, Non-Tortable, and Grande. In some embodiments, system 330 may include a combination of storage type and capability type (e.g., bin-type tortable product, bin-type non-totable product, bin-type grande product, pallet-type tortable product). , pallet-type non-tortable products or pallet-type grande products) to predict the ASC.

At step 703, system 330 may calculate a constraint capacity (eg, quantity of product, percentage of total capacity, etc.) associated with the FC. For example, system 330 may calculate a constrained capability based on an inbound infrastructure capability associated with the FC. For example, the inbound infrastructure capability may include the FC's workforce, the FC's workforce for each station, the FC's driver, the FC's forklift driver, the number of products that can be received based on the FC's available workforce, etc. (For example, the constrained capacity of an FC can be calculated based on the fact that there are 100 stations in the FC, but only 80 workers can work in the FC). In some embodiments, pharmaceutical capabilities may relate to stock keeping units (SKUs).

In some embodiments, system 330 may calculate a constrained capacity for a particular storage type. For example, storage types can include pallets and bins. In some embodiments, system 330 may calculate a constrained competency for a particular competency type. For example, competency types may include Torable, Non-Tortable, and Grande. In some embodiments, system 330 may include a combination of storage type and capability type (e.g., bin-type tortable product, bin-type non-totable product, bin-type grande product, pallet-type tortable product). , pallet-type non-tortable products or pallet-type grande products) can be calculated.

In some embodiments, system 330 may determine an inbound constraint for one or more FCs by determining a minimum value among the computed ASC and the computed constrained capabilities of the FC. In some embodiments, system 330 may calculate the ASC and constraint capabilities of each FC and determine inbound constraints for each day within the time period. For example, system 330 may determine inbound constraints for one or more FCs for each day of the week. System 330 may calculate an average daily inbound restriction by dividing the total inbound restriction for the week by the number of days in the week. System 330 can generate an inbound plan that takes into account fluctuations within a time period by calculating an average daily inbound constraint, thereby creating a more robust inbound plan.

At step 705, in some embodiments, the inbound demand forecasting system 340 may use the financial or inventory data to predict the demand capacity associated with the FC. For example, system 340 may forecast national demand for products for the selected EDD. In some embodiments, system 340 may forecast regional (eg, national) demand for a product for multiple EDDs within a time period. System 340 may use historical inventory data, supplier lead-time data, supplier delivery cycle data, and the like to predict national demand. In some embodiments, nationwide demand data may be per SKU. In some embodiments, the demand capability may be a forecast capability required to store multiple predicted demand products. System 340 may forecast demand capacity by predicting an outbound target for a product based on financial or inventory data and forecasting inbound inventory required to meet the outbound target. System 340 may forecast demand capacity by maximizing storage among one or more FCs capable of receiving inbound inventory required to meet outbound targets. In some embodiments, system 340 may calculate an average daily demand capacity by dividing the total demand capacity over a period of time (eg, 2 weeks) by the number of days in the week (eg, 14 days). System 340 may generate an inbound plan that takes into account fluctuations within a time period by calculating average daily demand capacity, thereby creating a more robust inbound plan.

In some embodiments, system 340 may predict demand capacity for a particular storage type. For example, storage types can include pallets and bins. In some embodiments, system 340 may calculate demand competencies for specific competency types. For example, competency types may include Torable, Non-Tortable, and Grande. In some embodiments, system 340 may include a combination of storage type and capability type (e.g., bin-type tortable product, bin-type non-totable product, bin-type grande product, pallet-type tortable product). , pallet-type non-tortable products or pallet-type grande products) can be predicted.

At step 707, preprocessing planning system 350 may use the predicted ASC from system 330, the calculated constrained capacity from system 330, and the predicted demand capacity from system 340 to perform preprocessing planning. . For example, system 350 may include storage type, capability type, or combination of storage type and capability type (e.g., bin-type tortable product, bin-type non-totable product, bin-type grande product, A pretreatment plan can be performed by selecting a pallet-type tortable product, a pallet-type non-totable product, or a pallet-type grande product). For selection, system 350 may retrieve the determined inbound constraints from system 330. System 350 may determine whether the determined inbound constraint is less than or equal to the predicted demand capacity.

If the determined inbound constraint is less than or equal to the predicted demand capacity, system 350 may calculate the difference between the predicted demand capacity and the determined inbound constraint. Since the calculated difference is negative, system 350 can determine that the calculated difference is excess demand capacity. If the determined inbound constraint is less than or equal to the predicted demand capacity, system 350 may calculate the difference between the predicted demand capacity and the determined inbound constraint. Since the calculated difference is positive, system 350 can determine that the calculated difference is the remaining available capacity. Based on the output of the preprocessing plan performed, system 300 may generate an inbound plan for one or more FCs. For example, if the determined inbound constraint exceeds the predicted demand capacity, the system 300 may generate an inbound plan since one or more FCs may store the predicted demand capacity. In some embodiments, the generated inbound plan is a bin-type toteble product, a bin-type non-totable product, a bin-type grande product, a pallet-type toteble product, a pallet-type non-totable product, or a pallet-type product. It may include capacity allocation for at least one of the type grande products, where the predicted demand capacity may be fully allocated or distributed among different capacity storage types.

In some embodiments, when the predicted demand capacity exceeds the determined inbound constraint (eg, during a peak period), the system 350 intersects one or more additional FCs based on the inbound plan generated for each additional FC. Excess demand capacity can be allocated. For example, the system 300 may add storage or capacity types of additional FCs (e.g., bin-type tortable products, bin-type non-totable products, bin-type grande products, pallet-type tortable products, The distribution of excess demand capacity may be optimized based on pallet-type non-tortable products or pallet-type grande products, etc.). In some embodiments, system 300 may optimize any distribution of expected demand capacity (eg, but not limited to, excess demand capacity) based on the priority associated with each SKU.

In some embodiments, one or more systems or components of system 300 may perform various simulations by adjusting one or more variables discussed above. System 300 may generate an inbound plan for one or more FCs based on an optimization in which the demand capacity that can be stored in the FC is maximized (eg, the available storage capacity in one or more FCs is maximized).

Although the present disclosure has been shown and described with reference to specific embodiments thereof, it will be understood that the present disclosure may be practiced in other circumstances, without modification. The foregoing description has been presented for purposes of illustration. It is not exhaustive or limited to the exact form or embodiment disclosed. Variations and adjustments will be apparent to those skilled in the art from consideration of the description and practice of the disclosed embodiments. Additionally, although the forms of the disclosed embodiments have been described as being stored in memory, those skilled in the art will understand that these forms can be stored on a secondary storage device, such as a hard disk or CD ROM, or other form of RAM or ROM, USB media, DVD , Blu-ray, or other optical drive media.

Computer programs based on the foregoing description and disclosed methods are well within the skill of the skilled developer. Several programs or program modules can be created using any technology known to those skilled in the art, or designed in conjunction with existing software. For example, a program section or program module may include the .NET Framework, .NET Compact Framework (and related languages, such as Visual Basic, C, etc.), Java, C++, Objective C, HTML, HTML/AJAX combinations, XML, or Java applets. can be designed in or by embedded HTML.

Moreover, although exemplary embodiments have been described herein, the scope of some or all embodiments is equivalent to elements, variations, omissions, combinations (eg, spanning multiple embodiments), as would be understood by those skilled in the art based on the present disclosure. combinations of shapes), adjustments and/or modifications. The limitations in the claims are intended to be broadly understood based on the language applied within the claims and are not limited to the examples set forth within this specification or during the performance of the application. The examples are intended to be construed on a non-exclusive basis. Additionally, steps of the disclosed methods may be altered in some other way, rearranged steps and/or may include inserting or deleting steps. Therefore, it is intended that the description and examples be considered illustrative only, with the true scope and spirit being indicated by the following claims and their full scope equivalents.

Claims (20)

memory to store instructions; and
at least one processor configured to execute the instructions;
The above command is
using the model to predict the amount of available product storage associated with a fulfillment center;
calculate a product pharmaceutical capability quantity associated with the fulfillment center;
predict national demand for the product based on at least one of historical inventory data, supplier lead-time data, or supplier delivery cycle data;
predicting a product demand capability quantity associated with the fulfillment center by predicting national demand for a competency type associated with the predicted national demand for the product, the competency type comprising at least three different competency types; and ,
Using the predicted available product storage quantity, the calculated product constraint capacity quantity and the predicted product demand capacity quantity,
determine an inbound product constraint quantity based on the predicted available product storage quantity and the calculated product constraint capacity quantity;
calculate a difference between the predicted product demand capacity quantity and the determined inbound product constraint quantity; and
By outputting an excess product demand capacity quantity or a remaining available product capacity quantity based on the calculated difference,
carry out the pretreatment plan, and
based on the output of the performed preprocessing plan, to generate an inbound plan for the fulfillment center. The method of claim 1 , wherein predicting the available product storage quantity comprises:
A computer-implemented system for generating an AI-based inbound plan comprising predicting at least one of an outbound quantity of products for bin-type storage or an outbound quantity of products for pallet-type storage. The method of claim 2, wherein predicting the outbound quantity of the product for the bin-type storage or pallet-type storage comprises:
Calculate historical inventory quantities of products;
Calculate the current stock quantity of the product; and
A computer-implemented system for creating an AI-based inbound plan that includes calculating the current inbound inventory quantity of a product. 4. The computer-implemented system of claim 3, wherein the current stock quantity of the product is calculated based on a past stock quantity of the product, a current inbound stock quantity of the product, and a current outbound stock quantity of the product. The computer-implemented system of claim 1 , wherein the product constraint capability quantities include product constraint capability quantities associated with each of at least three product classes in the fulfillment center. 6. The method of claim 5, wherein the at least three product classes at the fulfillment center include a product class that can be shipped in bags, a product class that can be shipped in boxes, and a product class that can be shipped in original packaging. A computer-implemented system for generating an AI-based inbound plan, including The computer-implemented system of claim 1 , wherein the product demand capability quantity comprises an expected capability quantity associated with a number of demand products. The computer-implemented system of claim 1 , outputting the excess product demand capacity quantity if the calculated difference is positive. 9. The computer-implemented method of claim 8, further comprising distributing the excess product demand capacity quantity among one or more additional fulfillment centers based on the inbound plans generated for each additional fulfillment center. system. The computer-implemented system of claim 1 , outputting the remaining available product capability quantity if the calculated difference is negative. The computer-implemented system of claim 1 , wherein the generated inbound plan includes capacity assignment for at least one of bin-type products or pallet-type products. A method for AI-based inbound plan generation, performed by one or more processors, the method comprising:
using the model to predict the amount of available product storage associated with a fulfillment center;
calculate a product pharmaceutical capability quantity associated with the fulfillment center;
predict national demand for the product based on at least one of historical inventory data, supplier lead-time data, or supplier delivery cycle data;
predicting a product demand capability quantity associated with the fulfillment center by predicting national demand for a competency type associated with the predicted national demand for the product, the competency type comprising at least three different competency types; and ,
Using the predicted available product storage quantity, the calculated product constraint capacity quantity and the predicted product demand capacity quantity,
determine an inbound product constraint quantity based on the predicted available product storage quantity and the calculated product constraint capacity quantity;
calculate a difference between the predicted product demand capacity quantity and the determined inbound product constraint quantity; and
By outputting an excess product demand capacity quantity or a remaining available product capacity quantity based on the calculated difference,
carry out the pretreatment plan, and
based on the output of the performed preprocessing plan, generating an inbound plan for the fulfillment center. 13. The method of claim 12, wherein the product constraint capability quantity comprises a product constraint capability quantity associated with each of at least three product classes in the fulfillment center. 14. The AI of claim 13, wherein the at least three product classes at the fulfillment center include a product class that can be shipped in bags, a product class that can be shipped in boxes, and a product class that can be shipped in original packaging. A method for creating a base inbound plan. The method of claim 12 , wherein the product demand capability quantity comprises an expected capability quantity associated with a number of demand products. The method of claim 12 , wherein if the calculated difference is a positive number, outputting the excess product demand capacity quantity. 17. The method of claim 16, further comprising distributing the excess product demand capacity quantity among one or more additional fulfillment centers based on the inbound plans generated for each additional fulfillment center. 13. The method of claim 12, outputting the remaining available product capability quantity if the calculated difference is negative. 13. The method of claim 12, wherein the generated inbound plan includes capacity assignment for at least one of bin-type products or pallet-type products. memory to store instructions; and
at least one processor configured to execute the instructions;
The above command is
using the model to predict the amount of available product storage associated with a fulfillment center;
calculate a product pharmaceutical capability quantity associated with the fulfillment center;
predict national demand for the product based on at least one of historical inventory data, supplier lead-time data, or supplier delivery cycle data;
predicting a product demand capability quantity associated with the fulfillment center by predicting national demand for a competency type associated with the predicted national demand for the product, the competency type comprising at least three different competency types; and ,
Using the predicted available product storage quantity, the calculated product constraint capacity quantity and the predicted product demand capacity quantity,
determine an inbound product constraint quantity based on the predicted available product storage quantity and the calculated product constraint capacity quantity;
calculate a difference between the predicted product demand capacity quantity and the determined inbound product constraint quantity; and
By outputting an excess product demand capacity quantity or a remaining available product capacity quantity based on the calculated difference,
carry out the pretreatment plan;
Based on the output of the performed preprocessing plan, create an inbound plan for the fulfillment center; and
calculate an average absolute percentage error between the predicted amount of product storage available and the actual amount of available storage;
select a new model to predict the available product storage quantity upon determining that the calculated mean absolute percentage error is greater than or equal to the threshold; and
by creating a new inbound plan for the fulfillment center based on the new model;
A computer-implemented system for AI-based inbound plan generation, performing validation tests on the model.

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