Patent Publication Number: US-2021174298-A1

Title: Computer-implemented systems and methods for intelligent prediction of out of stock items and proactive reordering

Description:
TECHNICAL FIELD 
     The present disclosure generally relates to computerized systems and methods for predicting out of stock items. Embodiments of the present disclosure relate to inventive and unconventional systems for predicting out of stock items by running a decision tree on information associated with items stored in a fulfillment center to determine a cause of the out of stock condition. 
     BACKGROUND 
     Fulfillment centers (FCs) encounter more than millions of products daily as they operate to fulfill consumer orders as soon as the orders are placed and enable shipping carriers to pick up shipments. Operations for managing inventory inside FCs may include ordering products and stocking the ordered products so the products can be shipped quickly as soon as the FCs receive the consumer orders. Although currently existing FCs and systems for inventory management in FCs are configured to forecast demands for products, a common issue arises when a FC runs out of stock by purchasing fewer products than an amount of consumer orders because of flawed predictions on product demand. For example, a consumer visits a website associated a merchant associated with an FC to purchase a desired product, but the consumer discovers that the desired product is out of stock. This leads to lost sales and poor customer satisfaction, and a review from the dissatisfied consumer may discourage potential sales from other buyers. 
     To mitigate such problems, conventional inventory management systems improve a prediction on demands of products by determining out of stock reasons. For example, the systems record one or more occurrences relating to an out of stock condition to determine a reason for the out of stock condition. While these systems attempt to determine out of stock reasons in an efficient manner, the process is manual and inconsistent. 
     Therefore, there is a need for improved methods and systems for predicting an out of stock item by determining a cause of out of stock condition. 
     SUMMARY 
     One aspect of the present disclosure is directed to a system including a memory storing instructions and at least one processor programmed to execute the instructions to perform a method for predicting out of stock items by running a decision tree against historical information associated with the out of stock items and contacting a supplier of the out of stock item to request more items based on the prediction. The method includes receiving information associated with an out of stock item from a system storing information associated with items in a fulfillment center, the information collected over an extended period, determining a cause of the out of stock condition by running a decision tree against the received information, the decision tree includes a plurality of conditions, and predicting an out of stock condition of the item based on the determined cause. The method further includes contacting a supplier of the out of stock item to request more items based on the prediction. 
     Another aspect of the present disclosure is directed to a method for predicting out of stock items by running a decision tree against historical information associated with the out of stock items and contacting a supplier of the out of stock item to request more items based on the prediction. The method includes receiving information associated with an out of stock item from a system storing information associated with items in a fulfillment center, the information collected over an extended period, determining a cause of the out of stock condition by running a decision tree against the received information, the decision tree includes a plurality of conditions, and predicting an out of stock condition of the item based on the determined cause. The method further includes contacting a supplier of the out of stock item to request more items based on the prediction. 
     Yet another aspect of the present disclosure is directed to a system including a memory storing instructions and at least one processor programmed to execute the instructions to perform a method for predicting out of stock items by running a decision tree against historical information associated with the out of stock items and contacting a supplier of the out of stock item to request more items based on the prediction. The method includes receiving information associated with an out of stock item from a system storing information associated with items in a fulfillment center, the information collected over an extended period, and a limited number of conditions. Based on the received information and the limited number of conditions, the system determines a cause of the out of stock condition by running a decision tree against the received information, the decision tree includes the limited number of conditions and predicts an out of stock condition of the item based on the determined cause. The system may contact a supplier of the out of stock item to request more items based on the prediction. 
     Other systems, methods, and computer-readable media are also discussed herein. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1A  is a schematic block diagram illustrating an exemplary embodiment of a network comprising computerized systems for communications enabling shipping, transportation, and logistics operations, consistent with the disclosed embodiments. 
         FIG. 1B  depicts a sample Search Result Page (SRP) that includes one or more search results satisfying a search request along with interactive user interface elements, consistent with the disclosed embodiments. 
         FIG. 1C  depicts a sample Single Display Page (SDP) that includes a product and information about the product along with interactive user interface elements, consistent with the disclosed embodiments. 
         FIG. 1D  depicts a sample Cart page that includes items in a virtual shopping cart along with interactive user interface elements, consistent with the disclosed embodiments. 
         FIG. 1E  depicts a sample Order page that includes items from the virtual shopping cart along with information regarding purchase and shipping, along with interactive user interface elements, consistent with the disclosed embodiments. 
         FIG. 2  is a diagrammatic illustration of an exemplary fulfillment center configured to utilize disclosed computerized systems, consistent with the disclosed embodiments. 
         FIG. 3A  shows an exemplary method for predicting an out of stock condition of item by running a decision tree on supply chain management system, consistent with the disclosed embodiments. 
         FIG. 3B  shows an exemplary method for determining a cause of out of stock condition by running a decision tree, consistent with the disclosed embodiments. 
         FIG. 4A  shows an exemplary table comprising data associated with out of stock condition of item. 
         FIGS. 4B and 4C  show exemplary methods for measuring a purity for each condition and choosing a condition with the highest purity. 
         FIG. 4D  shows an exemplary decision tree comprising a plurality of conditions. 
         FIG. 5A  shows an exemplary method for analyzing a reason for out of stock by running an out of stock root cause calculation algorithm or a decision tree construction algorithm on Supply Chain Management system, consistent with the disclosed embodiments. 
         FIG. 5B  shows an exemplary method for analyzing a reason for out of stock by running an out of stock root cause calculation algorithm, consistent with the disclosed embodiments. 
         FIG. 5C  shows an exemplary method for analyzing a reason for out of stock by running a decision tree construction algorithm on Supply Chain Management system, consistent with the disclosed embodiments. 
         FIG. 6A-D  show exemplary tables for analyzing a reason for out of stock by running an out of stock root cause calculation algorithm. 
         FIG. 7  shows an exemplary decision tree hierarchy list. 
     
    
    
     DETAILED DESCRIPTION 
     The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar parts. While several illustrative embodiments are described herein, modifications, adaptations and other implementations are possible. For example, substitutions, additions, or modifications may be made to the components and steps illustrated in the drawings, and the illustrative methods described herein may be modified by substituting, reordering, removing, or adding steps to the disclosed methods, or by performing non-dependent steps in parallel with each other. Accordingly, the following detailed description is not limited to the disclosed embodiments and examples. Instead, the proper scope of the invention is defined by the appended claims. 
     Embodiments of the present disclosure are directed to computer-implemented systems and methods configured for predicting an out of stock condition of item by running a decision tree. The disclosed embodiments provide innovative technical features that allow proactive ordering of an item in a fulfilment center by predicting an out of stock condition of the item, wherein the prediction is attained by running a decision tree against information related to the item. For example, the disclosed embodiments enable determination of a cause of out of stock condition by running a decision tree against information received from a system storing information associated with items in a fulfillment center, enable prediction on an out of stock condition of the item based on the determined cause, and enable efficient transmission of request to a supplier of the out of stock item to request more items based on the prediction. 
     Referring to  FIG. 1A , a schematic block diagram  100  illustrating an exemplary embodiment of a system comprising computerized systems for communications enabling shipping, transportation, and logistics operations is shown. As illustrated in  FIG. 1A , system  100  may include a variety of systems, each of which may be connected to one another via one or more networks. The systems may also be connected to one another via a direct connection, for example, using a cable. The depicted systems include a shipment authority technology (SAT) system  101 , an external front end system  103 , an internal front end system  105 , a transportation system  107 , mobile devices  107 A,  107 B, and  107 C, seller 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  119 A,  119 B, and  119 C (depicted as being inside of fulfillment center (FC)  200 ), 3 rd  party fulfillment systems  121 A,  121 B, and  121 C, fulfillment center authorization system (FC Auth)  123 , and labor management system (LMS)  125 . 
     SAT system  101 , in some embodiments, may be implemented as a computer system that monitors order status and delivery status. For example, SAT system  101  may determine whether an order is past its Promised Delivery Date (PDD) and may take appropriate action, including initiating a new order, reshipping the items in the non-delivered order, canceling the non-delivered order, initiating contact with the ordering customer, or the like. SAT system  101  may also monitor other data, including output (such as a number of packages shipped during a particular time period) and input (such as the number of empty cardboard boxes received for use in shipping). SAT system  101  may also act as a gateway between different devices in system  100 , enabling communication (e.g., using store-and-forward or other techniques) between devices such as external front end system  103  and FO system  113 . 
     External front end system  103 , in some embodiments, may be implemented as a computer system that enables external users to interact with one or more systems in system  100 . For example, in embodiments where system  100  enables the presentation of systems to enable users to place an order for an item, external front end system  103  may be implemented as a web server that receives search requests, presents item pages, and solicits payment information. For example, external front end system  103  may be implemented as a computer or computers running software such as the Apache HTTP Server, Microsoft Internet Information Services (IIS), NGINX, or the like. In other embodiments, external front end system  103  may run custom web server software designed to receive and process requests from external devices (e.g., mobile device  102 A or computer  102 B), acquire information from databases and other data stores based on those requests, and provide responses to the received requests based on acquired 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 comprise one or more of these systems, while in another aspect, external front end system  103  may comprise interfaces (e.g., server-to-server, database-to-database, or other network connections) connected to one or more of these systems. 
     An illustrative set of steps, illustrated by  FIGS. 1B, 1C, 1D, and 1E , will help to describe some operations of external front end system  103 . External front end system  103  may receive information from systems or devices in system  100  for presentation and/or display. For example, external front end system  103  may host or provide one or more web pages, including a Search Result Page (SRP) (e.g.,  FIG. 1B ), a Single Detail Page (SDP) (e.g.,  FIG. 1C ), a Cart page (e.g.,  FIG. 1D ), or an Order page (e.g.,  FIG. 1E ). A user device (e.g., using mobile device  102 A or computer  102 B) may navigate to external front end system  103  and request a search by entering information into a search box. External front end system  103  may request information from one or more systems in system  100 . For example, external front end system  103  may request information from FO System  113  that satisfies the search request. 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. The PDD, in some embodiments, may represent an estimate of when a package containing the product will arrive at the user&#39;s desired location or a date by which the product is promised to be delivered at the user&#39;s desired location if ordered within a particular period of time, for example, by the end of the day (11:59 PM). (PDD is discussed further below with respect to FO System  113 .) 
     External front end system  103  may prepare an SRP (e.g.,  FIG. 1B ) based on the information. The SRP may include information that satisfies the search request. For example, this may include pictures of products that satisfy the search request. The SRP may also include respective prices for each product, or information relating to enhanced delivery options for each product, PDD, weight, size, offers, discounts, or the like. External front end system  103  may send the SRP to the requesting user device (e.g., via a network). 
     A user device may then select a product from the SRP, e.g., by clicking or tapping a user interface, or using another input device, to select a product represented on the SRP, The user device may formulate a request for information on the selected product and send it to external front end system  103 . In response, external front end system  103  may request information related to the selected product. For example, the information may include additional information beyond that presented for a product on the respective SRP. This could include, for example, shelf life, country of origin, weight, size, number of items in package, handling instructions, or other information about the product. The information could also include recommendations for similar products (based on, for example, big data and/or machine learning analysis of customers who bought this product and at least one other product), answers to frequently asked questions, reviews from customers, manufacturer information, pictures, or the like. 
     External front end system  103  may prepare an SDP (Single Detail Page) (e.g.,  FIG. 1C ) based on the received product information. The SDP may also include other interactive elements such as a “Buy Now” button, a “Add to Cart” button, a quantity field a picture of the item, or the like. The SDP may further include a list of sellers that offer the product. The list may be ordered based on the price each seller offers such that the seller that offers to sell the product at the lowest price may be listed at the top. The list may also be ordered based on the seller ranking such that the highest ranked seller may be listed at the top. The seller ranking may be formulated based on multiple factors, including, for example, the seller&#39;s past track record of meeting a promised PDD. External front end system  103  may deliver the SDP to the requesting user device (e.g., via a network). 
     The requesting user device may receive the SDP which lists the product information. Upon receiving the SDP, the user device may then interact with the SDP. For example, a user of the requesting user device may click or otherwise interact with a “Place in Cart” button on the SDP. This adds the product to a shopping cart associated with the user. The user device may transmit this request to add the product to the shopping cart to external front end system  103 . 
     External front end system  103  may generate a Cart page (e.g.,  FIG. 1D ). The Cart page, in some embodiments, lists the products that the user has added to a virtual “shopping cart.” A user device may request the Cart page by clicking on or otherwise interacting with an icon on the SRP, SDP, or other pages. The Cart page may, in some embodiments, list all products that the user has added to the shopping cart, as well as information about the products in the cart such as a quantity of each product, a price for each product per item, a price for each product based on an associated quantity, information regarding PDD, a delivery method, a shipping cost, user interface elements for modifying the products in the shopping cart (e.g., deletion or modification of a quantity), options for ordering other product or setting up periodic delivery of products, options for setting up interest payments, user interface elements for proceeding to purchase, or the like. A user at a user device may click on or otherwise interact with a user interface element (e.g., a button that reads “Buy Now”) to initiate the purchase of the product in the shopping cart. Upon doing so, the user device may transmit this request to initiate the purchase to external front end system  103 . 
     External front end system  103  may generate an Order page (e.g.,  FIG. 1E ) in response to receiving the request to initiate a purchase. The Order page, in some embodiments, re-lists the items from the shopping cart and requests input of payment and shipping information. For example, the Order page may include a section requesting information about the purchaser of the items in the shopping cart (e.g., name, address, e-mail address, phone number), information about the recipient (e.g., name, address, phone number, delivery information), shipping information (e.g., speed/method of delivery and/or pickup), payment information (e.g., credit card, bank transfer, check, stored credit), user interface elements to request a cash receipt (e.g., for tax purposes), or the like. External front end system  103  may send the Order page to the user device. 
     The user device may enter information on the Order page and click or otherwise interact with a user interlace element that sends the information to external front end system  103 . From there, external front end system  103  may send the information to different systems in system  100  to enable the creation and processing of a new order with the products in the shopping cart. 
     In some embodiments, external front end system  103  may be further configured to enable sellers to transmit and receive information relating to orders. 
     Internal front end system  105 , in some embodiments, may be implemented as a computer system that enables internal users (e.g., employees of an organization that owns, operates, or leases system  100 ) to interact with one or more systems in system  100 . For example, in embodiments where network  101  enables the presentation of systems to enable users to place an order for an item, internal front end system  105  may be implemented as a web server that enables internal users to view diagnostic and statistical information about orders, modify item information, or review statistics relating to orders. For example, internal front end system  105  may be implemented as a computer or computers running software such as the Apache HTTP Server, Microsoft Internet Information Services (IIS), NGINX, or the like. In other embodiments, internal front end system  105  may run custom web server software designed to receive and process requests from systems or devices depicted in system  100  (as well as other devices not depicted), acquire information from databases and other data stores based on those requests, and provide responses to the received requests based on acquired information. 
     In some embodiments, internal front end system  105  may include one or more of a web caching system, a database, a search system, a payment system, an analytics system, an order monitoring system, or the like. In one aspect, internal front end system  105  may comprise one or more of these systems, while in another aspect, internal front end system  105  may comprise interfaces (e.g., server-to-server, database-to-database, or other network connections) connected to one or more of these systems. 
     Transportation system  107 , in some embodiments, may be implemented as a computer system that enables communication between systems or devices in system  100  and mobile devices  107 A- 107 C. Transportation system  107 , in some embodiments, may receive information from one or more mobile devices  107 A- 107 C (e.g., mobile phones, smart phones, PDAs, or the like). For example, in some embodiments, mobile devices  107 A- 107 C may comprise devices operated by delivery workers. The delivery workers, who may be permanent, temporary, or shift employees, may utilize mobile devices  107 A- 107 C to effect delivery of packages containing the products ordered by users. For example, to deliver a package, the delivery worker may receive a notification on a mobile device indicating which package to deliver and where to deliver it. Upon arriving at the delivery location, the delivery worker may locate the package (e.g., in the back of a truck or in a crate of packages), scan or otherwise capture data associated with an identifier on the package (e.g., a barcode, an image, a text string, an RFID tag, or the like) using the mobile device, and deliver the package (e.g., by leaving it at a front door, leaving it with a security guard, handing it to the recipient, or the like). In some embodiments, the delivery worker may capture photo(s) of the package and/or may obtain a signature using the mobile device. The mobile device may send information to transportation system  107  including information about the delivery, including, for example, time, date, GPS location, photo(s), an identifier associated with the delivery worker, an identifier associated with the mobile device, or the like. Transportation system  107  may store this information in a database (not pictured) for access by other systems in system  100 . Transportation system  107  may, in some embodiments, use this information to prepare and send tracking data to other systems indicating the location of a particular package. 
     In some embodiments, certain users may use one kind of mobile device (e.g., permanent workers may use a specialized FDA with custom hardware such as a barcode scanner, stylus, and other devices) while other users may use other kinds of mobile devices (e.g., temporary or shift workers may utilize off-the-shelf mobile phones and/or smartphones). 
     In some embodiments, transportation system  107  may associate a user with each device. For example, transportation system  107  may store an association between a user (represented by, e.g., a user identifier, an employee identifier, or a phone number) and a mobile device (represented by, e.g., an International Mobile Equipment Identity (IMEI), an International Mobile Subscription Identifier (IMSI), a phone number, a Universal Unique Identifier (UUID), or a Globally Unique Identifier (GUID)). Transportation system  107  may use this association in conjunction with data received on deliveries to analyze data stored in the database in order to determine, among other things, a location of the worker, an efficiency of the worker, or a speed of the worker. 
     Seller portal  109 , in some embodiments, may be implemented as a computer system that enables sellers or other external entities to electronically communicate with one or more systems in system  100 . For example, a seller may utilize a computer system (not pictured) to upload or provide product information, order information, contact information, or the like, for products that the seller wishes to sell through system  100  using seller portal  109 . 
     Shipment and order tracking system  111 , in some embodiments, may be implemented as a computer system that receives, stores, and forwards information regarding the location of packages containing products ordered by customers (e.g., by a user using devices  102 A- 102 B). In some embodiments, shipment and order tracking system  111  may request or store information from web servers (not pictured) operated by shipping companies that deliver packages containing products ordered by customers. 
     In some embodiments, shipment and order tracking system  111  may request and store information from systems depicted in system  100 . For example, shipment and order tracking system  111  may request information from transportation system  107 . As discussed above, transportation system  107  may receive information from one or more mobile devices  107 A- 107 C (e.g., mobile phones, smart phones. PDAs, or the like) that are associated with one or more of a user (e.g., a delivery worker) or a vehicle (e.g., a delivery truck). In some embodiments, shipment and order tracking system  111  may also request information from warehouse management system (WMS)  119  to determine the location of individual products inside of a fulfillment center (e.g., fulfillment center  200 ). Shipment and order tracking system  111  may request data from one or more of transportation system  107  or WMS  119 , process it, and present it to a device (e.g., user devices  102 A and  102 B) upon request. 
     Fulfillment optimization (FO) system  113 , in some embodiments, may be implemented as a computer system that stores information for customer orders from other systems (e.g., external front end system  103  and/or shipment and order tracking system  111 ). FO system  113  may also store information describing where particular items are held or stored. For example, certain items may be stored only in one fulfillment center, while certain other items may be stored in multiple fulfillment centers. In still other embodiments, certain fulfilment centers may be designed to store only a particular set of items (e.g., fresh produce or frozen products). FO system  113  stores this information as well as associated information (e.g., quantity, size, date of receipt, expiration date, etc.). 
     FO system  113  may also calculate a corresponding PDD (promised delivery date) for each product. The PDD, in some embodiments, may be based on one or more factors. For example, FO system  113  may calculate a PDD for a product based on a past demand for a product (e.g., how many times that product was ordered during a period of time), an expected demand for a product (e.g., how many customers are forecast to order the product during an upcoming period of time), a network-wide past demand indicating how many products were ordered during a period of time, a network-wide expected demand indicating how many products are expected to be ordered during an upcoming period of time, one or more counts of the product stored in each fulfillment center  200 , which fulfillment center stores each product, expected or current orders for that product, or the like. 
     In some embodiments, FO system  113  may determine a PDD for each product on a periodic basis (e.g., hourly) and store it in a database for retrieval or sending to other systems (e.g., external front end system  103 , SAT system  101 , shipment and order tracking system  111 ). In other embodiments, FO system  113  may receive electronic requests from one or more systems (e.g., external front end system  103 , SAT system  101 , shipment and order tracking system  111 ) and calculate the PDD on demand. 
     Fulfilment messaging gateway (FMG)  115 , in some embodiments, may be implemented as a computer system that receives a request or response in one format or protocol from one or more systems in system  100 , such as FO system  113 , converts it to another format or protocol, and forward it in the converted format or protocol to other systems, such as WMS  119  or 3 rd  party fulfillment systems  121 A,  121 B, or  121 C, and vice versa. 
     Supply chain management (SCM) system  117 , in some embodiments, may be implemented as a computer system that performs forecasting functions. For example, SCM system  117  may forecast a level of demand for a particular product based on, for example, based on a past demand for products, an expected demand for a product, a network-wide past demand, a network-wide expected demand, a count products stored in each fulfillment center  200 , expected or current orders for each product, or the like. In response to this forecasted level and the amount of each product across all fulfillment centers, SCM system  117  may generate one or more purchase orders to purchase and stock a sufficient quantity to satisfy the forecasted demand for a particular product. 
     Warehouse management system (WMS)  119 , in some embodiments, may be implemented as a computer system that monitors workflow. For example, WMS  119  may receive event data from individual devices (e.g., devices  107 A- 107 C or  119 A- 119 C) indicating discrete events. 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, a package identifier (e.g., a barcode or RFID tag data) may be scanned or read by machines at particular stages (e.g., automated or handheld barcode scanners, RFID readers, high-speed cameras, devices such as tablet  119 A, mobile device/PDA  119 B, computer  119 C, or the like). WMS  119  may store each event indicating a scan or a read of a package identifier in a corresponding database (not pictured) along with the package identifier, a time, date, location, user identifier, or other information, and may provide this information to other systems (e.g., shipment and order tracking system  111 ). 
     WMS  119 , in some embodiments, may store information associating one or more devices (e.g., devices  107 A- 107 C or  119 A- 119 C) with one or more users associated with system  100 . For example, in some situations, a user (such as a part- or full-time employee) may be associated with a mobile device in that the user owns the mobile device (e.g., the mobile device is a smartphone). In other situations, a user may be associated with a mobile device in that the user is temporarily in custody of the mobile device (e.g., the user checked the mobile device out at the start of the day, will use it during the day, and will return it at the end of the day). 
     WMS  119 , in some embodiments, may maintain a work log for each user associated with system  100 . For example, WMS  119  may store information associated with each employee, including any assigned processes (e.g., unloading trucks, picking items from a pick zone, rebin wall work, packing items), a user identifier, a location (e.g., a floor or zone in a fulfillment center  200 ), a number of units moved through the system by the employee (e.g., number of items picked, number of items packed), an identifier associated with a device (e.g., devices  119 A- 119 C), or the like. In some embodiments, WMS  119  may receive check-in and check-out information from a timekeeping system, such as a timekeeping system operated on a device  119 A- 119 C. 
     3 rd  party fulfillment (3PL) systems  121 A- 121 C, in some embodiments, represent computer systems associated with third-party providers of logistics and products. For example, while some products are stored in fulfillment center  200  (as discussed below with respect to  FIG. 2 ), other products may be stored off-site, may be produced on demand, or may be otherwise unavailable for storage in fulfillment center  200 . 3PL systems  121 A- 121 C may be configured to receive orders from FO system  113  (e.g., through FMG  115 ) and may provide products and/or services (e.g., delivery or installation) to customers directly. In some embodiments, one or more of 3PL systems  121 A- 121 C may be part of system  100 , while in other embodiments, one or more of 3PL systems  121 A- 121 C may be outside of system  100  (e.g., owned or operated by a third-party provider). 
     Fulfillment Center Auth system (FC Auth)  123 , in some embodiments, may be implemented as a computer system with a variety of functions. For example, in some embodiments, FC Auth  123  may act as a single-sign on (SSO) service for one or more other systems in system  100 . For example, FC Auth  123  may enable a user to log in via internal front end system  105 , determine that the user has similar privileges to access resources at shipment and order tracking system  111 , and enable the user to access those privileges without requiring a second log in process. FC Auth  123 , in other embodiments, may enable users (e.g., employees) to associate themselves with a particular task. For example, some employees may not have an electronic device (such as devices  119 A- 119 C) and may instead move from task to task, and zone to zone, within a fulfillment center  200 , during the course of a day. FC Auth  123  may be configured to enable those employees to indicate what task they are performing and what zone they are in at different times of day. 
     Labor management system (LMS)  125 , in some embodiments, 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 , WMA  119 , devices  119 A- 119 C, transportation system  107 , and/or devices  107 A- 107 C. 
     The particular configuration depicted in  FIG. 1A  is an example only. For example, while  FIG. 1A  depicts FC Auth system  123  connected to FO system  113 , not all embodiments require this particular configuration. Indeed, in some embodiments, the systems in system  100  may be connected to one another through one or more public or private networks, including the Internet, an Intranet, a WAN (Wide-Area Network), a MAN (Metropolitan-Area Network), a wireless network compliant with the IEEE 802.11a/b/g/n Standards, a leased line, or the like. In some embodiments, one or more of the systems in system  100  may be implemented as one or more virtual servers implemented at a data center, server farm, or the like. 
       FIG. 2  depicts a fulfillment center  200 . Fulfillment center  200  is an example of a physical location that stores items for shipping to customers when ordered. Fulfillment center (FC)  200  may be divided into multiple zones, each of which are depicted in  FIG. 2 . These “zones,” in some embodiments, may be thought of as virtual divisions between different stages of a process of receiving items, storing the items, retrieving the items, and shipping the items. So while the “zones” are depicted in  FIG. 2 , other divisions of zones are possible, and the zones in  FIG. 2  may be omitted, duplicated, or modified in some embodiments. 
     Inbound zone  203  represents an area of FC  200  where items are received from sellers who wish to sell products using system  100  from  FIG. 1A . For example, a seller ay deliver items  202 A and  202 B using truck  201 . Item  202 A may represent a single item large enough to occupy its own shipping pallet, while item  202 B may represent a set of items that are stacked together on the same pallet to save space. 
     A worker will receive the items in inbound zone  203  and may optionally check the items for damage and correctness using a computer system (not pictured). For example, the worker may use a computer system to compare the quantity of items  202 A and  202 B to an ordered quantity of items. If the quantity does not match, that worker may refuse one or more of items  202 A or  202 B. If the quantity does match, the worker may move those items (using, e.g., a dolly, a handtruck, a forklift, or manually) to buffer zone  205 . Buffer zone  205  may be a temporary storage area for items that are not currently needed in the picking zone, for example, because there is a high enough quantity of that item in the picking zone to satisfy forecasted demand. In some embodiments, forklifts  206  operate to move items around buffer zone  205  and between inbound zone  203  and drop zone  207 . If there is a need for items  202 A or  202 B in the picking zone (e.g., because of forecasted demand), a forklift may move items  202 A or  202 B to drop zone  207 . 
     Drop zone  207  may be an area of FC  200  that stores items before they are moved to picking zone  209 . A worker assigned to the picking task (a “picker”) may approach items  202 A and  202 B in the picking zone, scan a barcode for the picking zone, and scan barcodes associated with items  202 A and  202 B using a mobile device (e.g., device  119 B). The picker may then take the item to picking zone  209  (e.g., by placing it on a cart or carrying it). 
     Picking zone  209  may be an area of FC  200  where items  208  are stored on storage units  210 . In some embodiments, storage units  210  may comprise one or more of physical shelving, bookshelves, boxes, totes, refrigerators, freezers, cold stores, or the like. In some embodiments, picking zone  209  may be organized into multiple floors. In some embodiments, workers or machines may move items into picking zone  209  in multiple ways, including, for example, a forklift, an elevator, a conveyor belt, a cart, a handtruck, a dolly, an automated robot or device, or manually. For example, a picker may place items  202 A and  202 B on a handtruck or cart in drop zone  207  and walk items  202 A and  202 B to picking zone  209 . 
     A picker may receive an instruction to place (or “stow”) the items in particular spots in picking zone  209 , such as a particular space on a storage unit  210 . For example, a picker may scan item  202 A using a mobile device (e.g., device  119 B). The device may indicate where the picker should stow item  202 A, for example, using a system that indicate an aisle, shelf, and location. The device may then prompt the picker to scan a barcode at that location before stowing item  202 A in that location. The device may send (e.g., via a wireless network) data to a computer system such as WMS  119  in  FIG. 1A  indicating that item  202 A has been stowed at the location by the user using device  119 B. 
     Once a user places an order, a picker may receive an instruction on device  119 B to retrieve one or more items  208  from storage unit  210 . The picker may retrieve item  208 , scan a barcode on item  208 , and place it on transport mechanism  214 . While transport mechanism  214  is represented as a slide, in some embodiments, transport mechanism may be implemented as one or ore of a conveyor belt, an elevator, a cart, a forklift, a handtruck, a dolly, a cart, or the like. Item  208  may then arrive at packing zone  211 . 
     Packing zone  211  may be an area of FC  200  where items are received from picking zone  209  and packed into boxes or bags for eventual shipping to customers. In packing zone  211 , a worker assigned to receiving items (a “rebin worker”) will receive item  208  from picking zone  209  and determine what order it corresponds to. For example, the rebin worker may use a device, such as computer  119 C, to scan a barcode on item  208 . Computer  119 C may indicate visually which order item  208  is associated with. This may include, for example, a space or “cell” on a wall  216  that corresponds to an order. Once the order is complete (e.g., because the cell contains all items for the order), the rebin worker may indicate to a packing worker (or “packer”) that the order is complete. The packer may retrieve the items from the cell and place them in a box or bag for shipping. The packer may then send the box or bag to a hub zone  213 , e.g., via forklift, cart, dolly, handtruck, conveyor belt, manually, or otherwise. 
     Hub zone  213  may be an area of FC  200  that receives all boxes or bags (“packages”&#39; from packing zone  211 . Workers and/or machines in hub zone  213  may retrieve package  218  and determine which portion of a delivery area each package is intended to go to, and route the package to an appropriate camp zone  215 . For example, if the delivery area has two smaller sub-areas, packages will go to one of two camp zones  215 . In some embodiments, a worker or machine may scan a package (e.g., using one of devices  119 A- 119 C) to determine its eventual destination. Routing the package to camp zone  215  may comprise, for example, determining a portion of a geographical area that the package is destined for (e.g., based on a postal code) and determining a camp zone  215  associated with the portion of the geographical area. 
     Camp zone  215 , in some embodiments, may comprise one or more buildings, one or more physical spaces, or one or more areas, where packages are received from hub zone  213  for sorting into routes and/or sub-routes. In some embodiments, camp zone  215  is physically separate from FC  200  while in other embodiments camp zone  215  may form a part of FC  200 . 
     Workers and/or machines in camp zone  215  may determine which route and/or sub-route a package  220  should be associated with, for example, based on a comparison of the destination to an existing route and/or sub-route, a calculation of workload for each route and/or sub-route, the time of day, a shipping method, the cost to ship the package  220 , a PDD associated with the items in package  220 , or the like. In some embodiments, a worker or machine may scan a package (e.g., using one of devices  119 A- 119 C) to determine its eventual destination. Once package  220  is assigned to a particular route and/or sub-route, a worker and/or machine may move package  220  to be shipped. In exemplary  FIG. 2 , camp zone  215  includes a truck  222 , a car  226 , and delivery workers  224 A and  224 B. In some embodiments, truck  222  may be driven by delivery worker  224 A, where delivery worker  224 A is a full-time employee that delivers packages for FC  200  and truck  222  is owned, leased, or operated by the same company that owns, leases, or operates FC  200 . In some embodiments, car  226  may be driven by delivery worker  224 B, where delivery worker  224 B is a “flex” or occasional worker that is delivering on an as-needed basis (e.g., seasonally). Car  226  may be owned, leased, or operated by delivery worker  224 B. 
     According to an aspect of the present disclosure, a computer-implemented system for predicting an out of stock condition may comprise one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform operations. The out of stock condition can be predicted or analyzed by running an out of stock (OOS) Root Cause Calculation Algorithm, decision tree construction algorithm, or a decision tree against historical information associated with the out of stock item. In some embodiments, the disclosed functionality and systems may be implemented as part of SCM system  117 . The preferred embodiment comprises implementing the disclosed functionality and systems on SCM system  117 , but one of ordinary skill will understand that other implementations are possible. 
     Stock availability can be determined by one or more contributors associated with supply chain ordering and inventory replenishing. For example, contributors may be an error or mistake from commercial decisions, a defect from suppliers, a defect from ordering items, and a defect from fulfillment centers. The contributors may include one or more root causes for out of stock condition. For example, an error or mistake from commercial decisions may include root causes such as obsolete confirming and strategic decision; a defect from suppliers may include none delivery and short delivery; a defect from ordering items may include low recommended order quantity and sales spike; and a defect from fulfillment centers may include a delay in receiving items and a delay in stowing items. The root causes may be arranged in a decision tree by their priorities. The priorities may be determined by a static rule. For example, if a static rule for determining priorities of root causes prioritizes internal issues over external issues, then a root cause associated with a defect from fulfillment centers is prioritized over a root cause associated a defect from suppliers. 
       FIG. 3A  shows an exemplary method  300  for predicting an out of stock condition of item by running a decision tree on SCM system  117 . The method or a portion thereof may be performed by SCM system  117 . For example, the system may include one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the system to perform the steps shown in  FIG. 3A . 
     In step  301 , SCM  117  may receive information associated with an out of stock item from FO system  113 . As described above with respect to  FIG. 1A , FO system  113  may store information related to items stored in fulfillment center  200 . The stored information may also include one or more conditions causing an out of stock condition. For example, conditions causing an out of stock condition may include, but are not limited to, a low prediction on forecasting a demand for an item, an unexecuted purchase order for an item, that a purchase order was placed but not yet received by a fulfillment center, a supplier of out of stock item failed to deliver the ordered amount, a defect on an out of stock item, and a cancelation of purchase order associated with an out of stock item. When a quantity of an item stored in fulfillment center reaches zero (Out of Stock), FO system  113  may transmit information associated with the item to SCM system  117  for determining a cause of the out of stock condition. The transmitted information was collected over an extended period. For example, FO system  113  may transmit an exemplary table  400  in  FIG. 4A  (discussed further below) to SCM system  117 . The exemplary table  400  may include information related to one or more conditions causing an out of stock condition and whether an item was out of stock on a given day. The information presented in the exemplary table  400  was collected over ten days. The exemplary table  400  presents only three conditions and data collected over ten days but one of ordinary skill will understand that other configurations are possible. 
     In step  302 , SCM system  117  may determine a cause of the out of stock condition by running a decision tree against the received information, wherein the decision tree includes a plurality of conditions. Step  302  is further described with respect to step  311  in  FIG. 3B . In step  311  ( FIG. 3B ), SCM system  117  may limit a number of conditions. The number of conditions may refer to the length of the longest path from a root to a leaf in a decision tree. For example, as shown in  FIG. 4D  (discussed further below), decision tree  450  includes three conditions representing the length of the longest path from a root (Low Forecast?) to a leaf (Out of Stock or Not Out of Stock under Unexecuted Purchase Order). Limiting a number of conditions may assist in reducing overfitting of decision tree, wherein the overfitting results from creating over-complex decision trees that do not generalize data well. 
     In step  312 , SCM system  117  may measure a purity for each condition. The purity may provide a certainty about whether an item goes out of stock or not after a condition splits a decision tree. For example, as shown in  FIG. 4A , item was out of stock for six days over past ten days. In  FIG. 4B , a purity for each of conditions  410 ,  411 , and  412  is measured. For example, condition  410  has a pure set (3 Yes-Out of Stock on days D3, D7 and D10/0 No-Not Out of Stock) because an item as always out of stock when the item was forecasted low. The pure set provides a complete certainty on an occurrence of out of stock condition. The purity must be symmetric. For example, a condition comprising 4 Out of Stock/0 Not Out of Stock is as “pure” as 0 Out of Stock/4 Not Out of Stock. Unlike condition  410 , conditions  411  and  412  do not result a pure set thus in step  313 , SCM system  117  may choose condition  410 , the condition with the highest purity. The chosen condition (e.g., condition  410 ) may split a decision tree. SCM  117  may store the chosen condition further splitting the decision tree in a data structure. 
     In step  314 , SCM system  117  may determine whether all conditions split the decision tree into branches after measuring a purity for each of remaining conditions and choosing a condition with the highest purity. If all conditions do not split the decision trees into branches, SCM system  117 , in step  312 , may measure a purity for each of remaining conditions. For example, as shown in  FIG. 4C , SCM  117  may measure purities for conditions  411  and  412  after choosing highest purity condition  410  in  FIG. 4B . By way of further example, as shown in  FIG. 4C , an item was out of stock for three days and not out of stock for four days when the item was not forecasted low. Among days when the item as not forecasted low (Days D1, D2, D4, D5, D6, D8, and D9), condition  412  has a pure set (0 Yes-Out of Stock/2 No-Not Out of Stock on days D2 and D6) because the item was always not out of stock when purchase orders associated the item were not canceled. Thus, SCM system  117  may choose condition  412  as a condition splitting the tree after condition  410  and store the condition  412  in the data structure storing the decision tree and chosen conditions splitting the decision tree. As shown in  FIG. 4D , condition  412  splits the decision tree  450  branching out of condition  410 . Condition  411 , as an only remaining condition may split the decision tree after condition  412  in the exemplary decision tree  450  in  FIG. 4D . 
     If all conditions split the decision tree (e.g., decision tree  450  in  FIG. 4D ) into branches, SCM system  117 , in step  315 , may determine a cause of out of stock condition by finding a condition forming the longest path from a root of the decision tree. The condition forming the longest path from a root of the decision tree may represent a cause of out of stock condition. For example, as shown in  FIG. 4D , condition  411  (purchase orders associated with the out of stock item was unexecuted) forms the longest path from a root (condition  410 ) of the decision tree  450  and SCM  117  may determine that unexecuted purchase orders associated with the out of stock item is a cause of out of stock condition for the item. 
     After step  315  in  FIG. 3B , the process moves to step  303  in  FIG. 3A . In step  303 , SCM  117  may predict an out of stock condition of the item based on the determined cause. 
     In step  304 , SCM  117  may contact a supplier of the out of stock item to request more items based on the prediction. For example, if an item was predicted to be out of stock, SCM  117  may contact a supplier of the out of stock item to request more items. SCM  117  may also check status of purchase orders to prevent unexecuting the purchase orders if the determined cause is unexecuted purchase orders of the item. 
       FIG. 5A  shows an exemplary method  500  for analyzing a reason for out of stock by running an out of stock root cause calculation algorithm or a decision tree construction algorithm on SCM system  117 . The method or a portion thereof may be performed by SCM system  117 . For example, the system may include one or more processors and a memory storing instructions that, when executed by the one or ore processors, cause the system to perform the steps shown in  FIG. 5A . 
     In step  501 , SCM  117  may determine an out of stock scope. SCM  117  may receive information associated with an out of stock item from FO system  113 . As described above with respect to  FIG. 1A , FO system  113  may store information related to items stored in fulfillment center  200 . The stored information may provide a list of items (SKUs) that are out of stock when the quantity of the items stored in fulfillment center reaches zero (Out of Stock). 
     In step  502 , SCM  117  may determine attributes associated with out of stock items determined in step  501  from a data source. The data source may provide SKU-level data from purchase orders, receiving and stowing time, master data, sales, order cycles, etc. The data source may refer to FO system  113  which stores information associated with items stored in fulfilment centers. 
     In step  503 , SCM  117  may determine root cause conditions for each determined out of stock item from step  501 . For example, SCM  117  may determine a first determined item went out of stock from a mistake from commercial decisions such as obsolete confirming and strategic decision; a second determined item went out of stock from the supplier&#39;s defect such as none delivery and short delivery; a third determined item went out of stock from a defect in ordering items such as low recommended order quantity and sales spike; and a fourth determined item wen out of stock from a fulfillment center&#39;s defect such as a delay in receiving items and a delay in stowing items. SCM  117  may decide which category an out of stock item can be assigned based on determined attributes from step  502 . 
     In step  504 , SCM  117  may execute an out of stock root cause calculation algorithm to analyze the reason for out of stock. Step  504  is further described with respect to step  511  in  FIG. 5B . 
     In step  511  ( FIG. 5B ), SCM  117  may determine a single out of stock root cause level hierarchy. The single out of stock root cause is determined by locating a determined out of stock item (from step  501 ), in the determined data source from step  502 . For example, as shown in  FIG. 6A , list  601  provides a list of out of stock items determined in step  501  and data source  602  provides attributes associated with items such as a purchase order status code. As shown in box  603 , SCM  117  may determine a single out of stock root cause level hierarchy by locating item 10002 (depicted as SKUSEQ 10002 in  FIG. 6A ) in data source  602  and assigning corresponding PO_STATUS_CODE (new sku) as the single out of stock root cause. In another example, SCM  117  may assign level1_level1, level1_level12, or level2_level21 as a single out of stock root cause. Moreover, it is appreciated that SCM  117  may assign different single out of stock root cause based on the determined data source associated with the determined out of stock item. 
     In step  512 , SCM  117  may join all reason levels of out of stock root causes for each out of stock item. Each of the reason levels is determined in step  511 . For example, as shown in  FIG. 6B , determined out of stock root causes for each determined out of stock item (depicted in  611 ) is joined as depicted in  612 . By way of further example, out of stock reason levels level 1 and level 11 for item 10001 are joined to provide reasons for the out of stock condition of item 10001. The joined reason levels are arranged in table in respect to its priority. For example, as shown in exemplary table  612 , reason levels are arranged in respect to its priorities, wherein “NEW_SKU” is associated with the highest priority and “level2_level21” is associated with the least priority. 
     In step  513 , SCM  117  may determine a main out of stock root cause and an out of stock reason for each level. As shown in hierarchy node  621  in  FIG. 6C , SCM  117  may assign a first not null reason in hierarchy node as the main reason for out of stock for each item. As depicted in hierarchy node  621  in  FIG. 6C , level1_level11 is assigned as the main reason for out of stock for item 10001. Table  622  provides determined main out of stock causes for out of stock items. SCM  117  may further resolve out of stock reasons based on the main reason. For example, as shown in table  623  in  FIG. 6C , SCM  117  may determine that a first level of reason for out of stock is level 1 and a second level of reason for out of stock is level 11 for item 10001 based on its main reason level1_level11. By way of further example, level 1 may refer to a defect from suppliers and level 11 may refer to a non-delivery from the suppliers. Moreover, it is appreciated that reasons may comprise conditions suitable to cause out of stock conditions. 
     In step  514 , SCM  117  may add additional columns representing another root cause for each out of stock item. For example, SCM  117  may add information such as SKU description, SKU Bands, etc. As shown in  FIG. 6C , SCM  117  may add SKU names  623  for each out of stock item as additional information 
     In another embodiment, SCM  117  may, in step  505  (of  FIG. 5A ), execute a decision tree construction algorithm to analyze the reason for out of stock. Step  505  is further described with respect to step  521  in  FIG. 5C . In step  521 , SCM  117  may filter out a list of all none-root out of stock conditions. The determined attributes from step  502  may comprise information describing whether a condition is a root or none-root. For example, if a determined root condition from step  503  comprises information that the determined root condition is root, then the root condition is root while other root conditions are none-root conditions. 
     In step  522 , SCM  117  may filter a list of all leaf from the filtered list of none-root out of stock conditions. The determined attributes from step  502  may comprise information describing whether a condition is parent of other conditions. For example, if a determined root condition from step  503  comprises information that the determined root condition is not parent of other conditions, then the root condition is a leaf. 
     In step  523 , SCM  117  may build a list of hierarchy. SCM  117  may determine a parent condition of each leaf condition filtered in step  522 . The parent condition is included in the determined attributes from step  502  for each leaf condition. When the parent condition is determined, SCM  117  may add the determined parent condition to corresponding leaf condition as the new leaf condition. 
     In step  524 , SCM  117  may determine whether all hierarchy&#39;s last nodes are root. If all hierarchy&#39;s last nodes are not root, SCM  117  may, in step  525 , locate and add a parent of the last decision tree node for each hierarchy. If all hierarchy&#39;s last nodes are root, SCM  117  may, in step  526  reverse the hierarchy list. 
       FIG. 7  shows an exemplary decision tree hierarchy list. The exemplary decision tree hierarchy list depicts three tree hierarchies: a first tree hierarchy  701 , a second tree hierarchy, and a third tree hierarchy. First tree hierarchy  701  includes Global Sourcing leaves (including leaf  702 ). Second tree hierarchy includes an FC Defect leaf and a Stow Delay leaf. Third tree hierarchy may include a Supplier Defect leaf  703 , a None Delivery leaf  704 , and a Zero Confirmed leaf  705 . 
     While the present disclosure has been shown and described with reference to particular embodiments thereof, it will be understood that the present disclosure can be practiced, without modification, in other environments. The foregoing description has been presented for purposes of illustration. It is not exhaustive and is not limited to the precise forms or embodiments disclosed. Modifications and adaptations will be apparent to those skilled in the art from consideration of the specification and practice of the disclosed embodiments. Additionally, although aspects of the disclosed embodiments are described as being stored in memory, one skilled in the art will appreciate that these aspects can also be stored on other types of computer readable media, such as secondary storage devices, for example, hard disks or CD ROM, or other forms of RAM or ROM, USB media, DVD, Blu-ray, or other optical drive media. 
     Computer programs based on the written description and disclosed methods are within the skill of an experienced developer. Various programs or program modules can be created using any of the techniques known to one skilled in the art or can be designed in connection with existing software. For example, program sections or program modules can be designed in or by means of .Net Framework, .Net Compact Framework (and related languages, such as Visual Basic, C, etc.), Java, C++, Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with included Java applets. 
     Moreover, while illustrative embodiments have been described herein, the scope of any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., of aspects across various embodiments), adaptations and/or alterations as would be appreciated by those skilled in the art based on the present disclosure. The limitations in the claims are to be interpreted broadly based on the language employed in the claims and not limited to examples described in the present specification or during the prosecution of the application. The examples are to be construed as non-exclusive. Furthermore, the steps of the disclosed methods may be modified in any manner, including by reordering steps and/or inserting or deleting steps. It is intended, therefore, that the specification and examples be considered as illustrative only, with a true scope and spirit being indicated by the following claims and their full scope of equivalents.