Patent ID: 12254431

DETAILED DESCRIPTION

Aspects and applications of the invention presented herein are described below in the drawings and detailed description of the invention. Unless specifically noted, it is intended that the words and phrases in the specification and the claims be given their plain, ordinary, and accustomed meaning to those of ordinary skill in the applicable arts.

In the following description, and for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the various aspects of the invention. It will be understood, however, by those skilled in the relevant arts, that the present invention may be practiced without these specific details. In other instances, known structures and devices are shown or discussed more generally in order to avoid obscuring the invention. In many cases, a description of the operation is sufficient to enable one to implement the various forms of the invention, particularly when the operation is to be implemented in software. It should be noted that there are many different and alternative configurations, devices and technologies to which the disclosed inventions may be applied. The full scope of the inventions is not limited to the examples that are described below.

Embodiments of the following disclosure provide a probabilistic graphical model (PGM) resolution system and method to generate supply chain probabilistic graphical models and associated resolution actions and visualizations. Embodiments comprise a PGM resolution system that generates PGMs and identifies (1) probability relationships within quantities modeled by the PGMs and (2) mutual information common to separate supply chain features and attributes. Embodiments generate predictions using the PGMs, probability relationships, and mutual information, and based on one or more predicted instances in which the supply chain may not meet all service level agreement (SLA) targets and mutual information shared between the PGM variables, embodiments generate and recommend one or more resolution actions to address the SLA shortfalls and/or to impact one or more desired key process indicators (KPIs) while also making the fewest possible changes to the supply chain. Embodiments generate visualizations to display the PGMs and resolution actions, and provide hierarchical visualizations of the effects that one or more model features have on the PGM predicted outputs.

Embodiments of the following disclosure generate one or more PGM networks, including one or more Bayesian PGM networks, which comprise and graph only important features and feature classes drawn from supply chain data. The PGM networks and/or Bayesian PGM networks limit the complexity of data displayed and modeled by the probabilistic graphical models, and enable the drawing of inferences and other data from the probabilistic graphical models quickly and efficiently without slowing the analysis process with the addition of unnecessary data, features, and feature classes. Embodiments generate graphical displays to visualize one or more probabilistic graphical models and to recommend resolution actions to alter variables or values associated with one or more supply chain features, attributes, and/or supply chain entities, thereby improving one or more KPI and/or SLA targets using the fewest number of changes required to the supply chain network.

FIG.1illustrates a supply chain network100, in accordance with a first embodiment. The supply chain network100comprises probabilistic graphical model (PGM) resolution system110, archiving system120, transportation network130, warehouse management system133, inventory system136, supply chain planner140, one or more networked imaging devices150, one or more supply chain entities160, computer170, network180, and one or more communication links181-189. Although a single PGM resolution system110, a single archiving system120, transportation network130, warehouse management system133, inventory system136, supply chain planner140, a single networked imaging device150, one or more supply chain entities160, a single computer170, a single network180, and one or more communication links181-189are illustrated and described, embodiments contemplate any number of PGM resolution systems110, archiving systems120, transportation networks130, warehouse management systems133, inventory systems136, supply chain planners140, networked imaging devices150, supply chain entities160, computers170, networks180or communication links181-189, according to particular needs.

In one embodiment, PGM resolution system110comprises server112and database114. Server112comprises one or more modules that model supply chain network100and build probabilistic graphical models228of supply chain attributes, as described in greater detail below.

Archiving system120of supply chain network100comprises server122and database124. Although archiving system120is illustrated as comprising a single server122and a single database124, embodiments contemplate any suitable number of servers122or databases124internal to or externally coupled with archiving system120. Server122of archiving system120may support one or more processes for receiving and storing data from transportation network130, warehouse management system133, inventory system136, supply chain planner140, networked imaging devices150, one or more supply chain entities160, and/or computer170of supply chain network100. According to some embodiments, archiving system120comprises an archive of data received from transportation network130, warehouse management system133, inventory system136, supply chain planner140, networked imaging devices150, one or more supply chain entities160, and/or computer170of supply chain network100. Archiving system120provides archived data to PGM resolution system110and transportation network130, warehouse management system133, inventory system136, supply chain planner140to, for example, train one or more machine learning models. Server122may store the received data in database124. Database124of archiving system120may comprise one or more databases or other data storage arrangements at one or more locations, local to, or remote from, server122.

Transportation network130of supply chain network100comprises server131and database132. Although transportation network130is illustrated as comprising a single server131and a single database132, embodiments contemplate any suitable number of servers131or databases132internal to or externally coupled with transportation network130. According to embodiments, transportation network130directs one or more transportation vehicles to ship one or more items between one or more supply chain entities160, based, at least in part, on a supply chain plan, including a supply chain master plan, the number of items currently in stock at one or more supply chain entities160or other stocking location, the number of items currently in transit in transportation network130, a forecasted demand, a supply chain disruption, and/or one or more other factors described herein. One or more transportation vehicles comprise, for example, any number of trucks, cars, vans, boats, airplanes, unmanned aerial vehicles (UAVs), cranes, robotic machinery, or the like. The one or more transportation vehicles may comprise radio, satellite, or other communication that communicates location information (such as, for example, geographic coordinates, distance from a location, global positioning satellite (GPS) information, or the like) with PGM resolution system110, archiving system120, transportation network130, warehouse management system133, inventory system136, supply chain planner140, networked imaging device150, and/or one or more supply chain entities160to identify the location of the one or more transportation vehicles and the location of any inventory or shipment located on the one or more transportation vehicles.

Warehouse management system133of supply chain network100comprises server134and database135. Although warehouse management system133is illustrated as comprising a single server134and a single database135, embodiments contemplate any suitable number of servers134or databases135internal to or externally coupled with warehouse management system133. According to embodiments, server134comprises one or more modules that manage and operate warehouse operations, plan timing and identity of shipments, generate picklists, packing plans, and instructions. Warehouse management system133instructs users and/or automated machinery to obtain picked items and generates instructions to guide placement of items on a picklist in the configuration and layout determined by a packing plan. For example, the instructions may instruct a user and/or automated machinery to prepare items on a picklist for shipment by obtaining the items from inventory or a staging area and packing the items on a pallet in a proper configuration for shipment. Embodiments contemplate warehouse management system133determining routing, packing, or placement of any item, package, or container into any packing area, including, packing any item, package, or container in another item, package, or container. Warehouse management system133may generate instructions for packing products into boxes, packing boxes onto pallets, packing loaded pallets into trucks, or placing any item, container, or package in a packing area, such as, for example, a box, a pallet, a shipping container, a transportation vehicle, a shelf, a designated location in a warehouse (such as a staging area), and the like.

Inventory system136of supply chain network100comprises server137and database138. Although inventory system136is illustrated as comprising a single server137and a single database138, embodiments contemplate any suitable number of servers137or databases138internal to or externally coupled with inventory system136. Server137of inventory system136is configured to receive and transmit item data, including item identifiers, pricing data, attribute data, inventory levels, and other like data about one or more items at one or more stocking locations in supply chain network100. Server137stores and retrieves item data from database138or from one or more locations in supply chain network100.

Supply chain planner140of supply chain network100comprises server142and database144. Although supply chain planner140is illustrated as comprising a single server142and a single database144, embodiments contemplate any suitable number of servers142or databases144internal to or externally coupled with supply chain planner140. Server142of supply chain planner140comprises one or more modules, such as, for example, a planning module250, a solver254, a modeler252, and/or an engine, for performing activities of one or more planning and execution processes. Supply chain planner140may model and solve supply chain planning problems (such as, for example, operation planning problems). Supply chain planner140generates the supply chain planning problem solutions, which are used by PGM resolution system110to construct training data230. In one embodiment, Supply chain planner140may use probabilistic graphical model228to predict target supply chain attributes needed to reach a target state of the supply chain, or other predicted supply chain information or status, as described in further detail below. One or more networked imaging devices150comprise one or more processors154, memory156, one or more sensors152, and may include any suitable input device, output device, fixed or removable computer-readable storage media, or the like. According to embodiments, one or more networked imaging devices150comprise an electronic device that receives imaging data from one or more sensors152or from one or more databases in supply chain network100. One or more sensors152of one or more networked imaging devices150may comprise an imaging sensor, such as, a camera, scanner, electronic eye, photodiode, charged coupled device (CCD), or any other electronic component that detects visual characteristics (such as color, shape, size, fill level, or the like) of objects. One or more networked imaging devices150may comprise, for example, a mobile handheld electronic device such as, for example, a smartphone, a tablet computer, a wireless communication device, and/or one or more networked electronic devices configured to image items using one or more sensors152and transmit product images to one or more databases.

In addition, or as an alternative, one or more sensors152may comprise a radio receiver and/or transmitter configured to read an electronic tag, such as, for example, a radio-frequency identification (RFID) tag. Each item may be represented in supply chain network100by an identifier, including, for example, Stock-Keeping Unit (SKU), Universal Product Code (UPC), serial number, barcode, tag, RFID, or like objects that encode identifying information. One or more networked imaging devices150may generate a mapping of one or more items in supply chain network100by scanning an identifier or object associated with an item and identifying the item based, at least in part, on the scan. This may include, for example, a stationary scanner located at one or more supply chain entities160that scans items as the items pass near the scanner. In an embodiment, PGM resolution system110, archiving system120, transportation network130, warehouse management system133, inventory system136, supply chain planner140, networked imaging devices150, and/or one or more supply chain entities160may use the mapping of an item to locate the item in supply chain network100. The location of the item may be used to coordinate the storage and transportation of items in supply chain network100according to one or more plans and/or a reallocation of materials or capacity generated by supply chain planner140. Plans may comprise one or more of a master supply chain plan, production plan, operations plan, distribution plan, and the like.

In addition, one or more sensors152of one or more networked imaging devices150may be located at one or more locations local to, or remote from, one or more networked imaging devices150, including, for example, one or more sensors152integrated into one or more networked imaging devices150or one or more sensors152remotely located from, but communicatively coupled with, one or more networked imaging devices150. According to some embodiments, one or more sensors152may be configured to communicate directly or indirectly with one or more of PGM resolution system110, archiving system120, transportation network130, warehouse management system133, inventory system136, supply chain planner140, networked imaging device150, one or more supply chain entities160, computer170, and/or network using one or more communication links181-189.

One or more supply chain entities160may represent one or more suppliers, manufacturers, distribution centers, and retailers in one or more supply chain networks100, including one or more enterprises. One or more suppliers may be any suitable entity that offers to sell or otherwise provides one or more components to one or more manufacturers. One or more suppliers may, for example, receive a product from a first supply chain entity in supply chain network100and provide the product to another supply chain entity. One or more suppliers may comprise automated distribution systems that automatically transport products to one or more manufacturers based, at least in part, on a supply chain plan, the number of items currently in stock at one or more supply chain entities160, the number of items currently in transit in transportation network130, a forecasted demand, a supply chain disruption, a material or capacity reallocation, current and projected inventory levels at one or more stocking locations, and/or one or more additional factors described herein.

A manufacturer may be any suitable entity that manufactures at least one product. A manufacturer may use one or more items during the manufacturing process to produce any manufactured, fabricated, assembled, or otherwise processed item, material, component, good or product. Items may comprise, for example, components, materials, products, parts, supplies, or other items, that may be used to produce products. In addition, or as an alternative, an item may comprise a supply or resource that is used to manufacture the item, but does not become a part of the item. In one embodiment, a product represents an item ready to be supplied to, for example, another supply chain entity, such as a supplier, an item that needs further processing, or any other item. A manufacturer may, for example, produce and sell a product to a supplier, another manufacturer, a distribution center, a retailer, a customer, or any other suitable person or an entity. Such manufacturers may comprise automated robotic production machinery that produce products based, at least in part, on a supply chain plan, the number of items currently in stock at one or more supply chain entities160, the number of items currently in transit in transportation network130, a forecasted demand, a supply chain disruption, a material or capacity reallocation, current and projected inventory levels at one or more stocking locations, and/or one or more additional factors described herein.

One or more distribution centers may be any suitable entity that offers to sell or otherwise distributes at least one product to one or more retailers and/or customers. Distribution centers may, for example, receive a product from a first supply chain entity in supply chain network100and store and transport the product for a second supply chain entity. Such distribution centers may comprise automated warehousing systems that automatically transport to one or more retailers or customers and/or automatically remove an item from, or place an item into, inventory based, at least in part, on a supply chain plan, the number of items currently in stock at one or more supply chain entities160, the number of items currently in transit in transportation network130, a forecasted demand, a supply chain disruption, a material or capacity reallocation, current and projected inventory levels at one or more stocking locations, and/or one or more additional factors described herein.

One or more retailers may be any suitable entity that obtains one or more products to sell to one or more customers. In addition, one or more retailers may sell, store, and supply one or more components and/or repair a product with one or more components. One or more retailers may comprise any online or brick and mortar location, including locations with shelving systems. Shelving systems may comprise, for example, various racks, fixtures, brackets, notches, grooves, slots, or other attachment devices for fixing shelves in various configurations. These configurations may comprise shelving with adjustable lengths, heights, and other arrangements, which may be adjusted by an employee of one or more retailers based on computer-generated instructions or automatically by machinery to place products in a desired location, and which may be based, at least in part, on a supply chain plan, the number of items currently in stock at one or more supply chain entities160, the number of items currently in transit in transportation network130, a forecasted demand, a supply chain disruption, a material or capacity reallocation, current and projected inventory levels at one or more stocking locations, and/or one or more additional factors described herein.

Although one or more suppliers, manufacturers, distribution centers, and retailers are illustrated and described as separate and distinct entities, the same entity may simultaneously act as any one or more suppliers, manufacturers, distribution centers, and retailers. For example, one or more manufacturers acting as a manufacturer could produce a product, and the same entity could act as a supplier to supply a product to another supply chain entity. Although one example of supply chain network100is illustrated and described, embodiments contemplate any configuration of supply chain network100, without departing from the scope of the present disclosure.

As illustrated byFIG.1, supply chain network100comprising PGM resolution system110, archiving system120, transportation network130, warehouse management system133, inventory system136, supply chain planner140, one or more networked imaging devices150, and one or more supply chain entities160may operate on one or more computers170that are integral to or separate from the hardware and/or software that support PGM resolution system110, archiving system120, transportation network130, warehouse management system133, inventory system136, supply chain planner140, one or more networked imaging devices150, and one or more supply chain entities160. One or more computers170may include any suitable input device172, such as a keypad, mouse, touch screen, microphone, or other device to input information. One or more computers170may also comprise one or more output devices174, including but not limited to one or more computer monitors, which may convey information associated with the operation of supply chain network100, including digital or analog data, visual information, or audio information.

One or more computers170may include fixed or removable computer-readable storage media176, including a non-transitory computer readable medium, magnetic computer disks, flash drives, CD-ROM, in-memory devices or other suitable media to receive output from and provide input to supply chain network100. One or more computers170may include one or more processors and associated memory to execute instructions and manipulate information according to the operation of supply chain network100and any of the methods described herein. In addition, or as an alternative, embodiments contemplate executing the instructions on one or more computers170that cause one or more computers170to perform functions of the method. An apparatus implementing special purpose logic circuitry, for example, one or more field programmable gate arrays (FPGA) or application-specific integrated circuits (ASIC), may perform functions of the methods described herein. Further examples may also include articles of manufacture including tangible computer-readable media that have computer-readable instructions encoded thereon, and the instructions may comprise instructions to perform functions of the methods described herein.

PGM resolution system110, archiving system120, transportation network130, warehouse management system133, inventory system136, supply chain planner140, one or more networked imaging devices150, and one or more supply chain entities160may each operate on one or more separate computers170, network180of one or more separate or collective computers170, or may operate on one or more shared computers170. In addition, supply chain network100may comprise a cloud-based computing system having processing and storage devices at one or more locations, local to, or remote from PGM resolution system110, archiving system120, transportation network130, warehouse management system133, inventory system136, supply chain planner140, one or more networked imaging devices150, and one or more supply chain entities160. In addition, each of one or more computers170may be a work station, personal computer (PC), network computer, notebook computer, tablet, personal digital assistant (PDA), cell phone, telephone, smartphone, mobile device, wireless data port, augmented or virtual reality headset, or any other suitable computing device. In an embodiment, one or more users may be associated with PGM resolution system110, archiving system120, transportation network130, warehouse management system133, inventory system136, supply chain planner140, one or more networked imaging devices150, and one or more supply chain entities160.

These one or more users may include, for example, a “manager” or a “planner” handling supply chain planning, training PGM resolution system110, and/or one or more related tasks within supply chain network100. In addition, or as an alternative, these one or more users within supply chain network100may include, for example, one or more computers170programmed to autonomously handle, among other things, production planning, demand planning, option planning, sales and operations planning, operation planning, supply chain master planning, plan adjustment after supply chain disruptions, order placement, automated warehouse operations (including removing items from and placing items in inventory), robotic production machinery (including producing items), and/or one or more related tasks within supply chain network100.

In one embodiment, PGM resolution system110may be coupled with network180using communications link181, which may be any wireline, wireless, or other link suitable to support data communications between PGM resolution system110and network180during operation of supply chain network100. Archiving system120may be coupled with network180using communications link182, which may be any wireline, wireless, or other link suitable to support data communications between archiving system120and network180during operation of supply chain network100. Transportation network130may be coupled with network180using communications link183, which may be any wireline, wireless, or other link suitable to support data communications between transportation network130and network180during operation of supply chain network100. Warehouse management system133may be coupled with network180using communications link184, which may be any wireline, wireless, or other link suitable to support data communications between warehouse management system133and network180during operation of supply chain network100. Inventory system136may be coupled with network180using communications link185, which may be any wireline, wireless, or other link suitable to support data communications between inventory system136and network180during operation of supply chain network100. Supply chain planner140may be coupled with network180using communications link186, which may be any wireline, wireless, or other link suitable to support data communications between supply chain planner140and network180during operation of supply chain network100. One or more networked imaging devices150are coupled with network180using communications link187, which may be any wireline, wireless, or other link suitable to support data communications between one or more networked imaging devices150and network180during operation of distributed supply chain network100. One or more supply chain entities160may be coupled with network180using communications link188, which may be any wireline, wireless, or other link suitable to support data communications between one or more supply chain entities160and network180during operation of supply chain network100. One or more computers170may be coupled with network180using communications link189, which may be any wireline, wireless, or other link suitable to support data communications between a computer170and network180during operation of supply chain network100.

Although communication links181-189are illustrated as generally coupling PGM resolution system110, archiving system120, transportation network130, warehouse management system133, inventory system136, supply chain planner140, networked imaging device150, one or more supply chain entities160, and computer170to network180, each of PGM resolution system110, archiving system120, transportation network130, warehouse management system133, inventory system136, supply chain planner140, networked imaging device150, one or more supply chain entities160, and computer170may communicate directly with each other, according to particular needs.

In another embodiment, network180includes the Internet and any appropriate local area networks (LANs), metropolitan area networks (MANs), or wide area networks (WANs) coupling PGM resolution system110, archiving system120, transportation network130, warehouse management system133, inventory system136, supply chain planner140, networked imaging device150, one or more supply chain entities160, and computer170. For example, data may be maintained locally or externally of PGM resolution system110, archiving system120, transportation network130, warehouse management system133, inventory system136, supply chain planner140, networked imaging device150, one or more supply chain entities160, and computer170and made available to one or more associated users of PGM resolution system110, archiving system120, transportation network130, warehouse management system133, inventory system136, supply chain planner140, networked imaging device150, one or more supply chain entities160, and computer170using network180or in any other appropriate manner. Those skilled in the art will recognize that the complete structure and operation of network180and other components within supply chain network100are not depicted or described. Embodiments may be employed in conjunction with known communications networks and other components.

In accordance with the principles of embodiments described herein, supply chain planner140may generate a supply chain plan. Furthermore, one or more computers170associated with transportation network130, warehouse management system133, and inventory system136may instruct automated machinery (i.e., robotic warehouse systems, robotic inventory systems, automated guided vehicles, mobile racking units, automated robotic production machinery, robotic devices and the like) to adjust product mix ratios, inventory levels at various stocking points, production of products of manufacturing equipment, proportional or alternative sourcing of one or more supply chain entities160, and the configuration and quantity of packaging and shipping of items based on a supply chain plan, the number of items currently in stock at one or more supply chain entities160, the number of items currently in transit in transportation network130, a forecasted demand, a supply chain disruption, a material or capacity reallocation, current and projected inventory levels at one or more stocking locations, and/or one or more additional factors described herein. For example, the methods described herein may include computers170receiving product data284from automated machinery having at least one sensor and product data284corresponding to an item detected by the automated machinery. The received product data284may include an image of the item, an identifier, as described above, and/or product information associated with the item, including, for example, dimensions, texture, estimated weight, and the like. Computers170may also receive, from one or more sensors152of one or more networked imaging devices150, a current location of the identified item.

The methods may further include computers170looking up the received product data284in database system associated with supply chain planner140to identify the item corresponding to product data284received from automated machinery. Based on the identification of the item, computers170may also identify (or alternatively generate) a first mapping in database system, where the first mapping is associated with the current location of the identified item. Computers170may also identify a second mapping in database system, where the second mapping is associated with a past location of the identified item. Computers170may also compare the first mapping and the second mapping to determine if the current location of the identified item in the first mapping is different than the past location of the identified item in the second mapping. Computers170may send instructions to the automated machinery based, as least in part, on one or more differences between the first mapping and the second mapping such as, for example, to locate items to add to or remove from an inventory of or shipment for one or more supply chain entities160. In addition, or as an alternative, transportation network130, warehouse management system133, inventory system136, supply chain planner140monitors one or more supply chain constraints of one or more items at one or more supply chain entities160and adjusts the orders and/or inventory of one or more supply chain entities160at least partially based on one or more supply chain constraints.

FIG.2illustrates PGM resolution system110, archiving system120, and supply chain planner140ofFIG.1in greater detail, in accordance with an embodiment. PGM resolution system110comprises server112and database114, as described above. Although PGM resolution system110is illustrated as comprising a single server112and a single database114, embodiments contemplate any suitable number of servers112or databases114internal to or externally coupled with PGM resolution system110.

Server112of PGM resolution system110comprises probability module210, learning module212, inference and query engine214, ranking module216, and user interface module218. Although server112is illustrated and described as comprising a single probability module210, a single learning module212, a single inference and query engine214, a single ranking module216, and a single user interface module218, embodiments contemplate any suitable number or combination of these located at one or more locations, local to, or remote from PGM resolution system110, such as on multiple servers112or computers170at one or more locations in supply chain network100.

Database114of PGM resolution system110may comprise one or more databases or other data storage arrangements at one or more locations, local to, or remote from, server112. Database114of PGM resolution system110comprises, for example, supply chain network models220, supply chain states222, bucketized data224, KPI and SLA data226, one or more probabilistic graphical models228, training data230, test data232, ensemble data234, score-based ranking data236, delta distance data238, final ranking data240, root cause data242, L1 and L2 features data244, predictions data246, and resolutions data248. Although database114of PGM resolution system110is illustrated and described as comprising supply chain network models220, supply chain states222, bucketized data224, KPI and SLA data226, probabilistic graphical models228, training data230, test data232, ensemble data234, score-based ranking data236, delta distance data238, final ranking data240, root cause data242, L1 and L2 features data244, predictions data246, and resolutions data248, embodiments contemplate any suitable number or combination of these, located at one or more locations, local to, or remote from, PGM resolution system110according to particular needs.

In one embodiment, probability module210of PGM resolution system110constructs a graphical model from supply chain data282, such as, for example, supply chain states222of database114of PGM resolution system110, historical data260of archiving system120, data of database144of supply chain planner140(such as, for example, supply chain data282or inventory data286), and the like. The graphical model may comprise, for example, a Bayesian network. Probability module210identifies attributes of the supply chain to represent in the graphical model from supply chain data282and which will be used for probabilistic graphical model228constructed by learning module212, as described in further detail below. By way of example only and not by way of limitation, identified attributes may include inventory stock at a particular location, the current or average volume of orders for a particular product from a particular location, and the like. Probability module210may construct a graphical model in which each node represents one of the identified attributes. While constructing the graphical model, probability module210may generate edges connecting each node in the graph, with further refinement removing edges when learning module212calculates that they do not represent relationships present in supply chain data282.

Learning module212of PGM resolution system110refines the graphical model to generate probabilistic graphical model228. Using one or more machine learning algorithms, learning module212identifies and models relationships between the nodes of the graphical model. Continuing the example above, when the graphical model is a Bayesian network, learning module212calculates the relationships between each node and stores a probability table for each node indicating the probabilistic relationship between related nodes. By way of explanation only and not by way of limitation, consider a simplified graphical model having two nodes, ‘A,’ and ‘B.’ If A is related to B, then the probability table for B will indicate the probability that B is true for both the situation that A is true and the situation that A is false. In addition, learning module212models probabilistic relationships between the nodes such as conditional probabilities, joint probabilities, and marginal probabilities. According to embodiments, learning module212learns the probability of an attribute given the probabilities of one or more related “upstream” attributes. Learning module212traverses a network of attribute nodes, and determines the structure of the relationships as well as the associated probabilities.

Inference and query engine214of PGM resolution system110evaluates queries against probabilistic graphical model228. Inference and query engine214responds to queries formulated mathematically, that is, in a format compatible with probabilistic graphical model228, such as, for example, query comprising one or more desired states for one or more metrics of the supply chain. Based on the requested desired states, inference and query engine214may traverse probabilistic graphical model228to determine changes to one or more attributes that would result in an increased probability of reaching the desired states. Inference and query engine214may respond to queries with recommendations of modifying the supply chain plan, applying lever296, or adjusting the supply chain to increase the probability of reaching a desired state. In some embodiments, inference and query engine214sends recommendations to supply chain planner140, which automatically modifies the supply chain plan, applies lever296, or adjusts the supply chain to implement the recommendations.

Ranking module216of PGM resolution system110calculates a score and assigns a score-based rank to attributes of the supply chain. According to embodiments, the score-based rank establishes a hierarchy of attributes based, at least in part, on the score. Ranking module216may access PGM resolution system110database114and data stored therein, including but not limited to supply chain states222, bucketized data224, and/or one or more nodes of probabilistic graphical model228, in order to establish a hierarchy of relevance to the overall system. In various embodiments, ranking module216calculates ranks for the attributes based on historical data260of the supply chain, current data of the supply chain, or an ensemble combination of historical and current data of the supply chain. Ranking module216may also measure a delta distance for attributes of the supply chain, meaning the distance between the current, or nearly current, state of a particular attribute and a desired or optimal state of that particular attribute. Ranking module216may then combine the score-based ranks and the delta distances to arrive at a final ranking for the attributes.

According to embodiments, user interface module218receives and processes a user input, such as, for example, input received by input device172of one or more computers170. One or more computers170may transmit input to PGM resolution system110using one or more communication links181-189. User interface module218may register the input from one or more computers170and transmit the input to the modules and engines of PGM resolution system110. In an embodiment, user interface module218generates and displays a user interface (UI), such as, for example, a graphical user interface (GUI), that displays one or more interactive visualizations of data. User interface module218may generate one or more GUI displays. The one or more GUI displays may convey information, including supply chain plan data, segmentation data, and/or any other type of information about supply chain network100and segmentation. User interface module218display a GUI dashboard comprising visualizations of probabilistic graphical model228, supply chain data282, queries to probabilistic graphical model228as well as interactive visual elements that provide for user selection or adjustment of the values of variables to input into PGM resolution system110, or user entry of queries. In response to input from the user, PGM resolution system110may calculate responses to queries including one or more recommendations of changes to be made to the supply chain via Supply chain planner140. Further, the dashboard may display results of the query indicating, for example the probability of reaching a desired state of the supply chain currently, and the probability of reaching a desired state of the supply chain if the system recommendations are implemented. As described in further detail below, embodiments of PGM resolution system110provide a tool to identify the inputs having the greatest influence on one or more key performance indicators and may sort inputs according to the degree of their influence, variability, and risk likelihood.

Supply chain network models220represent the flow of materials through one or more supply chain entities160of supply chain network100. As descried in more detail below, modeler272of planning module270of supply chain planner140may model the flow of materials through one or more supply chain entities160of supply chain network100as one or more supply chain network models220comprising a network of nodes and edges. The material storage and/or transition units are modelled as nodes, which may be referred to as, for example, buffer nodes, buffers, or nodes. Each node may represent a buffer for an item (such as, for example, a raw material, intermediate good, finished good, component, and the like), resource, or operation (including, for example, a production operation, assembly operation, transportation operation, and the like). Various transportation or manufacturing processes are modelled as edges connecting the nodes. Each edge may represent the flow, transportation, or assembly of materials (such as items or resources) between the nodes by, for example, production processing or transportation. A planning horizon for supply chain network models220may be broken down into elementary time-units, such as, for example, time-buckets, or, simply, buckets. The edge between two buffer nodes may denote processing of material and the edge between different buckets for the same buffer may indicate inventory carried forward. Flow-balance constraints for most, if not every buffer in every bucket, model the material movement in supply chain network100. Supply chain network models220may include any dynamic supply chain data282, including for example, the one or more material constraints, one or more capacity constraints, lead times, yield rates, inventory levels, safety stock, demand dates, and/or the like. Although supply chain network models220are illustrated and described as comprising a network of nodes and edges, embodiments contemplate supply chain network models220comprising any suitable model that represents one or more components of supply chain network100using any suitable model, according to particular needs.

According to embodiments, supply chain network models220may model and display supply chain data282stored in supply chain planner140database144and/or archiving system120database124. In an embodiment, supply chain network model220may model the flow of materials from upstream nodes to downstream nodes along each of the edges from left to right from, for example, raw materials to finished products. However, flows may be bidirectional, and one or more materials may flow from right to left, from a downstream node to an upstream node. Supply chain network100represented by supply chain network model220comprises material buffers storing materials or items, operations for processing materials and items, and resources which represent capacity limitations on each of the operations to which they are connected. Operations may have a single material or item as input and a single material or item as output. In addition, or as an alternative, a single operation may require two or more materials or items as input (i.e. materials or items stored at buffers) and produces one or more items as output (materials or items stored at buffers).

Supply chain network100represented by supply chain network model220may begin at the most upstream nodes representing material buffers, such as, for example, raw material buffers. Raw material buffers may receive the initial input for a manufacturing process. For example, raw materials may comprise metal, fabric, adhesives, polymers, and other materials and compounds required for manufacturing. The flow of materials from the upstream material buffers is indicated by the edges, which identify which of the operations is a possible destination for the materials. For example, raw materials may be transported to operations comprising a production process, such as producing one or more intermediate items from the raw materials which are stored at material buffers comprising, for example, intermediate items buffers. The operations are coupled by the edges with the resources to indicate that the operations require the resource in order to process items or materials. According to embodiments, the resources may include, for example, particular manufacturing, distribution, or transportation equipment and facilities, and other such resources utilized in the supply chain.

Limitations on supplying materials and items to particular buffers may represent transportation limitations (e.g. cost, time, available transportation options) or outputs of various operations (such as, for example, different production processes, which produce different items, each of which may be represented by a different SKU, and which each may be stored at different buffers). Although the limitation of the flow of items between nodes of supply chain network model220is described as cost, timing, transportation, or production limitations, embodiments contemplate any suitable flow of items or limitations of the flow of items between any one or more different nodes of supply chain network100, according to particular needs. For the example manufacturing supply chain network100, transportation processes may transport, package, or ship finished goods to one or more locations internal to or external of one or more supply chain entities160of supply chain network100, including, for example, shipping directly to consumers, to regional or strategic distribution centers, or to the inventory of one or more supply chain entities160, including, for example, to replenish a safety stock for one or more items in an inventory of one or more supply chain entities160. Particular items and processes described herein comprise a simplified description for the purpose of illustration. For example, the items may be different sizes, styles, states of same or different physical material. Similarly, a process may be any process or operation, including manufacturing, distribution, transportation, or any other suitable activity of supply chain network100. In one embodiment, additional constraints, such as, for example, business constraints, operation constraints, and resource constraints, may be added to facilitate other planning rules.

Although a simplified supply chain network model220is described as having a particular number of buffers, resources, and operations with a defined flow between them, embodiments contemplate any number of buffers, resources, and operations with any suitable flow between them, including any number of nodes and edges, according to particular needs. In particular, a supply chain planning problem typically comprises supply chain networks100much more complex than the simplified supply chain network models220described above. For example, supply chain network100often comprises multiple manufacturing plants located in different regions or countries. In addition, an item may be processed by many operations into a large number of different materials and items, where the different operations may have multiple constrained resources and multiple input items, each with their own lead, transportation, production, and cycle times. In addition, material may flow bidirectionally (either, upstream or downstream).

Supply chain states222of PGM resolution system110database114may comprise various metrics and data points representing the current state of the supply chain and historical states of the supply chain. Supply chain states222may include data collected from locations of the supply chain such as the stock of inventory at a location, the safety stock of inventory at a location, the total volume of demand for products in the supply chain, the demand at particular product/location combinations in the supply chain, and/or the like. In addition, or as an alternative, supply chain states222include various metrics measuring the performance of the supply chain, such as one or more KPIs or SLAs. In other embodiments, the data pertaining to KPIs and SLAs (or other target metrics) may be separately stored as KPI and SLA data226. Supply chain states222may be used by probability module210to construct a graphical model of the supply chain represented by supply chain states222.

According to embodiments, data representing supply chain states222may be bucketized by probability module210and stored as bucketized data224. Probability module210may bucketize the data based on a functional grouping of the data in supply chain states222. For example, probability module210may place all data points related to inventory stock into a “stock” bucket. Bucketized data224may further have one or more restrictions modeled that prevent data in one bucket having an effect on data in another bucket type. For example, if data is sorted into four temporal buckets (past, current, future, and time-agnostic), then restrictions are included in the model to prevent test data232effecting past data and future data effecting current or past data. When using time-bucketized data to construct probabilistic graphical model228, past data nodes will be upstream of test data nodes, and test data nodes will be upstream of future data nodes.

KPI and SLA data226may relate to a current or historical state of a supply chain and its performance. KPI and SLA data226may also comprise one or more optimal or requested values for one or more features, attributes, other outputs, and/or supply chain entities160in supply chain network100. According to embodiments, learning module212may use KPI and SLA data226, in conjunction with supply chain states222, to predict the probability of a particular KPI or SLA being attained based on the state of the supply chain. In addition, or in the alternative, learning module212creates and/or adjusts probabilistic graphical model228based, at least in part, on the predicted probabilities of attaining particular KPIs or SLAs.

Probabilistic graphical model228is, as disclosed above, a graph-based model, such as a Bayesian network, constructed to model the relationship and effect of attributes on the KPIs, SLAs, or other metrics of a supply chain. Probability module210constructs a graphical model based on supply chain states222, bucketized data224, and/or root cause data242. Learning module212refines the graphical model by learning the probabilistic relationships between the nodes to construct probabilistic graphical model228. In an embodiment, PGM resolution system110uses probabilistic graphical model228to respond to queries and make recommendations of changes to the supply chain to improve the probability of meeting one or more desired metrics, such as the KPIs or SLAs.

According to embodiments, probabilistic graphical model228may comprise a probabilistic database composed of probability tables for the attributes of supply chain network100. PGM resolution system110may receive one or more queries, and the probabilistic database may respond to the queries by providing one or more insights. According to one embodiment, the query is sent to the probabilistic database. By way of further explanation only and not by way of limitation, the query may, for example, request, when given a first attribute in a first range, and a second attribute desired to be in a second range, the values for one or more other attributes. The response to the query provided by traversing the probabilistic database may be referred to as an inference or an insight into the way supply chain network100operates.

Training data230is used by probability module210and learning module212to train probabilistic graphical model228. Training data230may include data such as supply chain states222, bucketized data224, KPI and SLA data226, or other data related to the supply chain. In one embodiment, ranking module216uses training data230to determine score-based ranking data236for the attributes of the supply chain, based on the historical correlations between the attributes and the KPIs or the SLAs of the supply chain.

Test data232is data that is received by PGM resolution system110representing a current or near-current state of the supply chain. For example, test data232may be received by PGM resolution system110, such as via supply chain planner140, on a periodic basis. In other embodiments, test data232may be received by PGM resolution system110as part of a query sent to inference and query engine214. Test data232may include data such as supply chain states222, bucketized data224, or other data related to the supply chain. In one embodiment, ranking module216uses test data232to determine score-based ranking data236for the attributes of the supply chain, based on the current correlations between the attributes and the KPIs or the SLAs of the supply chain.

Ensemble data234is a combination of training data230and test data232, each as disclosed above. According to embodiments, ensemble data234provides PGM resolution system110with a more accurate representation of relevance of the various attributes than either training data230or test data232may provide alone. For example, if ranking module216uses only training data230, the analysis may become static as the supply chain evolves, such as when new locations are added to the supply chain, or when conditions at existing locations change. Thus, a training data-only approach may fail to address recent changes to the supply chain. Test data232is by its nature more current than training data230is, but typically test data232represents a small sample size which would thus be subject to significant noise. The noise may introduce increased uncertainty in a test data-only approach. Further, using such a small sample size may lead to the model overfitting to test data232.

When the modeled supply chain network100is highly similar to the historical supply chain network100, ranking module216may use training data230scoring method. In contrast, ranking module216may use test data232scoring method may when the changes are instance-based, such as, for example, when given a new row or new data for a production time. In other embodiments, ranking module216may use ensemble data234scoring method to attain the benefits of training data230and test data232, while avoiding the limitations of both. In one embodiment, ranking module216uses ensemble data234to determine score-based ranking data236for the attributes of the supply chain, based on both the historical correlations and the current correlations between the attributes and the KPIs or the SLAs of the supply chain.

Score-based ranking data236is, as described above, a ranking of the relative importance of the features and/or attributes of the supply chain, in terms of the impact each feature and/or attribute has on the KPI or SLA in question. A separate score-based ranking may be prepared for every KPI relevant to a query. That is, for a first KPI, attributes related to inventory stock may have the largest impact on the first KPI, while for a second KPI, features and/or attributes related to order volume may have the largest impact on the second KPI.

Delta distance data238is data indicating the difference between a current state of the features and/or attributes and a desired or optimal state of the features and/or attributes. Ranking module216may traverse probabilistic graphical model228to determine the optimal value of each supply chain feature or attribute in order to maximize the probability of reaching the desired value for the relevant KPI. For example, if a query sent to inference and query engine214seeks changes to the supply chain in order to meet an SLA of 80%, ranking module216traverses probabilistic graphical model228to determine the optimal value of each feature or attribute of the supply chain that would result in a maximized probability of achieving the specified SLA of 80%. Then, by comparing the current state of the attributes to these optimal states, a delta distance for each attribute may be calculated. If the data is discretized, such as by being binned, then the delta distance may be a whole number indicating the number of bins from optimal the attribute is currently in.

Final ranking data240comprises data ranking the feature and/or attributes used to qualify final rankings and make recommendations. According to embodiments, final ranking data240is based, at least in part, on score-based ranking data236and delta distance data238. Final rankings may indicate a hierarchy of features or attributes sorted by the magnitude of change each feature or attribute will cause for the KPI or SLA of the supply chain, given a current state of the supply chain. In one embodiment, PGM resolution system110calculates final ranking data240by sorting the features or attributes by absolute values for delta distance, (that is, the attributes furthest from their optimal states being first), with any ties in this ranking being broken by the score-based rank of the tied features or attributes. That is, if two features/attributes have a delta distance with magnitude equal to two 2 (such as, for example, two and −negative two, respectively), the feature/attribute with the highest score-based rank would then be ranked higher in the final rankings stored in final ranking data240. Embodiments contemplate other possible methods of combining score-based ranking data236and delta distance data238, such as, for example, a score-based rank-first approach, with ties being broken by delta distance, or a statistical combinations of the delta distance and score-based ranking data236. Inference and query engine214may use the final ranking in determining attributes of the supply chain to recommend changes, when responding to a query.

In other embodiments, different mechanisms for determining the final rankings may be appropriate. For example, in certain situations, a user of PGM resolution system110may have limited control or authority to enact all changes to the supply chain. In such a situation, the final rankings may instead be based on a calculation (or numerical approximation where appropriate) of attributes of the supply chain that have the largest impact on the desired supply chain metric with the smallest movement. That is, the final rankings may be based on a relative impact of the attributes rather than an absolute impact. In other cases, ranking module216may obtain data from supply chain planner140indicating which features or attributes of the supply chain can be modified, such as inventory policies288, store data290, customer data292, supply chain models294, or others. Using such data, ranking module216may exclude features or attributes which the user or PGM resolution system110cannot change from the final rankings. Another possible mechanism for determining the final ranking is to sort the features or attributes by which attributes can be changed the most. For example, in some supply chain systems, change management procedures may prevent any change over a certain size from happening, and the threshold for allowing changes may be different for different features or attributes. In such systems it may be preferable to provide to supply chain planner140a list that prioritizes changes that are possible under the controlling change management procedures, rather than suggesting changes which would have a large impact but cannot be performed.

Root cause data242is a set of attributes associated with a particular state or metric in the supply chain, as the root cause of that state. In some embodiments inference and query engine214may respond to a query seeking to uncover the root cause of a particular state in the supply chain, such as a particular metric falling below a threshold. In such a case, inference and query engine214may traverse probabilistic graphical model228to identify the features or attributes that have the largest impact on that metric, and store those features or attributes as root cause data242. In such embodiments ranking module216may rank root cause data242to sort root cause data242by which attribute had the largest impact on the metric in question.

Embodiments contemplate grouping features into any suitable categories using, for example, K percentile features (ranking the features according to the K percentile), top K % contributors (ranking the features according to the top K %), functional grouping (dictionary mapping to relate features placed together in a category such as, for example, business knowledge), and the like. Although particular methods of feature categorization are shown and described, embodiments contemplate using other suitable feature categorization methods, according to particular needs.

L1 and L2 features data244may store data290relating to L1 and L2 features generated by ranking module216. Predictions data246may store one or more predictions generated by inference and query engine214. Resolutions data248may store one or more resolution actions which alter the values or variables of one or more features, attributes, and/or supply chain entities160to improve the performance of one or more KPIs and/or SLAs.

As disclosed above, archiving system120comprises server122and database124. Although archiving system120is illustrated as comprising a single server122and a single database124, embodiments contemplate any suitable number of servers122or databases124internal to or externally coupled with archiving system120.

Server122of archiving system120comprises data retrieval module250. Although server122is illustrated and described as comprising a single data retrieval module250, embodiments contemplate any suitable number or combination of data retrieval modules250located at one or more locations, local to, or remote from archiving system120, such as on multiple servers122or computers170at one or more locations in supply chain network100.

In one embodiment, data retrieval module250of archiving system120receives historical data260from transportation network130, warehouse management system133, inventory system136, supply chain planner140and one or more supply chain entities160and stores the received historical data260in database124. According to one embodiment, data retrieval module250may prepare historical data260for use by supply chain planner140to generate variants of the supply chain planning problem by checking the historical supply chain data282for errors and transforming the historical supply chain data282to normalize, aggregate, and/or rescale the historical supply chain data282to allow direct comparison of data received from different transportation networks130, warehouse management systems133, inventory systems136, supply chain planners140and one or more supply chain entities160at one or more other locations local to, or remote from, archiving system120. According to embodiments, data retrieval module250receives data from one or more sources external to supply chain network100, such as, for example, weather data, special events data, social media data, calendars, and the like and stores the received data as historical data260.

Database124of archiving system120may comprise one or more databases or other data storage arrangements at one or more locations, local to, or remote from, server122. Database124of archiving system120comprises, for example, historical data260. Although database124of archiving system120is illustrated and described as comprising historical data260, embodiments contemplate any suitable number or combination of data, located at one or more locations, local to, or remote from, archiving system120, according to particular needs.

Historical data260is received from PGM resolution system110, archiving system120, transportation network130, warehouse management system133, inventory system136, supply chain planner140, one or more supply chain entities160, computer170, and/or one or more locations local to, or remote from, supply chain network100, such as, for example, weather data, special events data, social media data, calendars, and the like. According to one embodiment, historical data260comprises historic sales patterns, prices, promotions, weather conditions and other factors influencing demand of one or more items sold in one or more stores over a time period, such as, for example, one or more days, weeks, months, years, including, for example, a day of the week, a day of the month, a day of the year, week of the month, week of the year, month of the year, special events, paydays, and the like. When generating variants of the supply chain planning problem, supply chain planner140may calculate supply chain plans over a historical time period, such as, for example, any of the time periods represented by historical data260.

As disclosed above, supply chain planner140may comprise supply chain planner140comprising server142and database144. Although supply chain planner140is illustrated as comprising a single server142and a single database144, embodiments contemplate any suitable number of servers142or databases144internal to or externally coupled with supply chain planner140.

Server142of supply chain planner140comprises planning module270, the execution module276, and user interface module278. Although server142is illustrated and described as comprising a single planning module270, a single execution module276, and a single user interface module278, embodiments contemplate any suitable number or combination of planning modules270, execution modules276, and user interface modules278, located at one or more locations, local to, or remote from supply chain planner140, such as on multiple servers142or computers170at one or more locations in supply chain network100.

Database144of supply chain planner140may comprise one or more databases or other data storage arrangements at one or more locations, local to, or remote from, server142. Database144of supply chain planner140comprises, for example, transaction data280, supply chain data282, product data284, inventory data286, inventory policies288, store data290, customer data292, supply chain models294, and levers296. Although database144of supply chain planner140is illustrated and described as comprising transaction data280, supply chain data282, product data284, inventory data286, inventory policies288, store data290, customer data292, supply chain models294, and levers296, embodiments contemplate any suitable number or combination of data, located at one or more locations, local to, or remote from, supply chain supply chain planner140, according to particular needs.

Planning module270comprises modeler272and solver274. Although planning module270is illustrated and described as comprising a single modeler272and solver274, embodiments contemplate any suitable number or combination of these located at one or more locations, local to, or remote from planning module270, such as on multiple servers142or computers170at any location in supply chain network100.

Modeler272may model one or more supply chain planning problems of supply chain network100. According to one embodiment, modeler272of server142identifies resources, operations, buffers, and pathways, and maps supply chain network100using supply chain network models220, as disclosed above. For example, modeler272of server142models a supply chain planning problem that represents supply chain network100as supply chain network model220, an LP optimization problem, or other type of input to a supply chain solver274. As disclosed above, embodiments contemplate modeler272providing supply chain network model220to PGM resolution system110.

According to embodiments, solver274of planning module270generates a solution to a supply chain planning problem. The supply chain solver274may comprise an LP optimization solver, a heuristic solver, a mixed-integer problem solver, a MAP solver, an LP solver, a Deep Tree solver, and the like.

The execution module276executes one or more supply chain processes such as, for example, instructing automated machinery (i.e., robotic warehouse systems, robotic inventory systems, automated guided vehicles, mobile racking units, automated robotic production machinery, robotic devices and the like) to adjust product mix ratios, inventory levels at various stocking points, production of products of manufacturing equipment, proportional or alternative sourcing of one or more supply chain entities160, and the configuration and quantity of packaging and shipping of items based on a supply chain plan, the number of items currently in stock at one or more supply chain entities160, the number of items currently in transit in transportation network130, a forecasted demand, a supply chain disruption, a material or capacity reallocation, current and projected inventory levels at one or more stocking locations, root cause data242, a selected lever, and/or one or more additional factors described herein. For example, the execution module276may send instructions to the automated machinery to locate items to add to or remove from an inventory of or shipment for one or more supply chain entities160.

User interface module218of supply chain planner140generates and displays a UI, such as, for example, a GUI, that displays one or more interactive visualizations of transaction data280, supply chain data282, product data284, inventory data286, inventory policies288, store data290, customer data292, supply chain models294, and levers296. According to embodiments, user interface module218displays a GUI comprising interactive graphical elements for selecting one or more supply chain network100components, modeling supply chain network100as an object model, formulating supply chain network100as a supply chain planning problem, solving the supply chain planning problem, displaying predictions from PGM resolution system110, displaying and providing for selection of one or more levers296, and displaying one or more solutions or supply chain plans.

Transaction data280may comprise recorded sales and returns transactions and related data, including, for example, a transaction identification, time and date stamp, channel identification (such as stores or online touchpoints), product identification, actual cost, selling price, sales volume, customer identification, promotions, and or the like. In addition, transaction data280is represented by any suitable combination of values and dimensions, aggregated or un-aggregated, such as, for example, sales per week, sales per week per location, sales per day, sales per day per season, or the like.

Supply chain data282may comprise any data of one or more supply chain entities160including, for example, item data, identifiers, metadata (comprising dimensions, hierarchies, levels, members, attributes, cluster information, and member attribute values), fact data (comprising measure values for combinations of members) of one or more supply chain entities160. Supply chain data282may also comprise for example, various decision variables, business constraints, goals, and objectives of one or more supply chain entities160. According to some embodiments, supply chain data282may comprise hierarchical objectives specified by, for example, business rules, master planning requirements, scheduling constraints, and discrete constraints, including, for example, sequence dependent setup times, lot-sizing, storage, shelf life, and the like.

Product data284of database144may comprise products identified by, for example, a product identifier (such as a Stock Keeping Unit (SKU), Universal Product Code (UPC) or the like), and one or more attributes and attribute types associated with the product ID. Product data284may comprise data about one or more products organized and sortable by, for example, product attributes, attribute values, product identification, sales volume, demand forecast, or any stored category or dimension. Attributes of one or more products may be, for example, any categorical characteristic or quality of a product, and an attribute value may be a specific value or identity for the one or more products according to the categorical characteristic or quality, including, for example, physical parameters (such as, for example, size, weight, dimensions, color, and the like).

Inventory data286of database144may comprise any data relating to current or projected inventory quantities or states, order rules, or the like. For example, inventory data286may comprise the current level of inventory for each item at one or more stocking points across supply chain network100. In addition, inventory data286may comprise order rules that describe one or more rules or limits on setting an inventory policy, including, but not limited to, a minimum order volume, a maximum order volume, a discount, and a step-size order volume, and batch quantity rules. According to some embodiments, supply chain planner140accesses and stores inventory data286in database144, which may be used by supply chain planner140to place orders, set inventory levels at one or more stocking points, initiate manufacturing of one or more components, or the like in response to, and based at least in part on, a supply chain plan or other output of supply chain planner140. In addition, or as an alternative, inventory data286may be updated by receiving current item quantities, mappings, or locations from transportation network130, warehouse management system133, inventory system136, supply chain planner140and/or one or more networked imaging devices150.

Inventory policies288of database144may comprise any suitable inventory policy describing the reorder point and target quantity, or other inventory policy parameters that set rules for supply chain planner140to manage and reorder inventory. Inventory policies288may be based on target service level, demand, cost, fill rate, or the like. According to embodiment, inventory policies288comprise target service levels that ensure that a service level of one or more supply chain entities160is met with a certain probability. For example, one or more supply chain entities160may set a service level at 95%, meaning one or more supply chain entities160will set the desired inventory stock level at a level that meets demand 95% of the time. Although, a particular service level target and percentage is described; embodiments contemplate any service target or level, for example, a service level of approximately 99% through 90%, a 75% service level, or any suitable service level, according to particular needs. Other types of service levels associated with inventory quantity or order quantity may comprise, but are not limited to, a maximum expected backlog and a fulfillment level. Once the service level is set, PGM resolution system110and/or supply chain planner140may determine a replenishment order according to one or more replenishment rules, which, among other things, indicates to one or more supply chain entities160to determine or receive inventory to replace the depleted inventory. By way of example and not of limitation, an inventory policy for non-perishable goods with linear holding and shorting costs comprises a min./max. (s,S) inventory policy. Other inventory policies288may be used for perishable goods, such as fruit, vegetables, dairy, fresh meat, as well as electronics, fashion, and similar items for which demand drops significantly after a next generation of electronic devices or a new season of fashion is released.

Store data290may comprise data describing the stores of one or more retailers and related store information. Store data290may comprise, for example, a store ID, store description, store location details, store location climate, store type, store opening date, lifestyle, store area (expressed in, for example, square feet, square meters, or other suitable measurement), latitude, longitude, and other similar data. Store data290may include demand forecasts for each store indicating future expected demand based on, for example, any data relating to past sales, past demand, purchase data, promotions, events, or the like of one or more supply chain entities160. The demand forecasts may cover a time interval such as, for example, by the minute, hour, daily, weekly, monthly, quarterly, yearly, or any suitable time interval, including substantially in real time. Although demand forecasts are described as comprising a particular store, supply chain planner140may calculate a demand forecast at any granularity of time, customer, item, region, or the like.

Customer data292may comprise customer identity information, including, for example, customer relationship management data, loyalty programs, and mappings between one or more customers and transactions associated with those one or more customers such as, for example, product purchases, product returns, customer shopping behavior, and the like. Customer data292may comprise data relating customer purchases to one or more products, geographical regions, store locations, time period, or other types of dimensions.

Supply chain models294comprise characteristics of a supply chain setup to deliver the customer expectations of a particular customer business model. These characteristics may comprise differentiating factors, such as, for example, MTO (Make-to-Order), ETO (Engineer-to-Order) or MTS (Make-to-Stock). However, supply chain models294may also comprise characteristics that specify the supply chain structure in even more detail, including, for example, specifying the type of collaboration with the customer (e.g. Vendor-Managed Inventory (VMI)), from where products may be sourced, and how products may be allocated, shipped, or paid for, by particular customers. Each of these characteristics may lead to a different supply chain model.

FIG.3illustrates mutual information resolution method300of using probabilistic graphical model228and mutual information to generate resolution recommendations and graphical visualizations, according to an embodiment. Mutual information resolution method300comprises one or more activities, which although described in a particular order may be implemented in one or more combinations, according to particular needs.

At activity302of mutual information resolution method300, PGM resolution system110probability module210and learning module212initialize a graphical model of a supply chain. The graphical model may be based on a set of data representing the historical states of the supply chain using training data230, such as inventory stock, order volume, distribution center capacity, production ratios, logistical landscapes, and/or other measures, which may be referred to as supply chain attributes and/or supply chain features. Attributes and/or features may vary and correspond to one or more supply chain locations. By way of example only and not by way of limitation, a supply chain location may comprise, for example, one or more supply chain entities160(e.g. factories, warehouses, distribution centers, and the like), stocking locations, or any other locations where products may be produced, stored, or transported. In one embodiment, supply chain data282is obtained from supply chain network models220of supply chain planner140. Embodiments further contemplate the supply chain graphical model comprising a directed graph, such as a Bayesian network. The graphical model may be initialized as network180of nodes, wherein each node corresponds to one or more attributes of supply chain data282and coupled by edges connecting the nodes and emphasizing the relationships between the nodes. Having initialized a graphical model of the supply chain, including supply chain features, probability module210and learning module212store the graphical model and supply chain features in supply chain network models220.

At activity304of mutual information resolution method300, PGM resolution system110constructs probabilistic graphical model228by learning the probability relationships between the nodes of the graphical model using training data230. Probability module210and learning module212may access the graphical model stored in supply chain network models220and may use the graphical model to construct probabilistic graphical model228. According to embodiments, probability module210and learning module212determine which features or attributes of the supply chain (each feature or attribute represented by a node of the graphical model) impact other features attributes of the supply chain and the impact on the KPIs, SLAs, and/or other metrics used to measure the performance or productivity of the supply chain. As described above, each node of probabilistic graphical model228is associated with a probability table that describes the edges shared with other nodes and the probability relationship between the nodes. Having constructed probabilistic graphical model228, probability module210and learning module212store probabilistic graphical model228in probabilistic graphical models228of database114.

At activity306of mutual information resolution method300, ranking module216utilizes a scoring function to calculate a score-based rank for the features or attributes of the supply chain system by traversing probabilistic graphical model228. In addition, or as an alternative, PGM resolution system110bucketizes the features or attributes to create groups of features or attributes, such as, for example, one or more functional groupings of features or attributes. In an embodiment, ranking module216utilizes the following equation (1), training data230, and mutual information stored in probabilistic graphical model228to determine the significance and the ordering of the features or attributes, stored in supply chain network models220, that comprise the supply chain:

I⁡(U;C)=∑et∈{1,0}⁢∑ec∈{1,0}⁢P⁡(U=et,C=ec)⁢log2⁢P⁡(U=et,C=ec)P⁡(U=et)⁢P⁡(C=ec),(1)
In an embodiment, ranking module216may use equation (1) above to calculate differences between features and to determine information shared between variables and the connections between variables and associated features or attributes. Ranking module216may store the calculated score-based ranks for the features and/or attributes of the supply chain system, including but not limited to mutual information stored in probabilistic graphical model228determining the significance and ordering of features and attributes, in score-based ranking data236.

At activity308of mutual information resolution method300, ranking module216determines L1 and L2 features from score-based ranking data236and the one or more probabilistic graphical models228. In an embodiment, L1 may refer to one or more level one features (connected to a root node by a single direct connection or edge). L2 may refer to one or more level two features (connected to an L1 node by a single direct connection or edge), and so on. Having calculated L1 and L2 features, ranking module216stores the L1 and L2 features in L1 and L2 features data244.

At activity310of mutual information resolution method300, inference and query engine214generates an initial prediction using test data232. In an embodiment, inference and query engine214accesses probabilistic graphical model228stored in probabilistic graphical model228s228of database114, score-based ranking data236, and L1 and L2 features data244, and applies test data232to probabilistic graphical model228, score-based ranking data236, and L1 and L2 features data244to generate one or more initial predictions. Inference and query engine214stores the one or more initial predictions in predictions data246of database114. In an embodiment, the initial predictions based on test data232may indicate the predicted chance that supply chain network100will successfully fulfill—or fail to fulfill—one or more KPIs, SLAs, and/or other objectives given test data232. The initial predictions may record the predicted chance as a decimal value (such as, for example, SLA<0.8 indicating a less than 80% chance that the supply chain will successfully fulfill the SLA in question).

At activity312of mutual information resolution method300, ranking module216calculates the delta distance for the features, attributes, or buckets as calculated in the initial prediction, based on test data232, versus one or more optimal values for the features, attributes, or buckets stored in KPI and SLA data226. In one embodiment, ranking module216determines an optimal value for the features, attributes, or buckets of the supply chain system using an ensemble of training data230and test data232to maximize the probability of achieving the threshold of the KPI, SLA, and/or other objectives. Ranking module216may compare these optimal states to the current value of those attributes to determine the delta distance for the features, attributes, or buckets, using L1 and L2 features data244to ensure ranking module216is only comparing features that are relevant to the KPIs or SLAs that may not be successfully fulfilled according to the initial predictions. The delta distance may specify the distance that one or more features, attributes, and/or other supply chain variables need to move or be changed to bring the sub-performing KPIs and SLAs up to optimal values. Ranking module216may store the delta distance information in delta distance data238.

At activity314of mutual information resolution method300, ranking module216generates a final ranking. The final ranking may be based on the score-based ranking and the delta distances for the features and/or attributes. In one embodiment, the final ranking comprises the features and/or attributes sorted first by largest absolute delta distance, with any ties being broken by the feature and/or attribute with the higher-priority score-based ranking. In another embodiment, the final ranking may be limited to features and/or attributes over which transformations or other changes can be performed. For example, in some situations it may not be possible to alter the available stock at a particular location. In such a situation, attributes relating to stock at that location, or the stock bucket for that location, may be omitted from the final ranking. In an embodiment, ranking module216may utilize equation (1) as described above, test data232, score-based ranking data236, and L1 and L2 features data244to generate final rankings. The final rankings may comprise (1) final rankings for one or more features and/or attributes that specify the order of importance of each feature and/or attribute on affecting the one or more KPIs and/or SLAs that are not successfully fulfilled, as well as (2) one or more values at which the one or more features and/or attributes should be set at or changed to in order to affect change in the supply chain as efficiently as possible and to bring the one or more KPIs and/or SLAs that were not successfully fulfilled towards optimal values. Having generated final rankings, ranking module216store the final rankings in final ranking data240. In an embodiment, PGM resolution system110may use a hierarchical method, described in greater detail below in relation toFIG.8, to generate rankings and to assign L1/L2 rankings to features.

At activity316of mutual information resolution method300, inference and query engine214generates possible resolution actions. In an embodiment, inference and query engine214accesses probabilistic graphical models228of database114, score-based ranking data236, L1 and L2 features data244, delta distance data238, and final ranking data240, and generates one or more resolution actions to better fulfill the one or more KPIs and/or SLAs that are not successfully fulfilled by altering the values of one or more features and/or attributes based on score-based ranking data236, delta distance data238, and final ranking data240of the one or more features and/or attributes. For example, in an embodiment in which a supply chain manufacturer can only produce 90% of a requested manufacturing quota, PGM resolution system110executing the actions of mutual information resolution method300may determine that increasing available manufacturing stock at the supply chain manufacturer by 30% will enable the supply chain manufacturer to produce 99% of the requested manufacturing quota, and inference and query engine214may generate “increase available manufacturing stock at the supply chain manufacturer by 30%” as a possible resolution action. Having generated one or more resolution actions, inference and query engine214may store the resolution actions in resolutions data248of database114.

At activity318of mutual information resolution method300, user interface module218visualizes the rankings and resolution actions by generating one or more GUI displays. According to embodiments, user interface module218may access any data stored in PGM resolution system110database114, including but not limited to probabilistic graphical models228, score-based ranking data236, L1 and L2 features data244, delta distance data238, and final ranking data240, and may generate any GUI displays in any configuration to display the data stored in PGM resolution system110database114. By way of example only and not by way of limitation, user interface module218may generate, according to embodiments, risk mitigation GUI display400, illustrated byFIG.4; Bayesian network GUI display500, illustrated byFIGS.5A-5B; resolution recommendation GUI display600, illustrated byFIGS.6A-6B; mutual information location GUI display700, illustrated byFIG.7; and/or hierarchical GUI display900, illustrated byFIG.9. Having generated and displayed one or more GUI displays to visualize rankings and resolution actions, PGM resolution system110terminates mutual information resolution method300.

To illustrate the activities of PGM resolution system110executing mutual information resolution method300, the following example is provided. In this example, PGM resolution system110executes the activities of mutual information resolution method300to identify four features, and values associated with the four features, that will most directly resolve an identified out-of-stock problem at a supply chain retailer (for the purposes of this example, “Retailer X”). Although the provided example illustrates PGM resolution system110executing the activities of mutual information resolution method300in a particular order, embodiments not illustrated by the provided example contemplate PGM resolution system110and method executing the activities of mutual information resolution method300in any order, according to particular needs.

In this example, at activity302of mutual information resolution method300, PGM resolution system110probability module210and learning module212initialize a graphical model of the supply chain (henceforth “Supply Chain Y”) in which Retailer X operates, including Supply Chain Y features within the graphical model. The graphical model of Supply Chain Y comprises network180of nodes wherein each node corresponds to one or more attributes and/or features of supply chain data282and coupled by edges connecting the nodes and emphasizing the relationships between the nodes. Having initialized a graphical model of Supply Chain Y, including Supply Chain Y features, probability module210and learning module212store the graphical model and supply chain features in supply chain network models220.

Continuing the example, at activity304of mutual information resolution method300, PGM resolution system110constructs probabilistic graphical model228of Supply Chain Y by learning the probability relationships between the nodes of the Supply Chain Y graphical model using training data230. Probability module210and learning module212access the Supply Chain Y graphical model stored in supply chain network models220and use the Supply Chain Y graphical model to construct a Supply Chain Y probabilistic graphical model. Probability module210and learning module212determine which features or attributes of Supply Chain Y (each feature or attribute represented by a node of the graphical model) impact other features attributes of Supply Chain Y and the impact on the KPIs, SLAs, and/or other metrics used to measure the performance or productivity of Supply Chain Y. Having constructed the Supply Chain Y probabilistic graphical model, probability module210and learning module212store the Supply Chain Y probabilistic graphical model in probabilistic graphical models228of database114.

Continuing the example, at activity306of mutual information resolution method300, ranking module216utilizes a scoring function to calculate a score-based rank for the features or attributes of Supply Chain Y by traversing the Supply Chain Y probabilistic graphical model. In this example, ranking module216utilizes equation (1) as described above, training data230, and mutual information stored in the Supply Chain Y probabilistic graphical model to determine the significance and the ordering of the Supply Chain Y features or attributes, stored in supply chain network models220, that comprise Supply Chain Y. Ranking module216uses equation (1) to calculate differences between features and to determine information shared between variables and the connections between variables and associated features or attributes in Supply Chain Y. Ranking module216may store the calculated score-based ranks for the features and/or attributes of Supply Chain Y, including but not limited to mutual information stored in the Supply Chain Y probabilistic graphical model determining the significance and ordering of features and attributes, in score-based ranking data236.

Continuing the example, at activity308of mutual information resolution method300, ranking module216determines L1 and L2 features from score-based ranking data236and the Supply Chain Y probabilistic graphical model. Having calculated L1 and L2 features, ranking module216stores the L1 and L2 features in L1 and L2 features data244.

Continuing the example, at activity310of mutual information resolution method300, inference and query engine214generates an initial Supply Chain Y and Retailer X feature prediction using test data232. Inference and query engine214accesses the Supply Chain Y probabilistic graphical model, score-based ranking data236, and L1 and L2 features data244, and applies test data232to the Supply Chain Y probabilistic graphical model, score-based ranking data236, and L1 and L2 features data244to generate one or more initial Supply Chain Y predictions and to determine whether the outputs of Supply Chain Y and Retailer X within Supply Chain Y will meet desired features and targets (henceforth, the “Desired Supply Chain Y Features”). Inference and query engine214stores the one or more initial Supply Chain Y/Retailer X predictions in predictions data246of database114. In this example, the initial Supply Chain Y/Retailer X predictions indicate that Retailer X will sell only 70% of the targeted number of products within Supply Chain Y, given the current state of features, attributes, and other variables in Supply Chain Y (henceforth, the “Current Supply Chain Y Features”).

Continuing the example, at activity312of mutual information resolution method300, ranking module216calculates the delta distance for the Current Supply Chain Y Features, based on test data232, versus the Desired Supply Chain Y Features stored in KPI and SLA data226. In this example, the delta distance specifies the distance that one or more Supply Chain Y and Retailer X features, attributes, and/or other Supply Chain Y/Retailer X variables need to move or be changed to bring the sub-performing Retailer X sales (which were predicted to reach only 70% of the target number) up to desired values. Ranking module216stores store the delta distance information in delta distance data238.

Continuing the example, at activity314of mutual information resolution method300, ranking module216generates a final ranking. In this example, the final ranking comprises Supply Chain Y/Retailer X features406a-406gsorted first by largest absolute delta distance, with any ties being broken by the feature with the higher-priority score-based ranking. Ranking module216utilizes equation (1) as described above, test data232, score-based ranking data236, and L1 and L2 features data244to generate final rankings. The final rankings comprise (1) final rankings for one or more Supply Chain Y/Retailer X features406a-406gthat specify the order of importance of each feature affecting the Retailer X sales target numbers that are not successfully fulfilled, as well as (2) one or more values at which the one or more Supply Chain Y/Retailer X features406a-406gshould be set at or changed to in order to affect change in Supply Chain Y and boost the product sales at Retailer X as efficiently as possible. Having generated final rankings, ranking module216store the final rankings in final ranking data240.

Continuing the example, at activity316of mutual information resolution method300, inference and query engine214generates possible resolution actions for Retailer X to boost product sales. In an embodiment, inference and query engine214accesses the Supply Chain Y probabilistic graphical model, score-based ranking data236, L1 and L2 features data244, delta distance data238, and final ranking data240, and generates one or more resolution actions for Retailer X to better fulfill the one or more KPIs and/or SLAs that are not successfully fulfilled by altering the values of one or more Supply Chain Y/Retailer X features406a-406gbased on score-based ranking data236, delta distance data238, and final ranking data240of the one or more features. Having generated Supply Chain Y/Retailer X resolution actions, inference and query engine214stores the Supply Chain Y/Retailer X resolution actions in resolutions data248of database114.

Continuing the example, at activity318of mutual information resolution method300, user interface module218visualizes the Supply Chain Y/Retailer X feature rankings and resolution actions by generating, in this example, risk mitigation GUI display400, illustrated byFIG.4. User interface module218displays risk mitigation GUI display400on one or more computer170output devices174. PGM resolution system110then terminates mutual information resolution method300.

FIG.4illustrates risk mitigation GUI display400, according to an embodiment. In an embodiment, risk mitigation GUI display400may comprise Root Causes chart402and Risk Mitigation chart404. In other embodiments, risk mitigation GUI displays400may comprise any charts, features, or other visual displays of any data, according to particular needs.

In the embodiment illustrated byFIG.4, Root Causes chart402on the left-hand side of risk mitigation GUI display400displays a list of Supply Chain Y/Retailer X features406a-406g(beginning with “ATF_count_W0”, and then continuing with “stock_needs_ratio_W0”, and so on), which PGM resolution system110has identified as being most relevant to the predicted low sales figure at Retailer X (predicting Retailer X will sell only 70% of the targeted number of products within Supply Chain Y). Root Causes chart402lists Score Based Rank408, Delta Distance410, and Final Rank412for each identified Supply Chain Y/Retailer X feature.

In the embodiment illustrated byFIG.4, Risk Mitigation chart404on the right-hand side of risk mitigation GUI display400displays the estimated effect of modifying the values of one or more Supply Chain Y/Retailer X features406a-406gin order to address the Retailer X sales shortfall. Risk Mitigation chart404may display Supply Chain Y/Retailer X features406a-406gthat PGM resolution system110has determined to be the most relevant features to the Retailer X sales shortfall problem, and the features in which the fewest changes may make the largest impact on the Retailer X sales KPI. In an embodiment, by altering both the “Base”414aand “Stock”414bfeatures and associated feature variables, the percentage of product sales that Retailer X will be able to execute is estimated to increase to 0.8892 (indicating 88.92% of the targeted number of products, or an 18.92% increase over the initial 70% prediction). By further modifying the variables of the “Safety Stock”414cand “ATF”414dfeatures, the percentage of product sales that Retailer X will be able to execute further increases to 89.25%.

FIGS.5A-5Billustrate Bayesian network GUI display500, according to an embodiment. In an embodiment, user interface module218may access data stored in PGM resolution system110database114, including but not limited to one or more Bayesian networks stored in probabilistic graphical models228, and may use the data stored in PGM resolution system110database114to generate Bayesian network GUI display500. AlthoughFIGS.5A-5Billustrate user interface module218generating Bayesian network GUI display500in a particular configuration, embodiments contemplate user interface module218generating Bayesian network GUI displays500of any configuration and displaying any data stored in PGM resolution system110database114, according to particular needs.

In an embodiment, Bayesian network GUI display500may visualize or display various information about the Bayesian network stored in PGM resolution system110probabilistic graphical models228. Bayesian network GUI display500may display one or more KPIs or targeted variables, such as, for example, “ATF_volume_W3plus” root node502at the top left of Bayesian network GUI display500, in separate colors (such as, for example, green) to emphasize one or more target nodes or target variables associated with particular nodes. Bayesian network GUI display500may arrange and display nodes according to node strength and edges according to edge strength. In an embodiment, Bayesian network GUI display500may display nodes according to L1 and L2 features data244stored in PGM resolution system110database114. For example, in an embodiment, Bayesian network GUI display500may display node strength as a function of node size, wherein stronger nodes are displayed as larger than weaker nodes, and node color may be based on the level of the nodes (for example, in an embodiment, the one or more root nodes502may be illustrated in green, the L1 nodes504may be illustrated in orange, and the L2 nodes506may be illustrated in gray. In the example ofFIGS.5A-5B, level 3 (or L3) nodes508may be illustrated in yellow. That is, L1 nodes504have an indirect relationship with root node502, L2 nodes506have an indirect relationship (through L1 nodes504) with root node502, and L3 nodes508have an indirect relationship (through L2 nodes506and L1 nodes504) with root node502. Node size may, in an embodiment, indicate good candidate nodes on which to implement supply chain feature changes to bring about one or more desirable KPI or SLA changes, with larger nodes being more likely to affect one or more KPIs and/or SLAs as compared to smaller nodes.

FIGS.6A-6Billustrate resolution recommendation GUI display600, according to an embodiment. In an embodiment, user interface module218may access data stored in PGM resolution system110database114, including but not limited to one or more Bayesian networks stored in probabilistic graphical models228and one or more resolution actions stored in resolutions data248, and may use the data stored in PGM resolution system110database114to generate resolution recommendation GUI display600. AlthoughFIGS.6A-6Billustrate user interface module218generating resolution recommendation GUI display600in a particular configuration, embodiments contemplate user interface module218generating resolution recommendation GUI displays600of any configuration and displaying any data stored in PGM resolution system110database114, according to particular needs.

According to embodiment, resolution recommendation GUI display600may display two or more locations in the supply chain (in the example illustrated byFIGS.6A-6B, location 1602A and location 2602B). One assumption of the example ofFIGS.6A-6Bis that there exists a path between Location 1602A and Location 2602B. An analysis represented by resolution recommendation GUI display600may show only a possible opportunity, not network connectivity between the locations602. Resolution recommendation GUI display600may also display features and/or attributes604associated with each of location 1602A and location 2602B, such as “ATF_count_W0”, “stock_no_pending/SS_W3”, and so on. In an embodiment, PGM resolution system110may perform the actions of mutual information resolution method300, described above, to locate features that have the strongest impact across both of location 1602A and location 2602B. Resolution recommendation GUI display600may also display one or more potential resolution actions606to enable location 1602A and/or location 2602B to better fulfill one or more KPIs and/or one or more SLAs, and/or may recommend the modification of one or more features shared between location 1602A and/or location 2602B by, for example, recommending the transfer of 2 units of “stock_no_pending/SS_W3” from location 2602B to location 1602A to address a shortfall at location 1602A without unduly depleting reserves at location 2602B.

In an embodiment, resolution recommendation GUI display600may display each location (in the example illustrated byFIGS.6A-6B, location 1602A and location 2602B) using particular colors that correspond to whether or not each location can fulfill its target KPIs and/or SLAs. For example, in an embodiment, resolution recommendation GUI display600may display location 2602B using a green color, indicating that location 2602B will meet at least 80% of all target KPI and SLA objectives (other examples may use any percentage, including 90%, 95%, or any other number); resolution recommendation GUI display600may display location 1602A using a red color, indicating that location 1602A is predicted to fail to meet at least 80% of all target KPI and SLA objectives. In this embodiment, user interface module218may respond to input to one or more computer170input devices162, and may enable supply chain planners132dto “borrow” one or more resources, features, attributes, and/or other variables from location 2602B, and transfer the one or more resources, features, attributes, and/or other variables to location 1602A, to enable location 1602A to better meet at least 80% of all location 1602A KPI and SLA targets without unduly impacting the ability of location 2602B to meet at least 80% of location 2602B KPI and SLA targets. Resolution recommendation GUI display600may change the display colors of location 1602A and/or location 2602B as the proposed reallocations of resources, features, attributes, and/or other values between location 1602A and location 2602B affect each of location 1602A's and location 2602B's abilities to meet at least 80% of all KPI and SLA targets; other embodiments may change display colors in relation to any percentage of KPI and SLA targets.

FIG.7illustrates mutual information location GUI display700, according to an embodiment. In an embodiment, user interface module218may access data stored in PGM resolution system110database114, including but not limited to one or more Bayesian networks stored in probabilistic graphical models228, and may use the data stored in PGM resolution system110database114to generate mutual information location GUI display700. AlthoughFIG.7illustrates user interface module218generating mutual information location GUI display700in a particular configuration, embodiments contemplate user interface module218generating mutual information location GUI displays700of any configuration and displaying any data stored in PGM resolution system110database114, according to particular needs.

In an embodiment, mutual information location GUI display700may display location 1602A and location 2602B features that share mutual information702, including but not limited to statistical dependencies and/or shared values that are relevant to one or more KPIs and/or SLAs. Mutual information location GUI display700may display location 1602A and location 2602B features as L1 nodes706connected to one or more root nodes704, and/or as one or more L2 features as L2 nodes708connected to one or more L1 nodes706. Mutual information location GUI display700may emphasize the relationship of one or more L1 features and one or more L2 features on one or more KPIs and/or SLAs by, for example, emphasizing one or more L1 features that more directly affect one or more root nodes than do one or more L2 features, thereby enabling supply chain planners140to make alterations at one or more nodes that most directly affect one or more KPIs and/or SLAs.

FIG.8illustrates hierarchical method800of generating hierarchical relationships and visualizations, according to an embodiment. In an embodiment, hierarchical method800may analyze the impact of multiple separate features and feature levels on one or more KPI/SLA outputs. Hierarchical method800may comprise one or more activities, which although described in a particular order may be implemented in one or more combinations, according to particular needs.

At activity802of hierarchical method800, PGM resolution system110constructs a Bayesian network. At activity804of hierarchical method800, PGM resolution system110designs an entity-component mapping that groups individual features, components, and/or other nodes and/or variables of the Bayesian network into different business units based on shared features, components, and/or other variables. In an embodiment, PGM resolution system110may use following algorithm to design entity-component mappings and/or to execute other activities of hierarchical method800, according to particular needs.

The algorithm begins by PGM resolution system110assigning a target KPI as the root node. Thereafter, PGM resolution system110needs to create a hierarchy of features, such as L1 and L2 features, with respect to the target KPI. Based on business experience, PGM resolution system110maps all attributes under each category. As an example, this may include a feature dictionary. Then, PGM resolution system110iterates over each category to calculate an MI score for each feature under that category. Then PGM resolution system110assigns the features with the maximum MI score as the category representative, which is an L1 feature. This revised feature mapping is called a L1 feature set. An example of an L1 feature set may be the following:{‘ATF_count_W0’: [‘ATF_volume_W-2’, ‘ATF_count_W-2’, ‘ATF_count_W-1’, ‘ATF_volume_W0’, ‘ATF_volume_W-1’. ‘ATF_volume_W-3’, ‘ATF_count_W-3’], ‘stock_needs_ratio_W-1’: [‘STOCK/SS_W0’, ‘stock_no_pending/SS_W3’, ‘stock_no_pending/SS_W0’, ‘stock_needs_ratio_W2’, ‘STOCK/SS_W1’, ‘stock_needs_ratio_W0’, ‘STOCK/SS_W2’, ‘stock_nopending/SS_W1’, ‘STOCK/SS_W3’], ‘SS_W3/SS_W0’: [‘SS_W2/SS_W0’, ‘SS_W1/SS_W0’], ‘Logisitics_W-1’ [‘ratio_DC_capacity_new’], ‘R-1W_W0’: [‘R_3W_W0’, ‘R_2W_W2’, ‘R_3W_W2’, ‘R_1W_W2’, ‘R_2W_W1’, ‘R_2W_W0’, ‘R_3W_W1’]}

The algorithm then includes PGM resolution system110calculating a conditional MI score. The conditional MI score may be calculated by iterating over each feature in the above L1 category, and calculating the conditional MI score according to equation (2) below:

MI⁡(X;Y⁢❘"\[LeftBracketingBar]"Z)=∑x,y,z⁢P⁡(x,y,z)⁢log2⁢P⁡(x,y⁢❘"\[LeftBracketingBar]"z)P⁡(X⁢❘"\[LeftBracketingBar]"Z)⁢P⁡(y⁢❘"\[LeftBracketingBar]"Z)(2)

In equation (2), X is the iterated features, Y is the target KPI and Z is the L1 feature representative. Based on the conditional MI score, PGM resolution system110determines the L2 feature for each category. The L2 features for each category may be represented in TABLE 1 below.

feat denconditional colmi conditionalATF count W0ATF volume W + 3 ATF volume W0 ATF6.001715e−04count W0R 1W W0ATF volume W + 3 R 3W W2 R 1W W01.196396e−16SS W3/SS W0ATF volume W + 3 SS W2/SS W0 SS W3/SS3.454976e−17W0ratio DC capacity newATF volume w + 3 Logistics W − 1 ratio DC−4.417347e−17capacitystock needs ratio W0ATF volume W + 3 stock no pending/SS4.940359e−02W3/stock

For the L2 features shown in TABLE 2, PGM resolution system110may use a threshold to arrive at the L2 features. For example, if the threshold (12_threshold) is equal to 0.0001, then the L2 features may be determined according to df_12_grouped.loc [df_12_grouped [‘mi_conditional’]>12_threshold][‘conditional_col’].values.

Returning to method800, at activity806, PGM resolution system110generates a score for each entity-component group, based on mutual information. At activity808of hierarchical method800, PGM resolution system110may sort the scores for each entity-component based on mutual information to locate the highest-scored feature, to which PGM resolution system110assigns a level 1 (L1) feature rank for the particular entity-component. At activity810of hierarchical method800, for each entity-component, PGM resolution system110assigns the highest-scoring L1 feature selected during the fourth action to represent the entity component. At activity812of hierarchical method800, PGM resolution system110recursively creates next level ranked features (including but not limited to L2 features) within each entity-component conditioned on prior level features (for example, for L2 features, conditioned on prior L1 features). PGM resolution system110may then terminate hierarchical method800.

FIG.9illustrates hierarchical GUI display900, according to an embodiment. PGM resolution system110may generate and display one or more hierarchical GUI displays900after performing the actions of hierarchical method800described above, according to an embodiment. AlthoughFIG.9illustrates hierarchical GUI display900in a particular configuration, embodiments contemplate PGM resolution system110accessing any data stored in PGM resolution system110database114and displaying hierarchical GUI displays900in any configuration and comprising any data, according to particular needs.

In an embodiment, hierarchical GUI display900may display one or more root node KPIs/SLAs (as illustrated byFIG.9, “SLA”), one or more L1 features902which PGM resolution system110has identified as most directly influencing and/or affecting the performance of root node KPIs/SLAs904, and one or more L2 features906which influence L1 features902. By making changes to L1 features902, supply chain planner140may most directly influence the output of one or more root node KPIs/SLAs904.

Reference in the foregoing specification to “one embodiment”, “an embodiment”, or “some embodiments” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.

While the exemplary embodiments have been illustrated and described, it will be understood that various changes and modifications to the foregoing embodiments may become apparent to those skilled in the art without departing from the spirit and scope of the present invention.