Patent Publication Number: US-11645611-B1

Title: System and method of decoding supply chain signatures

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present disclosure is related to that disclosed in the U.S. Provisional Application No. 62/952,848, filed Dec. 23, 2019, entitled “System and Method of Decoding Supply Chain Signatures.” U.S. Provisional Application No. 62/952,848 is assigned to the assignee of the present application. The subject matter disclosed in U.S. Provisional Application No. 62/952,848 is hereby incorporated by reference into the present disclosure as if fully set forth herein. The present invention hereby claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/952,848. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates generally to supply chain planning and operation and specifically to decoding a supply chain signature of supply chain static or dynamic structure. 
     BACKGROUND 
     During supply chain planning, a supply chain plan may be generated by solving a supply chain planning problem modeled as a single- or multi-objective linear programming problem (LPP). For example, a supply chain planner may model a master production problem as a multi-objective hierarchical LPP. The supply chain planner may update and re-solve the supply chain planning problem from time-to-time when changes occur in the supply chain. However, even when there are few changes to the supply chain and these changes are known, re-solving the supply chain planning problem may take as much time to solve as the initial supply chain planning problem. This inefficiency to re-solve a previously-solved supply chain problem when there are only a few known changes to a supply chain is undesirable. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A more complete understanding of the present invention may be derived by referring to the detailed description when considered in connection with the following illustrative figures. In the figures, like reference numbers refer to like elements or acts throughout the figures. 
         FIG.  1    illustrates a supply chain network, in accordance with a first embodiment; 
         FIG.  2    illustrates the auto-encoder system, the archive system, and the planning and execution system of  FIG.  1    in greater detail, in accordance with the first embodiment; 
         FIG.  3    illustrates a simplified supply chain network model, in accordance with an embodiment; 
         FIG.  4    illustrates simplified supply chain networks, in accordance with an embodiment; 
         FIG.  5    illustrates a chart comparing supply chain signatures, in accordance with an embodiment; 
         FIG.  6    illustrates a method of generating supply chain signatures, in accordance with an embodiment; 
         FIG.  7    illustrates a one-layer auto-encoder, in accordance with an embodiment; 
         FIG.  8    illustrates a workflow of generating supply chain signatures, in accordance with an embodiment; 
         FIG.  9    illustrates an example image of a supply chain network, in accordance with an embodiment; 
         FIG.  10    illustrates an example array, in accordance with an embodiment; 
         FIG.  11    illustrates visualizations of images of ten exemplary supply chains networks as processed by an example seven-layer auto-encoder system, in accordance with an embodiment; 
         FIG.  12    illustrates visualizations of input images representing a supply chain network and visualizations of output images reconstructed from signatures by an auto-encoder model, in accordance with an embodiment; 
         FIG.  13    illustrates an array representing the input image of a supply chain network and an array representing the output image reconstructed from the signature by the auto-encoder system of  FIG.  1   , in accordance with an embodiment; 
         FIG.  14    illustrates an array representing the input image of a supply chain network and an array representing the output image for a second use case, in accordance with an embodiment; and 
         FIG.  15    illustrates an array representing the input image of a supply chain network and an array representing the output image for a third use case, in accordance with an embodiment. 
     
    
    
     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. 
       FIG.  1    illustrates supply chain network  100 , in accordance with a first embodiment. Supply chain network  100  comprises auto-encoder system  110 , archive system  120 , one or more planning and execution systems  130   a - 130   n , one or more networked imaging devices  140 , one or more supply chain entities  150 , one or more computers  160 , network  170 , and communication links  180   a - 180   g . Although a single auto-encoder system  110 , a single archive system  120 , one or more planning and execution systems  130   a - 130   n , one or more networked imaging devices  140 , one or more supply chain entities  150 , one or more computers  160 , and a single network  170  are shown and described, embodiments contemplate any number of auto-encoder systems, archive systems, planning and execution systems, networked imaging devices, supply chain entities, computers, or networks, according to particular needs. 
     In one embodiment, auto-encoder system  110  comprises server  112  and database  114 . Server  112  comprises one or more modules that generate a condensed representation of a supply chain as a vector space model using an auto-encoder. As described in further detail below, auto-encoder system  110  transforms supply chain network models  210  ( FIG.  2   ) into digital images and, further, uses the images to train auto-encoder model  206 . Auto-encoder model  206  is an artificial neural network (ANN) that is trained to encode an input as a representation having a lower dimensionality and decode the lower-dimensionality representation into an output that replicates the input. During training of the auto-encoder model, this structure (input to representation to output) is exploited to train auto-encoder model  206  by comparing the input with the output and tuning auto-encoder model parameters  216  using back propagation of the error. In this case, the input is an image representing supply chain network models  210 . When the input to auto-encoder model  206  converges to the output for a large number of training instances, the lower-dimensional representation uniquely encodes the input supply chain network model. As described in further detail below, the lower-dimensional representations, referred to as supply chain signatures  222 , are unique vector space representations of supply chain network models. In addition, or as an alternative, auto-encoder system  110  generates supply chain signatures  222  of transportation routes to compare the similarity or dissimilarity of routes and/or locate routes transporting similar items, as described in further below. According to some embodiments, auto-encoder system  110  creates signatures  222  to locate items in stocking locations of one or more warehouses. Further embodiments of auto-encoder system  110  may measure similarity or dissimilarity between supply chain networks by transforming the supply chain into a vector space model where an inner product exists (e.g. a Hilbert space), and uses one or more comparison methods, as described in further detail below. 
     Archive system  120  of supply chain network  100  comprises server  122  and database  124 . Although archive system  120  is shown as comprising a single server  122  and a single database  124 , embodiments contemplate any suitable number of servers or databases internal to or externally coupled with archive system  120 . Server  122  of archive system  120  may support one or more processes for receiving and storing data from one or more planning and execution systems  130   a - 130   n , one or more networked imaging devices  140 , one or more supply chain entities  150 , and/or one or more computers  160  of supply chain network  100 . According to some embodiments, archive system  120  comprises an archive of data received from one or more planning and execution systems  130   a - 130   n , one or more networked imaging devices  140 , one or more supply chain entities  150 , and/or one or more computers  160  of supply chain network  100 , and archive system  120  provides archived data to auto-encoder system  110 , and one or more planning and execution systems  130   a - 130   n  to, for example, train auto-encoder model  206 , generate supply chain signatures  222  that uniquely represent supply chain network models  210 , train a machine learning model, and generate solver-less solutions to supply chain planning problems. Server  122  may store the received data in database  124 . Database  124  of archive system  120  may comprise one or more databases or other data storage arrangement at one or more locations, local to, or remote from, server  124 . 
     One or more planning and execution systems  130   a - 130   n  of supply chain network  100  comprise transportation network  130   a , warehouse management system  130   b , supply chain planner  130   c , and any quantity of other planning and execution systems  130   n . Although one or more planning and execution systems  130   a - 130   n  are shown and described as comprising a single transportation network  130   a , a single warehouse management system  130   b , a single supply chain planner  130   c , and a single other planning and execution system  130   n , embodiments contemplate any number or combination of one or more planning and execution systems  130   a - 130   n  located internal to, or remote from, supply chain network  100 , according to particular needs. For example, one or more planning and execution systems  130   a - 130   n  typically perform several distinct and dissimilar processes, including, for example, assortment planning, demand planning, operations planning, production planning, supply planning, distribution planning, execution, forecasting, transportation management, warehouse management, inventory management, fulfilment, procurement, and the like. Servers  132   a - 132   n  of one or more planning and execution systems  130   a - 130   n  comprise one or more modules, such as, for example, planning modules, solvers, modelers, and/or one or more engines for performing activities of one or more planning and execution processes. In addition, servers  132   a - 132   n  store and retrieve data from databases  134   a - 134   n  or from one or more locations in supply chain network  100 . In addition, one or more planning and execution systems  130   a - 130   n  operate on one or more computers  160  that are integral to, or separate from, the hardware and/or software that support auto-encoder system  110 , archive system  120 , one or more networked imaging devices  140 , and/or one or more supply chain entities  140 . 
     As disclosed above, one or more planning and execution systems  130   a - 130   n  may include transportation network  130   a . As disclosed above and described in further detail below, one embodiment of auto-encoder system  110  creates a supply chain signature of transportation routes to compare the similarity or dissimilarity of routes and/or locate routes transporting similar items. Transportation network  130   a  comprises server  132   a  and database  134   a . According to embodiments, transportation network  130   a  directs one or more transportation vehicles to ship one or more items between one or more supply chain entities  150 , based, at least in part, a supply chain plan, including a supply chain master plan, the amount of items currently in stock at one or more supply chain entities  150  or other stocking location, the amount of items currently in transit in transportation network  130   a , 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 auto-encoder system  110 , archive system  120 , one or more planning and execution systems  130   a - 130   n , one or more networked imaging devices  140 , and/or one or more supply chain entities  150  to 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. 
     By way of a further example only and not by way of limitation, one or more planning and execution systems  130   a - 130   n  include warehouse management system  130   b . As disclosed above, auto-encoder system  110  creates a signature of warehouse items received from warehouse management system  130   b  and locate where similar items are placed in the warehouse. In one embodiment, warehouse management system  130   b  comprises server  132   b  and database  134   b . According to embodiments, server  132   b  comprises one or more modules that manage and operate warehouse operations, plan timing and identity of shipments, generate picklists, packing plans, and instructions. Warehouse management system  130   b  instructs 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 system  130   b  determining 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 system  130   b  may 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. 
     In addition, or as an alternative to one or more planning and execution systems  130   a - 130   n  comprising warehouse management system  130   b , embodiments contemplate one or more planning and execution systems  130   a - 130   n  comprising an inventory system. The inventory system comprises a server 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 network  100 . The server stores and retrieves item data from a database of the inventory system or from one or more locations in supply chain network  100 . 
     As disclosed above, one or more planning and execution systems  130   a - 130   n  include supply chain planner  130   c . Supply chain planner  130   c  solves supply chain planning problems (such as, for example, operation planning problems). Supply chain planner  130   c  accesses archived trained machine-learning models  276 , which may be received from supply chain planner  130   c . Using supply chain signatures  222  received from auto-encoder system  110 , supply chain planner  130   c  uses a machine-learning model  244  to create an autonomous and self-learning supply chain solver. In addition, supply chain planner  130   c  may transmit supply chain data  262  to auto-encoder system  110 , which uses the transmitted supply chain data  262  to create the supply chain signatures  222 . Although auto-encoder system  110  is shown and described as receiving supply chain data  262  from a supply chain solver  284 , embodiments contemplate auto-encoder system  110  using any type of supply chain planning or execution data as an input to create a supply chain signatures  222 . In addition, although auto-encoder system  110  is described as receiving an input comprising the supply chain planning problem, auto-encoder system  110  may generate a signature of any supply chain data including, for example, supply chain network models (e.g. supply chain data models, supply chain object models, and the like) or any other supply chain data received from archiving system  120 , any of one or more planning and execution systems  130   a - 130   n , and/or one or more locations local to, or remote from supply chain network  100 . 
     One or more networked imaging devices  140  comprise one or more processors  142 , memory  144 , one or more sensors  146 , 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 devices  140  comprise an electronic device that receives imaging data from one or more sensors  146  or from one or more databases in supply chain network  100 . One or more sensors  146  of one or more networked imaging devices  140  may 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 devices  140  may 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 sensors  146  and transmit product images to one or more databases. 
     In addition, or as an alternative, one or more sensors  146  may 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 network  100  by 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 devices  140  may generate a mapping of one or more items in supply chain network  100  by 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 entities  150  that scans items as the items pass near the scanner. As explained in more detail below, auto-encoder system  110 , archive system  120 , one or more planning and execution systems  130   a - 130   n , one or more networked imaging devices  140 , and/or one or more supply chain entities  150  may use the mapping of an item to locate the item in supply chain network  100 . The location of the item may be used to coordinate the storage and transportation of items in supply chain network  100  according to one or more plans and/or a reallocation of materials or capacity generated by one or more planning and execution systems  130   a - 130   n . 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 sensors  146  of one or more networked imaging devices  140  may be located at one or more locations local to, or remote from, one or more networked imaging devices  140 . For example, one or more sensors  146  are integrated into one or more networked imaging devices  140 , or one or more sensors  146  are remotely located from, but communicatively coupled with, one or more networked imaging devices  140 . According to some embodiments, one or more sensors  146  may be configured to communicate directly or indirectly with one or more of auto-encoder system  110 , archive system  120 , one or more planning and execution systems  130   a - 130   n , one or more networked imaging devices  140 , one or more supply chain entities  150 , one or more computers  160 , and/or network  170  using communication links  180   a - 180   g.    
     One or more supply chain entities  150  may represent one or more suppliers, manufacturers, distribution centers, and retailers in one or more supply chain networks  100 , 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 network  100  and 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 entities  150 , the number of items currently in transit in transportation network  130   a , 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 entities  150 , the number of items currently in transit in transportation network  130   a , 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 network  100  and 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 entities  150 , the number of items currently in transit in transportation network  130   a , 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 entities  150 , the number of items currently in transit in transportation network  130   a , 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 shown 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 a supply chain network is shown and described, embodiments contemplate any configuration of supply chain network  100 , without departing from the scope of the present disclosure. 
     As shown in  FIG.  1   , supply chain network  100  comprising auto-encoder system  110 , archive system  120 , one or more planning and execution systems  130   a - 130   n , one or more networked imaging devices  140 , and one or more supply chain entities  150  may operate on one or more computers  160  that are integral to or separate from the hardware and/or software that support auto-encoder system  110 , archive system  120 , one or more planning and execution systems  130   a - 130   n , one or more networked imaging devices  140 , and one or more supply chain entities  150 . One or more computers  160  may include any suitable input device  162 , such as a keypad, mouse, touch screen, microphone, or other device to input information. Output device  164  may convey information associated with the operation of supply chain network  100 , including digital or analog data, visual information, or audio information. 
     One or more computers  160  may include fixed or removable computer-readable storage media, including a non-transitory computer readable medium, magnetic computer disks, flash drives, CD-ROM, in-memory device or other suitable media to receive output from and provide input to supply chain network  100 . One or more computers  160  may include one or more processors  166  and associated memory to execute instructions and manipulate information according to the operation of supply chain network  100  and any of the methods described herein. In addition, or as an alternative, embodiments contemplate executing the instructions on one or more computers  160  that cause one or more computers  160  to 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. 
     Auto-encoder system  110 , archive system  120 , one or more planning and execution systems  130   a - 130   n , one or more networked imaging devices  140 , and one or more supply chain entities  150  may each operate on one or more separate computers, a network of one or more separate or collective computers, or may operate on one or more shared computers. In addition, supply chain network  100  may comprise a cloud-based computing system having processing and storage devices at one or more locations, local to, or remote from auto-encoder system  110 , archive system  120 , one or more planning and execution systems  130   a - 130   n , one or more networked imaging devices  140 , and one or more supply chain entities  150 . In addition, each of the one or more computers  160  may 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 auto-encoder system  110 , archive system  120 , one or more planning and execution systems  130   a - 130   n , one or more networked imaging devices  140 , and one or more supply chain entities  150 . 
     These one or more users may include, for example, a “manager” or a “planner” handling supply chain planning, training auto-encoder system  110 , training the machine learning model, and/or one or more related tasks within supply chain network  100 . In addition, or as an alternative, these one or more users within supply chain network  100  may include, for example, one or more computers  160  programmed 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 network  100 . 
     In one embodiment, auto-encoder system  110  may be coupled with network  170  using communication link  180   a , which may be any wireline, wireless, or other link suitable to support data communications between auto-encoder system  110  and network  170  during operation of supply chain network  100 . Archive system  120  may be coupled with network  170  using communication link  180   b , which may be any wireline, wireless, or other link suitable to support data communications between archive system  120  and network  170  during operation of supply chain network  100 . One or more planning and execution systems  130   a - 130   n  may be coupled with network  170  using communication link  180   d , which may be any wireline, wireless, or other link suitable to support data communications between one or more planning and execution systems  130   a - 130   n  and network  170  during operation of supply chain network  100 . One or more networked imaging devices  140  are coupled with network  170  using communication link  180   e , which may be any wireline, wireless, or other link suitable to support data communications between one or more networked imaging devices  140  and network  170  during operation of supply chain network  100 . One or more supply chain entities  150  may be coupled with network  170  using communication link  180   f , which may be any wireline, wireless, or other link suitable to support data communications between one or more supply chain entities  150  and network  170  during operation of supply chain network  100 . One or more computers  160  may be coupled with network  170  using communication link  180   g , which may be any wireline, wireless, or other link suitable to support data communications between one or more computers  160  and network  170  during operation of supply chain network  100 . Although communication links  180   a - 180   g  are shown as generally coupling auto-encoder system  110 , archive system  120 , one or more planning and execution systems  130   a - 130   n , one or more networked imaging devices  140 , one or more supply chain entities  150 , and one or more computers  160  to network  170 , each of auto-encoder system  110 , archive system  120 , one or more planning and execution systems  130   a - 130   n , one or more networked imaging devices  140 , one or more supply chain entities  150 , and one or more computers  160  may communicate directly with each other, according to particular needs. 
     In another embodiment, network  170  includes the Internet and any appropriate local area networks (LANs), metropolitan area networks (MANs), or wide area networks (WANs) coupling auto-encoder system  110 , archive system  120 , one or more planning and execution systems  130   a - 130   n , one or more networked imaging devices  140 , one or more supply chain entities  150 , and one or more computers  160 . For example, data may be maintained locally by, or externally of, auto-encoder system  110 , archive system  120 , one or more planning and execution systems  130   a - 130   n , one or more networked imaging devices  140 , one or more supply chain entities  150 , and one or more computers  160  and made available to one or more associated users of auto-encoder system  110 , archive system  120 , one or more planning and execution systems  130   a - 130   n , one or more networked imaging devices  140 , one or more supply chain entities  150 , and one or more computers  160  using network  170  or in any other appropriate manner. Those skilled in the art will recognize that the complete structure and operation of network  170  and other components within supply chain network  100  are 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, one or more planning and execution systems  130   a - 130   n  may generate a supply chain plan. Furthermore, one or more computers  160  associated with one or more planning and execution systems  130   a - 130   n  may 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 entities  150 , 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 entities  150 , the number of items currently in transit in transportation network  130   a , 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 one or more computers  160  receiving product data  264  from automated machinery having at least one sensor and product data  264  corresponding to an item detected by the automated machinery. The received product data may 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. One or more computers  160  may also receive, from one or more sensors  146  of one or more networked imaging devices  140 , a current location of the identified item. 
     The methods may further include one or more computers  160  looking up the received product data in the database system associated with one or more planning and execution systems  130   a - 130   n  to identify the item corresponding to product data  264  received from automated machinery. Based on the identification of the item, one or more computers  160  may also identify (or alternatively generate) a first mapping in the database system, where the first mapping is associated with the current location of the identified item. One or more computers  160  may also identify a second mapping in the database system, where the second mapping is associated with a past location of the identified item. One or more computers  160  may 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. One or more computers  160  may 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 entities  150 . In addition, or as an alternative, one or more planning and execution systems  130   a - 130   n  monitors one or more supply chain constraints of one or more items at one or more supply chain entities  150  and adjusts the orders and/or inventory of one or more supply chain entities  150  at least partially based on one or more supply chain constraints. 
       FIG.  2    illustrates auto-encoder system  110 , archive system  120 , and planning and execution system  130   n  of  FIG.  1    in greater detail, in accordance with an embodiment. Auto-encoder system  110  comprises server  112  and database  114 , as disclosed above. Although auto-encoder system  110  is shown as comprising a single server  112  and a single database  114 , embodiments contemplate any suitable number of servers or databases internal to or externally coupled with auto-encoder system  110 . 
     Server  112  of auto-encoder system  110  comprises data processing module  202 , training module  204 , auto-encoder model  206 , and user interface module  208 . Although server  112  is shown and described as comprising a single data processing module  202 , a single training module  204 , a single auto-encoder model  206 , and a single user interface module  208 , embodiments contemplate any suitable number or combination of these located at one or more locations, local to, or remote from auto-encoder system  110 , such as on multiple servers or computers at one or more locations in supply chain network  100 . 
     Database  114  of auto-encoder system  110  may comprise one or more databases or other data storage arrangement at one or more locations, local to, or remote from, server  112 . Database  114  of auto-encoder system  110  comprises, for example, supply chain network models  210 , n-dimensional vectors  212 , supply chain network training images  214 , auto-encoder model parameters  216 , archived trained auto-encoder models  218 , supply chain network samples images  220 , and supply chain signatures  222 . Although database  114  of auto-encoder system  110  is shown and described as comprising supply chain network models  210 , n-dimensional vectors  212 , supply chain network training images  214 , auto-encoder model parameters  216 , archived trained auto-encoder models  218 , supply chain network samples images  220 , and supply chain signatures  222 , embodiments contemplate any suitable number or combination of these, located at one or more locations, local to, or remote from, auto-encoder system  110  according to particular needs. 
     In one embodiment, data processing module  202  of auto-encoder system  110  receives data from archive system  120 , one or more planning and execution systems  130   a - 130   n , one or more networked imaging devices  140 , one or more supply chain entities  150 , one or more computers  160 , or one or more data storage locations local to, or remote from, supply chain network  100  and auto-encoder system  110 , and prepares the data for use in training auto-encoder model  206 , generating supply chain network training images  214  and supply chain network sample images  220 , classifying supply chain network models  210 , determining similarity between supply chain network models  210 , and transforming data received from one module to a different data structure for use by a second module. Data processing module  202  may check received data for errors in the range, sign, and/or value and use statistical analysis to check the quality or the correctness of the data. As described in further detail below, data processing module  202  transforms the object model of supply chain network  100  from a binary string to an integer array, each byte of the binary corresponding to one integer of the array. 
     In one embodiment, the binary comprises a hexadecimal binary generated by one or more solvers of a supply chain planner  130   c  to model and solve a supply chain planning problem. In one embodiment, solver  284  constructs a model representing the dynamic and static structure of a supply chain network and generates a solution or other output generated when solving the model. By way of example only and not by way of limitation, the model object may comprise a mathematical formulation of the supply chain planning problem, which includes the network structure and the dynamic properties, such as, for example, capacities, materials, operations, yield rate, lead time, and the like. Data processing module  202  stores the model and its solution together and generates a configuration file indicating the identity and location of each model and its corresponding solution. Machine learning model  240  retrieves the binary of the model and solver  284  output using the identity and location data stored in the configuration file, as described in further detail below. According to some embodiments, data processing module  202  converts the binary model object to n-dimensional vectors  212 . By way of example only and not by way of limitation, data processing module  202  converts an integer array into an 8-bit image, wherein each pixel of the image corresponds to one integer from the array. These images are used as supply chain network training images  214  and supply chain network sample images  220 . However, the binaries for supply chains having different sizes or complexity will have different sizes, as well. Generating images from differently-sized binaries results in differently-sized images. Embodiments of data processing module  202  convert the differently-sized images to the same size using an image transformation process prior to sending the image to an auto-encoder for training or sampling. A length and a width of the image may correspond to the number of rows and the number of columns of the integer array from which it was constructed, wherein each element of the array corresponds to a single pixel. The dimensions of the images may however be selected as any suitable dimensions to tune the performance of auto-encoder model  206  and machine learning model  240 , according to particular needs. 
     As disclosed above and as described in further detail below, training module  204  trains auto-encoder model  206  using images created from supply chain network models  210 . For example, training module  204  learns auto-encoder model parameters  216 , using back propagation of the error to minimize the differences between the image created from supply chain network  100  encoded by the supply chain signature and the output image, which is reconstructed by decoding the supply chain signature. In one embodiment, training module  204  calculates the error between the input image and the output image using the Root Mean-Squared Error (RMSE), according to Equation 1: 
     
       
         
           
             
               
                 
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                           i 
                           num_pixels 
                         
                           
                         
                           
                             ( 
                             
                               
                                 pixel 
                                 input 
                               
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                                 outut 
                               
                             
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     wherein, num_pixels is the quantity of pixels of the image, pixel input  is the value of a pixel of the input image, pixel output  is the value of the same pixel in the output image. 
     User interface module  208  of auto-encoder system  110  generates and displays a user interface (UI), such as, for example, a graphical user interface (GUI), that displays one or more interactive visualizations of supply chain network models  210 , supply chain network training images  214 , auto-encoder model parameters  216 , archived trained auto-encoder models  218 , supply chain network samples images  220 , and supply chain signatures  222 . According to embodiments, user interface module  208  displays a GUI comprising interactive elements for selecting one or more supply chain network models  210 , in response to the selection, calculating supply chain signatures  222 , and comparing the selected one or more signatures of supply chain signatures  222  to one or more signatures of one or more other supply chain networks. In one embodiment, user interface module  208  causes the GUI to display supply chain data  262  for the most similar supply chain network, as calculated by locating one or more signature of the network from supply chain signatures  222  using a vector space model. 
     The various types of data stored in database  114  of auto-encoder system  110  will now be discussed. 
     Supply chain network models  210  represent the flow of materials through one or more supply chain entities  150  of supply chain network  100 . As descried in more detail below, modeler  282  of planning module  260  of supply chain planner  130   c  models the flow of materials through one or more supply chain entities  150  of supply chain network  100  as one or more supply chain network models  210  comprising 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 models  210  may 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 network  100 . Supply chain network models  210  may include any dynamic supply chain data, 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 models  210  are shown and described as comprising a network of nodes and edges, embodiments contemplate supply chain network models  210  comprising any suitable model that represents one or more components of supply chain network  100  using any suitable model, according to particular needs. 
     According to embodiments, integer arrays, as disclosed above, correspond to n-dimensional vectors  212 , wherein data processing module  202  creates each integer of the array from each eight bits of a binary. Because data processing module  202  converts each integer of the array into a pixel of supply chain network training images  214  and supply chain network sample images  220 , each pixel of these images correspond to one dimension of n-dimensional vectors  212 . 
     As described in further detail below, supply chain network training images  214  may be created from, or transformed into, an array of integers or real numbers, wherein each element of the array indicates the value of a single pixel. As disclosed above, these images provide the input for auto-encoder model  206 . 
     The archived trained auto-encoder models  218  comprise previously trained auto-encoder models. Auto-encoder system  110  and one or more planning and execution systems  130   a - 130   n  may retrieve, load, and execute a particular previously-trained auto-encoder model in a first instance and retrieve, load, and execute a different trained model in a second instance. In one embodiment, auto-encoder system  110  selects the previously-trained auto-encoder model that was trained with instances of the same (or a similar) supply chain network, such as, for example, a supply chain network having a similar static supply chain structure. 
     Supply chain network sample images  220  comprise one or more images created by data processing module  202  from sample supply chain network models  210  and which are used as an input to a trained auto-encoder model to create supply chain signatures  222  for a sample supply chain network. In one embodiment, the sample supply chain network is the current supply chain network being analyzed by auto-encoder system  110  or supply chain planner  130   c . Supply chain signatures  222  for supply chain networks are generated from the representation layer between the encoder and decoder of auto-encoder model  206 , as disclosed in further detail below. 
     As disclosed above, archive system  120  comprises server  122  and database  124 . Although archive system  120  is shown as comprising a single server  120  and a single database  122 , embodiments contemplate any suitable number of servers or databases internal to or externally coupled with archive system  120 . 
     Server  122  of archive system  120  comprises data retrieval module  230 . Although server  122  is shown and described as comprising a single data retrieval module  230 , embodiments contemplate any suitable number or combination of data retrieval modules located at one or more locations, local to, or remote from archive system  120 , such as on multiple servers or computers at one or more locations in supply chain network  100 . 
     In one embodiment, data retrieval module  230  of archive system  120  receives historical supply chain data  232  from one or more planning and execution systems  130   a - 130   n  and/or one or more supply chain entities  150  and stores the received historical supply chain data in database  124 . According to one embodiment, data retrieval module  230  may prepare historical supply chain data  232  for use by supply chain planner  130   c  to generate variants of the supply chain planning problem by checking historical supply chain data  232  for errors and transforming historical supply chain data  232  to normalize, aggregate, and/or rescale historical supply chain data  232  to allow direct comparison of data received from different planning and execution systems and one or more supply chain entities at one or more other locations local to, or remote from, archive system  120 . According to embodiments, data retrieval module  230  receives data from one or more sources external to supply chain network  100 , such as, for example, weather data, special events data, social media data, calendars, and the like and stores the received data as historical supply chain data  232 . 
     Database  124  of archive system  120  may comprise one or more databases or other data storage arrangement at one or more locations, local to, or remote from, server  122 . Database  124  of archive system  120  comprises, for example, historical supply chain data  232 . Although database  124  of archive system  120  is shown and described as comprising historical supply chain data  232 , embodiments contemplate any suitable number or combination of data, located at one or more locations, local to, or remote from, archive system  120 , according to particular needs. 
     Historical supply chain data  232  comprises data received from auto-encoder system  110 , archive system  120 , one or more planning and execution systems  130   a - 130   n , one or more supply chain entities  150 , one or more computers  160 , and/or one or more locations local to, or remote from, supply chain network  100 , such as, for example, one or more sources for weather data, special events data, social media data, calendars, and the like. According to one embodiment, historical supply chain data  232  comprises 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 planner  130   c  may calculate supply chain plans over a historical time period, such as, for example, any of the time periods represented by historical supply chain data  232 . 
     As disclosed above, planning and execution systems  130   a - 130   n  comprise servers  132   a - 132   n  and databases  134   a - 134   n . Although planning and execution systems  130   a - 130   n  are shown and described a single planning and execution system  130   n  comprising a single server  132   n  and a single database  134   n , embodiments contemplate any suitable number of servers or databases internal to or externally coupled with any planning and execution systems  130   a - 130   n , according to particular needs. 
     By way of example only and not by way of limitation, server  132   n  comprises planning module  240 , execution module  242 , machine-learning model  244 , training module  246 , prediction module  248 , and user interface module  250 . Although server  132   n  is shown and described as comprising a single planning module  240 , a single execution module  242 , a single machine-learning model  244 , a single training module  246 , a single prediction module  248 , and a single user interface module  250 , embodiments contemplate any suitable number or combination of planning modules, execution modules, machine learning models, training modules, prediction modules, and user interface modules, located at one or more locations, local to, or remote from any of planning and execution systems  130   a - 130   n , such as on multiple servers or computers at one or more locations in supply chain network  100 . 
     Continuing with the example planning and execution system  130   n , database  134   n  may comprise one or more databases or other data storage arrangement at one or more locations, local to, or remote from, server  132   n . Databases  134   a - 134   n  of planning and execution systems  130   a - 130   n  may comprise, for example, transaction data  260 , supply chain data  262 , product data  264 , inventory data  266 , inventory policies  268 , store data  270 , customer data  272 , supply chain models  274 , archived trained machine-learning models  276 , and predictions data  278 . Although database  134   n  of planning and execution system  130   n  is shown and described as comprising transaction data  260 , supply chain data  262 , product data  264 , inventory data  266 , inventory policies  268 , store data  270 , customer data  272 , supply chain models  274 , archived trained machine-learning models  276 , and predictions data  278 , embodiments contemplate any suitable number or combination of data, located at one or more locations, local to, or remote from, supply chain the planning and execution system, according to particular needs. 
     Server  132   n  of planning and execution system  130   n  comprises planning module  240 . Planning module  240  comprises modeler  282  and solver  284 . Although planning module  240  is shown and described as comprising a single modeler  282  and a single solver  284 , embodiments contemplate any suitable number or combination of these located at one or more locations, local to, or remote from planning module  240 , such as on multiple servers or computers at any location in supply chain network  100 . 
     Modeler  282  may model one or more supply chain planning problems of supply chain network  100 . According to one embodiment, modeler  282  of server  132   n  identifies resources, operations, buffers, and pathways, and maps supply chain network  100  using supply chain network models  210 , as described in further detail below. For example, modeler  282  of server  132   n  models a supply chain planning problem that represents supply chain network  100  as a supply chain network model, an LP optimization problem, or other type of input to solver  284 . As discussed above, modeler  282  provides supply chain network  100  model to auto-encoder system  110 , which processes supply chain network  100  model into supply chain signatures  222 . 
     According to embodiments, solver  284  of planning module  240  generates a solution to a supply chain planning problem. Solver  284  may comprise an LP optimization solver, a heuristic solver, a mixed-integer problem solver, a MAP solver, LP solver, Deep Tree solver, and the like. According to embodiments, solver  284  solves a supply chain planning problem and auto-encoder model  206  generates supply chain signatures  222  for the same supply chain planning problem. 
     Execution module  242  of planning and execution system  130   n  executes 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 entities  150 , 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 entities  150 , the number of items currently in transit in transportation network  130   a , 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, execution module  242  may 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 entities  150 . 
     In one embodiment, machine-learning model  244  creates an autonomous and self-learning supply chain solver. Machine learning model  244 , after training by training module  246 , generates a solution to one or more supply chain planning problems using supply chain signatures  222  generated by auto-encoder model  206 . Server  132   n  comprises prediction module  248 , which receives supply chain signatures  222  from auto-encoder system  110  and applies supply chain signatures  222  to machine-learning model  244  to generate the solution to the supply chain planning problem, without using solver  284 , as disclosed above. 
     User interface module  250  of planning and execution system  130   n  generates and displays a UI, such as, for example, a GUI, that displays one or more interactive visualizations of transaction data  260 , supply chain data  262 , product data  264 , inventory data  266 , inventory policies  268 , store data  270 , customer data  272 , supply chain models  274 , archived trained machine-learning models  276 , and prediction data  278 . According to embodiments, user interface module  250  displays a GUI comprising interactive graphical elements for selecting one or more supply chain network components, modeling supply chain network  100  as an object model, formulating supply chain network  100  as a supply chain planning problem, solving the supply chain planning problem, generating predictions from archived trained machine-learning models  276 , and displaying one or more solutions and/or supply chain plans. 
     The various types of data stored in database  134   n  of planning and execution system  130   n  will now be discussed. 
     Transaction data  260  may 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 data  260  is 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 data  262  may comprise any data of one or more supply chain entities  150  including, 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 entities  150 . Supply chain data  262  may also comprise for example, various decision variables, business constraints, goals, and objectives of one or more supply chain entities  150 . According to some embodiments, supply chain data  262  may 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 data  264  of database  134   n  may 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 data  264  may 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 data  266  of database  134   n  may comprise any data relating to current or projected inventory quantities or states, order rules, or the like. For example, inventory data  266  may comprise the current level of inventory for each item at one or more stocking points across supply chain network  100 . In addition, inventory data  266  may 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, planning and execution system  130   n  accesses and stores inventory data  266  in database  134   n , which may be used by planning and execution system  130   n  to 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 planning and execution system  130   n . In addition, or as an alternative, inventory data  266  may be updated by receiving current item quantities, mappings, or locations from one or more planning and execution systems  130   a - 130   n  and/or one or more networked imaging devices  140 . 
     Inventory policies  268  of database  134   n  may comprise any suitable inventory policy describing the reorder point and target quantity, or other inventory policy parameters that set rules for planning and execution system  130   n  to manage and reorder inventory. Inventory policies  268  may be based on target service level, demand, cost, fill rate, or the like. According to embodiment, inventory policies  268  comprise target service levels that ensure that a service level of one or more supply chain entities  150  is met with a certain probability. For example, one or more supply chain entities  150  may set a service level at 95%, meaning one or more supply chain entities  150  will 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 level or target, 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, auto-encoder system  110  and/or planning and execution system  130   n  may determine a replenishment order according to one or more replenishment rules, which, among other things, indicates to one or more supply chain entities  150  to 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. Inventory policies  268  may 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 data  270  may comprise data describing the stores of one or more retailers and related store information. Store data  270  may 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 data  270  may 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 entities  150 . 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 planner  130   c  may calculate a demand forecast at any granularity of time, customer, item, region, or the like. 
     Customer data  272  may 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 data  272  may comprise data relating customer purchases to one or more products, geographical regions, store locations, time period, or other types of dimensions. 
     Supply chain models  274  comprise 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 models  274  may 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. 
     In one embodiment, archived trained machine-learning models  276  comprise one or more trained machine learning models used by planning and execution system  130   n  for predicting a solution to a supply chain planning problem, such as, for example, a supply chain plan. Predictions data  288  are the predictions generated by machine-learning model  244 . For machine-learning model  244  trained using signatures  222  and solutions of the supply chain planning problem, the predictions are the supply chain planning problem solution. 
     As disclosed above, modeling a supply chain network provides modelling of various material and capacity constraints and demand requirements. To create supply chain network models  210 , supply chain network  100  may be modelled to represent the flow of materials and resources between one or more supply chain entities  150  in accordance with the constraints at each operation, buffer, and resource. 
       FIG.  3    illustrates simplified supply chain network model  300 , according to an embodiment. Supply chain network  100  represented by simplified supply chain network model  300  comprises six material buffers  302   a - 302   f  (B1-B6) storing materials or items, five operations  304   a - 304   e  (O1-O5) for processing materials and items, and three resources  306   a - 306   c  (R1, R2, and R5), which represent capacity limitations on each of operations  304   a - 304   e  to which they are connected. Four operations  304   a ,  304   b ,  304   d  and  304   e  (O1, O2, O4, O5) have a single material or item as input and a single material or item as output. A single operation  304   c  (O3) requires two materials or items as input (i.e. materials or items stored at material buffers  302   c  and  302   e  (B3 and B5)) and produces a single item as output (materials or items stored at material buffer  302   d  (B4)). According to one embodiment of simplified supply chain network model  300 , materials flow from upstream nodes to downstream nodes along each of edges  310   a - 310   j  and  312   a - 312   c  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. 
     Simplified supply chain network model  300  may begin at the most upstream nodes representing material buffers  302   a  and  302   f  (B1, B6), 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 edges  310   a  and  310   j , which identify operations  304   a  and  304   e  (O1, O5) is a possible destination for the materials. For example, raw materials may be transported to operations  304   a  and  304   e  comprising a production process, such as producing one or more intermediate items from the raw materials which are stored at material buffers  302   b  and  202   e  (B2, B5) comprising, for example, intermediate items. Operations  304   a  and  304   e  (O1, O5) are coupled by edges  312   a  and  312   c  with resources  306   a  and  306   c  (R1, R5) to indicate that operations  304   a  and  304   e  (O1, O5) require resources  306   a  and  306   c  (R1, R5) in order to process items or materials from  302   a  and  302   f  (B1, B6). According to embodiments, resources  306   a  and  306   c  (R1, R5) may include, for example, particular manufacturing, distribution, or transportation equipment and facilities, and other such resources utilized in a 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 simplified supply chain network model  300  is 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 a supply chain network, according to particular needs. For a manufacturing supply chain network, transportation processes may transport, package, or ship finished goods to one or more locations internal to or external of one or more supply chain entities  150  of supply chain network  100 , including, for example, shipping directly to consumers, to regional or strategic distribution centers, or to the inventory of one or more supply chain entities  150 , including, for example, to replenish a safety stock for one or more items in an inventory of one or more supply chain entities  150 . 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 network  100 . 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, simplified supply chain network model  300  is shown and 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 a supply chain networks much more complex than simplified supply chain network model  300 . For example, a supply chain network often 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 bi-directionally (either, upstream or downstream). 
     By transforming supply chain data  262  to supply chain signatures  222  having the same dimensionality for each instance of training data, one or more planning and execution systems  130   a - 130   n  may train and predict using machine learning-based methods. For example, using supply chain signatures  222 , one or more planning and execution systems  130   a - 130   n  may apply machine-learning techniques directly to an input of solver  284 , such as, for example, an input comprising supply chain planning problems or supply chain models  274 . 
       FIG.  4    illustrates simplified supply chain networks  402   a - 402   c , in accordance with an embodiment. Simplified supply chain networks  402   a - 402   c  include first simplified supply chain network  402   a  with a similar static structure as third simplified supply chain network  402   c . Both first and third simplified supply chain networks  402   a  and  402   c  have a static structure that is dissimilar from the static structure of second simplified supply chain network  402   b . In these examples, the static structure is represented by simplified supply chain network models (such as disclosed in connection with simplified supply chain network model  300  of  FIG.  3   ), wherein material buffers are represented by upright triangles, resource buffers are represented by inverted triangles, and operations are represented by squares and rectangles. Each of simplified supply chain networks  402   a - 402   c  have different dynamic data, such as, for example, constraints of resources, materials, and operations (e.g. lead time constraints), represented by numerical values next to the buffers and operations. Supply chain signatures  404   a - 404   c  are associated with each of simplified supply chain networks  402   a - 402   c , and, for these examples, supply chain signatures  404   a - 404   c  identify the values of four-dimensional vectors representing the static and dynamic properties of the simplified supply chain networks  402   a - 402   c . Continuing with these examples, differences and similarities among the three supply chain signatures  404   a - 404   c  indicate differences and similarities of the static and dynamic supply chain data. In particular, supply chain signature  404   a  for first simplified supply chain network  402   a  is [3, 4, 16, 5], and supply chain signature  404   c  for third supply chain network  402   c  is [3.2, 4, 15.7, 5.2]. First and second supply chain signatures  404   a  and  404   c  are similar to each other, but different from second supply chain signature  404   b  of second supply chain network  402   c , which is [29, 36, 85, 25]. Small differences (such as the quantitatively small differences between first supply chain signature  404   a  and third supply chain signature  404   c ) indicate the encoded supply chain networks have a similar structure, but may have different dynamic data. Continuing with the explanatory and non-limiting example, both first and third simplified supply chain networks  402   a  and  402   c  share a similar structure, where a first material is transformed into a second material by a single operation having a single resource. Differences in dynamic data (e.g. third simplified supply chain network  402   c  has increased material storage, resource capacity, and operation lead time when compared with first simplified supply chain network  402   a ) do not result in great quantitative differences between first and third supply chain signatures  404   a  and  404   c , as disclosed above. Other changes in dynamic data may include, for example, a capacity increasing for a resource (such as, for example, capacity for manufacturing equipment increasing from ten days to fifteen days), changing a manufacturing process (such as, for example, producing different items, receiving items from a new or different supplier, changing one or more operations for processing, transporting, or storing one or more items), and the like. 
     When comparing first and third supply chain signatures  404   a  and  404   c  with second supply chain signature  404   b , the large quantitative difference indicates the supply chain structure of second simplified supply chain network  402   b  is different from first and third simplified supply chain networks  402   a  and  402   b . This dissimilarity in structure is indicated by, where second simplified supply chain network  402   b  has three operations with alternative pathways, first and third simplified supply chain networks  402   a  and  402   c  have a single operation with a single pathway. Other differences in static supply chain structure may include, for example, addition or removal of one or more supply chain entities  150  (e.g. a new manufacturer), supply chains of different customers or channels, comparisons of a different configurations of a supply chain that has evolved over time, or other like structural differences. In one embodiment, one or more planning and execution systems  130   a - 130   n  may compare the similarity or differences between supply chains by calculating an average Euclidean distance (e.g. the square of the distance) between two supply chain vectors from supply chain signatures  222 . When the calculated distance between the two vectors is zero, the supply chains represented by the vectors are identical. When the calculated distance is non-zero, the length of the distance corresponds to the expected similarity between the supply chains. 
       FIG.  5    illustrates chart  500  comparing supply chain signatures  222 , in accordance with an embodiment. Chart  500  comprises supply chain signatures  222  plotted using a t-Distributed Stochastic Neighbor Embedding (t-SNE) method to represent vectors of supply chain signatures  222  on a two-dimensional plot (x-axis  502  and y-axis  504 ). Plotted points represent three different supply chain network structures  506 - 510 . Each plotted point of the same shading represents iterations of a supply chain network having different dynamic data (such as, for example, different demand or capacity), but the same supply chain structure. As disclosed above, supply chain signatures  222  are dissimilar for supply chain networks having different structures, which is illustrated in the chart by the iterations of each shading not clustering well with iterations of a different shading. However, supply chain signatures  222  are similar for supply chain networks having the same structure regardless of the difference of dynamic data, as shown by the iterations of each shading (e.g. supply chain network structures  506 - 510 ) clustering well to other iterations having the same shading. 
       FIG.  6    illustrates method  600  of generating supply chain signatures  222 , in accordance with an embodiment. Method  600  comprises one or more activities, which although described in a particular order may be implemented in one or more combinations, according to particular needs. 
     At activity  602 , modeler  282  of one or more planning and execution systems  130   a - 130   n  creates a model of supply chain network  100 . In one embodiment, modeler  282  creates a model of a supply chain planning problem that is used as an input to one or more supply chain planning solvers, such as the object model, as disclosed above. 
     At activity  604 , data processing module  202  of auto-encoder system  110  receives the binary data representing supply chain network model. In one embodiment, data processing module  202  receives supply chain data  262  from one or more planning and execution systems  130   a - 130   n . For example, in one embodiment, supply chain planner  130   c  models a supply chain network object model used as an input to solver  284  of supply chain planner  130   c . Data processing module  202  may retrieve the object model directly from memory storing the supply chain object model. In another embodiment, data processing module  202  accesses the supply chain object model from a non-transitory computer readable medium associated with server  132   n  of planning and execution system  130   n.    
     At activity  606  of method  600 , data processing module  202  converts the binary to an integer array, wherein each consecutive byte of the binary is translated into an integer. Although the integer array is described as comprising one integer corresponding to each byte, data processing module  202  may convert a binary to an integer array using 16-bit, 32-bit, 64-bit, or any other suitable reading frame to construct each integer of the array, according to particular needs. 
     At activity  608 , data processing module  202  converts the integer array into an image. As disclosed above, each integer from the array is converted into a single pixel of an image. At activity  610 , training module  204  of auto-encoder system  110  trains auto-encoder model  206  using supply chain network training images  214 . After training, auto-encoder model  206  generates supply chain signatures  222 , which, as disclosed above, is a reduced-dimensionality representation of supply chain network  100  created as an intermediate layer during training of auto-encoder model  206 . For example, auto-encoder model  206  passes the image representing supply chain network models  210  through a series of nonlinear mappings (which may be similar to function mapping in mathematics). The mappings are constructed such that not only is the mapping nonlinear but also each layer reduces the dimensionality of the input to generate a finite and lower-dimensional vector. 
     At activity  612 , auto-encoder model  206  creates supply chain signatures  222  of a sample supply chain network model. As disclosed above, a particular supply chain network being analyzed may be referred to as a sample supply chain network Similar to supply chain network training images  214 , disclosed above, data processing module  202  creates an image of the sample supply chain network, supply chain network samples images  220 . Auto-encoder system  110  applies supply chain network sample images  220  to a trained auto-encoder model  206  which generates a supply chain signature of the sample supply chain by retrieving the output of the encoder. Supply chain signatures  222  uniquely represent supply chain networks and may be used as an input for machine learning-based methods of one or more planning and execution systems  130   a - 130   n.    
     For example, one or more planning and execution systems  130   a - 130   n  may use the T-SNE method and/or a Principle Component Analysis (PCA) method to calculate the similarity or dissimilarity between two or more supply chain networks. In one embodiment, auto-encoder system  110  creates the supply chain signature as a one-hundred-dimensional vector, which is not suitable for visualizations. Using the T-SNE/PCA techniques the relationships between the various supply chain signatures having a one-hundred-dimensional vector are visualized as two-dimensional vectors, as described in further detail below. Supply chain signatures  222  vary based on the dynamic properties and physical structure of the supply chain, as disclosed above, and may form a key input to AI-based methods, such as, for example, supply chain planning using machine-learning based methods. 
     At activity  614 , training module  244  receives training data comprising image of supply chain data  262 . Auto-encoder system  110  trains an auto-encoder to compress and decompress input data with as small an error as possible, as described in further detail below. 
     Although supply chain signatures  222  are described as comprising a compressed form of a physical supply chain structure for machine-learning based training and prediction, embodiments contemplate creating signatures for other types of supply chain data, as described in further detail below. 
       FIG.  7    illustrates one-layer auto-encoder  700 , in accordance with an embodiment. Auto-encoders, such as one-layer auto-encoder  700 , comprise encoder  702  and decoder  704 . Input layer  710  of encoder  702  receives an input, X, and transforms the input into a transformed representation  712 , represented by code h. Output layer  714  of decoder  704  receives representation  712  and transforms representation  712  into an output, X′, which is different than the input. Auto-encoder system  110  trains auto-encoders using a neural-network training process that compares the input of the auto-encoder with its output and, at each iteration, re-encodes the layers to minimize the difference between the input and output and reduce dimensionality. During early iterations of auto-encoder training, the input and the output may differ greatly. The training process continues to re-encode the layers at each iteration until training module  204  determines the differences between the input and output are less than a predetermined threshold. In addition, or as an alternative, training module  204  detects one or more stopping criteria, such as, for example, a user-selected margin of error, a convergence, a quantity of completed iterations, any other threshold, or other like one or more other stopping criteria. Although one-layer auto-encoder  700  is shown and described as comprising a single input layer  710  and a single output layer  712 , embodiments contemplate an auto-encoder having any number of layers, according to particular needs. 
     When the output is acceptably similar to the input, one or more machine-learning methods may use the transformed input from one of the layers as a signature of the input, allowing dissimilar inputs to be compared by a standard n-dimensional signature. In one embodiment, the supply chain signature is the output of the encoder and the input for the decoder, as described in more detail below. 
       FIG.  8    illustrates workflow  800  of generating supply chain signatures  222 , in accordance with an embodiment. According to embodiments of workflow  800 , supply chain data  262 , such as, for example, a supply chain planning problem generated by supply chain planner  130   c , is transformed into an image, such as, for example, supply chain network training images  214  and supply chain network sample images  220 . In one embodiment, auto-encoder system  110  generates image by converting an integer array generated from binary data received from modeler  282 . Multi-layer auto-encoder  802  comprises encoder  702  and decoder  704 , as disclosed above. Encoder  702  receives the image, processes the image through one or more input layers  810   a - 810   n  while reducing the dimensionality of the image. 
       FIG.  9    illustrates example image  900  of supply chain network  100 , in accordance with an embodiment. According to embodiments, auto-encoder system  110  generates an image, such as example image  900 , from supply chain network models  210 , one or more supply chain planning problems, or data from one or more planning and execution systems  130   a - 130   n  received by data processing module  202  of auto-encoder system  110 . Data processing module  202  may receive an in-memory object representing the supply chain or supply chain problem, which auto-encoder system  110  converts into a binary, which is ultimately transformed into an image, such as example image  900 . As disclosed above, image  900  comprises a converted integer array created from each byte of binary data received from modeler  282 . In one embodiment, a supply chain network model, or other data from one or more planning and execution systems is received by a data processing module of auto-encoder system  110 . The received data may comprise an in-memory object of the supply chain representation which is converted into a binary and then into an image. 
     Returning to workflow  800 , each layer of input layers  810   a - 810   n  learns a different representation of supply chain data  262  from the image. Signature (Code  812 ) is processed through one or more output layers  814   a - 814   n  comprising decoder  704  and transformed into an output image that is acceptably similar to the input image. Input layers  810   a - 810   n  of encoder  702  and output layers  814   a - 814   n  of decoder  704  are created by a neural-network training method. When the image is processed through the last input layer  810   n  of encoder  702  (after training), the image is signature (Code)  812 . The layers of the trained auto-encoder system may comprise one or more auto-encoder model parameters  216  such as, for example, one or more weights. Using a neural network training technique (such as, for example, a feed forward neural network, a convolution neural network, and the like), auto-encoder system  110  trains multi-layer auto-encoder  802  by adjusting auto-encoder model parameters  216  at each iteration, until auto-encoder model parameters  216  of the trained layers produce an output image from supply chain signature (Code)  812  that recreates the input image encoded by the supply chain signature. Once auto-encoder model parameters  216  are learned, encoder  702  generates a supply chain signature when presented with the image created by transforming the extracted binary of supply chain network models. In one embodiment, a seven-layer auto-encoder receives training data  250  comprising image of supply chain data  262 . Auto-encoder system  110  trains the seven-layer auto-encoder to compress and decompress input data with as small an error as possible. 
     In one embodiment, the training is represented by the following process, where training module  204  of auto-encoder system  110  receives two inputs: a seven-layer auto-encoder, A; and training data, D, comprising the supply chain image converted from binary data. Training module  204  trains the seven-layer auto-encoder, A, to generate the trained seven-layer auto-encoder, A′, by compresses and decompresses input data from one or more planning and execution systems  130   a - 130   n  by passing images through all layers of multi-layer auto-encoder  802  and observing data differences between input and output layer to error propagate to update all of the auto-encoder model parameters  216  using back propagation. According to one embodiment, training module  204  generates the trained seven-layer auto-encoder, A′, according to the following example code: 
     errorOld=Big number; errorNew=0 
     While (|errorOld−errorNew|!=0):
         error=0   For each image in D:
           Pass image through all layers of auto-encoder system and observe input and output layer data difference to error propagate to update all of the auto-encoder model parameters  216  using back propagation.   error=error+difference between input layer and output layer   
           errorOld=errorNew   errorNew=error       

     Return trained auto-encoder system ‘A’ 
       FIG.  10    illustrates an example array  1000 , in accordance with an embodiment. Example array  1000  is a one hundred cell array corresponding to a one-hundred-dimensional vector. As disclosed above, signatures  222  may comprise a one-hundred-dimensional vector, wherein each cell of the array represents one dimension of the vector. By way of example only and not by way of limitation, an input array representing an example supply chain network may comprise an 84,000 dimension vector. As disclosed above, the dimensionality of the 84,000 dimension vector is reduced at each layer of the encoder to a vector having a fewer number of dimensions. For example, a first layer may reduce the 84,000 dimension vector to a 10,000 dimension vector; the second layer may reduce the 10,000 dimension vector to a one-hundred dimension vector. Although supply chain network  100  is described as having a particular number of dimensions at each layer of auto-encoder system  110 , embodiments contemplate supply chain network  100  represented by a vector having any suitable number of dimensions, and each layer of the encoder reduces the number of dimensions to any suitable number, according to particular needs. 
       FIG.  11    illustrates visualizations  1102 - 1114  of images of ten exemplary supply chains networks as processed by an example seven-layer auto-encoder system, in accordance with an embodiment. As discussed above, auto-encoder model  206  comprises an encoder having one or more layers and a decoder having one or more layers. In one embodiment, auto-encoder system  110  comprises a seven-layer neural network wherein the encoder has three layers, the decoder has three layers, and a layer shared between the encoder and decoder comprises the code representing the supply chain signature. Visualizations of images  1102 - 1114  represent ten supply chain network models converted to images at each of the seven layers of auto-encoder system  110 . Top row visualizations  1102  comprise an input to auto-encoder system  110  and bottom row visualizations  1114  comprise an output of auto-encoder system  110 . First layer of the encoder receives the input of first row visualizations  1102  and generates the image of second row visualizations  1104 . The second layer of the encoder receive the images of the second row visualizations  1104  and generates the images of third row visualizations  1106 . Similarly, these images are received by the third layer and generate the images of the fourth row visualizations  1108 . The images of the fourth row represent supply chain signatures  222  for each of the exemplary ten supply chains. 
     Supply chain signatures  222  represent the output of the encoder and input of the decoder. For the ten exemplary supply chains, the fifth row visualizations  1110  the output generated by the first layer of the decoder in response to processing the supply chain signatures  222 . Sixth row visualizations  1112  comprise images is the output of the second layer of the decoder, and the seventh row visualizations  1114  comprise images is the output of the third layer of the decoder. As discussed above, training module  244  trains auto-encoder system  110  by minimizing the differences between the input (top row of images) and the output (bottom row of images) while creating a representational layer that creates supply chain signatures  222  as an image representing a lower-dimensional vector. 
       FIG.  12    illustrates visualizations  1202  of input images representing a supply chain network and visualizations  1204  of output images reconstructed from signatures  222  by auto-encoder model  206 , in accordance with an embodiment. Supply chain visualizations  1202  of the top row comprise supply chain images created from a binary of a supply chain network model, as disclosed above. Visualizations  1204  comprise images reconstructed by the decoder of the trained auto-encoder model from the signature created by the encoder from the input image. As disclosed above, the input and the output are used to train the encoder and decoder of auto-encoder system  110  until the error between the input and the output is below a predetermined threshold, or as low as possible. 
       FIG.  13    illustrates array  1302  representing the input image of a supply chain network and array  1304  representing the output image reconstructed from the signature by auto-encoder system  110 , in accordance with an embodiment. In this example, im1  1302  is the integer array corresponding to the pixels of the input image, and im2  1304  is the integer array corresponding to the pixels of the output image constructed from supply chain signatures  222 . When comparing each cell of the array (corresponding to a pixel of the image), the input corresponds with the output. For most pixels, the input value is close to the output value, such that when the input value is low, the output value is low; and, when the input value is high, the output value is high. Although only thirty-six pixels are shown for clarity, the input and output images comprises 84,000 pixels corresponding to an array having 84,000 cells. For this exemplary embodiment, the error calculated was less than 0.1% and the Mean-Squared Error (MSE) was equal to 9.41. 
     In other words, the one-hundred dimensional signature represents the 84,000 dimensions of the input image, and closely reproduced this input when decoded. As shown by the example array  1000  of  FIG.  10   , many of the dimensions comprise zero. Only a small number of non-zero numbers are required to represent a supply chain network as a one-hundred dimensional vector that may then be used as an input of various AI-based methods, as described herein. 
     Additional examples comparing arrays corresponding to input and output images of the trained auto-encoder model are provided in  FIGS.  14 - 15   . 
       FIG.  14    illustrates array  1402  representing the input image of a supply chain network and array  1404  representing the output image for a second use case, in accordance with an embodiment. Although example signature is described as comparing a one-hundred dimensional array, embodiments contemplate an array having any number of dimensions, according to particular needs. 
       FIG.  15    illustrates array  1502  representing the input image of a supply chain network and array  1504  representing the output image for a third use case, in accordance with an embodiment. In one embodiment, groupings of differently-sized supply chains (supply chain size is number of nodes like materials, capacities and operations in the supply chain) are fit to particular dimensions. By way of example only and not by way of limitation, an exemplary sizing policy may sort small supply chain into 50-dimensional space, mid-sized supply chains to 100-dimensional space, and large supply chains (e.g. over 5000 nodes) may have 200-dimensional space vectors. Although the supply chains are described as comprising three sized assigned to particular number of dimensional space vectors, embodiments contemplate any number of groupings, or no groupings, having any number of dimensions that give a useful auto-encoder model, according to particular needs. Importantly, the dimensionally may be configured while training the auto-encoder model. Additional exemplary use cases of auto-encoder model  206  are provided below. 
     Warehousing 
     By way of example only and not by way of limitation, auto-encoder system  110  may receive raw binary data from warehouse management system  130   b  of one or more distribution centers, manufacturer, retailers, suppliers, or the like. The received data may comprise item information, item images, shelf location, pricing, shelving sizes, locations, heights, shelf locations, inventory levels, packaging and shipping constraints, packing instructions, and other like data. Embodiments contemplate combining warehouse management system  130   b  with data received from one or more other supply chain planning and executions systems  130   a - 130   n , according to particular needs. As described above in connection with supply chain data  262 , auto-encoder system  110  may train a neural network model to construct one or more layers that reduce the dimensionality of the input data by calculating a difference between an input image and an output image, the images representing warehouse management system  130   b . In addition, warehouse management system  130   b  may generate an item-location combination matrix. According to one embodiment, the item location combination matrix has a first dimension representing locations for storing items in the warehouse, a second dimension representing particular items, and the element at each intersection comprises a 1 if the particular item is present at the corresponding warehouse location, or a 0 if the item is not at the location. When the input comprises an item-location combination matrix, the matrix may be converted directly to an image by the auto-encoder, as described above. 
     Similarly, embodiments contemplate auto-encoder system  110  generating supply chain signatures  222  for the item-location combination of a retail store, wherein each element of an item-location matrix may represent the presence or absence of an item at each stocking location (such as, for example, a store shelf space represented by a planogram). 
     Transportation 
     By way of a second example only and not by limitation, auto-encoder system  110  may receive raw binary data from transportation network  130   a  of supply chain network  100 . The received data may comprise mapping data, routing data (such as, for example, routes, addresses, delivery instructions, and the like), package identification information, item information, and the like, and other like data. Embodiments contemplate combining the planogram planning data with data received from one or more other supply chain planning and executions systems, according to particular needs. As described above, auto-encoder system  110  may train a neural network model to construct one or more layers that reduce the dimensionality of the input data by calculating a difference between an input digital image and an output digital image, the digital images representing transportation data  260 . In addition, as described in connection with warehouse management system  130   b , transportation network  130   a  may be modeled as an item-location combination matrix. In an embodiment representing transportation network  130   a , the first dimension comprises each particular transportation route, the second dimension represents particular items, wherein an element has a value of 1 when the item is present on a transportation route, and a value of 0, when it is not. 
     Planograms 
     By way of a third example only and not by limitation, auto-encoder system  110  may receive raw binary data from a planogram planner of one or more retailers of one or more supply chain entities  150 . The received data may comprise item information, item images, shelf location, pricing, shelving sizes, locations, heights, shelf locations, and the like, and other like data. Embodiments contemplate combining the planogram planning data with data received from one or more other supply chain planning and executions systems  130   a - 130   n , according to particular needs. As described above in connection with supply chain data  262 , auto-encoder system  110  may train a neural network model to construct one or more layers that reduce the dimensionality of the input data by calculating a difference between an input digital image and an output digital image, the digital images representing the planogram planning data. Embodiments additionally contemplate auto-encoder system  110  generates signatures  222  for an item location combination matrix for a retail location, such as, for example a store shelf space represented by a planogram. The item location combination matrix may comprise a location dimension corresponding to each possible item placement on a planogram and the item location corresponds to the particular items that are stocked by the retailer, and each element is a 1, if the item is present at the location, and a 0, if it is not present. 
     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 shown 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.