WO2019108240A1 - An infrastructure for automatic optimization of inventory and merchandise placement - Google Patents

Abstract

The inventory system may collect and compare high dimensional data sets relating to inventory and merchandise placement, identify correlations between them, and predict outcomes to optimize the placement schema. The inventory system may use input from human experience using templates which may be populated with information provided by a knowledgeable user. The inventory system may dynamically create and modify a model of the placement schema and its environment using a combination of user input, sensor measurements, time-dependent locations of inventory, merchandise, shoppers, and other entities, and imported external data. The process may include the step of traversing a graph database that orchestrates the combination of these data. The inventory system then generates the model which may allow the inventory system to identify what questions need to be asked and report their answers.

Description

An infrastructure for automatic optimization of inventory and merchandise placement

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application is a continuation of co-pending International Application No. PCT/US17/63917 filed on November 30, 2017 which is hereby incorporated by reference in its entirety.

BACKGROUND

FIELD OF THE INVENTION

[0002] This invention relates to systems for automatically identifying the optimal site for inventory storage and inventory placement. More specifically, the invention relates to optimizing inventory placement within a retail store, manufacturing facility, kitchen, or any other environment where inventory is stored.

BACKGROUND OF THE INVENTION

[0003] Businesses, homes, and other organizations often store inventory items for later retrieval followed by use or sale as merchandise. The overall efficiency and success of the organization is often significantly impacted by the efficiency of the inventory storage system, including the ease at which the inventory is located and retrieved from storage.

[0004] Equipment which conducts measurements may provide people with information to assist in assessing the status of the current inventory placement schema and in making decisions and adjustments thereto. However, the number of

measurements and other data that are available has become so great that it is difficult for a human to identify cause and effect relationships. It may even be difficult to identify which data sets are relevant to the task at hand and, therefore, worthwhile to analyze. For at least these reasons, associations between variables have become more difficult to identify. Consequently, the large amount of data sets becomes less useful for trouble shooting an inventory placement schema.

[0005] Artificial intelligence (hereinafter,“Al”) and algorithms designed to identify correlations within high dimensional data sets may be used to assist in managing large amounts of data related to inventory storage and retrieval. However, each organization and its inventory storage schema have unique questions to ask and unique problems to solve. Therefore, human input, particularly that of a person who is knowledgeable in the tasks and problems faced by the organization and its inventory storage schema, is still valuable in directing the Al and the algorithms. A system is needed which tracks the locations of inventory items and other related entities as well as their interactions in a time-dependent manner while combining the advantages of human knowledge. Such a system would streamline problem solving in optimizing an inventory storage schema thus creating greater efficiency in the organization as a whole.

BRIEF SUMMARY OF THE INVENTION

[0006] The disclosed inventory and merchandise placement optimization system (hereinafter,“inventory system”) provides a method for identifying patterns in an environment that includes inventory storage. The inventory may include items intended for sale in a retail store, items for use in a manufacturing facility, or food items for use in a kitchen. The system may include a graph database, a compute resource, a sensor measurement data stream manager, an entity tracking system, and a model framework which supports at least one specification-driven algorithm.

[0007] The inventory system may import and store measurements from multiple sensors. These sensors may include, but are not limited to, one or more of a camera, a pressure sensor, a temperature sensor, a proximity sensor, a humidity sensor, a microphone, and a VOC sensor. The cameras may scan computer-readable tags placed on various entities in the inventory and merchandise placement environment. In an example, these tags may include 2D codes such as UPC codes and QR codes. Accordingly, the inventory system may track the time-dependent location of each of the pieces of inventory, pieces of merchandise, and other entities related to the inventory or merchandise. The inventory system may also track a time dependent interaction of the pieces of inventory with each other and with the other entities in the environment.

[0008] The inventory system may use specification-driven algorithms to generate a model representation of the inventory and merchandise placement environment. The inventory system may automatically traverse the graph database to analyze model representation. In doing so, the inventory system may identify correlations between an outcome and the pieces of inventory, other entities, and between outcomes and the interactions between the pieces of inventory and other entities. Outcomes may include rate of sales, distance traveled to retrieve a piece of inventory, time spent retrieving a piece of inventory, rate of loss of inventory, time spent in meal preparation; and the number of separate trips to retrieve multiple pieces of inventory. The system may then generate a report to alert the user of possible improvements to be made in the inventory placement schema or problems therein. [0009] In some embodiments, the inventory system includes templates. Users who are knowledgeable about the inventory placement environment may enter information into the templates. The graph database may use this data to create model

representations of the current inventory placement schema. In this way, the input of human knowledge, experience and intuition is included in the model representation.

[0010] In some embodiments, the inventory system includes a probability distribution which assesses the likelihood of a cause and effect relationship between an outcome and one or more of the following list: entities the user entered in the template, a sensor measurement, a time-dependent location of pieces of inventory and other entities, and a time dependent interaction of the pieces of inventory with the other entities.

[0011] In some embodiments, the inventory system includes an actuarial risk quantification system which estimates the cost of certain relationships that occur between entities. The actuarial risk quantification system may construct an actuarial table which may be provided as a report to the user. The report may be provided through a user interface the inventory system creates.

[0012] The inventory system may include a user interface generator which dynamically generates a user interface in response to the template and other interactions with the inventory system. The user interface generator may create the user interface for the actuarial table as well as for a warning and notification system which informs the user of problems or developments within the inventory storage environment.

BRIEF DESCRIPTION OF THE DRAWINGS [0013] Non-limiting and non-exhaustive implementations of the disclosure are described with reference to the following figures, wherein like reference numerals refer to like parts throughout the various views unless otherwise specified. Advantages of the disclosure will become better understood regarding the following description and accompanying drawings where:

[0014] Figure 1 A illustrates an initial method of storing inventory items using the disclosed inventory system.

[0015] Figure 1 B illustrates a human employee retrieving some of the inventory items of Figure 1A while the system collects data.

[0016] Figure 1 C Illustrates an optimized method of storing the inventory items of Figure 1 A the inventory system designed using data gathered as shown in Figure 1 B.

[0017] Figure 2A illustrates an initial method of storing glass and plastic inventory items using the disclosed inventory system.

[0018] Figure 2B illustrates a human employee retrieving some of the inventory items of Figure 2A while the system collects data resulting in damage to a glass item.

[0019] Figure 2C Illustrates an optimized method of storing the inventory items of Figure 2A the inventory system designed using data gathered as shown in Figure 2B.

[0020] Figure 3 illustrates a human shopper pushing a shopping cart past a food item while the disclosed inventory system collects data.

[0021] Figure 4 illustrates a screen on the shopping cart of Figure 3 which displays information that is uniquely relevant to the shopper. [0022] Figure 5 illustrates the shopper of Figure 3 moving through a retail store as the disclosed inventory system collects and processes data, including the path the shopper takes through the store.

DETAILED DESCRIPTION OF THE INVENTION

[0023] Definitions

[0024] “Template,” as used herein, means a specification document into which one or more of the following may be entered: 1 ) a description of a class of users or objects, wherein the objects may include merchandise or inventory; 2) the properties, attributes and abilities that may be expressed by that class of users or objects; 3) relationships that may exist between classes of users or objects; and 4) properties, attributes or abilities that may be expressed because of the relationships.

[0025] “Templated,” as used herein, means that the system generates all the code needed to implement the processes disclosed herein.

[0026] “Unified data model representation,” as used herein, means a simulation of a real-world system, the real-world system comprising interactions between multiple real- world entities. The unified data model representation may predict how a change in a real- world entity, for example, a change in positioning of stored inventory, may impact the behavior of the real-world system based on relationships of collected data sets comprising the real-world entities. In an example, the behavior of the real-world system includes the workings of a retail store, a manufacturing facility, or a kitchen. [0027] “Entity tracking system,” as used herein, means 1 ) a collection of templates, measurements collected by sensors, and external data capable of identifying users and objects and their location as a function of time, and 2) an electronic system comprising computer-readable medium which continuously or periodically scans, collects, and analyzes identification and time-dependent location data from external databases, sensor measurements, and templates into which a user has entered data to discover interactions (in time and space) between users and objects.

[0028] “Specification-driven algorithm,” as used herein, means a sequence of transformations and feature extractions or combinations thereof which act on existing data to produce new data and in which the transformations and feature extractions are specified in a specification document. In an example, the specification document may be constructed via text or a drag-and-drop user interface.

[0029] “Inventory,” as used herein, means any item that may be stored.

[0030] “Merchandise,” as used herein, means a piece of inventory that is offered for sale.

[0031] “Shopping cart,” as used herein, means a cart, basket, dolly, or other apparatus designed to hold or transport merchandise throughout a store during merchandise selection.

[0032] “Computer-readable tag,” as used herein, means a scannable image which corresponds to information and which may be interpreted by a computer using an algorithm. In an example, a computer readable tag may be a quick response code, a Universal Product Code, or frequency identification tag. [0033] While this invention is susceptible of embodiment in many different forms, there are shown in the drawings, which will herein be described in detail, several specific embodiments with the understanding that the present disclosure is to be considered as an exemplification of the principals of the invention and is not intended to limit the invention to the illustrated embodiments.

[0034] In response to the problems described above, we disclose an inventory and merchandise placement optimization system (hereinafter, “inventory system”) which combines artificial intelligence and data analysis algorithms with human expertise and knowledge of abstract classes of entities, as well as time-dependent location data for tracked entities. The inventory system may be used to optimize inventory storage so that people placing the inventory in storage and then retrieving it may do so with the least effort and travel distance. The inventory system may also optimize storage of perishable inventory to minimize losses from decayed or outdated inventory. The inventory system may be used to place merchandise in a retail store in a pattern that optimizes sales. A kitchen or manufacturing facility may utilize the inventory system to optimize storage and retrieval of food ingredients or manufacturing supplies respectively. The inventory system may also identify the cause of a loss or other undesirable outcome by identifying associations between inventory or merchandise placement and other entities, for example, inventory placed in storage by a specific employee.

[0035] The inventory system employs the system core disclosed in co-pending International Patent Application No. PCT/US17/63917 filed on November 30, 2017 which is hereby incorporated by reference in its entirety. Initially, the method includes providing the system core and entering information that is relevant to the kitchen, manufacturing facility, retail sales business, or other organization which uses the disclosed system to place inventory in storage or merchandise on a retail floor. In some embodiments, a user that is knowledgeable about the goals, problems, and tasks that are relevant to the organization may enter this information into a template where it may be used to direct data analyses. The inventory system may also include continuous monitoring of many data sets to identify relationships which may be relevant to the problems the user has indicated are important to the organization. Over time, the algorithms may analyze increasingly more data sets and identify additional associations that are relevant to the organization, sometimes with the aid of input from the human user. This may improve the inventory system’s ability to identify relevant relationships between variables and to select data sets for analysis.

[0036] The disclosed innovation involves the creation of a central “brain” that is patterned after the entities within the inventory storage and/or merchandise placement environment and their interactions with each other. This brain maintains a strict one-to- one correspondence with entities directly entered into the inventory system by a user or a sensor and their counterparts which comprise external data derived from outside sources so that there is no ambiguity or confusion. In other words, an entity that may be recorded in different data sources under a different name is reconciled to be identified as a single entity for analysis. In an example, a data set from a U.S. source may refer to a shopping cart whereas another data set from a British source may call the same entity, a trolley. The inventory system would recognize that a shopping cart and a trolley are the same and consolidate the two data sets accordingly. [0037] The disclosed innovation may also involve populating templates with data from both human personal knowledge and experience as well as external data derived from outside sources. By using the templated architecture included in the disclosed inventory system, human intuition becomes automated and is included in the building and deployment of models. Then, the inventory system proposes actionable items to respond to flaws, inefficiencies, and changes in the user’s current inventory and merchandise placement schema.

[0038] The disclosed inventory system may include the mass deployment of sensors which may include, for example, cameras sensitive to varying wavelengths of light as well as 3D cameras. The sensors may scan computer-readable tags which may include, but are not limited to, quick response (hereinafter,“QR”) codes, Universal Product Codes (hereinafter“UPC”), and frequency identification (hereinafter,“RFID”) tags. The sensor measurements may be processed according to algorithms within the inventory system to analyze and interpret the tags from, for example, still photos or live video streams. Further, the disclosed inventory system may specify that all inventory and merchandise items to be tracked by the inventory system will have a unique tag associated with them.

[0039] In addition to entities that consist of inventory or merchandise, the disclosed inventory system may track the location and movement of other types of entities. These other types of entities may include, but are not limited to, employees, customers, shopping carts, rate and temporal patterns of sales, rate and temporal patterns of manufacturing processes, manufacturing process errors, inventory spoilage or damage, lighting, environmental temperature, source of inventory items, product expiration dates, and nutritional information of inventory comprising food products. The inventory system may track interactions between these other types of entities, between the other types of entities and inventory or merchandise, and interactions between inventory and/or merchandise.

[0040] In an example, well-placed cameras may be disposed in many locations throughout a retail store and its inventory storage area. Alternatively, a smaller number of cameras may be place in a home kitchen, for example, in the pantry, refrigerator, and stove, and food preparation area. In addition to inventory, computer-readable tags may also be placed on shopping carts to track their movement as shoppers move throughout the store. The shopping carts or baskets may be issued or“checked out” to each shopper in association with that unique shopper. The shopper’s movement may be tracked, at least in part, by tracking the movement of the shopping cart associated with that shopper. In another example, customers and employees may have a unique computer-readable tag on their persons which enables the inventory system to track customer and employee movement. Infrared cameras may also track the movement shoppers, employees, or kitchen workers using thermal imaging technology known in the art. Other methods, for example, facial recognition technology, may be combined with the cameras to identify and track the movement of specific employees, shoppers, or kitchen workers.

[0041] The cameras may read and record the information associated with the computer-readable tags and their locations to identify and track the movement of their associated entity. Associated algorithms within the inventory system may analyze and interpret the code associated with the computer-readable tags. Other sensors, for example, scales on inventory shelves which measure the mass of the item on the shelf, may be employed. The temperature in the environment where inventory is stored may also be measured and recorded. The tag tracking data and other sensor measurements may be imported to a computer controller where algorithms may track the interactions between the inventory, the humans, and any other entities on the premises that the inventory system tracks. Because this data collection is backed by the graph database within the disclosed inventory system, it becomes possible for graph traversals to be dynamically generated to detect the associations between interactions and the outcomes that they generate.

[0042] Once the representation of the user’s current inventory and merchandise placement method has been templated, a central graph within the inventory system may dynamically orchestrate the combination of sensor data with external data sources to provide a unified data model representation. By creating a unified data model representation, the algorithms within the inventory system may identify the questions that need to be asked as well as determine the likelihood that previously collected data may answer those questions.

[0043] The inventory system also may include the actuarial risk quantification system and as described in more detail in International Patent Application No. PCT/US17/63917. The actuarial risk quantification system may prepare actuarial tables that report the cost of certain relationships that occur between entities. The user may then reduce the cost by modifying these relationships.

[0044] The actuarial risk quantification system may allow risk to be quantified in monetary amounts for many or all interventions that a home or business may deploy on recommendation of the inventory system and its machine learning capabilities. In an example, the actuarial table may provide an indication of the projected loss due to spoiled food items over time. Because these assessments are generated dynamically by graph traversals, the risk assessments become dynamic“what-if” calculators. A user may adjust aspects of the current inventory or merchandise placement method and receive quantified and immediate feedback on the likely cost and outcome of the change in this method. The inventory system may include a Ul generator for these assessments which may provide Ul elements that allow user interaction with intervention parameters to adjust outcomes.

[0045] In an example, the disclosed inventory system may be used to track where a certain inventory item is stored, how far a store employee travels to retrieve the item to place in view of shoppers, how often the item is retrieved, and what other inventory items the employee may retrieve at the same time. The inventory system may find that the inventory item is retrieved more often than other inventory items which may justify moving its storage placement to a more readily accessible site. It may also save time and effort to place it near other inventory items which are typically retrieved at the same time.

[0046] The inventory system may store information about each shopper including purchase history and items the shopper has divulged are of interest to that shopper. The shopping cart may include a screen which provides a notice when the shopper approaches merchandise that is likely to be of interest to the shopper. The inventory system may have collected this information during previous shopping trips during which the cameras scanned the computer-readable tags associated with items as they were placed in the shopper’s cart or during checkout.

[0047] In another example, the inventory system may have recorded that the shopper previously purchased a perishable food item and that, based on the current date, the food item must have been consumed or become outdated. The screen on the shopping cart may suggest that the shopper purchase a replacement product.

[0048] In yet another example, the shopper may have provided information about dietary needs of individuals in the shopper’s household. These may include dietary needs related to medical history or simply a desire to purchase more healthy food items. The inventory system may provide notices that certain items are in compliance with those dietary needs or that the shopper has selected an item that is not within the dietary needs.

[0049] In another example, the inventory system may organize the placement of food items in food storage areas in a home kitchen according to templated information. The templated information may include a user’s desire to eat more healthy food items leading the inventory system to place such items in a more accessible position. The inventory system may organize perishable food items in the pantry such that those with expiration dates that are the fewest days from the current date are most accessible and are used first, thus discouraging food spoilage. A volatile organic compound sensor may also inform the user that a perishable food item has spoiled and should be disposed of. If the item continually spoils when stored in that site, the inventory system may propose a different storage site for that item, perhaps based on the local temperature of the site which may be suboptimal for that item.

[0050] A manufacturing facility may store items used in the manufacture of its product in its inventory storage area. The inventory system may optimize storage of these items in a manner similar to that used in the inventory storage of a retail store or kitchen.

[0051] The manufacturing facility, the retail store, and the kitchen may use the inventory system to identify the cause of accidents or errors. Certain interactions between entities may be identified and reported, for example, an employee in need of additional training.

[0052] Referring now to the drawings, Figures 1A-1 C illustrate shelves 110 and 120 which are used to store inventory items. Shelf 110 includes scales 130a-h on which inventory items may be placed. In Figure 1A, item 140a is placed on scale 130a, item 140b is placed on scale 130b, and item 140c is placed on scale 140c. Items 140a-c each have a barcode on the outer packaging, labeled barcode 135a-c respectively.

Shelf 120 includes scales 150a-f on which inventory items may be placed. Items 160a is placed on scale 150a, item 160b is placed on scale 150b, and item 160c is placed on scale 150c. Items 160a-c have barcodes 165a-c respectively on their outer packaging. Cameras 170 and 180 scan barcodes 135a-c and 165a-c which informs the system which inventory items are placed in which position. Scales 130a-h and 150a-f measure the mass of each item which is stored in the inventory system.

[0053] Figure 1 B illustrates a human employee 190 retrieving inventory items 140a, 140c, and 160a from shelves 110 and 120. Cameras 170 and 180 record the path of movement human employee 190 takes to move about the inventory storage area

(shown by arrows). Cameras 170 and 180 also scan barcodes 135a, 135c, and 165a as human employee 190 moves items 140a, 140c, and 160a from shelves 110 and 120. Scales 130a, 130c, and 150a measure the changes in mass when items 140a, 140c, 160a are removed. Algorithms use the location and mass measurements to make decisions about the optimal placement of inventory on shelves 110 and 120.

[0054] Figure 1 C illustrates an optimized inventory placement schema for the items presented in Figures 1A and 1 B. All the inventory items are moved to positions on shelf 110. Factors which may have entered into the design of the inventory system may include the need for human employee 190 to visit two separate shelves to retrieve items that are to be used at the same time. The placement schema in Figure 1 C requires a visit only to shelf 110. The volume or mass of the inventory items shown in Figures 1 A- 1 C may have impacted the decision to place items 160a-c in front of items 140a-c on shelf 110.

[0055] Figure 2A illustrates shelves 210 and 220 which are used to store inventory items. Glass items 230a and 230b are placed on shelf 210. Plastic items 250a and 250b are placed on shelf 220. Camera 240 reads barcodes 235a and 235b on glass items 230a and 230b respectively as well as barcodes 255a and 255b on plastic items 250a and 250b respectively. Information about glass items 230a and 230b and plastic items 250a and 250b is entered into the inventory system and stored.

[0056] In Figure 2B, human employee 250 has climbed a ladder to reach shelf 210 and retrieve glass item 230a. Fluman employee 250 has dropped glass item 230a to the floor while attempting to climb down the ladder carrying glass item 230a. Glass item 230a is shattered due to the fall resulting in loss of inventory. Camera 240 records this event which is stored as data in the disclosed inventory system. Algorithms within the inventory system use this and other data to design an alternative inventory placement system as shown in Figure 2C.

[0057] Figure 2C illustrates the alternative inventory system which places glass items 230a and 230b on shelf 220 which is within reach of human employee 250 without the aid of a ladder. Plastic items 250a and 250b are placed on shelf 210. If human employee 250 drops plastic items 250a or 250b while standing on a ladder, the plastic will not shatter, and inventory loss will be avoided.

[0058] Figure 3 illustrates shopper 310 navigating displays on the floor of a retail store. Shopper 310 pushes shopping cart 320 which includes screen 330. Shopping cart 320 displays barcode 340 which is read by camera 360 as shopping cart 320 passes by. Other cameras also read barcode 340 when it is within their range, thus tracking the movement of shopping cart 320 as shopper 310 navigates through the retail store.

[0059] Shopper 310 is shown approaching merchandise display 370 which offers oranges for sale. Barcode 375 is placed on merchandise display 370. Camera 360 reads barcode 375 along with other barcodes on other merchandise displays in the area, thus creating a map of the merchandise placement within the store. In some embodiments, shopping cart 320 is assigned to shopper 310 so that the data associated with shopping cart 320 is assigned to shopper 310. As shopper 310 placed

merchandise into cart 320, the cameras within the inventory system may record the selections by scanning a barcode on each item. These data may be used to improve the merchandize placement in the retails store to maximize the purchases shopper 310 makes.

[0060] Figure 4 is a close-up view of screen 330 which is placed on shopping cart 320. The display is an embodiment of that which may be shown as shopper 310 approaches merchandise display 370. Screen 330 informs shopper 310 that oranges (a perishable food item) were last purchased two weeks earlier suggesting that the oranges purchased at that time have either been consumed or have decayed. Screen 330 informs shopper 310 that oranges are currently offered at a reduced price.

Nutritional information associated with the oranges (which may have been derived from an external database) is displayed. In some embodiments, the inventory system may offer discounts on items, for example, oranges to a certain shopper who regularly purchases these items, who the inventory system indicates may be out of the item, or who has provided user preferences to the inventory system. The inventory preferences may suggest that the item is of interest to the shopper based on that shoppers purchasing goals and, thus, the shopper may be persuaded to buy the item.

[0061] Figure 5 provides an illustration of shopper 310 first shown in Figure 3 moving through the retail store. Arrow 530 illustrates shopper 310 moving toward merchandise display 370 which offers oranges for sale. Arrow 540 illustrates the path that shopper 310 took toward merchandise display 510 which displays barcode 515. Merchandise display 510 offers canned goods for sale. Arrow 550 illustrates the path that shopper 310 followed toward merchandise display 520 which offers deli items. Cameras throughout the store have scanned barcodes 375, 515, and 525 which provide information including the locations of their associated merchandise displays and the specific food items each presents. Note that shopper 310 could have traveled directly from merchandise display 370 (oranges) to merchandise display 520 (deli items). The path that shopper 310 actually chose is relevant to decisions the algorithms within the inventory system will make to achieve the goals of optimizing merchandise placement. By optimizing merchandise placement, the system may improve shopper convenience and, consequently, maximum sales. [0062] While specific embodiments have been described above, it is to be understood that the disclosure provided is not limited to the precise configuration, steps, and components disclosed. Various modifications, changes, and variations apparent to those of skill in the art may be made in the arrangement, operation, and details of the methods and systems disclosed, with the aid of the present disclosure.

[0063] Without further elaboration, it is believed that one skilled in the art can use the preceding description to utilize the present disclosure to its fullest extent. The examples and embodiments disclosed herein are to be construed as merely illustrative and exemplary and not a limitation of the scope of the present disclosure in any way. It will be apparent to those having skill in the art that many changes may be made to the details of the above-described embodiments without departing from the underlying principles of the disclosure. The scope of the present disclosure should, therefore, be determined only by the following claims.

Claims

CLAIMS We claim:

1. A method for managing inventories and optimizing merchandise placement comprising the steps of: providing a system core, the system core comprising:

a graph database;

a compute resource;

a sensor measurement data stream manager;

an entity tracking system; and

a model framework, wherein the model framework supports at least one specification-driven algorithm;

importing and storing a plurality of sensor measurements;

tracking a time-dependent location of each of a plurality of pieces inventory and at least one other entity,

tracking a time dependent interaction of the plurality of pieces of inventory with each other and with the at least one other entity;

generating a unified data model representation using the at least one specification-driven algorithm;

traversing the graph database to analyze the unified data model representation to identify one or more correlations between an outcome and the time-dependent locations and interactions of the plurality of pieces of inventory and at least one other entity; and

generating a report comprising the correlations.

2. The method of claim 1 , further comprising the following steps: providing a template, wherein the template comprises at least one entity entered by a user;

transferring the at least one entity entered by the user from the template to the graph database; and

generating a schema of the graph database using the template.

3. The method of claim 2, wherein the outcome comprises one or more of the following: rate of sales;

distance traveled to retrieve at least one of the plurality of pieces of inventory; time spent retrieving at least one of the plurality of pieces of inventory;

rate of loss within the plurality of pieces of inventory;

time spent in meal preparation; and

number of separate trips to retrieve at least one of the plurality of pieces of inventory.

4. The method of claim 3, wherein the rate of loss comprises a rate of damage of the plurality of pieces of inventory, a rate of spoilage of the plurality of pieces of inventory, or both the rate of damage and the rate of spoilage of the plurality of pieces of inventory.

5. The method of claim 2, wherein the at least one specification-driven algorithm generates a probability distribution, and wherein the probability distribution assesses the likelihood of a cause and effect relationship between an outcome and one or more of the following: the at least one entity entered by the user;

the at least one sensor measurement;

the time-dependent location of each of the plurality of pieces of inventory and the at least one other entity; and

the time dependent interaction of at least one of the plurality of pieces of inventory with at least one of the plurality of other entities.

6. The method of claim 5, wherein the at least one of the plurality of other entities comprises one or more of the following: employees, customers, shopping carts or baskets, rate and temporal patterns of sales, rate and temporal patterns of manufacturing processes, manufacturing process errors, inventory spoilage, inventory damage, lighting, environmental temperature, source of inventory items, product expiration dates, nutritional information of inventory comprising of food products, height at which each of the plurality of pieces of inventory is placed relative to a floor, and a footprint of each of the plurality of pieces of inventory.

7. The method of claim 2, wherein the following are automatically generated upon populating the template: the graph database schema;

the unified data model representation;

the scalable storage and access control layer; a scalable message queue and notification service

a sensor stream manager;

an external data adaptor and integration system;

the entity tracking system; and

the model framework.

8. The method of claim 2, further comprising a user interface generator, wherein the user interface generator dynamically generates a user interface in response to the template.

9. The method of claim 8, further comprising a warning and notification system, wherein the warning and notification system identifies at least one trigger based on a statistical analysis of the unified data model representation.

10. The method of claim 9, wherein the warning and notification system transmits a warning or notification to a device through the user interface.

11. The method of claim 2, wherein the step of importing and storing at least one sensor measurement comprises importing and storing measurements from one or more of the following list: a camera, a pressure sensor, a temperature sensor, a proximity sensor, a humidity sensor, a microphone, and a VOC sensor.

12. The method of claim 7, wherein the camera comprises one or more of the following: a 3D camera; a video camera; a visible light camera; an ultraviolet light camera; and an infrared camera.

13. The method of claim 2, wherein the unified data model representation is a function of one or more results obtained by the step of traversing the graph database.

14. The method of claim 2, further comprising an actuarial risk quantification system, wherein the actuarial risk quantification system analyzes one or more of the following: a cost of implementing a defined inventory placement schema for a defined period of time;

at least one correlation between at least one of the plurality of pieces of inventory and at least one of the plurality of other entities;

at least one correlation between at least two of the plurality of pieces of inventory;

a property associated with each of the plurality of pieces of inventory; a property associated with each of the plurality of other entities; and a fraction of the cost of implementing a defined inventory placement schema for a defined period of time that each of the plurality of entities is predicted to consume; and

wherein the actuarial risk quantification system constructs an actuarial table using data from the unified data model representation.

15. The method of claim 2, wherein each of the plurality of pieces of inventory is labeled with a computer-readable tag and further comprising the step of scanning the computer-readable tag when each of the plurality of pieces of inventory is placed on an inventory shelf and when each of the plurality of pieces of inventory is removed from the inventory shelf.

16. The method of claim 14, wherein the inventory shelf comprises a plurality of pressure sensors.

17. The method of claim 2, wherein shopping carts within the store comprise a computer-readable tag.

18. The method of claim 2, wherein each of the plurality of pieces inventory comprises a piece of merchandise, and further comprising the following steps: tracking an order in which a customer removes one or more of the plurality of pieces of merchandise from at least one display shelf;

tracking an amount of time that expires between a first time at which the customer removes a first piece of merchandise from the at least one display shelf and a second time at which the customer removes a second piece of merchandise from the at least one display shelf;

tracking a distance the customer traveled between the first time and the second time;

traversing the graph database to generate a unified data representation model;

simulating a reorganization of a placement schema of the plurality of pieces of merchandise; and

comparing outcomes that result from the reorganization.

19. The method of claim 17, wherein the step of simulating the reorganization comprises the step of creating a model in which a height from a store floor at which at least one of the plurality of pieces of merchandise is placed is modified.

20. The method of claim 17, wherein the plurality of other entities comprises a footprint and a volume of each of the plurality of pieces of merchandise.