Description:

FIELD OF THE DISCLOSURE
[0001] The embodiments discussed in the present disclosure are generally related to management of commodity data in a commodity production and supply chain environment. In particular, the embodiments discussed are related to the implementation of rules, ML, AI, cognition, self-learning, and trainable systems and methods for processing of commodity data and for generation of traceability information in a commodity production and supply chain environment.

BACKGROUND OF THE DISCLOSURE
[0002] In a supply chain associated with a product or a commodity, data is collected or generated at several nodes of the supply chain. This data, may be referred to as commodity data, provides valuable information about the commodity or the supply chain network. Management of commodity data may help centralize and synchronize data across supply chain networks and distributors. Additionally, efficient management of commodity data may help organizations to fully digitalize and build an autonomous supply chain. It may also enable distributors to anticipate disruptions and aid in real-time decision making.
[0003] Further, there is a need to track and trace products in a supply chain from production or manufacturing to the end customer. This is referred to as product traceability and it requires generation and organization of commodity and/or supply chain data. Product traceability ensures transparency of data and results in efficient and less costly product recalls. Further, for today’s conscious and aware consumer, the ability to trace the commodity they are consuming back to its source, is vital.
[0004] Accordingly, there is a need for technical solutions that address the needs described above, as well as other inefficiencies of the state of the art. Specifically, there lies a need to manage commodity data in a production and supply chain. Further, there lies a need to provide traceability information associated with a commodity to a user.

SUMMARY OF THE DISCLOSURE
[0005] The following represents a summary of some embodiments of the present disclosure to provide a basic understanding of various aspects of the disclosed herein. This summary is not an extensive overview of the present disclosure. It is not intended to identify key or critical elements of the present disclosure or to delineate the scope of the present disclosure. Its sole purpose is to present some embodiments of the present disclosure in a simplified form as a prelude to the more detailed description that is presented below.
[0006] In an embodiment, the subject matter of the present disclosure may include a method for managing commodity data in a computing environment. The method may include receiving commodity data from one or more sources in a commodity production and supply chain, determining an event by evaluating the commodity data using one or more rules associated with the commodity data, and generating an actionable insight based on the determined event, the actionable insight providing a recommendation related to the event enabling an action.
[0007] In an embodiment of the present disclosure, the method may further include receiving, by a commodity data processing module, the commodity data associated with at least one of: an item, a product, an article, a consumable product, an ingredient of a consumable product, and a part of a product.
[0008] In an embodiment of the present disclosure, the method may further include receiving, by a commodity data processing module, the commodity data from one or more sources including at least one of: an Internet of things (IoT) based data collection module, a Manufacturing and Processing data collection module, and a Sales and Distribution data collection module.
[0009] In an embodiment of the present disclosure, the method may further include determining, by a rules engine module, the event by applying a set of conditions associated with the one or more rules, each condition of the set of conditions determining an associated event; and generating, by a first insight module, the actionable insight using the determined event.
[0010] In an embodiment of the present disclosure, the method may further include detecting, by an Artificial Intelligence (AI) module, an anomaly associated with the commodity data by comparing the commodity data to a corresponding repository data, and generating, by a second insight module, the actionable insight based on the detected anomaly.
[0011] In an embodiment of the present disclosure, the method may further include detecting, by the AI module, an anomaly associated with a previously evaluated commodity data by comparing a current commodity data to the corresponding repository data, and generating, by the second insight module, the actionable insight based on the detected anomaly.
[0012] In an embodiment of the present disclosure, the method further includes aggregating, by a data aggregator and processing module, the commodity data received from the one or more sources, and generating, by a traceability presentation module, traceability information associated with the commodity based on the commodity data.
[0013] In an embodiment of the present disclosure, the method further includes generating, by the traceability presentation module, the traceability information associated with the commodity, the traceability information is at least one of: a source of the commodity, a quality of the commodity, dispatch data, processing parameters, livestock health status, and sales data.
[0014] In an embodiment of the present disclosure, the method further includes generating the actionable insight based on the determined event, the actionable insight providing one or more of: an instruction, an information, a suggestion, and an alert related to the event enabling an entity associated with the commodity to take an action based on the actionable insight.
[0015] In an embodiment, the subject matter of the present disclosure discloses a system for managing commodity data in a computing environment. The system includes a data aggregator and processing module configured to receive commodity data from one or more sources in a production and supply chain for a commodity. The system further includes a rules engine module configured to determine an event by evaluating the commodity data using one or more rules associated with the commodity data and generate an actionable insight based on the determined event, the actionable insight providing a recommendation related to the event enabling an action.
[0016] In an embodiment of the present disclosure, the system further includes a first insight module configured to generate an actionable insight using an associated event, the associated event determined by the rules engine module by applying a set of conditions associated with the one or more rules, each condition of the set of conditions determining an associated event.
[0017] In an embodiment of the present disclosure, the system further includes an Artificial Intelligence (AI) module configured to detect an anomaly associated with the commodity data by comparing the commodity data to a corresponding repository data. In an embodiment, the AI module includes a second insight module configured to generate the actionable insight based on the detected anomaly. Further, in an embodiment, the AI module is configured to detect an anomaly associated with a previously evaluated commodity data by comparing a current commodity data to the corresponding repository data, and generate the actionable insight based on the detected anomaly.
[0018] In an embodiment of the present disclosure, the system further includes a traceability presentation module configured to generate traceability information associated with the commodity based on the commodity data.
[0019] In an embodiment of the present disclosure, the system further includes a data aggregator and processing module configured to receive data from at least one of: an Internet of things (IoT) based data collection module, a Manufacturing and Processing data collection module, and a Sales and Distribution data collection module.
[0020] In an embodiment, the subject matter of the present disclosure may relate to non-transitory computer-readable storage medium, having stored thereon a computer-executable program which, when executed by at least one processor, causes the at least one processor to receive commodity data from one or more sources in a commodity production and supply chain, determine an event by evaluating the commodity data using one or more rules associated with the commodity data, and generate an actionable insight based on the determined event, the actionable insight providing a recommendation related to the event enabling an action.
[0021] In an embodiment of the present disclosure, the computer-executable program further causes the at least one processor to determine the event by applying a set of conditions associated with the one or more rules, each condition of the set of conditions determining an associated event; and generate the actionable insight using the determined event.
[0022] In an embodiment of the present disclosure, the computer-executable program further causes the at least one processor to detect an anomaly associated with the commodity data by comparing the commodity data to a corresponding repository data; and generate the actionable insight based on the detected anomaly.
[0023] In an embodiment of the present disclosure, the computer-executable program further causes at least one processor to generate traceability information associated with the commodity based on the commodity data.
[0024] The above summary is provided merely for the purpose of summarizing some example embodiments to provide a basic understanding of some aspects of the disclosure. Accordingly, it will be appreciated that the above-described embodiments are merely examples and should not be construed to narrow the scope or spirit of the disclosure in any way. It will be appreciated that the scope of the disclosure encompasses many potential embodiments in addition to those here summarized, some of which will be further described below.

BRIEF DESCRIPTION OF DRAWINGS
[0025] Further advantages of the disclosure will become apparent by reference to the detailed description of preferred embodiments when considered in conjunction with the drawings. In the drawings, identical numbers refer to the same or a similar element.
[0026] FIG. 1 illustrates an example computing environment for commodity data management, in accordance with the embodiments presented herein.
[0027] FIG. 2 is a schematic diagram illustrating inflow of commodity data from multiple sources, in accordance with the embodiments presented herein.
[0028] FIG. 3 is a schematic diagram illustrating an example Internet of things (IoT) based data collection module, in accordance with an embodiment of the present disclosure.
[0029] FIG. 4 is a schematic diagram illustrating an example Manufacturing and Processing data collection module, in accordance with the embodiments presented herein.
[0030] FIG. 5 is a schematic diagram illustrating an example Sales and Distribution data collection module, in accordance with the embodiments presented herein.
[0031] FIG. 6 is a schematic diagram illustrating an example commodity data processing module, in accordance with the embodiments presented herein.
[0032] FIG. 7 is a schematic diagram illustrating generation of actionable insights, in accordance with one or more embodiments presented herein.
[0033] FIG. 8 is a schematic diagram illustrating generation of actionable insights, in accordance with one or more other embodiments presented herein.
[0034] FIG. 9 is a schematic diagram illustrating an example commodity production and supply chain, in accordance with the embodiments presented herein.
[0035] FIG. 10 illustrates a sequential flow diagram for generating actionable insights, in accordance with the embodiments presented herein.
[0036] FIG. 11 illustrates a sequential flow diagram for generating traceability information, in accordance with the embodiments presented herein.

DETAILED DESCRIPTION
[0037] The following detailed description is presented to enable a person skilled in the art to make and use the disclosure. For purposes of explanation, specific details are set forth to provide a thorough understanding of the present disclosure. However, it will be apparent to one skilled in the art that these specific details are not required to practice the disclosure. Descriptions of specific applications are provided only as representative examples. Various modifications to the preferred embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the scope of the disclosure. The present disclosure is not intended to be limited to the embodiments shown but is to be accorded the widest possible scope consistent with the principles and features disclosed herein.
[0038] Certain terms and phrases have been used throughout the disclosure and will have the following meanings in the context of the ongoing disclosure.
[0039] “Commodity” may refer to a product or a material that can be bought and/or sold as is or is transformed into a product or goods for sale and/or distribution to consumers. In accordance with the embodiments of the present disclosure, the commodity may include, but is not limited to, an item, a product, an article, a consumable product, an ingredient of a consumable product, a part of a product, and the like.
[0040] “Commodity data” may refer to data associated with a commodity in a commodity production and supply chain. The commodity data may be structured or unstructured data. In accordance with the embodiments of the present disclosure, the commodity data may include real-time data, near-real-time data, predefined data, or collated data received from one or more sources.
[0041] “Commodity production and supply chain” may refer to a supply chain for a commodity that facilitates the transfer and transformation of the commodity into finished products. In accordance with the embodiments of the present disclosure, the commodity production and supply chain includes production and/or gathering of the commodity or the raw materials for the commodity, manufacturing and/or processing of the commodity, transportation, distribution, and sales of the commodity or a resulting product to a retailer or directly to a consumer.
[0042] A “network” may refer to a series of nodes or network elements that are interconnected via communication paths. In an example, the network may include any number of software and/or hardware elements coupled to each other to establish the communication paths and route data/traffic via the established communication paths. In accordance with the embodiments of the present disclosure, the network may include, but is not limited to, the Internet, a local area network (LAN), a wide area network (WAN), an Internet of things (IoT) network, and/or a wireless network. Further, in accordance with the embodiments of the present disclosure, the network may comprise, but is not limited to, copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
[0043] A “device” may refer to an apparatus using electrical, mechanical, thermal, etc., power and having several parts, each with a definite function and together performing a particular task. In accordance with the embodiments of the present disclosure, a device may include, but is not limited to, one or more IOT devices. Further, one or more IOT devices may be related, but are not limited to, connected appliances, smart home security systems, autonomous farming equipment, wearable health monitors, smart factory equipment, wireless inventory trackers, ultra-high speed wireless internet, biometric cybersecurity scanners, and shipping container and logistics tracking.
[0044] A “processor” may include a module that performs the methods described in accordance with the embodiments of the present disclosure. The module of the processor may be programmed into the integrated circuits of the processor, or loaded in memory, storage device, or network, or combinations thereof.
[0045] “Machine learning” may refer to as a study of computer based methodologies/techniques that may improve automatically through experience and by the use of data. Machine learning techniques build a model based at least on sample data, known as “training data,” in order to make predictions or decisions without being explicitly programmed to do so. Machine learning techniques are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to use conventional techniques to perform the needed tasks.
[0046] In machine learning, a common task is the study and construction of methodologies that can learn from and make predictions on data. Such methodologies function by making data-driven predictions or decisions, through building a mathematical model from input data. These input data used to build the model are usually divided in multiple data sets. In particular, three data sets are commonly used in various stages of the creation of the model: training, validation, and test sets. The model is initially fit on a “training data set,” which is a set of examples used to fit the parameters of the model. The model is trained on the training data set using a supervised learning method. The model is run with the training data set and produces a result, which is then compared with a target, for each input vector in the training data set. Based at least on the result of the comparison and the specific learning technique being used, the parameters of the model are adjusted. The model fitting can include both variable selection and parameter estimation.
[0047] Successively, the fitted model is used to predict the responses for the observations in a second data set called the “validation data set.” The validation data set provides an unbiased evaluation of a model fit on the training data set while tuning the model’s hyperparameters. Finally, the “test data set” is a data set used to provide an unbiased evaluation of a final model fit on the training data set.
[0048] “Deep learning” may refer to a family of machine learning models composed of multiple layers of neural networks, having high expressive power and providing state-of-the-art accuracy.
[0049] “Database” and/or “Repository” may refer to an organized collection of structured information, or data, typically stored electronically in a computer system.
[0050] “Data feed” is a mechanism for users to receive updated data from data sources. It is commonly used by real-time applications in point-to-point settings as well as on the World Wide Web.
[0051] “Ensemble learning” is the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem. Ensemble learning is primarily used to improve the (classification, prediction, function approximation, etc.) performance of a model, or reduce the likelihood of an unfortunate selection of a poor one. In an example, an ML model selected for gathering a user intent from an input is different from an ML model required for processing a statistical input for sensitivity.
[0052] In accordance with the embodiments of the disclosure, a system for managing commodity data in a computing environment is described. The system comprises a data aggregator and processing module configured to receive commodity data from one or more sources in a production and supply chain for a commodity, and a rules engine module configured to determine an event by evaluating the commodity data using one or more rules associated with the commodity data and generate an actionable insight based on the determined event, the actionable insight providing a recommendation related to the event enabling an action.
[0053] In an embodiment, the system includes a first insight module configured to generate an actionable insight using an associated event, the associated event determined by the rules engine module by applying a set of conditions associated with the one or more rules, each condition of the set of conditions determining an associated event. In an embodiment, the system further includes an Artificial Intelligence (AI) module configured to detect an anomaly associated with the commodity data by comparing the commodity data to a corresponding repository data. In an embodiment, the AI module includes a second insight module configured to generate an actionable insight based on the detected anomaly. Further, in an embodiment, the AI module is configured to detect an anomaly associated with a previously evaluated commodity data by comparing a current commodity data to the corresponding repository data, and generate an actionable insight based on the detected anomaly.
[0054] In an embodiment, the system further includes a traceability presentation module configured to generate traceability information associated with the commodity based on the commodity data. The system may further include a data aggregator and processing module configured to receive data from one or more of an Internet of things (IoT) based data collection module, a Manufacturing and Processing data collection module, and a Sales and Distribution data collection module.
[0055] The embodiments of the methods and systems are described in more detail with reference to FIGs. 1-11.
[0056] FIG. 1 illustrates an example networked computing environment 100 with which various embodiments of the present disclosure may be implemented. FIG. 1 is shown in simplified, schematic format for purposes of illustrating a clear example and other embodiments may include more, fewer, or different elements. FIG. 1 and the other drawing figures, and all of the description and claims in this disclosure are intended to present, disclose and claim a technical system and technical methods. The technical system and methods as disclosed includes specially programmed computers, using a special-purpose distributed computer system design and instructions that are programmed to execute the functions that are described. These elements execute to provide a practical application of computing technology to the problem of managing commodity intelligently and to provide traceability information related to a commodity. In this manner, the current disclosure presents a technical solution to a technical problem, and any interpretation of the disclosure or claims to cover any judicial exception to patent eligibility, such as an abstract idea, mental process, method of organizing human activity or mathematical algorithm, has no support in this disclosure and is erroneous.
[0057] In accordance with the embodiments of the disclosure, the computing environment 100 for management of commodity data in a commodity production and supply chain environment is shown in FIG. 1. As shown, the computing environment 100 includes one or more client computing devices 102, 104, 106, and 108 all communicating with a communication network 110. The ecosystem includes one or more server computing device 112 and a repository 114 in communication with the communication network 110. The computing environment 100 further includes commodity ecosystem 116 in communication with the communication network 110. Furthermore, the communication network 110 may be configured to implement different networking technologies, such as, fixed (Ethernet, fiber, xDSL, DOCSIS®, USB, etc.), mobile WAN (2G, 3G, 4G, 5G, etc.), Wireless LAN (WiFi®, etc.), and Wireless PAN (Bluetooth®, WiGig, ZWave®, ZigBee®, IrDA, etc.). In an embodiment, the computing environment 100 may implement a server-client architecture. In an embodiment, the communication network 110 may correspond to a medium through which content and messages flow between various devices of the computing environment 100, e.g., the client computing devices 102, 104, 106, 108, the server computing device 112, the repository 114, and the commodity ecosystem 116. In an embodiment, the repository 114 corresponds to a Postgres database (DB), or a NoSQL database. Various devices in the computing environment 100 can connect to the communication network 110 in accordance with the various wired and wireless communication protocols such as Transmission Control Protocol and Internet Protocol (TCP/IP), User Datagram Protocol (UDP), and 2G, 3G, 4G or 5G communication protocols.
[0058] In accordance with the present disclosure, the computing environment 100 may represent an ecosystem for commodity data management and traceability. In an embodiment, a human operator or person 118 will interact with one or more client computing devices in the computing environment 100 for providing inputs and consuming output from these devices. The human operator or person in the context of a commodity production and supply chain environment may refer to a supplier, a manufacturer, a sales manager, a consumer of the commodity, and the like, for the purposes of the ongoing description. In an embodiment, such a human operator may be employed with insurance companies, financial institutions, and/or farm/livestock management companies.
[0059] The server computing device 112 may include one or more computer programs or sequences of computer executable instructions. Computer executable instructions described herein may be in machine executable code in the instruction set of a CPU and may be compiled based upon source code written in Python, JAVA, C, C++, OBJECTIVE-C, or any other human-readable programming language or environment, alone or in combination with scripts in JAVASCRIPT, other scripting languages and other programming source text. In another embodiment, the programmed instructions may also represent one or more files or projects of source code that are digitally stored in a mass storage device such as non-volatile RAM or disk storage, in the systems of FIG. 1 or a separate repository system, which when compiled or interpreted cause generation of executable instructions that in turn upon execution cause the computer to perform the functions or operations that are described herein with reference to those instructions. In other words, the figure may represent the manner in which programmers or software developers organize and arrange source code for later compilation into an executable, or interpretation into bytecode or the equivalent, for execution by the server computing device 112.
[0060] Further, the server computing device 112 may be communicatively coupled to the repository 114. In some embodiments, the repository 114 may store a plurality of data feeds collected from various sources in a commodity production and supply chain, such as, production, manufacturing, processing, distribution, transportation, sales, or the like. A data feed may include data segments pertaining to real-time data associated with a commodity, such as, but not limited to, a source of the commodity, a quality of the commodity, dispatch data, processing parameters, livestock health status, sales data, and the like. In some embodiments, repository 114 may store predefined data, such as, a set of rules, repository commodity data, and the like. As described herein, the real-time data, near real-time data, collated data, and predefined data may be processed by various components of the server computing device 112 depending on the commodity data management objectives. Repository 114 may include additional databases and/or repositories storing data that may be used by the server computing device 112. Each database may be implemented using memory, e.g., RAM, EEPROM, flash memory, hard disk drives, optical disc drives, solid state memory, or any type of memory suitable for database storage.
[0061] Commodity ecosystem 116 in accordance with the present disclosure may refer to a commodity production and supply chain and entities associated with the production and supply chain. For example, commodity ecosystem 116 may include a commodity source, manufacturing units, distribution units, transportation units, sales unit, end customer, and the like.
[0062] FIG. 2 is a schematic diagram illustrating inflow of commodity data from multiple sources, in accordance with the embodiments presented herein. As shown in FIG. 2, the commodity data management system includes a commodity data processing module 200. The commodity data processing module 200 is configured to receive commodity data from multiple sources and process the received commodity data, as will be discussed later. As described above, the commodity data received by the commodity data processing module 200 may include real-time data, near-real-time data, predefined data, or collated data from one or more sources. In accordance with the present disclosure, the commodity data processing module 200 receives commodity data from an Internet of things (IoT) based data collection module 300, a Manufacturing and Processing data collection module 400, and a Sales and Distribution data collection module 500. Each of these modules, that is, the Internet of things (IoT) based data collection module 300, Manufacturing and Processing data collection module 400, and Sales and Distribution data collection module 500, will be described in detail with reference to FIGs. 3-5.
[0063] FIG. 3 is a schematic diagram illustrating the Internet of things (IoT) based data collection module 300, in accordance with an embodiment of the present disclosure. The IoT based collection module 300 may be configured to receive, aggregate, and/or process commodity data received from one or more data feeds or data sources associated with a source, production, and/or origin of the commodity or raw materials associated with the commodity. In an exemplary embodiment, as shown in FIG. 3, the commodity may refer to milk or a dairy based product, such as, cheese. In this case, the IoT based collection module 300 receives commodity data from a dairy farm 302 and a collection center 304. The dairy farm 302 and the collection center 304 may employ IoT based sensors or sub-systems to automatically collect data associated with the cattle and/or the produce. For example, the IoT based sensors may include sensors for sensing temperature of the livestock, movement of the livestock and the like. The activities sensed by the sensors may also include number of footsteps per hour, movement speed, resting time, blood pressure, pulse rate, etc. The sensors may include imaging sensors, temperature sensors, location sensors, pulse rate monitoring sub-systems, health monitoring devices, etc. Additionally, the IoT based sensors or sub-systems may collect data associated with the produce, such as, quality of milk, quantity of milk collected, time of pouring, temperature, etc.
[0064] Further, as shown in FIG. 3, the dairy farm 302 may provide data associated with cattle health, that is, cattle health data 306, and data associated with farm/herd management, that is, farm management data 308. Cattle health data 306 data may be captured from a farm setting and may include identification details of livestock, images of different body parts of livestock, health details of livestock since birth, historical data for the livestock treatment, medical history of ailments of livestock (if any), produce from individual livestock, details regarding the breed/ancestors of the livestock, nutrition consumption details consumed by the livestock, data associated with insurance of the livestock, data associated with the sale/purchase of the livestock including the details of the ownership history, vital statistics and body characteristics (height, weight, length, color etc.) of the livestock and the like. Cattle health data 306 may also include monitoring whether the animal is in any drug withdrawal period. For example, if a cow has been given an antibiotic for an ailment, it should not be milked during the drug withdrawal period as traces of antibiotics may be present in the produce if miking is done during that time. In an embodiment, cattle health data 306 may be monitored through embedded system which captures real time activity data of the cattle. Further, the captured data may be transferred to a server, such as the server computing device 112 and/or to the IoT based collection module 300.
[0065] Farm management data 308 may include data associated with the farm equipment, herd management systems, farm’s productivity data, and the like. In an embodiment, farm management data 308 may include data received from sensors disposed to measure or sense the various attributes of the produce, such as, milk, including but not limited to liquid conductivity, volume/yield, temperature of the liquid and so on. The attributes may include pH, fat, proteins, progesterone, milk solids-not-fat (SNF) present in the milk. In an embodiment, the farm management data 308 may include real-time data streams along with existing recorded data. Further, in an embodiment, the farm management data 308 may include data aggregated from a plurality of farms through a herd management system. The data may be categorized based on the farm it is associated with. Further, the captured data may be transferred to a server, such as the server computing device 112 and/or to the IoT based collection module 300.
[0066] Further, as shown in FIG. 3, the collection center 304 may provide Milk Quality & Quantity data 310 and Milk Storage data 312. The collection center 304 along with Milk Quality & Quantity data 310 and Milk Storage data 312 may provide data to the IoT based collection module 300 regarding possible sources of milk to a tanker and/or a collection center, quality index of the produce, time of pouring, time of transfer, quantity, and the like. As discussed above, additional details associated with the milk may also be captured, such as, traces of any antibiotics, pH, fat, proteins, progesterone, milk solids-not-fat (SNF) present in the milk, and the like. In an embodiment, the data may be collected automatically at each touchpoint of a collection center 304 with minimal human intervention by employing IoT based smart sensor technology. Thus, the collected data may be transferred to a server, such as the server computing device 112 and/or to the IoT based collection module 300.
[0067] IoT based collection module 300 may collate the data received from a plurality of sources, such as, the dairy farm 302 and the collection center 304. Additionally, the IoT based collection module 300 may arrange, process, and/or organize the collated data for further processing, as will be discussed later.
[0068] FIG. 4 is a schematic diagram illustrating the Manufacturing and Processing data collection module 400, in accordance with an embodiment of the present disclosure. The Manufacturing and Processing data collection module 400 may be configured to receive, aggregate, and/or process commodity data received from one or more data feeds or data sources associated with a manufacturing unit and/or a processing plant for the commodity. In an exemplary embodiment, as shown in FIG. 4, the Manufacturing and Processing data collection module 400 may receive commodity data from a plurality of teams in a manufacturing and/or processing plant. The Manufacturing and Processing data collection module 400 may receive product processing information from a Manufacturing team 402, product packaging information from a Packaging team 404, raw material and final product inventory information from an Inventory team 406, product dispatch information from a Dispatch team 408, quality control and product testing information from a Quality control team 410, quality material purchase information from a Purchase team 412, and product expiry and shelf life information from a Product team 414.
[0069] In an embodiment, the Manufacturing and Processing data collection module 400 may employ sensors or data collection sub-systems to automatically collect data associated with the manufacturing, packaging, inventory, quality, dispatch, and the like, of the commodity. In an embodiment, Manufacturing and Processing data collection module 400 may collect information associated with a list of tests conducted at each stage of processing the commodity and the associated results. Additionally, the Manufacturing and Processing data collection module 400 may arrange, process, and/or organize the collated data for further processing, as will be discussed later.
[0070] FIG. 5 is a schematic diagram illustrating the Sales and Distribution data collection module 500, in accordance with an embodiment of the present disclosure. The Sales and Distribution data collection module 500 may be configured to receive, aggregate, and/or process commodity data received from one or more data feeds or data sources associated with the sales and distribution of the commodity to retailers and/or end users. In an exemplary embodiment, as shown in FIG. 5, the Sales and Distribution data collection module 500 may receive commodity data including Promotions and Discount information 502, Product Distribution information 504, and Product and Storage information 506.
[0071] The Sales and Distribution data collection module 500 may employ data collection sub-systems to collect data associated with the sales, pricing, promotions, distribution, and the like, for the commodity. For example, the Sales and Distribution data collection module 500 may maintain a record of each retailer and/or a store where the commodity is distributed for sale along with details associated with the distribution, such as, a batch number, distribution date, transportation details, and the like. These details may be used by the server computing device 112 and/or the commodity data processing module 200 for making decisions associated with the commodity. Further, the Sales and Distribution data collection module 500 may arrange, process, and/or organize the collated data for further processing, as will be discussed later.
[0072] FIG. 6 is a schematic diagram illustrating an example commodity data processing module 200, in accordance with the embodiments presented herein. As described above, the commodity data processing module 200 receives commodity data from multiple sources in the commodity production and supply chain. The commodity data processing module 200 further processes the received commodity data, as will be described in detail with reference to FIGs. 6-8.
[0073] As shown in FIG. 6, the commodity data processing module 200 includes a data aggregator and processing module 602. The data aggregator and processing module 602 is configured to aggregate and intelligently process the received commodity data. The data aggregator and processing module 602 provides aggregated and/or processed commodity data to one or more of a rules engine module 604, an Artificial Intelligence (AI) module 606, and a traceability presentation module 608, as shown in FIG. 6. The rules engine module 604 and/or the Artificial Intelligence (AI) module 606 generate actionable insights 610 based on the commodity data received from the data aggregator and processing module 602. The traceability presentation module 608 generated traceability information 612 based on the commodity data received from the data aggregator and processing module 602.
[0074] Specifically, the data aggregator and processing module 602 may receive commodity data from a plurality of sources in the commodity production and supply chain. For example, as discussed above, the data aggregator and processing module 602 may receive commodity data from (IoT) based data collection module 300, Manufacturing and Processing data collection module 400, and Sales and Distribution data collection module 500. Further, the data aggregator and processing module 602 may receive commodity data from one or more databases and/or repositories associated with the commodity data. The received commodity data may be in different formats based on the source of the data. In an embodiment, the data aggregator and processing module 602 converts the received data into machine executable format for further processing. The data aggregator and processing module 602 may collate, arrange, modify, and/or process the received commodity data based on the requirements of the system. For example, for management of commodity data from a dairy farm, the data aggregator and processing module 602 may receive data regarding cattle health, milk quality, quantity, milk processing and/or transportation details, and the like. The data aggregator and processing module 602 may store the received data in a repository or a database, and may provide some or all of the data, received as is, or in a processed format to the rules engine module 604, Artificial Intelligence (AI) module 606, and traceability presentation module 608 for further processing.
[0075] In an embodiment, the data aggregator and processing module 602 may be implemented as a blockchain and/or a Trusted Execution Environment (TEE) to maintain data integrity and confidentiality. For example, the data aggregator and processing module 602 may be implemented as a secure area of a processor such that data loaded and/or processed inside the secure area is protected with respect to confidentiality and integrity. This prevents unauthorized entities from outside the Trusted Execution Environment from accessing, replacing, and/or modifying the data. In an embodiment, the TEE may be implemented using confidential architectural security such as Intel Software Guard Extensions (Intel SGX) which offers hardware-based memory encryption that isolates specific application code and data in a memory. Intel Software Guard Extensions (Intel SGX) allocates private regions of memory, called enclaves, which creates an isolated execution environment providing security features to users along with confidentiality of their assets.
[0076] Further, as shown in FIG. 6, the data aggregator and processing module 602 provides commodity data to the rules engine module 604. The rules engine module 604 evaluates the commodity data using one or more rules to determine an event associated with the commodity data. The term “event” as used in accordance with the present disclosure, may refer to a trigger, an activity, or an occurrence determined using a rules-based analysis of the commodity data. For example, the commodity data may include a shelf-life and/or expiration related information for a commodity. The rules engine module 604 may evaluate the expiration related information using one or more rules to determine a related event. In this case, the event may be an approaching expiration for a batch of commodity based on the analysis of the commodity data. In an embodiment, the event may be determined by the rules engine module 604 by applying a set of conditions associated with the rules, such that, each condition of the set of conditions indicates an associated event. The set of conditions, in the scenario discussed above, may relate to predefined rules with respect to expiration date ranges. For example, the rules engine module 604 may specify that for a perishable commodity, such as, milk, if the expiration date is with a first date range, there is probability of the product expiring soon, hence it must be distributed to retailers and/or consumers on priority. If the expiration date is within a second date range, there is probability of the product not being fit for its primary use. For example, in case of milk, it may indicate that there is chance of the milk turning sour. This may be detected as an event and alternate uses of the product may be suggested. Further, if it is detected that the product is past its expiration date, the product is identified as expired and unfit for consumption. Thus, the rules engine module 604 may detect a product expired event. The rules engine module 604 will be discussed further with reference to FIG. 7.
[0077] In accordance with the present invention, the detected event is further used by the rules engine module 604 to generate actionable insight 610. “Actionable insight” may refer to a recommendation related to the event enabling an action. The recommendation may include an instruction, a suggestion, information, alert, and the like, related to the detected event. Further, the actionable insight provides the recommendation related to the event enabling an entity associated with the commodity to take an action based on the actionable insight. “Entity” may refer to any member, device, system, operator, and the like, of the commodity production and supply chain. For example, entity may refer to the consumer of the commodity, manufacturer, distributer, a factory equipment, and the like. In the scenario discussed above, the detected event may be associated with an expiration date of the commodity, such as, milk. Based on the detected event, such as, an indication that a batch of milk is due to expire soon, the rules engine module 604 may generate an actionable insight providing a recommendation to retailers to offer a discount on the product. If the detected event indicates that the milk is about to turn sour, the rules engine module 604 may generate an actionable insight providing a recommendation to the processing plant to use the batch of milk to make cheese. Lastly, if the detected event indicates that the milk is past its expiration date, the rules engine module 604 may generate an actionable insight providing an alert to recall unsold batch of milk that is unfit for consumption. Thus, the rules engine module 604 and/or the commodity data processing module 200 intelligently analyzes the commodity data and automatically generates actionable insights based on the analysis.
[0078] Further, as shown in FIG. 6, the data aggregator and processing module 602 provides commodity data to the AI module 606. The AI module 606 detects an anomaly associated with the commodity data by comparing the commodity data to a corresponding repository data. The AI module 606 further generates actionable insight 610 based on the detected anomaly. The term “anomaly” as used in accordance with the present disclosure, may refer to an event that is different from the usual, peculiar, abnormal, or uncommon compared to the common order of things. Anomalies are data points that stand out amongst other data points in a dataset and do not confirm to the normal behaviour in the dataset. The AI module 606 compares the received commodity data to a corresponding repository data to intelligently detect an anomaly associated with the commodity data. For example, a group of cattle may have been milked during a drug withdrawal period, and the commodity data may indicate traces of the drug in the produce. Due to the traces of the drug in the milk, the AI module 606 may detect it as an anomaly compared to the predetermined and/or usual percentage (e.g., nil to a minimum) of the specific drug in the milk. Further, the AI module 606 generates an actionable insight based on the detected anomaly. For example, the actionable insight may be an alert and a recommendation to withdraw the batch of milk obtained during a drug withdrawal period of the cattle.
[0079] In an embodiment, the AI module 606 may use machine learning and/or AI-based anomaly detection methods to detect an anomaly. For example, the AI module 606 may analyze the commodity data to identify outliers, that is, anomalous patterns that appear in a non-systematic way in data collection, sudden change in events, and drifts, that is, slow, unidirectional, long-term change in the data. In an embodiment, simple statistical techniques such as mean, median, quantiles, data visualization, exploratory data analysis techniques, etc. can be used to detect anomalies in a commodity dataset. Further, the AI module 606 may use machine learning techniques to detect anomalies, such as, but not limited to, Local Outlier Factor that takes the density of data points into consideration to decide whether a point is an anomaly or not. The local outlier factor computes an anomaly score that indicates how isolated the point is with respect to the surrounding. It takes into account local as well as the global density to compute the anomaly score. Other techniques that are used by the AI module 606 may include Benchmarking, Isolation Forest, Robust Covariance, One-class SVM, and the like.
[0080] Further, as shown in FIG. 6, the data aggregator and processing module 602 provides commodity data to the traceability presentation module 608. The traceability presentation module 608 generates traceability information 612 associated with the commodity. Traceability information refers to information associated with a commodity based on the commodity data collected throughout the commodity production and supply chain. Traceability information may relate to a source of the commodity, a quality of the commodity, dispatch data, processing parameters, livestock health status, sales data, and the like. In an embodiment, the data aggregator and processing module 602 collates data from multiple sources and provides the data to the traceability presentation module 608. The traceability presentation module 608 may rearrange, sort, and/or modify the data to make it presentable to a user. For example, an end user of a commodity, such as, ice-cream, might want to know the origin of all ingredients of the ice-cream. Thus, the traceability presentation module 608 provides information about each ingredient such as milk, sugar, added flavors, etc. as a traceability information. Traceability information may include information about the farm and/or the cattle from where the milk was obtained, processing details, monetary rate at which the milk was purchased, tests that the ingredient went through, cattle health details, and the like. Such information may be provided for each ingredient of the product. In an embodiment, a user may obtain the traceability information through a traceability application accessible by one or more of the devices described with reference to FIG. 1. For example, a user may scan a QR code to access the traceability application. In response, the traceability presentation module 608 provides traceability information associated with the commodity to the user. It should be noted that any entity and/or a stakeholder in the commodity production and supply chain, such as, supervisors for collection centers, tankers, processing plants, etc. may also use the traceability information to gather information about the commodity being processed.
[0081] FIG. 7 is a schematic diagram illustrating generation of actionable insights, in accordance with one or more embodiments presented herein. As shown in FIG. 7, the data aggregator and processing module 602 collates commodity data from multiple sources and provides the commodity data to the rules engine module 604. The rules engine module 604 includes a processor 702. The processor 702 further includes an insight module 704. Further, a repository 706 including one or more rules, Rule a to Rule n, provides information with respect to an event to the rules engine module 604. As described above, the rules engine module 604 evaluates the commodity data using one or more rules to determine an event associated with the commodity data. Event may refer to a trigger, an activity, or an occurrence determined using a rules-based analysis of the commodity data. In an embodiment, the event may be determined by the rules engine module 604 by applying a set of conditions associated with the rules, such that, each condition of the set of conditions indicates an associated event. As is known in the art, the rules engine module 604 is a programmable logic having rules, such as, Rule a to Rule n, that combine to create an intelligent framework that enables automation of specific actions based on set of conditions. For example, the rules engine module 604 may run on If-Then formula, such that ‘If’ a condition is met, ‘Then’ an associated event is determined. The rules and/or the set of conditions of the rules engine module 604 may be customizable by an operator. In some embodiments, the rules engine module 604 may include multiple rules for more complex scenarios.
[0082] Further, once the rules engine module 604 determines an event associated with the commodity data, the insight module 704 determines an actionable insight using the determined event. As discussed above, the term actionable insight may refer to a recommendation related to the event enabling an action. The recommendation may include an instruction, a suggestion, information, alert, and the like, related to the detected event. Further, the actionable insight provides the recommendation related to the event enabling an entity associated with the commodity to take an action based on the actionable insight. The insight module 704 intelligently analyzes the commodity data based on the determined event and automatically generates actionable insights based on the analysis. In some embodiments, the insight module 704 employs AI techniques and self-learning models to intelligently identify relevant insights for one or more entities of the commodity production and supply chain based on the detected event. For example, if it is identified that a batch of produce, such as, milk, belongs to a diseased cow, the insight module 704 determines one or more actionable insights based on this event. An actionable insight may be for sales and distribution related entities to recall any distributed produce that belonged to the identified batch, another insight may be for manufacturing and/or processing plants to discard milk belonging to the identified batch, and lastly, an actionable insight may be provided to the cattle farmer alerting about the condition of the cow and suggesting means to ensure such an event is avoided in future. Thus, the insight module 704 intelligently converts an event determined based on a rules-based analysis of the commodity data into recommendations that enable an action.
[0083] It should be noted that the detection of an event and/or generation of actionable insights is not limited to a rules-based analysis, and the rules engine module 604 may employ machine learning techniques for complex scenarios where it may be difficult to predefine rules or a set of conditions. Thus, the rules engine module 604 may employ any computational model, machine learning technique, as an alternative to, or in combination with the rules-based analysis discussed above.
[0084] FIG. 8 is a schematic diagram illustrating generation of actionable insights, in accordance with one or more other embodiments presented herein. As shown in FIG. 8, the data aggregator and processing module 602 collates commodity data from multiple sources and provides the commodity data to the AI module 606. The AI module 606 includes a processor 802. The processor 802 further includes an insight module 804. Further, repository 806 is in communication with the AI module 606, in accordance with the present invention.
[0085] As discussed above, the AI module 606 detects an anomaly associated with the commodity data by comparing the commodity data to a corresponding data from the repository 806. Further, the insight module 804 generates actionable insight based on the detected anomaly. Anomaly may refer to an event that is different from the usual, peculiar, abnormal, or uncommon compared to the common order of things. Anomalies are data points that stand out amongst other data points in a dataset and do not confirm to the normal behaviour in the dataset. In accordance with the present invention, the AI module 606 compares the received commodity data to a corresponding repository data to intelligently detect an anomaly associated with the commodity data. As discussed, the AI module 606 may employ AI techniques, such as Benchmarking, Isolation Forest, Robust Covariance, One-class SVM, and the like, to detect an anomaly. The repository 806 may include predefined datasets and/or may be dynamically updated based on real-time data. The repository 806 may also include historical and/or self-learned data.
[0086] Further, the insight module 804 generates an actionable insight based on the detected anomaly. As described above, the insight module 804 may employ AI techniques and self-learning models to intelligently identify relevant insights for one or more entities of the commodity production and supply chain based on the detected anomaly. For example, if the transportation temperature of a batch of milk is not within the expected range due a faulty refrigeration system of a transport vehicle, the insight module 804 may recommend the quality of the milk to be checked at destination, before the milk is further processed. Thus, the insight module 804 intelligently converts a detected anomaly determined based on an AI-based analysis of the commodity data into recommendations that enable an action.
[0087] In an embodiment, the AI module 606 may detect an anomaly associated with a previously evaluated commodity data by comparing a current commodity data to the corresponding repository data. For example, based on the commodity data received from a dairy farm, the cattle health records may indicate that a specific cow has developed an ailment or a disease since the dispatch of a previous batch of milk. The commodity data associated with the previous batch of milk may have analyzed by the AI module 606 and no anomaly might have been detected then. However, based on the newly received commodity data, that is, cattle health records, the AI module 606 may identify the anomaly as being associated with the previous batch of milk. Accordingly, the insight module 804 may alert concerned parties and recommend an immediate recall of distributed produce. Thus, AI module 606 manages commodity data in a manner that an anomaly may be detected as being associated with a previously evaluated commodity data based on real-time information received from one or more sources in the commodity production and supply chain.
[0088] FIG. 9 is a schematic diagram illustrating an example commodity production and supply chain 900, in accordance with the embodiments presented herein.
[0089] In an embodiment, the commodity production and supply chain 900 may relate to a dairy based commodity. As shown in FIG. 9, the commodity production and supply chain 900 includes a dairy farm 902 that produces the commodity or produces the raw materials for the commodity. Next in the commodity production and supply chain 900 is a village level collection center 904 followed by a collection center 906. Produce from multiple dairy farms may be collected at these collection centers. Further, the commodity production and supply chain 900 includes transportation unit 908, such as, vehicles for transporting the commodity from the collection centers for further processing and/or distribution. Next, the commodity production and supply chain 900 includes a processing plant 910 and a production unit 912 for processing the commodity and/or the raw materials into a product ready for distribution. Optionally, the commodity production and supply chain 900 may include packaging unit 914 that interfaces with the production unit 912. Further, the commodity production and supply chain 900 includes distribution unit 916 for distributing the commodity and/or the product to retailers or end consumers, also shown as customer 918 in FIG. 9. Further, each of the nodes in the commodity production and supply chain 900 may generate corresponding commodity data, and may also receive actionable insights and/or traceability information, as discussed above.
[0090] FIG. 10 illustrates a sequential flow diagram for generating actionable insights, in accordance with the embodiments presented herein.
[0091] As shown in FIG. 10, the method 1000 starts at step 1002. In step 1004, the method includes receiving commodity data from one or more sources in a commodity production and supply chain. As described above, the commodity data processing module 200 may receive commodity data from an Internet of things (IoT) based data collection module 300, a Manufacturing and Processing data collection module 400, and a Sales and Distribution data collection module 500. Further, the received commodity data may include real-time data, near-real-time data, predefined data, or collated data from one or more sources.
[0092] In step 1006, the method includes determining an event by evaluating the commodity data using one or more rules associated with the commodity data. As described above, the data aggregator and processing module 602 may provide commodity data to the rules engine module 604. The rules engine module 604 may evaluate the commodity data using one or more rules to determine an event associated with the commodity data. For example, the commodity data may include a shelf-life and/or expiration related information for a commodity. The rules engine module 604 may evaluate the expiration related information using one or more rules to determine a related event.
[0093] In step 1008, the method includes generating an actionable insight based on the determined event, the actionable insight providing a recommendation related to the event enabling an action. As described above, an insight module 704 of the rules engine module 604 may generate an actionable insight based on the determined event associated with the commodity data. The actionable insight may include a recommendation, such as, an instruction, a suggestion, information, alert, and the like, related to the detected event. Further, the actionable insight enables an entity associated with the commodity to take an action based on the actionable insight.
[0094] FIG. 11 illustrates a sequential flow diagram for generating traceability information, in accordance with the embodiments presented herein.
[0095] As shown in FIG. 11, method 1100 starts at step 1102. In step 1104, the method includes receiving commodity data from one or more sources in a commodity production and supply chain. As described above, the commodity data processing module 200 may receive commodity data from an Internet of things (IoT) based data collection module 300, a Manufacturing and Processing data collection module 400, and a Sales and Distribution data collection module 500. Further, the received commodity data may include real-time data, near-real-time data, predefined data, or collated data from one or more sources.
[0096] In step 1106, the method includes aggregating and processing the commodity data received from one or more sources. In an embodiment, the data aggregator and processing module 602 may aggregate, arrange, collate, and process the commodity data received from the one or more sources in the commodity production and supply chain.
[0097] In step 1108, the method includes generating a traceability information associated with the commodity based on the aggregated commodity data. As described above, the data aggregator and processing module 602 may provide aggregated commodity data to the traceability presentation module 608. The traceability presentation module 608 may generate traceability information 612 associated with the commodity. Traceability information may relate to a source of the commodity, a quality of the commodity, dispatch data, processing parameters, livestock health status, sales data, and the like. For example, an end user of a commodity, such as, ice-cream, might want to know the origin of all ingredients of the ice-cream. Thus, the traceability presentation module 608 provides information about each ingredient such as milk, sugar, added flavors, etc. as traceability information. In an embodiment, a user may scan a QR code to access a traceability application to obtain traceability information.
[0098] In an embodiment, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
[0099] The terms “comprising,” “including,” and “having,” as used in the claim and specification herein, shall be considered as indicating an open group that may include other elements not specified. The terms “a,” “an,” and the singular forms of words shall be taken to include the plural form of the same words, such that the terms mean that one or more of something is provided. The term “one” or “single” may be used to indicate that one and only one of something is intended. Similarly, other specific integer values, such as “two,” may be used when a specific number of things is intended. The terms “preferably,” “preferred,” “prefer,” “optionally,” “may,” and similar terms are used to indicate that an item, condition, or step being referred to is an optional (not required) feature of the disclosure.
[00100] The disclosure has been described with reference to various specific and preferred embodiments and techniques. However, it should be understood that many variations and modifications may be made while remaining within the spirit and scope of the disclosure. It will be apparent to one of ordinary skill in the art that methods, devices, device elements, materials, procedures, and techniques other than those specifically described herein can be applied to the practice of the disclosure as broadly disclosed herein without resort to undue experimentation. All art-known functional equivalents of methods, devices, device elements, materials, procedures, and techniques described herein are intended to be encompassed by this disclosure. Whenever a range is disclosed, all subranges and individual values are intended to be encompassed. This disclosure is not to be limited by the embodiments disclosed, including any shown in the drawings or exemplified in the specification, which are given by way of example and not of limitation. Additionally, it should be understood that the various embodiments of the networks, devices, and/or modules described herein contain optional features that can be individually or together applied to any other embodiment shown or contemplated here to be mixed and matched with the features of such networks, devices, and/or modules.
[00101] While the disclosure has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this disclosure, will appreciate that other embodiments can be devised which do not depart from the scope of the disclosure as disclosed herein. , Claims:I/We claim:

1. A method for managing commodity data in a computing environment (100), the method comprising:
receiving commodity data from one or more sources in a commodity production and supply chain;
determining an event by evaluating the commodity data using one or more rules associated with the commodity data; and
generating an actionable insight based on the determined event, the actionable insight providing a recommendation related to the event enabling an action.

2. The method according to claim 1, further comprising:
receiving, by a commodity data processing module (200), the commodity data associated with at least one of: an item, a product, an article, a consumable product, an ingredient of a consumable product, and a part of a product.

3. The method according to claim 1, further comprising:
receiving, by a commodity data processing module (200), the commodity data from one or more sources including at least one of: an internet of things (IoT) based data collection module (300), a manufacturing and processing data collection module (400), and a sales and distribution data collection module (500).

4. The method according to claim 1, further comprising:
determining, by a rules engine module (604), the event by applying a set of conditions associated with the one or more rules, each condition of the set of conditions determining an associated event; and
generating, by a first insight module (704), the actionable insight using the determined event.

5. The method according to claim 1, further comprising:
detecting, by an Artificial Intelligence (AI) module (606), an anomaly associated with the commodity data by comparing the commodity data to a corresponding repository data; and
generating, by a second insight module (804), the actionable insight based on the detected anomaly.

6. The method according to claim 5, further comprising:
detecting, by the AI module (606), an anomaly associated with a previously evaluated commodity data by comparing a current commodity data to the corresponding repository data; and
generating, by the second insight module (804), the actionable insight based on the detected anomaly.

7. The method according to claim 1, further comprising:
aggregating, by a data aggregator and processing module (602), the commodity data received from the one or more sources; and
generating, by a traceability presentation module (608), traceability information associated with the commodity based on the commodity data.

8. The method according to claim 7, further comprising:
generating, by the traceability presentation module (608), the traceability information associated with the commodity, wherein the traceability information comprises at least one of: a source of the commodity, a quality of the commodity, dispatch data, processing parameters, livestock health status, and sales data.

9. The method according to claim 1, further comprising:
generating the actionable insight based on the determined event, the actionable insight providing one or more of: an instruction, an information, a suggestion, and an alert related to the event enabling an entity associated with the commodity to take an action based on the actionable insight.

10. A system for managing commodity data in a computing environment (100), the system comprising:
a data aggregator and processing module (602) configured to:
receive commodity data from one or more sources in a production and supply chain for a commodity; and
a rules engine module (604) configured to:
determine an event by evaluating the commodity data using one or more rules associated with the commodity data; and
generate an actionable insight based on the determined event, the actionable insight providing a recommendation related to the event enabling an action.

11. The system according to claim 10, further comprising:
a first insight module (704) configured to:
generate the actionable insight using an associated event, the associated event determined by the rules engine module (604) by applying a set of conditions associated with the one or more rules, each condition of the set of conditions determining an associated event.

12. The system according to claim 11, further comprising:
an Artificial Intelligence (AI) module (606) configured to:
detect an anomaly associated with the commodity data by comparing the commodity data to a corresponding repository data.

13. The system according to claim 12, wherein the AI module (606) further comprises:
a second insight module (804) configured to:
generate the actionable insight based on the detected anomaly.

14. The system according to claim 12, wherein the AI module (606) is further configured to:
detect an anomaly associated with a previously evaluated commodity data by comparing a current commodity data to the corresponding repository data; and
generate the actionable insight based on the detected anomaly.

15. The system according to claim 10, further comprising:
a traceability presentation module (608) configured to:
generate traceability information associated with the commodity based on the commodity data.

16. The system according to claim 10, wherein the data aggregator and processing module (602) is configured to:
receive data from at least one of: an internet of things (IoT) based data collection module (300), a manufacturing and processing data collection module (400), and a sales and distribution data collection module (500).

17. A non-transitory computer-readable storage medium, having stored thereon a computer-executable program which, when executed by at least one processor (702), causes the at least one processor (702) to:
receive commodity data from one or more sources in a commodity production and supply chain;
determine an event by evaluating the commodity data using one or more rules associated with the commodity data; and
generate an actionable insight based on the determined event, the actionable insight providing a recommendation related to the event enabling an action.

18. The non-transitory computer-readable storage medium according to claim 17, the computer-executable program further causes the at least one processor (702) to:
determine the event by applying a set of conditions associated with the one or more rules, each condition of the set of conditions determining an associated event; and
generate the actionable insight using the determined event.

19. The non-transitory computer-readable storage medium according to claim 17, the computer-executable program further causes the at least one processor (702, 802) to:
detect an anomaly associated with the commodity data by comparing the commodity data to a corresponding repository data; and
generate the actionable insight based on the detected anomaly.

20. The non-transitory computer-readable storage medium according to claim 17, the computer-executable program further causes the at least one processor (702) to:
generate traceability information associated with the commodity based on the commodity data.