TECHNICAL FIELD
[0001] The present subject matter generally relates to a food supply chain management system and particularly relates to a system for managing one or more assets of a cold supply chain in real-time.
BACKGROUND
[0002] Asset Management within any supply chain, and particularly, a food supply chain, has always been an important consideration. For example, each of the stake holder of the supply chain, manufacturers, retailers, brands, OEM sales, marketing or quality teams is concerned about various sensitive concerns related to the supply chain facility which may pose risks if not taken care of. For instance, assets involving devices working in a critical situation such as where leakage could be a problem, weather related asset / inventory issues, inventory stock management, customer experience, and so on, may have different unwanted impact on consistent and smooth flow of the supply chain. Accordingly, it may be understood that some of these unwanted situations may be caused due to physical impacts, some others due to environmental factors, and even others may be caused by information technology (IT) / and other functional / operational components of the supply chain. All such system components may probably face various possible concerns, such as quality, inventory, supply security, safety, across the supply chain which need to be timely and carefully managed. [0003] Further such unwanted events, factors, are potential risks which might be harmful to the inventory, safety and security of people, property, processes (e.g., processes of public entities, private industry, etc.) and special events. Such potential risks are usually easier to detect in the context of particular facility but not so readily detectable when considered in terms of geographical areas and locations through the supply chain.
[0004] Managing an asset generally involves the process of determining a deviation of various key performance identifier related factors either individually or in combination so as to determine if any unwanted interruptions / risk / threats might take place and therefore, taking informed decision accordingly to avoid possibility / probability of such unwanted situations through the life cycle. Additionally, managing assets in real-time is also another problem faced by people in different situations.
[0005] Currently there are several assets management system which often force the management to buy very expensive command center applications and software suites that cause a steep financial drain, are difficult and expensive to maintain and operate; and many a

times, do not meet requirements on ground. Further, such asset management systems merely consider only a fraction of factors relevant to determine a deviation and / or unwanted events. [0006] Some recent asset management systems employ sensor-based tracking systems having a plurality of tracking sensors. Such systems majorly consider only sensors and not functional parameters which must be utilized in a real time to truly determine and monitor the risks associated with a supply chain facility and determine deviation from an expected threshold associated thereto.
[0007] There are some custom applications that clients use which are built around their needs, and there are others which do not give clients full advantage as they are built around a fraction of available parameters available within the facility and are therefore, not comprehensive. Moreover, majority of such customized tools are resource hungry and are difficult to configure / manage / upgrade due to the legacy nature of existing systems. [0008] In order to accurately and timely determine any new threats, the underlying components of the associated threat parameters must be studied in detail and these threat parameters must be made available to an asset management system. Further, vast amount of such performance identifier data is readily available allowing the possibility of performing a more complicated and detailed analysis, but on the other hand making it more difficult to quickly sort through the data.
[0009] Therefore, there is a need for systems and methods that address these and other known problems in reliably detecting, evaluating and assessing deviation from expected performance to timely intimate the stake holders, and protecting the supply chain from, unwanted interruptions and situations.
[0010] Accordingly, it is desirable to provide a system and method that could accurately assess, determine and visualize threats within a supply chain that were conventionally undetectable. Moreover, such a system needs to be flexible so that it can be utilized with the existing infrastructure without requiring any changes therewithin. Additionally, such system must be configured to provide a detection and / or informed decision in real-time to avoid occurrences of unwanted events / interruptions.

SUMMARY
[0011] In an embodiment, a system for dynamically managing one or more assets related to a supply chain facility is disclosed. The system comprises of one or more tracking units, each installed onto one of the assets within the facility. The tracking unit includes a first processor and a first memory configured to execute one or more first programming instructions embodied thereon. The tracking unit further includes a first communication interface adapted to receive one or more data-sets from a plurality of data-sensors. The received data-sets pertains to at least one predetermined key performance identifier associated to said supply chain facility. Particularly, the data-sensors include at least one functional sensor adapted to sense data-sets related to operational functions related to assets within the supply chain facility, and an at least one environmental sensor adapted to sense data-sets related to environmental conditions and / or events within the facility. The data-sensors furthermore optionally includes process automating sensors such as including but not limited to various data-scanners, barcode scanners, NFC readers, RFID scanner, and the like. [0012] The system further includes a back-end server having a second processor and a second memory configured to execute one or more second programming instructions embodied thereon. The back-end server includes a data receiving component adapted to receive one or more data-sets from each of the tracking units. The back-end server further includes an asset management module adapted to assess the working of each of the assets within the supply chain facility. Particularly, the asset management module is configured to process the second programming instructions embodied onto the second memory to determine any deviation from a predetermined rule set / key performance identifier thresholds, either individually or in combination, as defined by the asset management model, for any of the assets present within the supply chain facility, using the received data-sets.
[0013] In an embodiment, a method for dynamically managing one or more assets related to a supply chain facility is disclosed. The method comprises receiving key performance identifier related data sets from one or more tracking units. The method further comprising storing the received data-sets within a central data repository. The method furthermore comprises processing collected data-sets by implementing the second programming instructions in accordance with an asset management module, to determine any deviation / interruptions / occurrences of unwanted events within the facility. The method further comprising visualizing the assessment onto a data visualization component and in turn

optionally automatically generate an alarm and / or to take informed decision to control the working of the assets within the facility so as to avoid occurrence of unwanted events. [0014] Numerous additional features, embodiments, and benefits of the methods and apparatus of the present invention are discussed below in the detailed description which follows.
OBJECTS OF THE INVENTION
[0015] An object of the invention is to dynamically identify any potential unwanted events
that may occur within a supply chain facility, which may be caused due to physical and / or
environmental and / or operational factors, in real time.
[0016] Another object of the invention is to visualize and generate alerts in real time based
on the detected deviation from an expected performance.
[0017] It is yet another object of the invention to provide an Internet of Things (loT) based
asset management system and method for providing safety and compliance in a supply chain
facility.
[0018] It is yet another object of the present invention to provide an asset management
system that can be installed onto the assets of a chain supply facility.
[0019] Yet another object of the present invention is to monitor assets associated to a
supply chain facility and take informed decisions to take an action to avoid turning them into
actual events.
[0020] The details of one or more implementations are set forth in the accompanying
drawings and the description below. Other aspects, features and advantages of the subject
matter disclosed herein will be apparent from the description, the drawings, and the claims.
BRIEF DESCRIPTION OF DRAWINGS
[0021] The accompanying drawings illustrate various embodiments of systems, methods, and other aspects of the disclosure. Any person having ordinary skill in the art will appreciate that the illustrated element boundaries (e.g., boxes, groups of boxes, or other shapes) in the figures represent one example of the boundaries. It may be that in some examples, one element may be designed as multiple elements or that multiple elements may be designed as one element. In some examples, an element shown as an internal component of one element may be implemented as an external component in another, and vice versa. Furthermore, elements may not be drawn to scale.

[0022] FIG. 1a is a system block diagram of an asset management system according to the
present invention.
[0023] FIG. 1 b is another exemplary system block diagram of an asset management system
according to the present invention.
[0024] FIG. 2 illustrates an exemplary tracking unit in accordance with the present invention.
[0025] FIG. 3 is an exemplary back end server in accordance with the present invention.
[0026] FIG. 4a through 4c illustrate an exemplary visualization dashboard in accordance
with the present invention.
[0027] Fig. 5 illustrates an exemplary smart fridge solution.
[0028] FIG. 6 is a flow chart illustrating a method of managing assets in real time, according
to the present invention.
[0029] FIG. 7a illustrates an exemplary embodiment of tracking unit, according to the
present invention.
[0030] FIG. 7b illustrates another exemplary embodiment of tracking unit, according to the
present invention.
[0031] FIG. 8a illustrates exemplary flow of configuring a tracking unit, according to the
present invention.
[0032] FIG. 8b illustrates exemplary flow of sending data-sets related to various assets
towards the backend server, according to the present invention.
[0033] Various embodiments will hereinafter be described in accordance with the appended
drawings, which are provided to illustrate, and not to limit the scope in any manner, wherein
like designations denote similar elements, and in which:
DETAILED DESCRIPTION
[0034] The present subject matter is best understood with reference to the detailed figures and description set forth herein. Various embodiments are discussed below with reference to the figures. However, those skilled in the art will readily appreciate that the detailed descriptions given herein with respect to the figures are simply for explanatory purposes as the methods and systems may extend beyond the described embodiments. For example, the teachings presented, and the needs of a particular application may yield multiple alternate and suitable approaches to implement the functionality of any detail described herein. Therefore, any approach may extend beyond the particular implementation choices in the following embodiments described and shown.

[0035] The present application discloses an asset management system for determining presence of various risks and / or unwanted events, which may be present and / or which may occur within a subject supply chain facility and thereafter, visualizing such risks in accordance with different categories on a data visualization component, preferably in form of an interactive dashboard. The system is further adapted to auto generate various alarms and / or to take informed decisions such that they can be taken up for correction within the supply chain facility. The system is generally provided in combination with a graphically visualized client application that could be accessed with a computer device, preferably in the form of a mobile application on an appropriate mobile device. However, in another embodiment, the system may be in form of a web-based automated service accessible on a generally known computing unit. [0036] Particularly, the system of the present subject matter is adapted to assess working status/ performance of various assets associated to the subject supply chain facility while considering all the possible key performance identifiers in combination with automation factors that may remotely be utilized for the purpose of determining any underlying threats and / or unwanted event within the supply chain facility. It is to be understood that unless otherwise indicated, this invention need not be limited to applications for supply chain facility. As one of ordinary skill in the art would appreciate, variations of the invention may be applied to other possible asset management system such as in field of industrial manufacturing, retail, medical treatments, including any other field of daily life where asset management is required. Moreover, it should be understood that embodiments of the present invention may be applied in combination with various other management systems such as facility management systems, access management systems, human resource management system, occupational management systems, clinical systems, and the like, for various other possible applications. It must also be noted that, as used in this specification and the appended claims, the singular forms "a," "an" and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, the term "a data-set" is intended to mean a single data-set or a combination of data-sets, "an algorithm" is intended to mean one or more algorithm for a same purpose, or a combination of algorithms for performing different program executions. [0037] References to "one embodiment," "an embodiment," "at least one embodiment," "one example," "an example," "for example," and so on, indicate that the embodiment(s) or example(s) so described may include a particular feature, structure, characteristic, property, element, or limitation, but that not every embodiment or example necessarily includes that particular feature, structure, characteristic, property, element or limitation. Furthermore,

repeated use of the phrase "in an embodiment" does not necessarily refer to the same embodiment.
[0038] FIG. 1a is a system block diagram of an asset management system 100 according to the present invention adapted to control and / or manage one or more assets within a supply chain facility 150. The system 100 includes a plurality of assets 105, adapted to be installed / positioned with a tracking unit 110, and connected to a backend server 120 through a communication medium 130. It is to be contemplated for a person skilled in the art that a system environment can have any number of assets 105 and / or tracking units 110 in accordance with the requirement within the supply chain facility 150 and may have multiple systems 100 connected to each other. Further, the system 100 includes a plurality of data-sensors 140 configured within the facility 150, each adapted to identify data-sets 145 related to at least one of the key performance identifier related to the one or more assets 105. [0039] Each of the tracking units 110 includes a first communication interface 112, and a second communication interface 114. In a preferred embodiment, the first communication interface 112 is a low energy communication interface, preferably in the form of a Bluetooth interface, adapted to communicate with one or more data-sensors 140 present within a vicinity thereof. Alternatively, the first communication interface 112 may be any suitable communication interface such as including various wired as well as wireless communication means selected from but not limited to USB, LAN, Wi-Fi, Bluetooth and other Cellular services (2G/4G or NB-loT).
[0040] In a preferred embodiment, the one or more data sensors 140 may be any conventionally known environmental data sensor suitable to sense at least one data type selected from the group consisting of temperature sensors, humidity sensors, leakage sensors, weight sensors, ambient light sensors, vibration sensors, wetness sensors, and geolocation sensors. In other embodiments, the one or more data sensors 140 may include functional / operational data-sensors selected from but not limited to sensors receiving data from one or more components present internally within the facility 150, such as including but not limited to sources such as power based systems, various employee management or other financial ERP installed within the facility, physical security incidents, system generated physical security alarms, facility systems alarms, security and safety incidents in facilities, data resiliency and information security feeds through infosec alarming systems such as Firewall alerts as well as SIEMs, or cyber threats such as vulnerabilities picked up during a pen-test, or through alarms received by firewall / cyber security assurance systems or any manual entries. In yet other embodiments, the data-sensors 140 may include an automation sensor such as including but

not limited to RFID scanner, various data-scanners, barcode scanners, NFC Reader, and the like. In yet other embodiments, the data-sensors 140 may include any combination of any of the above disclosed sensors in accordance with the requirement of the facility 150. [0041] In a preferred embodiment, the data-sensor 140 is inbuilt to the tracking units 110. However, in other embodiments, the data sensors 140 may be operatively connected to the tracking units 110 and may be embodied in a plurality of ways. For example, in some embodiment, the data sensors 140 may be in form of on-board sensor configured onto the tracking unitl 10. However, in other embodiments, each of the data-sensor 140 may be configured within the operational environment of the facility and may be operatively and / communicably connected to the one or more tracking units 110 using the first communication interface 114 selected from one or more of but not limited to various wired and / or wireless but not limited to a USB, LAN Wire and W-Fi, Bluetooth, mobile hotspot, internet, intranet and other communication harnesses.
[0042] It is to be understood that any different numbers, combinations and arrangements of data-sensors 140 could be used without deviating from the scope of the invention. [0043] The second communication interface 114 is generally adapted to communicatively connect the corresponding tracking unit 110 to the back-end server 120 through the communication medium 130. In a preferred embodiment, the second communication interface 114 is a high energy communication interface, generally in the form of a Wi-Fi interface, adapted to communicate with the back-end server 120 through the communication medium 130, generally in the form a network selected from one or more of but not limited to a WAN, Internet, Intranet, Cellular (2G/4G or NB-loT), BLE or Wi-Fi connectivity and the like. Each of the tracking unit is generally configured to store the received data-sets 145 from the data-sensors 140 within an inbuilt storage [not shown] and push towards the backend server 120. [0044] The back-end server 120 is generally a computing unit having one or more data-receiving component 122 adapted to receive data-sets 145 from the plurality of tracking units 110. The data-sets 145 pertains at least to a predetermined key performance identifier, received from the each of the tracking unit 110 and may also include other data-sets such as information from any out of the system data sensors 140 and / or other information from the tracking units 110 such as including but not limited to tracking unit's location 110, automated scanner inputs, and any manual configuration, and the like.
[0045] The back-end server 120 further includes a central data repository 125 connected to a plurality of performance management information sources 126 and consisting of a plurality of data-sets 127 pertaining to analyse performance for each of the assets within the facility

150. Such data-sets 127 may include one or more information that may pertain even remotely to occurrence of an unwanted event within the facility 150. In a preferred embodiment, the central repository 125 is remote to the back-end server 120 and works in a cloud based environment. Further, in some embodiments, the back-end server 120 may be distributed within one or more distributed central servers, which can enable distributed computing, such as cloud computing. Further as illustrated in an exemplary embodiment in Fig. 1 b, the backend server may be configured on a cloud server. However, in other embodiments, the central repository 125 may be positioned in any possible configuration, as known in the art. [0046] The back-end server 120 further includes an asset management module 124 adapted to assess various assets 105 and any risks of unwanted events and / or interruptions associated to said facility 150. Particularly, the asset management module 124 is configured to processes the received data sets 145 in accordance with one or more second programming instructions 160 so to determine a deviation of the predetermined key performance identifier related to the facility 150. In an embodiment of the present invention, asset management module 124 may compute and calculate deviation from expected values, based on the data-sets 145 processed in accordance to programming instructions 160 so as to calculate, identify, assess, rank, and determine a quantitative or qualitative value or level of threats / unwanted events based on known, anticipatory, historical, and/or premonitory data related to location(s) of, for example, the personnel, processes, and the equipment of the facility 150. [0047] In an embodiment, the data repository 125 may include a decision making database 165 comprising a plurality of decision data sets 167 such as for example, including but not limited to a plurality of deviation control parameters and / or features dataset, asset assessment reports comprising assessments of various supply chain facilities, performance identifiers, decision dataset comprising suggested recommendations and / or adjustment plan for overcoming said unwanted events. The data sets 167 comprises a historical database from a plurality of different supply chain facilities, across various geographic and demographic regions, races, origin, socio-economic, biological considerations, and various other similar variations. In some embodiments, the remedial data-sets may be collected from the external sources, such as asset control services providers and research institutes, and/or the management data accumulated by such asset management institutes. [0048] The data repository 125 including the decision database 165 and the plurality of data-sets 127, 167 are constantly upgraded on the basis of one or more learning models selected from but not limited to Natural language processing (NLP), Deep Learning, Machine Learning, statistical learning model, and the like.

[0049] In an embodiment of the present invention, the system 100 including the programming instructions 180 and the implementation to process the data-sets 115 and / or the data repository 125 are based on a deep learning model wherein the model is particularly applied to upgrade the data repository 125 including each of the data sets 127. [0050] Particularly, the deep learning model includes a number of pre-processing steps that are applied on the data stored in the all the individual data sets 115, 127, 167. The pre¬processing steps may include cleansing the data to remove any inconsistencies and assigning weights to each of the parameter for the consideration of assessments. Particularly, a list of parameters / features may be determined at this step.
[0051] Further, the machine learning model and / or the deep learning model includes a learning engine adapted to run a selected model (e.g., deep learning model, Random Forest, multi linear regression, Multilayered, feed-forward neural networks, statistical model or the like) on the data sets 145, 127, 167, and partitions them into either a training dataset or a testing dataset. In a preferred embodiment, the partitioning may apply an 80/20 split between the training dataset and the testing dataset, respectively.
[0052] Thereafter, the learning engine operates to then run the selected model on the training dataset to obtain a resulting output from the model. For example, in a preferred embodiment, the selected model is the Multilayered, feed-forward neural networks, with a Tensor flow backend to build and train the neural networks.
[0053] The learning engine then selects and tunes other model arguments of the training dataset to establish an error percentage. Once the error percentage (i.e., accuracy) established, the learning engine applies a ten-fold cross validation to establish a model stability of the selected model. Further, the learning engine operates dynamically by dynamically selecting the model arguments for each run of the selected model.
[0054] Further, the learning engine operates a final model run on the testing dataset to confirm the accuracy and/or fit of the selected model are within client acceptable limits. When the accuracy and/or fit of the selected model is not within the client acceptable limits or when there are more models left for consideration, a next model may be selected to begin the testing process over again. When the accuracy and/or fit of the selected model is determined to be within the client acceptable limits or when there are no more models left for consideration, selected model is established for use to predict unwanted events / interruptions within the supply chain facility 150.
[0055] In certain other embodiments, the programming instructions 160 may be based on any predetermined supply chain assessment model selected from a statistical models (e.g.,

linear regression, non-linear regression, Monte Carlo simulation), heuristic models (e.g., neural networks, fuzzy logic models, expert system models, state vector machine models useful in risk and safety prediction), and so on, that may be used to predict any unwanted events / risks / interruptions, well in advance within the supply chain facility 150.
[0056] In some embodiments, the back-end server 120 further includes an informed decision module 128 adapted to utilize the programming instruction set 160 to generate a decision plan adapted to reduce the chances of unwanted events / interruptions / risks / hazards within the facility 150. Further, such an informed decision module 128in accordance to informed decision data-sets 167 is adapted to provide a plurality of recommendations and / or suggestions suitable for the various assets 105 / stakeholders at risk so as to overcome the possibility of such events.
[0057] The system 100 further includes a visualization generation component 118 to generate an interactive visualization of the threats / interruptions / unwanted events in accordance with the possibilities determined by the asset management module 124. In a preferred embodiment, the visualization generation component 118 is configured on to the tracking units 110. In a preferred embodiment, the data visualization component displays graphical representations or visualizations of events categorized among multiple dimensions such as geographical areas, incident categories, inventory status, access management systems, and various possible metrics such as for example, financial, physical, IT based, for example. The visualization may further include predetermined as well as dynamically updateable filters on the data, such as risk-scores, dimension, and categorical filters, may require a user to go into a separate data view to select data sets of interest. [0058] In some embodiments, the visualization may include a time-based slider that may enable users to seamlessly switch between live and historical streams that can come from various sources (e.g., real-time store, temporary data cache, historical data store, etc.). Further, the real-time risks may be compared with a historical baseline based on simultaneously streaming from a real-time store, a temporary data cache, or a historical data store. It is understood that various features (e.g., components, operations, or other features) described herein may be implemented separately or in combination with other features. [0059] Examples of functional data set includes regulatory data, security data, utilities data, industrial data, logic controller data, compliance data, corporate IT data, and other types of data from within the facility but are not limited to data feed through a plurality of sources such as access control systems, CCTV cameras, various sensors installed across the facility energy, power based systems, various employee management or other financial ERP installed within

the facility, physical security incidents, system generated physical security alarms, facility systems alarms, security and safety incidents in facilities, data resiliency and information security feeds through infosec alarming systems such as Firewall alerts as well as SIEMs, or cyber threats such as vulnerabilities picked up during a pen-test, or through alarms received by firewall / cyber security assurance systems or any manual entries related to Energy Management IOT, HVAC, physical and cyber protection of Sensitive Assets & Intellectual Property sensitive asset diversion (dangerous chemicals, pathogens, nuclear material), Cyber Attacks - Utilities (Water, Power, Gas), Smart Grids, Transportation etc. [0060] Examples of environmental data set include but are not limited to such temperature information, humidity, light information, leakage information, wetness, storm, snow/fog, heavy rain, earthquake, etc.,
[0061] Examples of automation data set include information received through automatic data-scanners, RFID, data import, or any other source of information available and having an impact on the assessment of the assets 105 within the facility.
[0062] The tracking unit 110 is intended to represent various forms of portable electronic devices, adapted to communicatively connected with other disclosed sensors 140. Fig. 2 illustrates the exemplary tracking unit. The components shown here, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations described and/or claimed in this document.
[0063] In a preferred embodiment, as illustrated in FIG.3, the back-end server 120 is a computing device 300 having a processor 331, memory 332, a storage device 333, a high-speed interface connecting to memory and high-speed expansion ports, and a low speed interface connecting to low speed bus, one or more input/output (I/O) devices 334. Each of the components 331, 332, 333, 334 is interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate.
[0064] The processor 331 may communicate with a user through control interface [not shown] and display interface coupled to a display. The display may be, for example, a TFT LCD (Thin-Film-Transistor Liquid Crystal Display) or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. The display interface may comprise appropriate circuitry for driving the display to present graphical and other information to a user. The control interface may receive commands from a user and convert them for submission to the processor 331. In addition, an external interface in the form of data-receiving component 322 may be provided in communication with processor 331, so as to enable near area communication of the back-end server 300 with other tracking units 110 of the facility 150.

External interface may provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces may also be used.
[0065] The backend server 120 is shown as including the memory 332. The memory 232 may store the executable programming instructions 160. The executable instructions 180 may be stored or organized in any manner and at any level of abstraction, such as in connection with one or more applications, processes, routines, procedures, methods, functions, etc. [0066] In one implementation, the memory 332 is a volatile memory unit or units. In another implementation, the memory 332 is a non-volatile memory unit or units. The memory 332 may also be another form of computer- readable medium, such as a magnetic or optical disk. In one implementation, a computer program product is tangibly embodied in an information carrier. The computer program product contains instructions that, when executed, perform one or more methods, such as those described above. The information carrier is a computer- or machine-readable medium, such as the memory, expansion memory, or memory on processor. [0067] The instructions stored in the memory 332 may be executed by one or more processors, such as a processor 331. The processor 331 may be coupled to one or more input/output (I/O) devices 335.
[0068] In some embodiments, the I/O device(s) 335 may include one or more of a keyboard or keypad, a touchscreen or touch panel, a display screen, a microphone, a speaker, a mouse, a button, a remote control, a joystick, a printer, a telephone or mobile device (e.g., a smartphone), a sensor, etc.
[0069] In some embodiments, the memory 332 may include the central repository 325 for storing data pertaining to various threats related supply chain asset management within the facility 150 and received by the data-receiving component 322. The central repository 325 may further store details on all systems threat notifications and / or alarms and how the alarms were attended to. The central repository 325 furthermore store details on complete incident and event log that clearly shows incident types, incident types, actions taken when incidents happen - all in a nice, visualized format.
[0070] The back-end server 300 may communicate wirelessly with the communication interfaces 114 and / or of the tracking devices 110 through a back-end communication interface 337. The back-end communication interface 337 may provide for communications under various modes or protocols, such as HTTPS, MQTT, sMQTToverWIFI, LAN, or GPRS, among others. Such communication may occur, for example, through radio-frequency transceiver. In addition, short-range communication may occur, such as using a Bluetooth, Wi-Fi, or other

such transceiver (not shown). However, in other embodiments, the data receiving component 322 may use one or more application programming interface (API) connected to the tracking unitl 10 so as to receive data-sets 145 there from in a format acceptable by the source API and readable by the back end server 120. An exemplary back end server is depicted in Fig. 3. [0071] As disclosed above, the back-end server 120 can be operatively coupled to the tracking devices 110 through the communication medium 130 with the aid of the back-end communication interface 237. The communication medium 130 may be in form of Internet, an internet and/or extranet, or an intranet and/or extranet that is in communication with the Internet. The communication medium 130 in some cases is a telecommunication and/or data network. The communication medium 130, in some embodiments may be used to distribute the back-end server 120 within one or more distributed central servers, which can enable distributed computing, such as cloud computing. The system 100 is illustrative. In some embodiments, one or more of the entities may be optional. In some embodiments, additional entities not shown may be included. For example, in some embodiments the system 100 may be associated with one or more networks. In some embodiments, the entities may be arranged or organized in a manner different from what is shown in FIG. 1.
[0072] In an embodiment of the present invention, the processor 161 simultaneously compares the deviation for each of the performance identifier in the form of data-sets 145 and if deviation of one or more performance identifier exceeds a threshold value, then the informed decision making module automatic generates a decision ticket for the management to take a correction action on the basis of the same. In yet another embodiments, the deviation if well beyond the acceptable threshold value, the system may generate a visual / sound alarm so that the user managing the application may take over a quick action onto the same. [0073] FIG. 5 illustrates an exemplary embodiment of the present invention relating to a smart fridge, which can detect and measure various data points such as temperature, humidity, wetness, location, door open/ close status, and power consumption. A barcode scanner helps in inventory management in the fridge. Based on the various data sets received by the data sensors, the cooling performance of the asset, asset intelligence, number of sales per asset, compliance with standards and quality of the products can be analyzed. [0074] FIG. 6 illustrates a flow chart of a method of dynamically managing one or more assets related to a supply chain facility, and a visualization system according to the present invention. The method starts at step 602 and proceeds to step 604.

[0075] At step 604, one or more data sets 145 pertaining to key performance identifiers within the supply chain facility 150, are received at the data receiving component 122 of the back-end server 120.
[0076] At step 606,the collated data sets 145 is sent to the central processor 161 for the purpose of processing thereat using the asset management modules 124 and / or sub-modules 130.
[0077] At step 608, the receive data sets 145 is processed in accordance with the programming instructions 160 to determine a deviation of the key performance indicators of the assets 105 of the facility 150, and in turn a prediction of unwanted events / interruptions / risks / and the like.
[0078] Lastly, at step 610, the generated deviation is visualized by a visualization component 118 to display status of each of the assets 105 onto one or more dashboards, to present risks are identified as per business nature, geographic location and any specific risks identified by the system 100 in accordance to predetermined programming instructions 160. In an embodiment, the processor may utilize one or more computer languages and algorithms to compute the risk scores and completing a visualization thereof. In a preferred embodiment, the alert may be an audio and / or a visual alarm in the form of a flashing LED light on their respective tracking devices and / or high frequency beep / buzzer sound and / or vibration in respective tracking units 110
[0079] Further, the system 100 may publish over a variety of operating environments - web, smartphone and tablet devices. Further, the system 100 generate visual / sound notification to the user of system 100 which may be triggered based on detecting that the pre-determined deviation has been crossed and now the supply chain facility may be in an extremely interruption prone zone. Additionally, in some embodiments, the system 100 may also send reporting emails to one or more addresses as specified within a control interface of the system 100. Moreover, the deviations, including various data-sets etc. are automatically archived within the central repository 125 of the system 100. [0080] The process terminates at step 612.
[0081] Fig 4a to 4c illustrates an exemplary embodiment of the visualization dashboard in accordance with the present invention. According to an embodiment, the system 100 is exemplified with a client architecture system in the form a mobile application 100 as illustrated. The mobile application 100 includes a front-end user interface that can run off a standard web-browser on desktop environments, or a mobile based smartphone or tablet versions (for Android and iOS); and a backend server which can be a light weight workstation machine that

will collect and process the data-sets received from one or more data sources. In an embodiment, the front end user interface includes a login page. The logins for users are created and right management of the users are provisioned at the time of installation of the system 100 to enable security of the data-sets, reports, risks visualization generated on the user interface of the mobile application 100. In some embodiments, one or more users' roles may be provisioned by system administrator managing the system 100.
[0082] When the user logins, the user is presented with a main landing page as illustrated in FIG. 4a displaying a highlight of the critical alarms, total number of alerts, alert categories, incident tickets etc. The alerts are raised when the risk scoring module assesses the risk associated with the facility. The risk scoring module determine threats and / or hazards related to the facility using the data-sets received from different databases. The system envisages the overall risk based on certain business logics, extent of the physical risk, historical impacts that the business has seen as well as possible impacts that the business may see in due course. The module also assesses the supplemental data such as results of internal risk audits, a rundown of latest alerts, curated category and location wise etc. Each of such information is displayed onto a dedicated landing page.
[0083] FIG. 4a further illustrates location-wise risk alert that visualizes the risk applicable to various locations within the facility, and highlights risk as per color codes into high, medium and low risk categories.
[0084] The mobile application displays different dashboards based on the type of the risk alerts such as security risk alert, facility risk alerts, external risk alerts, district-wise risk etc. The security risk alerts dashboard represents all security related alarms and incident tickets that are currently open. These alarms can be categorized based on priority, and alarm type. In a preferred embodiment, the alerts are generated by Artificial Intelligence (Al) algorithm based on set thresholds. The data on the dashboard is presented as per total alarm volume over a period of time.
[0085] Further, the second dashboard as illustrated in Fig. 4b, shows the type of alarms generated for all facility. The dashboard further includes the incident tickets that are currently open. Another dashboard i.e., an external risk alert includes all external alarms picked up from open source media sites, news sites, weather feeds and manual inputs etc. The alarms therein can be categorized based on priority, and incident type, and the data is visualized on a GIS map.
[0086] Besides the above dashboards, the system includes a display for the open tickets over a period of time as illustrated in Fig. 4c. The visualization component generates the

average time between alerts received and response sent, alerts by locations and total tickets by various alert types (IOT, External, IOT, Internal), where internal refers to the device parameters and external refers to the process or machine data. Once the visualization component generates the alert, the user will be able to generate the ticket as per category and publish it into the ticket case manager. All user generated tickets are populated on this dashboard as per unique number, location, data and time stamp, alert code, city, device identifier or alarm category, location and user name in case of a manual input. [0087] Further, the system allows for self-auditing of security arrangements and facility management arrangements across a variety of disciplines for a particular site. The audit schematics will be configured and populated as per client's security and facility management and the system will provide an overall security and facility risk posture score for that facility -which will be highlighted in the landing page.
[0088] The system 100 of the present subject matter is primarily utilized for assessing an assets within a facility 150.The assets may include cold storages, cold supply chain, refrigerated containers, ice cream deep freezers, ice cream push carts, beverage cooler, processed food freezers, refrigerators, milk/dairy products freezers, refrigerators, refrigerated mobile containers such as delivery vans, trucks, containers, shipping containers, and condition controlled containers. These assets have applications across a very wide range of industries such as grocery delivery, food delivery, beverage and liquor delivery, e-pharmacy including condition sensitive pharmacy/medicines, door delivery, hospitality, retailing, pharmaceutical and other industries including condition sensitive pharmacy storage in healthcare unit and vaccines management to the remote villages, industries requiring condition monitored environments such as data centers and server rooms, service stations including in the automotive industry, manufacturing lines such as in the garment/apparel industry.. [0089] Advantageously, such an accurate and timely assessment of unwanted events is particularly beneficial in avoiding any interruptions within the supply chain facility 150. Further, the system 100 connects the physical and digital worlds by automating, collecting, and storing critical data, creating frictionless workflows to automate asset management process. [0090] Moreover, since the system 100 of the present subject matter is able to communicate via various possible communication interfaces known in the art, it provides flexibility to the organizations / facilities to choose the technology backhaul dependent on existing site infrastructure or requirements. Therefore, an infrastructure upgrade within the facility is not required.

[0091] Fig 7a and 7b illustrate two exemplary embodiments of the tracking unit. According to an embodiment, as illustrated in Fig. 7a, the tracking unit 110 is exemplified with a client architecture system. The tracking unit 110 includes a sensor and radio controller 182 which in turn is connected to a plurality of data-sensors 140 namely, wet sensor, door sensor, Digital temperature sensor, a spare digital interface which in turn is connected to power sensor and humidity sensor and vibration sensor, generally using sensor harness. The radio controller 182 is further connected to a cellular model to receive location information from a GPS sensor. In operation, the sensor and radio controller 182 receive readings from various sensors, evaluate and forward towards a processor 184 which additionally receives information from various automation sensors 140 such as for example, barcode scanner, NFC scanner, light sensor etc. Additionally, the tracking unit 110 in such an example includes an on device LED screen to display various alerts, alarms, configuration, readings and the like. [0092] The exemplary embodiment as illustrated in Fig. 7b, is generally same but different in that there is an integrated radio-modem 182 in place of a sensor and radio controller 182 which simply receives and forwards the various sensor values to the processor 184 without evaluating the sensed values.
[0093] Fig. 8a illustrates an exemplary method of configuring the tracking unit 110 of the embodiments as illustrated in Fig. 7a. The method includes installing a preconfigured tracking unit 110 onto one of the asset 105. Thereafter, the tracking unit 110 is connected to various sensors such as door sensor, power sensor and temperature sensor through the harness. Further, the tracking unit 110 is connected to a power source and is made ready to operate within the system 100 of the subject supply chain facility 150. Fig. 8b illustrates an exemplary method of managing assets 105. The method discloses that the tracking unit 110 is initially powered up which in turn starts an initialization sequence. The initialization sequence includes various checks such as cell network registration, location detection, and time synchronization followed by connecting to the back-end server 110 configured on a cloud, and the connection is kept alive to assure a real time management of assets. Thereafter, as illustrated in Fig. 8a, various events, and sensor values are collected and consolidated at the processor 184 of the tracking unit 110. The consolidated data-sets 145 related to the key performance identifiers are then sent to the back-end server 120 to perform the next steps, where a real time management / tracking of all the assets 105 is performed in accordance with the scope of the present disclosure.
[0094] It is noted that various connections are set forth between elements in the description and in the drawings (the contents of which are included in this disclosure by way of reference).

It is noted that these connections in general and, unless specified otherwise, may be direct or indirect and that this specification is not intended to be limiting in this respect. In this respect, a coupling between entities may refer to either a direct or an indirect connection. [0095] Various embodiments of the invention have been disclosed. However, it should be apparent to those skilled in the art that modifications in addition to those described, are possible without departing from the inventive concepts herein. The embodiments, therefore, are not restrictive, except in the spirit of the disclosure. Moreover, in interpreting the disclosure, all terms should be understood in the broadest possible manner consistent with the context. In particular, the terms "comprises" and "comprising" should be interpreted as referring to elements, components, or steps, in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced.
[0096] The disclosed methods and systems, as illustrated in the ongoing description or any of its components, may be embodied in the form of a computer system. Typical examples of a computer system include a general-purpose computer, a programmed microprocessor, a micro-controller, a peripheral integrated circuit element, and other devices, or arrangements of devices that are capable of implementing the steps that constitute the method of the disclosure. [0097] The computer system comprises a computer, an input device, a display unit and the Internet. The computer further comprises a microprocessor. The microprocessor is connected to a communication bus. The computer also includes a memory. The memory may be Random Access Memory (RAM) or Read Only Memory (ROM). The computer system further comprises a storage device, which may be a hard-disk drive or a removable storage drive, such as, a floppy-disk drive, optical-disk drive, and the like. The storage device may also be a means for loading computer programs or other instructions into the computer system. The computer system also includes a communication unit. The communication unit allows the computer to connect to other databases and the Internet through an input/output (I/O) interface, allowing the transfer as well as reception of data from other sources. The communication unit may include a modem, or other similar devices, which enable the computer system to connect to databases and networks, such as, LAN, WAN, and the Internet. The computer system facilitates input from a user through input devices accessible to the system through an I/O interface.
[0098] In order to process input data, the computer system executes a set of instructions that are stored in one or more storage elements. The storage elements may also hold data or

other information, as desired. The storage element may be in the form of an information source, or a physical memory element present in the processing machine.
[0099] The programmable or computer-readable instructions may include various commands that instruct the processing machine to perform specific tasks, such as steps that constitute the method of the disclosure. The systems and methods described can also be implemented using only software programming or using only hardware or by a varying combination of the two techniques. The disclosure is independent of the programming language and the operating system used in the computers. The instructions for the disclosure can be written in all programming languages including, but not limited to, "C", "C++", "Embedded C", "Visual C++," Java", "Python" and "Visual Basic". Further, the software may be in the form of a collection of separate programs, a program module containing a larger program or a portion of a program module, as discussed in the ongoing description. The software may also include modular programming in the form of object-oriented programming. The processing of input data by the processing machine may be in response to user commands, the results of previous processing, or from a request made by another processing machine. The disclosure can also be implemented in various operating systems and platforms including, but not limited to, "Unix," "DOS," "Android and "Linux."
[0100] The programmable instructions can be stored and transmitted on a computer-readable medium. The disclosure can also be embodied in a computer program product comprising a computer-readable medium, or with any product capable of implementing the above methods and systems, or the numerous possible variations thereof. [0101] Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
[0102] These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms "machine- readable medium" and "computer-readable medium" refer to any computer program product, apparatus and/or device

(e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor. [0103] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
[0104] A person having ordinary skills in the art will appreciate that the system, modules, and sub-modules have been illustrated and explained to serve as examples and should not be considered limiting in any manner. It will be further appreciated that the variants of the above disclosed system elements, or modules and other features and functions, or alternatives thereof, may be combined to create other different systems or applications. [0105] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), and the Internet.
[0106] The claims can encompass embodiments for hardware, software, or a combination thereof.
[0107] Although a few implementations have been described in detail above, other modifications are possible. Moreover, other mechanisms for performing the systems and methods described in this document may be used. In addition, the logic flows depicted in the figures may not require the particular order shown, or sequential order, to achieve desirable results. Other steps may be provided, or steps may be eliminated, from the described flows, and other components may be added to, or removed from, the described systems. Accordingly, other implementations are within the scope of the following claims.

WE CLAIM:

1. A system for dynamically managing one or more assets within a facility, in a real time, the
system comprising:
one or more tracking units, each of the tracking units comprising:
a first communication interface adapted to receive data-sets from plurality of sensors, the received data-sets pertaining at least to one of a predetermined key performance indicator associated to said food supply chain; and a processor and memory configured to execute one or more programming instructions embodied thereon, and cause the tracking unit to store and / or process the received key performance indicators form each of the plurality of sensors; and a back-end server communicatively connected to each of the tracking units, the back-end server comprising an asset management module adapted to receive the processed data-sets from each of the tracking unit so as to assess one or more threats within the facility and / or to determine informed decisions related to the facility using the received data-sets.
2. The system of claim 1 further comprising a visualization generation component that generates an interactive visualization of one or more unwanted events and / or threats on to an output screen.
3. The system of claim 1, wherein the tracking device comprises a portable device adapted to be installed and / or affixed onto the one or more assets within the facility.
4. The system of claim 1, wherein the first communication interface comprises a communication interface selected from various wired and / or wireless but not limited to a USB, LAN Wire and Wi-Fi, Bluetooth, mobile hotspot, internet, intranet and other communication harnesses.
5. The system of claim 1, wherein the tracking unit further comprises a second communication interface for sending data-sets towards the backend server, selected from one or more of various wireless communication interfaces but not limited to a W-Fi, Bluetooth, mobile hotspot, internet, intranet and the like.
6. The system of claim 1, wherein the plurality of sensors comprises one or more functional sensors selected form but not limited to sensors receiving data from one or more components present internally within the facility, such as including but not limited to sources such as power based systems, various employee management or other financial ERP installed within the facility, physical security incidents, system generated physical security alarms, facility systems

alarms, security and safety incidents in facilities, data resiliency and information security feeds through infosec alarming systems such as Firewall alerts as well as SIEMs, or cyber threats such as vulnerabilities picked up during a pen-test, or through alarms received by firewall / cyber security assurance systems or any manual entries.
7. The system of claim 1, wherein the plurality of sensors comprises one or more of environmental sensors selected from one or more of but not limited to temperature sensors, humidity sensors, leakage sensors, weight sensors, ambient light sensors, vibration sensors, wetness sensors, and geolocation sensors.
8. The system of claim 1, wherein the back-end server comprises a central repository adapted to store data received from the plurality of tracking units.
9. The system of claim 1 further comprising a visualization generation component that generates an interactive visualization of one or more risks and / or threats on to an output screen.
10. The system of claim 9, wherein the visualization generation component comprises a user interface graphically depicting the one or more risks and / or threats and / or informed decisions related to the facility onto an interactive dashboard.
11. A method for dynamically managing one or more assets within a facility, said method comprising:
receiving at a back-end server, one or more data-sets pertaining to at least one of a key
performance identifier within said facility from one or more tracking units;
storing the received data-sets within a central data-repository; and
processing the received data-sets in accordance with an asset management model to
predict possibility of one or more unwanted events and / or risks and / or threats and / or
determine one or more informed decision related to the supply chain facility.