FORM 2
THE PATENTS ACT 1970 [39 OF 1970] & THE PATENTS (AMENDMENT) RULES, 2006 COMPLETE SPECIFICATION [See Section 10; rule 13]
“METHOD AND SYSTEM FOR MANAGING EMISSIONS IN TRANSPORTATION OF
CONSIGNMENT”
Siemens Technology and Services Pvt. Ltd., of Unit 501/C-1, 5th Floor, Poonam Chambers, A Wing, Dr. Annie Besant Road, Worli, 400018 Mumbai, INDIA,
The following specification particularly describes the invention and the manner in which it is to be performed:

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METHOD AND SYSTEM FOR MANAGING EMISSIONS IN TRANSPORTATION OF
CONSIGNMENT
5 CROSS-REFERENCE TO RELATED APPLICATION
The present invention is an improvement over the IN Patent Application No. 202221041561 filed July 20, 2022, the entire contents of which are incorporated herein by reference.
10 FIELD OF TECHNOLOGY
The present invention relates to supply chain management, specifically to systems and methods for managing, tracking, and optimizing the environmental impact, particularly carbon dioxide (CO2) emissions, associated with the transportation of 15 consignments.
BACKGROUND
In supply chain management, the coordination and tracking of material shipments have traditionally relied on extensive and
20 often cumbersome documentation processes. One of challenges of traditional management of material shipments is the lack of transparency and visibility throughout the various stages of shipment, including transit, receipt, inspection, and integration into supply chain management systems. The reliance on manual
25 processes for handling consignments makes it difficult to track and address inefficiencies effectively. For instance, the tracking of consignments often involves direct communication with transporters or logistics coordinators, and upon arrival at the

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destination, the receipt and verification processes are largely manual, with records maintained in physical registers. This manual verification, often based on standard yet potentially inaccessible templates, poses significant hurdles for future audits and 5 compliance checks within supply chain management software.
While the logistics and operational challenges within supply chains are known issues, a growing concern also lies in the environmental impact of these operations, particularly the emissions generated from the transportation of goods. The
10 conventional methods of tracking and managing supply chains have largely overlooked the need to quantify and mitigate the carbon footprint associated with material shipments. This oversight is increasingly problematic as global awareness of environmental sustainability rises and regulatory pressures to reduce emissions
15 are intensifying.
Existing solutions to manage emissions within supply chains have been limited in scope and effectiveness. Conventional approaches often rely on assumptions, such as estimated travel distances and generalized vehicle types, to calculate emissions. These methods
20 lack the precision and reliability necessary to accurately gauge the environmental impact of transportation activities. Specifically, these approaches fail to account for the specific attributes of each consignment and the vehicles used for transportation, leading to generalized and often inaccurate
25 emission estimates. Moreover, the absence of real-time tracking and verification mechanisms prevents the accurate measurement of actual emissions and the timely update of emission records in supply chain management systems. This makes it difficult for organizations to set realistic emission reduction targets or to
30 implement effective sustainability practices

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The present invention seeks to address these shortcomings by providing a comprehensive solution that leverages real-time data, tracking technologies, and emission calculation methodologies. The present invention aims to transform the supply chain management 5 into a more transparent, efficient, and environmentally responsible process, aligning operational practices with the need for sustainability.
SUMMARY
10 In an aspect, a computer-implemented method for managing emissions in transportation of a consignment is provided. The method comprises receiving, from a consignment database, transportation data for the consignment being transported by an assigned vehicle, to be delivered to a target location from a source location,
15 including a vehicle ID and a travel distance. The method further comprises retrieving an emission factor including information about emission per unit distance for the assigned vehicle based on the vehicle ID thereof. The method further comprises determining estimated emissions for the transportation of the consignment via
20 the assigned vehicle based on the emission per unit distance and the travel distance. The method further comprises generating a unique machine-readable label for the consignment, including the transportation data and the estimated emissions. The method further comprises authenticating the assigned vehicle in which the
25 consignment is to be transported at the source location using the unique machine-readable label.
In one or more embodiments, the assigned vehicle is authenticated at the source location if the estimated emissions, as per the unique machine-readable label, is within a prescribed limit for 30 the consignment.

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In one or more embodiments, the vehicle ID of the assigned vehicle comprises vehicle registration number details. Herein, the method comprises accessing a regulatory database based on the vehicle registration number to fetch the emission factor for the assigned 5 vehicle.
In one or more embodiments, the vehicle ID of the assigned vehicle comprises vehicle attributes details including one or more of vehicle fuel type, vehicle capacity and vehicle make. Herein, the method comprises deriving the emission factor for the assigned 10 vehicle based on the vehicle attributes details.
In one or more embodiments, the transportation data further includes information about consignment load, and the emission factor further includes information about emission per consignment load for the assigned vehicle. Herein, the method comprises 15 adjusting the estimated emissions for the transportation of the consignment via the assigned vehicle based on the emission per consignment load and the consignment load.
In one or more embodiments, the method further comprises tracking the consignment during transportation from the source location to
20 the destination location using a Global Positioning System (GPS) of the assigned vehicle; determining an actual travel distance for the transportation of the consignment; and computing actual emissions for the transportation of the consignment via the assigned vehicle based, at least in part, on the emission factor
25 and the actual travel distance.
In one or more embodiments, the method further comprises creating emission logs in the consignment database, including information about the actual emissions and the actual travel distances associated with each consignment and by each assigned vehicle.

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In one or more embodiments, the method further comprises ranking suppliers based, at least in part, on actual emissions per unit actual distance associated with transportation of the consignments using one or more vehicles thereof, as obtained from the emission 5 logs in the consignment database; and providing the rankings of the suppliers within a consignment management system.
In another aspect, a consignment management system for managing emissions in transportation of a consignment is provided. The consignment management system comprises one or more processing
10 units. The consignment management system further comprises a consignment database communicatively coupled to the one or more processing units, wherein the consignment database is configured to store information associated with the consignments. The consignment management system further comprises a memory unit
15 communicatively coupled to the one or more processing units, wherein the memory unit comprises a consignment management module configured to perform the aforementioned method steps using the information stored in the consignment database.
In yet another aspect, a computer-program product having machine-20 readable instructions stored therein is provided, which when executed by one or more processing units, cause the one or more processing units to perform the aforementioned method steps.
Still, other aspects, features, and advantages of the invention are readily apparent from the following detailed description, 25 simply by illustrating a number of particular embodiments and implementations, including the best mode contemplated for carrying out the invention. The invention is also capable of other and different embodiments, and its several details may be modified in various obvious respects, all without departing from the scope of

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the invention. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.
BRIEF DESCRIPTION OF ACCOMPANYING DRAWINGS
5 A more complete appreciation of the present invention and many of the attendant aspects thereof will be readily obtained as the same becomes better understood by reference to the following description when considered in connection with the accompanying drawings:
10 FIG 1 is a flowchart of a computer-implemented method for managing emissions in transportation of a consignment, in accordance with one or more embodiments of the present invention;
FIG 2 is a block diagram representation of a system for managing emissions in transportation of a consignment, in accordance with 15 one or more embodiments of the present invention;
FIG 3 is an architecture for a supply chain management system with the present system for managing emissions in transportation of a consignment integrated therein, in accordance with one or more embodiments of the present invention;
20 FIG 4 is a block diagram of a cloud-based system for managing emissions in transportation of a consignment, in accordance with one or more embodiments of the present invention; and
FIG 5 is a block diagram of a server-based system for managing emissions in transportation of a consignment, in accordance with 25 one or more embodiments of the present invention.
DETAILED DESCRIPTION

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The present invention provides a method and a system for managing emissions in transportation of a consignment. Various embodiments are described with reference to the drawings, where like reference numerals are used in reference to the drawings. Like reference 5 numerals are used to refer to like elements throughout.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments. These specific details need not be employed to practice embodiments. In other instances, well known materials or methods 10 have not been described in detail in order to avoid unnecessarily obscuring embodiments.
While the present invention is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be 15 described in detail. There is no intent to limit the present invention to the particular forms disclosed. Instead, the present invention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present invention.
20 Referring now to FIG 1, illustrated is a flowchart of a computer-implemented method (as represented by reference numeral 100, and hereinafter simply referred to as "method 100") for managing emissions in transportation of a consignment, in accordance with an embodiment of the present invention. The method 100 provides a
25 comprehensive approach to managing emissions within the context of supply chain transportation, involving tracking, verifying, and assessing environmental impact of transportation of consignments. The method 100 integrates into existing supply chain operations, utilizing real-time data and analytics to provide a detailed
30 understanding of the emissions footprint of each consignment. The

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method 100 leverages technologies to enhance transparency, accuracy, and efficiency in the monitoring and reduction of emissions associated with the transportation of goods.
Referring to FIG 2, illustrated is a block diagram of a consignment 5 management system 200 (hereinafter, sometimes, referred to as system 200") for managing emissions in transportation of a consignment, in accordance with one or more embodiments of the present invention. It may be appreciated that the system 200 described herein may be implemented in various forms of hardware,
10 software, firmware, special purpose processors, or a combination thereof. One or more of the present embodiments may take a form of a computer program product comprising program modules accessible from computer-usable or computer-readable medium storing program code for use by or in connection with one or more computers,
15 processors, or instruction execution system. For the purpose of this description, a computer-usable or computer-readable medium may be any apparatus that may contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The
20 medium may be electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation mediums in and of themselves as signal carriers are not included in the definition of physical computer-readable medium include a semiconductor or solid state memory, magnetic
25 tape, a removable computer diskette, random access memory (RAM), a read only memory (ROM), a rigid magnetic disk and optical disk such as compact disk read-only memory (CD-ROM), compact disk read/write, and digital versatile disc (DVD). Both processors and program code for implementing each aspect of the technology may be
30 centralized or distributed (or a combination thereof) as known to those skilled in the art.

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In an example, the system 200 may be embodied as a computer-program product programmed for digitalizing and analysing the engineering diagram for the industry environment. The system 200 may be incorporated in one or more physical packages (e.g., chips). By 5 way of example, a physical package includes an arrangement of one or more materials, components, and/or wires on a structural assembly (e.g., a baseboard) to provide one or more characteristics such as physical strength, conservation of size, and/or limitation of electrical interaction. It is contemplated that in certain
10 embodiments the computing device may be implemented in a single chip. As illustrated, the system 200 includes a communication mechanism such as a bus 202 for passing information among the components of the system 200. The system 200 includes one or more processing units 204 and one or more memory units 206. Herein, the
15 memory unit 206 is communicatively coupled to the processing unit 204. In an example, the memory unit 206 may be embodied as a computer readable medium on which program code sections of a computer program are saved, the program code sections being loadable into and/or executable in a system to make the system 200
20 execute the steps for performing the said purpose.
Generally, as used herein, the term "processing unit" refers to a computational element that is operable to respond to and processes instructions that drive the system 200. Optionally, the processing unit includes, but is not limited to, a microprocessor, a
25 microcontroller, a complex instruction set computing (CISC) microprocessor, a reduced instruction set (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or any other type of processing circuit. Furthermore, the term "processing unit" may refer to one or more individual processors, processing
30 devices and various elements associated with a processing device that may be shared by other processing devices. Additionally, the

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one or more individual processors, processing devices and elements are arranged in various architectures for responding to and processing the instructions that drive the system 200.
Herein, the memory unit 206 may be volatile memory and/or non-5 volatile memory. The memory unit 206 may be coupled for communication with the processing unit 204. The processing unit 204 may execute instructions and/or code stored in the memory unit 206. A variety of computer-readable storage media may be stored in and accessed from the memory unit 206. The memory unit 206 may
10 include any suitable elements for storing data and machine-readable instructions, such as read only memory, random access memory, erasable programmable read only memory, electrically erasable programmable read only memory, a hard drive, a removable media drive for handling compact disks, digital video disks,
15 diskettes, magnetic tape cartridges, memory cards, and the like.
In particular, the processing unit 204 has connectivity to the bus 202 to execute instructions and process information stored in the memory unit 206. The processing unit 204 may include one or more processing cores with each core configured to perform
20 independently. A multi-core processor enables multiprocessing within a single physical package. Examples of a multi-core processor include two, four, eight, or greater numbers of processing cores. Alternatively, or in addition, the processing unit 204 may include one or more microprocessors configured in
25 tandem via the bus 202 to enable independent execution of instructions, pipelining, and multithreading. The processing unit 204 may also be accompanied with one or more specialized components to perform certain processing functions and tasks such as one or more digital signal processors (DSP), and/or one or more
30 application-specific integrated circuits (ASIC). Other specialized components to aid in performing the inventive functions described

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herein include one or more field programmable gate arrays (FPGA) (not shown), one or more controllers (not shown), or one or more other special-purpose computer chips.
The system 200 may further include an interface 208, such as a 5 communication interface (with the said terms being interchangeably used) which may enable the system 200 to communicate with other systems for receiving and transmitting information. The communication interface 208 may include a medium (e.g., a communication channel) through which the system 200 communicates
10 with other system. Examples of the communication interface 208 may include, but are not limited to, a communication channel in a computer cluster, a Local Area Communication channel (LAN), a cellular communication channel, a wireless sensor communication channel (WSN), a cloud communication channel, a Metropolitan Area
15 Communication channel (MAN), and/or the Internet. Optionally, the communication interface 208 may include one or more of a wired connection, a wireless network, cellular networks such as 2G, 3G, 4G, 5G mobile networks, and a Zigbee connection.
The system 200 also includes a consignment database 210. As used
20 herein, the consignment database 210 is an organized collection of
structured data, typically stored in a computer system and designed
to be easily accessed, managed, and updated. The consignment
database 210 may be in form of a central repository of information
about all the consignments that may be queried, analysed, and
25 processed to support various applications and business processes.
The consignment database 210 may include information about origin,
destination and contents of each consignment, and the vehicles
assigned for transportation thereof. The consignment database 210
serves as the primary source of data, facilitating the retrieval
30 of relevant transportation details for calculating estimated
emissions.

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The system 200 further includes an input device 212 and an output device 214. The input device 212 may take various forms depending on the specific application of the system 200. In an example, the input device 212 may include one or more of a keyboard, a mouse, 5 a touchscreen display, a microphone, a camera, or any other hardware component that enables the user to interact with the system 200. Further, the output device 214 may be in the form of a display, a printer, a communication channel, or the like, without any limitations.
10 In the present system 200, the processing unit 204 and accompanying components have connectivity to the memory unit 206 via the bus 202. The memory unit 206 includes both dynamic memory (e.g., RAM, magnetic disk, writable optical disk, etc.) and static memory (e.g., ROM, CD-ROM, etc.) for storing executable instructions that
15 when executed perform the method steps described herein for managing emissions in transportation of a consignment. In particular, the memory unit 206 includes a consignment management module 216 to perform steps for the said purpose.
Referring to FIGS 1-2 in combination, various steps of the method 20 100 (as described hereinafter), which may be executed in the consignment management system 200, or specifically in the processing unit 204 of the consignment management system 200, for managing emissions in transportation of a consignment, are described. It may be appreciated that although the method 100 is 25 illustrated and described as a sequence of steps, it may be contemplated that various embodiments of the method 100 may be performed in any order or different combinations, and need not include all of the illustrated steps.
At step 110, the method 100 includes receiving, from the 30 consignment database 210, transportation data for the consignment

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being transported by an assigned vehicle, to be delivered to a target location from a source location, including a vehicle ID and a travel distance. As used herein, the term "transportation data-refers to the specific set of information related to the 5 transportation of the consignment that is retrieved from the consignment database 210. The transportation data comprises various details, including but not limited to, the source location (the starting point of the consignment), the target location (the final destination where the consignment is to be delivered), and
10 the travel distance (the total distance that the assigned vehicle is expected to cover to transport the consignment from the source to the target location). The "assigned vehicle" refers to the specific vehicle designated for the transportation of the consignment. For instance, each vehicle in the consignment
15 management system 200 may be identified by vehicle ID. This identifier may include various forms of vehicle identification, such as the vehicle registration number or a unique code assigned by the supply chain management system. The vehicle ID may be utilized for retrieving specific vehicle-related information, as
20 discussed later.
Herein, the method 100 involves accessing the consignment database 210 to retrieve the transportation data for the specific consignment. This data provides the foundation for subsequent calculations and processes including the estimation of emissions, 25 the generation of machine-readable labels, and the tracking of the consignment. By capturing and processing this transportation data, the method 100 ensures that emissions management is based on precise, real-time information, enhancing the overall efficiency and sustainability of supply chain operations.
30 At step 120, the method 100 includes retrieving an emission factor including information about emission per unit distance for the

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assigned vehicle based on the vehicle ID thereof. The emission factor, in present context, represents a quantity of emissions produced by the assigned vehicle per unit of distance travelled. The emission factor may typically be expressed in terms of carbon 5 dioxide equivalent emissions (e.g., in kilogram) per unit distance (e.g., in kilometre). This information may be used for calculating the estimated emissions associated with transporting a consignment by the assigned vehicle. The emission factor takes into account various attributes of the vehicle, including its fuel efficiency, 10 engine type, age, and compliance with environmental standards, to provide an accurate measure of its environmental impact per unit distance.
Herein, the method 100 involves utilizing the vehicle ID to access a database or registry where vehicle-specific data is stored. This
15 may be an internal database maintained by the supply chain management system or an external regulatory database that contains detailed information on vehicle standards, fuel types, and associated emission data. Using the vehicle ID, the method 100 may involve querying such database to retrieve the specific emission
20 factor associated with the assigned vehicle. As may be appreciated, the querying process may be executed by using APIs (application programming interfaces) or the like. This emission factor is applied to calculate the estimated emissions for transportation of the consignment, taking into account the travel distance (as
25 discussed later in the description).
In an embodiment, the vehicle ID of the assigned vehicle comprises vehicle registration number details. Herein, the method 100 comprises accessing a regulatory database based on the vehicle registration number to fetch the emission factor for the assigned 30 vehicle. That is, the method 100 utilizes the registration number of the vehicle as a key identifier to interact with external

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regulatory databases. These databases are comprehensive repositories maintained by regulatory authorities (like Regional Transportation Offices (RTOs)), containing detailed information about registered vehicles, including but not limited to, their 5 make, model, year of manufacture, fuel type, engine specifications, and compliance with various environmental and emission standards (e.g., BS IV, BSVI, Euro 6, EPA standards).
The process may begin with securely connecting to the regulatory database. This connection may be established via an application
10 programming interface (API) or other data exchange protocols. The consignment management system 200 then submits a query to such database, using the vehicle registration number as the search criterion. The query is formulated to retrieve specific information relevant to calculating the emission factor for the
15 vehicle. Upon receiving the query, the regulatory database processes the request and returns the relevant vehicle information. This information is then analyzed by the consignment management system 200 to determine the emission factor. The analysis takes into account the various parameters that influence
20 emissions of the assigned vehicle, such as fuel type and engine efficiency, and may involve complex calculations or the application of standardized emission factor models specific to different vehicle categories and standards.
Alternatively, or additionally, the vehicle ID of the assigned 25 vehicle comprises vehicle attributes details including one or more of vehicle fuel type, vehicle capacity and vehicle make. Herein, the method 100 comprises deriving the emission factor for the assigned vehicle based on the vehicle attributes details. As used herein, the "fuel type" represents understanding whether the 30 vehicle operates on gasoline, diesel, electric, hybrid, or alternative fuels, as each fuel type has a different emission

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profile; the "vehicle capacity" represents the load capacity of the vehicle, which may influence fuel efficiency and, consequently, emissions per unit distance, especially under varying load conditions; and the "vehicle make and model" 5 represents specific makes and models of the vehicle, including engine efficiency, aerodynamics, and technology enhancements, which can impact its emissions. This data can be sourced directly from the consignment database where such details are recorded when the vehicle is assigned for a consignment or can be input manually 10 by operators or automatically via integrated vehicle management systems.
Using the vehicle attributes, the method 100 proceeds to derive the emission factor through a series of calculations and data analyses. Based on fuel type and engine specifications of the
15 vehicle (often tied to the make and model), the fuel efficiency is calculated. This may involve referencing manufacturer data or industry-standard efficiency ratings for similar vehicle types. Vehicles operating below or above optimal load conditions can exhibit different fuel efficiencies, thereby affecting emissions.
20 The capacity data of vehicle is used to adjust the emission factor based on typical load conditions. The compliance of the vehicle with emission standards (based on make, model, and year) is considered to refine the emission factor further. Vehicles designed to comply with stricter emission standards typically have
25 lower emission factors. The method 100 may further employ empirical data and theoretical models to estimate emissions based on the collected vehicle attributes. If available, historical emission data from similar vehicles under comparable conditions can be used to refine the emission factor, making it more representative of
30 real-world conditions.

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At step 130, the method 100 includes determining estimated emissions for the transportation of the consignment via the assigned vehicle based on the emission per unit distance and the travel distance. Such determination of the estimated emissions for 5 the transportation of a consignment via the assigned vehicle is utilized for managing and reducing environmental impacts within supply chain operations. This determination is made by utilizing the emission factor, which quantifies emissions per unit distance for the assigned vehicle, in conjunction with the total travel 10 distance the consignment is expected to cover from the source location to the target location.
The process begins by using the previously determined emission factor. Simultaneously, the process considers the travel distance. The travel distance is the total length of the journey that the
15 assigned vehicle will undertake to transport the consignment from its point of origin to its destination. This distance is either pre-determined based on route planning algorithms and geographical data, including information about any deviations from the planned route. Using the emission factor and the travel distance, the
20 process proceeds to calculate the estimated emissions for transportation of the consignment. This calculation involves multiplying the emission factor by the travel distance, providing a quantitative estimate of the total emissions that will be generated.
25 In some embodiments, the transportation data further includes information about consignment load, and the emission factor further includes information about emission per consignment load for the assigned vehicle. Herein, the method 100 comprises adjusting the estimated emissions for the transportation of the
30 consignment via the assigned vehicle based on the emission per consignment load and the consignment load. That is, the method 100

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incorporates an additional layer of specificity by considering the consignment load and its impact on emissions. This approach acknowledges that the weight and volume of the consignment can significantly influence the fuel efficiency of the assigned 5 vehicle and, consequently, the emissions produced during transportation. The transportation data, in this context, is expanded to include detailed information about the consignment load. This information encompasses the weight, volume, or both, of the consignment being transported. Parallelly, the emission factor 10 used in this calculation not only accounts for emissions per unit distance, but also includes a component that reflects emissions per unit of consignment load.
The method 100 then adjusts the estimated emissions based on such emission factor and the specific consignment load. This adjustment
15 involves a more complex calculation that integrates the basic emissions estimate, derived from the emissions per unit distance and the travel distance, with an additional component that accounts for the load-specific emissions. This may involve a proportional or algorithmic adjustment that scales the emissions estimate
20 according to the size and weight of the consignment relative to capacity and performance characteristics of the vehicle. Thereby, the method 100 provides a more accurate and realistic estimate of the environmental impact of transporting the consignment. This is particularly important for logistics operations that handle a wide
25 variety of consignment types and sizes, as it allows for more precise tracking and management of emissions. Furthermore, this approach enables more targeted strategies for reducing emissions, such as load optimization, vehicle selection based on load compatibility, and even the exploration of alternative
30 transportation modes that may offer lower emissions for specific types of consignments.

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In some embodiments, the method 100 further comprises tracking the consignment during transportation from the source location to the destination location using a Global Positioning System (GPS) of the assigned vehicle. This usually involves the activation of the 5 GPS device equipped in the assigned vehicle, which continuously communicates geographical location of the vehicle to the consignment management system 200. This real-time tracking capability allows the consignment management system 200 to monitor progress of the consignment as it moves from the source location 10 towards the destination location. The GPS data provides insights into the route taken by the vehicle, including any deviations from the planned path, stops made along the way, and variations in speed, all of which can influence fuel consumption and emissions.
The method 100, then, comprises determining an actual travel
15 distance for the transportation of the consignment. That is,
utilizing the data collected through GPS tracking, the consignment
management system 200 proceeds to determine the actual travel
distance covered by the assigned vehicle during the transportation
of the consignment. This involves analysing the GPS data to
20 construct a detailed trajectory of journey of the vehicle, from
which the total distance travelled can be accurately calculated.
This actual travel distance reflects the real-world conditions of
the transportation process, including any unforeseen circumstances
such as detours, traffic congestion, or changes in the planned
25 route.
The method 100, then, comprises computing actual emissions for the transportation of the consignment via the assigned vehicle based, at least in part, on the emission factor and the actual travel distance. That is, with the actual travel distance established, 30 the consignment management system 200 then computes the actual emissions generated during the transportation of the consignment.

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This computation leverages the previously determined emission factor, which provides a baseline measure of emissions per unit distance of the vehicle. By applying this emission factor to the actual travel distance, the consignment management system 200 5 calculates a more precise estimate of the total emissions produced during the transportation of the consignment.
This computation of actual emissions allows to assess and manage the environmental impact of supply chain operations. Unlike the initial estimation of emissions based on anticipated travel data
10 and vehicle specifications, this technique uses concrete data derived from the actual transportation process. This not only enhances the accuracy of the emissions data but also provides a basis for comparing estimated and actual emissions, offering insights into the efficiency of the transportation process and
15 identifying opportunities for further emissions reduction.
In some embodiments, the method 100 further comprises creating emission logs in the consignment database 210, including information about the actual emissions and the actual travel distances associated with each consignment and by each assigned
20 vehicle. Such creation of emission logs within the consignment database 210 helps in consolidating and archiving the environmental data associated with the transportation of consignments. This process involves systematically recording and storing information about the actual emissions produced and the
25 actual travel distances covered by each consignment and the respective assigned vehicles. For instance, the process may involve structuring the data into log entries that capture key details such as the consignment identifier, the vehicle ID, the actual travel distance covered during the transportation, and the
30 computed actual emissions for the journey. These emission logs serve as a comprehensive environmental record, enabling detailed

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analysis, reporting, and optimization strategies for reducing the carbon footprint of supply chain operations.
These logs facilitate the comparison of emissions data over time. By analysing trends and patterns within the emission logs, supply 5 chain managers can assess the effectiveness of implemented emission reduction strategies and identify areas for further improvement. For instance, a consistent reduction in emissions for consignments transported along a particular route could indicate the success of route optimization efforts or the beneficial impact
10 of using more fuel-efficient vehicles. The integration of emission logs into the consignment database also enhances transparency and accountability within supply chain operations. By maintaining a detailed record of the actual environmental impact associated with each consignment, the consignment management system 200 allows for
15 sustainability reporting, regulatory compliance, and stakeholder communication.
The method 100 and the consignment management system 200 of the present invention are not only limited to calculating emissions but are part of a broader supply chain management system,
20 implemented for enhancing transparency, efficiency, and sustainability in the logistics operations. The method 100 and the consignment management system 200 are designed to significantly enhance the transparency, efficiency, and sustainability of logistics operations. This holistic approach not only addresses
25 environmental concerns but also streamlines the entire supply chain process, ensuring that each stage from transportation to delivery is optimized for both performance and ecological impact.
At step 140, the method 100 includes generating a unique machine-readable label for the consignment, including the transportation 30 data and the estimated emissions. This unique machine-readable

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label serves as a digital fingerprint, interlinking logistical details of the consignment with its environmental footprint. The process of generating this unique machine-readable label begins once the estimated emissions for the consignment have been 5 calculated, utilizing the emission factor and travel distance as previously described. The consignment management system 200 then compiles the transportation data, which includes but is not limited to, origin and destination of the consignment, the assigned vehicle ID, and the estimated travel distance, along with the calculated 10 estimated emissions. This compiled data is then encoded into a machine-readable format, such as a QR code or barcode, to create the unique machine-readable label.
Once generated, the unique machine-readable label is affixed to the consignment or its documentation, serving multiple functions
15 throughout the journey. Such the unique machine-readable label provides a quick and easy way to authenticate the consignment and verify that the assigned vehicle matches the planned logistics and emissions estimates. This is particularly crucial at the source location, where the consignment is prepared for departure,
20 ensuring that all logistical arrangements align with sustainability goals. Further, as the consignment moves through the supply chain, the unique machine-readable label can be scanned at various checkpoints to update its status, track its location, and verify its progress. This real-time tracking capability,
25 facilitated by the unique machine-readable label, enhances the visibility and control over the consignment, contributing to improved logistical efficiency and security.
At step 150, the method 100 includes authenticating the assigned
vehicle in which the consignment is to be transported at the source
30 location using the unique machine-readable label. That is, upon
generating the unique machine-readable label, which includes the

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transportation data and the estimated emissions for the consignment, it is used in the authentication process. At the source location, where the consignment is prepared for dispatch, the assigned vehicle is presented for verification. The unique 5 machine-readable label, affixed to the consignment or its accompanying documentation, is scanned using a suitable device, such as a handheld scanner or a mobile device equipped with the appropriate scanning application. The scanning process retrieves the encoded data from the unique machine-readable label, including
10 the vehicle ID that was determined during the consignment planning phase. This vehicle ID is then cross-referenced with the actual vehicle present at the source location, specifically verifying the vehicle identification details, such as its registration number, make, and model, against the information stored within the
15 consignment management system 200. This verifies that the vehicle present is indeed the one that has been assigned to the consignment, ensuring consistency with the transportation plan.
This process may also be used to confirm that the vehicle aligns with the environmental considerations integral to the
20 transportation of the consignment, particularly the estimated emissions calculated based on specific characteristics of the vehicle and the planned travel distance. This is particularly important in scenarios where specific vehicles are selected for their lower emission factors or compatibility with certain types
25 of consignments, as part of the broader effort to reduce the carbon footprint of supply chain operations.
In an embodiment, the assigned vehicle is authenticated at the source location if the estimated emissions, as per the unique machine-readable label, is within a prescribed limit for the 30 consignment. For this purpose, when the assigned vehicle arrives at the source location (for example, to collect the consignment),

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the process involves scanning the unique machine-readable label to retrieve the encoded data, specifically the estimated emissions associated with transportation of the consignment. The consignment management system 200 then compares these estimated emissions 5 against a predefined emissions threshold, which represents the maximum allowable emissions limit for the consignment as per sustainability policies of the organization or regulatory standards. This threshold may be determined based on factors such as the nature of the consignment, the distance to be travelled, 10 and broader environmental objectives, such as carbon footprint reduction targets.
Now, if the estimated emissions for the consignment, as derived from the unique machine-readable label, fall within the acceptable range of the prescribed limit, the assigned vehicle is
15 authenticated and approved for the transportation task. This approval signifies that use of the assigned vehicle for transportation of the consignment is consistent with the desired environmental sustainability criteria. However, if the estimated emissions exceed the prescribed limit, the consignment management
20 system 200 flags a potential issue, indicating that the transportation of the consignment by the assigned vehicle may not align with the environmental objectives. In such cases, corrective actions can be initiated, such as re-evaluating the transportation plan, considering alternative routes or transportation modes that
25 may result in lower emissions, or selecting a different vehicle with a lower emission factor that meets the prescribed emissions criteria.
In some embodiments, the method 100 further comprises ranking
suppliers based, at least in part, on actual emissions per unit
30 actual distance associated with transportation of the consignments
using one or more vehicles thereof, as obtained from the emission

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logs in the consignment database 210. Here, the consignment database 210 serves as a central repository for all data related to the transportation of consignments, including emission logs. These logs include comprehensive information about the 5 consignment, including the actual distance travelled and the actual emissions produced by the vehicle or vehicles involved in the transportation. The term "actual emissions" refers to the measured output of greenhouse gases, particularly CO2 equivalents, generated during the transportation of consignments. It is 10 differentiated from "estimated emissions," which are based on predictive calculations prior to the actual transport taking place. The "actual distance" denotes the exact mileage or kilometres covered by the vehicle from the source to the target location, as opposed to a pre-determined or estimated distance.
15 To rank the suppliers, the method 100 takes into account cumulative actual emissions relative to cumulative actual distance for each supplier, which is a ratio that provides an emissions intensity measure for the transportation activities associated with each supplier. The suppliers with lower emissions intensity (i.e.,
20 fewer emissions per kilometre or mile) receive a more favourable ranking, reflecting more efficient and environmentally friendly transportation practices. This ranking data may then be used to order suppliers on a scale within the supply chain management system. This ranking is dynamic and can be updated continuously or
25 at regular intervals, depending on new data entries to the emission logs. Therefrom, organizations can use the supplier rankings to choose suppliers that align with their own environmental goals and compliance requirements.
Referring to FIG 3, illustrated is a schematic of an architecture
30 of a supply chain management system (as represented by reference
numeral 300) in which the consignment management system 200 of the

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present invention is integrated to provide a holistic approach to not only address environmental concerns but also streamline the entire supply chain process, ensuring that each stage from transportation to delivery is optimized for both performance and 5 environmental impact. The supply chain management system 300 provides a comprehensive approach to address environmental concerns through emissions tracking and also by streamlining the entire process from consignment pickup to final delivery. Each stage within this architecture is optimized for performance while 10 simultaneously minimizing environmental impact.
The supply chain management system 300 is broadly a combination of three interconnected portals, namely a vendor portal 300A, a supply chain portal 300B, and a consignment portal 300C. The consignment portal 300C is where the consignment management system 200 is 15 implemented. The process begins at block 302. This marks the initiation of the where the vendor begins interaction with the supply chain management system 300.
At block 304, a vendor logs in to access the vendor portal 300A. The vendor portal 300A may provide a secure interface where they
20 can manage shipments, submit transportation details, and engage with the consignment management system 200. At block 306, the vendor submits Advance Shipment Notifications (ASNs), which detail the transportation specifics, including the vehicle's identification data, driver details, and the size of the shipment.
25 Here, the vendor also generates a unique machine-readable label, encoding transportation data and estimated emissions information for the consignment.
The process then moves to the consignment portal 300C. Here, at
block 308, upon receiving the ASN and transportation details, the
30 consignment management system 200 proceeds to calculate key

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factors that will inform the subsequent supply chain stages. This includes identifying the 'From' and 'To' locations of the shipment using GPS for optimal route calculation, assessing the vehicle's type, make, and age to estimate emissions by querying a regulatory 5 database (such as https://vahan.parivahan.gov.in/nrservices/). At block 310, the consignment management system 200 generates an expected CO2 emission value for the vendor's reference. This ensures that the chosen route and vehicle are optimized not only for logistics but also for environmental efficiency (as discussed 10 in the preceding paragraphs).
Further, the process moves back to the vendor portal 300A. At block 312, the vendor, after reviewing the provided recommendations, makes informed decisions regarding the vehicle or route modifications to align with environmental and operational goals. 15 The vendor selects a vehicle and route based on a balance between performance metrics and the estimated emissions. The consignment is dispatched along with the unique machine-readable label, which facilitates authentication and real-time tracking throughout the transportation process.
20 The process then takes place at the supply chain portal 300B. Here, at block 314, at receiving end (for instance), the consignment is verified upon arrival. The unique machine-readable label affixed to the consignment is scanned to confirm the physical receipt of the materials. This scanning verifies the consignment against the
25 transportation data and ensures the authenticity of the delivery. This also serves as a checkpoint for environmental compliance, confirming that the transportation adhered to the prescribed emission limits.
After the consignment has been verified, at block 316, in the 30 consignment portal 300C, the process updates the consignment

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database 210. The actual travel distance and actual emissions data, ascertained through the GPS tracking and emissions calculation mechanisms of the consignment management system 200, are logged. This data is integrated into the consignment management system 5 200, completing the transportation of the consignment and enabling the consignment management system 200 to post the Goods Received Note (GRN), indicative of the successful completion of the delivery process. The process ends at block 318.
In some cases, the integrated consignment management system 200
10 may further incorporate an Artificial Intelligence (AI) engine
which is configured to analyse the vast amounts of data processed
throughout the supply chain lifecycle. The AI engine performs a
dynamic analysis of various data points to produce outputs that
significantly enhance supply chain decision-making and efficiency.
15 For instance, the AI engine may provide supplier ranking (as
discussed), where it conducts a comparative analysis of the
predicted versus the actual time of arrival for consignments. By
using this comparative data, the AI engine can assess and influence
the overall supplier ranking. This assessment takes into account
20 historical performance trends, and provides a continually updated
ranking.
In other examples, the AI engine may also be utilized for order pooling. By analysing current and historical logistics data, the AI engine may identify opportunities to consolidate materials from
25 multiple suppliers into fewer shipments. This consolidation helps in reducing carbon emissions, as it optimizes transport logistics to utilize vehicle capacity more efficiently and reduce the total number of trips required. Furthermore, the AI engine may enhance predictive capabilities of the supply chain management system 300
30 with material arrival forecast. By integrating real-time and historical data, the AI engine may predict whether the arrival of

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materials will be early, on-time, or delayed. These forecasts feed into inventory management, allowing for more precise inventory control and reducing the risk of overstocking or stockouts. Accurate forecasting also supports better resource allocation and 5 can lead to improved overall supply chain.
FIG 4 is a schematic representation of a cloud-based system 400 for managing emissions in transportation of a consignment, according to another embodiment. Particularly, the cloud-based system 400 includes a cloud computing system 402 configured for
10 providing cloud services for automated management of consignments. The cloud computing system 402 comprises a cloud communication interface 406, cloud computing hardware and OS 408, a cloud computing platform 410, the consignment management module 216, and the consignment database 210. The cloud communication interface
15 406 enables communication between the cloud computing platform 410, and client devices 412A-N such as smart phone, tablet, computer, etc. via a network 404.
The cloud computing hardware and OS 408 may include one or more servers on which an operating system (OS) is installed and includes
20 one or more processing units, one or more storage devices for storing data, and other peripherals required for providing cloud computing functionality. The cloud computing platform 410 is a platform which implements functionalities such as data storage, data analysis, data visualization, data communication on the cloud
25 hardware and OS 408 via APIs and algorithms; and delivers the aforementioned cloud services using cloud-based applications (e.g., computer-aided design application). The cloud computing platform 410 employs the consignment management module 216 for managing emissions in transportation of a consignment (as
30 described). The cloud computing platform 410 also includes the consignment database 210 for storing information associated with

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the consignments. The cloud computing platform 410 may include a combination of dedicated hardware and software built on top of the cloud hardware and OS 408.
The client devices 412A-N include graphical user interfaces 414A-5 N for receiving and displaying information associated with the consignment. Each of the client devices 412A-N may be provided with a communication interface for interfacing with the cloud computing system 402. Users of the client devices 412A-N can access the cloud computing system 402 via the graphical user interfaces
10 414A-N. For example, the users may send request to the cloud computing system 402 to access the information associated with the consignment. The graphical user interfaces 414A-N may be specifically designed for accessing the consignment management module 216 in the cloud computing system 402. The client devices
15 412A-N may include peripherals such as QR code scanner, Keyboard, Mouse, etc.
FIG 5 illustrates a block diagram of a server-based system 500 for managing emissions in transportation of a consignment, according to yet another embodiment. Particularly, the server-based system
20 500 includes a server 502 and a plurality of client devices 506A-N. Each of the client devices 506A-N is connected to the server 502 via a network 504 (e.g., Local Area Network (LAN), Wide Area Network (WAN), Wi-Fi, etc.). The server-based system 500 is another implementation of the system 100 of FIG 2, wherein the consignment
25 management module 216 resides in the server 502 and is accessed by client devices 506A-N via the network 504.
The server 502 includes the consignment management module 216, and
the consignment database 210. The server 502 may include a
processing unit(s), a memory unit, and a storage unit. The
30 consignment management module 216 may be stored on the memory unit

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in the form of machine-readable instructions and executable by the processing unit(s). The consignment database 210 may be stored in the storage unit. The server 502 may include a communication interface for enabling communication with client devices 506A-N 5 via the network 504.
When the machine-readable instructions are executed, the consignment management module 216 causes the server 502 to authenticate a transporter, track the consignment in real-time, verify the consignment using a unique machine-readable label 10 associated with the consignment. Method steps performed by the server 502 to achieve the above-mentioned functionality are described in greater detail in the preceding paragraphs.
The client devices 506A-N include graphical user interfaces 508A-N for receiving and displaying information associated with
15 consignments. Each of the client devices 506A-N may be provided with a communication interface for interfacing with the server 502. Users of the client devices 506A-N can access the server 502 via the graphical user interfaces 508A-N. The client devices 506A-N may include peripherals such as QR code scanner, Keyboard, Mouse,
20 etc.
Those skilled in the art will recognize that, for simplicity and clarity, the full structure and operation of all data processing systems suitable for use with the present invention is not being depicted or described herein. Instead, only so much of a data 25 processing system as is unique to the present invention or necessary for an understanding of the present invention is depicted and described. The remainder of the construction and operation of the data processing system may conform to any of the various current implementation and practices known in the art.

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The method 100 and the system 200 of the present invention provide significant advancements over existing solutions. For instance, the method 100 provides a more accurate and reliable means of calculating CO2 emissions by considering specific attributes of 5 each transportation event rather than relying on generalized assumptions. This precision enables more effective management and reduction strategies for emissions. The integration of this emissions management approach into the existing supply chain management systems leverages existing supply chain infrastructure, 10 facilitating adoption and implementation. Further, the ability to provide actionable insights, such as recommendations for alternative transportation options with lower emissions, not only enhances sustainability efforts but also promotes cost efficiency in logistics management.
15 While the present invention has been described in detail with reference to certain embodiments, it should be appreciated that the present invention is not limited to those embodiments. In view of the present invention, many modifications and variations would be present themselves, to those skilled in the art without
20 departing from the scope of the various embodiments of the present invention, as described herein. The scope of the present invention is, therefore, indicated by the following claims rather than by the foregoing description. All changes, modifications, and variations coming within the meaning and range of equivalency of
25 the claims are to be considered within their scope.

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33
Reference Numerals

method 100
step 110
step 120
step 130
step 140
step 150
consignment management system 200
bus 202
processing unit 204
memory unit 206
interface 208
consignment database 210
input device 212
output device 214
consignment management module 216
supply chain management system 300
vendor portal 300A
supply chain portal 300B
consignment portal 300C
block 302
block 304
block 306
block 308
block 310
block 312
block 314
block 316
block 318
cloud-based system 400

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34

cloud computing system
network
cloud communication interface
cloud computing hardware and OS
cloud computing platform
client devices
graphical user interfaces
server-based system
server
network
client devices
graphical user interfaces

402
404
406
408
410
412A-N
414A-N
500
502
504
506A-N
508A-N

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CLAIMS
We claim:
1. A computer-implemented method for managing emissions in
transportation of a consignment, the method comprising:
5 receiving, from a consignment database, transportation data for the consignment being transported by an assigned vehicle, to be delivered to a target location from a source location, including a vehicle ID and a travel distance;
retrieving an emission factor including information about 10 emission per unit distance for the assigned vehicle based on the vehicle ID thereof;
determining estimated emissions for the transportation of the consignment via the assigned vehicle based on the emission per unit distance and the travel distance; 15 generating a unique machine-readable label for the consignment, including the transportation data and the estimated emissions; and
authenticating the assigned vehicle in which the consignment is to be transported at the source location using the unique 20 machine-readable label.
2. The method according to claim 1, wherein the assigned vehicle
is authenticated at the source location if the estimated emissions,
as per the unique machine-readable label, is within a prescribed
25 limit for the consignment.
3. The method according to claim 1, wherein the vehicle ID of the
assigned vehicle comprises vehicle registration number details,
and wherein the method comprises accessing a regulatory database
30 based on the vehicle registration number to fetch the emission factor for the assigned vehicle.

36 202401309
4. The method according to claim 1, wherein the vehicle ID of the
assigned vehicle comprises vehicle attributes details including
one or more of vehicle fuel type, vehicle capacity and vehicle
5 make, and wherein the method comprises deriving the emission factor for the assigned vehicle based on the vehicle attributes details.
5. The method according to claim 1, wherein the transportation
data further includes information about consignment load, and the
10 emission factor further includes information about emission per consignment load for the assigned vehicle, and wherein the method comprises adjusting the estimated emissions for the transportation of the consignment via the assigned vehicle based on the emission per consignment load and the consignment load.
15
6. The method according to claim 1 further comprising:
tracking the consignment during transportation from the source location to the destination location using a Global Positioning System (GPS) of the assigned vehicle; 20 determining an actual travel distance for the transportation of the consignment; and
computing actual emissions for the transportation of the consignment via the assigned vehicle based, at least in part, on the emission factor and the actual travel distance. 25
7. The method according to claim 6 further comprising:
creating emission logs in the consignment database, including information about the actual emissions and the actual travel distances associated with each consignment and by each assigned 30 vehicle.
8. The method according to claim 7 further comprising:

37
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ranking suppliers based, at least in part, on actual emissions per unit actual distance associated with transportation of the consignments using one or more vehicles thereof, as obtained from the emission logs in the consignment database; and 5 providing the rankings of the suppliers within a consignment management system.
9. A consignment management system for managing emissions in
transportation of a consignment, comprising:
10 one or more processing units;
a consignment database communicatively coupled to the one or more processing units, wherein the consignment database is configured to store information associated with the consignments; and
15 a memory unit communicatively coupled to the one or more processing units, wherein the memory unit comprises a consignment management module configured to perform a method according to claims 1 to 8 using the information stored in the consignment database.
20
10. A computer-program product having machine-readable
instructions stored therein, which when executed by one or more
processing units, cause the one or more processing units to perform
a method according to any of the claims 1 to 8.