DESC:FIELD
The present disclosure relates to the field of an assessment system. More specifically, the present disclosure relates to the system for supply chain assessment.
DEFINITIONS
As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used indicates otherwise.
The expression “organization” used hereinafter in this specification refers to, but is not limited to, an entity generating product or services for the purpose of selling it to a customer.
The expression “supply chain” used hereinafter in this specification refers to, but is not limited to, is a system of organizations, people, activities, information, and resources involved in moving a product or service from the organization to a customer. Supply chain activities involve the transformation of natural resources, raw materials, and components into a finished product that is delivered to an end customer.
These definitions are in addition to those expressed in the art.
BACKGROUND
Conventionally, data related to the supply chain processes are fragmented, limited, and, in some cases, non-existent. The absence of timely communication and correlation between the different departments of the supply chain, results in higher costs and lower efficiency of the whole system. This leads to excess inventories and waste of product, unnecessary stock outs and rationing of products. An organization cannot effectively react to these concerns because of lack of data and information, which is required to make sound decisions.
There is, therefore, felt a need to provide a computer implemented system for assessing supply chain that alleviates the above mentioned drawbacks.
OBJECTS
Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:
It is an object of the present disclosure to ameliorate one or more problems of the prior art or to at least provide a useful alternative.
An object of the present disclosure is to provide a computer implemented system and method for supply chain assessment.
Another object of the present disclosure is to provide a computer implemented system and method for supply chain assessment which identifies areas of underperformance in an organization.
Yet another object of the present disclosure is to provide a computer implemented system and method for supply chain assessment which uses supervised learning methodology.
Still another object of the present disclosure is to provide a computer implemented system and method for supply chain assessment which improves overall performance of supply chain in an organization.
Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.
SUMMARY
The present disclosure envisages a computer implemented system and method for supply chain assessment. The system comprises a database, a data module, a relationship model generator, an optimization unit and a recommendation module.
The database is configured to store a pre-determined organization’s performance score, Enterprise Resource Planning (ERP) data wherein ERP data relates to predefined characteristic heads and parameters of the predefined characteristic heads, a set of pre-defined scripts, a set of pre-determined normalized rules, a set of pre-determined recommendation rules and a pre-determined minimum standard deviation. The data module is configured to cooperate with the database to extract the ERP data, based on the pre-defined scripts, and is further configured to normalize the ERP data, based on the pre-determined normalized rules.
The relationship model generator is configured to cooperate with the data module, to receive the normalized ERP data and generate a relative score for each of the predefined characteristic heads, and is further configured to compute an organization performance score based on randomly generated weights for each of the predefined characteristic heads and the parameters of the predefined characteristic heads. The relationship model generator is further configured to adjust the weights based on optimization instructions with respect to each of the predefined characteristic heads and the parameters of the predefined characteristic heads.
The optimization unit is configured to cooperate with the relationship model generator and the database. The optimization unit is configured to receive the computed organization performance score and the pre-determined organization performance score and compare the computed organization performance score and the pre-determined organization performance score to generate a comparison result. The optimization unit is further configured to compare the comparison result with the pre-determined minimum standard deviation to generate optimization instructions.
The recommendation module is configured to cooperate with the optimization unit and the database, to receive the adjusted weights and further configured to generate recommendations based on the pre-determined recommendation rules and the adjusted weights.
The data module, the relationship model generator, the optimization unit and the recommendation module are implemented using one or more processor(s).
In an embodiment, the data module includes a data soaking module and a data cleansing module. The data soaking module is configured to cooperate with the database to extract the ERP data with respect to the predefined characteristic heads and parameters of the predefined characteristic heads, based on the pre-defined scripts. The data cleansing module is configured to cooperate with the data soaking module and database, and is further configured to receive the extracted ERP data, and normalize the extracted ERP data, based on the pre-determined normalized rules.
In another embodiment, the relationship model generator includes a first weight assignor module, a first computation module, a second weight assignor module, and a second computation module. The first weight assignor module is configured to assign random weight to each of the parameters of the predefined characteristic heads. The first weight assignor module includes a first random number generator configured to generate the random weight for each of the parameters of the predefined characteristic heads. The first computation module is configured to compute a relative score for each of the predefined characteristic heads based on the random weight assigned to each of the parameters of the predefined characteristic heads.
The second weight assignor module is configured to assign the random weight to each of the predefined characteristic heads. The second weight assignor module includes a second random number generator configured to generate the random weight for each of the predefined characteristic heads. The second computation module is configured to cooperate with the first computation module, and is further configured to compute organization performance score based on the random weight assigned to each of the predefined characteristic heads and the relative score for each of the predefined characteristic heads.
In yet another embodiment, the optimization unit includes a first comparator, a second comparator and an instruction generator. The first comparator is configured to cooperate with the database and the relationship model generator, to compare the computed organization performance score and the pre-determined organization performance score, to generate the comparison result. The second comparator is configured to cooperate with the database and first comparator, and is further configured to compare the comparison result with the pre-determined minimum standard deviation to generate a final result. The instruction generator is configured to cooperate with the second comparator and the relationship model generator, and is further configured to generate the optimization instructions, and transmit the optimization instructions to the relationship model generator, to adjust the weights with respect to the parameters of the predefined characteristic heads and the characteristic heads, respectively to minimize the final result.
In still another embodiment, the relationship model generator is based on a neural network.
In an embodiment, the optimization unit employs an adaptive learning technique.
In another embodiment, the predefined characteristic heads are behavioral parameters associated with different processes/activities of organization.
In yet another embodiment, the predefined characteristic heads are selected from the group consisting of Velocity (vl), Agility (a), Lean (l), Variety (vr), Expense (e), Reliability (r), and Traceability (t).
In still another embodiment, each of the predefined characteristic heads has associated parameters.
The present disclosure envisages a computer implemented method for supply chain assessment.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING
A computer implemented system and method for supply chain assessment, of the present disclosure will now be described with the help of the accompanying drawing, in which:
Figure 1 illustrates a schematic block diagram of a computer implemented system supply chain assessment; and
Figures 2a and 2b illustrate a computer implemented method for supply chain assessment.
LIST AND DETAILS OF REFERENCE NUMERALS USED IN THE DESCRIPTION AND DRAWING:
Reference Numeral Reference
100 System
20 database
25 data module
30 data soaking module
40 data cleansing module
50 relationship model generator
52 first weight assignor module
53 first random number generator
54 first computation module
56 second weight assignor module
57 second random number generator
58 second computation module
60 optimization unit
62 first comparator
64 second comparator
66 instruction generator
70 recommendation module

DETAILED DESCRIPTION
Embodiments, of the present disclosure, will now be described with reference to the accompanying drawing.
Embodiments are provided so as to thoroughly and fully convey the scope of the present disclosure to the person skilled in the art. Numerous details are set forth, relating to specific components, and methods, to provide a complete understanding of embodiments of the present disclosure. It will be apparent to the person skilled in the art that the details provided in the embodiments should not be construed to limit the scope of the present disclosure. In some embodiments, well-known processes, well-known apparatus structures, and well-known techniques are not described in detail.
The terminology used, in the present disclosure, is only for the purpose of explaining a particular embodiment and such terminology shall not be considered to limit the scope of the present disclosure. As used in the present disclosure, the forms "a,” "an," and "the" may be intended to include the plural forms as well, unless the context clearly suggests otherwise. The terms "comprises," "comprising," “including,” and “having,” are open ended transitional phrases and therefore specify the presence of stated features, integers, steps, operations, elements, modules, units and/or components, but do not forbid the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The particular order of steps disclosed in the method and process of the present disclosure is not to be construed as necessarily requiring their performance as described or illustrated. It is also to be understood that additional or alternative steps may be employed.
When an element is referred to as being "mounted on," “engaged to,” "connected to," or "coupled to" another element, it may be directly on, engaged, connected or coupled to the other element. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed elements.
The terms first, second, third, etc., should not be construed to limit the scope of the present disclosure as the aforementioned terms may be only used to distinguish one element, component, region, layer or section from another component, region, layer or section. Terms such as first, second, third etc., when used herein do not imply a specific sequence or order unless clearly suggested by the present disclosure.
Terms such as “inner,” “outer,” "beneath," "below," "lower," "above," "upper," and the like, may be used in the present disclosure to describe relationships between different elements as depicted from the figures.
A computer implemented system and method for supply chain assessment as described herein compares the computed organization score and a pre-determined organization performance score to calculate the comparison result, and compares the comparison result with the pre-determined minimum standard deviation iteratively, so that the comparison result is minimum. The computed organization score is based on the relative scores calculated for each of the predefined characteristic heads based on the random weight assigned to each of the parameters of the predefined characteristic head, and the random weight assigned to each of the predefined characteristic heads.
A computer implemented system (100) for supply chain assessment, of the present disclosure is now described with reference to Figure 1.
The computer implemented system (100) for supply chain assessment (hereinafter referred to as system) includes a database (20), a data module (25), a relationship model generator (50), an optimization unit (60) and a recommendation module (70).
The database (20) is configured to store a pre-determined organization’s performance score, Enterprise Resource Planning (ERP) data wherein the ERP data relates to predefined characteristic heads and parameters of the predefined characteristic heads, a set of pre-defined scripts, a set of pre-determined normalized rules, a set of pre-determined recommendation rules and a pre-determined minimum standard deviation. The ERP data is related to various activities and working processes of the organization.
The predefined characteristic heads are behavioral parameters associated with the different processes/activities of the organization, which affects the performance of the supply chain. In an embodiment, the predefined characteristic heads are selected from the group consisting of Velocity (vl), Agility (a), Lean (l), Variety (vr), Expense (e), Reliability (r), and Traceability (t). In an embodiment, each of the predefined characteristic heads has associated parameters. Tables 1(a) and (b) illustrate the associated parameters with respect to the predefined characteristic heads.
Table 1(a)
Characteristic Heads
Velocity(vl) Agility (a) Lean(vr) Variety(vr)
Cube Movement Capacity Utilization Defects 80% sale v/s total SKUs
Inventory Turns Combined Supplier Capacity Energy Effectiveness Colour
Mean Warehouse Days Forecast accuracy history Inventory Destination/Routes
Order Fulfillment Cycle Time No. of contractors Motion Level of Customization
Planning Cycle Time No. of suppliers Over-processing Niche v/s Mass
Production Cycle Time Transportation Vendors (no.) Overproduction Packaging
Queues and Wait Time Subcontractor Category Types Raw Materials Effectiveness Promotional Offers
Supplier Lead Time W. Subcontractor Certification Staff Underutilization Number of Quality Bands
Transit Time (Inbound) W. Supplier Capacity Utilization Transportation Seasonal Effect on Variety
Transit Time (outbound) W. Logistics Vendor Utilization Waiting Time Specifications/Features

Table 1(b)
Characteristic Heads
Expense (e) Reliability (r) Tractability (t)
Energy Cost Availability of Transportation Customer IT integration
Factory Cost of Capital/Rent Backorders Inbound Freight Barcode
Inbound Logistics Cost Breakdown Time Inbound Freight GPS
Labour Cost Fill Rate Inbound Freight RFID
Outbound Logistics Cost Late Penalty Outbound Freight Barcode
Procurement Cost Manufacturing Defect Rate Outbound Freight GPS
Raw Materials Cost On Time v/s Total Outbound Freight RFID
Technology Cost Perfect Order Fulfilment Reverse Logistics IT Integration
Warehousing Cost Safety Certifications Supplier IT integration
WIP Cost of Capital Stockouts Warehouse Technology Level

In an embodiment, the organization performance score is based on the key performance indicator (KPI) of the organization such as earnings before interest, tax, depreciation and amortization (EBIDTA), operating margin, profits after tax (PAT) and revenue.
The data module (25) is configured to cooperate with the database (20) to extract the ERP data, based on the pre-defined scripts, and the data module (25) is further configured to normalize the ERP data, based on the pre-determined normalized rules.
The data module (25) includes a data soaking module (30) and a data cleansing module (40). The data soaking module (30) is configured to cooperate with the database (20) to extract the ERP data with respect to the predefined characteristic heads and the associated parameters, based on the pre-defined scripts.
In an embodiment, the data soaking module (30) extracts data periodically, such as day wise, weekly, monthly, bi-monthly, quarterly, yearly and the like.
The data cleansing module (40) is configured to cooperate with the data soaking module (30) and the database (20), and is further configured to receive the extracted ERP data, and normalize the extracted ERP data, based on the pre-determined normalized rules.
The relationship model generator (50) is configured to cooperate with the data module (25), to receive normalized ERP data and generate a relative score for each of the predefined characteristic heads. The relationship model generator (50) is configured to cooperate with the data module (25) to receive the normalized ERP data and generate a relative score for each of the predefined characteristic heads. The relationship model generator (50) is further configured to compute an organization performance score based on randomly generated weights for each of the predefined characteristic heads (Wij) and randomly generated weights for each of the parameters of the predefined characteristic heads (WI). The relationship model generator (50) is configured to adjust the weights based on optimization instructions with respect to each of the predefined characteristic heads and the parameters of the predefined characteristic heads.
The relationship model generator (50) comprises a first weight assignor module (52), a first computation module (54), a second weight assignor module (56), and a second computation module (58). In an embodiment, the relationship model generator (50) is based on a neural network.
The first weight assignor module (52) is configured assign random weight to each of the parameters of the predefined characteristic heads. The first weight assignor module (52) includes a first random number generator (53) configured to generate the random weight for each of the parameters of the predefined characteristic heads.
The first computation module (54) is configured to compute a relative score for each of the predefined characteristic heads based on the random weight assigned to each of the parameters of the predefined characteristic heads.
The working of the first computation module (54) can be illustrated by following equation:
P_i=?_(j=1)^10¦?P_ij*W_ij ? for all i?{vl,a,l,vr,e,r,t}
where,
P¬i¬ is the relative score with respect to each characteristic head;
P¬i¬j is parameters associated the predefined characteristic heads; and
Wij is weight assigned by the first weight assignor module (52).
The second weight assignor module (56) is configured to assign the random weight to each of the predefined characteristic heads. In an embodiment, the second weight assignor module (56) includes a second random number generator (57) which generates the random weight for each of the predefined characteristic heads.
The second computation module (58) is configured to cooperate with the first computation module (54), and is further configured to compute an organization performance score based on the random weight assigned to each of the predefined characteristic heads and the relative score for each of the predefined characteristic heads.
The working of the second computation module (58) can be illustrated by following equation:
P_B=?_(i=vl)^t¦?P_i*W_i ?
where,
PB is the compute organization performance score;
P¬i¬ is predefined characteristic heads; and
Wi is weight assigned by the second weight assignor module (56).
For example- Pe1 to Pe10 are the ten parameters belonging to the ‘Expense’ characteristic head which are multiplied with their respective random weightage (we1 to we10) by the first computation module (54) to generate the relative score for the ‘Expense’ characteristic head. Similarly, the relative scores are calculated for the other predefined characteristic heads.
Further, the relative scores of all the characteristic heads are multiplied with their respective weightages (wvl to wt) by the second computation module (58) to generate the computed organization performance score (PB).
The optimization unit (60) is configured to cooperate with the relationship model generator (50) and the database (20), to receive the computed organization performance score and the pre-determined organization performance score and compare the computed organization performance score and the pre-determined organization performance score to generate a comparison result. The comparison result is compared with the pre-determined minimum standard deviation to generate the optimization instructions.
The optimization unit (60) includes a first comparator (62), a second comparator (64) and an instruction generator (66). The first comparator (62) is configured to cooperate with the database (20) and the relationship model generator (50), to compare the computed organization performance score and the pre-determined organization performance score, to generate the comparison result. The second comparator (64) is configured to cooperate with the database (20) and the first comparator (62), and is further configured to compare the comparison result with the pre-determined minimum standard deviation to generate a final result. The instruction generator (66) is configured to cooperate with the second comparator (64) and the relationship model generator (50), and is further configured to generate the optimization instructions, and transmit the optimization instructions to the relationship model generator (50), to adjust weights (Wij) with respect to the parameters of the predefined characteristic heads and weights (Wi) with respect to the characteristic heads, to minimize the final result.
In an embodiment, the relationship model generator (50) iteratively generates the computed organization performance score, until the comparison result is not less than the pre-determined minimum standard deviation.
In an embodiment, the optimization unit (60) employs an adaptive learning technique to generate optimization instructions.
The recommendation module (70) is configured to cooperate with the optimization unit (60) and the database (20), to receive the adjusted weights (Wij) and (Wi), and is further configured to generate recommendations based on the pre-determined recommendation rules and the adjusted weights.
In an embodiment, the recommendation module (70) makes the recommendations based on the weights (Wij) with respect to the parameters and weights (Wi) with respect to the predefined characteristic heads.
The recommendation module (70) highlights the following recommendations:
weights (Wij) with respect to the parameters of the predefined characteristic heads and weights (Wi) with respect to the predefined characteristic heads;
relative ranking of predefined characteristics heads according to their impact on organization performance;
relative ranking of parameters for each of the characteristic head; and
Custom recommendations
In an embodiment, the data module (25), relationship model generator (50), optimization unit (60), and recommendation module (70) are implemented using one or more processors.
The processor is configured to cooperate with a memory to receive and process the set of pre-determined rules to obtain a set of system operating commands. The processor will be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor is configured to fetch and execute the set of predetermined rules stored in the memory to control modules of the system (100).
In an embodiment, the memory is configured to store a set of pre-determined rules. The memory includes any computer-readable medium known in the art, including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or a non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes, and/or a cloud based storage (cloud storage).
Figures 2a and 2b illustrate a flowchart of a computer implemented method for supply chain assessment.
Step 202- Storing, by a database (20), a pre-determined organization’s performance score, Enterprise Resource Planning (ERP) data wherein ERP data relates to predefined characteristic heads and parameters of the predefined characteristic heads, a set of pre-defined scripts, a set of pre-determined normalized rules, a set of pre-determined recommendation rules and a pre-determined minimum standard deviation.
Step 204- Extracting, by a data module (25), the ERP data, based on the pre-defined scripts.
Step 206- Normalizing, by the data module (25), the extracted ERP data, based on the pre-determined normalized rules.
Step 208- Receiving, by a relationship model generator (50), normalized ERP data.
Step 210- Generating, by the relationship model generator (50), a relative score for each of the predefined characteristic heads, and compute an organization performance score based on randomly generated weights for each of the predefined characteristic heads and the parameters of the predefined characteristic heads.
Step 212- Adjusting, by the relationship model generator (50), the weights based on optimization instructions with respect to each of the predefined characteristic heads and the parameters of the predefined characteristic heads.
Step 214- Receiving by an optimization unit (60), the computed organization performance score and the pre-determined organization performance score.
Step 216- Comparing, by the optimization unit (60), the computed organization performance score and the pre-determined organization performance score, to generate a comparison result.
Step 218- Comparing, by the optimization unit (60), the comparison result with the pre-determined minimum standard deviation to generate the optimization instructions.
Step 220- Receiving, by a recommendation module (70), adjusted weights.
Step 222- Generating, by the recommendation module (70), recommendations based on the pre-determined recommendation rules and received the adjusted weights.
The foregoing description of the embodiments has been provided for purposes of illustration and not intended to limit the scope of the present disclosure. Individual workpieces of a particular embodiment are generally not limited to that particular embodiment, but, are interchangeable. Such variations are not to be regarded as a departure from the present disclosure, and all such modifications are considered to be within the scope of the present disclosure.
TECHNICAL ADVANCEMENTS
The present disclosure described herein above has several technical advantages including, but not limited to, the realization of a computer implemented system for supply chain assessment, which:
identifies areas of underperformance in an organization;
uses supervised learning methodology; and
improves overall performance of supply chain in an organization.
The embodiments herein and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
The foregoing description of the specific embodiments so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.
The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the disclosure to achieve one or more of the desired objects or results.
Any discussion of documents, acts, materials, devices, articles or the like that has been included in this specification is solely for the purpose of providing a context for the disclosure. It is not to be taken as an admission that any or all of these matters form a part of the prior art base or were common general knowledge in the field relevant to the disclosure as it existed anywhere before the priority date of this application.
The numerical values mentioned for the various physical parameters, dimensions or quantities are only approximations and it is envisaged that the values higher/lower than the numerical values assigned to the parameters, dimensions or quantities fall within the scope of the disclosure, unless there is a statement in the specification specific to the contrary.
While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.

,CLAIMS:WE CLAIM:
1. A computer implemented system (100) for supply chain assessment, said system (100) comprising:
• a database (20) configured to store a pre-determined organization’s performance score, Enterprise Resource Planning (ERP) data wherein said ERP data relates to predefined characteristic heads and parameters of said predefined characteristic heads, a set of pre-defined scripts, a set of pre-determined normalized rules, a set of pre-determined recommendation rules and a pre-determined minimum standard deviation;
• a data module (25) configured to cooperate with said database (20), to extract said ERP data, based on said pre-defined scripts, and said data module (25) further configured to normalize said ERP data, based on said pre-determined normalized rules;
• a relationship model generator (50) configured to cooperate with said data module (25) to receive said normalized ERP data and generate a relative score for each of said predefined characteristic heads, and further configured to compute an organization performance score based on randomly generated weights for each of said predefined characteristic heads and said parameters of said predefined characteristic heads, and said relationship model generator (50) further configured to adjust said weights based on optimization instructions with respect to each of said predefined characteristic heads and said parameters of said predefined characteristic heads;
• an optimization unit (60) configured to cooperate with said relationship model generator (50) and said database (20), to receive said computed organization performance score and said pre-determined organization performance score and compare said computed organization performance score and said pre-determined organization performance score to generate a comparison result, said optimization unit (60) further configured to compare said comparison result with said pre-determined minimum standard deviation to generate said optimization instructions; and
• a recommendation module (70) configured to cooperate with said optimization unit (60) and said database (20), to receive said adjusted weights and further configured to generate recommendations based on said pre-determined recommendation rules and said adjusted weights,
wherein, said data module (25), said relationship model generator (50), said optimization unit (60), and said recommendation module (70) are implemented using one or more processor(s).
2. The system (100) as claimed in claim 1, wherein said data module (25) includes:
• a data soaking module (30) configured to cooperate with said database (20) to extract said ERP data with respect to said predefined characteristic heads and said associated parameters, based on said pre-defined scripts; and
• a data cleansing module (40) configured to cooperate with said data soaking module (30) and said database (20), and further configured to receive said extracted ERP data, and normalize said extracted ERP data, based on said pre-determined normalized rules.
3. The system (100) as claimed in claim 1, wherein said relationship model generator (50) includes:
• a first weight assignor module (52) configured to assign random weight to each of said parameters of said predefined characteristic heads, and said first weight assignor module (52) includes a first random number generator (53) configured to generate said random weight for each of said parameters of said predefined characteristic heads;
• a first computation module (54) configured to compute a relative score for each of said predefined characteristic heads based on said random weight assigned to each of said parameters of said predefined characteristic heads;
• a second weight assignor module (56) configured to assign said random weight to each of said predefined characteristic heads, and said second weight assignor module (56) includes a second random number generator (57) configured to generate said random weight for each of said predefined characteristic heads; and
• a second computation module (58) configured to cooperate with said first computation module (54), and further configured to compute organization performance score based on said random weight assigned to each of said predefined characteristic heads and said relative score for each of said predefined characteristic heads.
4. The system (100) as claimed in claim 1, wherein said optimization unit (60) includes :
• a first comparator (62) configured to cooperate with said database (20) and said relationship model generator (50), to compare said computed organization performance score and said pre-determined organization performance score to generate said comparison result;
• a second comparator (64) configured to cooperate with said database (20) and said first comparator (62), and further configured to compare said comparison result with said minimum standard deviation to generate final result; and
• an instruction generator (66) configured to cooperate with said second comparator (64) and said relationship model generator (50), and further configured to generate said optimization instructions, and transmit said optimization instructions to said relationship model generator (50), to adjust said weights with respect to said parameters of said predefined characteristic heads and said characteristic heads, to minimize said final result.
5. The system (100) as claimed in claim 3, wherein said relationship model generator (50) is based on a neural network.
6. The system (100) as claimed in claim 4, wherein said optimization unit (60) employs an adaptive learning technique.
7. The system (100) as claimed in claim 1, wherein said predefined characteristic heads are behavioral parameters associated with different processes/activities of organization.
8. The system (100) as claimed in claim 7, wherein said predefined characteristic heads are selected from the group consisting of Velocity (vl), Agility (a), Lean (l), Variety (vr), Expense (e), Reliability (r), and Traceability (t).
9. The system (100) as claimed in claim 8, wherein each of the predefined characteristic heads has associated parameters.
10. A computer implemented method for supply chain assessment, said method comprising the following steps:
• storing, by a database (20), a pre-determined organization performance score, Enterprise Resource Planning (ERP) data wherein ERP data relates to predefined characteristic heads and parameters of said predefined characteristic heads, a set of pre-defined scripts, a set of pre-determined normalized rules, a set of pre-determined recommendation rules and a pre-determined minimum standard deviation;
• extracting, by a data module (25), said ERP data, based on said pre-defined scripts;
• normalizing, by said data module (25), said extracted ERP data, based on said pre-determined normalized rules;
• receiving, by a relationship model generator (50), normalized ERP data;
• generating, by said relationship model generator (50), a relative score for each of said predefined characteristic heads, and compute an organization performance score based on randomly generated weights for each of said predefined characteristic heads and said parameters of said predefined characteristic heads;
• adjusting, by said relationship model generator (50), said weights based on optimization instructions with respect to each of said predefined characteristic heads and said parameters of said predefined characteristic heads;
• receiving, by an optimization unit (60), said computed organization performance score and said pre-determined organization performance score;
• comparing, by said optimization unit (60), said computed organization performance score and said pre-determined organization performance score, to generate a comparison result;
• comparing, by said optimization unit (60), said comparison result with said pre-determined minimum standard deviation to generate said optimization instructions;
• receiving, by a recommendation module (70), adjusted weights; and

• generating, by said recommendation module (70), recommendations based on said pre-determined recommendation rules and said adjusted weights.

Dated this 13th day of November, 2018

MOHAN DEWAN
of R.K. DEWAN & COMPANY
IN/PA-25
APPLICANT’S PATENT ATTORNEY

TO,
THE CONTROLLER OF PATENTS
THE PATENT OFFICE, AT MUMBAI