Description:FIELD OF INVENTION

[001] The present invention relates to organizational performance benchmarking metric system and a method thereof. Particularly, the present invention relates to determine performance benchmarking metric of an organization by a system integrated through a machine learning model-based system.

BACKGROUND OF THE INVENTION

[002] Organizational performance benchmarking is a systematic process where organizations compare their performance metric against industry standard or best practices from other companies, covering aspects such as financial performance, operational efficiency, customer satisfaction, and innovation. The primary objective is to identify areas for improvement, set performance target, and implement strategies that can lead to enhance outcomes.

[003] However, the present devices used for calculating benchmarking metrics face several limitations that affect their accuracy. Data quality and consistency are significant issues, as present devices are unable to process inconsistent data format and dealing with inconsistent data formats that lead to inaccuracies. Incomplete or outdated data can distort benchmarking results, but current devices often lack the capability to effectively handle and update this data. The lack of standardization is another major limitation, as present devices are unable to reconcile varying definition and calculations used by the different organizations for the same metric, making comparison difficult and unreliable. Data integration challenges also pose a problem, as presently available devices struggle to integrate data from multiple sources which is a complex and error prone process. Organizational silos further hindering comprehensive data collection and analysis. Moreover, the dynamic nature of market condition means that rapid changes can quickly render benchmark obsolete, necessitating periodic updates. Current devices are often not designed to manage these updates efficiently, making them resource intensive and difficult to handle.

[004] In addition to these limitations, present devices for benchmarking have several disadvantages. High cost associated with implementing and maintaining these systems are a significant barrier, encompassing expenses for collecting data, investing in software, and allocating human resources for analysis. The process for collecting and analyzing data is also time consuming, leading to delays in obtaining actionable insights. Furthermore, current devices have limited scope, often focusing narrowly on specific metrics and potentially neglecting broader performance indicators. Lastly, these devices tends to be inflexible, with static systems that do not adapt well to evolving organization needs, and a lack of customization options that limits the relevance of benchmarks to specific organizational contexts.

[005] There are several patent applications that disclose a system and method to obtain different metrics. One such United States patent application US4035627A discloses a battery powered hand-held calculator for performing arithmetic, trigonometric and logarithmic functions and displaying the results thereof is provided with a clock mode which performs the function of a clock and displays real time or the function of a stopwatch and stores and displays the times at which recorded events have taken place. However, the cited invention does not provide advance metrics to assess organization performance.

[006] In order to overcome the problem associated with state of arts, there is a need for the development of an efficient machine learning based system for determining organization performance benchmarking metric.

OBJECTIVE OF THE INVENTION
[007] The primary objective of the present invention is to provide an organizational performance benchmarking metric system and method for operation thereof.

[008] Another objective of the present invention is to provide a streamlined and automated assessment of organizational performance benchmarking metric against industry standards and competitors.

[009] Another objective of the present invention is to assist organizations to improve accuracy and efficiency of the organization based on the organizational performance benchmarking metric determined in the present invention.

[0010] Another objective of the present invention is to provide an extensive organizational performance benchmarking metric based on wide range of parameters.

[0011] Another objective of the present invention is to utilize machine learning models that indicate a broader organizational context and forecast future performance trends.

[0012] Another objective of the present invention is to assist organizations to improve the precision of organizational performance evaluation and reduce manual effort by leveraging advanced analytical capabilities.

[0013] Yet another objective of the present invention is to enable organizations to identify strengths, weaknesses, and opportunities for improvement, ultimately enhancing overall operational efficiency and strategic outcomes.

[0014] Yet another objective of the present invention is to help organizations allocate resources more efficiently, focusing on high-priority activities that derive competitive advantage.

[0015] Other objectives and advantages of the present invention will become apparent from the following description taken in connection with the accompanying drawings, wherein, by way of illustration and example, the aspects of the present invention are disclosed.

BRIEF DESCRIPTION OF DRAWINGS
[0016] The present invention will be better understood after reading the following detailed description of the presently preferred aspects thereof with reference to the appended drawings, in which the features, other aspects and advantages of certain exemplary embodiments of the invention will be more apparent from the accompanying drawing in which:

[0017] Figure 1 illustrates a block diagram of an organizational performance benchmarking metric system.
SUMMARY OF THE INVENTION
[0018] The present invention relates to an organizational performance benchmarking metric system and method for operation thereof. The system comprising: a computing device to receive input data and instructions by a user; an input device connected to the computing device and communicably connected to the processor for the user to provide input data and instructions to initiate analysis of the input data for determining performance benchmarking metrics of organizations; a processor integrated into the computing device to process the input data received from the user; a server wirelessly connected to the processor to receive and store the processed input data; a machine learning module installed in the server to determine performance benchmarking metrics of organizations based on the processed input data; and a display installed and connected to the computing device to display the determined performance benchmarking metrics of organizations; and a memory integrated into the computing device and connected to the processor to store the input data and determined performance benchmarking metrics of organizations. The present invention also provides a method for determining performance benchmarking metrics of the organization using the system. The method comprising steps of: providing input data and instructions to the computing device through the input device; storing the input data in the memory integrated into the computing device; transmitting the input data to the processor through the computing device to process/analyze the input data based on the instructions received by the user; transmitting the processed input data through the processor integrated in the computing device to the server wirelessly connected to the computing device; analyzing the processed input data received by the server through the machine learning module installed in the server to determine performance benchmarking of the organization; storing the determined performance benchmarking metrics of the organization, by the server; transmitting the determined performance benchmarking metrics of the organization from the server to the computing device wirelessly to display the determined performance benchmarking metrics of the organization on the display connected to the computing device. The machine learning module is trained on datasets encompassing a wide range of predefined parameters to determine organizational performance benchmarking metrics, thereby providing a nuanced appraisal of organizational performance.

DETAILED DESCRIPTION OF INVENTION
[0019] The following detailed description and embodiments set forth herein below are merely exemplary out of the wide variety and arrangement of instructions which can be employed with the present invention. The present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. All the features disclosed in this specification may be replaced by similar other or alternative features performing similar or same or equivalent purposes. Thus, unless expressly stated otherwise, they all are within the scope of the present invention.

[0020] Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the invention. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.

[0021] The terms and words used in the following description and claims are not limited to the bibliographical meanings but are merely used to enable a clear and consistent understanding of the invention. Accordingly, it should be apparent to those skilled in the art that the following description of exemplary embodiments of the present invention are provided for illustration purpose only and not for the purpose of limiting the invention.

[0022] It is to be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise.

[0023] It should be emphasized that the term “comprises/comprising” when used in this specification is taken to specify the presence of stated features, integers, steps, or components but does not preclude the presence or addition of one or more other features, integers, steps, components, or groups thereof.

[0024] Accordingly, the present invention relates to organizational performance benchmarking metric system and a method thereof. Particularly, the present invention relates to determine performance benchmarking metric of an organization by a system integrated through a machine learning model-based system.

[0025] In an embodiment, as shown in Figure 1, the system (100) comprises a computing device (102), a processor (104) integrated into the computing device (102), a display (106) connected to the computing device (102); an input device (108) connected to the computing device (102) and communicably connected to the processor (104), a memory (110) integrated into the computing device (102) and connected to the processor (104), a server (112) wirelessly connected to the processor (104), and a machine learning module (114) installed in the server (112).

[0026] The computing device (102) may be configured to receive input data as well as the instructions for the processor (104) to initiate analysis of the input data to determine performance benchmarking metrics of an organization, wherein the input data and instructions are provided by a user through an input device (108). In an exemplary embodiment, the computing device (102) may be selected from a group consisting of, such as, but not limited to, a computer, laptop, calculator, etc.
[0027] The input data provided by the user comprises of a plurality of pre-defined parameters that are used for determining performance benchmarking metrics of the organization. In an exemplary embodiment, the input data may be selected from a group consisting of such as, but not limited to, net sales; shares outstanding; dividend per share; stock price; Earnings Per Share (EPS); retained earnings; audit accuracy; forecasted sales; prepaid expenses; market capitalization; cash; total equity; total capital; net credit sales; accounts receivable; accounts payable; Price-to-Earnings (P/E) ratio; EPS growth rate; Return on Investment (ROI); total debt; procurement costs; procurement time; sales opportunities; total assets; revenue growth; lead response time; upsell sales; cross-sell sales; current assets; current liabilities; inventory; Cost of Goods Sold (COGS); operating profit; gross profit; Net profit; free cash flow; compliance violations; market size; industry size; and Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), external parameters such as potential target market size, number of competitors, suppliers, and vendors; internal parameters such as company’s productivity, profitability, cash flow, etcetera., or combination thereof.
[0028] In an exemplary embodiment, the input device (108) may be selected from a group consisting of, such as, but not limited to, a physical keyboard, touch-screen keyboard, mouse, etc.
[0029] The processor (104) may be configured to process/analyze input data on receiving the user’s instructions from the computing device (102) and thereafter wirelessly transmit data to the server (112) for determining performance benchmarking metrics of the organization by the machine learning module (114). In an exemplary embodiment, the processed input data may be transmitted to the server (112) through Wi-Fi, Bluetooth, Ethernet, GPRS module, and the like.
[0030] The server (112) receives processed data from the processor (104) and stores the processed input data. In an exemplary embodiment, the server (112) may store data in an architecture selected from a group consisting of, such as, but not limited to, Recurrent Neural Network (RNN) model architecture, an auto encoder, and the like. The machine learning module (114) installed in the server (112) determine performance benchmarking metrics of the organization based on the processed input data. The server (112) wirelessly transmits the determined performance benchmarking metrics data of the organization to the computing device (102) through such as, but not limited to, Wi-Fi, Bluetooth, etc., for displaying the determined performance benchmarking metrics of organization through the display (106) for the user.

[0031] The machine learning module (114) may be trained on datasets encompassing a wide range of pre-defined parameters to determine performance benchmarking of the organization, thereby providing an assessment of performance of the organizations in comparison to industry standards. The machine learning module (114) is trained to assign weightage to each pre-defined parameter, which involves segregating the data for training the machine learning module (114) into training data, which is used to train the machine learning module (114) and testing data, which is used to determine the performance of the trained machine learning module (114). In an exemplary embodiment, the machine learning module (114) may be selected from a group consisting of such as, but not limited to, linear regression, polynomial regression, random forest model, and the like, thereby making the process of assigning weight dynamic and automatic.

[0032] The machine learning module (114) also provides data-driven recommendations based on the input data. The machine learning module (114) is trained on training data of various industries and companies so as to evaluate a calibrated score reflective of the organization’s performance. In an exemplary embodiment, the machine learning module (114) is configured to evaluate the calibrated score based upon assigned weight, through a model selected from a group consisting of such as, but not limited to, linear regression, polynomial regression, random forest model, and the alike.

[0033] The memory (110) may be configured to store the data and instructions provided by the user and the evaluated organizational performance benchmarking metric. In an exemplary embodiment, the memory may be selected from a group consisting of, such as, but not limited to, flash memory, secondary memory, cache memory, and the alike.

[0034] In an embodiment, the present invention also provides a method for determining organizational performance benchmarking metric using the system of the present invention. The method comprises the following steps:
• providing input data and instructions to the computing device (102) through the input device (108);
• storing the input data in the memory (110) integrated into the computing device (102);
• transmitting the input data to the processor (104) through the computing device (102) to process/analyze the input data based on the instructions received by the user;
• transmitting the processed input data through the processor (104) integrated in the computing device (102) to the server (112) wirelessly connected to the computing device (102);
• analyzing the processed input data received by the server (112) through the machine learning module (114) installed in the server (112) to determine performance benchmarking of the organization;
• storing the determined performance benchmarking metrics of the organization, by the server (112);
• transmitting the determined performance benchmarking metrics of the organization from the server (112) to the computing device (102) wirelessly to display the determined performance benchmarking metrics of the organization on the display (106) connected to the computing device (102).

[0035] The input data provided by the user comprises of a plurality of pre-defined parameters that are used for determining performance benchmarking metrics of the organization. In an exemplary embodiment, the input data may be selected from a group consisting of such as, but not limited to, net sales; shares outstanding; dividend per share; stock price; Earnings Per Share (EPS); retained earnings; audit accuracy; forecasted sales; prepaid expenses; market capitalization; cash; total equity; total capital; net credit sales; accounts receivable; accounts payable; Price-to-Earnings (P/E) ratio; EPS growth rate; Return on Investment (ROI); total debt; procurement costs; procurement time; sales opportunities; total assets; revenue growth; lead response time; upsell sales; cross-sell sales; current assets; current liabilities; inventory; Cost of Goods Sold (COGS); operating profit; gross profit; Net profit; free cash flow; compliance violations; market size; industry size; and Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), external parameters such as potential target market size, number of competitors, suppliers, and vendors; internal parameters such as company’s productivity, profitability, cash flow, etcetera., , or combination thereof.
[0036] In an exemplary embodiment, the computing device may be selected from a group consisting of such as, but not limited to, a computer, laptop, calculator, etc.

[0037] In another exemplary embodiment, the input device (108) may be selected from a group consisting of, such as, but not limited to, a physical keyboard, touch-screen keyboard, mouse, etc.

[0038] In yet another exemplary embodiment, the memory may be selected from a group consisting of, such as, but not limited to, flash memory, secondary memory, cache memory, and the alike.

[0039] The instructions provided by the user include the instructions for analyzing the input data by the processor (104) for determining performance benchmarking metrics of the organization.

[0040] In yet another exemplary embodiment, the server (112) may store data in an architecture selected from a group consisting of, such as, but not limited to, Recurrent Neural Network (RNN) model architecture, an auto encoder, and the like.

[0041] The advantages of the present invention are enlisted herein:
• The present invention streamlines and automate the comparison of organization performance metrics against industry standards and competitors to improve accuracy and efficiency.
• The present invention covers wide range of parameters to provide a comprehensive view of organization performance.
• The present invention utilizes machine learning models to offer insights that consider the broader organization context and forecast future performance trends.
• The present invention improves the precision of organization performance evaluation and reduce manual effort by leveraging advance analytical capabilities.
• The present invention provides data drive recommendations to support informed decision making and strategic planning.
• The present invention enables organizations to identify strength, weaknesses, and opportunities for improvement, ultimately enhancing overall operational efficiency and strategic outcomes.
• The present invention helps organizations allocate resources more efficiently, focusing on high priority activities that derive competitive advantage.
• The present invention utilizes historical data and predictive models to forecast the potential impact of activities, aiding in proactive planning.
• The present invention provides machine learning based data driven insights, enabling an organization to make more informed and strategic decision.

[0042] While this invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
, Claims:1. A machine learning based system for determining performance benchmarking metrics of organizations (100), comprising:
(a) a computing device (102) to receive input data and instructions by a user;
(b) an input device (108) connected to the computing device (102) and communicably connected to the processor (104) for the user to provide input data and instructions to initiate analysis of the input data for determining performance benchmarking metrics of organizations;
(c) a processor (104) integrated into the computing device (102) to process the input data received from the user;
(d) a server (112) wirelessly connected to the processor (104) to receive and store the processed input data;
(e) a machine learning module (114) installed in the server (112) to determine performance benchmarking metrics of organizations based on the processed input data; and
(f) a display (106) installed and connected to the computing device (102) to display the determined performance benchmarking metrics of organizations; and
(g) a memory (110) integrated into the computing device (102) and connected to the processor (104) to store the input data and determined performance benchmarking metrics of organizations.
2. The system (100) as claimed in claim 1, wherein the computing device (102) is selected from a group consisting of a computer, calculator, and laptop.
3. The system (100) as claimed in claim 1, wherein the input data comprises of, Net sales; shares outstanding; dividend per share; stock price; Earnings Per Share (EPS); retained earnings; audit accuracy; forecasted sales; prepaid expenses; market capitalization; cash; total equity; total capital; net credit sales; accounts receivable; accounts payable; Price-to-Earnings (P/E) ratio; EPS growth rate; Return on Investment (ROI); total debt; procurement costs; procurement time; sales opportunities; total assets; revenue growth; lead response time; upsell sales; cross-sell sales; current assets; current liabilities; inventory; Cost of Goods Sold (COGS); operating profit; gross profit; Net profit; free cash flow; compliance violations; market size; industry size; and Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), external parameters such as potential target market size, number of competitors, suppliers, and vendors; internal parameters such as company’s productivity, profitability, cash flow, etcetera, or a combination thereof.
4. The system (100) as claimed in claim 1, wherein the input device (108) is selected from a group consisting of, a physical keyboard, touch-screen keyboard, mouse, or a combination thereof.
5. The system (100) as claimed in claim 1, wherein the machine learning module (114) is configured for continuous refinement through iterative training to alleviate reliance on human intervention.
6. The system (100) as claimed in claim 1, wherein the machine learning module (114) is configured to attribute weightage to pre-defined parameters based on a machine learning model.
7. The system (100) as claimed in claim 1, wherein the machine learning model is trained on datasets encompassing a wide range of pre-defined parameters to determine performance benchmarking metrics of the organization.
8. The system (100) as claimed in claim 7, wherein the machine learning model is selected from a group consisting of linear regression, polynomial regression, and random forest model.
9. A method for determining performance benchmarking metrics of organizations using the system as claimed in claim 1, comprising steps of:
• providing input data and instructions to the computing device (102) through the input device (108);
• storing the input data in the memory (110) integrated into the computing device (102);
• transmitting the input data to the processor (104) through the computing device (102) to process/analyze the input data based on the instructions received by the user;
• transmitting the processed input data through the processor (104) integrated in the computing device (102) to the server (112) wirelessly connected to the computing device (102);
• analyzing the processed input data received by the server (112) through the machine learning module (114) installed in the server (112) to determine performance benchmarking metrics of the organization;
• storing the determined performance benchmarking metrics of the organization, by the server (112);
• transmitting the determined performance benchmarking metrics of the organization from the server (112) to the computing device (102) wirelessly to display the determined performance benchmarking metrics of the organization on the display (106) connected to the computing device (102).
10. The method as claimed in claim 9, wherein the method comprise a step of providing iterative training to machine learning module (114) for continuous refinement.