A machine learning based system for determing performance metrics of organizations and method thereof

Abstract

The present invention relates to a machine learning based system for determining performance metrics for organizations. The system (100) comprises of a computing device (102) and a server (114) installed with a machine learning module (116) and wirelessly connected to the computing device (102). The computing device (102) comprises of a keyboard (108) configured to accept data and instructions from user. Further, the computing device (102) comprise of a processor (104) to process data and instruction from user. Furthermore, the computing device (102) transmits data and instructions to the server (114) for evaluation of performance metrics of organization by the machine learning module (116). The machine learning module (116) is trained on datasets encompassing a spectrum of global listed companies to provide data-driven recommendations, and the machine learning module (116) evaluates a calibrated score reflective of the organization's performance vis-à-vis industry benchmarks, thereby providing a nuanced appraisal of organizational performance.

Description

A MACHINE LEARNING BASED SYSTEM FOR DETERMING PERFORMANCE METRICS OF ORGANIZATIONS AND METHOD THEREOFFIELD OF INVENTION

[0001] The present invention relates to a system for determining performance metric of organizations. Particularly, the present invention relates to a system that utilizes machine learning model trained on datasets encompassing a spectrum of global listed companies to determine performance / purpose alignment metrics, thereby providing a nuanced appraisal of organizational performance.BACKGROUND OF THE INVENTION

[0002] The renaissance period saw the invention of mechanical calculators in 17th century by Schickard and Pascal which were later redefined by various scientists and industry player according to their needs. During the initial years, it was majorly covering aspects of basic algebra up to certain digits. In 1954, IBM introduced First All-Transistor Calculator which could perform more than 4000 additions per second.

[0003] Within the financial domain, any financial device can ascertain pertinent financial ratios. These Key Performing Indicators (KPIs) were able to give a picture of an organization with respect to the financials of the firm. Diverse tools and software significantly enhance financial analysis and decision-making processes. Prominent examples include Microsoft Excel, QuickBooks, SAP (Systems, Applications, and Products), Bloomberg Terminal, and Power BI (Business Intelligence), each serving distinct purposes to facilitate comprehensive financial data analysis.

[0004] However, business measurements extend beyond financial metrics, encompassing various functionalities necessitating a robust tracking system. In the broader market, this undertaking is undertaken by various Enterprise Resource 3 Planning (ERP) systems, encompassing accounting, procurement, project management, risk management, compliance, and supply chain operations. A comprehensive ERP suite incorporates enterprise performance management software, facilitatingplanning, budgeting, prediction, and reporting on organizational financial outcomes, albeit lacking relevant benchmarking mechanisms and opportunity analysis which can aid the managerial capabilities of the individual. Moreover, the industry has long neglected the purpose alignment metrics associated with firms, despite the critical role the purpose plays in each functionality of the firm; it becomes a necessity to measure the purpose to define strategy for any business. The Environmental Sustainability, Ethical Business Conduct, Corporate Governance and Transparency, Employee Welfare and Development, Community Engagement and Social Impact, Customer Satisfaction and Value Creation, innovation and sourcing play a pivotal role in the business that needs to be measured and benchmarked in the industry.

[0005] There are several patent applications that disclose a system and method to remove pollutants from air. 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 such as purpose alignment metric that are crucial for an organization decision making and growth.

[0006] 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 performance metric of organizations that can overcome the aforesaid limitations in a more efficient manner.OBJECTIVE OF THE INVENTION

[0007] The primary objective of the present invention is to provide a machine learning based system for determining performance metric of organizations.

[0008] Another objective of the present invention is to provide machine learning based data driven insights, enabling an organization to make more informed and strategic decision.

[0009] Another objective of the present invention is to help an organization in ensuring that organizational activities are closely aligned with the core mission and strategic goals, thereby enhancing overall effectiveness.

[0010] Another objective of the present is to help organizations allocate resources more efficiently, focusing on high priority activities that derive competitive advantage.

[0011] Yet another objective of the present invention is to utilize historical data and predictive models to forecast the potential impact of activities, aiding in proactive planning.

[0012] Yet another objective of the present is to increase efficiency of organization by reducing time and effort spent on low value tasks.

[0013] Yet another objective of the present invention is to provide a machine learning based system for determining performance metrics that is simple, easy to use, and cost-efficient.

[0014] Yet another objective of the present invention is to engender a symbiotic interaction among disparate KPIs, thus unravelling intricate correlations that often evade traditional methodologies.

[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 block diagram of a computing device; and

[0018] Figure 2 illustrates a schematic diagram of a machine learning based system for determining performance metrics of organizations.SUMMARY OF THE INVENTION

[0019] The present invention relates to a machine learning based system for determining performance metrics of organizations. The system comprises of a computing device, and a server installed with a machine learning module and wirelessly connected to the computing device. The computing device comprises of a keyboard configured to accept data and instructions from user. Further, the computing device comprise of a processor to process data and instruction from user. Furthermore, the computing device transmits data and instructions to the server for evaluation of performance metrics of organization by the machine learning module. The machine learning module is trained on datasets encompassing a spectrum of global listed companies to provide data- driven recommendations, and the machine learning module evaluates a calibrated score reflective of the organization’s performance vis-a-vis industry benchmarks, thereby providing a nuanced appraisal of organizational performance.

[0020] The present invention also provides a method for operating the machine learning based system for determining performance metrics of organizations. The method comprising steps of: providing data and instructions to the computing device through the keyboard; storing the data in the memory; processing of the data and the instructions by the processor; transmitting the instructions and data to the machine learning module on the server; training the machine learning module on datasets encompassing a spectrum of global listed companies; extracting feature by the machine learning module; evaluating of purpose alignment metrics by the machine learning module; and displaying the purpose alignment metric on the display of the computing device.DETAILED DESCRIPTION OF INVENTION

[0021] 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 sameor equivalent purposes. Thus, unless expressly stated otherwise, they all are within the scope of the present invention.

[0022] 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.

[0023] 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.

[0024] It is to be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise.

[0025] 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.

[0026] Accordingly, the present invention relates to a system for determining performance metric of an organization. Particularly, the present invention relates to a system that utilizes machine learning model trained on datasets encompassing a spectrum of global listed companies to determine performance metrics, wherein the performance metrics are based on purpose alignment model. The system provides a nuanced appraisal of organizational performance.

[0027] The purpose alignment model is used to evaluate organization’s performance vis-a-vis industry benchmarks. The purpose alignment model evaluate organization not only on financial metrics but also on various operational facets intrinsic to business operations. The parameter usedto calculate purpose alignment model are: Environmental sustainability metrics; Ethical Business Conduct; Corporate Governance and Transparency; Employee Welfare and Development; Community Engagement and Social Impact; Innovation and Industry Leadership; and Supply Chain and Responsible Sourcing.

[0028] In a preferred embodiment of the present invention, the system comprises of a plurality of computing devices (102), a processor (104) integrated in the computing device (102), a display (106) installed in the computing device (102) and connected to the processor (104), a keyboard (108) installed in the computing device (102) and connected to the processor (104), a memory (110) integrated on the computing device (102) and connected to the processor (104), a communication unit (112) located in the computing device (102), a server (114) wirelessly connected to the computing device (102) through the communication unit (112), and a machine learning module (116) installed in the server (114).

[0029] Figure 1 illustrates the computing device (102). The computing devices (102) is configured to accept data and instructions from user through keyboard (108). Further, the computing device (102) comprise of a processor (104) to process data and instruction received from user through the keyboard (108). Additionally, the data and instructions are stored in the memory (110) integrated on the computing device (102). Furthermore, the computing device (102) transmits data and instructions to the server (114) through the communication unit (112) for evaluation of performance metrics of organization by the machine learning module (116). The communication unit (112) provides communication to the server (114) or any external device through any one or combination of: Wi-Fi, Bluetooth, Ethernet, GPRS module, and the alike.

[0030] The server (114) is wirelessly connected to the computing device (102) through the communication unit (112). The server (114) receives data and instructions from the computing device (102) and provide to the machine learning module (116), wherein the machine learning module (116) evaluates performance / purpose alignment metrics. The server (114) stores data received from the computing device (102) through a Recurrent Neural Network (RNN) model architecture. Lastly, the server (114) transmit performance metrics / purpose alignment model metrics to the computing device (102) for displaying the performance metrics to user through thedisplay (106).

[0031] The machine learning module (116) is trained on datasets encompassing a spectrum of global listed companies. Further, the machine learning module (116) is configured for continuous refinement through iterative training so as to alleviate reliance on human intervention. Additionally, the machine learning module ( 116) is configured to attribute weights to pre-defined ratios and key performance indicators (KPI) contingent upon prevailing industry norms and the dimensions of the entity.

[0032] Furthermore, the machine learning module (116) provides data-driven recommendations based on the extensive reservoir of quantitative firm data. Lastly, the machine learning module (116) evaluates a calibrated score reflective of the organization’s performance vis-a-vis industry benchmarks, as shown in Figure 2.

[0033] The machine learning module (116) is trained on the pre-defined ratios and key performance indicators (KPI) so as to evaluate organization not only on financial metrics but also on various operational facets intrinsic to business operations, wherein the pre-defined ratios and KPI include: Environmental sustainability metrics; Ethical Business Conduct; Corporate Governance and Transparency; Employee Welfare and Development; Community Engagement and Social Impact; Innovation and Industry Leadership; and Supply Chain and Responsible Sourcing.

[0034] The pre-defined ratios and KPIs included are described in detail below:• Environmental sustainability metrics such as Greenhouse gas emissions (Scope 1 , 2, and 3), energy consumption and efficiency, water consumption and management, waste generation and recycling rates, use of renewable energy sources, sustainable packaging and product design, deforestation and biodiversity impact.• Ethical Business Conduct such as Incidents of corruption, bribery, or anticompetitive behavior, human rights violations in operations and supply chain, ethical training andawareness programs, whistleblower reports and resolution, supplier code of conduct compliance, data privacy and cybersecurity breaches, responsible marketing and advertising practices.• Corporate Governance and Transparency includes board independence and diversity, executive compensation and incentive alignment, shareholder rights and voting practices, lobbying and political contributions, tax strategy and transparency, risk management and internal control systems, cyber and data governance.• Employee Welfare and Development includes employee engagement and satisfaction scores, diversity, equity, and inclusion metrics, investment in training and professional development, workplace health, safety, and wellbeing initiatives, work-life balance and flexible work arrangements, fair compensation and living wage practices, labor relations and collective bargaining agreements.• Community Engagement and Social Impact contains corporate philanthropy and community investments, local job creation and economic development, partnerships with non-profit organizations, access to essential products and services indigenous rights and community relations, support for education and skill development programs, disaster relief and humanitarian aid efforts.• Customer Satisfaction and Value Creation can be measured by Net Promoter Score (NPS) or customer satisfaction ratings, product quality, safety, and reliability, customer complaint resolution and response time, customer privacy and data protection, accessibility and affordability of products / services, innovation and new product / service launches, customer retention and loyalty rates.• Innovation and Industry Leadership needs to be measured by Research and development (R&D) investment, patents, trademarks, and intellectual property, adoption of new technologies and business models, industry awards and recognitions, leadership in sustainability and ethical practices, collaboration with academia and research institutions, thought leadership and influence in the industry.• Supply Chain and Responsible Sourcing such as, responsible sourcing and procurement practices, supplier diversity and inclusion, traceability and transparency in supply chain, fair trade and ethical sourcing certifications, environmental and social impact of supply chain, worker rights and labor practices in the supply chain.

[0035] In an embodiment, the present invention also provides method for operating the machine learning based system for determining performance metrics of organizations, comprises the following steps• providing data and instructions to the computing device (102) through the keyboard (108);• storing the data in the memory (110);• processing of the data and the instructions by the processor (104);• transmitting the instructions and data to the server (114);• training the machine learning module (116) on datasets encompassing a spectrum of global listed companies;• extracting feature by the machine learning module (116);• evaluating of purpose alignment / performance metrics by the machine learning module (116); and• displaying the purpose alignment / performance metrics on the display (106) of the computing device (102).

[0036] In an embodiment the advantages of the present invention are enlisted herein:• The present invention provides machine learning based data driven insights, enabling an organization to make more informed and strategic decision.• The present invention enables an organization in ensuring that organizational activities are closely aligned with the core mission and strategic goals, thereby enhancing overall effectiveness.• The present invention enables 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 increases efficiency of organization by reducing time and effort spent on low value tasks.• The present invention provides a machine learning based calculator that is simple, easy to use, and cost-efficient.• The present invention engenders a symbiotic interaction among disparate KPIs,thus unravelling intricate correlations that often evade traditional methodologies.

[0037] 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

CLAIMS:

1. A machine learning based system for determining performance metrics of organizations (100), comprising:(a) a plurality of computing devices (102);(b) a processor (104) integrated in the computing device (102);(c) a display (106) installed on the computing device (102) and connected to the processor (104);(d) a keyboard (108) installed in the computing device (102) and connected to the processor (104);(e) a memory (110) integrated on the computing device (102) and connected to the processor (104);(f) a communication unit (112) located in the computing device (102);(g) a server (114) wirelessly connected to the computing device (102) through the communication unit (112); wherein,• a machine learning module (116) is installed in the server (114);• the machine learning module (116) is trained on datasets encompassing a spectrum of global listed companies;• the machine learning module (116) provides data-driven recommendations based on the extensive reservoir of quantitative firm data; and• the machine learning module (116) evaluates a calibrated score reflective of the organization’s performance vis-a-vis industry benchmarks based on the training data.

2. The system (100) as claimed in claim 1, wherein the machine learning module (116) is configured to calculate a dynamic score, predicated on firm-specific data relative to peer cohort so as to provide a nuanced appraisal of organizational performance.

3. The system (100) as claimed in claim 1, wherein the server (114) employs a Recurrent Neural Network (RNN) model architecture for data storage.

4. The system (100) as claimed in claim 1, wherein the machine learning module (116) is configured for continuous refinement through iterative training so as to alleviate reliance on human intervention.

5. The system (100) as claimed in claim 1, wherein the machine learning module (116) is configured to attribute weights to pre-defined ratios and key performance indicators (KPI) contingent upon prevailing industry norms and the dimensions of the entity.

6. The system (100) as claimed in claim 1, wherein the machine learning module (116) is trained on the pre-defined ratios and key performance indicators (KPI) so as to evaluate organization not only on financial metrics but also on various operational facets intrinsic to business operations.

7. The system (100) as claimed in claim 6, wherein the pre-defined ratios and KPI include: Environmental sustainability metrics; Ethical Business Conduct; Corporate Governance and Transparency; Employee Welfare and Development; Community Engagement and Social Impact; Innovation and Industry Leadership; and Supply Chain and Responsible Sourcing.

8. The system as claimed in claim 1, wherein the performance metrics are based on purpose alignment model.

9. A method for operating the machine learning based system for determining performance metrics of organizations as claimed in claim 1, comprising: a. providing data and instructions to the computing device (102) through the keyboard (108); b. storing the data in the memory (110); c. processing of the data and the instructions by the processor (104); d. transmitting the instructions and data to the server (114) through the communication unit (112); e. training the machine learning module (116) on datasets encompassing a spectrum of global listed companies; f. extracting feature by the machine learning module (116); g. evaluating of purpose alignment / performance metrics by the machine learning module (116); and h. displaying the purpose alignment / performance metrics on the display (106) of thecomputing device (102).

10. The method as claimed in claim 9, wherein the method comprise a step of providing iterative training to machine learning module (116) for continuous refinement.

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