Method and system for managing performance indicators for addressing goals of enterprise facility operations management

Patent metadata

Field	Value
Publication number	US-10530666-B2
Application number	US-201715443529-A
Country	US
Kind code	B2
Filing date	Feb 27, 2017
Priority date	Oct 28, 2016
Publication date	Jan 7, 2020
Grant date	Jan 7, 2020

Abstract

The present disclosure discloses a method and a system for dynamically managing performance indicators for an enterprise to address goals for facility operations management. The method comprises integrating operations data associated with an enterprise, deriving Key Performance Indicators (KPIs), thresholds and metrics, determining factors affecting performance of the KPIs and factors affecting performance of the thresholds, normalizing the factors affecting the performance of the KPIs based on a comparability matrix, assessing performance of KPIs based on the one or more thresholds, the comparability matrix and associated patterns of the normalized data to derive KPI performance insights, fine-tuning the KPIs and enhancing the system, along with the one or more KPIs and the interactive visualization based on one or more patterns of usage of the interactive visualization and the fine-tuning, thereby dynamically managing performance indicators for an enterprise to address the goals of facility operations management.

First claim

What is claimed is:

1. A method of dynamically managing performance indicators for an enterprise to address goals of facility operations management, comprising:
integrating, by an auto-learning performance device operations data associated with an enterprise, received from one or more sources and an indicator management database;
deriving, by the auto-learning performance device, one or more Key Performance Indicators (KPIs), one or more thresholds and one or more metrics based on the integrated data and goals of facility operations management;
determining, by the auto-learning performance device, one or more factors affecting performance of the KPIs and one or more factors affecting performance of the one or more thresholds;
creating, by the auto-learning performance device, a comparability matrix based on the one or more factors affecting performance of the KPIs and the one or more factors affecting performance of the one or more thresholds to identify clusters of objects, corresponding data points and techniques to be used for normalization of the one or more KPIs;
normalizing, by the auto-learning performance device, the one or more KPIs based on at least one of the clusters of objects, the corresponding data points or the techniques for normalization to provide normalized data;
assessing, by the auto-learning performance device, performance of KPIs based on the one or more thresholds, the comparability matrix and associated patterns of the normalized data to derive KPI performance insights;
providing, by the auto-learning performance device, the one or more KPIs, the KPI performance insights and the one or more factors affecting performance of the KPIs to a user, through an interactive visualization based on user profile;
fine-tuning, by the auto-learning performance device, the one or more KPIs based on the performance of KPIs, the one or more thresholds and the one or more metrics through machine-learning techniques;
enhancing the auto-learning performance device, along with the one or more KPIs and the interactive visualization based on one or more patterns of usage of the interactive visualization and the fine-tuning, thereby dynamically managing performance indicators for an enterprise to address the goals of facility operations management;
wherein the one or more KPIs are classified as at least one of asset indicators and process indicators, the asset indicators including at least one of energy efficiency ratio (EER) and coefficient of performance (COP) for heating, ventilation and air condition (HVAC) assets, the process indicators including efficiency of a cooling process by the HVAC assets.

2. The method of claim 1 , wherein the operations data comprises at least one of enterprise type, enterprise requirements, number of sensors and measurement units of devices present in the enterprise.

3. The method as claimed in claim 1 , wherein the operations data is received from at least one of the one or more sources comprising, a Building Management System (BMS), site instrumentation, one or more utility system interfaces, one or more vendor system interfaces, one or more web interfaces and an enterprise Management Information System (MIS).

4. The method as claimed in claim 1 , wherein the goals used for deriving KPIs are defined in the form of one or more Key goal indicators (KGI).

5. The method as claimed in claim 4 , wherein the indicator management database comprises the one or more KG Is, KPIs associated with the KGIs, the one or more thresholds, the one or more metrics and polices contributing to the performance of KPI's to be maintained which are categorized based on one or more of type of enterprise, operations, location, building attributes, make and model of assets.

6. The method as claimed in claim 1 , wherein deriving KPIs comprises determining frequency at which each KPI is calculated.

7. The method as claimed in claim 1 , wherein the one or more thresholds are derived based on at least one of historical data and data associated with the indicator management database.

8. The method as claimed in claim 1 , wherein the one or more thresholds are tuned based on real time data.

9. The method as claimed in claim 1 , wherein a graphical user interface (GUI) provides insights on improvement opportunities, preventive maintenance, problem resolutions and usage of KPIs.

10. An auto-learning performance device comprising:
a processor; and
a memory, communicatively coupled to the processor, which stores processor executable instructions, which, on execution, causes the processor to:
integrate operations data associated with an enterprise, received from one or more sources and an indicator management database;
derive one or more Key Performance Indicators (KPIs), one or more thresholds and one or more metrics based on the integrated data and goals of facility operations management;
determining, by the auto-learning performance system, one or more factors affecting performance of the KPIs and one or more factors affecting performance of the one or more thresholds;
create a comparability matrix based on the one or more factors affecting performance of the KPIs and the one or more factors affecting performance of the one or more thresholds to identify clusters of objects, corresponding data points and techniques to be used for normalization of the one or more KPIs;
normalize the one or more factors affecting the performance of the KPIs based on at least one of the clusters of objects, the corresponding data points or the techniques for normalization to provide normalized data;
assess performance of KPIs based on the one or more thresholds, the comparability matrix and associated patterns of the normalized data to derive KPI performance insights;
provide the one or more KPIs, the KPI performance insights and the one or more factors affecting performance of the KPIs to a user, through an interactive visualization based on user profile;
fine-tune the one or more KPIs based on the performance of KPIs, the one or more thresholds and the one or more metrics through machine-learning techniques;
enhance along with the one or more KPIs and the interactive visualization based on one or more patterns of usage of the interactive visualization and the fine-tuning, thereby dynamically managing performance indicators for an enterprise to address the goals of facility operations management;
wherein the one or more KPIs are classified as at least one of asset indicators and process indicators, the asset indicators including at least one of energy efficiency ratio (EER) and coefficient of performance (COP) for heating, ventilation and air condition (HVAC) assets, the process indicators including efficiency of a cooling process by the HVAC assets.

11. The auto-learning performance system as claimed in claim 10 , wherein the operations data comprises at least one of enterprise type, enterprise requirements, number of sensors and measurement units of devices present in the enterprise.

12. The auto-learning performance system as claimed in claim 10 , wherein the operations data is received from at least one of the one or more sources comprising, a Building Management System (BMS), site instrumentation, one or more utility system interfaces, one or more vendor system interfaces, one or more web interfaces and an enterprise Management Information System (MIS).

13. The auto-learning performance system as claimed in claim 10 , wherein the goals used for deriving KPIs are defined in the form of one or more Key goal indicators (KGI).

14. The auto-learning performance system as claimed in claim 13 , wherein the indicator management database compris

Assignees

• Carrier Corp

Inventors

• Venkitapathi Prabhu Raja Subbarayalu
• Singh Jasmine

Classifications

• H04L67/306

  User profiles · CPC title

• H04L43/16

  Threshold monitoring · CPC title

• H04L43/045

  for graphical visualisation of monitoring data · CPC title

• G06Q10/06393 Primary

  Score-carding, benchmarking or key performance indicator [KPI] analysis · CPC title

• H04L41/5009 Primary

  Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF] · CPC title