Title: Develop a solution to identifo discrepancies in Quality Monitoring Data on OMMAS and generate reports that highlight areas where corrective action is Required.. Category: Smart Automation. Subcategory: Software. Organization: Ministry of Rural Development. Description: The task is to develop a solution that leverages the quality monitoring data available on OMMAS to identify discrepancies between various data sources, The data is collected through an Eform report and test datasheet filled by second and thirdtier quality monitors QMs and a quality control register QCR scanned copy uploaded by State Project lmplementation Units PlUl. 1. The QM report which has been digitalized and called an Efrom we currently get from OMMAS is filled in by Quality Monitors QMs. Ihe report contains data on all road layers and other pertinent information, such as geometry road furniture, drains, etc. The report includes values of important parameters from DPR, values recorded at the site by second and thirdtier quality monitors, YesNo answers, and values from QCR. 2. The test reportdatasheet contains values recorded at the site for every test. Test results need to be recorded in the QM report Eform for each test. This report is cunently uploaded by QMs as a scanned pdf. lt will be digitized in a few days, and we will receive the inspection data in digitized format only. 3. Qualfi Control Registers QCR are the record of the tests conducted at the site by the Contractor engineer and the authority engineer JE, AE and EE. eCn has two parts. eCR partr needs to be filled and maintained by the contractor, and part_ll needs io be filled bv the authority project imprementation unit pru. prUs currentry ,pu.J ecn, ., scanned pdf copies. The solution should be able to compare the data from these sources with the standard values for each test and analyse the three sources separately. The aim is to identifo discrepancies in the data and enable effective quality monitoring of the processes involved. The solution should be able to process large volumes of data and provide actionable insights that can be used to improve the quality of the processes being monitored. Probable Outputs 1. Develop a machine learning algorithmtool that can analyze the quality monitoring data on OMMAS and identifo discrepancies between the different sources of data. The algorithm can be trained on historical data to identif patterns and use thess patterns to predict discrepancies in realtime. The output of the algorithm can be used to generate reports that highlight areas where corrective action is required. 2. Build a dashboard that visualizes the quality monitoring data on OMMAS and enables users to compare the data from different sources. The dashboard can be customized to display data in various formaG, such as tables, charts, and graphs. The users can interact with the dashboard to drill down into specific data points and identifo discrepancies between the different sources of data. 3. Develop a data processing pipeline that automates the process of analyzing the quality monitorin8 data on OMMAS. The pipeline can be designed to intest data from different sources, such as the QM report Eform, test reports, and QCRs. The pipeline can thenprocess the data using various tools and techniques, such as data cleaning transformation, and analysis. The output of the pipeline can be used to generate reports that highlight areas where corrective action is required. 4. Use natural language processing NLP techniques to analyze the quality monitoring data on OMMAS and identifo discrepancies between the different sources of data. NLP techniques can be used to extract relevant information from the QM report Eform, test reports, and QCRs, and compare this information to identifo discrepancies. The output of the NLP analysis can be used to generate reports that highlight areas where corective action is required