System and method for category management

Abstract

The present invention discloses a method, a system and a computer program product for Autonomous sourcing and Category management. The invention includes demand sensing and generation through a category workbench interface providing actionable insights for sourcing operation. The invention includes an AI engine configured for recommending a sourcing strategy through prediction analysis and auto negotiation in sourcing operation of Supply chain.

What is claimed is: 
     
         1 . A system for Category management, the system comprises:
 a category workbench application user interface configured to generate a plurality of data patterns related to one or more object categories for providing actionable insights to a user through at least one dashboard of the interface;   an intelligent bot configured for injecting the data patterns into a recommended strategy for generating at least one object characteristic data set; and   a processor configured to process historical data from a data lake and the object characteristic data set to identify one or more suppliers for executing the recommended strategy,   wherein the actionable insight includes a set of qualitative and quantitative data generated by processing of historical data from the data lake to analyze trends in supply chain for category management by enabling execution of at least one task.   
     
     
         2 . The system of  claim 1  further comprises:
 generating a code for the recommended strategy through prediction analysis by processing the historical data from a data lake. 
 
     
     
         3 . The system of  claim 2  wherein the category workbench application interface triggers a plurality of predictive data models to identify the one or more object categories. 
     
     
         4 . The system of  claim 1  wherein the bot is configured to generate backend scripts based on the recommended strategy for injecting the aggregated data using AI based dynamic processing logic to generate the object characteristic data set. 
     
     
         5 . The system of  claim 1  further comprises a plurality of task tools configured for triggering the at least one task based on a received demand from one or more data sources. 
     
     
         6 . The system of  claim 1  further comprises one or more trend indicators configured for providing the actionable insights to the user through the dashboard of the workbench. 
     
     
         7 . The system of  claim 1  wherein the actionable insights include category spend monitoring data, category classification and positioning data, supply market analysis data, supplier spend monitoring data, cost driver data, strategy data, opportunity identification data, risk assessment data. 
     
     
         8 . The system of  claim 1  further comprises AI engine coupled to the processor and configured for tracking and monitoring a plurality of parameters driving one or more supply chain operation wherein the plurality of parameters includes category strategies, key projects, supplier risk factors, contract performance indicator, and costs. 
     
     
         9 . The system of  claim 1  wherein the quantitative data includes market indices, commodity prices, stock price of supplier, delivery turn-around time (TAT), changes in market shares, demand and supply forecasts, expected lead times, savings expectations and tracking, compliance, percentage of managed spend, benchmarks for spend and prices, and should cost models with cost evolution. 
     
     
         10 . The system of  claim 1  wherein the qualitative data includes newsfeeds, about innovation, litigation, Merger and Acquisition, spin-offs, bankruptcy, entry and exit of key executives, path breaking innovation, supply shocks, strategic changes. 
     
     
         11 . The system of  claim 1  wherein the trends include supply, demand and pricing trends in supply chain. 
     
     
         12 . The system of  claim 1  further comprises:
 a sub network having at least one server configured to process a plurality of backend scripts generated by the bot to identify a relevant script for the at least one recommended strategy; and 
 a control unit configured to process the at least one strategy based on the identified relevant script for automating at least one task, wherein the control unit selects an Artificial Intelligence based dynamic processing logic using the bot to reduce the processing time of the task. 
 
     
     
         13 . The system of  claim 1  wherein the category workbench application user interface is configured to provide the actionable insights into the one or more data patterns being selectable to trigger an application associated with each of the one or more data patterns and enable the selected data pattern to be seen within the application and providing details on spend category, supplier regions spend, actual/vs target spend, top cost drivers and strategies. 
     
     
         14 . A method of Category management, the method comprises:
 generating a plurality of data patterns related to one or more object categories for providing actionable insights to a user through at least one dashboard of a category workbench application user interface;   injecting by an intelligent bot, the data patterns related to one or more object categories into a recommended strategy for generating at least one object characteristic data set;   processing historical data from a data lake and the object characteristic data set to identify one or more suppliers for executing the recommended strategy, and   generating a set of quantitative and qualitative data on the dashboard to analyze trends in supply chain for category management by enabling execution of at least one task initiated by a user through the interface.   
     
     
         15 . The method of  claim 14  further comprises:
 generating a code for the recommended strategy through prediction analysis by processing the historical data from a data lake. 
 
     
     
         16 . The method of  claim 15  further comprises the step of analyzing historical data through the workbench application interface and perform AI based budget predictions and demand aggregation by overlaying a historical spend data with disparate forecasting models built on various data sources available to analyze spend and pricing trends. 
     
     
         17 . The method of  claim 15  wherein the bot is configured to generate backend scripts based on the recommended strategy for injecting the aggregated data using AI based dynamic processing logic to generate the object characteristic data set. 
     
     
         18 . The method of  claim 15  further comprises:
 initiating automated tactical execution process based on the recommended strategy wherein the recommendation is auto-flipped into projects with a pre-populated responsibility assignment matrix, a savings target, one or more impacted categories and a supplier data. 
 
     
     
         19 . The method of  claim 18  further comprises:
 encapsulating one or more awarding scenario on the category workbench application user interface by the bot wherein an AI engine incorporates rules and target constraints including preferable number of suppliers, preferential awards to incumbent suppliers, minimum lead times, and savings goals to automatically arrive at a most efficient cost for executing recommended strategy. 
 
     
     
         20 . A computer program product for category management in supply chain management application of a computing device with memory, the product comprising:
 a computer readable storage medium readable by a processor and storing instructions for execution by the processor for performing a category management method, the method comprises:
 generating a plurality of data patterns related to one or more object categories for providing actionable insights to a user through at least one dashboard of a category workbench application user interface; 
 injecting by an intelligent bot, the data patterns related to one or more object categories into a recommended strategy for generating at least one object characteristic data set; 
 processing historical data from a data lake and the object characteristic data set to identify one or more suppliers for executing the recommended strategy, and 
   
       generating a set of quantitative and qualitative data on the dashboard to analyze trends in supply chain for category management by enabling execution of at least one task initiated by a user through the interface.