Method and system for performing root cause analysis in warehouse management system using artificial intelligence

A computer-implemented system for performing root cause analysis in a warehouse management system using artificial intelligence, the computer-implemented system comprising:

• a computing device comprising:
• a processor;
• and
• a memory communicably coupled to the processor, wherein the memory stores processor-executable instructions, which when executed by the processor, cause the processor to:
• train a generative artificial intelligence (AI) model by collecting, cleansing, unifying, annotating, and labelling binary data received by the computing device, wherein the binary data comprising one or more images, one or more videos, sensor data, and system data depicting a comprehensive data of both successful and error data in the warehouse, wherein the generative AI model is trained based on the labelled binary data;
• receive real-time binary data comprising one or more images, one or more videos, sensor-data and system data depicting a process error in the warehouse, wherein the real-time binary data is captured in real-time and/or from archived records;
• extract contextual data related to the process error from one or more warehouse databases, trained/labelled data related to historical process execution comprising both success and error, wherein the contextual data comprising package information, warehouse process related information, packing information, quality check information, device data, process data, human factors, environmental data, historical anomaly data, customer feedback data, and operator-captured screenshots and videos;
• pre-process the real-time binary data by cleaning, unifying, annotating, and labelling the received real-time binary data;
• generate an initial hypothesis for a root cause of the process error based on the real-time binary data and the contextual data using a generative artificial intelligence (AI) model;
• construct a directed acyclic graph (DAG) representing potential causal relationships among variables associated with the process error using a DAG generation module, wherein the variables are derived from the contextual data and historical trained data, wherein the DAG captures both direct and indirect causal dependencies among the variables, which are derived from the contextual data;
• identify one or more root causes of the process error by applying statistical and machine learning and advanced Artificial Intelligence (AI) techniques to the DAG to estimate strength and direction of causal relationships between the variables associated with the process error, using a structured causal model;
• and
• validate the identified root causes by comparing results of the structured causal model against historical data and known outcomes.

A non-transitory computer-readable medium storing computer-executable instructions for performing root cause analysis in a warehouse management system using artificial intelligence, the computer-executable instructions configured for:

• training a generative artificial intelligence (AI) model by collecting, cleansing, unifying, annotating, and labelling binary data received by the computing device, wherein the binary data comprising one or more images, one or more videos, sensor data, and system data depicting a comprehensive data of both successful and error data in the warehouse, wherein the generative AI model is trained based on the labelled binary data;
• receiving real-time binary data comprising one or more images, one or more videos, sensor-data and system data depicting a process error in the warehouse, wherein the real-time binary data is captured in real-time and/or from archived records;
• extracting contextual data related to the process error from one or more warehouse databases, wherein the contextual data comprising package information, warehouse process related information, packing information, quality check information, device data, process data, human factors, environmental data, historical anomaly data, customer feedback data, and operator-captured screenshots and videos;
• pre-processing the real-time binary data by cleaning, unifying, annotating, and labelling the received real-time binary data;
• generate an initial hypothesis for a root cause of the process error based on the real-time binary data and the contextual data using a generative artificial intelligence (AI) model;
• construct a directed acyclic graph (DAG) representing potential causal relationships among variables associated with the process error using a DAG generation module, wherein the variables are derived from the contextual data, wherein the DAG captures both direct and indirect causal dependencies among the variables, which are derived from the contextual data;
• identify one or more root causes of the process error by applying statistical and machine learning techniques to the DAG to estimate strength and direction of causal relationships between the variables associated with the process error, using a structured causal model;
• and validate the identified root causes by comparing results of the structured causal model against historical data and known outcomes.