ABT-CPD Zero Customer Complaint – AI-Based Complaint Analytics, Root Cause Identification & Prediction System
Problem Overview
ABT-CPD has accumulated approximately 19 years of customer complaint records, covering different products, customers and types of quality and service-related complaints. Complaints include product quality deviations, physical defects, packaging issues, contamination/foreign matter, infestation, sensory characteristics, weight/quantity issues, process-related defects and other customer-specific concerns. At present, the historical data represents a valuable but largely untapped source of information. The challenge is to analyse this historical database using data analytics and AI to identify recurring patterns, major root causes, high-risk products/processes, ineffective or recurring corrective actions and early warning indicators. The ultimate objective is to develop a system capable of identifying the probability and likely nature of future customer complaints and recommending preventive actions before the product reaches the customer.
Industrial Context & Background
AI & Machine Learning / Data Analytics & Business Intelligence / Digital Quality Assurance / Quality Improvement & Process Optimization
Technical Requirements
1) Historical customer complaint database of approximately 19 years. 2) Complaint information such as date, customer, product and nature of complaint. 3) Wherever available, additional data such as batch number, process parameters, test results, root cause, corrective action and effectiveness of action. 4) The system should permit addition of new complaint data and continuously improve its analysis. 5) The solution should focus on actionable preventive recommendations rather than merely displaying historical statistics. 6) A prototype dashboard/alert mechanism should be demonstrated.
Suggested Technology Stack: Python / SQL / Power BI or equivalent BI tools / Natural Language Processing (NLP) / Machine Learning / Classification & Clustering / Predictive Analytics / Root Cause & Pareto Analytics / Risk Scoring / Dashboard & Data Visualization
Expected Solution & Prototype
A prototype Zero Customer Complaint Analytics & Prediction System that: 1) classifies historical complaints into meaningful categories; 2) identifies Pareto/major contributors to complaints; 3) identifies recurring complaints and chronic problem areas; 4) establishes relationships between product, customer, complaint type and possible process/root causes; 5) identifies high-risk products/processes; 6) predicts potential complaint categories/risk based on historical patterns; 7) recommends preventive/corrective actions; 8) provides a management dashboard for complaint trends and risk monitoring; and 9) establishes measurable KPIs for moving towards Zero Customer Complaints.
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