The prevalent global competition in manufacturing sector has resulted in evolution of warranty programs into tools for managing costs, tracking fraudulent claims, and driving customer satisfaction. This has in turn resulted in a persistent demand for enhancing product quality and reliability. However, lack of approaches to detect defects and reduce claim expenses, is having a detrimental effect on the manufacturer’s brand. Manufacturers spend a great deal of money on warranty costs, something between 2 to 5% of sales. Thus, even a small change in the claim payment can have a huge impact on bottom line and improve customer satisfaction.
Warranty analytics seems to be the only key to identify root cause of possible defects and provide possible solutions by transforming raw data into meaningful insights with actionable plans. Organizations not only require the right analysis techniques but also good information technology systems to enable data collection and integration of various databases across the departments.
Despite availability of many warranty analytics software in the market, implementing them is still a challenge for the organizations. Below given three reasons illustrate the existing loopholes in current analytics solutions:
- The warranty analytics software is not specifically architected analyzing for warranty specific data, the especially technical jargon used by the technicians. Also, the tools do not have the necessary algorithms or models to build warranty specific reports showing trends, specific skews, heatmaps, and diagnostics.
- Current warranty analytics platforms lack proper data analyst support to prepare, detect, and tackle real-time errors before uploading the data.
- Lack of warranty data expertise also makes current analytics platforms incapable of finding exact happenings and form a predictive model for decision making.
How BIRD helps Warranty Analytics
BIRD’s powerful full-stack data management and augmented business intelligence platform provides the one-stop solution to collect, process, and analyze complicated warranty specific data with appropriate warranty data expertise. Utilize our specially designed algorithms and tools to easily model, analyze, and visualize tons of complicated data to instantly recognize product shortcomings, and provide relevant warranty services.
Forecasting failure rate
- Forecast the number of claims for the next 6 to 12 months.
- Get forecast with respect to product categories, models, and even down to part level.
- Use forecasting to plan for inventory, budget, and model warranty agreements.
- Perform root cause analysis on models or parts not following the estimated failure rate.
- Use advanced deep learning and text analytics to detect anomalies and questionable patterns in the data.
- Mark various claims to dig deeper into the outliers.
- Identify exploited patterns to subsequently improve warranty processes.
- Take preventive and proactive steps in product management, thus saving millions.
Automated claims processing
- Automate warranty claim rejection or approval through advanced machine learning models.
- Save hours in doing manual processing of claims.
- Use advanced machine learning models to detect more patterns in claims.
- Train and develop models to reduce rejections of claims.
- Segregate different suppliers on the basis of failure rates, delivery times, and other SLAs.
- Analyze patterns in each of the supplier segments to constantly monitor SLAs and take subsequent decisions for the related suppliers.
Key benefits with BIRD
Eliminate data silos
Use our connectors to integrate your data one place
Use multiple ML models for forecasting, prediction and text analytics
Avail real time analytics with advanced visualizations
Big Data architecture
Event driven architecture to ingest and process real-time data
Use high performance and extensive data preparation features
Universal data model
Create single data models with multiple fact tables
Augmented analytics through BIRD
Leverage BIRD’s augmented analytics feature to achieve solutions for all types of queries ranging from simple search keywords to complex human queries in natural language format. BIRD’s advanced analytics approach utilizes combination of machine learning and natural language generation to instantly display any kind of searched report or patterns.
BIRD comes with an in-built library of analytical functions and algorithms which would run in the background to analyze data and predict business outcomes. Leverage these predictions to simulate impacts of all decisions and alleviate any possible risks. Enable your business to transit from:
Get into action from Insights
Leverage BIRD’s automated insights to take relevant business actions by identifying growth opportunities and prevailing loopholes. BIRD uses artificial intelligence and machine learning techniques to transfer raw data into recommended actions. It delivers personalized in-context information and helps save time from analysis to action.
The powerful and collaborative on-the go storyboards ensure that the insights are displayed seamlessly, regardless of user location, and more importantly, on time to take key business decisions.