Warranty service is an important component of the warranty provider's business operations, and accurate warranty forecasting is a driving force customers satisfaction. Statistical analysis of warranty data, after-sales evaluation of product quality, and using warranty data to realize reverse logistics have long been neglected by warranty providers. A large amount of available information is hidden in massive warranty data yet it is difficult to effectively mine and hard to provide reliable references for the warranty provider's operational strategy.

The operation status of the enterprise's internal assets directly affects the production capacity and efficiency. Accurate fault prediction can demonstrate the enterprise's efficient production and operation. Long fault intervals, lost repair data, and unrecorded repair data make it difficult to analyze and organize asset repair data, and leads to difficulties for operators to understand status of the asset.

The Data Analysis can effectively solve the difficult problems of collecting, checking, and analyzing warranty data for users. It provides professional data processing and adds value to the data by analyzing it.

The Data Analysis function is mainly divided into six stages: data statistics, quality analysis, part statistics, warranty forecasting, warranty costs, and strategy-making:

Statistics

By filtering the massive data generated by warranty management and repair services, data counting and analysis are conducted to show the quantity of warranty claims by product, batch, machine, and part through charts.

Data Analysis

Quality Analysis

Based on warranty data supplemented by manufacturing data, the quality of products is effectively analyzed to provide a solid and effective basis for process improvement and quality enhancement.

Data Analysis

Spare Parts Management

Obtain part replacement records and calculate part quality data to provide data basis for part replacement and estimation, and pre-stock based on the actual usage of parts to effectively shorten the waiting time.

Data Analysis

Warranty Forecasting

Apply mathematical models to scientifically forecast future warranty and spare parts quantities, while maintaining high customer service levels, effectively control inventory investment, and increase asset return rates.

Data Analysis

Decision-making

Adjust different service parameters using scientific forecast. Provide optimal cost-effective solutions through simulating warranty conditions and improve strategic decision-making accuracy.

Data Analysis

Warranty Costs

Accurately control the occurred and estimating warranty costs.

Data Analysis

wareconn will satisfy your imagination of warranty service information mining with its complete Data Analysis function.

Advantages

  • Failure Analysis

    Analyze repair data from multiple dimensions and enhance data visualization.

  • Quality Assurance

    Statistically calculate the MTBF and failure rate of products, to strengthen product quality control.

  • Model Forecasting

    Using Weibull distribution model to forecast the next failure time or future warranty claims quantity and reduce operating costs.

  • Spare Parts Estimation

    Estimate the spare parts quantity based on the procurement cycle and safety stock coefficient to reduce costs and inventory.

  • Costs Statistics

    Calculate the warranty costs and estimate future costs to reduce operational risks.

  • Strategic Decision Making

    Simulate warranty settings, calculate and compare the expected costs before and after simulation as a strategy reference.

    Warranty Provider

    Data Setting

    Create a data set

    Build a massive data foundation

    Shipping Data

    Fetch or upload shipping data

    Bedding quality analysis

    Warranty Quantity

    Get useful information from massive warranty data

    Professional process warranty data

    Analyze the warranty quantity based on factors such as customers and part numbers

    Defective Batch

    Filter and process valid warranty data

    Analyze defective batches based on factors such as batches and months of service

    Defective Product

    Filter and process valid warranty data

    Analyze product defects and draw Pareto diagrams based on customer statement and warranty diagnosis

    Defective Parts

    Solve diverse data types

    Filter the warranty data of the parts under the product

    Realize data value-added

    Batch Yield

    Filter batch actual shipment data

    Multi-dimensional statistical analysis of the failure rate of a batch

    Product Yield

    Filter actual product shipment data

    Multi-dimensional statistical analysis of the failure rate of a product

    Parts Yield

    Organize and analyze the effective data of the components under the product

    Plot the frequency distribution of component failure time

    Analyze the reasons for the failure rate

    Charts

    Combine shipping information and platform data

    Customized analysis chart

    Prediction Model

    Provide prediction model building conditions

    Support multiple prediction model options

    Weibull, three-parameter, index and other algorithms

    Warranty Forecast

    Data mining, processing, analysis

    Use mathematical models to estimate future warranty numbers and improve forecast accuracy

    Inventory Estimates

    Optimize inventory indicators

    Refer to the scrap rate and monthly production capacity to estimate the inventory of spare parts and improve the return on inventory assets

    Known Warranty

    Historical warranty cost data collation

    Analyze data according to products and customers

    Pending Warranty

    Relying on model prediction logic

    Estimate future maintenance costs

    Life Cycle

    Add future shipment data

    Calculate the total cost of repairs for all shipments

    Warranty Simulation

    Data science practical applications

    Improve decision-making activities

    Simulate warranty settings to obtain the best solution

    Data Analysis

    Process

    1

  • Data Analysis
  • Setting warranty and shippingdata

  • 2

  • Data Analysis
  • Filter valid data from massive data, and query warranty information at multiple levels

  • 3

  • Data Analysis
  • Analyze product quality from multiple dimensions through data processing.

  • 4

  • Data Analysis
  • Create and select models, use different models for the data and come out with different information.

  • 5

  • Data Analysis
  • Predict future warranty based on the statistics or even compare different models for the best result.

  • 6

  • Data Analysis
  • Input procurement cycle and safety stock coefficient, forecast spare part demand and calculate safety stock based on future warranty.

  • 7

  • Data Analysis
  • Classify and calculate warranty costs occurred, forecast the future to assist strategy-making

  • 8

  • Data Analysis
  • Forecasting and simulating reverse service to achieve maximum benefits

    Data Analysis Chart Presentation

    Data Mining

    Data mining, clarify, and structural reorganizing Warranty Data

    Users can select the data to analyze from the massive warranty data generated by WM and RS, filter and restructure them into a data set that can be analyzed and forecasted. After obtaining the data, users can apply data verification function and obtain meaningful analysis results.

    Data Analysis

    Statistics

    Warranty Data Statistics and Analysis Based on Established Dataset

    Automatically generate statistical graphs for product lifecycle, failure code, and product batch repair quantity based on the established dataset. It can also perform statistical analysis on part replacement quantity and lifespan. Users can complete preliminary data analysis based on the charts.

    Data Analysis

    Quality Analysis

    Calculate quality indicators and compare product quality from multiple dimensions.

    Data analysis can quickly calculate quality indicators such as MTBF, reliability rate, product failure rate, WCR, etc. and generate quality graphs from multiple dimensions such as product, batch, machine, part, month, etc., helping users understand the product quality and identify the issues accordingly.

    Data Analysis

    Model Building

    Applying various mathematical methods, establish product failure models and forecast the failures.

    The data analysis function applies both non- and parametric methods to establish exclusive failure models for each machine/product. Among them, the parametric method applies the Weibull distribution model widely used in reliability engineering to forecast failures or repairs. Users can perform maintenance or quality improvement in advance based on the results.

    Data Analysis

    Warranty Simulation

    Cost-effective solution through simulated warranty conditions

    The platform can calculate and analyze all the expenses occurred in the repair process, and categorize and graph them accordingly. It can also forecast the costs based on the results of warranty and spare parts. wareconn also has a warranty simulation function, where users can adjust the warranty period, item costs, and product quality data based on the conditions in order to assist in making warranty strategies for new products.

    Data Analysis