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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
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Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
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8 Ways to Improve Accuracy of Machine Learning Models
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Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Data Science: Automotive Industry-Warranty Analytics-Use Case

Trupti
Trupti Last Updated : 27 Jan, 2021
5 min read

This article was published as a part of the Data Science Blogathon.

Introduction

The automotive industry has mobilized the economy for decades. In the Automobile industry, the value chain starts at Inbound Logistics (raw material, warehouse) Production (Operation), Marketing and Sales, Maintenance (Service) given the Car design, vendors, the process is already ready.

automative industry

Though Data science is used at all levels in this value chain like optimization the schedule, transportation optimization, inventory management, workforce optimization, process optimization, and process control, targeting the right audience for the right segment of car, today I will be describing more on Maintenance or Warranty period analytics use case I have worked on.

 

Why Warranty Analysis?

After-sales, automobiles get post-sale services from dealers. A warranty analysis is mainly based on the data collected from those services, claims over a certain period.

In the Warranty Analysis typically Gamma, Weibull, or lognormal Distribution is observed for the failure of the product over the period.

 

How Does Warranty Analysis Data look like and how do we fit the distribution to data?

A warranty analysis is the analysis of time-to-event/failure data. In our example, the individual part is followed from the car sold time to its failure.

As in typical model building, we split the data into train and test datasets. With the training data, we first estimate the parameters of the distribution, and then using test data, we see if the model fitted works well on that data or not.

Gamma: The probability density function (pdf) of the Gamma distribution can be expressed as:

Gamma Distribution warranty analytics

Gamma Distribution

with α >0 and β >0 and x ≥0.

This also means it has two parameters, alpha– which represents the shape parameter, and beta- which represents the scale parameter, and its support consists only of positive values.

In Gamma distribution, using all xi ‘s, and using Maximum likelihood estimates, I estimated the α  and β for each item(spare part in analysis). But I observed that Gamma distribution when fitted on test data, the actual time to fail was not close to the predicted time to fail by model for that item. So I discarded the Gamma Distribution approaches.

Weibull: The probability density function (pdf) of the Weibull distribution can be expressed as:

Weibull Distribution warranty analystics

Weibull Distribution

with α >0 and β >0 and 0 < x < ∞

This means, it has two parameters, alpha– which represents the shape parameter, and beta- which represents the scale parameter, and its support consists only of positive values.

Again here using the maximum likelihood estimate for each spare item’s data I estimated α and β and tested with test data. The accuracy of the time to fail for each item was on test data with the estimated α  and β for all items were in acceptance criteria. and hence all the items of cars were modeling using Weibull distribution. For each item, I had different α and β, and all those models were stored for scoring purposes for the future.

The scenarios under which conditions the car is driven, driver’s driving skills, the age of the part are a few of the parameters of which some can be measured, and some are qualitative. And hence the modeling of the item failure needs to be done on a large amount of data. Else model will have a bias from the modeling data and the accuracy of the model will drop over the period of deployment /implementation.

 

Challenges: Typical challenges would be the accuracy and timeliness of data, the length of historical data, the number of parts to be modeled, and model selection based on accuracy on testing data. In my analysis I had the last two challenges due to the reasons like the car will have 1000’s of items in it and the model which fits best for one item may not fit well for another item.

Solution: There are many research papers that have explained this problem, mentioned the three distribution is way forward. To the problem, I was solving Weibull distribution fit well to all the part items. Once that is fit, testing data should support the model chosen and once that is done, I developed the process so that each item is modeled, and its scoring function is stored in such a way that periodically the forecast of failures per item is generated.

Once this solution is implemented in the business process, every month, manufactures will have an estimate of which items and their count he needs to be ready with for possible warranty claims. This enables saving in customer wait time for parts to be available, better inventory management, reduction in the cost of unnecessary storage of non-required parts and check on fraudulent warranty claims too.

Conclusion: For every manufacturer, warranty analytics can solve the following problems:

  1. Identify the patterns of claims (based on season, mileage, etc)
  2. Predicting the expected number of claims or cost of claims
  3. Predicting fraudulent claims( particular dealer always filing warranty claims, the life of an item is very long, but those
    parts are frequently coming for a warranty claim, etc)
  4. Investigating the association between different types of claims ( the two spare parts that get used same time has the
    same failure time)
  5. Identifying issues before they become severe and stop functioning
  6. What-if analysis such as if we increase the mileage what will impact on warranty costs

The benefits of the warranty analytics are:

  1. Increased customer satisfaction, product quality & brand reputation
  2. Tremendous impact on the bottom line due to early issues identification
  3. Optimized warranty policies for maximum financial performance
  4. Increase efficiency of support logistics such as optimum stocking of replacement parts or deployment of technicians.

It can also help answers the questions like:

  1. If we can raise the product warranty from 3 years to 6, if data supports the failure of the product occurs only after 10 years. If a competitor has raised his warranty and if we do adopt the same, how much more warranty costs we will incur? If we don’t, how much revenue we will we lose from reduced market share?
  2. Given a new product with no historical data, should we play it safe and offer only a one year warranty, or can we offer a three-year warranty to improve our brand perception?

This article summarizes the background, solution approach, and benefit for Warranty analytics which is a very big industry due to the increase in the sale and competition in the automobile market.

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

Trupti
Trupti

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