W2S Solutions 
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Outperform with technology 
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W2X LABS 
Contact us
Contact us
W2S Solutions -----
Outperform with technology -----
W2X LABS
USA
Automobile Manufacturing
Cross-platform application and Warranty cost analysis tool
Python, Flask, Angular, Cloud foundry, Data Science, Machine Learning & Natural Language Processing
4 Engineers
52+ Weeks and ongoing
Since the dawn of the automobile industry, our client has been a key player in the market and is known for their high-performance yet affordable cars that changed the course of industrialization itself! 117 years have passed since they made their first car, but they are now stronger than ever, keeping the spirit of the automotive industry intact!
With manufacturing plants across 83 locations and 21 countries, our client is on a mission to optimize their global manufacturing operations to reduce their warranty costs and claims and further elevate the quality of their products.
Our client’s spending on warranty costs and claims took a hit on their treasury. With the existing market competition, streamlining the resources seemed to be a huge problem.
Transcending the cultural borders proved to be a daunting task for our client, as most of the messages were lost in translation across these abstract borders.
Amid the client’s draining resources, the congruency between innovation and its cost took a severe hit. Known for their out-of-the-box approach, this also took a toll on their overall image.
The warranty costs alone had a huge impact on the client's resources and their product. Lack of data on the behaviour of the spare parts was the main problem.
Data Analytics
Enterprise SolutionsAnalyzing the behaviour of the spare parts lifecycle allowed us to understand more about the warranty costs and the nature of the warranty claims.
Our primary goal is to analyze the warranty claims and map out the corresponding vehicle lines, the cost being claimed, and the nature of the warranty claims.
We used ML to Identify areas of high vulnerability that could show up during warranty claims. These hotspots also opened up the space for Innovation and Improvisation.
We create a framework that allows our client to introspect their product and understand the spots that could be reinforced. These improvements could be implemented in the upcoming editions of the manufacturer.
We created a diverse tech panel composed of people from different backgrounds and skill sets. The team is hand-picked based on their experience and proficiency in the latest technologies.
1Data Analyst
1Cloud Architect
1Quality Analyst
2Backend Developer
1Projects Manager
1Front End Developer
Results can only be driven by choosing the correct technology stack. As we have great experience in handling such Digital Transformation Solutions, we had hand picked the following tech stack to build a rock solid application that should be capable of handling big data and should also provide flexibility for easy customization in future.
The nature of our client’s operations and their problems are highly complex. So, we focused on creating minimalistic solutions that are highly modular and adaptive. This helps our client to understand even the minute characteristics of the spare parts and reduce the overall warranty expenses.
By leveraging data, our client understood the critical pressure points in their spare parts and addressed those issues to build a better functioning product.
After implementation, the warranty costs gradually reduced, and once massive data sets were available, our client was able to cut the warranty expenses by more than 37% globally!
Keeping the Innovation factor fresh allows our clients to stay relevant in the market. It also had a significant impact on our client’s brand image.
Frequently Asked Questions
Data analytics in the automotive industry is used to estimate consumer behavior and collect customer data using data science. By anticipating future customer expectations, enhancing product quality, and customizing the shopping experience, it enhances the customer journey.
Big data analytics for automotive businesses enables the automotive manufacturing sector to gather information from ERP systems and integrate it with data from other company functional units and supply chain participants.
Predictive analytics in the automotive industry is widely utilized in the automobile sector to identify fundamental consumer buying habits and forecast future utilization, using methods like data mining/modelling, machine learning (ML), and artificial intelligence (AI) (AI).
In automotive predictive analytics, numerous sectors utilize predictive analytics to enhance their outcomes and foresee future events so that they can respond appropriately. Therefore, the real-time automotive data analytics the successful uses are retail, finance, insurance, telecommunications, energy, and other industries.
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