Global Data Science Forum

 View Only

Machine Learning in Warranty Management

By Stephen Crenshaw posted Sat August 28, 2021 11:00 AM

  

Manufacturing businesses usually view warranty repairs as unfavourable. After all, they cost the company in cash to pay for warranty assistance and replacement elements. But, warranty repairs also present data that a manufacturer can use to develop its understanding of its products, finances, suppliers and third-party OEMs, and clients. 

The automotive business has mobilized the market for decades. In Automobile production, the value chain begins at Inbound Logistics Production, Marketing and Sales, Maintenance (Service) given the Car design, merchants, the process is already ready.

Though Data science is practiced at all levels in this value chain like optimization the program, transportation optimization, inventory control, workforce optimization, process optimization, and process control, targeting the right audience for the right section of the car.

Introduction

The automotive business has prepared the economy for decades. In the Automobile business, the value chain starts at Inbound Logistics Production, Marketing and Sales, Maintenance given the Car design, vendors, the process is already available.

 

Though Data science is practiced at all levels in this value chain like inventory management, workforce optimization, optimization the schedule, transportation optimization, process optimization, and process administration, targeting the right audience for the right section of car.

 

Why Warranty Analysis?

After-sales, automobiles get post-sale services from merchants. A warranty report is mainly based on the information collected from those services, claims over a specified interval.

In the Warranty Analysis, Gamma, Weibull, or lognormal Distribution is recognized for the breakdown of the product over the time.

 

How Does Warranty Analysis Data seem like and how do we fit the delivery to data?

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

As in standard model building, we split the information into train and analysis datasets. With the practice data like extended auto warranty solutions from olive, we first consider the distribution parameters, and then using test information, we see if the model provided works well on that data.

What are Essential Data Used in Warranty Analytics?

Warranty requests are submitted with a wealth of data. In addition to client information and equipment place, the warranty claim will recognize the product, defect, serial number, and even cover the amount of usage. For instance, a warranty claim for a vehicle might combine the vehicle model, the vehicle identification number, the defect – a fuel pump with insufficient pressure, and vehicle mileage.

The regular software warranty module and SAP ACS Warranty Management upgrade handle this warranty data from authorized service providers and dealers that present warranty service for SAP users. To receive authorization for warranty repairs, service hubs submit warranty claims that are verified and certified by the manufacturer.

This warranty information is needed to approve the warranty claims. For instance, a repair required under a three-year, 30,000-mile guarantee must include the vehicle mileage to guarantee that the vehicle is still under warranty. Furthermore, the VIN can be used to support the warranty claim by verifying the sale date. Nevertheless, this data can also be used with SAP's company analytics module.

Furthermore, information like warranty prices, labor costs and time, repair turnaround, and client satisfaction can also be gathered after the warranty repair is achieved. A manufacturer can gain an ambitious advantage by taking this data, feeding it into an analytical model, and creating data visualizations.

4 Warranty Analytics Use Facts You Need to Know

The most apparent use of warranty information is to improve product quality. But, warranty data can also give penetration into your products' use and misuse, enhance product safety, improve repair methods, reduce repair times, and improve warranty assistance.

  • Product Quality and Safety

Warranty data gives direct data about how and when your products fail. With this knowledge, you can recognize parts and systems that need repair. Warranty data will even give you insight into what needs improvement to reduce failure rates and improve client satisfaction.

For instance, warranty analytics may record that your fasteners fail at a higher rate than foreseen, leading to structural collapse. This would direct you to either change the design of the pins or alter the manufacturing method used for the fasteners. If you outsource the production of your fasteners, you might have a third choice – change fastener suppliers.

As important as increasing product quality and decreasing product failures, warranty analytics may assist you identify a systemic security problem that may need a product recall to resolve. For instance, automotive analytics may reveal that brake warranty repairs are unacceptably high and act a substantial risk of injury, death, and property destruction. Based on the high number of brake repairs as revealed by the warranty analytics, your company may choose to admit a product recall to safely repair all brake operations rather than waiting for clients to make warranty cases.

  • Product Use

While product damage might be excluded from warranty claims, a defect reported through ordinary use and foreseeable misuse might still be covered. But, warranty analysis of requirements for product damage incurred through ordinary use and foreseeable misuse might lead to a few things by a manufacturer.

  • Redesign: Redesign the product to restrict the use or misuse that is causing the product failure.
  • Update notifications and instructions: Presenting additional warnings and warnings against damaging use or perversion might reduce warranty claims and decrease your risk of product liability lawsuits.
  • Change the manufacturing method: The product may be manufactured separately against damaging use or misuse.
  • Design a new product: Add a product to your line that can accommodate the customers' use.

  • Repair Methods

Warranty analytics might also give insight into ways your service centers can fix repairs. For instance, you might find that a replacement for which you budgeted one hour of labor requires three hours of work. This might point to an analysis of the repair methods provided to the service centers. By refining the repair method, your business might save cash on warranty labor and improve the repairability of the goods.

  • Warranty Service

Advancing warranty service for you, your service hubs, and your clients might improve customer comfort and reduce your warranty costs.

Warranty information analysis will tell you which elements are likely to fail. As explained above, this can assist you in redesigning your products to reduce warranty requests. But, you can use this data to predict which parts will be required by service centers for products already in the field. Addressing these parts to the service hubs in anticipation of warranty claims will support the service centers to complete warranty repairs fast and maintain or improve client satisfaction with the warranty claim method.

Furthermore, you may be ready to reduce your warranty expenses by predicting which parts are unlikely to fail and reducing your inventory of those parts.

Warranty analytics can also assist you in managing suppliers. For instance, if the failure rate from one of your automotive OEMs is much lower than your other pieces suppliers, you might shift more work to that OEM. This will reduce your warranty costs by lowering the number of warranty claims.

Experience the Business Benefits of Warranty Analytics

Warranty analytics gives a unique insight into products that are in service—investigating the information from the results that fail shows you how your creations hold up over time. Identify, when a product is first submitted for a warranty repair in its third year in service, you can understand that the product did not need repair during the first two years.

Practicing this information, you can receive a complete picture about your company's results. You will develop a data visualization showing failure rates over time for various systems and components. This will tell your company what it is doing right and what it is doing wrong with its design and production.

Furthermore, warranty analytics can better your business to tailor its warranty policies and produce designs based on actual product use rather than engineering theory. When applied to your goods, these warranty policies will disclose when a product is truly broken, providing even more warranty data for review.

0 comments
3 views

Permalink