AFTER-MARKET SERVICE PROCESS DIGITIZATION
Among other things, techniques are described for an after-market service process digitization. Service data is obtained that is associated with at least one asset and comprises at least historical warranty data and current IoT data. Predictive analysis to generate an asset survival prediction is performed based on current data associated with a first asset and the service data. Troubleshooting data associated with the first asset from at least one knowledge data source is received. A warranty coverage metric is determined based on the asset survival prediction and the troubleshooting data, wherein the warranty coverage metric is calculated in real time according to the asset survival prediction and the troubleshooting data. The warranty coverage metric is transformed at a device into human-readable form.
1 . A method, comprising:
obtaining, by at least one processor, service data, originating from at least one service data source, wherein the service data is associated with at least one asset and comprises at least historical warranty data and current IoT data;
performing, using at the least one processor, predictive analysis to generate an asset survival prediction based on current data associated with a first asset and the service data;
receiving, using the least one processor, troubleshooting data associated with the first asset from at least one knowledge data source;
determining, using the at least one processor, a warranty coverage metric based on the asset survival prediction and the troubleshooting data, wherein the warranty coverage metric is calculated in real time according to the asset survival prediction and the troubleshooting data; and
transforming, using the at least one processor, the warranty coverage metric at a device into human-readable form.
2 . The method of claim 1 , wherein the service data includes data associated with asset health of an entity.
3 . The method of claim 1 , wherein performing the predictive analysis is based on, at least in part, feedback associated with the troubleshooting data.
4 . The method of claim 1 , wherein the predictive analysis comprises survivability modeling.
5 . The method of claim 1 , wherein the at least one knowledge data source is a virtual assistant decision tree.
6 . The method of claim 1 , wherein the at least one knowledge data source is a deep-learning image classification model or a machine learning classification model.
7 . The method of claim 1 , wherein the warranty coverage metric is a warranty pricing strategy.
8 . The method of claim 1 , wherein the asset survival prediction is a Weibull distribution, Kaplan-Meier (KM) estimator, Cox-proportional hazard model, or Random Survival Forest model.
9 . A system, comprising:
at least one processor; and
at least one memory storing instructions thereon that, when executed by the at least one processor, cause the at least one processor to:
obtain service data originating from at least one service data source, wherein the service data is associated with at least one asset and comprises at least historical warranty data and current IoT data;
perform predictive analysis to generate an asset survival prediction based on current data associated with a first asset and the service data;
receive troubleshooting data associated with the first asset from at least one knowledge data source;
determine a warranty coverage metric based on the asset survival prediction and the troubleshooting data, wherein the warranty coverage metric is calculated in real time according to the asset survival prediction and the troubleshooting data; and
transform the warranty coverage metric at a device into human-readable form.
10 . The system of claim 9 , wherein the service data includes data associated with asset health of an entity.
11 . The system of claim 9 , wherein performing the predictive analysis is based on, at least in part, feedback associated with the troubleshooting data.
12 . The system of claim 9 , wherein the predictive analysis comprises survivability modeling.
13 . The system of claim 9 , wherein the at least one knowledge data source is a virtual assistant decision tree.
14 . The system of claim 9 , wherein the at least one knowledge data source is a deep-learning image classification model.
15 . A non-transitory computer-readable storage medium comprising at least one program for execution by at least one processor of a first device, the at least one program including instructions which, when executed by the at least one processor, carry out a method comprising:
obtain service data originating from at least one service data source, wherein the service data is associated with at least one asset and comprises at least historical warranty data and current IoT data;
perform predictive analysis to generate an asset survival prediction based on current data associated with a first asset and the service data;
receive troubleshooting data associated with the first asset from at least one knowledge data source;
determine a warranty coverage metric based on the asset survival prediction and the troubleshooting data, wherein the warranty coverage metric is calculated in real time according to the asset survival prediction and the troubleshooting data; and
transform the warranty coverage metric at a device into human-readable form.
16 . The computer-readable storage medium of claim 15 , wherein the service data includes data associated with asset health of an entity.
17 . The computer-readable storage medium of claim 15 , wherein performing the predictive analysis is based on, at least in part, feedback associated with the troubleshooting data.
18 . The computer-readable storage medium of claim 15 , wherein the predictive analysis comprises survivability modeling.
19 . The computer-readable storage medium of claim 15 , wherein the at least one knowledge data source is a virtual assistant decision tree.
20 . The computer-readable storage medium of claim 15 , wherein the at least one knowledge data source is a deep-learning image classification model.
21 . A method, comprising:
obtaining, using at least one processor, at least one exploded technical view diagram associated with an asset; and
inputting, using the at least one processor, the at least one exploded technical view diagram to a machine learning model that is trained to output a scalable vector graphics model corresponding to the at least one exploded technical view diagram, wherein an exploded view label in the scalable vector graphics model is hyperlinked to a product page.
22 . The method of claim 1 , wherein the machine learning model is trained using identified labels in training exploded technical view diagrams.
23 . The method of claim 1 , wherein optical character recognition is used to identify part numbers in the at least one exploded technical view diagram, and digits of the part numbers are converted to a hyperlink to the product page.
24 . The method of claim 1 , wherein the product page is viewed using a troubleshooting mobile application.