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A Data Warehouse-Based System for Service Customization Recommendations in Product-Service Systems.

Laila Esheiba1, Iman M A Helal1, Amal Elgammal1,2

  • 1Faculty of Computers and Artificial Intelligence, Cairo University, Giza 12613, Egypt.

Sensors (Basel, Switzerland)
|March 26, 2022
PubMed
Summary

This study introduces a recommender system for Product-Service Systems (PSS) customization. It uses product usage data to recommend optimal IoT sensors for enhancing smart connected products and improving service customization.

Keywords:
data analyticsdata warehousingdecision support systemsproduct usage dataproduct-service systems (PSSs)product-service systems customizationrecommender systems (RSs)sensors

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Area of Science:

  • Engineering
  • Computer Science
  • Manufacturing Systems

Background:

  • Manufacturers are transitioning to Product-Service Systems (PSS), integrating products with services.
  • PSS customization involves configuring products and adding smart IoT devices for enhanced functionality.
  • Challenges exist in selecting optimal sensors and their placement during PSS service customization.

Purpose of the Study:

  • To propose a data warehouse-based recommender system (RS) for PSS customization.
  • To address the challenge of selecting appropriate IoT sensors and their locations.
  • To leverage historical product usage data for informed sensor recommendations.

Main Methods:

  • A data warehouse collects and analyzes usage data from similar products.
  • Analysis identifies critical components with high incident rates and their causes.
  • The RS recommends sensor types based on identified incident causes.

Main Results:

  • The system successfully recommends sensor types for PSS customization.
  • A case study on CNC milling machine rotary spindle units demonstrated the system's utility.
  • The approach facilitates informed decision-making in service customization.

Conclusions:

  • The proposed recommender system effectively supports PSS customization by recommending IoT sensors.
  • Data-driven insights from product usage analysis are crucial for optimizing service offerings.
  • This method enhances the transition towards smart, connected products within PSS frameworks.