Personalized customization: Service resource configuration optimization driven by customer requirements accurately.

Area of Science:

• Operations Research
• Industrial Engineering
• Human-Computer Interaction

Background:

• Personalized customization presents challenges in optimizing service resource configuration.
• Existing methods often fail to adequately capture diverse customer requirements for effective resource allocation.

Purpose of the Study:

• To propose and validate a customer-driven approach for optimizing service resource configuration in personalized customization.
• To enhance the alignment between customer needs and service resource allocation.

Main Methods:

• Integrated importance judgment matrix, KANO model, and competitiveness evaluation for customer requirement assessment.
• Utilized House of Quality (HoQ) and technical attributes to link customer requirements to service modules.
• Introduced a 'one-to-many' relationship mechanism between service modules and candidate itemsets.
• Applied utility measure functions to determine customer perceived utility.
• Formulated and solved an optimization model for service resource configuration scheme.
• Employed an improved genetic algorithm (IGA) for demonstration.

Main Results:

• Successfully evaluated the relative importance of customer requirements.
• Established weights and correlations between service modules and customer needs.
• Determined service candidate itemsets based on specific customer requirements.
• Quantified customer perceived utility.
• Demonstrated the approach's viability and efficacy through a living room customization example.

Conclusions:

• The proposed customer-driven approach effectively optimizes service resource configuration for personalized customization.
• The methodology provides a robust framework for aligning service resources with dynamic customer demands.
• The improved genetic algorithm effectively solves the optimization problem, showcasing practical applicability.