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Conference on Performance and Management (COPERMAN) aims to bring together researchers and practitioners to present and discuss innovative contributions concerning the measurement and management of organizational performance in a modern business environment.

COPERMAN maintains an interdisciplinary and multifaceted approach, as performance measurement and management are not limited to a specific area of competence. For this reason, scientific contributions addressing the Performance Measurement and Management Systems field with ground-breaking theoretical, experimental, and practical models and techniques are welcome, as well as studies on the latest organizational/technological trends.

Participation is free of charge.


  • Organizational Change Management
  • Leadership and Performance
  • Innovation and Creativity in Management
  • Sustainable Development Strategies
  • Digitalization and Performance Management
  • Predictive Analysis in Performance Management
  • Performance Management in the Public Sector
  • Performance Measurement in Circular Production
  • Advanced Management Models for Industry 4.0 and 5.0
  • Management Models for Human-Robot Collaborative Environments
  • Models and Case Studies of Performance Improvement/Optimization
  • Digitalisation for Operational Excellence
  • Continuous Improvement of Processes, Products, and Services
  • Reliability, Availability, and Risk Analysis
  • Asset Integrity Management
  • Maintenance Management
  • Definition of Decision Support Tools
  • Discrete Event and Agent-Based Simulation Models
  • Performance Measurement and Management Systems (PMMSs)
  • Key Performance Indicators (KPIs)
  • Sustainability performance
  • Human performance
  • Company performance drivers
  • Performance in Industry 4.0 contexts
  • Business strategy deployment
  • Strategic alignment
  • Complexity-driven PMMSs
  • Performance in Human-Robots working environments
  • Additive manufacturing performance
  • Metrics for social performance measurement in Industry
  • Performance measurement in Machine Learning and Artificial Intelligence

First Announcement Call-for-Abstracts

We are pleased to invite all researchers to submit full papers that will be peer reviewed for acceptance with the essential corrections and proposed for oral presentation.

All the accepted papers will be published in Lecture Notes in Production Engineering, a series published by Springer. Moreover, the best papers of the conference will be awarded.


Please contact us at for additional information on the conference.

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