Digital Model, Digital Shadow or Digital Twin? A Life-Cycle Cost-Based Framework for Integrating Simulation and Artificial Intelligence in Logistics Process Development

Background: Logistics process development increasingly relies on simulation and artificial intelligence (AI), yet existing approaches provide limited methodological guidance for integrating these technologies throughout the process development lifecycle and selecting economically justified digital maturity levels. Methods: Based on a structured literature review, this study develops an integrated methodological framework covering logistics process development from problem formulation and model design through validation to operational application. The framework assigns algorithm-independent AI functions to individual development stages and combines technical feasibility criteria with discounted net life-cycle cost (NLCC) evaluation to select among digital models, digital shadows and digital twins. Its applicability is illustrated through an industry-inspired proof-of-concept case study involving simulation-based resource optimisation and neural network predictions. Results: The framework establishes a systematic relationship between process development objectives, simulation modelling, AI-based decision support and digital maturity selection. The case study demonstrates how AI can approximate simulation-derived optimal operating parameters to support logistics process improvement. Under the assumed economic conditions, the digital model was the most favourable alternative. Conclusions: The proposed framework provides a structured basis for integrating simulation and AI throughout logistics process development while supporting technically feasible and economically justified decisions.