Predictive Analytics

I4S 4/2025: Smart Logistics

I4S 4/2025: Smart Logistics

Sustainable, resilient processes along the entire value chain
Logistics is entering a new era. Climate change and geopolitical uncertainties are shifting the focus to resilience and sustainability. The concept of smart logistics is gaining importance. But what exactly makes logistics smart, and how can it help us organize our societies and the economy? Approaches such as predictive analytics, demand analysis, and machine learning show why smart logistics is more than just a technological trend.
Requirements Analysis for Predictive Analytics in SCM

Requirements Analysis for Predictive Analytics in SCM

Decision support for research and practice
Iris Hausladen ORCID Icon, ABM Ali Hasanat
Predictive analytics opens up opportunities to improve decision-making in manifold areas, including in supply chain management (SCM). Yet, the complete realization of its potential requires the identification of the corresponding needs upfront. This paper provides a structured concept that guides through the complex and interdisciplinary endeavor of requirements analysis for predictive analytics in SCM. Due to the generic nature of this approach, it can be applied for any use case and be adapted or enhanced in case of need.
Industry 4.0 Science | Volume 41 | Edition 4 | Pages 86-92
Open-Source and Cost-Effective Digital Twin

Open-Source and Cost-Effective Digital Twin

A case study with two weeks to succeed
Shantall Cisneros Saldana ORCID Icon, Sonali Pratap, Parth Punekar, Sampat Acharya, Heike Markus ORCID Icon
Digital Twin (DT) adoption remains a challenge due to high costs, complexity and lack of skills. This study proposes a cost-effective, TRL 5-validated DT model that can be built using open-source and office suite tools within just two weeks. Integrating real-time sensor data, predictive analytics, anomaly detection and notification, the model improves efficiency and sustainability in agriculture. Even with cloud service constraints, the system delivers a 7.76% average relative error and rapid, automated notifications. The findings show how open-source in combination with common commercial tools technologies can make advanced digital tools accessible to all, creating scalable, human-centered, and affordable solutions in line with Industry 5.0 principles.
Industry 4.0 Science | Volume 41 | Edition 3 | Pages 62-68 | DOI 10.30844/I4SE.25.3.62