DIGITAL TEMPORALITY AS A DRIVER OF RESILIENCE AND AGILITY IN PRODUCTION LOGISTICS AND SUPPLY CHAIN MANAGEMENT

Abstract

The digital transformation of logistics under Industry 4.0 shifts supply chains from static models to dynamic ecosystems. However, the temporal characteristics of data flows − their frequency, latency, and synchronization − remain undertheorized despite their growing importance for operational decision-making. This study introduces the construct of “digital temporality” and examines its influence on the balance between supply chain resilience and flexibility. A multi-method approach was employed, combining systems analysis of production and logistics processes, real-time data flow modelling with explicit representation of time lags, and comparative case study analysis of IoT and predictive analytics implementations across twelve manufacturing enterprises. Indicators were developed to measure digital temporality levels, including data update frequency, control signal latency, and EDI system response speed. Higher digital temporality correlates with reduced bullwhip effect intensity. Real-time data frequency improves resilience through early fault detection and faster recovery, while expanding the decision window for resource reallocation. However, excessive temporality generates “digital noise”, degrading decision quality by 15–25% beyond optimal frequency thresholds, necessitating AI-based filtering mechanisms. Digital temporality transforms logistics from a passive executor into an active agent of organizational adaptation. Its effectiveness depends on data quality, workforce digital maturity, and organizational culture rather than technology alone. The study proposes a temporal classification of supply chains (slow, fast, real-time) and identifies future research directions, including psychological aspects of hyper-temporality management and temporal safety standards for logistics systems.