Logistics outsourcing enters new era with AI, simulation and process standardization

The new framework developed in the study breaks down the outsourcing process into three key phases: preparation, process development, and digital technology integration. Each phase includes a structured series of steps - from defining objectives and key performance indicators (KPIs), to collecting and analyzing operational data, and finally integrating advanced tools such as discrete-event simulation, AI forecasting, and augmented reality for staff training and error reduction.

As global supply chains grow increasingly complex, a new study suggests that logistics outsourcing evaluations remain outdated, fragmented, and under-leveraged. A peer-reviewed article "New Dimensions in the Study of Outsourcing Logistics Services: The Role of Digitalization in Enhancing Efficiency" published in Logistics journal today introduces a comprehensive framework that integrates digitalization technologies into outsourcing-based process development, offering companies a structured and efficient model for transforming logistics operations.

The study, authored by Professor Péter Tamás of the University of Miskolc, finds that while logistics outsourcing continues to gain momentum as a strategic tool for cost reduction and operational focus, most companies still rely on ad hoc evaluation methods and ignore the potential of digital tools such as simulation modeling, artificial intelligence (AI), and digital twins.

The new framework developed in the study breaks down the outsourcing process into three key phases: preparation, process development, and digital technology integration. Each phase includes a structured series of steps - from defining objectives and key performance indicators (KPIs), to collecting and analyzing operational data, and finally integrating advanced tools such as discrete-event simulation, AI forecasting, and augmented reality for staff training and error reduction.

Drawing from both systematic literature analysis and prior industrial projects - including warehouse redesign and supply chain restructuring - Tamás emphasizes that the competitive edge lies in combining traditional logistics evaluations with modern digital insight.

The study begins with a comprehensive literature review that reveals a lack of consistency and innovation in current outsourcing assessment practices. Tamás analyzed over 100 publications from databases including Scopus, ScienceDirect, and Web of Science. He found that while outsourcing research has surged since 2015 - largely due to digital transformation - the applied methodologies have failed to keep pace.

His analysis highlighted four core outsourcing strategies: in-house operations, outsourcing, co-sourcing, and insourcing. Despite the recognized advantages of outsourcing, such as reduced costs, focus on core competencies, and deferred infrastructure investments, the study notes significant risks that continue to plague decision-makers. These include vendor underperformance, service disruptions, intellectual property theft, and long-term dependency on external providers.

To mitigate these risks, Tamás proposes a model that supports scenario analysis using real-time and predictive data. For example, companies can simulate the performance of logistics service providers (LSPs) using Plant Simulation software to test allocation strategies, warehouse maturity, delivery accuracy, and inventory fidelity. Objective function models are applied to weigh cost efficiency against service quality, with automated optimization identifying the best outcomes.

A case study presented in the paper demonstrates how the framework was applied to a fictional company evaluating whether to outsource, co-source, or retain control over its distribution warehousing. The company analyzed 5,400 potential allocation variants and, by integrating simulation tools and normalized KPI weights, selected the most efficient model, achieving a 21% improvement in operational performance over its initial baseline.

Key digital technologies outlined in the framework include monitoring systems, digital models, digital shadows, digital twins, business intelligence tools, AI, virtual reality, and augmented reality. Each technology is mapped to a relevant subprocess, enabling targeted applications such as simulation-based process planning, predictive maintenance, and real-time KPI tracking.

AI plays a pivotal role in enhancing data accuracy, forecasting future logistics demands, and optimizing service provider selection. Digital twins enable organizations to mirror physical logistics systems virtually, allowing for real-time troubleshooting and continuous optimization. Virtual and augmented reality tools further reduce operational risks by supporting employee training, layout validation, and process visualization.

That said, the study warns that digital integration is not without its challenges. Key barriers include poor data standardization, legacy IT infrastructure, internal resistance to change, and compliance risks related to contract law and data privacy (e.g., GDPR). Tamás recommends phased implementation strategies, pilot programs, and robust change management to ease adoption and reduce friction.

The study also highlights the need for clearer governance over performance monitoring and legal safeguards. Service-level agreements must be transparent, measurable, and enforceable to ensure alignment between client expectations and service delivery.

The author calls for continued research into AI-enabled outsourcing optimization, real-time analytics, and cross-sectoral applicability of the framework. He advocates for expanding the toolset to accommodate diverse logistics sectors, including manufacturing, retail, pharmaceuticals, and construction, each with unique needs and constraints.