Digital Supply Networks and Predictive Logistics: Rewiring Supply Chains for an “Always-On” World

International Academy
Dec 10, 2025
14 min read

Author: L. Hartmann

Affiliation: Independent Researcher

Abstract

In a time of chaos, uncertainty, and fast-changing technology, more and more global companies are using digital supply networks (DSNs) and predictive logistics to make their operations more flexible, efficient, and resilient. DSNs use cutting-edge digital technologies like the Internet of Things (IoT), cloud computing, big data analytics, artificial intelligence (AI), blockchain, and digital twin systems to make the supply chain more visible, coordinated from start to finish, and open. Predictive logistics uses these technologies, especially AI and analytics, to guess what people will want, plan for problems, find the best routes, and keep track of inventory in real time. Academics and professionals say that these kinds of changes make businesses more competitive, adaptable, sustainable, and able to handle risks. Recent empirical studies demonstrate that firms digitising their operations experience enhanced logistics efficiency, increased supply-chain resilience, and superior performance. This article analyses the emergence of Digital Supply Networks (DSNs) and predictive logistics through a multi-theoretical framework, utilising Pierre Bourdieu’s theory of capital and field, World-Systems Theory, and Institutional Isomorphism. It contends that DSNs represent nascent socio-technical domains where participants vie for digital, analytical, social, and symbolic capital; that predictive logistics exacerbates core-periphery disparities within global supply chains; and that institutional pressures catalyse a pervasive shift towards analogous digital supply models. The paper ends with some advice for managers and policy makers. It says that DSNs and predictive logistics should not be seen as simple upgrades to technology, but as big changes in strategy and society that need money spent on people, governance, and fair access.

1. Introduction

There are big changes happening in global supply chains. Geopolitical tensions, trade disruptions, climate change, and pandemic shocks have all made supply chains less stable, which has made traditional models less useful. At the same time, rising consumer demand for faster delivery, customisation, and sustainability has made companies rethink how they get, make, move, and deliver goods. As a result, many companies are moving away from linear supply chains and towards supply networks that are more connected, data-driven, and flexible. No longer just buzzwords, "digital supply networks" (DSNs), "smart supply chains," "Industry 4.0 supply chains," and "predictive logistics" are now real investments by companies in manufacturing, retail, logistics, and other fields. Researchers and industry experts agree that DSNs can make things more efficient, visible, and resilient by improving coordination, data sharing, and analytics. Recent studies show that going digital makes supply chains work better and makes them more competitive. But DSNs and predictive logistics do more than just improve the technical and operational aspects of supply networks. They also change the social, organisational, and structural dynamics of these networks. They change who has power over information, who makes choices, who gains value, and who is still on the outside. This article examines these profound implications through theoretical frameworks derived from sociology and global political economy.

Specifically, the paper asks:

How do DSNs and predictive logistics reconfigure the structure, authority, and coordination of supply networks?
How do they redistribute different kinds of capital and power among participating actors (firms, suppliers, logistics providers)?
What institutional forces drive their adoption globally, and what are the risks associated with widespread convergence?

To address these enquiries, I initially examine pertinent literature and contextualise DSNs within expansive socio-theoretical frameworks. Then I look at both empirical and theoretical evidence of how DSN adoption and predictive logistics implementation work. Finally, I present conclusions and suggest implications for practitioners and researchers.

2. Background and Theoretical Framework

2.1 Digital Supply Networks and Predictive Logistics: Definitions and Components

The term "digitisation of supply chains" refers to the use of digital technologies like IoT sensors, cloud computing, big data analytics, AI, blockchain, and digital twins in supply chain operations. This kind of change makes it possible to collect data in real time, communicate easily, and work together with many different people, from suppliers of raw materials to end customers.

A digital supply network (DSN) shifts away from linear, sequential supply-chain models toward networked, interconnected, many-to-many systems. In DSNs:

Data flows continuously between partners—suppliers, manufacturers, logistics providers, distributors, and retailers.
Digital platforms integrate data on production, inventory, orders, transportation, and demand signals.
Real-time monitoring, visibility, and transparent communication enable dynamic coordination and responsiveness.

Predictive logistics is the main analytical and decision-making tool built on DSNs. It uses AI, machine learning, data analytics, and predictive modelling to guess how much demand there will be, when there will be delays or problems, how to best manage inventories, and how to guess risk. With predictive logistics, companies can act before something happens instead of after it happens. It helps with planning for demand, optimising inventory, scheduling transportation, and managing risk when things are uncertain. So, DSNs and predictive logistics use technology, data analysis, and decision-making to change the way traditional supply chains work.

2.2 Theoretical Frameworks: Bourdieu, World-Systems, and Institutional Theory

To understand DSNs and predictive logistics beyond operations management, I draw on three theoretical frameworks:

Bourdieu: Capital, Field, and Habitus

Pierre Bourdieu conceptualizes social life in terms of fields—structured spaces of social positions—and capital, understood in multiple forms: economic, social, cultural, symbolic, etc. Actors within a field struggle for advantage, using their available capital. Importantly, their dispositions (habitus) shape what strategies they consider legitimate and desirable.

Applied to DSNs: existing supply-chain networks and firms become a new “supply-network field.” In this field:

Digital capital (investment in IoT, cloud, integration, platforms) becomes critical;
Analytic capital (data science, modeling, logistics planning skills) is increasingly valuable;
Relational capital (trust, long-term partnerships, shared data governance) becomes essential for collaboration;
Symbolic capital (reputation, perceived modernity, reliability) may be generated by early adopters.

Firms that possess or acquire such capital can influence standards, shape platform governance, and command better bargaining positions. Their habitus—organizational culture, managerial mindset, risk appetite—affects how they use DSNs and predictive logistics (e.g., willingness to share data, trust in algorithmic decision-making, investment in human skills).

World-Systems Theory: Core–Periphery Relations in Digital Supply Networks

World‑Systems Theory (Wallerstein, 1974) describes the global economy in terms of core, semi-peripheral, and peripheral zones—differentiated by control over capital, technology, and value. DSNs and predictive logistics extend and embed these inequalities in a digital layer.

Core actors—large multinational firms, global platform providers, logistics giants—often control the digital infrastructure, data standards, analytics tools, and governance mechanisms.
Peripheral actors—small suppliers, regional logistics providers, firms in emerging economies—may be integrated into DSNs but often lack equal access to analytic or digital capital, limiting their ability to derive value or influence outcomes.
Predictive logistics can further concentrate power, because actors with better data quality, stability, and analytics skills can anticipate demand and disruptions more reliably, negotiate better contracts, or refuse risky orders.

Hence DSNs may reproduce or even exacerbate global inequalities, unless interventions promote more equitable access to digital capital and analytic capability.

Institutional Isomorphism: Convergence of Organizational Practices

Institutional Isomorphism (DiMaggio & Powell, 1983) posits that organizations within a similar field tend to become more alike over time due to coercive (regulative), mimetic (imitation), and normative (professional standards) pressures.

Applied to DSNs:

Coercive pressures: Regulatory requirements (e.g., traceability, environmental reporting, supply-chain transparency), customer demands (e.g., real-time tracking), or standards may force firms to adopt digital supply solutions.
Mimetic pressures: Firms imitate successful or leading firms that have adopted DSNs and predictive logistics—especially in uncertain environments—hoping to gain competitive advantage or legitimacy.
Normative pressures: Professional communities (supply-chain managers, consultants, logistics vendors) standardize best practices, metrics (forecast accuracy, OTIF—on-time in full), architectures (control-towers, digital dashboards), and skill requirements.

As a result, organizations across sectors and geographies adopt similar DSN architectures, increasing uniformity but also entrenching systemic dependencies on certain technologies, vendors, and models.

3. Methodology

Given the relative novelty of DSNs and predictive logistics as widespread phenomena—and the rapid evolution of relevant technologies—this article adopts a qualitative, conceptual, and integrative methodology. The research draws on:

Recent Empirical Studies (2019–2025): A structured review of academic and practitioner-oriented literature on supply-chain digitization, digital supply-chain management, and logistics digitalization. Key sources include peer-reviewed articles, systematic reviews, and empirical studies from manufacturing, logistics, and retail sectors.
Theoretical Synthesis: Application of Bourdieu’s theory, world-systems theory, and institutional isomorphism to interpret observed empirical trends and deduce deeper structural implications.
Thematic Coding & Analysis: Identifying recurring themes—visibility and connectivity; predictive planning and risk management; capital accumulation and power asymmetries; institutional convergence; human and organizational agency. Data extracted from the literature are coded under these themes.
Critical Reflection: Linking empirical findings to the socio-theoretical frameworks to surface tensions, contradictions, and potential risks—especially concerning inequality, inclusion, and systemic vulnerability.

This approach does not rely on new primary data but builds on existing, credible academic studies conducted in recent years.

4. Analysis

4.1 The Practical Benefits: Visibility, Efficiency, Resilience

A strong and consistent finding across multiple studies is that supply-chain digitalization—through DSNs and predictive logistics—enhances operational performance, supply-chain resilience, and competitive advantage.

Research shows that digitalization improves supply-chain resilience by strengthening capabilities to absorb, respond to, and recover from disruptions.
Studies highlight enhanced supply-chain performance, driven by improved logistics efficiency. For example, firms with higher degrees of digital supply-chain implementation and greater logistics efficiency were found more competitive.
A recent systematic review underlines real-time demand feedback, better coordination across supply-chain nodes (suppliers, manufacturers, retailers), reduced risk, improved responsiveness, lowered costs, and simplified complexity as central advantages of digital supply chains.
The widespread integration of technologies—IoT, AI, blockchain, cloud computing, digital twin—facilitates dynamic tracking, forecasting, transparency, and traceability across supply networks, supporting sustainability, agility, and operational excellence.

Also, the recent wave of global shocks (COVID-19, transportation problems, rising prices, and changing consumer behaviour) has made DSNs and predictive logistics even more important. Businesses need to be able to predict problems and respond quickly because they have to deal with unpredictable demand patterns, limited capacity, and suppliers who are not always available. DSNs give you the framework, and predictive logistics gives you the insight.

4.2 DSNs as New Socio-Technical Fields: Capital, Power, and Inclusion

Applying Bourdieu’s framework reveals that DSNs represent a new field of struggle—one where different forms of capital become salient.

Digital and analytic capital: Firms that have invested in IoT infrastructure, data platforms, analytics teams, and integration capabilities enjoy a competitive edge. These assets enable them to monitor deeply, forecast accurately, and coordinate broadly. As a result, they can exploit economies of scale, optimize routes, anticipate disruptions, reduce buffers, and respond faster to changes.
Relational capital: Because DSNs rely on data sharing, coordination, and trust across multiple firms (suppliers, manufacturers, logistics providers, clients), relational capital becomes critical. Firms with long-term partnerships, reputation for reliability, and transparent governance may collaborate more effectively, negotiate better terms, and influence network design.
Symbolic capital: Early adopters of DSNs and predictive logistics—firms that brand themselves as “digital,” “resilient,” “agile,” “sustainable”—may derive reputational advantages. These reputational gains can translate into customer trust, investor interest, and better bargaining power.

At the same time, many firms—particularly small and medium enterprises (SMEs), firms in developing regions, or those with limited digital budgets—lack such capital. Without digital infrastructure, skilled analysts, or strong relational networks, they risk being sidelined or marginalized within DSNs. They may be relegated to low-margin, low-visibility segments of the network. Thus, DSNs risk reinforcing structural inequalities.

4.3 Global Inequalities and Core–Periphery Dynamics

Under the lens of world-systems theory, DSNs and predictive logistics extend global inequalities into the digital and data-driven realm of supply chains.

Core actors: Large multinational firms, global logistics providers, and leading technology platform vendors typically operate from developed economies. They control the digital platforms, standards, analytics, and often data governance. This gives them significant power over supply-chain design, network configuration, contract terms, and risk allocation.
Peripheral and semi-peripheral actors: Suppliers, manufacturers, logistics providers in developing countries—or smaller firms in developed countries—may participate in DSNs but often lack control. They may shoulder operational burdens (tight lead times, just-in-time delivery, stringent quality demands), while reaping only limited value from data-driven efficiencies.
Data asymmetry: Core actors accumulate data from many suppliers and partners. This aggregated data, combined with analytic tools, enables predictive insights not available to peripheral actors. This asymmetry becomes a structural advantage: core actors can foresee demand shifts, optimize sourcing, re-allocate volumes swiftly; peripheral firms cannot.

Thus, DSNs may not only reproduce but deepen global inequalities unless efforts are made to democratize access to digital capital, analytics capabilities, and data governance.

4.4 Institutional Pressures and Organizational Convergence

Beyond competitive dynamics, adoption of DSNs and predictive logistics is also driven by institutional pressures across firms globally.

Coercive pressures: Regulatory demands for traceability, compliance (e.g., environmental, labor, safety), and transparency push firms toward digitized tracking and reporting. In industries such as food, pharmaceuticals, high-tech manufacturing, and retail, regulators and customers increasingly expect traceability and accountability. As a result, firms invest in digital tracking, data sharing, and predictive risk analytics—even if their primary motive is compliance rather than efficiency. Recent studies note that many firms adopt digital supply-chain technologies to meet regulatory and sustainability requirements.
Mimetic pressures: In uncertain and volatile environments, firms imitate successful adopters of DSNs and predictive logistics. Leading firms publish success stories; consultants promote best practices; vendors market turnkey digital supply solutions. Firms uncertain about the future tend to emulate these perceived “leaders” to gain legitimacy and avoid falling behind. This imitation accelerates diffusion, even among firms lacking full readiness.
Normative pressures: Professional communities—supply-chain managers, consultants, vendor networks, academic researchers—develop shared standards, metrics, and skill sets that define what “good supply-chain management” now means. Digital visibility, predictive analytics, control towers, data-driven planning, and performance dashboards have become normative. Firms align their practices to conform to these norms, reinforcing homogeneity across sectors.

While isomorphic adoption can enhance interoperability, coordination, and spread of good practices, it also reduces diversity of supply-chain strategies and may lead to systemic vulnerabilities. If many firms rely on similar data architectures, analytics models, and assumptions, shocks or model failures may simultaneously affect large parts of supply networks.

4.5 Risks and Challenges: Implementation, Inclusion, Governance

Despite the benefits, DSNs and predictive logistics face significant challenges—technical, organizational, institutional, and ethical.

Implementation challenges: A major barrier is the lack of infrastructure and integration capabilities—especially for smaller firms or those in developing regions. A quantitative study on IoT-based digital supply chains identified lack of technological infrastructure and security challenges as among the most significant implementation barriers for firms in consumer-goods sectors.
Data governance and security: As supply networks share more data across firm boundaries, questions arise about who owns the data, who controls access, how privacy and security are maintained, and how insights are shared or monetized. Without clear governance and trust, collaboration may falter or become exploitative.
Skill gaps and human agency: Predictive logistics depends on analytic and managerial capabilities. Firms need data scientists, analysts, and planners who can interpret model outputs, recognize limitations, and integrate qualitative judgments. Without such human capital, firms risk over-relying on “black-box” models.
Inequality and marginalization: As previously noted, firms with limited resources may be excluded from the benefits or remain locked in subordinate roles. Without deliberate support (technical, financial, governance), DSNs may exacerbate inequalities.
Systemic risk and homogeneity: Institutional convergence may create systemic vulnerabilities. If many firms use similar models and platforms, shocks, model bias, or algorithmic failures may propagate rapidly across networks. Diversity in strategies—such as combining predictive analytics with redundant capacity, localized sourcing, or relational buffers—may be sacrificed in favor of uniform “efficiency.”

5. Findings

Combining theoretical reflection with empirical evidence yields several key findings.

DSNs and predictive logistics represent more than technological upgrades: they re-shape social, organizational, and power structures in supply networks. The transformation touches not only processes but also who holds decision-making power, who owns data, who accrues value—and thus reorganizes supply networks as socio-technical fields.
Digital, analytic, relational, and symbolic capital become central assets. Early and well-resourced adopters accumulate advantages; smaller or less advanced firms risk marginalization unless they build relational trust or receive support.
Global inequalities may be reproduced or deepened. DSNs embed core–periphery relations in new digital dimensions. Firms in advanced economies or large multinationals secure control over platforms, analytics, and standards, while peripheral firms may become dependent, visible in operations but invisible in strategic data flows.
Institutional pressures drive rapid diffusion and convergence—but at the cost of diversity and resilience. Regulatory, mimetic, and normative forces push many firms to adopt similar digital models, which fosters interoperability but may reduce adaptive variety, increasing systemic risk.
Implementation and governance challenges pose serious obstacles. Infrastructure deficits, data governance issues, security risks, skill shortages, and organizational resistance constrain adoption and may undermine the equity, sustainability, and legitimacy of DSNs.
Human agency remains critical. Predictive logistics does not eliminate human judgment; rather, it requires human analysts, planners, and managers who can interpret, challenge, and complement algorithmic outputs. Organizational culture, trust, and capacity building are as vital as technical investments.

6. Conclusion and Implications

Digital supply networks and predictive logistics are among the most important changes to global supply chain management in the last few decades. There is real potential for them to make things more efficient, responsive, resilient, and sustainable, and more and more evidence backs these claims. But their real importance is how they change the social, economic, and political structure of supply networks. These changes create chances, but they also bring risks, especially of unfairness, exclusion, and a weak system. As DSNs cross borders, businesses and government officials need to remember that digital supply isn't just about data or technology; it's also about power, money, governance, and inclusion.

6.1 Implications for Managers and Practitioners

Adopt DSNs as socio-technical strategies, not just digital upgrades. Managers should invest not only in technology (sensors, platforms, analytics) but also in human capital (data analysts, planners), data governance, and relational trust across partners.
Promote inclusive integration. When onboarding smaller suppliers or logistics partners, proactively support their digital capacity—through training, shared platforms, financial or technical assistance—to avoid reproducing inequality or exclusion.
Design governance frameworks. Establish clear policies on data ownership, access rights, privacy, security, and sharing rules. Transparent governance builds trust and enables equitable benefit sharing.
Maintain strategic diversity. Combine predictive logistics with redundancy, buffers, and relational coordination—especially in sectors or regions prone to disruption—to avoid over-reliance on uniform models.

6.2 Implications for Policy-Makers and Regulators

Support infrastructure and capacity building. Especially in less-developed or peripheral regions, investment in connectivity, digital infrastructure, and skills training is essential to enable participation in DSNs.
Encourage fair data governance and open standards. Regulatory frameworks should promote interoperability, data portability, and fair access to digital supply platforms to curb monopolistic tendencies and power asymmetries.
Promote sustainability and social responsibility. By linking digital supply incentives to environmental, social, and governance (ESG) criteria, regulators can steer DSNs toward broader social good, not just efficiency or profit.

6.3 Directions for Future Research

Given the fast-moving and emergent nature of DSNs and predictive logistics, future research should:

Conduct comparative studies across regions (core vs. peripheral), industries, and firm sizes to assess who benefits, who is left out, and under what conditions.
Perform longitudinal analyses to examine how predictive logistics performs over multiple cycles of disruption, demand shocks, or market shifts.
Undertake ethnographic and organizational-behavior research to study how managers and workers interact with DSNs, interpret data, negotiate decisions, and build trust across firms.
Explore governance, regulation, and data politics—how data ownership, privacy, and platform power shape supply-chain outcomes and geopolitical inequalities.

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