Does deglobalization imply the end of global supply chains?
时间：2025-09-10
作者：Enver Yücesan
Abstract
Deglobalization, the process through which economic agents are increasingly severing their international economic links and relocating economic activity toward their domestic economies, has been affecting supply chains spanning the globe. In the face of major economic and political challenges, companies have always been redesigning their networks through which inputs are sourced, products are manufactured, and customer demand is satisfied. While the literature has addressed the traditional complexities arising from market volatility, cost differentials and emerging technologies, further guidance is needed on the impact of government policies for companies operating on a global scale. It is therefore necessary to identify the forces that drive deglobalization and understand how they shape the evolution of global supply chains. Anchored on the global value chain framework from macroeconomics, political science, international business, and operations management, the author constructs and validates systems dynamics models to assess the evolution of global supply chains where decisions taken at the macro and strategic levels would act as constraints or enablers at the tactical level. The author observes that, independent from any other forces, the landed cost advantage, which was the principal driver of offshoring decisions, gets eroded over time through both higher competition and convergence of wages. Moreover, as competition shifts towards higher responsiveness, offshored facilities find themselves at a greater disadvantage. Political pressure in terms of tariffs and sanctions as well as management control in terms of ownership restrictions and the challenges in enforcing the rule of law reduce the attractiveness of offshoring. The rate at which offshoring decisions are reversed, however, depends on the complexity of both the manufacturing asset and the sophistication of the local ecosystem. Finally, political pressure does not necessarily lead to reshoring; nearshoring, the transfer of manufacturing facilities to a territory closer to the final market, emerges as a viable alternative. In short, these findings signal a slow transition from global to regional supply chains. As a result, supply chain professionals and policy makers are urged to adopt a broader perspective that incorporates the impact of operational, strategic, and geo-political parameters on the entire supply network in their decision or policy making processes. Going forward, emerging technologies and sustainability considerations will continue setting the boundary conditions within which global supply chains will continue to evolve.
Highlights
The landed cost advantage, which was the principal driver of offshoring decisions, gets eroded over time through both higher competition and convergence of wages. Political pressure as well as restrictions in management control further reduce the attractiveness of offshoring.
The rate at which offshoring decisions are reversed depends on the complexity of both the manufacturing asset and the sophistication of the local ecosystem.
Political pressure does not necessarily lead to reshoring; nearshoring, the transfer of manufacturing facilities to a territory closer to the final market, emerges as a viable alternative. We therefore observe a slow transition from global to regional supply chains.
Emerging technologies and sustainability considerations will continue setting the boundary conditions within which global supply chains will continue to evolve.
1. Introduction
We study the impact of the macro-economic and geo-political drivers of deglobalization on the design and management of global supply chains. Deglobalization, the process in which economic agents are severing their international economic links and are relocating economic activity toward their domestic economies (Antràs, 2020), has been gaining momentum. Macro-level data show that foreign direct investment (FDI) has fallen by 48 % in 2020 from its peak in 2007 in parallel with the stagnation in international trade (UNCTAD, 2024). In fact, a recent analysis of 23 value chains across 43 countries, which account for 96 % of global trade, indicates that trade intensity, the share of output that is traded across the globe, has declined from 28.1 % to 22.5 % of gross output from 2007 to 2017 in goods-producing value chains (McKinsey Global Institute, 2019). Another often-cited measure of globalization, the ratio of world trade to world GDP, shows that, while the period between 1986 and 2008 indicates an era of hyperglobalization (Krugman, 2020), the rate of growth during the following decade has been much more restrained (and comparable to that over 1970–1985). The share of global value chains (GVC) in global trade, which identifies the share of a country’s exports that flow through at least two borders, shows that this indicator has recently stagnated following significant growth during the period of hyperglobalization.
One of the economic structures on which deglobalization is having a significant impact is the supply chains that span the globe (Ellram, 2013, de Vries et al., 2019). Supply chains have been ”global” since the rise of the Silk Road as the main artery for trade, starting with basic commodities, textiles, simple manufactured goods and, ultimately, with complex products such as microprocessors, airplanes, and electric vehicles. As a result, the world merchandise trade has reached US$24.91 trillion in 2022, while global trade in commercial services topped $7.54 trillion, (World Trade Organization, 2023). Offshoring, moving manufacturing away from the country of the parent company mainly in search of lower costs and new markets, has been the principal initiative in global supply chains, facilitated by advanced information and communication technologies (ICT) and powered by innovative and efficient transportation solutions (Cohen et al., 2018). In addition to the breakthroughs in ICT and lower transportation costs, political initiatives such as trade liberalization (e.g., dismantling of trade barriers whereby world average tariffs going from 13.6 % in 1986 to 7.5 % in 2008 and ultimately to 5.2 % in 2017), regional trade agreements (e.g., the European Union, USMCA, and RCEP), and the remarkable increase in the share of world population participating in the process of globalization (from countries representing 43 % of the world population in 1990 providing a large pool of highly skilled workers) served as the principal forces behind hyperglobalization (Antràs, 2020).
However, this growth appears to be losing steam, prompting The Economist to label this deceleration as ”slowbalization” (D’Urbino, 2019). In fact, recent global trade statistics paint a mixed picture on the evolution of global supply chains signaling shifts that are driven by a combination of strategic and operational parameters (Contractor, 2021; Wong and Swanson, 2022; Harput, 2022; Han et al., 2024; Mukherjee et al., 2023; Nguyen-Quoc, 2024) such as structural transformations in the global economy (e.g., convergence of wages and increasing importance of services) and policy (e.g., a weakening WTO and the rise of political blocs). Krugman (2020) states that the observed slowdown in globalization is a natural sequel to the unsustainable hyperglobalization by pointing out that, in addition to escalating trade tensions and a slowing world economy, globalization requires progress in transportation to outpace technological progress in domestic production. For example, while containerization coupled with information and communication technologies has indeed produced a productivity surge in ocean shipping (Levinson, 2016), it may indeed have trailed progress in domestic production technologies.
In the face of major economic and political challenges, firms have always been under enormous pressure to restructure their supply networks through which input materials are sourced, products are manufactured, and customer demand is satisfied. In addition to the traditional complexities arising from market volatility, cost differentials, and emerging technologies, government policies such as tariffs, quotas, tax incentives or ownership restrictions, aimed at increasing the domestic portion of the supply chains that operate in their jurisdiction, also provide significant challenges for these companies that operate in multiple regions and generate extensive cross-borders flows of material, cash, and information (Cohen & Lee, 2020). It is therefore necessary to question how deglobalization will shape global supply chains. In particular, it is desirable to understand how global supply chains would evolve as a function of macroeconomic forces, inherent complexities, and emerging technologies through onshoring/reshoring/nearshoring decisions.
To this end, the objective of this paper is to study the evolution of global supply chains under the influence of geo-political forces both to help guide supply chain managers in redesigning their supply networks for ensuring resilience in the face of escalating geo-political tensions and active conflicts, and to motivate academics conduct further empirical work on this phenomenon for validating or refuting our predictions as more data become available over the coming years. It is worth noting that the paper focuses exclusively on supply chains in manufacturing. Even though they represent 20 % of Foreign Direct Investment (FDI) stocks and flows, manufacturing ecosystems tend to be more asset heavy —hence, stickier— than service supply chains. In other words, it is relatively easier to relocate a contact center than a microprocessor fab. This is the reason why we explicitly incorporate the complexity of both the asset and the ecosystem in analyzing the evolution of the global supply network.
To avoid ambiguities, we will adopt the following crisp terminology. Offshoring (or backshoring) is the process of relocating factories that produce goods and services from more costly countries to lower cost regions. It is distinct from outsourcing in which operations are transferred to a third party –at home or abroad. With offshoring, the parent company remains in charge of all operational decisions. Reshoring is the process of relocating factories that had been previously offshored to a foreign country back to the parent company’s domestic territory. The decision is principally aimed at lowering freight and transport costs, reducing lead times, and meeting the growing demand for locally made products. Nearshoring is the process of moving manufacturing operations geographically closer to the country where the goods or services will ultimately be sold.
Anchored in GVC models from international business (IB), macroeconomics, political science, and operations management (OM), we construct and validate systems dynamics (SD) models to assess the evolution of global supply chains whereby decisions taken at the macro and strategic levels would serve as constraints or enablers at the tactical level. Through simulation experiments, we study the key parameters that drive the design of global supply chains and the way the forces driving deglobalization may impact those parameters —hence, the decisions of offshoring, reshoring, and nearshoring. Our study leads to the following conclusions: independent from any macro or GVC-level forces, the landed cost advantage, which was the principal driver of offshoring decisions, gets eroded over time through both higher competition and convergence of wages. Moreover, as competition shifts towards higher responsiveness, offshored facilities find themselves at a greater disadvantage. Political pressure in terms of tariffs and sanctions as well as management control in terms of ownership restrictions and the challenges in enforcing the rule of law reduce the attractiveness of offshoring. The rate at which offshoring decisions are reversed, however, depends on the complexity of both the manufacturing asset and the sophistication of the local ecosystem. Finally, political pressure does not necessarily lead to reshoring; nearshoring, the transfer of manufacturing facilities to a territory closer to the final market, emerges as a viable alternative. We therefore conclude that deglobalization serves as a catalyst that changes the “geometry of global trade”1; by replacing global supply chains by regional supply networks.
Computer simulation is a numerical analysis tool that enables us to fast forward time, test out counterfactuals, and generate predictions about the behavior of complex systems stimulated by external interventions. This paper uses SD modeling to predict the impact of the macro-economic and geo-political drivers of deglobalization on the design and management of global supply chains with the ultimate goal of triggering empirical research to validate or refute our predictions as more data become available over the coming years.
The remainder of the manuscript is organized as follows. Section 2 highlights the key streams of literature on global value chains (GVC) and proposes a hierarchical analysis framework. Section 3 constructs the SD models and validates them through existing literature. Section 4 describes the experimental settings for addressing the questions of interest and reports on the results of the numerical experiments, ending with the answer to the question posed in the title of the paper. Section 5 provides anecdotal evidence from recent business initiatives to illustrate the observations from the numerical experiments. It also encourages both supply chain professionals and policy makers to adopt a broader perspective in their decision-making processes. The paper concludes with a call for further investigation into two important drivers, namely emerging technologies and sustainability considerations.
2. Modeling global supply chains
Supply chain strategy consists of developing and deploying a portfolio of assets to match supply with demand on a global scale by considering inherent trade-offs associated with fixed and variable costs, capacities and capabilities, technology choices, customer requirements, competitive positioning, and revenue models that determine after-tax profit. The strategy, which must be formulated within the constraints imposed by government policies and regulations within each country of operation, also includes mitigation strategies against various sources of risk, including exchange rate fluctuations, price volatility, uncertainties in trade policies, and other potentially disruptive events such as natural or man-made catastrophes (Buckley, 2011). Global supply chains have been studied from a variety of perspectives, including Economic Geography, which focuses on the efficiency of international trade flows and value creation; Economic Sociology, which is mainly concerned with the social consequences of economic exchange and their impact on local know-how; International Business, which investigates how firms can profitably exploit their unique firm-specific advantages and create value by forging business relationships across national borders; and OM, which studies optimal configuration and coordination strategies in global production networks. As depicted in Fig. 1, these streams of research can be synthesized into a three-tier hierarchical framework (Kano et al., 2020): at the micro level, we study key flows of material, information, and cash in an effort to match supply with demand. At the strategic (GVC) level, we focus on governance, including network structure and orchestration, to conceive high-performance global value chains. At the macro level, we explore the interactions between the global value chains and their environment, including cultural, institutional, and geopolitical characteristics. For completeness, we provide further details for each tier in the following subsections.
Fig. 1. Analyzing global value chains.
2.1. Macro models: Geo-political drivers
At the macro level, policy makers monitor three key measures of globalization, which encompass the integration of goods, labor, and capital markets (Antràs, 2020). The first measure focuses on international trade flows, including both the ratio of world trade to world GDP and GVC trade as a percentage of world trade. These measures are based on Input-Output (I-O) tables that reflect the amount of production needed to satisfy a given level of demand where each industry’s production is used in the production of other goods and services or is consumed as final demand (Leontief, 1936, Borin and Mancini, 2019). More concretely, xs, which denotes the output of goods and services in monetary terms as a function of intermediate production that is used by other industries and final consumer demand, is defined as(2.1)xs = Asxs + cs
where, for an ecosystem with n industries, A is an n × n technical coefficient matrix describing the economic interdependency among industries whereby industryj requires a·sij dollars of production input from industry i to produce one dollar of production output, and cs is a vector of length n denoting final consumption. While Eq. (2.1) looks deceptively simple, it is challenging in practice to capture these flows across complex global supply chains. Due to the complexities in compiling global I-O tables, they become available with a non-negligible time lag. Moreover, as they rely on aggregated input-output data, I-O tables yield a significantly coarse picture when disaggregated to measure GVC flows. Antràs et al. (2012) provides a macro metric to capture network complexity. Johnson and Noguera (2012) and Koopman et al. (2014) propose approaches to enhance the accuracy of the figures.
The I-O framework outlined above necessarily captures only the flow of goods and services across countries. To reflect the multi-faceted nature of globalization, economists also monitor the stock of international migrants as a percentage of world population. Reported by the United Nations Population Division, the share of migrants in the world population has increased from 6.9 % to 10.9 % in Europe and from 9.2 % to 15.7 % in the United States since 1990. Finally, capital flows across countries, which include FDI and portfolio investment flows, constitute the third measure of globalization. While capital flows tend to be fickle, FDI driven by multinational companies may be more relevant from the perspective of global supply chains (Adarov, 2021).
Trade policy —in particular, tariff regimes, rules of origin, and local content requirements— can affect the geography of production (Gereffi et al., 2021). For example, in an effort to protect local manufacturers, US Section 232 tariffs on steel (25 %) and aluminum (10 %) products imposed by the United States in 2018 immediately triggered retaliatory tariffs by the affected countries, including Canada, China, the European Union, and Mexico. Similarly, the $50-billion US Section 301 tariffs imposed on China led to retaliatory tariffs on American agricultural exports. Such moves have an immediate impact on the landed cost of a product manufactured abroad, triggering, first, a modification of the flows in the network, and, ultimately, a reconfiguration of the network. Curran et al. (2019) therefore propose an analytical framework to enable the effective integration of trade policies into global production network (GPN) analysis.
2.2. Strategic (GVC) models: international business practices
At the strategic level, product, process, and supply chain characteristics must be considered simultaneously (Fine, 1999). Products with low intellectual property (IP) tend to be prime candidates for offshoring. Apparel is a perfect example of such products with low risk of IP leakage. It therefore comes as no surprise that, over the years, apparel sourcing has been shifting to lower-cost countries. In contrast, when product design necessitates sophisticated process capabilities as in technologically intensive manufacturing, firms locate close to knowledge centers with highly skilled resources. Product and process complexity therefore results in different levels of “stickiness” in supply chain relationships under economic uncertainty (Martin et al., 2023).
Finally, supply chain characteristics are also shaped by important economic and social factors. A basic concern is the availability of key resources such as suitable human capital, infrastructure, raw materials and energy, and access to markets (Fatás & Mihov, 2009). Acemoğlu and Robinson (2013) further emphasize the importance of institutions as a necessary condition for “ease of doing business.” Companies also need to consider the transaction costs of deploying these resources in business activities, which in turn reflect the quality and reliability of local institutions. Co-determined by these transaction costs, the governance structure that is put in place to orchestrate the network is a key input into supply chain design. Tsay (2014) indicates that location decisions are often conflated with governance decisions, which reflect the level of administrative control of the operations, ranging from complete (in-house hierarchical governance) to intermediate (close partnerships with other stakeholders, i.e., hybrid governance), to essentially no control (pure procurement, arms-length, market governance) (Williamson, 1991). Partnerships typically involve extensive buyer-supplier interaction, which may be facilitated by local production, permitting greater collaboration between manufacturing and other functions (Gray et al., 2017). Jenkins and Tallman (2010) elucidate how these networks evolve over time.
Finally, Schmitt and Van Biesebroeck (2013) emphasize the role of geographical, cultural, and relational proximity in supply network configuration. In a large study of contracts from the European automotive industry, they capture proximity effects along multiple dimensions: geographical proximity at the production and plant level (to minimize logistics costs), cultural proximity at the decision-making level (to minimize conflicts and misunderstandings in management), and relational proximity at the contract level (to ensure seamless transactions). Witt et al. (2018) analyze the business systems in 61 major economies, developing a taxonomy and a metric that reflects the closeness (proximity) of pairs of countries in terms of their business practices.
2.3. Micro models: operations and supply chain management
Ferdows et al. (2016) dissects the complexity of global supply chain networks. Mihalache and Mihalache (2016) provide a comprehensive review of the OM literature on global supply chains. To reflect the inherent challenges at the micro level, a parsimonious optimization model can be formulated to match production capacities in different regions to customer demands in various geographies over a given planning horizon to minimize the total landed cost or to maximize the total after-tax profit (Cohen & Lee, 1988 and 1989). More specifically, consider the following simplified mathematical programming formulation where P denotes the set of production facilities and M denotes the set of potential markets around the globe. The decision variable, Xij, denotes the quantity of goods produced at a production facility of country i ∈ P and sold in the market of country j ∈ M. (Note that the I-O Tables introduced in Eq. (2.1) can be used here to capture the flow of intermediate goods.) Similarly, cij captures the landed cost of a unit produced at the production facility i ∈ P and sold in the market j ∈ M. Note that cij = ci + tij, where the first term on the right-hand side of the equality reflects the unit production cost at the production facility i ∈ P while the second term represents the transportation cost from production facility i ∈ P to market j ∈ M. tij might also include import duties and other tariffs. dj denote the demand at market j ∈ M. Finally, Ci denotes the production capacity at production facility i ∈ P. Note that Krugman’s observation (2020) that progress in transportation needs to outpace technological progress in domestic production to drive globalization is captured in this formulation by the condition cij = ci + tij,≤ cj. The configuration that minimizes the landed cost can then be computed as the solution to the following mathematical program:
minimize ∑i∈P ∑j∈M cji Xij
Subject to ∑i∈P Xij ≥ dj,∨j∈M,
∑j∈M Xij ≤Ci ,∨i∈P . (2.2)
While the first constraint ensures that the demand is met in every market, the second constraint enforces the capacity constraints at each production facility. It is straight-forward to extend this formulation to incorporate other relevant parameters, for example, lead times. It is also possible to extend the above model to incorporate various sources of uncertainties that global supply chains face. For instance, through stochastic optimization, we incorporate demand fluctuations (Bassamboo et al., 2010), supply (capacity) uncertainty (DeCroix, 2013), exchange rate fluctuations (Huchzermeier & Cohen, 1996) among other sources of uncertainties. To capture the impact of non-negligible capital investment, it is possible to calculate the net present value of a discounted cash flow stream generated by this investment over a finite planning horizon through dynamic programming (Fine & Freund, 1990).
In a similar vein, Simchi-Levi et al. (2018) proposes a two-stage decision process within the context of risk management against supply disruptions triggered by natural (e.g., floods) or man-made (e.g., sanctions) catastrophes. In the first stage, the firm determines inventory levels for each product to ensure a certain level of customer service. In the second stage, the firm observes the product demand and available plant capacities and determines its global production schedule using the formulation in Fig. 4, where the first constraint determines lost sales after demand is satisfied by either production or inventory. The second constraint enforces plant capacity limits. The third constraint ensures that lost sales are bounded by a target service level. Lima and Huchzermeier (2017) consider operational and financial hedging simultaneously to manage risk in a more effective fashion. De Vericourt and Gromb (2017) adopt a contracting approach for financing capacity investment under demand uncertainty.
It is worth noting that the I-O tables described in Section 2.1 have been extended to incorporate uncertainty (Santos, 2008) as well as mitigation strategies such as inventory holding (Barker & Santos, 2010), alternate routes during a transportation disruption (Gordon et al., 2005), and the substitution of different inputs (MacKenzie & Barker, 2011). Hallegatte (2008) develops an adaptive regional I-O model to measure the impact of supply constraints with an illustration of the impact of Hurricane Katrina. Similarly, MacKenzie et al., 2012 assesses the impact of the Fukushima disaster on the automotive industry. Okuyama et al. (2004) considers the timing effects that differ across industries.
2.4. Synthesis
Under this three-tiered framework, Fig. 2 provides a broader perspective for recognizing the forces driving deglobalization, which ultimately create enablers or impose constraints in reshaping global supply chains. That is, from the perspective of the network flow formulation (2.2), decisions at the macro and GVC levels render certain nodes and edges infeasible. In other words, the initiatives undertaken at the macro and at the GVC levels translate into constraints that define the feasible region within which optimal supply chain decisions can be generated.
To be more specific, consider the following “clippings” from recent news reports:
“The photolithography equipment made by the Dutch high-tech company ASML Holdings uses novel optical technology to define ultrasmall circuitry on chips. With a price tag of more than $150 million, the complex machine is widely acknowledged as necessary for making the most advanced chips, an ability with geopolitical implications. The Trump administration successfully lobbied the Dutch government to block shipments of such a machine to China in 2019, and the Biden administration has shown no signs of reversing that stance. ASML’s machine has effectively turned into a choke point in the supply chain for chips. The tool’s three-continent development and production —using expertise and parts from Japan, the United States and Germany— is also a reminder of just how global that supply chain is” (The New York Times, 4 July 2021).
“Instead of the French PAI, the board of directors of Sanofi entered exclusive negotiations with the American private equity firm CD&R to sell part of its Opella division dedicated to consumer health. The fund is said to be offering 15.5 billion euros to take over 50 % or more of Opella, a sum higher than that offered by PAI, allied with the sovereign funds of Abu Dhabi and Singapore. Before approving the sale, the government has asked the company to produce a list of all the state subsidies it had received over the past decade. In addition to concerns about losing manufacturing jobs, the Ministry of Health also expressed concerns about the supply of critical medications in France” (Le Figaro, 14.10.2024). The French government is reported to consider securing equity in the company with the objective of obtaining a seat on the board of directors (Les Echos, 15.10.2024).
“When U.S. Steel put itself up for sale in 2023, executives at Nippon Steel in Tokyo saw an opportunity: Buying the American steel maker could help it offset anemic demand in its home country and strengthen its hand in a global business dominated by China. On Dec. 18, the companies announced that Nippon Steel had agreed to acquire U.S. Steel for $14.9 billion, a 40 percent premium to U.S. Steel’s share price at the time. Analysts praised Nippon Steel as a potential savior of U.S. Steel, a onetime backbone of the American economy that had fallen behind rivals. But almost immediately, the merger incited a backlash in the United States that has prevented it from being completed. U.S. politicians from both parties have condemned the prospect that a storied 123-year-old American industrial company would be acquired by a foreign corporation” (The New York Times, 26.08.2024). The Biden administration ultimately blocked the transaction (The New York Times, 06.01.2025).
To meet the anticipated demand following the end of the confinement period, the French car maker Peugeot was planning to staff a third shift in its factory in Hordain, France, by transferring Polish workers from its factory in Gliwice, Poland — benefitting from the network flexibility offered by its European supply chain. The plan was blocked by the French government, which opposed this transfer and forced the car maker to hire temporary workers in France (Le Figaro, 12.06.2020).
In the above vignettes, while the ASML episode reflects the way macro-level (political) constraints reduce network flexibility “by creating a choke point in the supply chain for chips,” the US Steel and Sanofi stories reveal the constrains imposed by policy makers on ownership and governance at the GVC level, impairing the flexibility generated by the network structure. Finally, the Peugeot story reflects operational restrictions at the micro-level tightening the capacity constraints of the network. This hierarchical framework can then be succinctly formulated as the following optimization problem:
To recap, macro and GVC-level constraints provide the boundary conditions that define the feasible region within which supply chains can be optimized. Hence, supply chain management must be fully resilient in reconfiguring the value networks in response to GVC constraints (or enablers), which, in turn, are shaped by macro-level constraints (or enablers). Hence, to understand the impact of deglobalization on supply chains, we need to first understand the macro and GVC drivers. To this end, in a two-echelon instance of the above formulation, Dong and Kouvelis (2020) evaluate the impact of tariffs on the location of production and consumption, i.e., on the configuration of the resulting supply network. A large-scale illustrative example is also provided by Hausman et al. (2010) with a mapping and analysis of the logistics process in global trade management.
3. The study
We will reflect this hierarchical perspective in our systems dynamics (SD) modeling to assess the evolution of global supply chains under the geo-political forces driving deglobalization whereby initiatives undertaken at the macro and strategic (GVC) levels would serve as constraints or enablers at the tactical (operational) level. SD models capture information feedback and time delays that make it possible to simulate complex dynamic behavior (Forrester and Jay, 1961, Sterman, 2001). Given their modeling power, SD is used to study the evolution of dynamic systems in a wide variety of application domains, including predator-prey models (Hofbauer & Sigmund, 1998), population dynamics (de Roos, 2019), epidemics and public health (Bailey, 1957, Homer and Hirsch, 2006), natural disasters such as forest fires (Collins et al., 2013), climate studies (Martinez-Valderrama & Ibanez, 2023), agricultural and natural resource management (Turner et al., 2016), plant growth models (Yin & Struik, 2010), diffusion of technological innovations (Tsai and Hung, 2014), and sustainable business models (Jonsdottir et al., 2024).
Global value chains are dynamic systems that evolve over time. SD is therefore a natural modeling framework to study the evolution of these systems “disturbed” by macro-economic and geo-political stimuli. Gray et al. (2017) have pioneered SD modeling in analyzing the offshoring/reshoring decisions of small and medium-sized enterprises (SME). SMEs, which had originally offshored their manufacturing operations in search of lower costs, started reshoring their activities after encountering unanticipated operational problems as well as difficulties in protecting their intellectual property. Moreover, increased competition not only eroded the cost advantage of offshored operations, but also necessitated a more responsive supply chain, which became increasingly difficult to achieve with the geographic distance.
Inspired by the work of Gray et al. (2017), we study the impact of the initiatives at the macro and GVC levels on global supply chains through SD models. While maintaining the fundamental trade-off between lower cost and higher responsiveness, we extend the model of Gray et al. (2017) along two dimensions. First, reflecting an increasingly popular practice, we incorporate the possibility of nearshoring, the transfer of manufacturing activities to a territory closer to the home market, which is also referred to as regionalization. Such a practice aims at not only enhancing responsiveness without a significant increase in costs, but also at reducing the likelihood of unanticipated problems thanks to enhanced proximity. Second, in accordance with our hierarchical framework, we incorporate both the macro and GVC-level constraints in the form of political pressure (e.g., tariffs, sanctions, rules of origin, local content requirements or sourcing bans) and degree of management control (e.g., ownership stake, strength of institutions, and rule of law), respectively. We validate the model parameters through the results published in the literature in Economics, International Business, and Operations Management. We also conduct sensitivity analyses to ensure that our observations are robust to the adopted parameter values (Barlas, 1996). We verify the model implementation by reproducing the results of Gray et al. (2017)
Following convention, we first introduce, in Fig. 3, the causal loop diagram (CLD) to describe the underlying dynamics by mapping the relevant state variables, associated relationships, and time lags. In SD modeling, the relevant state variables are typically expressed as “stocks,” depicted as boxes. The values of these variables (i.e., the levels of stocks) are determined by (in and out) “flows,” depicted by (incoming or outgoing) directed edges, respectively. The edges, which may have different flow rates and may be affected by delays, also reflect the relationship between its origin and its destination: a positive “+ ” (negative “-“) sign at the tip of the directed edge indicates a positive (negative) relationship between the state variables at the origin and at the destination of the edge, i.e., a higher value at the origin results in a higher (lower) value at the destination.
Fig. 3. The causal loop diagram.
Description of the CLD. In Fig. 3, the global manufacturing footprint of a firm is captured through three variables (stocks): the boxes labeled as Offshoring, Nearshoring, and Reshoring represent the proportion of manufacturing activities that are currently offshored, nearshored, and reshored, respectively. We note that the sum of these three variables is equal to 1. The evolution of this footprint is driven by two different dynamics at the micro level: depicted in the upper part of the CLD, the Landed Cost (as financial performance) and, depicted in the lower part of the CLD, Responsiveness (as service performance).
The key driver of offshoring, the determinant of the rate with which Offshoring stock is accumulated, is the Gap in Landed Cost, which represents the difference between the desired and realized landed costs. The former is driven by Competition in Cost in that a higher cost competition leads to a lower Desired Landed Cost (a negative relationship). The latter, Landed Cost, is a weighted average of the costs of offshored, nearshored and reshored production. As this gap increases, offshoring becomes more attractive from a purely financial perspective, thereby increasing the rate of inflow into Offshoring. As the gap narrows, offshoring becomes less attractive, thereby increasing the rate of outflow from Offshoring and increasing the rate of inflows into Reshoring or Nearshoring.
We also consider several parameters acting as a catalyst or an inhibitor, reflecting the macro and GVC-level drivers. At the GVC level, one of the drivers of the Landed Cost is Liability of Foreignness, which reflects the costs that firms operating outside their home countries experience above and beyond those incurred by local firms (Nachum, 2003), driven by Offshoring Experience as a function of the current proportion of offshored facilities. Further note that the Liability of Foreignness is driven by both geographic and cultural distance (or proximity) whereby a smaller distance would facilitate the development and maintenance of close relationships with the local community, ultimately reducing Liability of Foreignness (Witt et al., 2018).
Over time, competition drives Desired Landed Cost down, eroding the cost advantage of Offshored and Nearshored production, reflected by the narrowing Gap in Landed Cost. The erosion in the attractiveness of offshore production drives the rate of nearshoring and reshoring. Such moves, however, are mitigated by the inherent Mobility of an industry mainly driven by Asset Complexity as well as Ecosystem Complexity (Contractor et al., 2010; McKinsey Global Institute, 2019; Martin et al., 2023). This is because building a new factory or a distribution platform as a link in the supply chain necessitates a non-negligible set up cost. As delineated by Antràs (2020), at the GVC level, the set-up cost consists of a string of investments in information (search for potentially suitable suppliers), in physical assets (factory, specialized equipment, capital), and “relational capital” (to navigate in an environment with imperfect contracting). Moreover, most of these investments are sunk in nature as specialized equipment is not easily sold or redeployed while relational capital and search costs are often abandoned when breaking off a relationship and exiting a country. In addition, at the macro level, closing an existing facility could incur exit (termination) costs consisting of severance, environmental cleanups as well as possible financial penalties for reimbursing the tax subsidies originally offered by the government to encourage the investment. Actions such as nearshoring or reshoring therefore involve significant fixed costs in addition to potential future costs to undo them (Cohen & Lee, 2020).
The other determinant of the global manufacturing footprint is the Gap in Performance. As Competition in Performance increases, the level of the Desired Performance also goes up. As the Gap in Performance, the difference between the desired and the actual performance, broadens, the attractiveness of offshoring diminishes. Recall that we model performance as Responsiveness (or agility) in customer service (de Treville et al., 2014), which, in turn, is driven by Logistical Constraints. At the micro level, Logistical Constraints not only reduce responsiveness, but also increase Landed Cost (Hausman et al., 2013, de Treville et al., 2014). At the GVC level, Logistical Constraints become more binding as Unanticipated Problems surface, driven by the Liability of Foreignness and a low Degree of Management Control (Williamson, 1991) impacted by Ownership Stake (Kouvelis et al., 2001). At the macro level, these parameters are impacted by the strength of the institutions such as IP protection reinforcing the Rule of Law in the local country (Fatás and Mihov, 2009, Bilir, 2014). We also consider the impact of Political Pressure, which captures the trade barriers ranging from tariffs to sanctions and sourcing and export bans imposed by the home country (Witt, 2019).
We investigate the impact of macro and GVC-level initiatives on the evolution of global supply chains in terms of reshored, nearshored, and offshored production. After establishing the base case, which considers exclusively the competitive pressures and logistical constraints, we study the impact of three key factors, namely
At the micro level, supply chain complexity: asset complexity and ecosystem complexity
At the GVC level, management control: ownership stake
At the macro level, the rule of law and political pressure: tariffs and sanctions
on the resulting global manufacturing footprint. We also comment on the role that technology might play in re-shaping global supply networks.
