Simulation of International Logistics Costs – Anticipate Variations

Does your operation depend on international trade? Learn how to simulate your logistics costs and anticipate changes in rates, routes, and transit times.

Why simulate logistics costs in global operations?

Global operations are exposed to multiple changing variables that directly impact international logistics costs. From tariff rates to port delays or freight price fluctuations, anticipating these elements enables better decision-making and avoids significant budget deviations.

Logistics simulation becomes a key tool for visualizing these scenarios before they occur. It allows companies to test routes, suppliers, consolidation schemes, and modal alternatives without incurring real costs. For businesses that frequently import or export, this predictive capability can make a difference in profitability and compliance.

Main variables that impact international costs

Simulating means correctly identifying the critical variables that may affect costs. Below are the most relevant ones in international contexts:

Tariffs and trade restrictions

Bilateral trade agreements, customs policies, and legislative changes directly affect import/export duties. Simulations allow for the analysis of different scenarios: for example, what happens if the rules for preferential origin change, or if a new specific tax is applied to a particular product.

In addition, embargoes or temporary sanitary restrictions can influence total logistics operation costs, affecting both timing and penalties.

Freight, insurance, and handling fluctuations

Ocean and air freight rates can fluctuate sharply due to global demand, geopolitical crises, port congestion, or seasonality. The same goes for insurance, consolidation costs, port charges, and handling fees.

Modeling different behaviors of these variables helps understand the possible impact range and prepare mitigation strategies (e.g., long-term contracts or contingency logistics schemes).

Hidden costs due to delays or lack of coordination

Often, the highest costs don’t come from official tariffs but from operational deviations: delays due to incorrect documentation, unnecessary customs storage, lack of synchronization among suppliers, etc.

These situations create hidden costs that can be detected in advance through end-to-end logistics process simulations, helping identify bottlenecks or systemic risks in the operation.

How to build an effective simulation model

The value of a simulation largely depends on how the model is built. To accurately represent an international logistics operation, it’s essential to integrate real data and capture uncertainty precisely.

Input data from real systems

Integrating data from ERP, WMS, TMS, or customs platforms allows the model to more accurately reflect current operating conditions. This includes:

  • Historical import/export volumes
  • Average border crossing or customs clearance times
  • Specific rates by item, route, and supplier

Good integration prevents the model from becoming outdated or overly reliant on theoretical assumptions.

Use of statistical distributions for uncertain processes

International logistics processes are full of uncertainty: shipment durations, container availability, customs inspections, etc. Using statistical distributions instead of fixed values captures this variability and allows for more realistic scenario simulations.

This not only improves simulation accuracy but also enables risk and sensitivity analysis based on the results.

Dynamic visualization and scenario analysis

Effective models don’t just calculate — they communicate. Incorporating interactive dashboards, logistics maps, and scenario comparisons makes it easier for management and non-technical areas to make decisions.

It also enables the simulation of multiple scenarios in parallel: what happens if a supplier changes, if the logistics network is reconfigured, or if a nearshoring strategy is implemented?

Benefits of anticipating logistics deviations before execution

Implementing an international logistics simulation model offers tangible benefits:

  • Reduced operating costs by avoiding miscoordination and unplanned overruns
  • Greater negotiation power with suppliers and logistics operators by better understanding real margins
  • Resilience in disruptive scenarios, with alternative routes and configurations already evaluated
  • Agility to respond to regulatory or tariff changes without halting operations

In short, simulation doesn’t replace experience — it enhances it with data, visualization, and foresight.

Use cases for cost simulation in imports/exports

Companies in various sectors are using this approach to optimize their international operations. Some examples include:

  • Electronics component importers: simulate combined routes and consolidations to minimize the impact of air freight.
  • Agro-industrial exporters: model different combinations of ports, operators, and brokers to maximize efficiency during harvest seasons.
  • Retail multinationals: evaluate the impact of new trade agreements and tariff restrictions on their supply network.

Each case allows decision-making to be validated before execution, reducing high-cost errors.

Recommendations for scaling models in complex operations

When operations become larger or more diverse, scaling the simulation model is also possible, but it requires certain considerations:

  • Use professional simulation platforms that support large data volumes and multiple nodes
  • Modularize the model to simulate regions or products separately
  • Incorporate optimization algorithms that combine simulation with ideal configuration search
  • Automate data updates from internal systems and external sources

In this way, simulation becomes a living tool — evolving with the business and continuously delivering strategic value.

Nearshoring: How It Affects the Supply Chain and How to Anticipate It with Predictive Models

The global supply chain is undergoing an unprecedented transformation. Nearshoring—a strategy that seemed marginal a decade ago—has become the centerpiece of today’s most critical business decisions. The disruptions caused by the COVID-19 pandemic, geopolitical tensions between the United States and China, and rising logistics costs have prompted companies to radically rethink their sourcing approach.

This migration toward geographically closer suppliers is not just a reactive response to crises but a proactive strategy that is redefining the fundamentals of supply chain management. However, successfully implementing nearshoring requires more than good intentions: it demands sophisticated predictive models that help anticipate and mitigate the risks inherent in this transition.

What Is Nearshoring and Why Is It Being Adopted?

Nearshoring refers to the relocation of production or service operations from distant locations to countries or regions geographically closer to the target market. Unlike traditional offshoring, which focused solely on cost reduction by relocating to low-wage countries, nearshoring seeks a balance between cost efficiency, geographic proximity, and risk mitigation.

This strategy has gained relevance due to structural factors that have transformed the global business landscape. The U.S.-China trade war increased tariffs and regulatory uncertainty, making Chinese manufacturing less attractive for the U.S. market. Simultaneously, the pandemic exposed the fragility of overly long supply chains reliant on a single region or provider.

Maritime transport costs—which remained relatively stable for decades—soared dramatically by 300% to 500% on some routes during 2021 and 2022. This volatility eroded the traditional competitive advantages of Asian offshoring. Meanwhile, wages in China have risen significantly, reducing the labor cost gap with other regions.

Time has also become a critical factor. Consumers expect faster deliveries, and companies seek more agility to respond to demand changes. A product manufactured in Mexico can reach the U.S. market in days, whereas from Asia it can take weeks. This time difference translates into tangible competitive advantages in increasingly dynamic markets.

Additionally, sustainability and corporate social responsibility considerations have gained importance. Shorter supply chains generally mean lower carbon emissions from transportation, and geographic proximity makes it easier to monitor labor and environmental standards.

Structural Changes to the Supply Chain

The shift toward nearshoring is not simply a matter of changing supplier locations. It involves a fundamental reconfiguration of the supply chain’s architecture, with ramifications spanning from strategic planning to day-to-day operational execution.

Redesigning Logistics Nodes

Nearshoring forces companies to completely redesign their distribution and storage networks. Distribution centers optimized to receive large volumes from Asia via maritime transport may not be suitable for more frequent, smaller-volume shipments from nearby locations.

This transformation requires significant investment in new logistics infrastructure. Companies must establish or expand regional distribution centers, develop new overland transport routes, and reorganize cross-docking operations. The placement of these new nodes must account not only for proximity to final markets but also for connectivity with new nearshore suppliers.

Warehouse management systems (WMS) and enterprise resource planning (ERP) systems must adapt to different flow patterns. While traditional offshoring managed large batches at low frequencies, nearshoring favors more frequent, smaller deliveries, demanding greater flexibility in management systems.

Changes in Lead Times and Stock Levels

Reduced delivery times are one of nearshoring’s most tangible benefits—but this improvement entails complex adjustments to inventory management. Shorter lead times allow for lower safety stock levels, freeing up working capital that can be reinvested elsewhere in the business.

However, the transition is not automatic. Demand planners must recalibrate their forecasting models to take advantage of the supply chain’s greater responsiveness. Automated replenishment algorithms require adjusted reorder point and economic order quantity parameters.

Lead time variability also changes. Maritime transport from Asia involves high variability due to port congestion, weather, and other factors, whereas overland transport from nearshore locations tends to be more predictable—though it may face disruptions such as border blockades or trucker strikes.

Relocating Critical Suppliers

Nearshoring involves identifying and developing new suppliers in geographically closer regions—a process that may take years and require substantial investment in audits, certifications, and capacity building.

Diversifying suppliers becomes more complex when geographic proximity is a priority. Companies must balance regional risk concentration with operational efficiency. Having multiple suppliers in the same nearshore region reduces coordination costs but increases exposure to regional shocks such as natural disasters or political crises.

Developing nearshore suppliers often requires technology transfer, specialized training, and financing. Companies leading this transition invest in strengthening their new partners—an effort that can yield sustainable competitive advantages but requires long-term commitment.

How to Anticipate the Impact with Predictive Models

The shift toward nearshoring presents significant uncertainties that can be mitigated through the strategic use of advanced predictive models. These tools allow companies to simulate different scenarios, quantify risks, and optimize decisions in a highly complex environment.

Advanced Forecasting of Demand and Capacity

Modern predictive models go beyond traditional time-series forecasting. They integrate multiple data sources—including economic indicators, consumption patterns, seasonal events, and real-time market signals—to generate more accurate demand projections.

In the context of nearshoring, these models must account for different regional market dynamics. Demand in the U.S. may show seasonal patterns that differ from those in Europe or Latin America, and nearshore suppliers must be ready to accommodate these variations. Machine learning algorithms can uncover complex correlations between regional economic variables and specific demand patterns.

Predicting the capacity of nearshore suppliers presents unique challenges. Many are still in the process of expanding or transforming, introducing volatility in productive capacity. Predictive models can incorporate leading indicators such as equipment investment, hiring trends, and expansion projects to anticipate changes in available capacity.

Scenario Modeling Under Uncertainty

Monte Carlo simulation and other stochastic modeling methods allow companies to evaluate thousands of potential scenarios for their nearshoring strategy. These models incorporate probability distributions for key variables such as transport costs, delivery times, exchange rates, and political risk.

Scenario modeling must consider complex variable correlations. For example, a political crisis in a nearshore country can simultaneously affect labor costs, currency stability, and border transit times. Advanced models capture these interdependencies to provide more realistic risk assessments.

Real options theory offers a valuable conceptual framework for evaluating nearshoring decisions under uncertainty. Establishing nearshore operations can be seen as purchasing an option that provides future flexibility to respond to changing market conditions. Real options models quantify this flexibility’s value and help determine the optimal timing for investments.

Evaluating Cost vs. Time Trade-offs

Multi-objective optimization models allow companies to quantify the inherent trade-offs in nearshoring decisions. These tools can identify Pareto frontiers showing the optimal combinations of cost, delivery time, quality, and risk for various supply chain configurations.

Genetic algorithms and other heuristic optimization techniques can explore complex solution spaces to identify optimal network configurations. These models simultaneously consider supplier selection, distribution center locations, transport routes, and inventory levels to maximize total system value.

Incorporating Total Cost of Ownership (TCO) metrics into these models is essential. Nearshoring may involve higher production costs than traditional offshoring, but it can generate savings in inventory, transportation, and risk management that justify the difference. Advanced TCO models capture all these factors to provide comprehensive comparisons between strategies.

Real-World Cases of Nearshoring

General Electric exemplifies a successful nearshoring transition in its appliance division. The company relocated washer and refrigerator production from China to Mexico, establishing a plant in San Luis Potosí serving the North American market. This move, implemented between 2018 and 2020, reduced delivery times by 30% and generated significant logistics cost savings that offset Mexico’s higher labor costs.

Apple has gradually diversified its supply chain from China to other Asian countries like Vietnam, India, and Thailand, but has also explored nearshore options for the U.S. market. The company has increased component production in Mexico and established final assembly centers in Brazil for the Latin American market.

Ford Motor Company is a textbook case of nearshoring in the automotive industry. It consolidated its production operations in Mexico, with plants in Hermosillo and Cuautitlán serving both U.S. and Mexican markets. This strategy allowed Ford to benefit from NAFTA (now USMCA) while staying close to

Whirlpool Corporation moved refrigerator production from China to Mexico in 2019, establishing operations in Apodaca, Nuevo León.
The company reported that although labor costs increased by approximately 15%, savings in transportation, inventory, and time-to-market generated a positive ROI in the second year of operation.

In the textile sector, Gildan Activewear exemplifies a regional nearshoring approach.
The Canadian company established manufacturing operations in Honduras, Nicaragua, and El Salvador to serve the North American market, leveraging regional trade agreements and geographic proximity to effectively compete with Asian products.

These cases demonstrate that nearshoring success does not rely solely on geographic relocation, but on a comprehensive reconfiguration of the supply chain, including development of local suppliers, investment in technology, and adaptation of operational processes.

Considerations in LATAM: Infrastructure, Capacity, and Political Risk

Latin America presents significant opportunities as a nearshore destination for the U.S. market, but also unique challenges that companies must carefully evaluate. The region offers competitive advantages in labor costs, geographic proximity, and favorable trade frameworks, but faces limitations in infrastructure and political stability that require specific mitigation strategies.

Latin America’s logistics infrastructure has improved considerably over the last decade, but there are still significant disparities between countries and regions. Mexico has the most developed infrastructure, with modern logistics corridors connecting major industrial zones with the U.S. border. The Port of Manzanillo and the border crossings at Tijuana and Ciudad Juárez handle significant volumes with relative efficiency.

Colombia has invested heavily in port and transport infrastructure, positioning itself as a logistics hub for South America. The ports of Cartagena and Buenaventura have modernized their operations, and the country has developed specialized free trade zones offering fiscal and logistical advantages for nearshore operations.

Brazil, due to its size and internal market, represents a special case. Its internal infrastructure is complex due to vast distances, but ports such as Santos have reached international standards. The country offers advantages for companies looking to serve both the Brazilian market and export to other countries in the region.

Manufacturing capacity varies significantly across Latin American countries. Mexico has developed sophisticated industrial clusters, particularly in automotive and electronics, with multi-tier suppliers and advanced technical capabilities. Costa Rica has specialized in high-tech manufacturing, attracting investments from companies like Intel and Boston Scientific.

Political risk is a crucial consideration for nearshoring decisions in Latin America. Government changes can significantly alter trade, labor, and tax policies. Venezuela and Nicaragua exemplify how political instability can destroy nearshore investment value, while countries like Chile and Uruguay have maintained political stability and a predictable regulatory framework.

Currency fluctuations add another layer of complexity. Currencies like the Mexican peso, Brazilian real, and Colombian peso can experience significant volatility against the U.S. dollar, affecting the competitiveness of nearshore operations. Companies should implement currency hedging strategies or structure contracts in U.S. dollars to mitigate this risk.

The availability of qualified talent varies greatly between countries and sectors. Mexico has a solid base of engineers and technicians specialized in manufacturing, while Costa Rica and Uruguay have developed capabilities in technology services and business processes. Brain drain to the United States remains a persistent challenge for several countries in the region.

Is It Worth Adopting Nearshoring Now or Waiting?

The decision to implement nearshoring involves complex strategic considerations that go beyond a traditional cost-benefit analysis. Companies must evaluate their current competitive position, the urgency of their risk mitigation needs, and their capacity to execute a complex transition in an uncertain environment.

Companies operating in time-sensitive sectors, such as fast fashion or consumer electronics, may justify an accelerated transition to nearshoring. The ability to quickly respond to changes in demand or market trends can generate competitive advantages that outweigh the transition costs. These industries also face growing pressure from sustainability-conscious consumers who favor products with lower carbon footprints.

Timing is crucial. Early adopters of nearshoring can secure access to the best suppliers and locations before competition increases the demand for nearshore resources. However, being a pioneer also entails taking on market and supplier development risks that may not materialize as expected.

The company’s financial situation significantly influences the viability of nearshoring. The transition requires substantial investments in supplier development, system reconfiguration, and potentially new infrastructure. Companies with capital constraints may prefer gradual approaches or strategic partnerships that reduce upfront investment requirements.

Product complexity and quality requirements also affect timing decisions. Simple products with standardized manufacturing processes can be transitioned more quickly to nearshore suppliers. Products requiring specialized technical capabilities or extremely high quality standards may need longer supplier development and knowledge transfer periods.

Geopolitical considerations add urgency for some companies. Trade tensions between the U.S. and China, though subject to change with new administrations, represent a structural risk for companies highly dependent on Chinese suppliers. Companies in sectors considered strategic or sensitive to national security may face regulatory pressure to diversify their supply chains.

The general recommendation favors a gradual and strategic approach. Companies can start with pilot projects in less critical product categories, develop nearshore supplier management capabilities, and gradually scale their operations. This approach allows for organizational learning, risk mitigation, and iterative adjustments that improve long-term success prospects.

However, the cost of waiting must also be considered. Global supply chain disruptions persist, maritime transport costs remain volatile, and competition for nearshore resources is intensifying. Companies that indefinitely postpone nearshoring assessments may find themselves at a competitive disadvantage when circumstances force them to act under pressure.

The optimal decision requires company-specific analysis, considering the industry, competitive position, organizational capabilities, and strategic goals. Predictive models and scenario analysis can inform this decision, but successful implementation will depend on the company’s execution capacity and its willingness to manage the inherent complexity of supply chain transformation.

Hybrid Models in Supply Chain: Combining Simulation and Analytics

The increasing complexity of modern supply chains has led companies to seek more sophisticated solutions for strategic and operational decision-making. Hybrid models, which integrate simulation techniques with advanced analytical methods, are emerging as an innovative response to the challenges faced by supply chain professionals in the digital age.

The convergence of big data, enhanced computational capabilities, and more efficient algorithms has opened new possibilities for developing modeling tools that leverage the best of both worlds: the mathematical precision of analytical models and the representational flexibility of simulation. This evolution addresses the need for systems capable of handling both the structural complexity and inherent variability of contemporary logistics operations.

What Are Hybrid Models in Supply Chain?

Hybrid supply chain models represent an advanced methodology that systematically combines discrete event simulation techniques with mathematical optimization models and predictive analytics. This integration enables the resolution of complex supply chain problems that require both uncertainty management and the optimization of limited resources.

A typical hybrid model incorporates deterministic components to optimize strategic decisions, while using stochastic simulation to evaluate system behavior under varying scenarios. This methodological duality allows planners to obtain robust solutions that consider both optimization goals and operational risk factors.

The distinguishing feature of these models lies in their ability to switch between different modeling paradigms according to the specific needs of the problem. For example, they may use linear programming to determine optimal production capacities, while employing Monte Carlo simulation to assess the impact of disruptions in the distribution network.

These systems integrate multiple real-time data sources, including transactional data from ERP systems, operational data from IoT sensors, and external variables such as weather conditions or market fluctuations. This connectivity allows for continuous model parameter updates and adaptive recommendations.

Why Combine Simulation and Analytical Models?

The evolution toward hybrid models addresses the inherent limitations of purely analytical or purely simulation-based approaches. This synergistic combination overcomes the individual weaknesses of each methodology while enhancing their respective strengths.

Traditional analytical models, though capable of producing mathematically verifiable optimal solutions, often require simplifications that may not accurately reflect real operational complexity. On the other hand, simulation models offer detailed representations of system behavior but may lack explicit optimization mechanisms and require extensive analysis to identify optimal configurations.

The integration creates more robust decision support systems capable of managing both resource optimization and operational risk quantification. This duality is especially valuable in environments where strategic decisions must be considered alongside tactical and operational variability.

Deterministic vs. Stochastic Approaches

Deterministic approaches are characterized by using fixed values for all model parameters, assuming full certainty about future conditions. These methods provide precise and reproducible solutions, ideal for strategic planning problems where optimal configurations are sought under known or highly confident conditions.

In contrast, stochastic approaches explicitly incorporate uncertainty through probability distributions for key parameters. This allows modeling of inherent variability in demand, processing times, resource availability, and other critical factors affecting supply chain performance.

The fundamental difference lies in how each approach handles uncertainty: while deterministic models seek the optimal solution for a specific scenario, stochastic models aim for robust solutions that maintain strong performance across multiple possible scenarios.

Hybrid models leverage this complementarity by using deterministic optimization to establish the basic decision structure and stochastic simulation to assess robustness and identify potential vulnerabilities under various operational conditions.

Strengths and Limitations of Each Method

Analytical models excel in their ability to guarantee mathematical optimality within defined constraints, providing computationally efficient solutions that can scale to large problems. Their deterministic nature facilitates result interpretation and trade-off analysis between conflicting objectives.

However, these advantages come with significant limitations, including the need to simplify complex real-world representations, difficulty in incorporating nonlinear behaviors, and a tendency to underestimate risks associated with operational variability.

Simulation models stand out for their ability to faithfully represent complex systems with multiple interactions, incorporate realistic statistical distributions, and evaluate emergent system behaviors. They can model detailed temporal dynamics and feedback effects that are difficult to capture analytically.

Simulation’s limitations include the lack of explicit optimization mechanisms, the need for extensive exploration of the solution space to identify superior configurations, and significant computational requirements to obtain statistically reliable estimates.

Architecture of a Hybrid Model Applied to Logistics

The architecture of a hybrid model for logistics applications requires a modular structure that enables seamless integration between analytical and simulation components while maintaining the flexibility needed to adapt to different operational contexts and decision-making requirements.

A typical architectural design includes distinct layers for data management, analytical processing, simulation, optimization, and results presentation. This separation facilitates maintenance, scalability, and reuse of components across different applications.

The data layer centralizes information management from multiple sources, implementing validation, cleaning, and transformation mechanisms to ensure the quality and consistency required by downstream models. This layer also handles historical storage and synchronization with operational systems.

Integration Between Discrete Event Simulation and Optimization Models

Effective integration between discrete event simulation and optimization models requires sophisticated mechanisms for information exchange and temporal coordination between the two paradigms. This integration can be implemented through various architectural patterns, each with specific advantages depending on the application context.

The most common pattern uses optimization to generate initial policies or configurations that are then evaluated and refined through simulation. This approach leverages the computational efficiency of optimization to explore the solution space while using simulation to validate performance under realistic conditions.

A more advanced variant embeds optimization within the simulation loop, where specific decisions are dynamically optimized based on the current state of the simulated system. This approach is particularly valuable for problems requiring continuous adaptation to changing conditions.

Temporal coordination between components requires synchronization mechanisms to ensure consistency between the different time horizons handled by optimization (typically strategic or tactical) and simulation (usually operational). This includes protocols for parameter updates, system state transfer, and result aggregation.

Connectivity with Real Data (ERP, MES, IoT Sensors)

Connectivity with real operational systems is a critical aspect that differentiates academic hybrid models from practical industrial implementations. This connectivity must handle the heterogeneity of data sources, communication protocols, and update frequencies typical of the modern industrial environment.

Integration with ERP systems provides access to core master data, including product structures, resource capacities, standard costs, and supplier information. This connection also enables bidirectional synchronization where the hybrid model can directly influence ERP operational planning.

MES (Manufacturing Execution Systems) provide real-time visibility into manufacturing operations, including production order progress, equipment utilization, product quality, and maintenance events. This information allows continuous model parameter updates and validation of predictions against real performance.

Incorporating data from IoT sensors introduces continuous monitoring capabilities that significantly enhance model accuracy. This data includes environmental conditions, equipment vibrations, real-time inventory levels, and quality metrics that support predictive maintenance and adaptive optimization.

Use Cases in Distribution, Production, and Warehousing

Hybrid models are especially valuable in areas where operational complexity combines with the need for optimization under uncertainty. These applications range from strategic network design decisions to tactical optimization of daily operations.

In distribution, hybrid models enable simultaneous optimization of the logistics network structure and operational routing and scheduling policies. This capability is particularly valuable for companies operating multi-tier distribution networks with multiple transportation modes and variable delivery windows.

In production, applications focus on coordinating capacity planning, production scheduling, and inventory management under uncertain demand. Hybrid models can optimize product mix decisions while simulating the impact of variability in processing times and material availability.

Planning with Dynamic Constraints

Planning with dynamic constraints represents one of the most challenging and valuable use cases for hybrid models, where system constraints continuously change in response to operational conditions, external events, or evolving business policies.

Such problems frequently arise in flexible manufacturing environments where available capacity varies based on equipment configurations, specialized personnel availability, and scheduled or corrective maintenance requirements. Hybrid models can optimize resource allocation while simulating the impact of different availability scenarios.

In logistics, dynamic constraints include changes in transportation capacity due to temporary regulations, weather conditions, or urban congestion. Hybrid models allow re-optimization of routes and schedules while evaluating solution robustness against anticipated disruptions.

Effective implementation requires adaptive algorithms that can efficiently update existing solutions when constraints change, avoiding the need to re-solve the entire optimization problem. This includes incremental optimization techniques and solution repair heuristics.

Contingency Simulation and Multi-Stage Decision Making

Contingency simulation using hybrid models allows systematic evaluation of disruptive events and the development of robust contingency plans that can be quickly implemented when real disruptions occur.

This approach is particularly valuable for low-probability but high-impact events such as natural disasters, labor strikes, or massive equipment failures. Hybrid models can pre-compute optimal responses for different contingency scenarios, significantly reducing response time during real crises.

Multi-stage decision-making adds further complexity by considering how current decisions affect future options under uncertainty. Hybrid models can use stochastic dynamic programming to optimize decision sequences while employing simulation to evaluate the performance of specific policies.

Integrating decision trees with Monte Carlo simulation enables the evaluation of adaptive strategies that modify the course of action based on the gradual resolution of uncertainties. This approach is especially useful for capacity investment or supplier selection decisions under uncertain demand.

Advantages Over Traditional Tools

Hybrid models offer significant competitive advantages compared to traditional planning and optimization tools, particularly in terms of predictive accuracy, solution robustness, and adaptability to changing conditions.

The most notable advantage lies in the ability to deliver solutions that are simultaneously mathematically optimal and robust to operational variability. This duality eliminates the need to choose between theoretical precision and practical applicability—a common dilemma with traditional tools.

Hybrid models also overcome the scalability limitations typical of purely analytical or simulation-based approaches.

Reshoring Scenario Evaluation: How to Simulate Impacts on Tariffs and Operations

Reshoring Scenario Evaluation: How to Simulate Impacts on Tariffs and Operations
Do you want to technically compare reshoring scenarios? We’ll show you how to use flexible models to analyze costs, tariffs, and lead times in the supply chain.

What is reshoring and why is it back at the center of logistics strategy?
Reshoring is the process of bringing back production activities that were previously offshored to the country of origin or nearby regions. This strategy has gained prominence in recent years due to geopolitical factors, disruptions in global supply chains, rising costs in Asia, and the need for greater resilience and control over production.
For decades, many companies moved operations to countries with lower labor costs. However, events like the pandemic, rising tariffs, logistics delays, and volatility in global markets have led many to reconsider those decisions. Today, reshoring is emerging as a viable alternative to improve time-to-market, reduce external dependency, and strengthen the connection with end customers.

Key variables affected by reshoring
The decision to relocate production operations involves evaluating multiple strategic, financial, and operational variables. Each of them directly impacts business results and should be carefully simulated before implementing changes.

Labor costs, tariffs, and duties
One of the main drivers of offshoring was the reduction in labor costs. In a reshoring process, this component tends to increase but can be offset by reduced tariffs, import duties, and international logistics costs.
In addition, changes in trade agreements, protectionist policies, or government incentives can significantly alter the economic balance of the process. Modeling these impacts across different scenarios is crucial for making well-founded decisions.

Lead times and operational flexibility
Relocating production closer to the final market can reduce lead times from weeks to days, enabling faster responses to demand changes. Operational flexibility improves, allowing for shorter production cycles and less inventory.
This agility can become a decisive competitive advantage in industries where customization, seasonality, or speed are critical.

Local infrastructure and production capacity
Not all countries have the same industrial or technological infrastructure. The local ability to absorb production processes, access quality suppliers, and attract technical talent are key factors.
Reshoring scenario evaluation must consider these structural limitations or advantages, which influence both costs and the scalability of the operating model.

How to evaluate reshoring scenarios using flexible models
The complexity of these decisions requires tools that allow comparison of multiple possible configurations, considering dynamic variables and real business constraints.

Discrete event simulation
Discrete event simulation allows for detailed representation of a logistical or industrial operation, including times, resources, queues, material flows, and state changes.
This technique is particularly useful for assessing how reshoring would impact a plant, warehouse, or distribution network, measuring effects on time, costs, productivity, and bottlenecks. Different layouts, shifts, technologies, or demand scenarios can be tested.

Optimization models under multiple constraints
In addition to simulating operations, optimization models help find the most efficient configuration among many possible scenarios, considering capacity, cost, demand, and regulatory constraints.
Applied to reshoring, these models help determine which volumes to relocate, which plants to use, how to supply each region, and which logistics routes to optimize. They can be combined with advanced heuristics for large-scale problems.

Risk and sensitivity evaluation
A proper reshoring scenario evaluation goes beyond average results to include variability and risk associated with different decisions. Sensitivity analysis, Monte Carlo simulations, or pessimistic/optimistic scenarios help understand system behavior under unexpected changes.
This perspective is key to avoiding investments that are only profitable under ideal conditions and to designing resilient supply chains.

Comparison with offshoring and nearshoring scenarios
To justify a reshoring process, it is essential to compare all possible scenarios: continuing offshoring, moving operations to nearby countries (nearshoring), or fully relocating back home.

Decision matrices based on quantifiable outputs
Using decision matrices that compare quantifiable variables such as logistics costs, initial investment, delivery times, expected quality, political risk, and scalability helps make strategic decisions on solid grounds.
These visual tools incorporate multiple dimensions into the analysis and present results clearly for executive decision-making.

Practical cases in industrial companies
Several industrial companies across Latin America, Europe, and the U.S. have started using simulation and optimization tools to evaluate reshoring projects. Some typical cases include:

  • Automotive industry: Simulation of supply chains with distributed production in Mexico, the U.S., and China to assess total costs and delivery times.
  • Pharmaceutical companies: Risk analysis to relocate critical processes and reduce dependency on active ingredients from Asia.
  • Appliance manufacturers: Optimization of local assembly lines versus direct imports from Asia.

These cases show there is no one-size-fits-all answer: the key lies in having adaptable models tailored to each company’s specific conditions.

When is reshoring truly profitable?
Reshoring becomes profitable when the benefits in time, control, quality, and flexibility outweigh increased labor or operational costs. It is also worthwhile when it helps reduce systemic risks, meet local regulations, or better serve the end customer.
However, each case must be analyzed individually. Simulation and optimization tools allow for a technical, quantified analysis aligned with each organization’s strategic goals.

Big Data in Supply Chains: What It Is, Benefits, and Practical Applications

Discover How Big Data Is Transforming Supply Chains with Efficiency, Prediction, and Smart Decision-Making. Real Applications in Argentina and Key Trends.

What Is Big Data Applied to Logistics?
Big Data in logistics refers to the ability to capture, process, and analyze massive volumes of data from multiple sources within the supply chain. It goes beyond mere data storage by extracting valuable insights from both structured and unstructured information.
In the logistics sector, it encompasses diverse information: goods movement, transit times, consumer behavior, weather conditions, traffic status, and market fluctuations. The integration and simultaneous analysis of this heterogeneous data enable the identification of patterns and opportunities that transform traditional logistics management.
What makes Big Data distinctive is its capacity to process these enormous volumes in real-time, allowing for dynamic adjustments and proactive decision-making—shifting from reactive operations to predictive systems.

Why Is It Important in Modern Supply Chains?

Operational Efficiency Improvement
Big Data optimizes every link in the logistics chain through detailed analysis. Companies implementing these solutions experience significant improvements, from warehouse management to route planning.
Advanced analytics uncover inefficiencies invisible to traditional systems: suboptimal patterns, underutilized resources, or redundant processes. Continuous analysis allows for real-time adjustments, reducing operational costs by 15% to 30%.
For instance, route optimization based on historical data combined with real-time traffic information significantly cuts delivery times and fuel consumption.

Data-Driven Decision Making
Big Data transforms decision-making by replacing intuition with precise analysis. This paradigm shift allows for more informed decisions, risk reduction, and maximized outcomes.
Analytical dashboards and visualization tools deliver critical information instantly. Algorithms can simultaneously process hundreds of variables to recommend the best option in complex situations.
This data-driven approach improves decision quality and reduces the time needed to make them—a crucial factor in a sector where operational agility is a competitive advantage.

Greater Forecasting and Planning Capabilities
One of the most transformative benefits is predictive capability. By analyzing historical trends and correlating multiple variables, organizations can anticipate demand changes, foresee incidents, and plan future scenarios with accuracy.
Predictive models allow companies to forecast seasonal demand peaks, estimate delivery times considering external factors, or predict maintenance needs. This foresight optimizes resources, reduces inventory costs, and improves customer service.
Organizations with predictive capabilities report up to a 20% reduction in inventory levels while maintaining or improving responsiveness.

Real-World Applications of Big Data in Logistics

Examples in Retail, Agribusiness, and Manufacturing
Big Data’s impact is evident across multiple sectors, adapting to each industry’s specific needs.
In retail, chains like Walmart have revolutionized operations using analytics systems that process sales, inventory, consumption, and external data to optimize distribution. In Argentina, Grupo Arcor uses Big Data to optimize its complex distribution chains by analyzing regional consumption patterns and logistics data.
In agribusiness, Big Data transforms the entire value chain. Grain processors use predictive analytics to anticipate harvests and plan capacity. Satellite data, combined with historical records and soil analysis, enables accurate production volume estimates.
In manufacturing, industrial plants implement predictive maintenance systems based on IoT sensor data, reducing downtime and optimizing internal logistics flows.

Most Used Platforms and Tools in Argentina
Argentina’s tech ecosystem has significantly developed Big Data solutions for logistics, combining local development with adapted international platforms.
SAP Supply Chain Management stands out, used by YPF Logística and Andreani to manage complex operations with advanced analytics. Microsoft Azure solutions have gained ground in manufacturing and retail for their scalability and flexibility.
The open-source ecosystem (Apache Hadoop, Spark, Tableau) is widely adopted among mid-sized operators and startups. Argentina’s tech community has developed local market-specific adaptations.
Companies like Tango Logística have incorporated advanced analytics capabilities into their platforms, offering solutions tailored to local SMEs.

Technologies Enhancing Big Data Usage

Internet of Things (IoT)
IoT has revolutionized real-time data capture and transmission through interconnected smart sensors monitoring every physical aspect: goods location and status, environmental conditions, or equipment performance.
In Argentina, these devices are essential for long-distance logistics management. GPS sensors combined with telemetry provide continuous information on location, temperature, driving patterns, and fuel consumption.
In warehouses, RFID sensors and beacons enable precise tracking and optimization of internal flows, generating heat maps of movements and identifying bottlenecks.
IoT’s revolution lies in its ability to generate continuous data streams that were previously invisible, turning every element into an information source for analysis and optimization.

Artificial Intelligence and Machine Learning
AI and machine learning represent the qualitative leap that transforms vast data volumes into actionable insights—identifying complex patterns, making accurate predictions, and generating optimized recommendations.
In Argentine logistics, these algorithms transform demand planning, route optimization, and inventory management. Mercado Libre uses predictive algorithms to anticipate order volumes and optimize distribution. Cruz del Sur uses AI to dynamically optimize routes based on multiple variables.
The transformative power lies in continuous learning: systems improve with each operation, adapting to new patterns.

Blockchain and Data Traceability
Blockchain technology is emerging as a solution for logistics data traceability and security. Its decentralized architecture guarantees data integrity throughout the chain—a critical factor in industries like pharmaceuticals and food.
In Argentina, sectors like wine and meat exports adopt blockchain to ensure full traceability, immutably recording every stage of the logistics process.
Integrating blockchain with Big Data multiplies value: while blockchain ensures authenticity and immutability, analytics tools extract valuable insights, creating a trustworthy data ecosystem to support critical decisions.

What Are the Challenges in Implementing Big Data in Logistics?

System and Data Source Integration
A major challenge is the effective integration of multiple legacy systems and disparate sources. Technological fragmentation is common: ERP, WMS, TMS, and departmental apps often operate in silos with limited interoperability.
This fragmentation hinders the creation of a real-time unified view. Integration projects require specialized resources and significant investments. The challenge intensifies when incorporating external sources like weather or traffic data.
Successful organizations address this with service-oriented architectures or data lakes that store both structured and unstructured information.

Cybersecurity and Data Protection
Big Data implementation involves handling sensitive and valuable information. Data about routes, inventories, customers, or pricing are critical assets, and exposure could have serious consequences.
In Argentina, where cyberattacks have significantly increased, protecting these assets is a top priority. Threats include both external and internal attacks—compounded when data must be shared with logistics partners.
Effective strategies include “Zero Trust” security models, anonymization and tokenization systems, and role-based access controls. Ongoing staff training and a security culture are fundamental.

Training and Resistance to Change
The shift to data-driven logistics represents a deep cultural transformation. Resistance to change and lack of analytical skills are major barriers.
In Argentina’s logistics sector—traditionally reliant on practical experience—the move to algorithm-based decisions may generate resistance from experienced professionals, often in the form of skepticism or reluctance.
The digital skills gap is another challenge. There’s a shortage of professionals combining logistics expertise with advanced analytics skills.
Successful initiatives tackle this with structured change management programs, tailored training, transparent communication, and mixed teams of logistics and analytics specialists.

Are We Heading Toward Predictive and Smart Logistics?

Digital Twins and Logistics Simulations
The digital twin concept is revolutionizing planning by creating accurate virtual replicas of entire physical systems, enabling visualization, simulation, and optimization in a virtual environment.
In Argentina, pioneering companies are developing digital twins of distribution centers and transport networks. These models, continuously fed with real data from IoT sensors, allow experimentation with different configurations without impacting ongoing operations.
Operators like Andreani use digital twins to assess new warehouse setups. In urban areas, simulations help optimize last-mile delivery in congested cities.
Their transformative potential lies in the ability to integrate multiple variables and create detailed “what-if” scenarios previously unimaginable.

Autonomous Logistics and Prescriptive Analytics
The natural evolution of Big Data leads to increasingly autonomous systems—capable not only of analyzing and predicting but also of making and executing decisions with minimal human intervention.
Prescriptive analytics, which recommends specific actions to optimize outcomes, is gaining ground in complex operations. Advanced systems can automatically suggest route adjustments, reallocate resources, or modify priorities based on changing conditions.
In Argentina, companies like Mercado Libre are at the forefront of autonomous system implementation. Algorithms continuously manage order assignment, optimizing routes in real-time based on traffic conditions and delivery windows.
The future points to integration with emerging technologies like advanced robotics and autonomous vehicles. This isn’t about replacing human intervention but evolving toward a collaborative model where autonomous systems handle routine tasks and professionals focus on strategic aspects.

Conclusion
Big Data is deeply transforming supply chains, turning traditionally reactive operations into predictive and intelligent systems. This change is not just technological but a fundamental shift in how we conceive and manage the flow of goods.
Leading organizations understand that the value doesn’t lie in accumulating data but in converting it into actionable insights that drive smarter decisions, more efficient processes, and better customer experiences.
As IoT, AI, and blockchain mature and converge, we foresee increasingly predictive, autonomous, and adaptive logistics. This evolution brings challenges, but the opportunities to improve efficiency, reduce costs, and enhance sustainability justify the transformation efforts.
Big Data is redefining what’s possible in modern logistics, enabling previously unimaginable levels of optimization and synchronization. Argentine companies embracing this transformation are not only improving current operations but preparing for a future where logistics excellence is inseparable from analytical excellence.

Geopolitics and Supply Chain: How to Prepare for an Unstable World

Geopolitics is increasingly impacting the supply chain. Discover how to anticipate its effects and what strategies to adopt to operate with resilience.

What is the relationship between geopolitics and supply chains?
The interaction between geopolitics and supply chains has become inseparable in today’s globalized world. Political decisions, tensions between nations, and the reshuffling of international power directly affect trade flows, logistics routes, and the availability of essential resources for business operations.

This two-way relationship has gained unprecedented prominence in recent years. On one hand, power struggles between nations increasingly use logistics chains as a tool of strategic pressure. On the other, companies have realized that international political stability is just as crucial for their operations as internal process efficiency or market demand.

In this new scenario, a purely technical and operational view of logistics management is no longer sufficient. Organizations that thrive are those integrating sophisticated geopolitical analysis into their strategic planning, anticipating changes on the international stage that could impact their operations.

Examples of geopolitical events that affected global logistics

Wars, sanctions, and trade blockades
Armed conflicts and economic sanctions represent some of the most direct geopolitical impacts on global logistics chains. The Russia-Ukraine conflict perfectly exemplifies this dynamic, having caused disruptions at multiple levels: from grain and fertilizer supplies to critical tech components.

Sanctions imposed on Russia have reconfigured trade flows of energy, raw materials, and manufactured products. Companies reliant on Russian inputs have been forced to completely rethink their sourcing strategies, often at significantly higher costs and with less supply certainty.

Meanwhile, the trade war between the United States and China has profoundly altered global production dynamics. Tariffs, technology restrictions, and limits on foreign investment have forced multinationals to reconsider their supply chains, seeking alternatives to reduce exposure to these geopolitical tensions.

Post-pandemic energy and logistics crises
The COVID-19 pandemic and its uneven recovery exposed the vulnerability of a global logistics system optimized for efficiency but not for resilience. The initial collapse of entire chains was followed by crises in specific sectors like semiconductors, triggering domino effects in dependent industries.

The European energy crisis following the invasion of Ukraine showed how strategic dependencies can rapidly become critical vulnerabilities. The exponential rise in energy costs altered the economic viability of numerous industrial and logistics operations, forcing deep relocations and restructurings.

These events also revealed the fragility of the just-in-time model in high-volatility situations. Container shortages, port congestion, and lack of skilled personnel created bottlenecks that lengthened delivery times and undermined the operational predictability essential to modern supply chains.

Reconfiguration of trade alliances
The landscape of trade agreements and economic blocs is undergoing accelerated transformation, driven by both economic and national security considerations. The trend toward regionalization is gaining strength over the hyper-globalized model of previous decades.

New initiatives like the RCEP (Regional Comprehensive Economic Partnership) in the Asia-Pacific are creating commercial zones with their own rules and standards. Simultaneously, concepts like “friend-shoring” or “ally-shoring” are gaining relevance, prioritizing trade with reliable partners over sheer economic efficiency.

This fragmentation of global trade into blocs with different regulatory systems, compliance requirements, and technical standards poses a major challenge for transnational logistics chains, which must now adapt to multiple operational frameworks simultaneously.

Main geopolitical risks for the supply chain

Increased logistics costs
Geopolitical instability directly impacts logistics costs through multiple mechanisms. The most evident is the rise in transportation costs, especially maritime and air, due to the need to use longer or less efficient alternative routes to avoid conflict zones.

Regulatory compliance costs have also surged amid a proliferation of sanctions, restrictions, and traceability requirements depending on origin. Companies must invest heavily in verification and documentation systems to navigate this complex international regulatory environment.

Added to this is the increase in inventory costs, as many organizations are moving away from minimal inventory models to build strategic reserves that allow greater autonomy in the face of disruptions. While this trend boosts resilience, it marks a fundamental change in traditional logistics cost structures.

Shortage of inputs and raw materials
Competition for critical resources has intensified dramatically. Strategic materials like rare minerals, semiconductors, or renewable energy components have become subjects of geopolitical dispute, with nations implementing export restrictions to protect their strategic industries.

The geographical concentration of certain essential resources amplifies this risk. For example, over 70% of the world’s cobalt production comes from the Democratic Republic of the Congo, while China processes around 85% of the world’s rare earth elements. These concentrations create systemic vulnerabilities when geopolitical tensions arise.

Companies face not only the challenge of securing physical supplies of these elements, but also of managing the extreme price volatility that accompanies periods of geopolitical uncertainty, significantly complicating financial and operational planning.

Instability in operational planning
Predictability, a fundamental pillar of modern logistics, is being severely undermined by geopolitical volatility. Traditional planning cycles are inadequate in the face of sudden changes in trade policies or border restrictions that can materialize within days.

Companies report growing difficulties in establishing reliable delivery commitments, especially in transcontinental operations crossing multiple jurisdictions. This uncertainty affects the entire chain, amplifying the bullwhip effect and complicating the synchronization between production and demand.

Traditional ERP (Enterprise Resource Planning) systems, designed for relatively stable environments, show significant limitations in modeling high-volatility geopolitical scenarios, forcing many organizations to develop adaptive planning capabilities based on dynamic risk analysis.

Strategies to mitigate geopolitical impact on logistics

Supplier diversification
Diversifying supply sources has become a cornerstone of logistics resilience. The most adaptive companies are moving from single-supplier models to geographically distributed multi-supplier configurations to minimize the impact of regional events.

This diversification involves not only increasing the number of suppliers, but establishing strategic redundancies in different geopolitical regions. The “China plus one” trend illustrates this approach, with companies maintaining operations in China while developing parallel capabilities in countries like Vietnam, Mexico, or India.

The main challenge lies in balancing the increased complexity and potential loss of economies of scale with the benefits of greater resilience. The most effective organizations are implementing AI-powered supplier management platforms to optimize this balance.

Relocation of strategic operations
The reshoring or nearshoring phenomenon is gaining traction as a response to geopolitical vulnerabilities. Companies are reevaluating decades of labor-cost-based offshoring to incorporate considerations of national security, operational continuity, and proximity to end markets.

Critical sectors such as semiconductors, medical equipment, and green technologies are leading this trend, with significant investments to reduce dependency on geopolitically volatile regions. Governments are actively incentivizing this process through subsidies, tax benefits, and special economic zones.

Relocation does not necessarily mean a full return to home countries but rather a strategic reconfiguration of the global production footprint. The concept of “regionalization” is emerging as an alternative that maintains some global distribution while reducing vulnerabilities tied to overly extended chains.

Digitalization for greater flexibility and monitoring
Digital transformation has become a key ally in managing geopolitical risks. Technologies like blockchain, IoT, and digital twins are enhancing real-time visibility across the entire supply chain, enabling rapid detection and response to disruptions.

Logistics intelligence platforms with predictive capabilities are incorporating geopolitical variables into their models, combining operational data with political risk analysis to anticipate vulnerabilities. These tools enable scenario simulation and contingency planning tailored to different types of disruptions.

Advanced automation and robotics are reducing dependency on labor in specific locations, granting more flexibility to adapt operations in response to geopolitical changes. This flexibility is crucial for agile responses to reconfigurations forced by sanctions, tariffs, or trade restrictions.

Latin America’s role in the new global logistics context

Opportunities for Argentina in exports and nearshoring
Argentina holds a strategic position amid new geopolitical dynamics due to its abundance of critical natural resources and relatively neutral stance in international conflicts. The country has the potential to capitalize on the growing demand for food, raw materials, and energy in a context of global trade fragmentation.

The nearshoring trend offers significant opportunities, particularly to supply the North American market in sectors where Argentina has comparative advantages: agribusiness, knowledge-based services, non-traditional mining, and renewable energy. Companies that successfully adapt their processes to international standards are capturing value from this global reconfiguration.

The main challenge lies in overcoming structural limitations such as poor logistics infrastructure, macroeconomic instability, and regulatory complexity. Companies and regions proactively addressing these issues through strategic investments and trade facilitation improvements are successfully integrating into new global value chains.

Latin America as a whole has the potential to emerge stronger in this new geopolitical landscape, offering stable supply alternatives to markets seeking to diversify sources and reduce dependence on more volatile regions. However, seizing this historic opportunity requires coordinated strategies between the public and private sectors to develop sustainable competitive capabilities.

How to strengthen the supply chain in the face of crises and economic shocks

In an increasingly volatile world, supply chains face unprecedented challenges. From global pandemics to abrupt economic fluctuations, a company’s ability to anticipate, withstand, and recover from disruptions has become a critical factor for survival and success. At Eurystic, we understand that process optimization and simulation are key not only for day-to-day efficiency but fundamentally for building the resilience needed in the face of uncertainty. This article explores how to strengthen your supply chain using proven strategies and advanced technological tools.

What Is a Resilient Supply Chain?

A resilient supply chain goes beyond simple efficiency or cost reduction. It is defined by its ability to anticipate, withstand, adapt to, and quickly recover from disruptions—whether operational, economic, or geopolitical. Unlike traditional chains, often optimized for a single “steady state” scenario, a resilient chain incorporates:

  • Visibility: Deep, real-time insight into all nodes and flows across the chain.
  • Flexibility: Ability to quickly modify routes, suppliers, or transportation modes.
  • Collaboration: Strong, transparent relationships with key suppliers and partners.
  • Controlled Redundancy: Strategic alternatives (suppliers, inventory, routes) without incurring excessive costs.
  • Agility: Rapid decision-making and implementation of changes.

In essence, it’s about designing a logistics system that not only survives crises but can even find opportunities amid them.

Impact of Recent Economic Shocks on Logistics

Recent years have revealed the fragility of global supply chains in the face of disruptive events. Understanding the specific impact of these shocks is the first step toward building more robust defenses.

Examples: Pandemic, Inflation, and Currency Devaluations

The COVID-19 pandemic was a massive disruption catalyst. We witnessed factory shutdowns, labor shortages, border restrictions, and dramatic shifts in demand patterns (surge in health and home products, decline in other sectors). This created major bottlenecks and exposed overreliance on single regions or suppliers.

Subsequently, global inflation and currency devaluations in various economies added another layer of complexity. Rising costs of raw materials, energy, and labor directly impact margins. Economic uncertainty affects the financial capacity of suppliers and clients, while currency volatility adds further risk to international transactions.

Transport Disruptions and Volatile Demand

Transport disruptions became the norm: port congestion, container and driver shortages, and skyrocketing ocean and air freight rates. These factors not only increased costs but also caused unpredictable delays, affecting planning and customer satisfaction.

At the same time, demand became extremely volatile. The “bullwhip effect” (where small variations in end-consumer demand amplify as they move up the chain) was intensified by panic buying, changes in consumer habits (e.g., more e-commerce), and general uncertainty. Predicting demand became a major challenge, leading to stockouts or overstocking.

Strategies to Strengthen the Supply Chain

In the face of these challenges, passivity is not an option. Proactive strategies are essential to building resilience. At Eurystic, we recommend focusing on the following key areas:

Contingency Planning and Risk Management

This goes beyond mere reaction. It requires exhaustive mapping of the supply chain to identify critical failure points (key suppliers, single routes, concentrated logistics nodes). Specific contingency plans should be developed for likely scenarios (supplier failure, route closure, natural disaster), with clear actions, responsibilities, and required resources. Ongoing risk assessment (geopolitical, financial, operational) must be integral to management.

Digitalization of Critical Processes

Digitalization is the backbone of visibility and agility. Implementing systems that centralize and analyze inventory, orders, production, and transportation data in real time is crucial. This includes cloud-based platforms, IoT solutions for asset tracking, and digital documents (purchase orders, invoices, shipping documents) to reduce errors and speed up workflows.

Geographic and Supplier Diversification

Overdependence on a single source or region is an inherent risk. A smart diversification strategy involves identifying and qualifying alternative suppliers in different geographies. This doesn’t mean duplicating everything, but having viable, vetted options. A mix of local, regional, and global suppliers (nearshoring, friend-shoring, offshoring) can balance costs and risks. Strengthening relationships and collaboration with these suppliers is equally important.

Technological Tools That Improve Resilience

Technology is a fundamental enabler of the strategies mentioned above. At Eurystic, we see immense value in leveraging the following tools:

ERP and Supply Chain Management (SCM) Platforms

Enterprise Resource Planning (ERP) systems and specialized Supply Chain Management (SCM) platforms integrate data across departments (finance, purchasing, inventory, logistics, production). They provide a single source of truth, enhance end-to-end visibility, facilitate collaboration, and standardize processes for greater efficiency and control.

Scenario Simulation and Digital Twins

This is where Eurystic’s expertise makes a difference. Process simulation enables modeling of the supply chain to test the impact of different disruptive scenarios (“What if my main supplier shuts down?”, “What happens if a maritime route is blocked?”, “How does a sudden demand spike affect operations?”). Digital twins (dynamic virtual replicas of the chain) go further, allowing real-time monitoring and simulation of changes. These tools enable:

  • Risk-free testing of contingency plans
  • Optimization of safety stock levels
  • Identification of hidden bottlenecks
  • Data-driven, predictive decision-making—not just reactive

Automation and Real-Time Monitoring

Automation in warehouses (robots, AGVs) and processes (RPA for administrative tasks) reduces labor dependency and minimizes errors. Real-time monitoring enabled by IoT sensors and GPS provides instant visibility into shipment location and condition (temperature, humidity), allowing for early alerts and rapid responses to deviations or incidents.

Tangible Benefits of a Strengthened Chain

Investing in resilience isn’t just a defensive cost—it delivers measurable, strategic benefits.

Fewer Disruptions and Faster Response Times

A resilient chain with tested contingency plans and real-time visibility can identify problems faster and activate alternative solutions efficiently. This minimizes the impact of disruptions on production and deliveries, reduces downtime, and improves business continuity.

Improved Cash Flow and Traceability

Inventory optimization (enabled by simulation and better forecasting), lower costs from fewer disruptions (expedited freight, penalties), and more efficient operations all contribute to healthier, more predictable cash flow. Additionally, digitalization and monitoring drastically improve product traceability—essential for quality control, product recalls, and regulatory compliance.

Fast Adaptation to Market Changes

The flexibility inherent to a resilient chain—combined with intelligence gained from data and simulations—enables companies to quickly adapt to changes in demand, new regulations, emerging opportunities, or competitor moves. This agility translates into a significant competitive advantage.

Sectors Where Supply Chain Resilience Is Critical

While resilience is important across all industries, it is absolutely critical in certain sectors.

Food and Healthcare

In these sectors, disruptions can have serious consequences. The cold chain for perishable food and pharmaceutical products must be maintained flawlessly. Food and medicine availability is essential to public safety and health. Traceability is vital for food safety and strict regulatory compliance (e.g., ANMAT, FDA). Resilience here saves lives and ensures well-being.

E-commerce and Retail Distribution

The rise of e-commerce has raised consumer expectations to unprecedented levels (fast delivery, high availability, easy returns). Managing last-mile complexity, extreme demand volatility (especially during peaks like Black Friday), and reverse logistics (returns) requires a supply chain that is exceptionally agile, visible, and resilient to maintain customer satisfaction and profitability.

Conclusion

Building a resilient supply chain is no longer optional—it is a strategic necessity in today’s environment. It requires a proactive approach that combines rigorous planning, smart diversification, and adoption of advanced technologies. Tools like scenario simulation and digital twins—Eurystic’s specialty—are essential for testing strategies, optimizing responses, and making informed decisions before crises strike. Strengthening your supply chain today is an investment in the stability, profitability, and growth of your business tomorrow.

Ready to build a future-proof supply chain? Contact Eurystic and discover how our simulation and optimization solutions can help you identify risks and design the resilience your business needs.

MES Software in Manufacturing: What It Is, Its Benefits, and How to Apply It in Argentina

Industrial Digitization Is Advancing Quickly—and Local Plants Need Tools That Bridge the Gap Between Production Plans and Shop Floor Reality

The Manufacturing Execution System (MES) is the key component that closes this gap, enabling Industry 4.0 with real-time data, traceability, and decision-making based on performance indicators. Below, we break down its functionalities, benefits, and adoption keys, with a focus on the Argentine context.

What Is an MES and How Does It Work?

An MES is a software platform that captures and orchestrates everything that happens between the release of the work order and the finished product. It sits between the ERP and plant systems (SCADA, PLC), integrating data from machines, operators, and materials to synchronize production execution minute by minute.

In practice, the MES:

  • Receives production orders from the ERP.
  • Breaks down operations and assigns resources (equipment, labor, materials).
  • Monitors each workstation in real time.
  • Records quality, consumption, downtime, and performance (OEE).
  • Returns KPIs and alerts to act before waste appears.

Core MES Functions in Industry

At the heart of the MES are three functional blocks working in an integrated manner.

Real-time Process Monitoring
It connects sensors, PLCs, and workstations to display online status, pace, and cycle times. This enables immediate action on micro-stoppages or bottlenecks.

Quality Management and Traceability
Each batch is linked to process parameters, raw materials, and inspection results. In case of deviations, the traceable batch can be isolated within minutes and documented for ISO 9001, BRC, or SENASA audits.

Production Order and Resource Control
The MES sequences orders, reserves machines, releases materials, and guides operators with digital instructions—avoiding rework and paper-based chaos.

Benefits of MES Software in Manufacturing

Adopting MES is not just “adding screens”; it directly impacts the bottom line.

Reduced Downtime and Errors
By detecting unplanned stoppages in seconds and documenting root causes, scrap and rework are significantly reduced. Studies report annual savings of up to US$2 million in medium-sized plants.

Improved Operational Efficiency
Companies integrating MES increase their OEE by 5% to 20% thanks to dynamic scheduling and data-driven preventive maintenance.

Data-Driven Decision-Making
Dashboards and automatic reports turn thousands of signals into actionable KPIs (performance, quality, delivery). Management can respond hour by hour—not just at month-end.

How MES Integrates with Other Systems (ERP, SCADA, PLM)

A successful project doesn’t replace existing platforms—it connects them.

Plant-Wide Data Synchronization
MES receives the master plan from the ERP, feeds SCADA with recipe parameters, and returns actual consumption and cycle times to fine-tune planning.

Automated Information Flow
When an operator releases a batch, MES triggers labels, quality certificates, and updates inventory in the WMS/ERP without manual intervention, speeding up financial closing.

MES Implementation Examples in Argentine Industries

Although each sector has its nuances, three verticals clearly demonstrate the path to adoption.

Automotive Industry
Assembly plants in Pacheco and Córdoba are digitizing their welding and assembly lines with MES to unify data from multiple robots, ensure part traceability, and comply with IATF 16949 standards. Benefits include global plant visibility and maintenance cost savings.

Food and Beverage
Dairy and snack producers implement MES suites (e.g., FactoryTalk) to comply with HACCP, log batches, and monitor OEE in pasteurizers, fillers, and packagers—reducing waste and recalls.

Electronics Assembly
In Tierra del Fuego, TV and smartphone manufacturers manage thousands of SMT boards per shift. An MES consolidates AOI quality data, component usage, and line balancing to sustain >85% integration rates.

Considerations for MES Implementation

Introducing MES requires alignment between technology, processes, and people.

Cost and Return on Investment (ROI)
A typical project pays for itself within 12–24 months through reduced scrap, overtime, and downtime. Key: quantify potential savings before selecting a provider.

Staff Training
Operators, supervisors, and IT staff must be trained in terminal use, data handling, and alert management to prevent the system from going “dark” after go-live.

Software Scalability and Customization
Choose modular solutions (OEE, traceability, maintenance) that can scale to multiple plants and connect with legacy equipment via RS-485 or OPC UA protocols.

Conclusion

The MES is the practical enabler of Industry 4.0: it transforms production into a continuous, reliable data flow—ready to optimize through analytics, simulation, and heuristic algorithms. Integrating it into Argentine plants today makes manufacturing more competitive, resilient, and ready to meet the global market’s demands for cost-efficiency, quality, and flexibility.

What Are Flow Algorithms? Graph Theory

Logistics and productivity optimization are essential for improving any organization’s operational efficiency. In this context, flow algorithms are key tools that help optimize complex industrial processes, facilitating strategic decision-making. At Eurystic, we use these methodologies to provide quantitative solutions tailored to our clients’ specific needs.

What Is a Flow Algorithm in Logistics and Production?

A flow algorithm is a quantitative technique used to solve problems related to the efficient movement of goods, services, or information through a network. These algorithms help determine optimal routes, allocate resources efficiently, and significantly improve operational management in companies with complex logistical or production processes.

Difference Between Flow Algorithms and Other Optimization Techniques

While other optimization techniques, such as heuristics or simulations, offer approximate and fast solutions, flow algorithms provide exact solutions based on solid mathematical models. The main difference lies in the ability of flow algorithms to handle complex systems with multiple constraints and clearly defined objectives.

Most Commonly Used Flow Algorithms

Several flow algorithms are widely used in logistics and production optimization, depending on business needs:

Maximum Flow Algorithm (Max Flow)

The maximum flow algorithm identifies the maximum possible flow that can pass through a network from an origin point to a destination point, considering constraints such as transport or storage capacity.

Minimum Cut Algorithm (Min Cut)

The minimum cut algorithm identifies the most vulnerable or limiting point within a network. This is crucial for diagnosing bottlenecks and establishing strategies to improve system resilience and efficiency.

Shortest Path Algorithms

These algorithms seek the most efficient route in terms of cost, time, or distance between two points in a network. They are particularly useful for optimizing distribution processes, reducing delivery times, and improving customer service.

Benefits of Applying Flow Algorithms in Industry

Implementing flow algorithms provides multiple competitive advantages for companies:

Reduction of Operational Costs

By optimizing resource use and eliminating inefficiencies, these algorithms significantly reduce operational costs related to transportation, storage, and inventory management.

Efficient Resource Distribution

Flow algorithms enable intelligent resource allocation, improving production, distribution, and storage planning, optimizing installed capacity, and reducing waste.

Route and Delivery Time Optimization

With specific algorithms like Shortest Path, route management can be improved, significantly reducing delivery times, increasing customer satisfaction, and strengthening the company’s competitive advantage.

Recommended Software Tools and Programming Languages

To effectively implement flow algorithms, it is advisable to use specialized tools like AnyLogic, a leading simulation and optimization software. Additionally, programming languages like Python and Java are particularly suitable due to their versatility and support for advanced optimization libraries.

Step-by-Step Implementation of a Flow Algorithm

  1. Requirements Analysis and Objective Definition
    Clearly identify business objectives and specific system constraints to properly select and configure the algorithm to be used.
  2. Selection of the Appropriate Algorithm Type
    Determine whether a maximum flow, minimum cut, or shortest path algorithm is the most suitable for the organization’s specific problem.
  3. Pilot Testing and Scalability
    Conduct pilot tests to validate the obtained results and ensure the solution can be effectively scaled to the entire system.

Relevant Use Cases and Examples

Logistics and Distribution

Used to optimize routes, reduce delivery times, and improve fleet management, increasing operational efficiency and reducing costs.

Production Planning

Helps optimally manage the flow of materials and products through different stages of the production process, avoiding bottlenecks and improving adherence to production plans.

Supply Chain Management

Facilitates the planning and control of supply from multiple points, ensuring material availability, optimizing stock levels, and minimizing operational costs.

At Eurystic, we apply these advanced quantitative methodologies to solve complex logistical and production challenges, delivering measurable results tailored to each client’s specific needs.

Internal Logistics Optimization: Strategies to Maximize Resources and Productivity

Explore techniques for optimizing and automating internal logistics to maximize resources and increase productivity. The implementation of advanced technologies and improved process management enables companies to reduce costs and enhance operational efficiency.

What is Internal Logistics?

Internal logistics is the central system of business operations, responsible for efficiently managing the flow of materials, information, and resources within an organization. It includes processes such as receiving, storage, inventory management, and preparation for final distribution. It also encompasses personnel coordination, time management, and the optimization of warehouse space usage.

Why is Internal Logistics Optimization Important?

Optimizing internal logistics is not just about efficiency; it is a key strategy for improving profitability and competitiveness. Efficient internal logistics help reduce costs, minimize production times, and enhance customer satisfaction. Companies that prioritize this optimization can respond swiftly to market fluctuations and improve their adaptability to changes in demand.

Why is Internal Logistics Crucial in the Value Chain?

Differences Between Internal and External Logistics

Internal logistics manages processes within the organization, from raw material reception to product preparation for distribution. On the other hand, external logistics handles the movement of products to the final consumer. The efficiency of internal logistics directly impacts the responsiveness of external logistics and the quality of service provided to customers.

Impact on Costs, Time, and Customer Satisfaction

Optimized internal logistics reduce operational times, eliminate waste, and improve inventory management, leading to faster deliveries and lower operating costs. Additionally, it enhances customer experience by providing more accurate and reliable service. The digitization of logistics processes also facilitates demand forecasting and strategic decision-making based on real-time data.

Main Challenges Hindering Internal Logistics Efficiency

Bottlenecks in Daily Operations

Bottlenecks are critical points where operational capacity is insufficient to meet demand, causing delays throughout the supply chain. Identifying and eliminating these obstacles through process optimization and automation is crucial for improving logistics flow. Implementing simulation models can help anticipate these issues and design more effective solutions.

Lack of Real-Time Visibility and Data

The absence of real-time monitoring hinders efficient decision-making. Implementing technological tools that provide a comprehensive view of logistics flow is essential for anticipating problems and improving operational planning. Systems like ERP and WMS (Warehouse Management Systems) enable full control over stock and optimize warehouse space utilization.

Manual Processes and Risk of Human Errors

Excessive reliance on manual processes increases the likelihood of errors, slows down operations, and generates additional costs. Digitalization and task automation help minimize these risks and improve productivity. Training personnel in new technologies is also fundamental for ensuring a smooth transition to automation.

Automation in Internal Logistics

Automated Transport Systems

AGV (Automated Guided Vehicles)

AGVs optimize the internal transport of materials without human intervention, reducing transfer times and accident risks. These systems can operate autonomously in production and storage environments, enhancing operational efficiency.

Conveyor Belts

Modern conveyor belts incorporate sensors and automated control systems to ensure a constant and efficient material flow. Implementing these systems in distribution centers and factories increases productivity and reduces handling costs.

Mobile Robots

Collaborative mobile robots can adapt to different operational environments, improving flexibility and precision in internal logistics. With artificial intelligence, these robots can optimize routes and perform repetitive tasks with a high degree of accuracy.

Identification and Tracking Technologies

RFID

Radio Frequency Identification (RFID) enables automated and real-time tracking of products and materials, enhancing traceability and inventory control. Its implementation reduces losses and improves data accuracy.

Barcodes

An efficient and cost-effective solution for quick product identification and stock control. Integrating barcodes with ERP systems enables more efficient inventory management.

IoT

The Internet of Things (IoT) connects devices throughout the logistics chain, providing real-time data to optimize decision-making. Sensors in warehouses and transportation equipment can detect problems and generate alerts before they impact operations.

Optimization of Algorithms in Internal Logistics

Use of Routing and Resource Allocation Algorithms

Advanced algorithms optimize routes, reduce travel times, and improve resource allocation within internal logistics. These algorithms can dynamically adapt to demand changes and enhance operational efficiency.

Applications of Artificial Intelligence and Machine Learning

AI-based solutions analyze demand patterns, forecast inventory needs, and optimize operational planning. AI also facilitates anomaly detection in logistics processes, reducing response times to unforeseen events.

Simulation Models and Digital Twins for Decision-Making

Digital twins simulate logistics scenarios to anticipate problems and optimize strategies before implementing them in the real environment. This helps reduce operating costs and improve overall supply chain efficiency.

What Are the Tangible Benefits of Optimized Internal Logistics?

Reduced Processing Times and Faster Deliveries

Automating and optimizing internal processes speeds up operations, reducing production and delivery times. This improves customer satisfaction and strengthens a company’s competitiveness.

Cost Savings and Improved Cash Flow

Efficient logistics eliminate waste, lower storage costs, and improve cash flow by accelerating inventory turnover. This allows for better financial planning and greater profitability.

Reduced Errors and Improved Product Traceability

Digitizing internal logistics reduces human errors and enhances traceability, ensuring more efficient and secure product management.

Examples and Applications in Different Industries

Manufacturing and High-Turnover Assembly

Factories integrate automated transport systems and identification technologies to optimize production lines, improving efficiency and reducing operating costs.

Retail Warehouses and Distribution Centers

The use of mobile robots and RFID in warehouses enables efficient inventory management and quick order preparation, ensuring more precise and agile deliveries.

E-commerce Logistics and Peak Demand Management

E-commerce companies employ artificial intelligence and automation to manage demand surges and optimize order processing and shipping. This allows them to handle large order volumes efficiently and accurately.

Optimizing internal logistics not only improves operational efficiency but also strengthens a company’s competitiveness in a dynamic market. Implementing advanced technologies and automation strategies is key to maximizing resources and productivity.