Is it the end of globalization? Or is a commitment to critical infrastructure supply chains?

Instituto de Ingeniería Industrial, Universidad Nacional de Cuyo, Mendoza, Argentina

Di³ Doctorado en Ingeniería Industrial, Universidad Nacional de Cuyo, Mendoza, Argentina

Di³ Doctorado en Ingeniería Industrial, Universidad Nacional de Cuyo, Mendoza, Argentina

Agenda

• Climate change and manufacturing
• United Nations Timeline
• Metrics of the globalization
• One man is One man
• The role of hegemonic powers
• What a critical infrastructure is?
• Post-COVID and CI protection
• Wine Supply Chain Case
• AI and Logistics for Water Management
• Conclusion

Conference Paper

Disclaimer

• This presentation had been developed following the Open Science paradigm.

• Furthermore, we have chosen to uphold the principles of a high gradient of reproducibility.

You can download code, datasets and new version of this document from : https://rpubs.com/ricardorpalma/1351475

DOI 10.5281/zenodo.17262615

https://zenodo.org/records/17262615

Climate change timeline

# Instalación de paquetes necesarios (descomenta si no los tienes)
# install.packages("ggplot2")
# install.packages("dplyr")
# install.packages("ggtext")
options(warn = -1)
library(ggplot2)

suppressPackageStartupMessages(library(dplyr))

suppressWarnings(library(ggtext))

# Datos de las Conferencias COP
cop_data <- data.frame(
  year = c(1997, 2005, 2007, 2009, 2011, 2012, 2015, 2021, 2024, 2025),
  event = c("COP3", "COP11/MOP1", "COP13", "COP15", "COP17", "COP18", "COP21", "COP26", "COP29", "COP30"),
  title = c(
    "Protocolo de Kioto",
    "Entrada en vigor de Kioto",
    "Plan de Acción de Bali",
    "Acuerdo de Copenhague",
    "Plataforma de Durban",
    "Enmienda de Doha",
    "Acuerdo de París",
    "Pacto de Glasgow",
    "COP29",
    "COP30"
  ),
  location = c(
    "Kioto, Japón",
    "Montreal, Canadá",
    "Bali, Indonesia",
    "Copenhague, Dinamarca",
    "Durban, Sudáfrica",
    "Doha, Catar",
    "París, Francia",
    "Glasgow, Reino Unido",
    "Bakú, Azerbaiyán",
    "Belém, Brasil"
  ),
  description = c(
    "Metas vinculantes de reducción de\nemisiones 6-8% vs 1990",
    "Primera reunión de las Partes\ndel protocolo. +10,000 delegados",
    "Cronograma para el marco post-2012.\nGrupo de acción cooperativa",
    "192 países. Compromiso de\n$30 mil millones 2010-2012",
    "Negociaciones para acuerdo vinculante.\nFondo Verde $100 mil millones",
    "Segundo período de compromiso\nKioto 2013-2020",
    "Mantener calentamiento <2°C.\nParticipación universal",
    "Primer balance global del\nAcuerdo de París. +40,000 participantes",
    "Acuerdo de $300,000 millones\nanuales para países en desarrollo",
    "Prevista para noviembre 2025.\nNuevas NDCs 3.0"
  ),
  highlight = c(TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE),
  side = c("left", "right", "left", "right", "left", "right", "left", "right", "left", "right")
)

# Crear el gráfico
p <- ggplot(cop_data, aes(x = year, y = 0)) +
  
  # Línea de tiempo central
  geom_segment(aes(x = min(year), xend = max(year), y = 0, yend = 0),
               color = "#667eea", size = 1.5, lineend = "round") +
  
  # Puntos en la línea de tiempo
  geom_point(aes(color = highlight), size = 8, show.legend = FALSE) +
  scale_color_manual(values = c("TRUE" = "#e74c3c", "FALSE" = "#667eea")) +
  
  # Líneas conectoras a las cajas de texto
  geom_segment(data = cop_data %>% filter(side == "left"),
               aes(x = year, xend = year, y = 0, yend = 0.3),
               color = "#667eea", size = 0.8, linetype = "solid") +
  geom_segment(data = cop_data %>% filter(side == "right"),
               aes(x = year, xend = year, y = 0, yend = -0.3),
               color = "#667eea", size = 0.8, linetype = "solid") +
  
  # Cajas de eventos (lado izquierdo/arriba)
  geom_label(data = cop_data %>% filter(side == "left"),
             aes(x = year, y = 0.35, 
                 label = paste0("**", year, " - ", title, "**\n",
                               "📍 ", location, "\n\n",
                               description),
                 fill = highlight),
             hjust = 0.5, vjust = 0, size = 3.5,
             lineheight = 0.9, label.padding = unit(0.5, "lines"),
             label.size = 0.5, show.legend = FALSE) +
  
  # Cajas de eventos (lado derecho/abajo)
  geom_label(data = cop_data %>% filter(side == "right"),
             aes(x = year, y = -0.35, 
                 label = paste0("**", year, " - ", title, "**\n",
                               "📍 ", location, "\n\n",
                               description),
                 fill = highlight),
             hjust = 0.5, vjust = 1, size = 3.5,
             lineheight = 0.9, label.padding = unit(0.5, "lines"),
             label.size = 0.5, show.legend = FALSE) +
  
  scale_fill_manual(values = c("TRUE" = "#667eea", "FALSE" = "#ecf0f1")) +
  
  # Etiquetas de años en la línea
  geom_text(aes(label = year), y = -0.05, vjust = 1.5, 
            size = 3, fontface = "bold", color = "#2c3e50") +
  
  # Configuración de escalas y tema
  scale_x_continuous(breaks = cop_data$year, limits = c(1995, 2027)) +
  scale_y_continuous(limits = c(-0.8, 0.8)) +
  
  labs(
    title = "🌍 Conferencias de la ONU sobre Cambio Climático",
    subtitle = "Del Protocolo de Kioto (1997) a la COP30 (2025)",
    caption = "Fuente: UNFCCC | Eventos destacados: Kioto, París y COP30"
  ) +
  
  theme_minimal() +
  theme(
    plot.title = element_text(size = 20, face = "bold", hjust = 0.5, 
                              color = "#2c3e50", margin = margin(b = 5)),
    plot.subtitle = element_text(size = 14, hjust = 0.5, 
                                 color = "#7f8c8d", margin = margin(b = 20)),
    plot.caption = element_text(size = 10, color = "#95a5a6", hjust = 0.5,
                               margin = margin(t = 20)),
    axis.text.x = element_blank(),
    axis.text.y = element_blank(),
    axis.title = element_blank(),
    axis.ticks = element_blank(),
    panel.grid = element_blank(),
    plot.background = element_rect(fill = "#f8f9fa", color = NA),
    panel.background = element_rect(fill = "#f8f9fa", color = NA),
    plot.margin = margin(30, 30, 30, 30)
  )

# Mostrar el gráfico
print(p)

# Guardar el gráfico (opcional)
# ggsave("timeline_cop_conferencias.png", plot = p, 
#        width = 16, height = 10, dpi = 300, bg = "#f8f9fa")

suppressPackageStartupMessages(library(kableExtra))
# kable(cop_data[ ,1:5], format = "html")
cop_data[ ,1:5] %>%
  kbl() %>%
  kable_material(c("striped", "hover"))

year	event	title	location	description
1997	COP3	Protocolo de Kioto	Kioto, Japón	Metas vinculantes de reducción de emisiones 6-8% vs 1990
2005	COP11/MOP1	Entrada en vigor de Kioto	Montreal, Canadá	Primera reunión de las Partes del protocolo. +10,000 delegados
2007	COP13	Plan de Acción de Bali	Bali, Indonesia	Cronograma para el marco post-2012. Grupo de acción cooperativa
2009	COP15	Acuerdo de Copenhague	Copenhague, Dinamarca	192 países. Compromiso de $30 mil millones 2010-2012
2011	COP17	Plataforma de Durban	Durban, Sudáfrica	Negociaciones para acuerdo vinculante. Fondo Verde $100 mil millones
2012	COP18	Enmienda de Doha	Doha, Catar	Segundo período de compromiso Kioto 2013-2020
2015	COP21	Acuerdo de París	París, Francia	Mantener calentamiento <2°C. Participación universal
2021	COP26	Pacto de Glasgow	Glasgow, Reino Unido	Primer balance global del Acuerdo de París. +40,000 participantes
2024	COP29	COP29	Bakú, Azerbaiyán	Acuerdo de $300,000 millones anuales para países en desarrollo
2025	COP30	COP30	Belém, Brasil	Prevista para noviembre 2025. Nuevas NDCs 3.0

Metrics of the globalization

Those are the so-called “end of globalization” metrics.

---

Offshoring

Offshoring is the practice of relocating a business process, manufacturing, or service to a distant foreign country, typically to take advantage of lower labor costs or other economic benefits (environmental).

• Location: A country far from the company’s home country (e.g., a U.S. company moving manufacturing to China or a European company moving IT support to India).

• Primary Motivation: Significant cost savings, access to specialized skills, and global market expansion.

• Trade-offs: Potential for long supply chains, significant time zone differences, cultural/language barriers, and reduced supply chain visibility/control.

---

Re-shoring (also called Onshoring)

Re-shoring is the process of bringing business operations, production, or services back to the company’s original home country after they were previously moved overseas (offshored).

• Location: The company’s home country.

• Primary Motivation: Increasing supply chain resilience and control, reducing lead times, improving product quality oversight, avoiding geopolitical risks, and leveraging the “Made in [Home Country]” appeal.

• Trade-offs: Generally involves higher labor and operational costs compared to offshore locations.

---

Near-shoring

Near-shoring involves relocating business operations or services to a nearby foreign country, typically one that is geographically proximate, often sharing a border or being within a close time zone.

• Location: A neighboring or geographically close country (e.g., a U.S. company moving production to Mexico or a French company moving IT to Romania).

• Primary Motivation: Seeking a balance between cost savings (lower costs than the home country) and the benefits of proximity, such as shorter shipping times, fewer time zone issues, and easier communication and travel for oversight.

• Trade-offs: Costs are typically lower than re-shoring but potentially higher than far-off offshoring. The goal is to mitigate the complexity and risk associated with long-distance offshoring while still achieving some cost advantages

One man is One man everywhere

* A US citizen emits seven times more than a European citizen. China proposes reducing emissions based on quotas per country.


* A Chinese citizen emits 15 times less than a European, but uses coal. Furthermore, they have a huge population. The US proposes reducing China's emissions and quality without altering its own emition rate. (MAGA)


* India has lowered its energy intensity (TOE vs. GDP), proposing that one man equals one man. Developed countries have already "burned" their share of fuel. Then their need to reduce his emissions and allow developing countries to close the inequality gap.

The role of hegemonic powers

There are hundreds of examples throughout history of what happens when a hegemonic power disappears (the Roman Empire). There is a transition period with wars and a dramatic reduction in "international" trade.

It is a painful process that requires a change in **culture** until the new power manages to establish itself. The hegemonic power that tends to disappear uses a **copy of the strategies** that allowed the emerging power to grow.**Trust** is also lost between strategic partners.

The US is breaking ties with Europe. It wants to become great again by imitating the economic processes of Industry 2.0. It's closing its doors to brains. It doesn't support electric cars or 5G.

It create Industry 5.0 in Silicon Valley and operate only with tariffs that impact the global supply chains.

The end of globalization

The “end of globalization” is a widely discussed concept suggesting that the deep, fast, and extensive integration of the world’s economies, cultures, and politics—a process known as globalization—is slowing down, stagnating, or even reversing.

1- Rise of Economic Nationalism and Protectionism

2- Trade Wars and Tariffs

3- Populist Backlash

4- Growing erosion of institutionalism (including Western democracies)

5- Supply Chain Vulnerability

6- Shocks and Disruptions: Major events like the 2008 financial crisis, the COVID-19 pandemic, and the Russia-Ukraine war exposed the fragility of highly optimized, just-in-time global supply chains.

7- Focus on Resilience

What a critical infrastructure is?

Infrastructure is no longer just bridges, roads, and the like—it now includes elements ranging from digital networks to clean-energy systems to electric-vehicle charging corridors.

To meet the growing demand for infrastructure, McKinsey estimates that a cumulative $106 trillion in investment will be necessary through 2040. Asia is expected to receive more than half of the total infrastructure investment through 2040, driven by rapid urbanization, population growth, and continued industrial expansion.

Infranomics

Introducing Infranomics, as a crucial discipline for this century. Neither authorities, nor industrial or academic bodies could afford to ignore the advent of the convolution of opportunities and risks accompanying the implementation of the new generation of infrastructures. The shape of our society will be determined by the characteristics of, and the services delivered through, those infrastructures. It is argued that Infranomics is the body of disciplines supporting the analysis and decision-making regarding the metasystem

Introducing Infranomics, as a crucial discipline for this century. Neither authorities, nor industrial or academic bodies could afford to ignore the advent of the convolution of opportunities and risks accompanying the implementation of the new generation of infrastructures. The shape of our society will be determined by the characteristics of, and the services delivered through, those infrastructures. It is argued that Infranomics is the body of disciplines supporting the analysis and decision-making regarding the metasystem

Post-COVID and CI protection

https://ricardorpalma.github.io/IC_SCM/

What decisions and paths to take

The Institute of Industrial Engineering and Di³ are working on the following lines of research that use artificial intelligence and language models to investigate ways to optimize critical infrastructure, which appears to be the focus of attacks and cyberattacks to reduce competitiveness in these end-of-globalization environments.

• We firmly believe that universities cannot silence their voices and must work toward a culture of peace.

• Playing war games, as many leaders are doing, is more dangerous than playing Russian roulette with nuclear bullets.

• As academics, our duty is to research, discover, and put on the agenda of governments and companies what our models tell us should be the right thing to do.

Use CI as intelligently as possible

It is very clear that the governments of Western democracies will no longer have the capacity to invest in infrastructure in the way they did in the first half of the 20th century.

The infrastructure we inherited will soon be overwhelmed by exponential population growth.

New environmental restrictions force us to become more efficient or endure increasingly intense and adverse weather events.

A Clever Decisión

  USE ARTIFICIAL INTELLIGENCE TO GET THE MAXUMUN OF YOUR CRITICAL INFRASTRUCTURE

EXAMPLE 1 - Wine Supply Chain Case

China and Australia Wine Routing

Yuval Harari and the Fait	Goverment and Wine	Chinese Wine?

China’s shares of the volume of world wine:

Year	Production	Consumption	Imports
2014	4.0	5.6	3.6
2015	4.0	6.7	5.2
2016	4.0	6.8	6.0
2017	3.7	6.7	7.0
2018	2.1	5.4	6.5
2019	1.6	4.3	5.8
2020	1.2	3.1	4.1
2021	1.0	2.9	4.0
2022	0.7	2.2	3.2
2023	0.6	2.4	3.5
2024	0.3	2.9	3.1

shares_volume <- c(2014 , 4.0, 5.6, 3.6 ,
2015 , 4.0, 6.7, 5.2 ,
2016 , 4.0, 6.8, 6.0 ,
2017 , 3.7, 6.7, 7.0 ,
2018 , 2.1, 5.4, 6.5 ,
2019 , 1.6, 4.3, 5.8 ,
2020 , 1.2, 3.1, 4.1 ,
2021 , 1.0, 2.9, 4.0 ,  
2022 , 0.7, 2.2, 3.2 ,
2023 , 1.6, 2.4, 3.5 ,
2024 ,  3, 1.3, 1.5 )

shares_volume <- matrix(shares_volume,ncol = 4, byrow = TRUE)
colnames(shares_volume) <- c("Year", "Consumption", "Consumption", "Imports")
# shares_volume

suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(plotly))
suppressPackageStartupMessages(library(tidyr))

# Crear el vector y la matriz
shares_volume_vec <- c(2014, 4.0, 5.6, 3.6,
                       2015, 4.0, 6.7, 5.2,
                       2016, 4.0, 6.8, 6.0,
                       2017, 3.7, 6.7, 7.0,
                       2018, 2.1, 5.4, 6.5,
                       2019, 1.6, 4.3, 5.8,
                       2020, 1.2, 3.1, 4.1,
                       2021, 1.0, 2.9, 4.0,
                       2022, 0.7, 2.2, 3.2,
                       2023, 1.6, 2.4, 3.5,
                       2024, 3, 1.3, 1.5)

shares_volume <- matrix(shares_volume_vec, ncol = 4, byrow = TRUE)
colnames(shares_volume) <- c("Year", "Production", "Consumption", "Imports")

# Convertir a data frame
df <- as.data.frame(shares_volume)

# Transformar de formato ancho a largo para ggplot2
df_long <- pivot_longer(df, 
                        cols = c("Production", "Consumption", "Imports"),
                        names_to = "Variable",
                        values_to = "Value")

# Crear gráfico con ggplot2
p <- ggplot(df_long, aes(x = Year, y = Value, color = Variable, group = Variable)) +
  geom_line(size = 1.2) +
  geom_point(size = 3) +
  labs(title = "Shares Volume: Production, Consumption & Imports",
       x = "Year",
       y = "Value",
       color = "Variable") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
        legend.position = "right")

# Convertir a plotly para interactividad
interactive_plot <- ggplotly(p, tooltip = c("x", "y", "colour"))

# Mostrar el gráfico interactivo
interactive_plot

China vs Australia vs Chile

Index of intensity of wine exports to China from Australia, Chile and France,a and share of Australia’s wine exports going to China, by value, 2014 to 2022

Country	2014	2015	2016	2017	2018	2019	2020	2021	2022
% Export Au to Ch	1.9	2.8	3.7	5	5.1	6	0.5	0.3	6
Australia Index	2.8	2.8	3	4	4.6	5.5	6.5	0.3	0.1
Chile Index	1.95	1.9	1.8	2	2.3	2.6	2.5	4.1	4.4
France Index	1.3	1.1	1.0	1.1	0.9	0.8	0.95	1.3	1.35

export_index <- c(
 1.9  , 2.8  , 3.7  , 5  ,  5.1, 6   , 0.5 , 0.3  , 6   ,
 2.8  , 2.8  , 3    , 4  , 4.6 , 5.5 , 6.5 , 0.3  , 0.1 ,
 1.95 , 1.9  , 1.8  , 2  , 2.3 , 2.6 , 2.5 , 4.1  , 4.4 ,
 1.3  , 1.1  , 1.0  , 1.1, 0.9 , 0.8 , 0.95, 1.3  , 1.35 )

export_index <- matrix(export_index, ncol= 9 , byrow = "TRUE")

colnames(export_index) <- c( "2014", "2015", "2016","2017","2018","2019" ,"2020" ,"2021","2022")

rownames( export_index) <-  c("Bult wine export Australy to China", "Australia Index", "Chile Index","France Index") 
  
# export_index

# library(ggplot2)
# library(plotly)
suppressPackageStartupMessages(library(tidyr))
suppressPackageStartupMessages(library(dplyr))

# Crear la matriz
export_index <- c(
  1.9, 2.8, 3.7, 5, 5.1, 6, 0.5, 0.3, 0.2,
  2.8, 2.8, 3, 4, 4.6, 5.5, 6.5, 0.3, 0.1,
  1.95, 1.9, 1.8, 2, 2.3, 2.6, 2.5, 4.1, 4.4,
  1.3, 1.1, 1.0, 1.1, 0.9, 0.8, 0.95, 1.3, 1.35
)

export_index <- matrix(export_index, ncol = 9, byrow = TRUE)
colnames(export_index) <- c("2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022")
rownames(export_index) <- c("Bulk wine export Australia to China", "Australia Index", "Chile Index", "France Index")

# Convertir a data frame largo
df <- as.data.frame(export_index)
df$Category <- rownames(export_index)

df_long <- df %>%
  pivot_longer(cols = -Category, names_to = "Year", values_to = "Value") %>%
  mutate(
    Type = ifelse(Category == "Bulk wine export Australia to China", "Bar", "Line"),
    Year = factor(Year, levels = colnames(export_index))
  )

# Crear el gráfico con ggplot2
p <- ggplot(df_long, aes(x = Year, y = Value, color = Category, group = Category)) +
  # Líneas para los índices
  geom_line(data = filter(df_long, Type == "Line"), linewidth = 1) +
  geom_point(data = filter(df_long, Type == "Line"), size = 2.5) +
  # Barras para exportaciones de vino
  geom_col(data = filter(df_long, Type == "Bar"), 
           aes(fill = Category), alpha = 0.7, color = NA) +
  scale_color_manual(values = c(
    "Bulk wine export Australia to China" = "#8B0000",
    "Australia Index" = "#FF6B6B",
    "Chile Index" = "#FFFF00",
    "France Index" = "#45B7FF"
  )) +
  scale_fill_manual(values = c("Bulk wine export Australia to China" = "#00f200")) +
  labs(
    title = "Export Index: Wine and Country Indices (2014-2022)",
    x = "Year",
    y = "Index Value",
    color = "Category",
    fill = "Category"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", size = 14),
    axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "right"
  )

# Convertir a plotly para interactividad
fig <- ggplotly(p, tooltip = c("x", "y", "colour"))

# Mostrar el gráfico
fig

Example 2 - AI and Logistics for Water Management

The model initially built in LOGO was implemented in R-Cran. CLAUDE was then used to generate a dynamic model that would improve the reliability of water distribution and conservation strategies for human, agricultural, and industrial consumption, minimizing last-mile logistics costs.

Example 3 - Quality of Data for Operational Excellence

In this research, the quality documentation systems of 50 companies in the retail and automotive sectors were used as training material for a language model (using Llama 3 and Deep Seek on low-cost local servers). By using the PRISMA methodology for systematic literature review, the agent was improved, enabling it to generate recommendations for SMEs that lead to operational excellence.

Conclusion

Climate problem

Achieving the Sustainable Development Goals (SDGs) by 2030 has become extremely difficult, as most countries are lagging behind and progress is either null or regressive, aggravated by global crises. Despite this, it is still possible through a decadeFIVE YEARS of ambitious action and policy changes, with key investments in critical and not critical infrastructure, support for the most vulnerable, and active participation from all sectors of society.

What about Hegemonic Powers

• Most major economies (except Japan) have postponed the Sustainable Development Goals until 2050.

• Unlike what happened in the First and Second World Wars, infrastructures have become the targets of attack in every war that has appeared.

• This reduces the possibility of nuclear conflicts and opens up the possibility for academia to conduct R&D in infrastructure at borders, as a means of connecting rather than dividing. Ports, roads, airways, water, disaster mitigation, and farms for food independence appear as key topics for applying AI models, and all of them involve last-mile logistics.

Lessons learned from examples 1,2 and 3

• Last-mile logistics isn’t limited solely to urban traffic or drone delivery issues.

• China has increased its vineyard production using smart rolling vehicles that travel through small vineyards. They haven’t used drones, and these vehicles don’t have drivers or remote pilots.

• The case of irrigation water distribution is another example of last-mile logistics to which we intend to apply the arsenal of methods that AI offers.

• Finally, the case of language models is closely linked to the Japanese proposal we referred to in the previous paragraphs. As with the quality paradigm, which has been a cultural construct in Japan with an ecumenical character, they propose operational excellence as an alternative to Industry 4.0 and 5.0.

The end of globalization?

• Ok, I can grant you that this may not be the end of globalization, but let’s at least agree that it’s a HUGE change.

• This operational excellence is part of what they have called SOCIETY 5.0. Who knows? Perhaps this is the most plausible answer we have found to all these questions.