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Utilizing real data from Kenya’s vaccination campaign, this article evaluates performance metrics crucial for effective vaccine dissemination with simulation modelling support.

Measuring Supply Chain Carbon Footprint When assessing the supply chain sustainability, various key performance indicators (KPIs) must be considered. The most commonly used is the supply chain carbon footprint estimation, which serves as a major proxy for environmental sustainability.  Carbon footprint refers to the total greenhouse gas (GHG) emissions, measured in CO2 equivalent, generated directly or indirectly by an activity, company, product, or service. Importance of Supply Chain Carbon Footprint The supply chain carbon footprint serves as a key proxy for environmental sustainability and is increasingly used in regulations such as the European Green Deal, the Paris Agreement, and the EU’s Net-Zero Industry Act. These initiatives highlight its role as a target for reducing emissions. Its prominence stems from the ability to summarize all sources of GHG emissions within a company’s supply chain, offering a clear metric for evaluating its impact on climate change. Given the importance of carbon footprint indicator, it is necessary to focus on its computation to comprehend how it could be embedded in a sustainable supply chain simulation model. Calculating Supply Chain Carbon Footprint The calculation of carbon footprint involves three main inputs: Activity Volume (A): Measures the quantity of activity that produces emissions (e.g., in kg, liters, kWh, etc.). Emission Factor (EF): A coefficient defining how much GHG is released per unit of activity. Global Warming Potential (GWP): A conversion factor that expresses GHG emissions in CO2 equivalent. Mathematical operators Activitiy Volume Emission Factor Global Warming Potential Mathematical operators The operators at the start of the formula are interpreted as follows: Integral over time (t): Emissions are calculated across the entire defined time period. Summation over activities (j): All activities generating emissions are included in the carbon footprint calculation. Summation over GHG gases (g): All greenhouse gases generated or used in the activities are considered, with their effects converted to CO2 equivalents using the GWP coefficient. Activitiy Volume Represents the quantity of the flows associated to a certain activity (j) in the time period considered (t). Depending on the nature of the activity considered, this variable may assume differents measurment unit: kg, ton, liters, m3, MJ, kWh, km,… Choosing the right level of detail for activity mapping affects data collection, estimation accuracy, and the ability to target emission-reduction efforts. Detailed mapping yields precise results but requires extensive data, while a high-level approach may lead to less accurate estimates. Emission Factor An emission factor (EF) represents the amount of a pollutant released relative to the activity causing the emission. These factors are usually expressed as the pollutant’s weight per unit of activity and help estimate emissions from various pollution sources. The coefficient is related to a specific activity (j) that defines the quantity of emissions for a particular GHG (g) per unit processed by that activity (volume/mass/distance/energy, etc.).  They are typically averages of available quality data and assumed to be representative over the long term. Global Warming Potential The Global Warming Potential (GWP) factor allows for comparison between different GHGs by converting their emissions into CO2 equivalent.It is a conversion factor to CO2 equivalent mass relative to the global warming potential of a specific greenhouse gas (g).  The GWP coefficient is purely scientific and does not vary by application. Typically, the 100-year GWP is used. In carbon footprint calculations, the GWP factor is sourced from scientific references. Carbon Footprint according to the GHG Protocol The GHG Protocol is the primary framework for calculating a supply chain carbon footprint. It defines emissions in three scopes: Scope 1: Direct emissions from owned or controlled sources (e.g., fuel, chemicals, and factory emissions). Scope 2: Indirect emissions from purchased energy like electricity, heat, and steam. Scope 3: Indirect emissions across the value chain, both upstream and downstream. This scope often accounts for the largest share of emissions and has become mandatory under the EU’s Corporate Sustainability Reporting Directive (CSRD). By integrating carbon footprint estimation into supply chain models, companies can monitor, optimize, and reduce their environmental impact, contributing to global sustainability efforts. 01 Modeling Sustainable supply chain Sustainable development modeling approaches and strategies. Deep dive 02 Supply chain carbon footprint How to measure one of the most important indicators for sustainability. Deep dive 03 The simulation dictionary Model, Scenarios, Simulations, Digital Twin: what is what? Deep dive 04 Skills and technologies Learn about the skills and technologies involved in this cutting-edge technology Deep dive 05 Simulation modelling roadmap From beginner to level expert: the road ahead Deep dive 06 Applying Simulation to the S&OP process MRP, Scheduling, and in between Simulation Modelling: discover how Deep dive Photo by HD Wallpapers on StockSnap

modeling sustainable supply chain The evolution of Sustainable Supply Chain Modeling In recent years, sustainable supply chain has become a critical priority for managers and companies, driven by growing awareness of environmental and social responsibilities. However, the concept of sustainable economic development has deep historical roots, with its origins tracing back to ancient practices. Early examples include crop rotation and hunting restrictions, implemented to balance human activities with environmental needs and ensure consistent access to food resources. Over the past two centuries, the study of sustainable development has evolved significantly, embracing scientific methodologies and economic theories: 1798 Thomas Maltus publishes an Essay on the Principle of Population, making the first attempt in sustainable development modeling. 1972 The Limits to Growth explored the potential outcomes of unchecked economic and population growth in the face of limited resources, analyzed through computer simulations.  2021 The Sixth Assessment Report (AR6) from the United Nations Intergovernmental Panel on Climate Change (IPCC) is the latest in a series of comprehensive evaluations of the current scientific understanding of climate change. T. R. Malthus – An Essay on the Principle of Population – 1798 Malthus, despite certain mathematical and logical limitations, introduced the innovative idea of modeling the macro environment. His approach utilized mathematics and physics to predict future societal developments and propose measures to ensure long-term prosperity and sustainability. The book warns about potential challenges ahead, based on the idea that while the population would grow exponentially (doubling approximately every 25 years), food production would only rise at a linear rate. This imbalance could lead to food shortages and famine unless there was a reduction in birth rates. Since Malthus’s time, numerous initiatives and academic studies have advanced the understanding of sustainable development. Club of Rome – The Limits to Growth – 1972 The Club of Rome, an interdisciplinary group of scientists, developed a macroeconomic model to explore the limits of economic growth. Their research culminated in the influential report, The Limits to Growth (1972), which highlighted the potential consequences of unchecked economic expansion on the environment and human well-being. The study employed the World3 model to simulate the effects of interactions between human systems and the Earth’s environment. The report concluded that without substantial changes in how resources are used, there is a high probability of a sudden and uncontrollable decline in both population and industrial output. IPCC – Sixth Assessment Report (AR6) – 2021 More recently, in 2021, the Intergovernmental Panel on Climate Change (IPCC) released its Sixth Assessment Report (AR6), a landmark study in sustainable development modeling. The AR6 report, which represents the culmination of decades of research, covers three critical areas: The Physical Science Basis, Impacts, Adaptation and Vulnerability, and Mitigation of Climate Change. This comprehensive analysis utilized over 20 different modeling frameworks, each incorporating various sub-models, to connect human activities with planetary conditions. By simulating scenarios of economic growth and emissions reduction, the report assessed the potential impacts on both society and the planet. The findings of the AR6 report underscore the urgency of sustainability, particularly the need to limit global temperature increases to below 1.5°C over the next century. This report has laid the foundation for many subsequent regulations on emission reductions, influencing policies set by governments and organizations worldwide. The report offers both immediate and long-term strategies for addressing the issue. It identifies the primary driver of global warming as the rise in CO2 emissions, warning that global temperatures are likely or very likely to exceed 1.5°C under scenarios of higher emissions. Some key statements from the report include: Human activities, particularly greenhouse gas emissions, have definitively caused global warming, raising global surface temperatures by 1.1°C from pre-industrial levels. Continued emissions are projected to push global warming past the 1.5°C threshold soon, with each increase in temperature heightening multiple risks. However, significant and rapid reductions in emissions could slow warming within two decades. Climate change is a critical threat to human well-being and planetary health, with a rapidly closing window to secure a sustainable and livable future. As businesses and policymakers increasingly prioritize sustainability, these historical and contemporary studies provide valuable insights into how we can achieve sustainable development while balancing economic growth and environmental stewardship. Understanding the evolution of sustainable development and its importance is crucial for businesses aiming to thrive in a rapidly changing world. By learning from past studies and current models, companies can implement strategies for sustainable supply chain that not only support economic growth but also contribute to the well-being of society and the preservation of our planet. Enhancing Sustainable Supply Chain through Simulation Modeling Sustainable development has traditionally been a focus of macroeconomic modeling, providing valuable insights at a broad scale. However, the growing importance of sustainability at the microeconomic level—particularly within individual companies—necessitates aligning traditional business performance metrics with sustainability goals, such as reducing carbon footprints and other environmental impacts. One key challenge in environmental sustainability modeling is the reliance on indicators that are not directly measurable, such as greenhouse gas (GHG) emissions. These indicators are often estimations rather than exact measurements because it is nearly impossible to measure emissions directly from most sources. Instead, GHG emissions are typically estimated using sophisticated methodologies and reliable data from scientific research. Given the theoretical nature of emissions data, applying simulation modeling to this field is a natural choice. Simulation allows companies to create detailed models of their supply chains, enabling them to estimate environmental impacts more accurately. Moreover, simulation provides the ability to validate these estimations by comparing them with more traditional performance indicators. For example, if a simulation model can accurately replicate a supply chain’s operational or financial historical performance, it can be considered a reliable representation of material and resource flows. By converting these flows using scientifically validated environmental data—such as GHG emission factors and global warming potential (GWP) factors—businesses can accurately assess the carbon footprint of their supply chains. This is achieved by simulating the behavior of the supply chain, considering factors like stock levels, production policies, transportation logistics, and material …

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Utilizing real data from Kenya’s vaccination campaign, this article evaluates performance metrics crucial for effective vaccine dissemination with simulation modelling support.

Utilizing real data from Kenya’s vaccination campaign, this article evaluates performance metrics crucial for effective vaccine dissemination with simulation modelling support.

Utilizing real data from Kenya’s vaccination campaign, this article evaluates performance metrics crucial for effective vaccine dissemination with simulation modelling support.

Utilizing real data from Kenya’s vaccination campaign, this article evaluates performance metrics crucial for effective vaccine dissemination with simulation modelling support.

Master the supply chain basics Navigating Uncertainty safely Introduction to Supply Chain Management: An overview of what supply chain management entails, including its importance, key concepts, and objectives. Supply Chain Components: Exploring the various components of a supply chain, such as suppliers, manufacturers, distributors, retailers, and customers. Supply Chain Planning: Discussing the process of supply chain planning, including demand forecasting, inventory management, and production scheduling. Procurement and Sourcing: Explaining the procurement process, strategic sourcing, supplier selection, and vendor management. Inventory Management: Covering inventory control methods, safety stock, just-in-time (JIT) inventory, and inventory optimization techniques. Transportation and Logistics: Detailing transportation modes, logistics management, freight forwarding, warehousing, and distribution. Supply Chain Technologies: Introducing technologies like RFID, GPS tracking, blockchain, and supply chain management software, and their role in optimizing supply chain operations. Risk Management in the Supply Chain: Discussing risk identification, assessment, mitigation strategies, and contingency planning to manage risks effectively. Supply Chain Sustainability: Exploring sustainable practices in the supply chain, including ethical sourcing, reducing carbon footprint, and waste management. Supply Chain Performance Measurement: Covering key performance indicators (KPIs) for evaluating supply chain performance, such as on-time delivery, inventory turnover, and cost-to-serve. Global Supply Chain Management: Addressing challenges and opportunities in managing global supply chains, including cultural differences, trade regulations, and geopolitical risks. Supply Chain Resilience: Exploring strategies for building resilience in the supply chain to withstand disruptions such as natural disasters, supplier bankruptcies, or geopolitical conflicts. 01 Simulation modelling for disruption analysis and mitigation action testing Learn how simulation modelling can support deeper understanding of disruptions and enable your business to test mitigation actions in a risk-free environment Coming soon! 02 Monitoring risks via probabilistic modelling and bayesian networks Discover how to continuously monitor the risk profile thanks to an Enterprise Risk Management tool based on Bayesian Networks, Direct Acyclic Graphs and conditional probabilities Deep dive 03 Monte carlo method to discover all about risks based on their severity Estimate the potential effect of the power of chaos and randomicity on your supply chain. Empower any static risk analysis with steroids thanks to Monte carlo method Deep dive Photo by John Wardell on flickr

enterprise risk management for resilient supply chain Mitigating risks via Enterprise Risk Management tools Risk management in the supply chain is undoubtfully a very complex topic, requiring a structured approach to effectively navigate the myriad uncertainties and potential disruptions. Without a systematic framework in place, identifying, assessing, and mitigating risks can be daunting tasks fraught with ambiguity. However, leveraging and embracing advanced methodologies and tools based on probabilistic modeling offers a structured means of quantifying and monitoring risks. By integrating probabilistic techniques with data-driven insights, organizations can gain a deeper understanding of the probabilistic dependencies and interrelationships within the supply chain ecosystem. This enables them to proactively identify vulnerabilities, assess the likelihood and impact of various risk events, and implement targeted mitigation strategies to enhance resilience and mitigate the potential impact of disruptions on supply chain operations.   Bayesian Networks for effective risk monitoring In the labyrinth of decision-making, where uncertainties lurk at every corner, Enterprise Risk Management tools, mainly based on Bayesian Networks, emerge as guiding beacons, illuminating pathways through the darkness of ambiguity. At their core lies a mathematical schema that intertwines probability theory with graphical representations, enabling the modeling, inference, and understanding of complex systems.  Conditional Probability: Central to Bayesian networks is conditional probability, a cornerstone of probability theory. It quantifies the likelihood of an event occurring given the occurrence of another event. Mathematically, it’s expressed as: P(A∣B)= P(A∩B)/P(B) Here, P(A∣B) denotes the probability of event A occurring given that event B has occurred, P(A∩B) represents the joint probability of both events A and B occurring, and P(B) is the probability of event B occurring. Graphical Representation: Bayesian networks are epitomized by directed acyclic graphs (DAGs), where nodes represent variables or events. Directed edges between nodes denote probabilistic dependencies or causal relationships. Each node is associated with a conditional probability distribution, quantifying the likelihood of the node’s value given the values of its parent nodes. Conditional Probability Tables (CPTs): Conditional probability tables are the scaffolding upon which Bayesian networks are built. They capture the conditional probabilities of each node given its parent nodes’ values. For nodes with parents, the CPT specifies the probability distribution of the node’s possible states conditioned on the parent nodes’ states. Bayes’ Theorem: Bayes’ theorem, a fundamental principle underpinning Bayesian networks, facilitates the updating of probabilities based on new evidence. Mathematically, it’s expressed as: P(A∣B)=P(B∣A)×P(A)/P(B)​ Here, P(A∣B) is the posterior probability of A given B, P(B∣A) is the likelihood of B given A, and P(A) and P(B) are the prior probabilities of A and B, respectively. The mathematical schema characterizing Bayesian networks embodies the fusion of probability theory, graphical representations, and inferential methodologies. As organizations traverse the landscapes of uncertainty, Bayesian networks stand as formidable allies, guiding them through the complexities of decision-making and illuminating pathways to resilience and innovation. In an era defined by uncertainty, Bayesian networks serve as lighthouses, casting rays of clarity amidst the fog of ambiguity. Main applications of Bayesian Networks Bayesian networks empower diverse applications, from machine learning and artificial intelligence to risk management and diagnostic systems. In this article, we will explore two main usage of such methodology: Supply chain risk management and Predictive maintenance. In the first case the DAG is used to explore the impact of risks on the whole supply chain and simulate plausible combination of events (i.e. a port closure and lack of containers). In the second, DAGs support diagnostic maintenance by leveraging probabilistic inference to identify the root causes of failures and prognostic maintenance by predicting future machinery health. Both the two cases deal with risk management, but at a different level of abstraction. 1. ERM for the supply chain In supply chain risk management, Bayesian networks offer probabilistic modeling, scenario analysis, and decision support, enabling organizations to navigate uncertainties with confidence. They aid in building anti-fragile supply chains by identifying vulnerabilities, dynamically managing risks, and optimizing resilience strategies. Risks, in the supply chain, can derive from many different sources, here shortly summarized. Supplier Reliability: In a manufacturing supply chain, a company relies on multiple suppliers to provide raw materials. However, the reliability of each supplier varies, and disruptions in the supply chain can occur if a critical supplier fails to deliver on time. A Bayesian network can be constructed to assess the reliability of each supplier based on historical performance data, lead times, geographical location, and other relevant factors. By incorporating conditional probabilities, the network can calculate the likelihood of a supplier meeting delivery deadlines or encountering disruptions, enabling the company to prioritize suppliers and implement contingency plans accordingly. Demand Forecasting and Inventory Management: Accurate demand forecasting is essential for optimizing inventory levels and ensuring customer satisfaction. A Bayesian network can be used to model the probabilistic relationships between various factors influencing demand, such as seasonality, marketing campaigns, economic indicators, and competitor activities. By incorporating historical sales data and external variables, the network can generate probabilistic forecasts of future demand. These forecasts can then be used to optimize inventory levels, reduce stockouts, and minimize excess inventory, leading to improved supply chain efficiency and cost savings. Disruptions: Supply chains are vulnerable to various risks, including natural disasters, geopolitical tensions, transportation delays, and quality issues. A Bayesian network can be employed to assess the likelihood and impact of different risks on supply chain operations. By incorporating historical data, expert knowledge, and external factors, the network can quantify the probabilities of various risk events occurring and their potential consequences. This information can then be used to prioritize risks, develop mitigation strategies, and allocate resources effectively to minimize the impact of disruptions on the supply chain. Supplier Selection and Contract Negotiation: When selecting suppliers and negotiating contracts, companies must consider various factors such as cost, quality, lead times, and reliability. A Bayesian network can assist in this process by modeling the probabilistic relationships between supplier attributes and performance metrics. By analyzing historical data and expert assessments, the network can calculate the probabilities of different suppliers meeting performance targets and the expected costs associated with …

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RISK MANAGEMENT IN THE SUPPLY CHAIN Risk management in supply chain Risk management in the supply chain is a multifaceted endeavor aimed at identifying, assessing, and mitigating potential disruptions and uncertainties that could impact the flow of goods and services from suppliers to customers. With global supply chains becoming increasingly complex and interconnected, effective risk management strategies are essential for ensuring operational resilience and continuity. However, as all supply chain are constantly exposed to a long list of potential risks and threaths of different nature, also the management must investigate, estimate, monitor and mitigate constantly such threaths Sources of risks in the supply chain Risk in supply chain management can arise from various sources across the entire supply chain ecosystem. Some of the primary sources of risk include: Supplier Risks: Suppliers play a critical role in the supply chain, and risks associated with suppliers can have significant implications for downstream operations. Supplier risks include disruptions in the supply of raw materials or components, supplier bankruptcies, quality issues, and ethical or compliance violations. Demand Risks: Demand risks stem from uncertainties in customer demand and market dynamics. Fluctuations in consumer preferences, changes in market trends, economic downturns, and unforeseen events (e.g. pandemics) can impact demand forecasting accuracy and lead to excess inventory or stockouts. Logistics and Transportation Risks: Logistics and transportation risks encompass disruptions and challenges related to the movement of goods throughout the supply chain. These risks include delays, capacity constraints, congestion, accidents, infrastructure failures, customs delays, and geopolitical tensions affecting trade routes. Inventory and Stock Management Risks: Poor inventory management practices can introduce various risks into the supply chain, including overstocking, stockouts, obsolescence, and carrying costs. Inaccurate demand forecasting, supply disruptions, and inefficient inventory control processes can exacerbate these risks. Financial Risks: Financial risks in the supply chain encompass factors such as currency fluctuations, payment delays, credit risks, and financial instability among suppliers or customers. Cash flow constraints, liquidity issues, and exchange rate fluctuations can impact the financial health and stability of supply chain partners. Operational Risks: Operational risks arise from internal processes, systems, and capabilities within the organization. These risks include production disruptions, equipment failures, quality control issues, labor shortages, and supply chain complexity. Inadequate contingency planning, lack of redundancies, and reliance on manual processes can heighten operational risks. Regulatory and Compliance Risks: Regulatory and compliance risks stem from non-compliance with laws, regulations, and industry standards governing various aspects of the supply chain. Failure to meet regulatory requirements, environmental regulations, labor laws, or product safety standards can result in fines, penalties, reputational damage, and legal liabilities. Geopolitical and Environmental Risks: Geopolitical factors such as trade policies, tariffs, sanctions, political instability, and regional conflicts can disrupt global supply chains. Environmental risks, including natural disasters, climate change, and sustainability concerns, can also impact supply chain operations by affecting transportation networks, production facilities, and sourcing locations. Sources of risks in the supply chain  Let’s explore some of the most used methodologies to reduce risks in the supply chain: 1. Risk Identification: The first step in supply chain risk management is to identify potential risks. This involves analyzing the entire supply chain ecosystem to identify vulnerabilities, such as supplier dependencies, geopolitical instability, natural disasters, demand fluctuations, quality issues, and transportation delays. 2. Risk Assessment: Once risks are identified, they need to be assessed in terms of their likelihood and potential impact on the supply chain. Various tools and techniques, such as risk matrices, scenario analysis, and risk scoring models, are used to quantitatively and qualitatively evaluate risks based on factors such as probability, severity, and exposure. 3. Risk Mitigation: After assessing risks, mitigation strategies are implemented to reduce their likelihood or impact. Common risk mitigation strategies include: Diversification of suppliers and supply chain partners to reduce dependency on single sources. Inventory optimization and buffer stock management to mitigate supply disruptions. Contractual agreements and risk-sharing mechanisms to allocate responsibilities and liabilities. Implementing robust quality control measures and supplier performance monitoring systems. Investing in technology solutions such as predictive analytics, IoT sensors, and blockchain for enhanced visibility and traceability. 4. Contingency Planning: Despite proactive risk mitigation efforts, disruptions may still occur. Contingency planning involves developing response plans and alternate courses of action to minimize the impact of disruptions when they occur. This includes establishing communication protocols, emergency response teams, alternative sourcing strategies, and backup logistics routes. 5. Continuous Monitoring and Improvement: Supply chain risk management is an ongoing process that requires continuous monitoring and improvement. Organizations should regularly reassess their risk landscape, update mitigation strategies, and incorporate lessons learned from past incidents to enhance resilience and adaptability. 6. Collaborative Risk Management: Collaborative risk management involves fostering partnerships and collaboration across the supply chain ecosystem. By sharing information, best practices, and resources, organizations can collectively identify and address common risks, enhancing the overall resilience of the supply chain network.   01 Simulation modelling for disruption analysis and mitigation action testing Learn how simulation modelling can support deeper understanding of disruptions and enable your business to test mitigation actions in a risk-free environment Coming soon! 02 Monitoring risks via probabilistic modelling and bayesian networks Discover how to continuously monitor the risk profile thanks to an Enterprise Risk Management tool based on Bayesian Networks, Direct Acyclic Graphs and conditional probabilities Deep dive 03 Monte carlo method to discover all about risks based on their severity Estimate the potential effect of the power of chaos and randomicity on your supply chain. Empower any static risk analysis with steroids thanks to Monte carlo method Deep dive