# SCNODE.COM


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- [Planning the supply chain and improving the collaboration in the Contract Manufacturing businesses through simulative digital twin](https://scnode.com/index.php/2023/12/06/contract-manufacturing/): ABSTRACT Operations planning in contract manufacturing presents unique challenges due to the dynamic nature of product life cycles and partnerships. The constant phase-in and phase-out of products, coupled with shifts in manufacturing partners, introduce complexities that hinder smooth operational planning. Additionally, the inherent uncertainty of productive resources, just-in-time adjustments of demand and the lack of seamless communication and system integration further complicate the process. Successfully navigating these challenges requires a proactive and flexible approach from CMOs, emphasizing the adaptability and close collaboration between the contracting company and its manufacturing partners. A software solution, based on a simulative digital twin, can support all phases of the partnerships lifecycles and enhance the collaboration between the counterparts. Download the Full Paper Your Name Your e-mail 23 Dicembre 2023 ABSTRACT Operations planning in contract manufacturing presents unique challenges due… Digital twin 9 Dicembre 2023 Welcome to SCNode.com

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- [Enhancing Master Production Scheduling with simulation](https://scnode.com/index.php/enhancing-master-production-scheduling-with-simulation/): Disvoer how to elevate a company's sustainability game with the innovative approach to Sustainable Supply Chain (SSC) management. Explore a real-world case study in the dairy industry to see this methodology in action.
- [Digital twin standards](https://scnode.com/index.php/digital-twin-standards/): Utilizing real data from Kenya's vaccination campaign, this article evaluates performance metrics crucial for effective vaccine dissemination with simulation modelling support. 
- [Digital Twin for sustainability standards](https://scnode.com/index.php/digital-twin-for-sustainability-standards/): Digital Twin for sustainability standards (ISO and CSRD) The growing focus on industrial sustainability has prompted the creation of numerous standards and certifications, established by international organizations like ISO and global alliances. These frameworks aim to ensure that companies align with global objectives, including limiting temperature increases to 1.5°C, as stated in the Paris Agreement. While adopting these standards demonstrates transparency and commitment to environmental sustainability, many organizations face challenges in practically implementing them. The lack of standardized tools to integrate, compute, and manage the required data creates significant hurdles. Digital twins and simulation models present a transformative solution by providing the structure and analytical power needed to meet these requirements effectively. ISO Standards: Quantitative Approaches to Sustainability ISO 14064-1:2018 – Greenhouse Gas Inventories for Organizations The ISO 14064-1:2018, Greenhouse Gas Inventories for Organizations, provides a framework for quantifying and reporting greenhouse gas (GHG) emissions and removals. The standard emphasizes accuracy, consistency, and reproducibility in its “Quantification Approach” section, stating: “The organization shall select and use quantification methodologies that minimize uncertainty and yield accurate, consistent, and reproducible results.” “GHG emissions or removals can be obtained through measurement or modeling.” “A model is a simplification of physical processes with assumptions and limitations, converting source or sink data into emissions or removals.” These requirements highlight the critical role of digital twins in supply chain modeling. Digital twins help organizations meet these guidelines by offering: Minimization of uncertainty: A simulation model is tailored to the company’s specific characteristics, refined over time to represent actual and expected performance reliably. This process ensures a high degree of accuracy while balancing data availability, modeling effort, and result precision. Consistency in results: Supply chain simulations validate traditional metrics (e.g., costs, service levels, and productivity) against real-world observations. Fine-tuning ensures models align operationally and environmentally, supporting consistent outcomes. Reproducibility over time: Once validated, models can be reused periodically (e.g., monthly or quarterly) to monitor performance and adjust strategies based on evolving company needs. Additionally, simulation models allow: Dynamic scenario analysis: Testing interventions for emissions reductions under varying operational conditions. Granular insights: Identifying inefficiencies across the supply chain, such as energy usage, transportation modes, or resource allocation, to improve overall performance. ISO 14068-1:2023 – Carbon Neutrality The ISO 14068-1:2023, Carbon Neutrality, focuses on achieving and maintaining carbon neutrality through comprehensive planning and robust decision-making. Key elements include: Establishing a carbon neutrality management plan with clear timelines, reduction targets, and methodologies for GHG quantification. Identifying a base period and defining target years for achieving residual GHG reductions, supported by rationale for timing. Developing systems to track and report progress toward carbon neutrality goals. Digital twins address these requirements by: Tracking emissions over time: Simulating current and future emissions scenarios to support effective planning. Integrating financial and environmental data: Aligning carbon neutrality efforts with financial objectives to ensure long-term viability. Scenario-based planning: Evaluating the impact of different strategies to prioritize the most effective interventions. Moreover, digital twins enable organizations to: Monitor real-time performance: Ensuring alignment with both short-term goals and long-term targets. Evaluate systemic impacts: Understanding interdependencies within the supply chain to identify opportunities for simultaneous cost savings and emissions reductions. Validate assumptions: Continuously improving accuracy by refining models with real-world data. CSRD and ESRS: Aligning Strategies with Sustainability Goals The Corporate Sustainability Reporting Directive (CSRD) and its Environmental, Social, and Governance Reporting standards, specifically ESRS E1, require companies to disclose comprehensive information on their environmental impact. These standards push organizations to think systemically and integrate sustainability into their core strategies. Key ESRS E1 Requirements The ESRS E1 framework mandates the disclosure of: A carbon footprint assessment of GHG emissions, covering seven key gases: carbon dioxide (CO₂), methane (CH₄), nitrous oxide (N₂O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride (SF₆), and nitrogen trifluoride (NF₃). Plans to align with the Paris Agreement’s 1.5°C target and achieve carbon neutrality by 2050. An integrated strategy combining sustainability with financial objectives, supported by CapEx and OpEx budgets for decarbonization. Risk assessments related to climate change and organizational resilience under different scenarios. Digital twins serve as essential tools for meeting these requirements by: Integrating supply chain data: Linking production, logistics, and sales data to compute a detailed carbon footprint. Scenario modeling: Simulating interventions to evaluate their impact on emissions and financial performance, identifying optimal strategies. Evaluating trade-offs: Balancing sustainability initiatives with financial goals, such as profitability and operational efficiency. Dynamic resource allocation: Adjusting plans in real-time to address emerging risks or opportunities. Simulating climate resilience: Testing various scenarios to assess supply chain vulnerabilities and develop robust contingency plans. Optimization of sustainability levers: Exploring combinations of actions to maximize environmental and financial performance. Bridging Financial and Non-Financial Metrics A significant innovation introduced by ESRS E1 is the integration of financial and non-financial indicators. This requirement encourages companies to assess the systemic relationship between sustainability and business performance. Digital twins, with their ability to simulate complex interactions, offer the ideal platform for achieving this integration. By modeling the interplay between sustainability measures (e.g., emissions reductions) and business metrics (e.g., costs, revenues, and productivity), digital twins provide insights that are otherwise difficult to uncover. This capability not only supports regulatory compliance but also strengthens strategic decision-making. Conclusion Sustainability standards like ISO, CSRD, and ESRS provide a structured framework for companies to address global environmental challenges. However, the path to compliance requires advanced tools capable of bridging data, strategy, and operations. Digital twins and simulation models empower organizations to navigate this complexity. They provide the means to quantify emissions, plan sustainability initiatives, and align these efforts with financial goals. By adopting these technologies, companies demonstrate their commitment to sustainability while enhancing their operational resilience and competitiveness. 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 Digital twins for sustainability Quantify emissions, plan sustainability initiatives and align with financial goals with digital twins You are here Photo by HD Wallpapers on StockSnap
- [Media Hub](https://scnode.com/index.php/media-hub/): Disvoer how to elevate a company's sustainability game with the innovative approach to Sustainable Supply Chain (SSC) management. Explore a real-world case study in the dairy industry to see this methodology in action.
- [Measuring Supply Chain Carbon Footprint](https://scnode.com/index.php/measuring-supply-chain-carbon-footprint/): 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. You are here 03 Digital twins for sustainability Quantify emissions, plan sustainability initiatives and align with financial goals with digital twins Deep dive Photo by HD Wallpapers on StockSnap
- [Modeling Sustainable Supply Chain](https://scnode.com/index.php/modeling-sustainable-supply-chain/): 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: 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 usage. To effectively apply simulation to sustainable supply chain, it is essential to create a comprehensive digital twin of the system under analysis. This digital twin allows companies to simultaneously simulate both financial and environmental performance. It provides a foundation for exploring different scenarios and assessing how changes in operations might impact both sustainability and profitability. One of the significant advantages of this approach is that, once the model is developed and validated, businesses no longer need … Leggi tutto ""
- [System interaction for closed-loop digital twins](https://scnode.com/index.php/system-interaction-for-closed-loop-digital-twins/): Utilizing real data from Kenya's vaccination campaign, this article evaluates performance metrics crucial for effective vaccine dissemination with simulation modelling support. 
- [Level of abstraction digital twin](https://scnode.com/index.php/level-of-abstraction-digital-twin/): Utilizing real data from Kenya's vaccination campaign, this article evaluates performance metrics crucial for effective vaccine dissemination with simulation modelling support. 
- [Types and characteristics of digital twins](https://scnode.com/index.php/types-and-characteristics-of-digital-twins/): Utilizing real data from Kenya's vaccination campaign, this article evaluates performance metrics crucial for effective vaccine dissemination with simulation modelling support. 
- [Exploring the Dimensions of Digital Twins: Types, Abstraction Levels, and System Interactions](https://scnode.com/index.php/exploring-the-dimensions-of-digital-twins-types-abstraction-levels-and-system-interactions/): Utilizing real data from Kenya's vaccination campaign, this article evaluates performance metrics crucial for effective vaccine dissemination with simulation modelling support. 
- [Resilient warehouse](https://scnode.com/index.php/resilient-warehouse/): Experience the resilience revolution in supply chain management with our exclusive article. Delve into the intricacies of warehouse design, operational strategies, and risk assessment to uncover the secrets of building a bulletproof warehouse. Through three simulation studies, witness the transformation of warehouse optimization, navigating through strategical, tactical, and operational intricacies for operational excellence. Explore the critical importance of risk analysis and discover how businesses can fortify their warehouses against disruptions, ensuring continuity and profitability. Join us as we present a compelling business case from a multinational consumer goods market, showcasing the power of data-driven decision-making in enhancing warehouse resilience.
- [Master the supply chain basics](https://scnode.com/index.php/master-the-supply-chain-basics/): 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](https://scnode.com/index.php/enterprise-risk-management-for-resilient-supply-chain/): 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 … Leggi tutto ""
- [Risk management in the supply chain](https://scnode.com/index.php/risk-management-in-the-supply-chain/): 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
- [Build your best-in-class consulting toolkit](https://scnode.com/index.php/build-your-best-in-class-consulting-toolkit/): Empower your personal Supply chain toolkit to be the rockstar in any project Become a Supply Chain guru with SCNode In the intricate world of supply chain management, efficiency and innovation are paramount. Whether you’re a seasoned professional or just dipping your toes into the complexities of logistics, having access to the right tools and resources can make all the difference. That’s where we can support you with our innovative platform designed to empower professionals like you by providing free supply chain materials and a comprehensive toolkit to streamline your operations. free Innovative content Expertise at Your Fingertips In the fast-paced world of supply chain management, staying ahead requires continuous learning and adaptation. That’s why SCNode is committed to providing you with access to cutting-edge technical materials. Dive into topics such as supply chain analytics, optimization algorithms, and risk management strategies, all presented in a clear and accessible format. Our goal is to equip you with the knowledge and skills needed to tackle even the most challenging supply chain scenarios with confidence.   Read More Discover your Talents Harness the Power of Advanced Analysis In today’s data-driven world, advanced analytical techniques are indispensable for optimizing supply chain performance. From simulation modeling to Monte Carlo analysis and machine learning, SCNode offers in-depth resources and practical business cases to help you leverage these powerful tools effectively. Learn how to simulate different scenarios, analyze risks, and harness the predictive capabilities of machine learning algorithms to drive informed decision-making and achieve tangible results. Read More Learn complex topics easily Simplify Complexity with Ease Navigating through the maze of supply chain intricacies can often feel like solving a puzzle with missing pieces. At SCNode, we address complex topics, but we truly believe in the added value of simplifying complexity. Our curated collection of resources offers practical insights, tips, and strategies to help you unravel the complexities of supply chain management. Whether you’re grappling with inventory optimization, demand forecasting, or network design, our easy-to-understand materials provide valuable guidance every step of the way. Read More FOLLOw us on linkedin free Innovative content Expertise at Your Fingertips In the fast-paced world of supply chain management, staying ahead requires continuous learning and adaptation. That’s why SCNode is committed to providing you with access to cutting-edge technical materials. Dive into topics such as supply chain analytics, optimization algorithms, and risk management strategies, all presented in a clear and accessible format. Our goal is to equip you with the knowledge and skills needed to tackle even the most challenging supply chain scenarios with confidence.   Read More Discover your Talents Harness the Power of Advanced Analysis In today’s data-driven world, advanced analytical techniques are indispensable for optimizing supply chain performance. From simulation modeling to Monte Carlo analysis and machine learning, SCNode offers in-depth resources and practical business cases to help you leverage these powerful tools effectively. Learn how to simulate different scenarios, analyze risks, and harness the predictive capabilities of machine learning algorithms to drive informed decision-making and achieve tangible results. Read More Learn complex topics easily Simplify Complexity with Ease Navigating through the maze of supply chain intricacies can often feel like solving a puzzle with missing pieces. At SCNode, we address complex topics, but we truly believe in the added value of simplifying complexity. Our curated collection of resources offers practical insights, tips, and strategies to help you unravel the complexities of supply chain management. Whether you’re grappling with inventory optimization, demand forecasting, or network design, our easy-to-understand materials provide valuable guidance every step of the way. Read More FOLLOw us on linkedin   Join the SCNode Community Today Whether you’re a supply chain professional, a student, or an aspiring entrepreneur, SCNode is your one-stop destination for supply chain excellence. Join our growing community of like-minded individuals and gain access to a wealth of resources designed to elevate your supply chain expertise. Best of all, everything on our platform is completely free, because we believe that knowledge should be accessible to all. Would you like to share your article on our platform, for free? Contact us now!   Write for us With our three distinct collections of materials, namely “Master the Supply Chain Basics,” “Become a Supply Chain Data Science Master,” and “How to Communicate Complexity Effectively,” we offer tailored content to meet your specific needs and aspirations. Whether you’re just starting out and need to solidify your understanding of fundamental concepts, aiming to delve into the realm of data science to unlock deeper insights, or seeking to enhance your communication skills to navigate complex scenarios with ease, we have you covered. Our commitment to empowering individuals across all levels of expertise in supply chain management drives us to continually expand and refine our resources. By embracing the knowledge and tools available on SCNode, you’ll be equipped to tackle challenges head-on, drive innovation, and optimize efficiencies in your supply chain operations. 01 Master the supply chain BASICS Learn about the ABC of the supply chain management Deep dive 02 Broadening Perspectives: Supply Chain and Beyond Go beyond the ABC of the Supply Chain to provide deeper insights and guidance to your team on more advanced topics in the Supply Chain and its satellite topics Deep dive 03 How to communicate complexity effectively Structured approaches, charts, tips and tricks to address complex problems and to delivering simple, effective presentations  Deep dive Photo by Nick Youngson on Alpha Stock Images
- [How to communicate complexity effectively](https://scnode.com/index.php/effectively-communicating-complexity/): How to communicate complexity effectively Simplifying complexity In today’s fast-paced and dynamic business environment, decision-makers are constantly faced with uncertainty and complexity. Every choice carries the potential for both success and failure, making strategic decision-making a daunting task. However, amidst this uncertainty, there exists a powerful ally – Simulation Modeling. With its “what-if” approach, Simulation Modeling empowers organizations to explore countless scenarios and evaluate any possible outcome, providing invaluable insights to support decision-making processes. While Simulation Modeling offers a sophisticated methodology for analyzing complex systems, its success lies in the challenge to present results and scenarios clearly and effectively. Despite the complexity of the underlying algorithms and simulations, decision-makers require concise and actionable insights to inform their choices. This necessitates a structured approach from initial scenario generation and screening of potential operating alternatives to summarizing and visualizing results, ensuring that decision-makers can easily interpret and compare different scenarios. The Data-driven scenario funnel for optimal corporate positioning In the context of scenario selection, a typical funnel is a structured approach that narrows down a broad set of potential scenarios into a focused subset that warrants further analysis and consideration. The funnel serves as a filtering mechanism, guiding decision-makers through successive stages of evaluation to identify the most relevant and impactful scenarios for strategic decision-making. Here’s a breakdown of a typical funnel in scenario selection: 1. Preliminary analyisis In the initial stages, diverse scenarios are generated considering various factors such as market trends and regulatory changes. Screening criteria are established to assess scenario relevance and feasibility, including alignment with strategic goals. Preliminary evaluation involves a high-level assessment of scenarios, utilizing initial efficiency and efficacy metrics to gauge their potential strategic value. 1. Preliminary scenario generation The process begins with the generation of a wide range of possible scenarios. This may involve brainstorming sessions, data analysis, market research, or scenario modeling techniques. The goal is to capture a diverse set of scenarios that reflect different potential futures and uncertainties. 2. Screening criteria Next, decision-makers establish screening criteria to assess the relevance and feasibility of each scenario. These criteria may include factors such as strategic alignment, feasibility, relevance to business objectives, potential impact, and likelihood of occurrence. 3. Preliminary evaluation Scenarios that meet the screening criteria proceed to a preliminary evaluation stage. Here, decision-makers conduct a high-level assessment of each scenario to gauge its potential implications and relevance. This may involve qualitative analysis, expert judgment, or preliminary modeling to identify key drivers and uncertainties. By following a structured funnel approach, decision-makers can systematically evaluate and prioritize scenarios, enabling them to make informed decisions and navigate uncertainties with confidence. The funnel serves as a roadmap for scenario selection, guiding decision-makers through a series of stages to identify the most relevant and impactful scenarios for strategic planning and decision-making. 2. The first level of deep dive – scenario comparison Leveraging a first pre-screening of the scenarios support in gathering a good understanding of the problems and its constraints, while also streamlining the first level of deep dive by discarding some unfeasible or less interesting options. In the first level of deep dive, the scenarios pictured now undergo two stress test during the first level of deep dive, whose scope is to compare the potential scenarios in order to select even less options. Quantitative Analysis: Selected scenarios undergo a more detailed quantitative analysis to assess their impact on key performance metrics or objectives. This is where simulation modeling, and sensitivity analysis, come into place to quantify the potential outcomes and risks associated with each scenario. Strategic Alignment: Scenarios are evaluated based on their alignment with strategic objectives and priorities. Decision-makers consider how each scenario aligns with the organization’s mission, vision, goals, and values, as well as its potential to create value or mitigate risks. 2.1. The strategical frontier One effective method for presenting the results is through the use of a “strategical frontier” chart. In this chart, the X-axis represents the efficiency of a scenario, while the Y-axis represents the efficacy of the scenario. By plotting various scenarios on this chart, decision-makers can visually compare their performance based on these two key metrics. The concept of “isoquantum” lines can further enhance the utility of the strategical frontier chart. Isoquantum lines connect scenarios that achieve the same payoff, the sum of the level of efficiency or efficacy, allowing decision-makers to identify trade-offs between these two metrics. Additionally, the chart can include a “non-compliance area” to highlight scenarios that fail to meet predefined expectations and should be discarded. By leveraging the strategical frontier chart, decision-makers can quickly identify promising scenarios that offer a good balance between efficiency and efficacy, hopefully better than the AS-IS situation. They can also pinpoint areas of improvement and potential risks by analyzing the distribution of scenarios relative to the isoquantum lines and non-compliance area. This structured approach facilitates informed decision-making, enabling organizations to select strategies that align with their goals and objectives. 3. The second level of deep-dive – anti-fragility test While with the first level deep dive the funnel narrowed some more, the analysis maturity level and the understanding of the problem and constraints should be enough to proceed to stressing further more the remaining scenarios: it is time to conduct the anti-fragility test. Risk Assessment: The remaining scenarios undergo a comprehensive risk assessment to identify potential vulnerabilities, uncertainties, and downside risks. Decision-makers evaluate the likelihood and impact of various risks associated with each scenario and develop mitigation strategies to address them. Final Selection: Based on the results of the evaluation process, a final selection of scenarios is made. These scenarios represent the most relevant, impactful, and strategically significant outcomes for further analysis and consideration. Refinement and Iteration: The selected scenarios may undergo further refinement and iteration as new information becomes available or as strategic priorities evolve. Decision-makers continue to monitor and evaluate the chosen scenarios over time, adapting their strategies and actions as needed.   2.1. Mono and multivariate analysis In the pursuit of strategic excellence, organizations must delve deeper into … Leggi tutto ""
- [Monte Carlo method for anti-fragile supply chain](https://scnode.com/index.php/monte-carlo-method-for-anti-fragile-supply-chain/): monte carlo method for anti-fragile supply chain Navigating Uncertainty safely In an era marked by global disruptions, resilient supply chains have become the cornerstone of organizational success. Traditional supply chain management strategies often struggle to withstand the volatility and uncertainty of today’s world. However, by integrating Monte Carlo methods and simulation modeling, businesses can not only weather disruptions but also emerge stronger – embracing the concept of anti-fragility. Monte Carlo Methods: the foundation of uncertainty management Monte Carlo methods, named after the famed casino city, are a statistical technique used to understand and manage uncertainty in various scenarios. By simulating numerous possible outcomes based on input variables, Monte Carlo methods provide valuable insights into the range of potential outcomes and their probabilities. In the context of supply chain management, Monte Carlo methods offer a strategic advantage. They enable businesses to assess risks, optimize inventory levels, and forecast demand amidst fluctuating market conditions. By quantifying uncertainties, organizations can make informed decisions, minimizing vulnerabilities and maximizing resilience.   Embracing Anti-fragility While traditional supply chains aim for robustness – the ability to resist disruptions – the concept of anti-fragility goes a step further. Anti-fragile systems not only withstand shocks but also thrive in the face of adversity, gaining strength from volatility. Monte Carlo methods play a pivotal role in the journey towards anti-fragility. By systematically analyzing risks and uncertainties, businesses can identify areas of vulnerability and proactively implement measures to enhance resilience. From supplier diversification to dynamic inventory management, Monte Carlo methods empower organizations to adapt and thrive amidst uncertainty.   The Synergy of Monte Carlo Methods and Simulation Modeling While Monte Carlo methods excel at uncertainty quantification, simulation modeling takes resilience a step further by providing a dynamic framework for scenario analysis. By combining these two approaches, businesses can unlock a new realm of strategic insights and decision-making capabilities. Simulation modeling extends the capabilities of Monte Carlo methods by incorporating dynamic interactions and feedback loops within supply chain systems. It enables businesses to simulate various scenarios, assess their impact in real-time, and identify optimal strategies for mitigating risks and enhancing resilience. Creating Anti-Fragile Supply Chains: A Case for Integration Imagine a scenario where a global pandemic disrupts supply chains worldwide. By leveraging Monte Carlo methods and simulation modeling, businesses can simulate the potential impacts of such disruptions, identify critical vulnerabilities, and implement agile strategies to mitigate risks. From predictive demand forecasting to real-time supply chain optimization, the integration of Monte Carlo methods and simulation modeling empowers organizations to build anti-fragile supply chains capable of thriving in an uncertain world. By embracing uncertainty as an opportunity for innovation and adaptation, businesses can turn volatility into a competitive advantage. In conclusion, Monte Carlo methods and simulation modeling represent powerful tools in the pursuit of supply chain resilience and anti-fragility. By harnessing the synergies between these approaches, businesses can navigate uncertainty with confidence, ensuring continuity and sustainability in an ever-changing world. 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
- [Hardware and software for prescriptive maintenance](https://scnode.com/index.php/hardware-and-software-for-prescriptive-maintenance/): Discover the evolution of maintenance strategies, transitioning from reactive approaches to sophisticated prescriptive plans in this insightful article. Delve into the hardware and software essentials at every readiness level, from basic plug-and-play solutions to advanced prescriptive maintenance, leveraging cutting-edge technologies like simulation models, AI, and machine learning. Plug-and-play solutions boast standard sensors with embedded 5G/LTE antennas and software for data gathering, offering advantages such as ease of deployment, cost-effectiveness, and scalability. However, for competitive edges, customized predictive maintenance solutions shine, utilizing tailored machine learning algorithms and AI to enhance decision-making and reduce downtime, albeit with higher upfront costs and longer timelines. Despite challenges, the paper presents a comprehensive business case for implementing advanced prescriptive maintenance in the energy sector, aligning with the traditional Automation Pyramid framework. From on-field sensors to cloud infrastructure and beyond, witness how this innovative approach revolutionizes operational efficiency, minimizing downtime, and optimizing resource utilization to unprecedented levels.
- [Vaccine supply chain](https://scnode.com/index.php/vaccine-supply-chain/): Diving deep into the labyrinth of the COVID-19 pandemic, this article unveils the intricate dance between vaccine development and global distribution strategies. With a keen eye on the breakneck speed of vaccine creation, it navigates through the complexities of ensuring fair and efficient access worldwide. From the delicate balance of supply and demand to the logistical hurdles of maintaining the cold chain, every facet of the vaccine journey is dissected. Delving into the heart of the supply chain structure, it exposes the deceptive simplicity of nodes contrasted with the intricate dance required for distribution. But the challenges don't end there; the last mile delivery, particularly in lower-income nations, emerges as a focal point of struggle. Enter simulation modeling - the article introduces it as the beacon illuminating strategic decisions, navigating through hypothetical scenarios to uncover optimal pathways. As the simulation unfolds, it sheds light on bottlenecks, inventory nuances, and the economic ripples of every choice made. Ultimately, it champions the systemic perspective granted by simulation modeling, transcending the confines of traditional planning methods. In a world gripped by crisis, this paper advocates for knowledge-driven strategies to chart a course through the storm.
- [Cookie policy](https://scnode.com/index.php/cookie-policy/): Cookie Privacy policy Welcome to SCNode (“we” or “us” or “our”). We are committed to protecting the privacy and security of your personal information. This Cookie Privacy Policy explains how we use cookies and similar technologies to recognize you when you visit our website at https://scnode.com/ (“Website”). It explains what these technologies are and why we use them, as well as your rights to control our use of them. What are cookies? Cookies are small data files that are placed on your computer or mobile device when you visit a website. Cookies are widely used by website owners in order to make their websites work, or to work more efficiently, as well as to provide reporting information. How do we use cookies? We use cookies to: Make our Website function properly. Enable functionality and enhance your user experience. Analyze how our Website is used and how it performs. Provide personalized advertising. What types of cookies do we use? Essential cookies: These cookies are necessary for the website to function properly. They enable basic functions like page navigation and access to secure areas of the website. The website cannot function properly without these cookies. Analytics cookies: These cookies allow us to analyze your use of the Website, including which pages you visit, how long you stay on each page, and what links you click on. This information helps us understand how our Website is used and how we can improve it. Advertising cookies: These cookies are used to make advertising messages more relevant to you. They perform functions like preventing the same ad from continuously reappearing, ensuring that ads are properly displayed for advertisers, and in some cases selecting advertisements that are based on your interests. Functionality cookies: These cookies enable us to remember choices you make when you use the Website, such as remembering your login details or language preference. The purpose of these cookies is to provide you with a more personal experience and to avoid you having to re-enter your preferences every time you visit the Website. How can you control cookies? You have the right to decide whether to accept or reject cookies. You can exercise your cookie preferences by clicking on the cookie settings link located at the bottom of the page. You can also set or amend your web browser controls to accept or refuse cookies. If you choose to reject cookies, you may still use our Website though your access to some functionality and areas of our Website may be restricted. Changes to this Cookie Privacy Policy We may update our Cookie Privacy Policy from time to time. Any changes we make will be posted on this page. We will also provide you with additional notice if the changes are significant and require your consent. Contact Us If you have any questions about our use of cookies or this Cookie Privacy Policy, please contact us at info@scnode.com
- [Terms & Conditions](https://scnode.com/index.php/terms-conditions/): Last updated: March 18, 2024 Please read these terms and conditions carefully before using Our Service. Interpretation and Definitions Interpretation The words of which the initial letter is capitalized have meanings defined under the following conditions. The following definitions shall have the same meaning regardless of whether they appear in singular or in plural. Definitions For the purposes of these Terms and Conditions: Affiliate means an entity that controls, is controlled by or is under common control with a party, where “control” means ownership of 50% or more of the shares, equity interest or other securities entitled to vote for election of directors or other managing authority. Country refers to: Italy Company (referred to as either “the Company”, “We”, “Us” or “Our” in this Agreement) refers to SCNODE, Italy, Milan. Device means any device that can access the Service such as a computer, a cellphone or a digital tablet. Service refers to the Website. Terms and Conditions (also referred as “Terms”) mean these Terms and Conditions that form the entire agreement between You and the Company regarding the use of the Service.  Third-party Social Media Service means any services or content (including data, information, products or services) provided by a third-party that may be displayed, included or made available by the Service. Website refers to SCNODE, accessible from https://scnode.com/ You means the individual accessing or using the Service, or the company, or other legal entity on behalf of which such individual is accessing or using the Service, as applicable. Acknowledgment These are the Terms and Conditions governing the use of this Service and the agreement that operates between You and the Company. These Terms and Conditions set out the rights and obligations of all users regarding the use of the Service. Your access to and use of the Service is conditioned on Your acceptance of and compliance with these Terms and Conditions. These Terms and Conditions apply to all visitors, users and others who access or use the Service. By accessing or using the Service You agree to be bound by these Terms and Conditions. If You disagree with any part of these Terms and Conditions then You may not access the Service. Your access to and use of the Service is also conditioned on Your acceptance of and compliance with the Privacy Policy of the Company. Our Privacy Policy describes Our policies and procedures on the collection, use and disclosure of Your personal information when You use the Application or the Website and tells You about Your privacy rights and how the law protects You. Please read Our Privacy Policy carefully before using Our Service. Links to Other Websites Our Service may contain links to third-party web sites or services that are not owned or controlled by the Company. The Company has no control over, and assumes no responsibility for, the content, privacy policies, or practices of any third party web sites or services. You further acknowledge and agree that the Company shall not be responsible or liable, directly or indirectly, for any damage or loss caused or alleged to be caused by or in connection with the use of or reliance on any such content, goods or services available on or through any such web sites or services. We strongly advise You to read the terms and conditions and privacy policies of any third-party web sites or services that You visit. Termination We may terminate or suspend Your access immediately, without prior notice or liability, for any reason whatsoever, including without limitation if You breach these Terms and Conditions. Upon termination, Your right to use the Service will cease immediately. Limitation of Liability To the maximum extent permitted by applicable law, in no event shall the Company or its suppliers be liable for any special, incidental, indirect, or consequential damages whatsoever (including, but not limited to, damages for loss of profits, loss of data or other information, for business interruption, for personal injury, loss of privacy arising out of or in any way related to the use of or inability to use the Service, third-party software and/or third-party hardware used with the Service, or otherwise in connection with any provision of this Terms), even if the Company or any supplier has been advised of the possibility of such damages and even if the remedy fails of its essential purpose. Some states do not allow the exclusion of implied warranties or limitation of liability for incidental or consequential damages, which means that some of the above limitations may not apply. In these states, each party’s liability will be limited to the greatest extent permitted by law. Governing Law The laws of the Country, excluding its conflicts of law rules, shall govern this Terms and Your use of the Service. Your use of the Application may also be subject to other local, state, national, or international laws. Disputes Resolution If You have any concern or dispute about the Service, You agree to first try to resolve the dispute informally by contacting the Company. For European Union (EU) Users If You are a European Union consumer, you will benefit from any mandatory provisions of the law of the country in which You are resident. Changes to These Terms and Conditions We reserve the right, at Our sole discretion, to modify or replace these Terms at any time. If a revision is material We will make reasonable efforts to provide at least 30 days’ notice prior to any new terms taking effect. What constitutes a material change will be determined at Our sole discretion. By continuing to access or use Our Service after those revisions become effective, You agree to be bound by the revised terms. If You do not agree to the new terms, in whole or in part, please stop using the website and the Service. Intellectual Property The Service and its original content, features and functionality are and will remain the exclusive property of SCNode and its licensors. The Service is protected by copyright, trademark, and other laws … Leggi tutto ""
- [Humanitarian Supply Chain](https://scnode.com/index.php/humanitarian-supply-chain/): Resilient Humanitarian Supply Chain: A Simulation Study of COVID-19 Vaccine Logistics in Kenya ABSTRACT The paper presents a case study employing simulation modeling to analyze and improve a Humanitarian Supply Chain (HSC) dealing with the distribution system of the Pfizer-BioNTech COVID-19 vaccine in Kenya. The study explores the existing distribution network (As Is model) and proposes two alternative configurations (To Be models) based on global health organizations’ guidelines. The simulation utilizes actual data from Kenya’s COVID-19 vaccination campaign and the national vaccine distribution network. The context of the study is Kenya, a low-middle income country in East Africa, divided into 47 counties. The As Is model reflects the current vaccine distribution structure, involving vaccine production factories, a national depot in Kitengela, regional depots, and hospitals. To Be scenarios consider variations in the number and positioning of central depots, focusing on proximity or centralization. The simulation assesses key performance indicators, including demand coverage, flexibility of depots, fleet utilization, and fulfillment time. The study aims to enhance the efficiency and effectiveness of the humanitarian supply chain, emphasizing fair and rapid vaccine access. The To Be 2 scenario, featuring a centralized distribution with increased transport strength, outperforms others, achieving a higher service level (99.7%) and reducing vaccine inventory in the network. This is facilitated by innovations such as aerial drones for remote deliveries, which prove effective in mitigating uncertainties in transport times. Results highlight the sensitivity of the As Is and To Be 1 scenarios to variations in system parameters, emphasizing the need for a robust supply chain configuration. The study underscores the importance of simulation models in testing proposed improvements, complementing analytical optimization methods. The research contributes to the exploration of alternative distribution network designs in humanitarian contexts and underscores the necessity for further modeling and analysis to enhance the resilience and efficiency of vaccine distribution systems Download the full paper Abilita JavaScript nel browser per completare questo modulo.Abilita JavaScript nel browser per completare questo modulo.Full name *Email * Download about the authors Pietro Negri Supply chain consultant BROWSE THROUGH OUR PAPER HISTORY DIGITAL TWIN Leveraging dynamic production policies: a Textile Digital Twin sustainable supply chain Greening the Chain Humanitarian Supply Chain COVID-19 Vaccine Logistics in Kenya Prescriptive maintenance Prescriptive maintenance from mirage to reality: an energy business case Vaccine Supply chain How to manage global cold chains Process design How to build a resilient warehouse MPS with Simulation Enhancing Master Production Schedule with Simulation More papers coming soon…
- [Applying Simulation to S&OP Process](https://scnode.com/index.php/applying-simulation-to-sop-process/): Utilizing real data from Kenya's vaccination campaign, this article evaluates performance metrics crucial for effective vaccine dissemination with simulation modelling support. 
- [Simulation modelling roadmap: beginning a digital journey](https://scnode.com/index.php/simulation-roadmap-beginning-digital-journey/): Simulation modelling roadmap: beginning a digital journey As we have mentioned in previous articles, simulation turns out to be an important decision support tool in SCM fields, where a 360-degree view of the possible impacts of decision alternatives is required. In this article we will analyze the main phases inherent to the development of a model to support a generic SCM process. Developing the model The simulation model is created on a one-off basis as the model, which represents the real functioning of the supply chain (process) considered, will not be subject to substantial changes during its existence unless structural changes occur in the analyzed business context. The creation of a simulation model for an SCM process involves 5 phases: requirements collection, conceptual design, model development, testing, scenario assesment.  1. Requirements collection Establishing the correct perimeter of the supply chain that you want to analyze and model is essential for the successful outcome and use of the tool. In this phase it is necessary to define the macro characteristic of the supply chain of interest.Without the presumption of being exhaustive, below we report some areas of requisites collection. 1.1 Layers and actors of the supply chain First of all is necessary to point out the number of levels, upstream and downstream,which need to be mapped in the model. This activity requires an analysis of the main suppliers and customers, as well as a careful study of the existing production and distribution network.  As regards suppliers, it is necessary for example to understand which suppliers are critical and/or have the most impact on procurement decisions and possibly extend the scope also to more upstream levels (suppliers of suppliers). Let’s think, for example, of a car manufacturer interested in developing a simulation tool to support the sales and operations planning (S&OP) process: an important choice could concern the mapping of microchip suppliers (highly critical in the value chain) and the mapping of the underlying market of semiconductors. This can be particularly useful for evaluating the effects that upheavals in the semiconductor market can have on the final automotive market, helping management to structure parallel sourcing or risk containment strategies to guarantee production continuity. The same reasoning can be extended to number of downstream actors: in B2B contexts it could be useful to map customers’ customers to evaluate the impacts that changes in final product demand  may carry on company operations. This obviously requires a in-depth knowledge of the mechanisms of demand propagation in the upstream levels of the supply chain as well as expected distribution of demand over time and its variation depending on characteristic market parameters. In addition to the world of demand and supply, it is then necessary to carefully consider the existing distribution and production network: given the growing complexity of these networks, mainly due to the use of outsourcing of particular production phases (contracting) and distribution (use of logistics operators), it is critical to also monitor phases and processes in the model that are not directly under company control.   1.2 Products, components and raw materials Mapping the master data of finished products, semi-finished products and raw materials constitutes another important input for the construction of the model. Depending on the context to be modeled, it may be necessary to map the single finished product or serial production code (in the case of make-to-order productio), while in other cases it may be sufficient to limit the analysis to the product family (as often happens in traditional tools of S&OP). The choice of the level of granularity in this case critically impacts the quality of the results produced by the model as well as the computational time necessary to develop the different simulation analyses. Consequently to the definition of the products and components, it is also necessary to define which are the bill of materials (BOMs) to be considered in the model, in order to link the different product codes together according to supply-demand mechanisms. 1.3 Resources and capabilities Usually in high-level decisions there is no need to consider the individual resources of the supply chain (such as vehicles, personnel, machinery, etc.) but it is preferred to aggregate their meaning in the term of capacity, understood as the sum of the availability of the individual resources. Considering supply chain capacity as dependent on the constraints present is essential to produce eligible results and provide important points of analysis. While traditionally in strategic SCM processes, such as S&OP, the match between supply and demand is done with infinite capacity, in the simulation model it is already possible to define macro capacity constraints to consider the structural limits of the current supply chain. Setting capacity does not only mean defining medium-term production capacity, but also considering other important capacity constraints that can affect the balance between supply and demand.Let’s think for example about the distribution capacity of the network: in some cases it may be necessary to explain the storage capacity of critical nodes of the network to verify compliance over time and evaluate eventual temporary increases (for example through the rental of storage spaces). In other contexts, however, it could be advantageous to consider the limits of transport capacity, if the company has its own fleet of vehicles or relies on logistics service providers with limited capacity (think for example of the availability of maritime journeys and containers, seriously affected by changes and oscillations in recent years). In summary, considering the different types of capacity (production, distribution, storage) can be particularly useful both for carrying out a correct balance between supply and demand and for evaluating in the scenarios the effects of potential risks impacting these macro-variables (think for example to the analysis of the impact of a fire in a warehouse, a strike in the transport sector, an interruption in a maritime course, etc.). 1.4 Planning policies Defining the policies on which the company conducts its business can in some cases be a difficult abstraction of the way in which people, processes, technology and machines operate. However, planning … Leggi tutto ""
- [Skills and technologies](https://scnode.com/index.php/skills-and-technologies/): Skills and technologies A determining factor in the success or failure of a project for the creation of a simulation model is certainly the degree of knowledge of both the business context and the software to be used, which clearly identify two different set of skills. The first category of skills, related to the business dimension, often includes a mix of professional figures, partly relating to the business object of the simulation (e.g. supply chain manager, operation manager, etc.) and partly coming from specific professional fields (such as universities, research, consultancy firms, etc.). Moving on to the second category of modeling skills we need to make some clarification: everyone has modeling skills, as our brain leads us to reason daily with patterns and models that we learn over time.However, in order to correctly deal with simulation, the need to seek for a very analytical and objective approach arises. The objectivity requested to the modelizer is usually insufficient in those who live daily in contact with the reality or problem to be modeled. This is precisely due to the “contamination” that reality exerts on our perception: a person accustomed to work and operate according to a certain scheme and with a predetermined set of information will most likely tend to model reality according to his/her own mental scheme. This type of cognitive bias can be particularly dangerous when modeling complex situations, where the imposition of a partially objective modeling scheme can lead to overlooking important variables or links that have a non-negligible effect on the outcomes of interest. Therefore, when taking about creating the conceptual model, it is best to rely on external experts who, based on the experiential support of the business people, are able to analyze the situation “from the outside”, guaranteeing the correct threshold of objectivity required for the analysis of the problem . The ability to rationalize and objectify the problem or reality is often acquired through experience in carrying out simulation projects: the need to translate processes, resources and information into virtual objects through the use of technical resources (software, programming, etc.) leads the modeling expert to develop the right mindset over time and to understand how to represent any situation (real or abstract) with the right technique and above all with the adequate effort and level of detail. In summary we can conclude that the resources necessary for the correct implementation of a dynamic simulation project must involve a small number of modeling experts (variable based on the extension of the perimeter) and a fairly varied audience of actors belonging to the business world, in order to capture different aspects and particularities of the situation to be modeled. The topic of technical simulation skills inevitably opens up the discussion of the tools necessary for model development. At the state of the art, there are various software applications that allow even users who are not experts in IT and programming to develop simulation models. Obviously these applications must be managed by an expert user in order to create a model that is reliable and usable as a decision support tool.We recommend to practice by starting to develop simple models and then increasing and transferring knowledge into the business environment. For what concerns hardware, for a generic project of medium complexity it can be easily managed on a personal computer with standard performance, as simulation software usually requires as its main requirement the availability of correct RAM memory to be able to operate and iterate all the variable calculation steps.This lead us to conclude that the software and hardware aspects of a simulation project are not critical, making it manageable on traditional platforms (such as personal computers). It should also be noted that the license for the use of simulation software for professional and/or commercial purposes has a fair cost and its purchase by a company it is not always justifiable. For this reason, simulation projects, at least in the first applications, are usually outsourced to consultancy companies or professionals capable of providing what is requested with a reasonable effort. In the most advanced and widespread applications, where the simulation model is usually used to interact directly with other company information systems, it may instead be necessary to purchase one or more licenses of the simulation software to enable company users the operability on the model(s) realized. In summary we can conclude that for pilot projects in the field of simulation or in any case for applications with limited complexity, it is best to outsource the design and creation of the model as the implementation effort is lower than the cost of acquiring licenses and skills. On the contrary, when opting to include one or more simulation models within the corporate digital strategy that must be used on a regular basis or which involve a high degree of integration with the technological infrastructure, it is better to acquire or build the appropriate skills in the company. This objective, which is much more difficult than the development of a model, can be achieved with the assistance, in the initial developments, by professionals/trainers who can teach resources the rudiments of simulation modeling as well as how to design the necessary models. 01 Problem solving in supply chain processes Problem by problem: Analytical methods and Dynamic Simulation compared Deep dive 02 Dynamic simulation as decision support How to choose the right simulation methodology for a tailor made approach to reliable data-driven decision 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
- [Simulation dictionary](https://scnode.com/index.php/simulation-dictionary/): Simulation dictionary: Models, Scenarios, Simulations and Digital Twins In previous articles we have repeatedly mentioned the words “models”, “scenarios” and “simulation(s)” without however giving a clear definition, which can help in understanding the functioning of the simulation model within Supply Chain processes. 1. Models With the term “model” we entail the representation of an object, entity or process in an artificial (virtual) environment, capable of reproducing its functioning and performance according to a certain degree of reliability . This definition therefore allows us to state that a model supporting a generic SCM process, whether based on simulation or not, must replicate the input data flows (sales plan, production plan, financial objectives, etc.), process them correctly and provide the decision maker with a vision of the expected results. The definition of model also introduces some important concepts which we summarize below: A model constitutes a representation of something observable/perceptible in reality: just as the same object can be represented in various ways depending on the painter who creates the painting, the way the modeler decides to represent the observed phenomenon is often subjective and it lends itself to the interpretation of the person who creates the model and to his perception of reality. Therefore it is important that in the phase of defining the model requirements there is always an expert figure about the reality studied, whether linked to the business or technical field. The phenomenon represented by the model can be of any type: from a physical object (e.g. the functioning of a warehouse or a factory) to an abstract process (e.g. the phases of the management process of a tender). The description of the phenomenon to be modeled must be as framed as possible, to avoid having to include factors, variables and interactions in the model that are not necessary for its analysis. The environment in which the model is conceived, developed and simulated is purely artificial, or better yet, virtual: while for a model of a building there are different implementation environments (from CAD software to 3D printing of the model), the simulation model exists only virtually and must therefore be developed using specific software. If on the one hand the virtuality of the simulation model does not allow easy recognition of the effort and complexity necessary to create it, on the other hand it enables a key advantage of this type of applications: the possibility of creating copies of the model on which is possible to make experiments with variations, additions, changes, etc. at zero cost and without having any type of repercussion on the reality analyzed. The difficulty and burden of creating a precise simulation model is therefore compensated by the fact that it can be modified freely and infinitely without incurring any danger or additional cost. This advantage leads the decision maker to adopt a new type of decision-making approach based on experimentation (trial and error approach) at zero cost and time. This approach allows to test a multitude of alternative ideas and solutions and to direct the decision maker’s mind towards non-standard solution paths. These way of reasoning is in any case different from the traditional decision making mindset, with evident benefits regarding effectiveness and efficiency of the implemented solutions. The model must reproduce the functioning of the observed phenomenon: this presupposes in-depth knowledge of how the phenomenon operates. However, although the functioning of processes, companies, etc. is often known, the results often tend to deviate from those expected. This is mainly due to the presence of stochastic and irrational factors within any type of phenomenon. In the simulation model, unlike other applications, it is also possible to take these biases into consideration and study their effect on the results, to establish effective containment or abatement strategies. The creation of a simulation model requires establishing a priori the appropriate level of detail to analyze the phenomenon. This is a key aspect as it affects on the one hand the quality of the results provided by the model and on the other the time and design effort for its implementation. Therefore, even for simulation, as for any project, there is a trade-off between quality and time (cost) of implementation: a clear definition of how the model must work for determining the results is fundamental for the correct estimate of the development time. Having briefly clarified the fundamental concepts underlying the definition of model, we then move on to analyze how many and which models may be necessary depending on the analysis needs. We can distinguish two main cases: the one inherent to the creation of a single model and the one which instead requires the creation of multiple models. 1.1 Unique model In the case of single development, it is necessary to create a model because we want to know the state of the art and the performance of a phenomenon (usually process) that may exist or still be created. An application example may regards a situation where management needs to obtain information regarding company processes that are not able to be monitored correctly through the existing information systems and technologies.Let’s think for example about monitoring and estimating CO2 equivalent emissions in relation to a supply chain: it is unthinkable to analytically measure emissions at each step of the supply chain and for each process. However, in order to have an estimate of the environmental impact and what to do to mitigate it, the creation of a simulation model can represent the right application for the calculation and tracking of all emissions, both direct (scope 1) and indirect (scope 2 and 3). In others situations in which the model concerns a process that does not yet exist, it is clear that the simulation in this case not only performs an analysis function but also a design function of the characteristics of the new process. A classic example of this use concerns the design and subsequent set up of a supply chain starting from scratch (for example for the marketing of a new product or for … Leggi tutto ""
- [Dynamic simulation as decision support tool](https://scnode.com/index.php/dynamic-simulation-as-decision-support-tool/): Dynamic simulation as decision support tool So far we have mentioned how simulation presents itself as an alternative tool to the traditional problem solving methods used by decision makers within the various SCM processes. Let us now go into the detail of this application to evaluate the main aspects inherent to the application of this methodology in the SCM context: how is a simulation model developed? What is needed for its operation? How is it actually used? In the next paragraphs we will answer these and other questions, trying to provide a complete discussion of all the aspects inherent to the implementation of a dynamic simulation model for the governance of decisions made within SCM processes. Which type of simulation? When talking about simulation, we are considering a vast range of methodologies and applications that are used in very different fields. For example, FEM analysis (Finite Elements analysis), which is often carried out during the design and prototyping of a new product to test its mechanical properties in advance, represents a particular use of simulation. In cases of application of simulation to the supply chain, we can summarize three different approaches existing in the literature:   1. System Dynamics System dynamics is a modeling approach that captures the dynamic interrelationships within complex systems over time. It employs feedback loops, stocks, and flows to represent the system’s structure and behavior, allowing for the simulation of various scenarios. Developed by Jay W. Forrester of MIT, this methodology is widely used in areas such as business, economics, and environmental studies. System Dynamics aids in understanding and predicting system behavior, facilitating strategic decision-making by illustrating how changes in one part of the system can reverberate throughout the entire interconnected system. A simple example of most used System Dynamics models are SIR (Susceptible, Infectious, Recovered) epidemiologic models. 2. Agent-Based modelling Agent Based Modeling: considers groups (clusters) of agents and how they influence each other depending on the relationships that bind them. A cluster is representQative of the (appropriately simplified) behavior of a certain object or individual, therefore the level of detail of this approach can vary widely depending on how much of the real behavior of individuals needs to be represented. Traditional applications of this methodology concern epidemiological models for estimating the market share and/or level of adoption of new products within a closed (market) system. 3. Discrete Event Discrete Event Modeling: it is the simulation method that allows you to reach the maximum level of detail, whereby each object in reality is represented as an entity , depicted by its own typical parameters and its own operating and interaction logics. It is often used to solve complex problems relating to the branch of queuing theory, whereby the analyzed process is broken down into its main elements in order to determine the expected performance when the stochastic behavior of the parameters and inputs to the system varies (e.g. queue of customers at a post office). To select the correct simulation method, a first critical decisional level is the level of abstraction of the methods. In facts, each method serves a specific range of abstraction levels, and a modeler should be deciding upon which method to use based on the problem’s characteristics. System Dynamics (SD): System Dynamics operates at a high level of abstraction, focusing on the overall structure and behavior of a system. It represents key variables and their interconnections using causal loop diagrams (CLDs) and Stock and Flows Diagrams (SFDs), emphasizing feedback loops and accumulations. SD provides a holistic view of system dynamics but may simplify individual components, behavior, and intricate interactions. In facts, the system behavior is defined by simple parameters interactions and mathematical relationships of parameters. For example, in an SD environmental model, the model can estimate the resource cost over time (e.g. oil) based on a mathematical the interaction of other parameters (e.g. oil extraction rate, oil consumption), but does not allow to detail the consumption in the whole world (e.g. considering tariffs) nor define different consumer behaviors (e.g. drivers in the US tend to make more miles by car than people in the Netherlands). Agent Based Modelling can overcome the SD limitations. Agent-Based Modeling (ABM): Agent-Based Modeling allows for a more granular level of abstraction by simulating individual agents with unique characteristics and behaviors. ABM captures the heterogeneity and autonomy of entities within a system, offering a detailed perspective on how individual components interact. This method provides a nuanced understanding of emergent patterns and behaviors, making it suitable for studying complex systems with diverse actors. Bringing back the example from SD, in this case a modeler would be able to define oil consumers from the US and from the Netherlands. Eventually, both of them could be clustered in sub-clusters based on their wealth or location (e.g. people living in big cities run less miles). This is configurable, up to each single entity behavioral simulation. In this case, oil consumption can be estimated with more precision compared to SD, and the model can predict the future consumption based on the people elasticity to price changes. Discrete Event Modeling (DEM): Discrete Event Modeling focuses on specific occurrences or events within a system, providing a detailed level of abstraction. It is best used to represents processes as sequences of discrete events, capturing the timing and order of activities. DE specifically focuses on process flows, making it the best method to address Supply Chain problems, which can be broken down in many sub-processes (e.g. logistics, storage, manufacturing…). While also Discrete Events allow the creation of agents, it is important to clarify the difference between Discrete Event and ABM agents. In DE, there are two types of agents: passive and active agents. In ABM, agents are only active due to the fact that each agent has its own defined behavior and interact with each other.  To give an example of “passive” agent in DE, we can think of a production order or a lot in a manufacturing simulation model. This agent is made of … Leggi tutto ""
- [Problem solving in supply chain processes](https://scnode.com/index.php/problem-solving-scm/): PROBLEM SOLVING IN SUPPLY CHAIN PROCESSES Modern supply chains are identified by a high degree of complexity, which may be divided into some fundamental aspects: articulated production and distribution processes; characteristic parameters subject to variability; risks of various kinds; physical, procedural or conceptual constraints; changing behavior of system variables over time. In general we can state that the processes of Supply Chain Management (SCM) field, such as planning, production, logistics, transportation and so on entail data-driven decision-making situations. Data must be appropriately collected and organized in databases to be subsequently converted into useful information through processing and visualization tools. However, “traditional” data processing (for example by exploiting reports) may not be sufficient to support the decision-making process: the consequences deriving from a decision have in fact a systemic effect on many other company areas.Understanding the effects on different company performances then becames simultaneousluy necessary and onerous. Choosing the right approach to represent and manage supply chain complexity can therefore prove to be a decisive factor for the decision-making effectiveness of SCM processes. Among the most commonly used and recognized methods for evaluating the effects of a decision there are: analytical methods and dynamic simulation. Let’s analyze them briefly. Analytical Methods Analytical methods represent the problem as a model of equations which include  the variables to be optimized. These equations are solved using some solving algorithms such as, for example, CPLEX. Equations cannot be excessively complex: for this reason the constraints and variables of a given problem (which are deterministic and non-stochastic in nature) must be simplified and standardized as much as possible. The time interval considered is an integer, discretized dimension (subject to the so called bucketing) and the events are static. The analytical method provides the decision maker with a black box view of the model’s solutions: there is visibility only on the input and output data, while understanding how a certain result was obtained may not be simple or intuitive. Finally, when optimizing the parameters considered, a predefined set of objectives is normally considered, typically of an economic nature. Dynamic Simulation In the case of simulation, the problem considered is modeled at user’s discretion, through a series of entities that interact with each other through cause-effect relationships. The model obtained describes, over time, how the entities behave and what types of interactions they will establish with a very high level of detail (e.g. individual operators). In this case time, unlike the analytical method, is represented as a continuous dimension. The simulation process does not have the task of optimizing any variable: on the contrary, it allows the user to understand the dynamics that regulate the model and verifies the performance based on the hypotheses provided, commonly called scenarios. Choosing the right method for supporting decision making Which method should then be used to make the decision-making process effective?  There is no single answer: any tool, whether dictated by experience, an electronic spreadsheet, an analytical method orsimulation, can be more or less useful for the purpose. However, you can think of using two metrics to evaluate the choice of the most suitable tool: speed of problem resolution and precision of the result achieved.  The first concerns the time necessary to logically formulate the problem and to find the solution, while the second concerns the reliability of the results obtained and the ability of the tool to report the complexity of what is being considered. It is possible to briefly present the main problem solving tools as shown below, with their relative advantages and disadvantages linked to their use. The choice between a simulation-based method and an analytical method is not exclusive: in some cases, using both tools combined allows you to achieve the best results. One could think of analyzing a decision using an analytical approach and then developing alternative scenarios using simulation or vice versa, generating sub-optimal scenarios for a given problem through simulation and then moving on choosing the optimal scenario using an analytical method . Some typical decisions of SCM processes that find valid decision support in simulation may concern: Design (or re-design) of the supply chain: if the value chain presents systematic inefficiencies, it is necessary to intervene on the variables of the logistics network to ensure the correct level of service and cost efficiency. For example, the repositioning of storage or production sites can be one of the typical decisions made in this context. In this case the simulation can help to develop a Network Redesign that provides valid alternatives for the positioning of the sites, both at a geographical, operational capacity and economic level. The input data considered by the model will therefore concern the current location of the facilities, their storage capacity, production capacity and the reduction/increase in fixed and variable costs. As an output, the model will allow to verify in detail how the level of service, the structure of costs and revenues as well as any environmental impact vary depending on the different scenarios hypothesized for the new distribution network. Product portfolio review: the simulation approach is particularly useful for evaluating the introduction or removal of SKUs in the market. An analysis of this type allows to verify the saturation of production and storage capacity, the effectiveness in responding to market needs and the possibility of cannibalization of existing products. Long-term production planning: based on the sales plans agreed with the commercial function, it is possible to simulate various scenarios for verifying the saturation of production capacity to anticipate any critical issues in the medium-long term. This results in decisions to modulate the production capacity of the plants in order to adapt it to events expected in the future, such as: seasonal peaks, new market trends, changes in market share, promotions, etc. Review of inventory policies: it is a decision-making situation that is taken by mutual agreement between the production function and the sales function, when there are important changes in the structure of the supply chain, in the demand presentation patterns or in  financial targets for reducing working capital. In this case it may be necessary to … Leggi tutto ""
- [Greening the Chain](https://scnode.com/index.php/greening-the-chain/): Disvoer how to elevate a company's sustainability game with the innovative approach to Sustainable Supply Chain (SSC) management. Explore a real-world case study in the dairy industry to see this methodology in action.
- [All Case Studies](https://scnode.com/index.php/all-case-studies/): Browse through the case studies list All Posts Blog Enhancing Master Production Scheduling with simulation 10 Giugno 2025/ Disvoer how to elevate a company's sustainability game with the innovative approach to Sustainable Supply Chain (SSC) management. Explore a… Read More Resilient warehouse 30 Aprile 2024/ Experience the resilience revolution in supply chain management with our exclusive article. Delve into the intricacies of warehouse design, operational… Read More Hardware and software for prescriptive maintenance 21 Marzo 2024/ Discover the evolution of maintenance strategies, transitioning from reactive approaches to sophisticated prescriptive plans in this insightful article. Delve into… Read More Vaccine supply chain 20 Marzo 2024/ Diving deep into the labyrinth of the COVID-19 pandemic, this article unveils the intricate dance between vaccine development and global… Read More Humanitarian Supply Chain 8 Febbraio 2024/ Resilient Humanitarian Supply Chain: A Simulation Study of COVID-19 Vaccine Logistics in Kenya ABSTRACT The paper presents a case study… Read More Greening the Chain 12 Gennaio 2024/ Disvoer how to elevate a company's sustainability game with the innovative approach to Sustainable Supply Chain (SSC) management. Explore a… Read More Textile Digital twin 23 Dicembre 2023/ Discover how the dynamic fusion of Make-to-Stock (MTS) and Make-to-Order (MTO) production strategies can be achieved, from theory to practice…. Read More
- [Textile Digital twin](https://scnode.com/index.php/textile-digital-twin-2/): Discover how the dynamic fusion of Make-to-Stock (MTS) and Make-to-Order (MTO) production strategies can be achieved, from theory to practice. Surf any mix of production policies with the support of simulation modeling for seamless management of complexities and impact driven decision support. 
- [Meet The Team](https://scnode.com/index.php/meet-the-team/): Meet the team Tommaso Cesati Tommaso is an experienced operations consultant renowned for his extensive expertise in the design and implementation of simulation models and digital twin solutions spanning diverse industries. With a comprehensive understanding of how these solutions can address varied needs, Tommaso possesses deep expertise in optimizing Supply Chain and manufacturing processes. With an extensive skill set, his expertise encompasses various aspects of operations management. Key strengths include: Resource and capacity sizing proficiency Production planning expertise adeptness in production and maintenance scheduling experience in process redesign Knowledgeable in warehouse sizing and planning In his role, Tommaso delivered solutions that significantly enhance efficacy and efficiency, ultimately contributing to organizational excellence.  Tommaso’s impact spans a wide array of industries, including Pharmaceuticals, Semiconductors, Consumer Goods, Textile, Steel, Telco, and more. His commitment to driving innovation and efficiency makes him a trusted advisor in the ever-evolving landscape of digital operations. At SCNODE.COM Tommaso shares his deep expertise regarding digital twins and simulation through articles and case studies, enabling the readers to build a professional-level toolbox to address the entire supply chain.  Pietro Negri Pietro is a results-driven professional in the field of supply chain management with a proven track record of optimizing operations and company’s performance. As reflected in his diverse experience, Pietro excels in leveraging cutting-edge simulation modeling to tackle the complexities of modern supply chain management challenges. His main areas of activity regard:  the study and enhancement of supply chain performance the analysis of supply chain environmental impact (carbon footprint) and related improvement actions the publishing of academic and non-academic articles for divulgation purpose the development of ad-hoc training and educational courses for companies and professionals With a ten-year background rooted in strategic consulting, Pietro has successfully navigated multifaceted challenges across various industries, such as: white goods, telecommunication, food & beverage, pharma, agriculture, packaging, oil & gas, heavy machinery, distribution and logistic. Besides, in the last years, his commitment to innovation and environmental protection has turned him in a sought-after professional in the ever-evolving landscape of sustainable supply chain management. His expertise lies in investigating the ingredients for resilient supply chains, providing valuable insights that empower businesses to build robust, adaptable, and future-proof networks. At SCNODE.COM Pietro shares his wealth of knowledge through engaging articles and case studies, contributing to the collective understanding of supply chain dynamics.  Hey, this could be you! At SCNODE We strive to hear from you! Get in touch with us to publish your research or share your experience in supply chain management through an article, for free!  We can offer you some visibility here, you just need to provide us the article, a short bio and a picture of you, and we can make it happen! View our article guidelines and offering Hey, this could be you! At SCNODE We strive to hear from you! Get in touch with us to publish your research or share your experience in supply chain management through an article, for free!  We can offer you some visibility here, you just need to provide us the article, a short bio and a picture of you, and we can make it happen! View our article guidelines and offering Photo by Austin Ban on StockSnap
- [Simulation & Digital twin explained](https://scnode.com/index.php/simulation-digital-twin-explained/): SIMULATION MODElLING AS A DECISION MAKING TOOL WHAT IS SIMULATION MODELLING? Simulation modelling is a sophisticated methodology for strategic planning and data-driven decision making. Picture a scenario where you’re orchestrating a complex business strategy or a complex process flow. Instead of relying solely on intuition, simulation modelling enables you to create a virtual replica of the real world in a computer. By inputting various parameters and running simulations, you can predict outcomes, identify potential bottlenecks, and optimize processes. It’s akin to a virtual rehearsal, providing a nuanced understanding of how different variables interact in complex systems. To learn how advanced such methodologies are and to understand the extent of support that they can offer to decision-making processes, we can map them on the analytics pyramid.   The journey from descriptive to prescriptive analytics is closely intertwined with the adoption and integration of advanced technologies such as simulation modelling, machine learning, business intelligence (BI) tools, and digital twins. These technologies play pivotal roles in enhancing the capabilities of each analytical stage, contributing to a more comprehensive and insightful decision-making process. 1. Descriptive Analytics and BI Tools: Descriptive analytics, with its focus on summarizing historical data, is greatly facilitated by BI tools. These tools enable organizations to visually explore and interpret data trends through intuitive dashboards and reports. Business intelligence platforms provide an interactive and user-friendly interface for stakeholders to analyze and understand the past, facilitating data-driven insights that are crucial for strategic planning and performance evaluation. 2. Diagnostic Analytics and Machine Learning: Diagnostic analytics involves the examination of historical data to understand the reasons behind past events or outcomes. It aims to identify patterns, correlations, and causation within the data. For instance, in a business context, diagnostic analytics might explore why sales dropped during a specific period or why certain marketing strategies were more effective than others. 3. Predictive Analytics and Machine Learning: Predictive analytics, essential for anticipating future trends, heavily relies on machine learning algorithms. Machine learning enables organizations to build predictive models that learn from historical data patterns and make accurate forecasts. The dynamic nature of machine learning algorithms allows businesses to adapt to changing environments and make data-driven predictions, enhancing decision-making in areas such as demand forecasting, resource optimization, and risk management. 4. Prescriptive Analytics, Simulation Modelling and Digital Twins: At the apex of the analytical spectrum, prescriptive analytics is further strengthened by the concept of digital twins. A digital twin is a virtual representation of a physical system or process. They enable organizations to simulate different scenarios in a virtual environment to make impact-driven decisions. By closely mirroring real-world conditions, digital twins provide a platform for testing and refining prescriptive recommendations, ensuring that actions are tested in a risk-free environment and are based on accurate and up-to-date information. 5. Optimized Prescriptive Analytics, Simulation Modelling and Machine Learning: To evaluate various decision options and recommend the most advantageous course of action, considering constraints and objectives, simulation modelling and machine learning can be applied. Simulation Modeling enables the representation of complex systems through virtual scenarios, providing a dynamic environment to test and analyze different strategies. Machine Learning, on the other hand, empowers systems to learn from data and make predictions, enhancing the decision-making process with predictive insights. Together, these technologies synergize to create a comprehensive framework that not only diagnoses current situations but also prescribes optimal solutions, while constantly adapting and improving based on real-time feedback. This integration facilitates proactive decision-making, fosters efficiency, and propels organizations toward more agile and intelligent operations.   1. Descriptive Analytics and BI Tools Descriptive analytics, with its focus on summarizing historical data, is greatly facilitated by BI tools. These tools enable organizations to visually explore and interpret data trends through intuitive dashboards and reports. Business intelligence platforms provide an interactive and user-friendly interface for stakeholders to analyze and understand the past, facilitating data-driven insights that are crucial for strategic planning and performance evaluation. 2. Diagnostic Analytics and Machine Learning Diagnostic analytics involves the examination of historical data to understand the reasons behind past events or outcomes. It aims to identify patterns, correlations, and causation within the data. For instance, in a business context, diagnostic analytics might explore why sales dropped during a specific period or why certain marketing strategies were more effective than others. 3. Predictive Analytics and Machine Learning Predictive analytics, essential for anticipating future trends, heavily relies on machine learning algorithms. Machine learning enables organizations to build predictive models that learn from historical data patterns and make accurate forecasts. The dynamic nature of machine learning algorithms allows businesses to adapt to changing environments and make data-driven predictions, enhancing decision-making in areas such as demand forecasting, resource optimization, and risk management. 4. Prescriptive Analytics, Simulation Modelling and Digital Twins At the apex of the analytical spectrum, prescriptive analytics is further strengthened by the concept of digital twins. A digital twin is a virtual representation of a physical system or process. They enable organizations to simulate different scenarios in a virtual environment to make impact-driven decisions. By closely mirroring real-world conditions, digital twins provide a platform for testing and refining prescriptive recommendations, ensuring that actions are tested in a risk-free environment and are based on accurate and up-to-date information. 5. Optimized Prescriptive Analytics, Simulation Modelling and Machine Learning To evaluate various decision options and recommend the most advantageous course of action, considering constraints and objectives, simulation modelling and machine learning can be applied. Simulation Modeling enables the representation of complex systems through virtual scenarios, providing a dynamic environment to test and analyze different strategies. Machine Learning, on the other hand, empowers systems to learn from data and make predictions, enhancing the decision-making process with predictive insights. Together, these technologies synergize to create a comprehensive framework that not only diagnoses current situations but also prescribes optimal solutions, while constantly adapting and improving based on real-time feedback. This integration facilitates proactive decision-making, fosters efficiency, and propels organizations toward more agile and intelligent operations. In essence, the synergy between all the 5 approaches to analytics … Leggi tutto ""
- [Home](https://scnode.com/): Welcome to SCNODE.COM The first free Supply Chain-specific knowledge sharing platform, built with passion by professionals, for professionals Our mission We’re dedicated to unraveling the intricacies of supply chain challenges. Using simulation and digital twins, we showcase real-world examples from various industries, guiding our readers on the journey to build robust, adaptable, and future-proof supply chain networks. Simulation & Digital twin explained goals At SCNODE, we understand the challenges of gaining visibility, developing competencies, and establishing a personal brand and network, particularly for young professionals. However, we are here to assist you! Our objective is to create a community of supply chain enthusiasts focused on freely exchanging experiences. We aim to support you in building your Supply Chain toolbox and enhancing your visibility. Interested in joining our team or sharing your knowledge with an article? Let’s connect! Be a node in our Supply Chain! Who are we SCNODE is a free non-commercial website that serves as a collaborative platform for Supply Chain professionals, hosting a variety of articles on diverse supply chain topics. Even though SCNODE is a non-commercial entity, our scope is to build trust around an open source of information based on competence and on-field professional experiences. Whether you’re a seasoned professional or a newcomer eager to share discoveries, we encourage you to contribute with your unique perspective. Join us in building a network of shared knowledge, fostering discussions, and shaping the future of supply chain excellence. Together, let’s explore, learn, and connect within this dynamic field. Be part of the team Browse through our articles DIGITAL TWIN Leveraging dynamic production policies: a Textile Digital Twin sustainable supply chain Greening the Chain Humanitarian Supply Chain COVID-19 Vaccine Logistics in Kenya Prescriptive maintenance Prescriptive maintenance from mirage to reality: an energy business case Vaccine Supply chain How to manage global cold chains Process design How to build a resilient warehouse MPS with Simulation Enhancing Master Production Schedule with Simulation More papers coming soon…
- [Contacts](https://scnode.com/index.php/contacts/): Contacts Contact usAbilita JavaScript nel browser per completare questo modulo.Abilita JavaScript nel browser per completare questo modulo. Name * NomeCognome Email *Message Send Photo by Antoine Barrès on StockSnap
- [Write for us](https://scnode.com/index.php/write-for-us/): Write for us AT SCNODE, WE STRIVE TO HEAR FROM YOU! We are constantly looking for new sources of information regarding every aspect of the supply chain. We don’t have any pre-requirement regarding your age or level of experience. Get in touch with us, and we will support you in gaining some visibility here, all for free! Are you passionate about supply chain or do you have some experience to share with us? Get in touch with us! CONTACT US WHAT WE OFFER Submit your article and a short bio, in any format, by mail at info@scnode.com Our selection committee will evaluate your work and let you know asap if it fits SCNODE values and philosophy. Once your article will be considered suitable for publication, you will receive an email with eventual adjustments requested and the template for formatting your article. Once finalized, the article will be published on SCNODE and sponsored via social media channels. ARTICLE SUBMISSION GUIDELINES Thank you for considering SCNODE as a platform to share your insights and knowledge in the fields of Logistics, Operations, and Supply Chain Management. To ensure a cohesive and informative collection of articles, we have outlined the following submission guidelines: Scope and Purpose: The focus of article publication on SCNODE is divulgative, with no fees charged for submissions. Authors will not receive any revenue for their published materials; recognition will be given to the author(s). Content Criteria: Articles should relate to processes or current events within the Logistics, Operations, and Supply Chain Management sectors. A minimum bibliography and reference sources are required for credibility. Self-referential materials will not be eligible for publication. Materials created with the assistance of artificial intelligence tools will not be considered for publication. Structure: Articles should follow a general structure: introduction, theoretical section, and a case study part illustrating practical applications. Maximum article length is 20 pages, encompassing cover, annexes, references, etc. Author Information: A mandatory author(s) bio should be submitted alongside the article. Commercial Advertisements: Any form of commercial advertisement within the article, directly or indirectly promoting specific products or services, will lead to removal or rejection of the submission. Sensitive Topics: References to political, religious, or ethnic issues are strictly prohibited and will result in exclusion from publication. Ethical Considerations: SCNODE adheres to ethical values and will not publish articles related to military/defense, tobacco (farming and processing), alcoholic beverages, or any objects or services harmful to living beings. Reporting Ethical Concerns: If you come across material on SCNODE that you believe violates ethical standards, please contact us with an explanation of your concerns. Authorship Inclusivity: SCNODE welcomes articles from freelance professionals, universities, companies, associations, and public sector representatives. Rights and Responsibilities: By submitting an article, the author grants all rights on the material to SCNODE. Authors will be duly recognized when the material is published on SCNODE. Authors bear all responsibilities and liabilities for the published material, including copyrights on images, data, and information shared. Anonymous Publication: Authors can request anonymous publication, where no information about the author’s biography will be shared on the SCNODE website or social media channels. We appreciate your commitment to contributing valuable content to SCNODE. Please ensure your submission aligns with these guidelines for a smooth review and publication process. If you have any questions, feel free to contact us. Photo by Matthew Henry on StockSnap
- [Privacy Policy](https://scnode.com/index.php/privacy-policy/): Privacy Policy 1. An overview of data protection General information The following information will provide you with an easy to navigate overview of what will happen with your personal data when you visit this website. The term “personal data” comprises all data that can be used to personally identify you. For detailed information about the subject matter of data protection, please consult our Data Protection Declaration, which we have included beneath this copy. Data recording on this website Who is the responsible party for the recording of data on this website (i.e. the “controller”)? The data on this website is processed by the operator of the website, whose contact information is available under section “Information Required by Law” on this website. How do we record your data? We collect your data as a result of your sharing of your data with us. This may, for instance be information you enter into our contact form. Other data shall be recorded by our IT systems automatically or after you consent to its recording during your website visit. This data comprises primarily technical information (e.g. web browser, operating system or time the site was accessed). This information is recorded automatically when you access this website. What are the purposes we use your data for? A portion of the information is generated to guarantee the error free provision of the website. Other data may be used to analyze your user patterns. What rights do you have as far as your information is concerned? You have the right to receive information about the source, recipients and purposes of your archived personal data at any time without having to pay a fee for such disclosures. You also have the right to demand that your data are rectified or eradicated. If you have consented to data processing, you have the option to revoke this consent at any time, which shall affect all future data processing. Moreover, you have the right to demand that the processing of your data be restricted under certain circumstances. Furthermore, you have the right to log a complaint with the competent supervising agency. Please do not hesitate to contact us at any time under the address disclosed in section “Information Required by Law” on this website if you have questions about this or any other data protection related issues. Analysis tools and tools provided by third parties There is a possibility that your browsing patterns will be statistically analyzed when your visit this website. Such analyses are performed primarily with what we refer to as analysis programs. For detailed information about these analysis programs please consult our Data Protection Declaration below. 2. Hosting and Content Delivery Networks (CDN) External Hosting This website is hosted by an external service provider (host). Personal data collected on this website are stored on the servers of the host. These may include, but are not limited to, IP addresses, contact requests, metadata and communications, contract information, contact information, names, web page access, and other data generated through a web site. The host is used for the purpose of fulfilling the contract with our potential and existing customers (Art. 6 para. 1 lit. b GDPR) and in the interest of secure, fast and efficient provision of our online services by a professional provider (Art. 6 para. 1 lit. f GDPR). Our host will only process your data to the extent necessary to fulfil its performance obligations and to follow our instructions with respect to such data. We are using the following host: Hostinger International Ltd., Cyprus private limited companyregistered address: 61 Lordou Vironos str., 6023 Larnaca, Cyprus Execution of a contract data processing agreement In order to guarantee processing in compliance with data protection regulations, we have concluded an order processing contract with our host. 3. General information and mandatory information Data protection The operators of this website and its pages take the protection of your personal data very seriously. Hence, we handle your personal data as confidential information and in compliance with the statutory data protection regulations and this Data Protection Declaration. Whenever you use this website, a variety of personal information will be collected. Personal data comprises data that can be used to personally identify you. This Data Protection Declaration explains which data we collect as well as the purposes we use this data for. It also explains how, and for which purpose the information is collected. We herewith advise you that the transmission of data via the Internet (i.e. through e-mail communications) may be prone to security gaps. It is not possible to completely protect data against third-party access. Information about the responsible party (referred to as the “controller” in the GDPR) The data processing controller on this website is: Pietro NegriMilan, Italy E-mail: pietro.negri@scnode.com The controller is the natural person or legal entity that single-handedly or jointly with others makes decisions as to the purposes of and resources for the processing of personal data (e.g. names, e-mail addresses, etc.). Storage duration Unless a more specific storage period has been specified in this privacy policy, your personal data will remain with us until the purpose for which it was collected no longer applies. If you assert a justified request for deletion or revoke your consent to data processing, your data will be deleted, unless we have other legally permissible reasons for storing your personal data (e.g. tax or commercial law retention periods); in the latter case, the deletion will take place after these reasons cease to apply. Information on data transfer to the USA Our website uses, in particular, tools from companies based in the USA. When these tools are active, your personal information may be transferred to the US servers of these companies. We must point out that the USA is not a safe third country within the meaning of EU data protection law. US companies are required to release personal data to security authorities without you as the data subject being able to take legal action against this. The possibility cannot therefore be … Leggi tutto ""

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