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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 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 …

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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

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

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 …

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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: 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 …

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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 …

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