1.0SCNODE.COMhttps://scnode.comscnode.comhttps://scnode.com/index.php/author/scnode-com/rich600338<blockquote class="wp-embedded-content" data-secret="PSMjZnJODR"><a href="https://scnode.com/index.php/dynamic-simulation-as-decision-support-tool/">Dynamic simulation as decision support tool</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://scnode.com/index.php/dynamic-simulation-as-decision-support-tool/embed/#?secret=PSMjZnJODR" width="600" height="338" title="“Dynamic simulation as decision support tool” — SCNODE.COM" data-secret="PSMjZnJODR" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script> /*! This file is auto-generated */ !function(d,l){"use strict";l.querySelector&&d.addEventListener&&"undefined"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!/[^a-zA-Z0-9]/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret="'+t.secret+'"]'),o=l.querySelectorAll('blockquote[data-secret="'+t.secret+'"]'),c=new RegExp("^https?:$","i"),i=0;i<o.length;i++)o[i].style.display="none";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute("style"),"height"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):"link"===t.message&&(r=new URL(s.getAttribute("src")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener("message",d.wp.receiveEmbedMessage,!1),l.addEventListener("DOMContentLoaded",function(){for(var e,t,s=l.querySelectorAll("iframe.wp-embedded-content"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute("data-secret"))||(t=Math.random().toString(36).substring(2,12),e.src+="#?secret="+t,e.setAttribute("data-secret",t)),e.contentWindow.postMessage({message:"ready",secret:t},"*")},!1)))}(window,document); //# sourceURL=https://scnode.com/wp-includes/js/wp-embed.min.js </script> 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 ""https://scnode.com/wp-content/uploads/2024/01/Simulation-modelling-level-of-abstraction.png975652