{"id":1673,"date":"2024-02-02T11:58:22","date_gmt":"2024-02-02T10:58:22","guid":{"rendered":"https:\/\/scnode.com\/?page_id=1673"},"modified":"2024-11-29T11:58:46","modified_gmt":"2024-11-29T10:58:46","slug":"simulation-roadmap-beginning-digital-journey","status":"publish","type":"page","link":"https:\/\/scnode.com\/index.php\/simulation-roadmap-beginning-digital-journey\/","title":{"rendered":"Simulation modelling roadmap: beginning a digital journey"},"content":{"rendered":"\t\t
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Simulation modelling roadmap: beginning a digital journey<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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As we have mentioned in previous articles, simulation<\/strong> turns out to be an important decision support tool in SCM<\/strong> fields, where a 360-degree view of the possible impacts of decision<\/strong> alternatives is required.
In this article we will analyze the main phases inherent to the development of a model<\/strong> to support a generic SCM process.<\/p><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Developing the model<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The simulation model is created on a one-off basis<\/strong> as the model, which represents the real functioning of the supply chain (process) considered, will not be subject to substantial changes<\/strong> during its existence unless structural changes occur in the analyzed business context.<\/p>

The creation of a simulation model for an SCM process involves 5 phases: requirements collection, conceptual design, model development, testing, scenario assesmen<\/strong>t.\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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1. Requirements collection <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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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.<\/strong>
Without the presumption of being exhaustive, below we report some areas of requisites collection.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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1.1 Layers and actors of the supply chain<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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First of all is necessary to point out the number of levels, upstream and downstream,which need to be mapped in the model<\/strong>.
This activity requires an analysis of the main suppliers and customers<\/strong>, as well as a careful study of the existing production and distribution network<\/strong>.\u00a0<\/p>

As regards suppliers<\/strong>, 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<\/strong> (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.<\/p>

The same reasoning can be extended to number of downstream actors<\/strong>: in B2B contexts it could be useful to map customers’ customers<\/strong> to evaluate the impacts that changes in final product demand\u00a0 may carry on company operations. This obviously requires a in-depth knowledge of the mechanisms of demand propagation<\/strong> in the upstream levels of the supply chain as well as expected distribution<\/strong> of demand over time and its variation<\/strong> depending on characteristic market parameters.<\/p>

In addition to the world of demand and supply, it is then necessary to carefully consider the existing distribution and production network<\/strong>: given the growing complexity<\/strong> of these networks, mainly due to the use of outsourcing<\/strong> of particular production phases (contracting) and distribution (use of logistics operators), it is critical to also monitor phases and processes<\/strong> in the model that are not directly under company control<\/strong>.<\/p>

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1.2 Products, components and raw materials<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Mapping the master data of finished products, semi-finished products and raw materials<\/strong> constitutes another important input for the construction of the model.<\/p>

Depending on the context to be modeled, it may be necessary to map the single finished product<\/strong> or serial production code<\/strong> (in the case of make-to-order productio), while in other cases it may be sufficient to limit the analysis to the product family<\/strong> (as often happens in traditional tools of S&OP).
The choice of the level of granularity<\/strong> 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)<\/strong> to be considered in the model, in order to link the different product codes together according to supply-demand mechanisms.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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1.3 Resources and capabilities<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Usually in high-level decisions there is no need to consider the individual resources<\/strong> of the supply chain (such as vehicles, personnel, machinery, etc.) but it is preferred to aggregate their meaning in the term of capacity<\/strong>, understood as the sum of the availability of the individual resources.<\/p>\n

Considering supply chain capacity as dependent on the constraints present is essential to produce eligible results<\/strong> and provide important points of analysis<\/strong>. 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<\/strong> to consider the structural limits<\/strong> of the current supply chain.<\/p>\n

Setting capacity<\/strong> does not only mean defining medium-term production<\/strong> capacity, but also considering other important capacity constraints<\/strong> that can affect the balance between supply and demand.
Let’s think for example about the distribution capacity of the network<\/strong>: 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<\/strong>, 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).<\/p>\n

In summary, considering the different types of capacity (production, distribution, storage) can be particularly useful<\/strong> both for carrying out a correct balance between supply and demand<\/strong> and for evaluating in the scenarios the effects of potential risks <\/strong>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.).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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1.4 Planning policies<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Defining the policies<\/strong> on which the company conducts its business can in some cases be a difficult abstraction<\/strong> of the way in which people, processes, technology and machines operate.
However, planning policies constitute the “mind” according to which the model carries out the calculations and simulations<\/strong>: if they are not clearly defined, there is a risk of obtaining results that are meaningless and devoid of applicability. Therefore it is good to spend time to analyze and abstract the main policies, which usually concern:<\/p>