{"id":229006,"date":"2026-03-19T11:11:09","date_gmt":"2026-03-19T10:11:09","guid":{"rendered":"https:\/\/www.ak-online.de\/?p=229006"},"modified":"2026-04-08T14:33:59","modified_gmt":"2026-04-08T12:33:59","slug":"logistical-optimisation-through-empirical-simulation","status":"publish","type":"post","link":"https:\/\/www.ak-online.de\/en\/logistical-optimisation-through-empirical-simulation\/","title":{"rendered":"Logistical optimisation through empirical simulation"},"content":{"rendered":"\t\t
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Logistical optimisation through empirical simulation<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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The potential of digital twins in supply chain management<\/em><\/p>\n

The increasing complexity of global supply chains presents new challenges for supply chain management. Fluctuating demand, volatile procurement markets and rising expectations regarding delivery reliability are putting greater pressure on planning and control processes. At the same time, product portfolios, distribution networks and data volumes are growing continuously. Under these conditions, traditional methods of logistics process optimisation often reach their limits. A promising approach to the systematic analysis and improvement of logistics structures lies in empirical simulation based on digital twins of the supply chain.<\/p>\n

The idea of first testing and optimising complex systems virtually has long been established in other industrial sectors. In product development across many sectors, variants and stress scenarios have been analysed in simulation environments for years before physical prototypes are created. By shifting testing to a digital environment, development cycles can be shortened, thereby reducing costs. A similar approach is now also gaining significance in logistics and supply chain management.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t

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Digital twins as the basis for analysis<\/strong><\/p>\n

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Logistics networks are characterised by a multitude of interdependent factors. Sales forecasts, inventory strategies, lead times, production capacities and scheduling rules all influence the behaviour of the entire value chain simultaneously. Changes to individual parameters can therefore trigger unexpected effects elsewhere. Empirical knowledge and static analyses are often insufficient to fully capture these interactions. An empirical simulation, on the other hand, makes it possible to realistically model the dynamic behaviour of the entire supply chain and compare different courses of action under identical conditions.<\/p>\n

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The basis of such an approach is a digital twin of the logistics processes. This is created from existing company data, for example from ERP or merchandise management systems. Product master data, historical demand trends, order and delivery data, as well as structural data such as bills of materials or work plans, form the basis of the simulation model. On this basis, a representation of the real value streams and planning processes is generated, which can map the dynamics of material flows across several stages of the supply chain.<\/p>\n

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A key difference from traditional analytical methods lies in the dynamic analysis of real-time time series. Whilst traditional value stream analyses often work with average values, a simulation-based model takes into account fluctuations in demand, delivery times or production capacities. This yields results that are closer to actual processes and provide a more robust basis for decision-making. Particularly in the case of volatile demand or complex product structures, a static analysis can obscure important effects, whereas a dynamic simulation makes their impacts visible.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Application scenarios in strategy and operational planning<\/strong><\/h4>\n

The analysis is usually carried out using historical data. Changes to planning parameters, inventory strategies or organisational structures are implemented in the digital model and then simulated over a defined period. The simulation results can be compared with the actual results observed in the past. This reveals which measures would have led to improvements under realistic conditions and what effects can be expected.<\/p>\n

The use of empirical simulations enables both strategic and operational analyses. At the strategic level, alternative structures of the logistics business model can be examined, such as the positioning of decoupling points, the design of distribution networks or the alignment of planning processes along the value chain. Operational issues concern, for example, forecasting methods, inventory sizing or planning parameters. The aim is always to achieve a balance between inventory levels, delivery capability and operational efficiency.<\/p>\n

Practical examples from various industries illustrate the range of possible applications. At a manufacturer of textile products, a simulation-based analysis showed that a planned reduction in lead times would have had only a limited impact on stock levels. Instead, an improvement in sales forecasting proved to be the decisive lever for reducing inventory levels. In another case, an automated replenishment system was developed for a branch network at a wholesaler in the automotive aftermarket. Through simulation-based optimisation of the replenishment parameters, inventory levels were sustainably reduced by a high double-digit million figure.<\/p>\n

The method also opens up new perspectives in industrial manufacturing. At a manufacturer of electrical components, the simulation of the entire value chain led to the development of a new logistics business model. Among other things, decoupling points were repositioned, Kanban structures were introduced and replenishment rules were adapted. The implementation of these measures enabled a significant reduction in working capital and improved the efficiency of production and logistics processes.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Iterative optimisation and economic effects<\/strong><\/p>\n

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Optimisation takes place in several iterative steps. First, potential solutions are defined and modelled. Different scenarios are then simulated and evaluated against defined objectives. Based on the results, the parameters are adjusted and tested again. Modern simulation systems partially support this process through automated optimisation mechanisms, which identify, from a multitude of possible parameter combinations, those variants that best fulfil a given objective.<\/p>\n

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Despite the high performance of digital simulation tools, specialist expertise remains a key success factor. Interpreting the results and deriving practical measures require a deep understanding of the logistics processes and the specific operating conditions of a company. Simulation projects therefore typically begin with a detailed analysis of existing processes and are accompanied by workshops in which specialist departments and the project team jointly identify potential for optimisation.<\/p>\n

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In addition to analytical quality, cost-effectiveness also speaks in favour of simulation-based optimisation approaches. As complex value stream models can be rapidly constructed using existing company data, extensive product portfolios can be analysed without a proportional increase in project effort. At the same time, decision-making and implementation times are shortened, as various scenarios can be evaluated in advance. In many cases, the resulting reductions in inventory or increases in efficiency lead to a rapid return on investment in the simulation project.<\/p>\n

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Empirical simulation is thus increasingly becoming a key tool for data-driven supply chain optimisation. The combination of real company data, dynamic modelling and iterative analysis creates a robust foundation for strategic and operational decisions. Given the rising complexity and growing demands for resilience and efficiency in logistics networks, the importance of such approaches in supply chain management is likely to continue to grow.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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The potential of digital twins in supply chain management The increasing complexity of global supply chains presents new challenges for supply chain management. Fluctuating demand, volatile procurement markets and rising expectations regarding delivery reliability are putting greater pressure on planning and control processes. At the same time, product portfolios, distribution networks and data volumes are<\/p>\n","protected":false},"author":25,"featured_media":228820,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1433],"tags":[],"class_list":["post-229006","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-newsletter_en"],"acf":[],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t