{"id":10449,"date":"2025-11-04T11:00:00","date_gmt":"2025-11-04T10:00:00","guid":{"rendered":"https:\/\/optano.com\/?p=10449"},"modified":"2026-02-04T08:58:22","modified_gmt":"2026-02-04T07:58:22","slug":"optimizing-transport-mode","status":"publish","type":"post","link":"https:\/\/optano.com\/en\/media\/insight\/1\/optimierung-der-transportmodi\/","title":{"rendered":"Optimization of transport modes"},"content":{"rendered":"
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Optimization of transport modes in tactical distribution planning<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Between cost, speed and sustainability - data-based decisions for complex supply networks<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Fast or cheap? Direct or consolidated? Anyone making decisions about means of transportation in distribution logistics is often faced with precisely these conflicting objectives. The choice between rail or air freight, FTL (full truck load) or LTL (less-than-truckload) is not a purely operational question - it has a significant impact on a company's profitability, service quality and ecological balance sheet.<\/p>

But how can the optimum mode be determined in a complex network with hundreds of routes and changing requirements? The answer lies in mathematical optimization - an approach that makes it possible to make well-founded decisions based on data, scenarios and conflicting objectives.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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The reality of distribution planning: conflicting goals and uncertainty<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Cost, service level and sustainability in conflict<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The reality in many companies shows that the choice of transportation mode is often made under uncertainty and time pressure. Planners are faced with the challenge of reconciling contradictory objectives. On the one hand, transportation costs should be reduced, but on the other hand, service levels must not be undercut. At the same time, there is growing pressure to achieve ecological goals and reduce CO\u2082 emissions. These requirements are often in direct conflict with each other. For example, although the use of rail transport can significantly reduce emissions, it can result in longer transit times and therefore jeopardize the ability to meet delivery deadlines.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Lack of transparency in carrier performance data<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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In addition, many companies do not take information about the actual performance of carriers - such as punctuality rates, damage rates or average delays - into account in their planning. This often leaves untapped efficiency potential that is not visible at first glance. A concrete example: a carrier with a punctuality rate of 89 % may incur eight percent higher costs than the cheapest provider, but its reliability can make a decisive contribution to meeting service levels - especially for time-critical deliveries. Such trade-offs can only be made if the corresponding performance data is systematically recorded and integrated into the planning.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Complexity of multi-level networks as an additional hurdle<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The complexity is further increased by the structure of modern distribution networks. In multi-level systems with central hubs, regional distribution centers and thousands of customers, there are countless possible transport routes. Manually evaluating all these options is practically impossible. Specialized software solutions are needed that are capable of mastering this complexity and at the same time mathematically mapping conflicting objectives.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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