{"id":11340,"date":"2025-07-09T15:15:00","date_gmt":"2025-07-09T13:15:00","guid":{"rendered":"https:\/\/optano.com\/?p=11340"},"modified":"2026-01-13T12:26:50","modified_gmt":"2026-01-13T11:26:50","slug":"service-level-vs-costs","status":"publish","type":"post","link":"https:\/\/optano.com\/en\/media\/insight\/1\/servicelevel-vs-kosten\/","title":{"rendered":"Service level vs. costs"},"content":{"rendered":"
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Supply Chain Triangle:\nThe balancing act between costs and service level<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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How companies use mathematical optimization to make their supply chain more efficient<\/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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In today's business world, efficiency in the supply chain is a decisive competitive factor. Companies are under constant pressure to get their products to customers faster, more cheaply and more reliably. However, these requirements are often at odds with each other. If you cut costs, you risk longer delivery times. Those who increase service levels often have to accept higher stock levels. This area of conflict is known as the supply chain triangle - a classic conflict of objectives between costs, service level and cash flow (working capital).<\/p>

This is where many companies face a key challenge: how can a balance be found between these objectives without one of them suffering permanently? In this article, we show how companies use mathematical optimization and the OPTANO solution for network optimization to successfully master the conflicting goals of cost and service level - and achieve impressive results in the process.<\/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 in the supply chain triangle: costs versus service level<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The supply chain triangle is not just a theoretical framework, but a daily reality for those responsible for planning in companies. Every decision - whether on warehousing, transport logistics or the choice of location - influences at least two of the three target variables. For example, reducing stock levels reduces costs, but at the same time risks supply bottlenecks. On the other hand, those who increase the service level through high availability must expect higher storage and transportation costs.<\/p>

These conflicting objectives are further exacerbated by external factors:<\/h4><\/div><\/div>
  • Global supply chains<\/strong>\u00a0with long transportation routes and geopolitical risks<\/li>
  • Volatile markets<\/strong>\u00a0with fluctuating demand<\/li>
  • Rising customer expectations<\/strong>\u00a0delivery speed and transparency<\/li>
  • Sustainability requirements<\/strong>, which entail new restrictions<\/li><\/ul>Many companies try to cope with this complexity using traditional tools such as Excel. However, these tools quickly reach their limits when it comes to optimizing several target values at the same time and taking thousands of variables and restrictions into account. The result: decisions are made on the basis of gut feeling or isolated key figures - with correspondingly limited success.<\/div><\/div><\/div><\/div><\/div><\/section>\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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