The 5 Biggest Benefits of Supply Chain Optimization with Decision Intelligence

In recent years the dynamics and complexity of supply chains have increased significantly – and this trend will become even more critical in the future. Amongst the most important factors contributing to this are accelerated globalization, advancing digitalization and massive geopolitical risks. As a result there’s growing pressure, especially for internationally operating companies, to make their supply chains more resilient in order to remain competitive.

With paretos they can rely on a partner that provides them with state-of-the-art Decision Intelligence (DI) tools based on AI and predictive analytics. Not only do these tools enable informed supply chain improvement decisions to be made in real time, they also enable a higher level of automation and process integration. Below we have listed the five biggest advantages that companies could benefit from should they utilize DI by paretos to optimize their supply chain.

1. Better cost efficiency

By applying DI technologies to their supply chain, companies can develop a data-driven, proactive approach to optimizing it. This allows them to identify and adjust potential cost drivers that will reduce – or even eliminate – unnecessary costs. One way to do this is to provide deeper insight into the supply chain by collecting data from multiple sources such as suppliers, production processes, inventories and distribution channels and then interpreting it accordingly using data analysis and modeling tools. By analyzing real-time data, for example, it’s possible to optimize delivery times and so avoid unnecessary shipments or excess inventories and, as a result, reduce warehousing costs. Another option is to use simulations and optimization models to determine which measures could make the supply chain even more efficient.

2. Greater flexibility

Using DI to optimize your supply chain enables you to react in the best possible way to critical variables in supply chain management. These can include changes to the market, supplier bottlenecks, commodity prices or even natural disasters. This is achieved via analysis of real-time data and predictive models to enable a faster and, thus, more flexible response and adaptation to changing conditions.

3. Increased transparency

Nowadays the supply chains of many global players such as BMW consist of an average of around 60,000 suppliers. This presents a significant challenge for supply chain management whilst including the need to ensure compliance with ethical, sustainable and other fundamental corporate principles throughout the supply chain. The use of DI enables companies to be able to smartly aggregate data from all sources involved and view their current status. This information plus a comprehensive overview allow companies to gain important insights into all underlying decision-making processes. This means that they can identify problems much more quickly to help increase the efficiency and productivity of their supply chain.

4. Increased sustainability

A supply chain based on DI enables companies to incorporate sustainability into their decision-making processes and so increase the positive impact of the business on both the environment and society as a whole. Examples include selecting suppliers that produce sustainably and are committed to social responsibility, optimizing transport routes to reduce fuel consumption and, thus, CO2 emissions and managing inventories to avoid overstocking (and the associated waste) and any unnecessary consumption of resources.

5. Reduced supplier risk

One of the biggest risk factors along any supply chain is issues with suppliers. These can include, among others, both internal and external factors such as production bottlenecks, quality fluctuations, delivery delays and even insolvencies. By using DI within the supply chain it’s possible to reliably identify, evaluate and minimize these negative factors. Based on the analysis of historical data, information on supplier performance and market information, a company can make predictions and recommend measures that significantly reduce potential supplier risks or enable companies to react more quickly. For example, they can find alternative delivery options to ensure a continuous supply of materials or services to enable the company to maintain, or even increase, its efficiency and effectiveness.