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Supply Chain Management

How Is External Data Used in Supply Chain Management?

AI in supply chain management depends on road quality, distribution centers, weather, and the capabilities of suppliers and third parties. Monitoring and updating these data sources is therefore crucial for any operation.

Additional data includes foot and store traffic, future events, and competitor analysis. Competitor analysis, in particular, impacts product demand, forecasting, and new technologies that a company may consider utilizing themselves.

How are Machine Learning Models Used in Supply Chain Management?

Machine learning models can develop and run algorithms that predict product needs by individual stores and the best traffic routes. However, they do not limit themselves to making predictions; they can also analyze spending and vehicle status in order to reduce fuel and repair costs.

Which Companies Lead the Way in Building Advanced Analytics and Machine Learning Products in This Field?

IBM Sterling
IBM’s Sterling platform uses AI in supply chain management to optimize every aspect of the product journey. It also offers solutions to industry-specific problems with both transparency and security.
SAP Digital Supply Chain Management
SAP offers a suite of supply chain solutions, with IoT technology and predictive analytics via machine learning models. Additional services include warehouse management, a certified supplier network, and manufacturing capabilities.
Oracle Supply Chain Management (SCM) and Manufacturing
Oracle offers clouds solutions for supply chain management that integrate with its other business services. It also provides product trend and other supply chain-related predictive capabilities powered by AI.

Supply Chain Management Use Cases

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Wikiroutes Transit Data

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Wikiroutes Transit Data provides public transport information—routes, stop points, and more—via crowd-sourcing. The data is constantly updated and can be easily converted and integrated into your own software system. 

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