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.
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.
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.
PipeCandy eCommerce Leads & Insights for Fulfillment tracks company data and company shipping details: shipping volume, which companies they use to ship products, whether they ship internationally or not, and so on. You can find company leads easily with this dataset with filter capabilities.
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.
Wikiroute’s Transit Data is used by individuals, private companies, and government agencies of all types and sizes.
TrackStar’s Predictive Credit Technology uses fifteen years of financial dispute data to create predictive models of future borrowing potential. With this data and AI technology, your bank or other lending company can mitigate the risk of fraud, improve existing customer relations, and reduce your operating costs.