Machine learning draws from wide-ranging data (e.g., data related to inventory, orders, transportation, network partners, grids), as well as external factors, such as the weather forecast, traffic, and currency rates, to help teams gauge market dynamics and risk levels around order fulfillment, as well as reliably facilitate on time and in full delivery, control costs, and flag high-risk orders for close monitoring, giving sensitive orders the VIP treatment when necessary.
Supply chain management is becoming more and more complex as organizations expand their multi-party networks of suppliers, warehouses, distributors, and logistics service providers. Therefore, technology, such as artificial intelligence and machine learning best serve organizations as part of multi party orchestration platforms. As part of stand alone solutions, machine learning will only serve to optimize specific processes and solutions, whereas when part of an end to end supply chain management technology solution, business can optimize across entire networks, building flexibility, agility, and resilience.
Supply chain cloud platforms that are integrated network-wide have access to a pool of data that’s continuously growing and becoming more reliable. When updates occur in real-time from multiple data pipelines, the information is often more representative of actual conditions. Data science and machine learning make it possible to aggregate data then apply the information to strategy and planning.
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