Supply Chain AI and ML: IBM Watson
Watson Machine Learning is a service on IBM Cloud that provides capabilities for training and deploying machine learning models and neural networks. Watson Machine Learning is built on a scalable open source platform based on Kubernetes and Docker components, enabling users to build, train, deploy and manage machine learning and deep learning models.
Watson first rose to fame in 2011 when he won the US game show Jeopardy! when he played against two human champions. When it comes to supply chains, Watson helps end-to-end visibility with intelligent dashboards and his KPIs.
Supply Chain AI and ML: SAP
SAP Integrated Business Planning (IBP) is a cloud-based planning solution for analyzing, managing, and transforming data to help companies meet their logistical challenges. ML capabilities built into SAP IBP drive planning accuracy and automation with solutions that leverage ML capabilities across planning areas such as forecasting and operations.
SAP also offers intelligent asset management solutions built on AI and machine ML algorithms that analyze sensor data, identify patterns, and predict potential failures.
SAP recently announced that it will incorporate IBM Watson technology into SAP solutions to provide AI-driven insights and automation across the SAP application portfolio.
Oracle Cloud Infrastructure (OCI) AI Services is a collection of services with pre-built machine learning models that make it easy for developers to apply AI to their applications and business operations. You can custom train your model to get more accurate business results.
In April 2023, Oracle introduced new capabilities across the Oracle Fusion Cloud Applications Suite to help customers accelerate supply chain planning, improve operational efficiency, and improve financial accuracy.
Supply Chain AI and ML: Siemens
Siemens is a global engineering and technology leader, providing a range of predictive maintenance solutions to industries such as manufacturing, energy and healthcare. In 2022, Siemens will acquire Senseye, a leading provider of predictive maintenance solutions for manufacturing and industrial companies.
Predictive maintenance is critical to supply chains because it enables real-time asset intelligence across factories around the world, helping to proactively prevent and prevent production problems.
The platform combines cutting-edge AI and human insight to help organizations increase productivity, work more sustainably, and accelerate their digital transformation.
Supply Chain AI and ML: Dataiku
Many top supply chain and logistics organizations and companies with supply chain or procurement departments use Dataiku. It’s a data science, machine learning, and AI platform used for more granular and precise management. This will help enable a more efficient supply chain that will result in significant cost savings and increased profits.
Dataiku supports a wide range of machine learning and analytics tasks such as forecasting, clustering, time series and image classification.
Founded in 1976, SAS develops and markets a suite of analytical software that helps you access, manage, analyze and report on data to support decision making.
SAS Machine Learning on SAS Cloud supports the latest statistical and ML techniques in a single, scalable, in-memory processing environment for developing, testing and deploying models.
Supply chains can use real-time “what-if” insights into supply and demand dynamics to help avoid inventory shortages or excesses. You can also use ML models to connect customer and supply chain intelligence, connecting shopper engagement to product demand.
Supply Chain AI and ML: Databricks
Databricks Machine Learning empowers ML teams to prepare and process data, streamlining cross-team collaboration. The Databricks Lakehouse Platform will allow companies to build a resilient and predictable supply chain “by eliminating the trade-off between accuracy or depth of analysis and time,” the company said.
This enables scalable forecasting, which can help supply chain management by forecasting demand, driving supply chain planning and optimization, and improving decision-making accuracy.
