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Data Science With ML in Supply Chain and Commerce Systems
Data Science is a multidisciplinary approach that uses scientific methods, processes, and algorithms to extract insights from structured and unstructured data to influence tactical and strategic decisions.
Artificial Intelligence (AI) is a subset of Data Science and Machine Learning (ML) is a subset of AI that uses algorithms to learn from data patterns and make predictions or decisions without explicit programming. ML enables systems to autonomously learn and model tasks from data, identify patterns and make decisions with little to no human intervention.
These disciplines are highly overlapped and symbiotically linked as Data Science uncovers the insights and ML applies those insights to make intelligent decisions or predictions. Together, these technologies are transforming industries like supply chain commerce, where their impact is highly beneficial and often critical to successfully automating and optimizing many analytical tasks and operations.
Data Science leverages ML to enhance supply chain commerce solution capabilities, performance, and robustness by handling numerous types of complex analytical tasks across a variety of operations. A subset of ML called Generative AI or GenAI uses Large Language Models (LLM) to create new content or data like text, images, music, or other creative content. It can even be used to translate languages or write programming code.
Data Science and Manhattan
The speed, connectivity, and complexity demands on modern supply chain commerce networks necessitate the capacity to process vast amounts of complex data to model analytical tasks, discover and recommend actionable insights, and predict likely outcomes autonomously and reliably. Manhattan infuses Data Science and ML in its solutions to improve operational decision-making, allowing organizations to adapt to changes and more robustly anticipate and plan accordingly.
Examples of Data Science in Supply Chain Commerce
During transportation shipment planning, advanced Data Science methods and ML models evaluate demand, order, and shipment patterns to optimize transportation policies and create predictive models to anticipate hundreds of outcomes—such as whether or not a carrier will accept a shipment request—which streamlines and optimizes the shipment planning process.
ML algorithms autonomously learn and then model the fulfillment and distributional dynamics associated with the processing, shipping, and delivery of customer orders across every fulfillment node in the network. ML enhances the predictive promising capabilities and improves accuracy for estimating shipment and delivery dates within the order management system, thereby improving online purchase conversion and consumer service levels.
Accurate demand forecasting is one of the most crucial aspects of supply chain management, and ML models can process historical data alongside other demand-influencing factors to strengthen predictive capabilities. ML improves predictive promising models for ship date and delivery date estimation from order management systems, increasing online consumer purchase conversion and satisfaction by providing estimates that meet expectations and actual delivery dates.
- Data Science extracts insights from raw data to influence both tactical and strategic decisions.
- Machine Learning is a sub-field of Artificial Intelligence that enables systems to autonomously learn and model tasks with little to no human intervention.
- Generative AI is a subset of Machine Learning that understands and communicates using human languages and generates new content or code rapidly.
- Manhattan has a long history of infusing Data Science into its supply chain commerce solutions to ensure you receive the best possible business outcomes and customer experiences.
Learn more about the groundbreaking technologies that make up the Manhattan Active® Platform experience.
Let’s Make Supply Chain Commerce Smarter
Learn how Manhattan’s commitment to advanced computational intelligence technologies like Data Science with Machine Learning and GenAI, Decision Science, Adaptive Systems, and Optimization are efficiently working to provide a competitive advantage for supply chain commerce organizations.