Decision Science in Supply Chain Commerce
Decision science is vital to modern supply chain commerce as it turns information into action, resulting in operational efficiency, agility, resilience, and optimal business outcomes.
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Decision Science in Supply Chain and Commerce Systems
Decision Science combines mathematics, statistics, machine learning, economics, and behavioral science to analyze and solve complex decision-making problems. It uses these analytical techniques to make informed, evidence-based decisions by evaluating alternatives and choosing the best course of action.
Decision Science vs Data Science
Decision Science may sound very similar to Data Science, as they both center on using data to inform choices and actions. However, their core objectives, methodologies, and areas of emphasis differ. While Data Science is primarily concerned with how to extract the most accurate insights, patterns, predictions, and knowledge from data, Decision Science focuses on what to do once we have those insights. What is the optimal decision given many different choices and their potential outcomes? Decision Science deals with uncertainties, tradeoffs, and complex scenario analyses to find the best path forward.
More Comprehensive
Data Science is a more tactical analytical field, leveraging data processing, statistical models, and machine learning. Decision Science is more comprehensive and can even incorporate Data Science concepts with other disciplines, including mathematical optimization, game theory, and simulation modeling.
Data Science helps us answer questions like "What patterns exist within my data?" or "Can we predict future outcomes based on this historical data?" Decision Science answers questions like, "Given our knowledge of the data and understanding of possible solution strategies, which strategy is likely to yield the best result?".
Decision Science and Manhattan
In today's rapidly evolving and complex supply chain commerce environment, organizations face multifaceted challenges ranging from volatile customer demand patterns and continuously expanding varieties of supply disruptions to growing expectations for faster and more optimal operational efficiencies, responsiveness, and adaptability. Decision Science is essential to creating structured prescriptive methodologies for systems to navigate these intricacies, transforming vast amounts of data into actionable insights.
Manhattan solutions infused with Decision Science allow your supply chain to remain efficient, resilient, and aligned with both near-term operational needs and overarching business goals in a data-driven, hyper-competitive globalized marketplace.
Examples of Decision Science in Supply Chain Commerce
Warehouse Management
Decision Science optimizes processes within the warehouse—including picking efficiency, optimal picking paths, space utilization, and inventory placement—by considering inventory turnover and a wide variety of execution operations that impact inventory. It helps Manhattan Active® Warehouse Management dynamically adapt and tune to changes in demand patterns, seasonality, or other trends to keep the warehouse operations at peak efficiency.
Transportation Management
Decision Science is used in Manhattan Active® Transportation Management to simulate and develop models to find the optimal transportation policies and routes, taking into consideration variables like costs, constraints, time-phased asset availability, demand/order/shipment patterns, congestion patterns, and delivery windows. The result is reduced transportation costs, improved resource and asset utilization, and improved customer satisfaction due to more timely deliveries.
Order Management
Decision Science helps Manhattan Active® Order Management analyze customer buying behavior, geographical trends, and channel preferences to enable more intelligent order routing and allocation. It ensures that consumer demand and expectations are met and that orders are processed in the most timely and cost-effective way, leveraging all available network inventory and balancing factors like shipping costs and customer location.
Inventory Management
By leveraging machine learning, statistical models, and optimization algorithms, Decision Science helps Manhattan Demand Forecasting and Inventory Optimization solutions find the ideal balance between holding too much or too little inventory. It helps predict future demand more accurately and suggests optimal ordering policies, reducing stockouts and overstock situations, thereby reducing capital expenses and improving customer service levels.
Key Takeaways
- Decision Science is the strategy of selecting between options to ensure efficient, agile, resilient, and optimal outcomes.
- Decision Science handles the uncertainties, trade-offs, and complexities of available options to find the best path forward.
- Manhattan has a long history of infusing Decision Science into its supply chain commerce solutions, built to provide the best possible outcomes and customer experiences for your enterprise.
Related Insights
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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.