Sense. Understand. Respond.
Adaptive Systems in Supply Chain and Commerce Systems
Adaptive Systems combine advanced analytical and modeling techniques from Statistics, Decision Science, and Data Science to automatically sense and respond, often preemptively, to detected changes or opportunities in operational conditions. They continuously tune parameters and adjust logic to repeatedly refine their models, algorithms, and engines. As a result, Adaptive Systems are perfect for intelligently anticipating and optimally responding to supply chain commerce events like drifting data distributions, evolving customer and consumer demand patterns, shifting operational dynamics, and general supply chain volatility. They are self-sufficient, versatile, and resilient.
Within today's highly interdependent and often volatile environment of supply chain commerce, autonomous Adaptive Systems capabilities are crucial for ensuring rapid, robust, and continuous optimization of science and optimization engines.
Autonomous adaptation is the reason why Manhattan Active applications are infused with computational intelligence capabilities like Adaptive Systems. Manhattan Active solutions are purposefully engineered for supply chain commerce complexity, with expectations of non-stop optimization that continuously adapts 24 hours a day, seven days a week, and 365 days a year with zero downtime while requiring little to no human interaction. That means every machine learning, algorithmic, or mathematical model within must also reliably operate in this manner, regardless of its complexity or lifecycle management needs. There is no downtime while the system experiments with parameter settings or builds, tests, and deploys a new model.
Manhattan's autonomous Adaptive Systems logic seamlessly and continuously self-governs, self-tunes, and self-heals each analytical, predictive, and prescriptive model as well as the system itself that's managing these models. Manhattan's science engines are doing all of this simultaneously while autonomously processing and sifting through massive amounts of supply chain commerce data, from extreme transactional volumes to enormous distributed heterogeneous datasets. Manhattan's solutions use Adaptive Systems capabilities to quickly analyze data, characterize it, and automatically make appropriate adjustments to parameters, policies, models, and/or engines to either guide minor or major course corrections on the path to improved performance.
Examples of Adaptive Systems in Supply Chain Commerce
Adaptive Systems has been a part of commerce, distribution, and logistics since Manhattan started more than three decades ago and over the years its usage has expanded and matured. Today, Decision Science is an integral component of computational intelligence in supply chain commerce.
Adaptive Systems ensure that demand forecasting remains agile, accurate, and relevant by continuously adjusting to the ever-evolving landscape of market dynamics, external factors, and consumer behavior. Adaptive Systems make demand sensing, preemptive forecasting model adjustments, and automated policy tuning a reality, enabling robust, adaptive forecasting models and improved forecast accuracy.
Adaptive Systems enable autonomous fulfillment algorithms that continuously adjust and optimize inventory sourcing decisions, increasing utilization of network inventory, improving inventory turns, reducing markdowns, and increasing shopper retention and customer satisfaction.
Adaptive Systems science uses advanced algorithms to optimize work release. It continuously balances orders and allocations in response to real-time demand and predicted downstream impacts to ensure all resource utilization is maximized and order cycles are minimized, reducing click-to-ship times and increasing order fulfillment throughput.
Adaptive Systems and Manhattan
In today's supply chain commerce environment, Manhattan infuses autonomous Adaptive Systems and algorithms into Manhattan Active® solutions to ensure continuously consistent, robust, and efficient optimization. These sophisticated science and optimization model management systems are designed for round-the-clock operation with little to no human intervention. They self-adapt, self-tune, and self-heal.
Amidst supply and demand volatility, intricate operational complexities, and a tsunami of transactional data, Manhattan's solutions can autonomously process, analyze, and refine system approaches, ensuring optimal supply chain operations.
- 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 to ensure your business outcomes and customer experiences are optimal.
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 drive efficiency and resilience, providing a competitive advantage for supply chain commerce organizations.