One Network Enterprises Enhances Automated Intelligence and Machine Learning to Improve End-to-End Supply Chain Planning

One Network Enterprises (ONE), the leading global provider of intelligent control towers and the AI-driven Digital Supply Chain Network, is pleased to announce significant advancements to the NEO Platform’s supply chain planning capabilities. These capabilities span the entire supply chain network ecosystem and functions, from revenue planning, demand planning, IBP/S&OP, supply chain planning, logistics, and network optimization.

With the release of NEO 3.5, One Network continues to make major strides in the area of AI and machine learning (ML), advancing the underlying proprietary framework of its intelligent agent, known as NEO, to increase the effectiveness of its learning capabilities. Two new capabilities are now available: Network BOM Constrained Supply Planning and Field Service Optimization. These new capabilities combine optimization with machine learning, enabling best-in-class prediction accuracy compared to traditional approaches.

NEO benefits organizations on the Digital Supply Chain Network with customer and supplier insights, full network collaboration, and increased network density. NEO captures network-wide data, making that data available for big data analytics through a supply chain data warehouse. Predictive analytics are generated from this current and historical data by applying advanced analytics such as ML, neural networks, and combinatorial optimization, along with traditional analytical techniques.

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Prescriptions are generated from predictive analytics based on solving root causes combined with targeted business and process KPIs. The prescriptions are “smart” in the sense that they incorporate both local and global objectives, and the relationship between demand, supply, and logistics. They offer a series of dynamic prescriptions that are sensitive to current conditions and constraints, to optimize execution and completely resolve problems.

NEO’s learning capabilities now include the ability to learn successful prescription sequences that generate optimal outcomes, so that it can offer those patterns in similar contexts in the future. The combination of the digitized supply chain network and smart prescriptions enables continuous and incremental planning on a near real-time basis. Thus, NEO 3.5 attains a significant milestone, enabling autonomous decision-making (based on user-defined KPI “guardrails”) across the supply network.

NEO 3.5 also introduces the concept of “bring your own intelligence” (BYOI), enabling companies to leverage insights from elsewhere as part of their decision-making on the Digital Supply Chain Network. NEO enables BYOI with ML “plug points,” so customers can extend the solution based on their own analytics. Any such SDK-created extensions are guaranteed to be supported in future NEO releases.

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These new capabilities are made possible by the distinctive architecture of One Network’s NEO Platform. On the platform, planning and execution run concurrently, using the same data objects and the same data model, enabling true planning married to execution. Forecasts, orders, and deliveries move through the network in a seamless flow across all time horizons, without the need for a bridge between planning and execution. Continuous and incremental planning provides near real-time demand-supply matching across the network. Opportunities and problems are handled through interactive workbenches, where they can be autonomously or collaboratively engaged. Due to the fact that NEO’s distributed transaction management spans demand, supply, and logistics, it enables more powerful, dynamic workflow problem resolution across all functions, and significantly increases the chances of completely resolving issues.

NEO 3.5 also introduces a new NEO capability called “Optimized Execution,” which is enabled by unifying planning and execution on one platform. Optimized Execution is equipped with sophisticated forecasting tools, real-time insights, decision support, and decision execution. It can run autonomously, based on KPI “guardrails” and user-defined business rules, or present exceptions, problems, and potential issues to users via the workbench. Users can review and execute NEO’s smart prescriptions, or collaborate with relevant trading partners to determine the best path forward.

Optimized Execution brings the traditionally distinct functions of planning and execution together in a unique way. Its smart prescriptions are designed to solve issues caused by demand and supply variation, to meet both local and network-wide objectives. Smart prescriptions bridge the gap between decision support and execution, by enabling plans to be executed even as demand, supply, and logistics conditions vary; and to ensure that execution continues to align with objectives.

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