Next-generation demand management incorporating replenishment planning, segmentation capabilities, new product introduction, and machine learning for planners.
Transform qualitative and quantitative signals from diverse sources into business insights.
Dynamically group items, locations, and customers with similar behavior into segments that share a demand profile.
Use machine learning to generate more accurate forecasts, while measuring and tracking the root cause of forecast error.
Analyze the cause and effect of seasonality, promotional events, product attributes, and leading indicators to make adjustments to inventory.
Simulate forecast scenarios, collaborate with stakeholders, and manage new product introductions to improve business performance.
Track changes, monitor exceptions, and synchronize with S&OP and supply plans to ensure a well-functioning process.
Make replenishment planning easy and effective. Drive more accurate fulfillment with tailored inventory policies and consumption rules to demand segments.
Integrate demand-driven consumption, inventory and fulfillment to meet customer-specific requirements.
Forecast demand to anticipate seasonal, trend, and event-based changes and keep supply within inventory thresholds.
Automatically assign inventory policies to groups of item locations that have similar replenishment requirements.
Select and compare the effect of different inventory policy parameters on stockouts, overstocks, and inventory costs.
Graphically simulate and adjust the replenishment strategy for individual items and locations to improve performance.
Calculate replenishment requirements and release of planned orders automatically as consumption occurs.
Identify and execute the best supply to demand plans. Plan items across multi-tier locations, identify and address the most important problems, and simulate potential responses to optimize customer service and the cost of inventory.
Balance demand and supply, optimize inventory for service and cost, and plan for multiple material and capacity scenarios.
Plan process, discrete, project-driven, configure-to-order, and outsourced production, as well as drop-ship and back-to-back fulfillment.
Use embedded analytics to review impact on KPIs, such as margin and turns, and navigate to focus on problem areas.
Review and collaborate on planned supply order changes with internal and external stakeholders. Utilize built-in machine learning or integration to IoT systems.
Schedule production during the day to maximize use of bottleneck resources and available components. Reduce work in process inventory, waste, and expedites. Visualize how work order operations are allocated to resources. Drag and drop to simulate the best response to issues or changes, and release schedules for execution in real time.
Use real-time resource availability and work orders in Oracle Cloud Manufacturing to create feasible schedules that take the latest material, capacity, and calendar constraints into account.
Increase throughput and asset utilization with schedules that make efficient changeovers based on your industry attributes, minimizing idle time.
Schedule work order operations with your desired attribute sequence to maximize the efficiency of your production lines. Reduce bottlenecks by offloading to alternate resources.
Easily inspect schedules and analyze production issues in a visual Gantt chart. Make drag and drop adjustments and edit dispatch lists as necessary. Run quick, in-line simulations and what-ifs to resolve issues.
Run and repair schedules dynamically to deal with unexpected machine outages, labor shortages, material delays, and changes in order priorities. Release adjusted schedules as needed during the day for instant shop floor execution.
Evaluate how your factory is scheduled at a glance, with interactive analytics that reveal insights about changeover time, resource utilization, and late orders.
Prioritize your open orders to reduce delivery delays, increase sales, or achieve margin targets when supply or demand changes. Simulate multiple fulfillment alternatives, and select those that best meet your business objectives.
Use available supply to schedule new orders and reduce delays while respecting existing promise dates.
Prioritize orders in the backlog using flexible business rules that maximize order revenue, margin, and service.
Model what-if changes to sourcing, transit mode, and shipping constraints to respond quickly and find the best solutions.
Simulate the scheduling and availability impact of a potential order on the rest of the backlog.