Sense What’s Next
Sensium is an AI-native supply chain solution that deploys in just 8-12 weeks for even the largest and most complex manufacturers. It integrates seamlessly with your legacy tools and existing datasets, giving you real-time visibility, automated forecasting, and clear next steps to solve your biggest challenges, all without a long and heavy implementation.
Sensium transforms fragmented ERP data into unified, predictive intelligence in just a few weeks.
It then catches OTIF failures 7-14 days before they occur, and recommends specific mitigation actions your planners can execute immediately.
OTIF Improvement
from predictive exception alerts and inventory optimization
working capital reduction
from tighter management of exceptions and inventory levels
less time spent firefighting
from automatically identifying risks and how to resolve them before they happen
Sensium gives you one complete solution to sharpen and streamline your supply chain planning process. It’s purpose-built to turn messy, siloed data into clean answers, delivered before you need them.
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Connect your existing systems and turn all of your supply chain data into a single source of truth, without a manual clean-up exercise.
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Use AI to forecast demand, detect anomalies, and identify your supply chain’s evolving risks and opportunities.
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View the current state of your supply chain, get recommendations on how to improve it, and see the top 3 actions to take today.
Sensium was designed to fully implement and deliver value within 8 – 12 weeks, without replacing existing systems, performing manual data cleaning, or disrupting operations.
We follow a structured, logical implementation roadmap that combines our proprietary AI with white-glove support services to minimize customer lift and accelerate time-to-value. We focus our initial implementations on solving your highest-impact business problems to ensure we drive outcome-driven metrics improvement in record time.
Sensium AI is a Microsoft-native platform for mid-market manufacturers that unifies operational data, applies AI forecasting and optimization, and helps teams act on insights. It connects data from ERP, planning, and external sources into a single governed model, predicts demand and supply risks, and supports decision-making and execution through dashboards and an AI-powered chatbot - Sunny.
Sensium is designed specifically for mid-market manufacturers and is built entirely on the Microsoft ecosystem. It focuses on fast time-to-value, typically 8 to 12 weeks for an MVP, rather than long multi-year transformations. It augments existing ERP systems instead of replacing them and emphasizes measurable outcomes across service, cost, and working capital.
No. Sensium is designed to work alongside existing ERP systems. It integrates with platforms like SAP, Oracle, Microsoft Dynamics 365, and other data sources via APIs and Microsoft Fabric. There is no ERP rip-and-replace required.
Yes. While Sensium is Microsoft-native, it can ingest data from non-Microsoft systems such as third-party ERPs, WMS, spreadsheets, and external data sources through APIs and data connectors.
Initial value is typically realized within 8 to 12 weeks. Data is usually connected and baselined within the first 2 to 3 weeks, with forecasting models and decision dashboards live shortly after.
Based on documented pilot targets and benchmarks, Sensium aims to deliver measurable improvements such as increased forecast accuracy, reduced inventory levels, lower expedited freight costs, and improved planner productivity. Results are tracked against baselines established during the MVP.
An MVP is a focused 8-week pilot that demonstrates AI-driven demand and production alignment. It typically includes data unification, baseline KPI measurement, demand forecasting models, scenario simulations, and limited AI-assisted actions.
Engagement typically starts with a scoped assessment and agreement on a pilot use case. Data access is established, baseline metrics are defined, and an MVP is deployed to validate value before any broader rollout.
