Proteus - Supply Chain Control Tower

"Almost 80% of [surveyed supply-chain executives] said they need to improve and invest in digital planning to increase supply-chain visibility."

- Knut Alicke, McKinsey & Company Insights.



Supply-chain disruptions cost the average organization 45% of one year's profits over a decade. The recent unpredictabilities due to the COVID-19 pandemic have led to soaring demand for agility and visibility to achieve operational excellence. Modern technologies like Order Management Systems, Transport Management Systems and Enterprise Resource Planning often operate in silos while focusing on discrete areas of the supply chain process. These existing systems have deprived executives of the big picture and their ability to see this process as a whole.



Proteus(Control Tower) is an AI-SaaS platform for multi-enterprise supply chain networks* that enables visibility and actionable insights to help executives spot issues/alerts at a bird's eye level. Executives can dive deep into resolving these critical problems in real time with an intelligent, collaborative workflow. They can further deliver the ultimate value chain by minimizing carbon footprint through the KPI scorecards and diversification of supplier(s)/service.



By serving as a nerve centre for the supply chain, it consolidates data from various sources into a centralized platform for real-time monitoring of the supply chain health score.



Incoming Orders: It informs executives about potential risk/optimization across the order journey through predictive data analytics using AI/ML. It classifies them as critical, moderate and important, impacting the supply chain's score.



Active Orders: This provides a map-based view of issues, KPIs, order stage tracking and an overall health score for the supplier, manufacturing, and logistics phases.



Order history: This shows each order's final status and issues resolved and highlights the cost/time saved by resolving these issues.



Resolving supply chain issues with AI/ML recommendation:



The predictive models use ML algorithms to forecast customer demand, optimize inventory levels, optimize logistics routes, or determine optimal production schedules.



The decision support system utilizes AI/ML to provide recommendations and insights to supply chain executives by detecting anomalies or outliers in data, indicating potential issues.