From Alerts to Agents: Turning Unified Visibility into Autonomous Action with SCOTI

Companies are rapidly transitioning from traditional AI to agentic AI to transform their supply chain. Unlike traditional AI, which largely relies on rules and human intervention, agentic AI can autonomously execute and manage supply chain operations.

The agents are trained to make informed decisions and learn from feedback, enabling them to effectively manage the complex and dynamic nature of the modern supply chain.

It helps companies lower operating costs, streamline supply chain management, and proactively respond to disruptions and seasonal peaks.

Companies primarily use agentic AI for:

  • Sending alerts: Agentic AI can send real-time alerts to companies, notifying them about deviations and enabling them to respond proactively without waiting for human instructions.

  • Inventory rebalancing: Companies can utilize agentic AI to analyze historical and real-time data on demand, supplier delays, and seasonal fluctuations, and rebalance their inventory to adjust for overstocking or stockouts.

  • Dynamic slotting: Depending on factors such as pick frequency, order patterns, and labor availability, agentic AI can dynamically assign product storage locations. This enables companies to accelerate picking, increase throughput, and improve fulfillment speed.

  • Route optimization: Based on factors such as weather conditions, traffic, and fuel efficiency, agentic AI can make independent decisions on rerouting vehicles and ensure timely deliveries.

However, for an agentic AI to work efficiently, companies need a well-integrated ecosystem of supply chain platforms.

That’s where SCOTI – our proprietary agentic AI tool comes into the picture. 

SCOTI – The Agentic AI That Turns Unified Visibility Into Autonomous Action

Modern supply chains are becoming increasingly complex. Companies use legacy tools like spreadsheets and systems like Transportation Management (TMS), Warehouse Management (WMS), Enterprise Resource Planning (ERP), and a variety of IoT devices to manage storage, transportation, and order fulfillment.

However, each of these systems operates in silos, providing limited visibility into supply chains to companies. This prevents companies from making timely decisions.

At a time when companies are expected to meet the dynamic needs of markets, delayed responses or a fragmented supply chain could put the business at risk.

That’s when companies need an agentic AI tool like SCOTI.

SCOTI is an Agentic AI-powered Logistics Assistant that orchestrates various fragmented systems, including TMS, WMS, ERP, and IoT devices, and harmonizes data across these systems to build a single source of truth. 

It helps companies:

  • Manage exceptions: SCOTI can handle exceptions such as traffic delays or unexpected disruptions by optimizing routes and adjusting dock schedules, ensuring shipments stay on track and business operations continue smoothly.

  • Streamline operations:

    Actionable Recommendations: Identify the most cost-effective routes and reduce dwell times to boost efficiency.

    End-to-End Workflow Management: Automates and manages processes from order management to dock scheduling and invoicing.

    Proven Cost Savings: A food distribution company saved $80,000 annually by optimizing a shipping route that was 15% more expensive, based on SCOTI’s recommendations.

  • Predict events: SCOTI leverages NLP to forecast events, such as demand spikes and other anomalies, across the supply chain.

Besides these benefits, SCOTI is platform-agnostic. It can be easily deployed on cloud and on-premise environments. This makes it adaptable to different IT landscapes. 

By improving supply chain efficiency and providing unified insights to companies, SCOTI has helped companies reduce logistics costs by 20-30%, improve order accuracy by 2X, and accelerate order fulfilment time by 40-60%.

How SCOTI Connects TMS, WMS, and Other Enterprise Applications To Streamline Operations?

SCOTI provides a unified integration layer that enables companies to integrate disparate systems, such as WMS, TMS, and ERP, regardless of the vendor or data format. This improves the end-to-end supply chain visibility. It ensures a smooth flow of real-time data from all systems in a standardized format, making it easier for companies to manage exceptions and make informed decisions. Moreover, SCOTI uses AI bots that are trained on Natural Language Processing (NLP). This enables the bots to interpret and act on the data from different systems more efficiently.

SCOTI’s specialized bots have proven to have helped companies in the following areas:

  • Automating dock schedules: SCOTI eliminates the time-consuming process of coordinating with various stakeholders for booking dock slots. It automatically creates and adjusts appointments depending on the real-time data of when the shipment arrives. It assigns the best dock door based on real-time data to reduce security gate delays and accelerate the unloading and loading process.

  • Route optimization: SCOTI ingests real-time data on live traffic, weather conditions, and warehouse updates into its AI models. This enables it to recommend route changes in case of delays or disruptions automatically. It can also adjust the delivery schedule and notify customers accordingly.

  • Cold chain management: SCOTI collects temperature, humidity, and other data from reefer trucks, containers, and warehouses. It predicts temperature fluctuations and equipment failure, and triggers automated workflows to reroute the vehicles or reschedule dock appointments to prevent spoilage.

  • Dynamic and Optimized Shipment Planning: SCOTI replaces hours of manual planning with automated optimization in seconds, enabling the planning process with AI-Powered Intelligence. SCOTI autonomously orchestrates order planning, route optimization and exception handling by triggering CSCS Consolidation Engine that intelligently groups orders and plans them into optimal shipments based on business rules, compliance, product class compatibility, infeasibilities and many other efficiency factors. Additionally, SCOTI also supports dynamic adjustments to the plan to accommodate last minute changes and overflows. 

  • Intelligent Driver & Equipment Assignment: SCOTI, assists the dispatchers with the intelligent assignment of drivers and equipment to the trips. What once took hours of manual effort is now done in just seconds, even when matching hundreds of trips. SCOTI matches each driver’s schedule with trip arrival and departure times, assigning the most optimal set of drivers while simultaneously pairing the right set of tractors, trailers, and IoT devices for seamless tracking and delivery.

Why Human Intervention Is Important While Implementing Agentic AI

Balanced Automation: While SCOTI automates most parts of supply chain workflows, companies cannot completely eliminate human intervention.

Human Oversight: Agentic AI assists with task automation and decision-making, but humans are essential for monitoring performance and setting boundaries.

Governance & Guardrails: Enforcing governance ensures accountability, transparency, and ethical AI use across operations.

Data-Driven Decision-Making: Human experts interpret AI insights to make informed, strategic business decisions.

Continuous Alignment: Human validation of AI-generated outcomes helps train and adapt AI agents to align with evolving business goals.

The 30-60-90 Day Roadmap For Agentic AI Implementation

Overhauling existing systems and workflows can be risky, especially in today’s fast-paced environment. We recommend that companies take a phased approach when implementing agentic AI. Here’s a roadmap they can follow:

  • 0-30 days: At this stage, assess the existing systems, pain points, and prioritize use cases that could significantly improve the supply chain management. We would also recommend auditing the data quality and integration capabilities of the various systems and defining the scope for the pilot launch.

  • 31-60 days: Deploy SCOTI agentic AI for the chosen use case. The use case could be something as critical as optimizing routes for high-friction lanes or automating picking to ensure hassle-free shipment. Track the KPIs, such as improvement in ETA accuracy or picking productivity, to gauge the AI’s effectiveness. Run multiple simulations and what-if tests to identify exceptions, collect feedback, and modify the AI agents.

  • 61-90 days: If the pilot is successful, establish governance and security rules to manage agentic AI effectively. Do demand planning is required to ensure the agentic AI can manage unusual scenarios and seasonal demands. This is also the time when companies can consider adopting agentic AI for other workflows and use cases and scaling it across all functions. 

Need help with integrating SCOTI and transitioning from unified visibility to autonomous actions? Contact us for more details.

Frequently Asked Questions (FAQ)

 

What is SCOTI, and how does it integrate TMS, WMS, and ERP?

COTI is an Agentic AI-powered Logistics Assistant that provides an integration layer to connect different systems like TMS, WMS, and ERP. It harmonizes data, offers better visibility across the supply chain, and makes autonomous decisions depending on real-time data from the systems.

Q. Why do companies need more than traditional AI to manage supply chains?

While traditional AI can unify different systems and provide a unified view of the entire supply chain, companies need more to adapt to the changing market scenarios. Agentic AI learns from historical and real-time data and makes decisions autonomously. This helps companies to respond to dynamic situations more proactively.

Q. How to implement agentic AI like SCOTI within the company?

Take a 30-60-90-day approach instead of implementing agentic AI all at once. This will help companies assess the current environment and processes, gauge the effectiveness of agentic AI, measure performance, modify the agents, and expand to other workflows. 

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