Supply Chain Analytics Services, Solutions & Consulting

Harness AI, data, and strategy to fuel end-to-end transformation

Accelerate your AI/ML journey with a connected approach from defining the right implementation strategy and validating it through proof-of-value and design, to building a scalable supply chain data foundation that fuels growth. We implement and scale AI/ML use cases, ensure seamless end-to-end orchestration with production-ready pipelines and monitoring, and drive change management and value tracking to maximize adoption and measurable impact.

AI/ML Strategy

Assess the current AI/ML landscape, compare with industry best practices and define the future of connected Supply Chain. We align business goals with the latest technologies like Agentic AI to create a clear roadmap from current state to future state.

AI/ML Operationalization (Foundry)

Turn ideas into actionable, value-driven AI/ML initiatives. We develop Proofs of Concept (POCs) and Proofs of Value (POVs) like demand sensing agents, on-shelf availability agents etc. to validate potential and design implementation approaches that deliver measurable impact.

AI/ML Implementation (Factory)

Bring supply chain AI/ML strategies to life through the development and deployment of high-impact use cases at an enterprise scale.

Supply Chain Data Foundation

Build a robust data backbone that fuels scalable AI/ML in supply chain operations. We design functional data models, integrate pipelines, and create a unified data layer that serves all business needs at scale.

End-to-End Orchestration

Ensure seamless execution from demand planning to last-mile delivery with integrated data pipelines, production readiness, and real-time performance monitoring. We connect every node of the supply chain planning, sourcing, production, inventory, logistics, and fulfillment into a unified AI/ML-powered ecosystem that drives visibility, agility, and sustained business value.

Change Management & Value Tracking

Drive adoption across supply chain functions, align global stakeholders, and track value delivered post-implementation. We ensure AI-led supply chain initiatives are embraced, sustained, and transparently delivering measurable impact in cost reduction, service levels, and resilience.

Pre-built AI/ML agents to plan, optimize, and orchestrate your supply chain

Leverage a suite of specialized AI/ML agents from Planning and Sourcing & Procurement to Inventory, Warehouse & Logistics, Intelligent Store Operations, and the Supply Chain Command Center. These accelerators work together to enhance forecasting, optimize inventory, streamline procurement, improve logistics performance, and drive last-mile efficiency all while enabling end-to-end visibility, control, and rapid disruption response. Deliver measurable value by reducing cost and waste, improving service levels, and accelerating decision-making across the supply chain.

Integrated Planning Agent

Optimizes key planning functions across the supply chain, including demand forecasting, inventory optimization, distribution planning, production scheduling, and procurement planning enabling data-driven decisions that improve efficiency and resilience.

Sourcing & Procurement Agent

Transforms sourcing and procurement through inbound/outbound control towers, supplier collaboration, digital procurement, spend analytics, smart negotiation, and vendor management reducing costs while strengthening supplier relationships.

Inventory Optimization Agent

Drives inventory accuracy and efficiency with store replenishment optimization, waste reduction strategies, and real-time stock visibility ensuring the right products are in the right place at the right time.

Warehouse & Logistics Agent

Enhances warehouse and logistics performance through warehouse control towers, delivery promise optimization, distribution center capacity planning, freight capacity forecasting, and other solutions that streamline operations end-to-end.

Intelligent Store Ops Agent

Improves last-mile and in-store operations with capabilities like shrink reduction, returns analytics, rider assignment optimization, and efficient delivery management boosting customer satisfaction and operational agility.

Supply Chain Command Center

Acts as a supervisory layer, monitoring the entire supply chain and coordinating with underlying agents to quickly resolve disruptions, manage deviations, and maintain seamless orchestration across functions.

Here’s how we’ve helped our customers win at the last mile

Accelerating AI-native forecasting for a U.S. grocery chain

Faced with a legacy Blue Yonder system, a leading U.S. grocer needed a scalable forecasting solution that could meet modern demands across demand, labor and production planning.

Tredence architected an AI-native forecasting engine on Azure Databricks. The system integrated Delta Lake and Unity Catalog for traceability, scaled data models across millions of items, and automated planning workflows.

A campaign and pricing model POC was also initiated, and the transformation was anchored by a highly engaging executive “Data Day” to align stakeholders and accelerate adoption.

$220M EBITA value unlocked

10% forecast improvement

$13M in labor savings

90%+ replenishment match rate

Creating a supply chain control tower for a Fortune 10 retailer

Due to a recent acquisition and complex operations, this global retailer had petabytes of data but struggled with daily data-related challenges.

The retailer developed a comprehensive data strategy to ensure architecture and data quality, enable real-time analytics, leverage AI/ML tools, and improve delivery capabilities.

The retailer built a unified data foundation and created connected visibility and intelligence with control tower capabilities, spanning reporting, inventory, fulfillment, and transportation processes.

45+ dashboards and 60+ KPIs, driven by a retail data model

Real-time data streaming

100% automated report production

Improving asset and batch throughput for a large pharmaceutical company

A multinational pharmaceutical company’s data and tech silos hindered trend detection, repair prioritization, and shutdown planning, leading to long issue resolution times and extended asset downtime, which impacted throughput and quality.

Tredence built an Azure-Databricks solution using a domain-specific asset and batch model to drive optimization and predictive maintenance. A traceability graph and predictive model help identify deviations, integrate with machines, and boost asset and batch throughput.

12% reduced assets mean time to failure (MTTF)

7% decreased asset maintenance costs

12% improved asset throughput

15% higher production uptime

Driving demand forecast accuracy for a large convenience retailer

A large convenience store chain experienced challenges in creating accurate sales estimates at the SKU level, resulting in manual assortment decision-making, excessive safety stock, and hot food waste. In addition, manual detection processes used to manage fuel pump outages resulted in lost sales.

The company enabled advanced demand forecasting, connected planning, and digital merchandising to drive business value. The retailer also uses an AI algorithm to predict fuel pump faults and automate work order production, reducing outages.

1000 bps improvement in forecast accuracy

$170M gain in incremental fuel sales due to lower pump downtime

$109M reduction in safety stock, improving margins