Racking Up CPG Logistics Success

Timeliness in supply chain management, particularly in outbound logistics, is integral to maintaining strong retailer relationships and ensuring customer satisfaction. However, managing on-time deliveries amidst the complexity of numerous shipments and multiple stages in each outbound shipment is difficult.

In the intricate logistics landscape, it’s vital to ensure punctual deliveries, especially in the face of penalties from retailers for any delays. The key is identifying at-risk shipments and determining the necessary actions to expedite them — a task made daunting by the sheer volume of daily shipments.

A leading multinational company was grappling with a costly logistics challenge in the highly competitive consumer packaged goods (CPG) industry. They were incurring approximately $24 million in fines annually due to delayed deliveries.

Analyzing the logistics play

The company was at a critical juncture, facing the overwhelming task of optimizing its complex logistics operations. This was compounded by the need to significantly maximize on-time deliveries to reduce these hefty penalties. The challenge was not just logistical but also analytical, requiring a deep dive into the multifaceted dynamics of supply chain management.

Analytics for precision delivery

To address this issue effectively, the company recognized the necessity of a robust analytics system. The aim was to develop a sophisticated, data-driven approach that could accurately predict and mitigate the risks of late shipments. Implementing this solution would minimize financial losses, strengthen retailer relationships, and enhance the company’s reputation in the market.

We started with discovery sessions to understand the outbound shipment process and the various steps and teams involved. We also identified all source systems and how they are used to track shipments. Based on all the information gathered, we leveraged AED methodology to determine the model requirements, infrastructure, and UI design, which the relevant stakeholders then validated and signed off.

What we provided:

Cued up analytics innovations

We implemented a structured, phased solution that aligned technology and analytics to transform the client’s logistics performance.

Objective

• Develop a predictive logistics model to minimize late deliveries
• Integrate the solution with existing ERP and TMS systems
• Pilot and scale the solution across North America
• Achieve end-to-end automation in supply chain management
• Implement within a 12-month timeline

Technology Stack

• Bayesian inference modeling, Azure cloud platform
• Azure cloud services, ERP, and TMS integration
• Azure-based POC, gradual scaling approach
• Azure automation, full-stack deployment
• Comprehensive Azure deployment, phased approach

Reason

• To accurately forecast and mitigate the risk of late shipments by understanding the conditional probabilities of each step in the supply chain
• To ensure seamless data flow and real-time analytics, enabling proactive decision-making for shipment risk management
• To test the effectiveness of the solution in a controlled environment and then systematically expand its application across all customer lanes in North America
• To streamline processes, reduce manual intervention, and enhance overall efficiency in logistics operations
• To move from concept to deployment efficiently, ensuring that the solution is user-ready within one year

Striking the perfect balance of precision and strategy for unparalleled logistics efficiency

The most significant data point is the on-time delivery percentage (OT%). Its relevance lies in its direct correlation with incurred penalties — the company faces financial penalties if it falls below a certain threshold. Maintaining a high OT% is thus essential for operational success and cost-effectiveness.

The immediate impact:

Streamlined logistics and enhanced decision-making

Achieving breakthroughs in on-time deliveries for game-changing results