AWS for Industries

Overcome CPG OTIF Challenges with Predictive Supply Chain Planning and Execution

AWS and Noodle.ai are on a mission to help Consumer Packaged Goods (CPG) manufacturers solve the on time, in full (OTIF) dilemma that began in 2020. In our recent blog, How Retail/CPG Can Improve On-Time, In-Full (OTIF) Deliveries, we introduced the concept of OTIF and why it matters to CPGs. As put by Michael Connor, Global Head of Solutions for CPG at AWS, “Our customers tell us that OTIF is their biggest supply chain challenge, costing them millions in compliance fees and revenue loss from stockouts.”

In this blog, we’ll provide an overview of AWS Partner Noodle.ai’s supply chain planning and execution solution, which leverages AWS infrastructure for Noodle.ai’s AI-based applications.

“Through our collaboration with Noodle.ai, AWS is helping CPGs avoid these fees and get products to their consumers, with an agile software-as-a-service approach that doesn’t require a significant monetary investment,” says Connor.

The Challenges of OTIF

Let’s start with some background about why OTIF is so difficult for CPGs. The end-to-end supply chain process is comprised of many different elements that are required to move products from raw materials to consumer. With each discrete process or hand-off, there is system-wide variability, creating the possibility of delays and fill mishaps. For a CPG with global operations, OTIF performance spans the entire supply chain and can’t be solved at a single functional level.

As we’ve worked with many leading CPGs, we’ve found three common issues that make it difficult to meet and exceed OTIF goals.

  1. Addressing OTIF too narrowly as a transportation issue (e.g., last mile)—Yes, third-party logistics play an important role in OTIF, and refining lead times with logistics vendors can help improve OTIF. However, addressing OTIF performance at only the transportation level ignores the many other upstream stages that contribute to OTIF.
  2. Solving for OTIF by looking in the rear-view mirror—Looking at past OTIF trends through the lens of business intelligence tools or a rules-based ERP system won’t holistically improve OTIF. We’ve found that by only looking at a few discrete data sources for insights into OTIF issues, many CPGs reach incorrect conclusions and therefore don’t address the big picture challenges of OTIF.
  3. OTIF aggregation—It’s easy to get caught in the trap of trying to improve OTIF performance through aggregate metrics. However, to address OTIF challenges, you need to focus on deep segmentation (SKU-week-DC-level) of key metrics that contribute to OTIF, so you don’t miss the nuances that exist across your supply chain.

The supply chain profile of a direct-to-store product will vary from a product that’s distributed through a warehouse. Supply chain profiles also vary by SKU and distribution center (DC) because things like raw materials and transportation methods are different for virtually every product on the market. Because of the variances, each product category requires a different treatment to drive successful OTIF outcomes. In fact, many of our customers tell us that legacy ERP tools do not provide the level of granularity they need to meet their OTIF goals.

Enter AWS and the Noodle.ai OTIF Solution

To address the many challenges of OTIF for CPGs, AWS and Noodle.ai are partnering to deliver a robust, predictive planning and execution solution that uses artificial intelligence (AI) and machine learning (ML) to improve planning for better OTIF outcomes. It’s a unified system of intelligence for complex supply chains. It empowers inventory planners to make the best execution-horizon allocation and expedite decisions with recommendations based on patterns previously hidden in the data.

OTIF Solution Overview

An Overview of the AWS and Noodle.ai Partner Solution:

  1. We ingest historical data from ERP and planning systems and enhance the data with external signals from the AWS Data Exchange and the Noodle.ai Supply Chain Data Cartridges.
  2. The Noodle.ai Data Engine processes the enhanced, consolidated dataset following a two-step procedure. First, the Data Engine cleanses, normalizes, and transforms the data into a usable state. Next, the Data Engine converts the data into signals to accurately predict OTIF.
  3. The solution detects patterns in historical data (Sentinel) using Noodle.ai’s AI engine, computes large scale probabilities at the SKU/DC level (Precog), and makes recommendations to planners (Pathfinder) to improve OTIF outcomes. As more data accumulates in the Data Engine, the accuracy of OTIF predictions improves.
  4. Noodle.ai’s Flow Operations applications for supply chains give planners visibility across demand, inventory, and production, enabling them to improve OTIF holistically by identifying value-at-risk across the supply chain network.
  5. The data output from the AWS/Noodle.ai application is uploaded to customers’ supply chain data lakes and ERP/planning systems to ensure a single source of truth.

To learn more about the AWS and Noodle.ai OTIF solution, register to attend our informative webcast, AWS + Noodle.ai Cures CPG’s OTIF Challenges, live on Wednesday, June 17, at 9:00am PDT or receive the on-demand replay. You’ll hear from experts Michael Connor (AWS), Gaurav Palta, (Noodle.ai), and Mike Hulbert (Noodle.ai) as they discuss the importance of OTIF, the limitations of legacy supply chain planning solutions, and how AWS and Noodle.ai combined can help you overcome the challenges of OTIF.

And look out for the next blog in our OTIF series in which we’ll feature a customer using the solution today and how Noodle.ai’s Flow Operations software is helping them improve fill rates, reduce inventories, and reduce OTIF penalties.

Michael Connor

Michael Connor

As Global Solutions Lead for Consumer Products at AWS, Michael Connor helps customers meet their goals for revenue growth and digital transformation using cloud technologies. Michael brings a wealth of Consumer Goods experience from his previous role as Chief Architect for Coca-Cola Freestyle, where he led Digital Innovation, Data Science & Analytics, and Enterprise Architecture. Michael also led Coca-Cola North America’s consumer and enterprise cloud migrations as part of a digital transformation, and while leading the innovation group in Coca-Cola, the IP developed represented three of the company’s top four innovations in 2020. While at Coca-Cola, Michael was a five-year member of the AWS Customer Advisory Board. His passions include artificial intelligence, automation, privacy, culture, and ethics.

Amit Saini

Amit Saini

Amit Saini leads Alliances at Noode.ai, creating “better-together” outcomes for Noodle.ai customers and partners. Previously, he was responsible for Enterprise AI Services at Noodle.ai, where he led AI-based supply chain transformation initiatives with Noodle.ai’s Flow Ops suite of applications. Prior to this, Amit spent over a decade at MicroStrategy—a leading provider of business intelligence software—and held various roles. He is passionate about the power of Human+AI for better decision-making and the reduction of global waste.