The problem is wasteful supply chains clogged with excess inventory and defective parts. The goal is creating “perfect flow” for manufacturers and retailers to reduce waste running into the hundreds-of-billions dollars annually.
Part of the solution, according to supply chain analytics specialist Noodle.ai, is its “FlowOps” platform, an emerging category of business software that helps manufacturers and retailers cope with logistics volatility and complexity.
FlowOps uses explainable AI to detect and anticipate supply chain disruptions, then recommend work-arounds to optimize Manufacturing and distribution channels. The platform provides early warnings of impending supply issues, a Bayesian analysis tool to determine possible causes and a recommendation engine designed to maintain and tune Manufacturing and product workflows.
The supply-chain waste problem is measured in trillions of dollars annually, along with industrial waste on a similar scale, according to Stephen Pratt, Noodle.ai’s co-founder and CEO. The FlowOps platform is aimed at the estimated $440 billion in excess inventory in 2019 and $860 billion in defective parts that don’t meet manufacturing standards. Pratt argues that static resource planning tools can’t cope with supply chain disruptions that have grown worse during the pandemic.
The San Francisco-based company has invested more than $100 million over the last five years in developing its FlowOps software. “We’re seeing tremendous disruptions and seemingly unpredictable events and seemingly random things that are happening in supply chains that are causing disruptions,” Pratt noted.
The startup and is its growing customer list of retailers and manufacturers are using the tool to restore not only product workflows by also cash and “team flow,” a reference to how resource planners keep supply chains humming.
For manufacturers, the platform also seeks to streamline asset and quality workflows. “Manufacturing defects are not random, they are predictable,” Pratt told Datanami. “The problem is using static systems” unable to anticipate factory or inventory disruptions.
Noodle.ai’s platform leverages explainable AI to anticipate problems and reduce waste in the form of faulty components, excess inventory and underutilized distribution capacity. For example, Pratt reckons half of available space on long-haul trucks is unused. Carriers often make return trips with empty trucks.
Hence, the shelf may be empty when looking for your favorite consumer product.
The FlowOps platform starts with a Sentinel early warning system that leverages three years of times-series data, feeding it into long short-term memory models to help detect disruptions. A demand flow engine helps with inventory forecasting.
