Element AI for supply chain

Supply chains meet AI

Supply chains face disruptive new trends

The customer experience is multifaceted and ever-evolving

Especially in today’s day-and-age, customers now buy the supply chain as much as they buy products, and they’re demanding convenience, personalization and transparency.
Increased resilience via more effective use of digital signals

The past 30 years have been spent trying to make supply chains more efficient at or near equilibrium. Differentiated supply chains of the next ten years will be better able to “bend, not break” and offer more alternatives for when things go awry.
Increased focus on triple bottom line (profit, planet, people)

Middle-class spending will triple by 2030, while global climate goals and consumer expectations place constraints on how growth is achieved.
Companies are losing tribal knowledge

As baby boomers retire, they’re being replaced by gig economy workers who lack vital institutional know-how.

From disruption to opportunity

The new and rising demands facing retailers, manufacturers and CPGs can’t be satisfied with solutions born before the internet. New streams of data—from weather and competitive promotions to social sentiment and shelf sensors—are providing a greater abundance of information than ever.

But it's not always clear how to identify which signals are relevant, let alone how to make sense of it all. AI changes all of that.

Feeding off of these diverse streams of data, AI can rapidly process and analyze large quantities of information, paving the way to a more orchestrated and agile supply chain.

AI can help address problems of scale, tedium, and volatility -- all in an effort to better understand causality and sensitivity to make smarter decisions at the right time.

The supply chain problems worth solving need a multifaceted array of capabilities

High Accuracy, short-term forecasting

React to disruptions more quickly and more accurately using SOTA forecasting techniques in circumstances of high volatility and complexity where incremental improvements drive great value. Eliminate the need for costly feature engineering or model proliferation.
Speed up decision making during disruptions

Make decisions in complex, multi-dimensional spaces more quickly with a conversational assistant to rapidly provide aggregated information, presented in the right manner to make the most effective decision possible. Using a novel combination of forecasting, optimization, and simulation, explore future execution scenarios to ease the navigation of future disruption events.
Localized inventory optimization

Create inventory plans for each store, warehouse, and node in the network, taking into account the characteristics that make each node unique. Use this to build optimized, custom planograms.
Visual Anomaly Detection for defects and inconsistencies

A groundbreaking application of computer vision to detect defects in complex environments, our visual anomaly detection is easier to both implement and sustain. It requires less data up front, has the ability to continually learn, is more explainable, and is far more robust to changes in the environment or to the fundamental part itself.