The evolution of supply chains in 2024 will, according to Gartner, be defined by the integration of human and robotic workforces.

It’s an interesting time to be in the supply chain business. Not only are supply chains under increasing pressure to unlock greater value for the business, but executives find themselves operating in a landscape that might be even more challenging than that of the pandemic just a few years ago. 

Geopolitical tensions are rising, disrupting global trade routes. The spectre of economic downturn looms over Europe and the US, intermingling with growing unrest over the cost of living, wage stagnation, and the broken promises of 21st century capitalism. And, over everything, the worsening climate crisis throws a long shadow. Crop failures, extreme weather events, and disrupted ecosystems are conspiring to create a world where, for supply chains, disruption is the norm. 

Many organisations are turning to technology as a way to improve their efficiency, cost-containment, and resilience. Supply chain operators are being driven by the need to “ensure their foundation can support both past and future investments, while also looking ahead for new differentiation opportunities,” noted Christian Titze, VP analyst in Gartner’s Supply Chain Practice

A key theme emerging for Titze is the role that emerging technologies like artificial intelligence (AI) and maturing technologies like robotics will play for supply chains. However, Gartner’s report stresses that the success of adoption when it comes to these technologies is tied to “ the complementary integration of humans and machines.” 

Humans and machines in the supply chain

Gartner’s report identifies a number of trends connecting the broader theme of humans and machines in collaboration. 

“These technology trends are not isolated, but rather interconnected and mutually reinforcing,” said Dwight Klappich, VP Analyst and Fellow in the Gartner Supply Chain practice. 

Composite AI, AI-enabled vision systems, augmented connected workforce initiatives, next generation humanoid robots, and machine customers are all contributing to the increasingly collaborative relationship between humans and machines in the supply chain.

AI-Enabled Vision Systems

AI-enabled vision systems combine industrial 3D cameras, computer vision software and advanced AI pattern recognition technologies to autonomously capture, interpret, and make inferences based on unstructured images in real time.

Augmented Connected Workforce (ACWF)

ACWF initiatives aim to reduce onboarding time for employees to achieve full productivity and enhance decision-making. 

Augmented connected forkforce is a strategy that combines the value of intelligent technology, workforce analytics, and skills augmentation to accelerate talent growth and optimisation.

Composite AI

Individual AI applications are impressive, but it’s in aggregate that the technology really begins to shine. Composite AI uses multiple AI techniques together to increase the efficiency and accuracy with which these models learn. Composite AI also broadens the level of knowledge representations, and could ultimately solve business problems in a way that drives supply chain performance improvements.

Next-Generation Humanoid Working Robots

Robotics have been an established part of the supply chain for over a decade at this point. Signalling the next phase in the technology’s journey towards maturity, humanoid working robots combine sensory awareness, mobile manipulation, and dynamic locomotion to perform tasks previously done by humans. 

They typically feature sensor-equipped heads, power and mechanical bodies, manipulative arms/hands, and legs that allow for more dynamic movement than previously seen.

Machine Customers

Machine customers are nonhuman entities capable of selling or purchasing goods or services autonomously. 

Examples include IoT devices placing independent orders, intelligent replenishment algorithms maintaining product availability, and automated assistants suggesting new deals to consumers.

  • Digital Supply Chain

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