Gartner Identifies Composite AI and Supply Chain Data Governance as Highest Impact Supply Chain Planning Technology Trends

Experts to Discuss Key Supply Chain Planning Issues at Gartner Supply Chain Planning Summit in London

Composite AI and supply chain data governance are the highest impact technology trends for supply chain planning (SCP) leaders, according to Gartner, Inc.

"SCP leaders are in the process of developing transformation roadmaps that will prioritize delivering on advanced decision intelligence and automated decision making," said Eva Dawkins, Director Analyst in Gartner’s Supply Chain practice.

"Composite AI, which is the combined application of different AI techniques to improve learning efficiency, will drive the optimization and automation of many planning activities at scale, while supply chain data governance is the foundational key for digital transformation,” said Dawkins.

SCP leaders are tasked with developing a roadmap that demonstrates how technology investments can help their businesses remain successful and drive competitive advantage, despite growing uncertainties and economic pressures. This task is complicated by frequent disruptions and the rapid pace of technological innovation.

To increase and accelerate the realized value of technology investments, Gartner recommends facilitating a shift from IT-led to business-led digital leadership, with SCP leaders taking ownership of multidisciplinary teams to advance business operations, channels and products. This includes establishing a clear governance process that engages different stakeholders and solicits their feedback for using and prioritizing supply chain planning technologies.

Top Technology Trends for Supply Chain Planning

As supply chain decision makers look to improve the decision making process, they are targeting supply chain technology investments. The top technology trends for SCP leaders are both directly supportive of this objective.

These include:

• Composite AI: This trend is increasingly important to SCP leaders as uncertainty, complexity and the drive for automation must factor into their plans and objectives. Broadening the portfolio of AI techniques used (rather than seeking a one-size-fits-all approach) is the first step to optimizing and automating SCP activities. Once organizations have a broader portfolio available, they can define and implement composite strategies to leverage the wider variety of techniques.

• Supply Chain Data Governance: This trend ensures data quality by formalizing the framework in which supply chain data is identified, defined, controlled and accessed, both internally and externally. Establishing or evolving supply chain data governance capabilities is often a preparatory activity for organizations embarking on a digital transformation journey. Relevant, accurate and complete data is necessary to establish trust and encourage adoption by end users.

“A sound data governance strategy supports advanced technologies, such as composite AI, while also facilitating collaboration throughout the supply chain technology ecosystem,” said Dawkins. “Without attention to data governance, SCP leaders will likely struggle to achieve their expected return on key technology investments.”

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Interplay Between Humans and Machines Is a Key Theme Driving Multiple Trends in Supply Chain Technology

"This year’s trends are driven by themes that encourage supply chain technology leaders to ensure their foundation can support both past and future investments, while also looking ahead for new differentiation opportunities,” said Christian Titze, VP analyst in Gartner’s Supply Chain Practice. “AI variants continue to be both a driver of trends, such as in robotics, and a trend itself, this year represented by ‘Composite AI.’”

This year’s supply chain technology trends were driven by two broad themes: the need for supply chain leaders to leverage emerging technologies to control and protect their businesses and new opportunities for competitive differentiation through the complementary integration of humans and machines (see Figure 1).

Figure 1: Top Trends in Supply Chain Technology 2024, By Theme

Source: Gartner (March 2024)

Cyber Extortion

Cyber criminals are highly successful at executing ransomware attacks to extort funds from supply chain organizations. It is highly likely that they will utilize the power of AI to generate advanced attack tools, making their attacks even more effective. Supply chain technology leaders should collaborate with IT leadership to confirm ransomware attack scenarios are included in the corporate risk management processes and develop a detailed ransomware incident response playbook.

Supply Chain Data Governance

The emergence of powerful tools for advanced analytics and AI techniques is massively scaling the capabilities for cross-functional visibility, scenario modeling and decision automation. As those technologies are increasingly adopted, the importance of maintaining a high level of data quality and strict governance process is becoming business mission critical.

End-to-end Sustainable Supply Chains

Sustainability-related legislation is growing globally and driving a shift from voluntary to regulatory compliance. As a result, sustainability data accuracy needs to be uplifted from indicators to being investment grade to meet stakeholder requirements, while also driving internal decision making.

AI-Enabled Vision Systems

AI-enabled vision systems are novel hyper-automation solutions that combine industrial 3D cameras, computer vision software and advanced AI pattern recognition technologies. These solutions can autonomously capture, interpret, and make inferences based on the unstructured images the vision systems see in real-time.

Augmented Connected Workforce

Augmented connected workforce (ACWF) initiatives reduce the time required after onboarding for an employee to become fully productive and improve their decision making. The ACWF is a strategy to optimize the value derived from a human worker by establishing a connective tissue that optimizes the use of intelligent technology, workforce analytics and skills augmentation. It treats these capabilities as a unified, cohesive strategy to accelerate and scale talent.

Composite AI

Composite AI is the combined application of multiple AI techniques to improve the efficiency and accuracy of learning to broaden the level of knowledge representations, and ultimately, to solve a variety of business problems that drive supply chain performance improvements. Depending on the context of a specific use case, different AI techniques, or more often a combination of techniques, will make more sense than relying any on a “one-size-fits-all” approach.

Next-Generation Humanoid Working Robots

Next-generation humanoid robots combine sensory awareness with mobile manipulation and dynamic locomotion to perform productive work that was previously relegated to biological humans. Humanoid robots will typically imitate the human body with a head with sensors and cameras for sensing its environment; a body that houses the power and mechanicals; arms and hands/grippers for grasping, manipulating and carrying items; and legs for dynamic locomotion.

Machine Customers

Machine customers are nonhuman economic actors that autonomously obtain goods or services in exchange for payment. Examples include: IoT-connected devices or assets that place orders independently of a human command, intelligent replenishment algorithms that maintain availability of consumables and intelligent assistants that suggest deals to consumers.