The dawn of AI in supply chain: Redefining efficiency and innovation

Key drivers fueling rapid adoption include proliferating data complexity, need for enhanced visibility and agile decision-making amidst disruptions

Artificial intelligence (AI) is reinventing supply chain efficiency with groundbreaking innovations pioneered by leaders like Utkarsh Mittal. Mittal introduced generative AI to optimise inventory across multiple brands. This transformed supply planning with data-driven insights, generating millions of dollars in cost savings.

Industry reports forecast exponential growth for AI in supply chain, projecting the market to surge from $2.3 billion in 2020 to $19.9 billion by 2027 at a CAGR of over 20 per cent. Key drivers fueling rapid adoption include proliferating data complexity, need for enhanced visibility and agile decision-making amidst disruptions.

"AI is revolutionising supply chains from reactive to predictive with incredible precision," says Mittal. "It enables granular visibility across suppliers, manufacturing, warehouses and transit routes while continuously learning and adapting."

The Power of AI

AI allows supply chains to operate in a self-learning, self-healing capacity by leveraging capabilities like:

* Predictive Analytics: Advanced forecasting algorithms analyse sales data, customer trends and macroeconomic variables to anticipate demand swings and mitigate stockouts.

* Prescriptive Modeling: By simulating myriad scenarios, optimisation engines prescribe ideal procurement volumes, transfer batches and inventory buffers to balance working capital and service levels.

* Automated Warehouses: Computer vision, RFID and robots allow rapid put-away, precision tracking and automated picking/packing without human intervention.

* Dynamic Route Planning: Leveraging traffic data and geo-spatial analytics, logistics routes are continuously re-optimised in real-time for on-time delivery with minimal mileage.

The Next Frontier

Generative adversarial networks (GANs) also expand possibilities by generating synthetic yet realistic datasets for training predictive models and creating digital twin environments to simulate innovations at scale prior to implementation.

However, as AI accelerates, pertinent challenges around security, privacy and job automation must also be examined. Partnerships between governments, academia and industry can guide ethical and inclusive AI adoption.

"Supply chains stand transformed from fragmented linear systems to fully integrated self-learning networks driven by AI’s tremendous potential," concludes Utkarsh. "Those who modernise early will gain sustained competitive advantage."

• KT Network