Can Artificial Intelligence Truly Boost the Digital Economy?

In today’s fast-evolving market, adapting through intelligent technologies is key to digital transformation. While e-commerce companies are born digital, traditional manufacturing and consumer packaged goods (CPG) companies are often bogged down by outdated systems and processes, which hinders their competitiveness against digital natives.

As consumer demands for faster delivery increase, traditional supply chains struggle with old economic models. The COVID-19 pandemic has further highlighted the disparity between agile e-commerce firms and their slower-moving counterparts.

Supply chain leaders are keenly aware of this issue. Their inability to swiftly react to market changes impacts both revenue and brand image. To compete more effectively, companies across the supply chain are rethinking their tools, systems, and processes. Many are turning to artificial intelligence (AI) and machine learning (ML) as potential solutions.

The Challenge of Transforming Supply Chains

Traditional companies, unlike digital giants such as Amazon or Airbnb, cannot manage inventory virtually. Instead, they must find new ways to gain a competitive edge. Future competitiveness depends on agility, adaptability, and resilience—qualities that can surpass traditional metrics of size and scale.

Despite recognizing the need for change, many traditional companies have invested heavily in infrastructure and cannot start anew. They must adapt their existing investments to achieve greater flexibility, effectively transforming their systems mid-operation. AI can play a crucial role in this transformation.

Integrating AI into Supply Chain Transformation

Currently, a vast majority of supply chain companies are piloting AI/ML projects. However, many are adopting new technologies without a clear strategic vision. AI’s true value lies in interpreting supply chain activities, learning from them, and supporting human decision-making. This augmented decision-making can significantly enhance supply chain operations.

While some low-impact decisions might be automated, high-impact decisions require human judgment and accountability. AI should be strategically applied to automate manual processes, thus improving cost efficiency and operational agility.

High-Impact Areas for AI Application

1. Demand Forecasting: Traditionally performed by humans using methods like the 80/20 rule, demand forecasting has grown more complex due to vast data volumes. AI and ML can process this data efficiently, offering substantial benefits.
2. Demand Sensing: By integrating real-time data into traditional demand planning, AI helps manage the complexity of predicting demand, especially in volatile environments like the COVID-19 economy. AI handles large data sets to enhance decision accuracy, with humans finalizing the decisions.
3. Supply and Raw Material Planning/Inventory: With reliable demand signals, companies must respond quickly. AI aids in deciding inventory levels, enhancing organizational agility and providing a competitive advantage.

AI can transform supply chains by embedding intelligence into processes rather than simply adding it on. Proven AI applications can expand to areas such as predictive maintenance. While AI is not a universal solution, it is a powerful tool when integrated into business processes, leading to significant improvements.

This perspective highlights how AI can revolutionize the digital economy, particularly by transforming traditional supply chains to meet contemporary demands.

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