The Role of Artificial Intelligence in Supply Chain Planning

The Role of Artificial Intelligence in Supply Chain Planning

Are you aware that AI is quickly becoming a foundational element in modern supply chain planning?

In Supply Chain Management (SCM), Artificial Intelligence (AI) is driving improvements in efficiency and introducing sustainable practices. As projected by Gartner, in 2023, over half of the leading supply chain companies will have incorporated artificial intelligence into their systems. This shift towards AI isn't just a temporary movement; it highlights the significant impact of AI in enhancing, simplifying and anticipating supply chain demands.

According to a report by McKinsey, companies who adopted AI for SCM earlier have witnessed improvements in logistics costs by up to 15% and significant positive changes in inventory management. The transformative power of AI is becoming very evident as businesses struggle with the complexities of modern technology in supply chains. This evolution underscores the importance of AI's role in redefining and optimising supply chain planning for the future. Gartner also states that supply chain organisations expect that the level of machine automation in their SCM will double in the coming years. 

In this article, we will learn about the effective and efficient role of AI in Supply Chain Planning and what it holds for the future of organisations. 

Artificial Intelligence in Supply Chain Planning

Supply chain planning includes a range of activities like producing, sourcing or delivering products. However, it depends on industry to industry. 

So, how exactly is Artificial Intelligence reshaping the supply chain management landscape? What has propelled us from the basic logistics of transporting goods to anticipating the future trajectories of supply chain dynamics? Dive in as we unravel the transformative role of AI in revolutionising supply chain planning. 

Inventory Management

Inventory management involves the organised method of procuring, storing and distributing inventory, which includes both raw materials and final products. It has helped companies track their inventory accurately, even if the order is being sent across the globe. The biggest advantage is that it saves human work hours and allows them to focus on other work which cannot be done with the help of AI. 

As per findings from Future Market Insights, there's an anticipated growth in the inventory management software sector at a rate of 11.2% CAGR between 2022 and 2028. By 2028, the market's valuation is projected to touch approximately $US 3,291 million. Let’s see how AI helps in inventory management: 

  • Real-time tracking
  • Predictive analysis
  • Integration with other systems
  • Automated reordering

Predictive Analysis

Predictive analysis provides insights into predicting the demand beforehand. This came in exceptionally handy during the post-COVID era when supply chains faced volatility and changing demands. 

AI analyses historical and current data trends and can optimise inventory levels specific to regions or countries. This technology also helps in predictive maintenance, allowing organisations to anticipate machine downtime and ensure that operations are uninterrupted. Furthermore, route optimisation through predictive analytics helps enhance shipping and logistics by determining the most efficient paths for transportation, thereby reducing costs and delivery times. 

Warehouse Automation

Warehouse automation combines digital and physical asset automation, including data analytics and robotics. Digital automation can include warehouse management systems that track orders and fulfilments, while physical automation might involve technologies like conveyor belts or mobile shelf loaders. 

Advanced warehouse automation technologies range from autonomous mobile robots to automated storage and retrieval systems. These innovations streamline warehouse operations, reduce manual labour, and increase efficiency, ensuring that products are stored and retrieved in the most optimal manner.

Route Optimisation

AI-driven route optimisation is transforming the logistics sector. By analysing real-time data combined with existing map information, AI-powered systems can quickly identify and create the most efficient routes for transportation. This reduces fuel consumption and delivery times and ensures that goods reach their destinations in the shortest time possible, leading to increased customer satisfaction.

Enhanced Visibility

One of the significant advantages of integrating AI into supply chain management is the enhanced visibility it offers. AI collects vast sets of logistic data and presents it in an easily understandable manner. This includes information on shipping times, inventory locations, predicted delays, and potential shortages. For the first time, organisations can gain a comprehensive view of their supply chain, making informed decisions and optimising operations.

Customer Satisfaction

AI plays a crucial role in improving customer service. Advanced AI-powered chatbots can instantly address common customer queries, reducing wait times and enhancing the overall customer experience. By predicting customer behaviour and automating responses, AI ensures that customer concerns are addressed promptly and efficiently. This enhances the customer's trust in the brand and ensures repeat business.

To Sum Up

The fusion of Artificial Intelligence with supply chain planning signifies more than just a tech-driven leap; it marks a transformative change in how supply chain operations are perceived and executed. Analytics in supply chain management has evolved from merely analysing historical data to proactively forecasting future trends and challenges. 

This evolution has spurred a growing interest in specialised supply chain management courses and training in supply chain analytics. Such educational endeavours aim to prepare professionals to leverage advanced analytics capabilities, ensuring supply chains remain agile and adaptive amidst unpredictable challenges. As the journey continues, the collaboration between advanced analytics and supply chain management promises to set new standards for operational efficiency and customer-centricity.

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