Data and Machine Learning are Transforming Supply Chain Management

A huge volume of data is generated today from a variety of data sources as well as business processes. But how are businesses using all this data? Do they have the technology and tools to decode this data and utilize it to their advantage?

This is where a business needs advanced technology such as machine learning.

In fact, machine learning and artificial intelligence-powered technology are changing things quickly in many business functions. From analyzing large volumes of data quickly to assessing market conditions and making accurate predictions, these technologies are making a huge impact across all functions.

Business leaders have been quick to realize the transformative potential of these technologies. Many of them believe disruptive technologies like these can create several opportunities, especially as they look to build resiliency, gain competitive advantage and thrive in an increasingly uncertain environment.

Like other business functions, supply chain management has also benefited from the adoption of machine learning in one form or the other. Among other benefits, technology has helped derive meaning from the vast amount of data generated in supply chains.

Machine learning algorithms can run through the entire supply chain data and discover patterns. They can consolidate and analyze the data to generate actionable insights, which in turn can help make informed decisions and optimize processes.

Let’s now look at some of the key supply chain functions where machine learning can make a huge impact.

Also Read: Supply Chain Digital Magazine Interviews GEP President Neha Shah on Gen AI’s Impact on Supply Chain and Procurement

How Machine Learning Can Transform Supply Chain Operations

Automation:

Technology can automate several supply chain functions, eliminating the need for manual labor and freeing up staff for strategic tasks. It can automate processes such as order tracking, inventory management, order fulfillment and even customer service. By automating manual, time-consuming tasks, it enhances efficiency and reduces costs.

Demand forecasting:

With demand patterns fluctuating too often in recent times, forecasting demand is perhaps the biggest challenge for businesses today. To accurately predict demand, there is an immediate need to upgrade existing techniques such as baseline statistical analysis and advanced simulation modeling to machine learning algorithms that can use predictive analytics and take into account a wide range of factors. Accurate demand forecasting can be especially helpful when a business plans to launch a new product and estimates potential sales.

Inventory and warehouse management:

By accurately forecasting demand and analyzing sales data, machine learning also helps streamline inventory management. Businesses can align supply with demand, prevent stockouts or surplus inventory and optimize costs in this process. Effective inventory management also enhances customer satisfaction as they can get what they need when they need it.

In the warehouse, machine learning can be used to make the best possible use of available space. It can analyze data from sensors and cameras to optimize the layout, placement of inventory and order-picking process. It can also identify products that are selling faster than others and suggest an arrangement to make them easily accessible.

Vendor selection:

Many businesses are also leveraging machine learning for the vital process of vendor selection. Analysis of supplier performance data, delivery times, pricing, sustainable practices and other indicators can help identify the right suppliers and negotiate favorable rates.

Logistics:

Logistics is another area where machine learning can be used to plan and optimize transport routes. It can look at real-time data on traffic and weather conditions to determine the shortest and most efficient delivery routes. This, in turn, can save fuel costs, lower emissions and ensure timely delivery.

Also read: 6 Ways Generative AI Can Improve Supply Chain & Procurement Productivity

Risk management:

Machine learning can analyze historical data to help identify potential bottlenecks and supply chain risks. As a result, businesses can act proactively and develop contingency plans. Additionally, technology can continuously monitor and evaluate risk indicators, allowing a business to anticipate potential issues before they can occur.

Quality inspection:

The ability to recognize visual patterns makes machine learning useful for the physical inspection of assets in the supply chain network. The technology can be deployed in logistics hubs to automate quality inspection and isolate damaged or worn product shipments.

Maintenance of physical assets:

The manufacturing supply chain has found another use case of machine learning for extending the life of fixed supply chain assets such as machinery, engines and warehouse equipment. By finding patterns in usage data and analyzing this data, machine learning can determine causal factors that most affect machinery performance.

Finally, businesses are using machine learning in tandem with advanced analytics and real-time monitoring to achieve end-to-end supply chain visibility. Such visibility is crucial for all businesses today, given the uncertain, disruption-prone business environment.

Where to Begin

High-quality data is key to implementing machine learning. Clean, well-structured data can ensure that machine learning algorithms provide accurate predictions and recommendations. Additionally, to begin with, you may like to implement machine learning in a small part of the operations. This can give time to prepare the workforce and help them understand its benefits. When the workforce gets familiar, it can be scaled and expanded to other areas of supply chain management.

Know more about the growing role of AI and ML in supply chain management.