ML In Supply Chain Management

Artificial Intelligence

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30 Mar 2022
5 Min read

Importance Of ML In Supply Chain Management

Supply chain management is a complicated jumble of activities in which even a minor lapse in visibility or synchronization can result in massive losses and costs. However, thanks to recent advancements in artificial intelligence and machine learning, we can now use historical and real-time supply chain data to uncover patterns that can help us understand what factors influence the various components of the supply chain network.

These insights assist businesses in gaining a competitive advantage, streamlining processes, reducing costs and improving revenues, and using recommendations to improve customer experience. According to Gartner, by 2023, at least half of all global organizations will be employing AI-related transformative technologies such as machine learning in supply chain operations.

The Impact Of Machine Learning On Supply Chain Management

Management Of Inventory

Keeping the proper merchandise in stock to meet future market demand has always been difficult for producers. Manufacturers can use big data supply chain analytics to examine many sorts of data, such as past sales demand, channel performance, product returns, POS data, and promotions data, to gain insights into:

  • What is the best inventory to keep on hand to meet demand while keeping stock levels low?
  • How can out-of-stock occurrences be minimized?
  • What can be done to mitigate the impact of product recalls?
  • How to facilitate cross-selling and boost the performance of slow-moving stocks

When fed with the most recent supply and demand data, machine learning can help a corporation better its attempts to solve the over or under stocking problem.

Predictive And Preventative Maintenance

Supply chain disruptions can be caused by various factors, including equipment failures and machine malfunctions. Unexpected and lengthy downtimes might lead to out-of-stock problems and revenue losses.

To avoid these scenarios, companies are replacing the reactive and wasteful break-fix service model with proactive maintenance approaches — predictive and preventive maintenance.

This entails advanced analytics data from intelligent parts and sensors using machine learning to forecast when a machine or part will break and choose the best time for repairs and replacements.

This enables businesses to eliminate surplus inventory, mitigate the expenses and disruption associated with unplanned downtime, and, as a result, boost customer happiness and brand loyalty.

Furthermore, machine learning can assist in determining ways to extend the life of existing assets, identifying prevalent causes of failure, and taking preventative measures.

Logistics

In supply chain management, last-mile logistics is prone to operational inefficiencies and accounts for up to 28% of total delivery costs.

The following are some of the most typical issues in this field:

  • Not being able to find a parking area for huge delivery trucks near the customer’s location and having to carry the box to the customer’s location on foot
  • Customers are not at home to sign for things, causing a delivery delay.
  • During this last leg of transport, the package was damaged.

In most circumstances, it’s pretty tricky for businesses to pinpoint exactly what’s going on in the final mile. This final phase is sometimes referred to as the supply chain’s “black box.”

A worldwide brewing firm recently collaborated with the MIT Megacity Logistics Lab to use data and machine learning to handle last-mile logistics operations and increase operational efficiency. In this case, machine learning technologies assessed historical route plans and delivery records to discover customer-specific delivery difficulties for thousands of clients worldwide. Customers whose delivery difficulties caused the most substantial interruptions to the company’s last-mile logistics operations were identified. The corporation then restructured its distribution services for a specific group of consumers.

Production Scheduling

The challenges of generating production plans can be simplified with machine learning. For example, CPG and food and beverage companies use machine learning to analyze weather forecast data (temperature and sunshine data) to predict demand for specific product categories better and manage production and inventories.

Management Of Supplier Relationships

Improved supply chain resilience necessitates robust Supplier Relationship Management strategies. Businesses can use machine learning algorithms to evaluate supplier data and gain insights into supplier compliance, performance patterns, and potential threats. By forecasting and recognizing new supplier risks, supply chain and procurement experts may improve their supplier selection process and reduce supply chain interruptions.

Conclusion

Please get in touch with one of our ONPASSIVE team if you want to learn more about machine learning applications in supply chain management.

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