Supply chain management is a complex medley of processes in which even a slight lack of visibility or synchronization can lead to enormous losses and overheads. But with the recent developments in AI & machine learning, we can now harness historic and real-time supply chain data to discover patterns that help us understand what factors influence the different aspects of the supply chain network.
These insights help companies in getting a competitive edge, streamline processes, cutting down on costs and increasing profits, and leveraging recommendations to enhance the customer experience. According to Gartner, at least 50% of global companies would be using AI-related transformational technologies such as Machine Learning in supply chain operations by 2023.
5 Ways In Which ML Acts As A Game Changer In Supply Chain Management
1. Inventory Management
Ensuring the right amount of product availability in the inventory as per the future market demand has always been a constant challenge for manufacturers. With big data analytics, manufacturers can analyze different types of data including past sales demand, chanel performance, product returns, POS data, promotions data etc. to get insights around:
- What is the optimal inventory required to meet demand while ensuring stock levels are at a minimum
- How to reduce out of stock situations
- How to control the impact of product recalls
- How to enable cross-selling and improve slow-moving stock’s performance
When feeded with the latest supply and demand data, machine learning can enable a continuous improvement in a company’s efforts towards solving the over or under stocking problem.
2. Predictive and Preventive Maintenance
Equipment failures and machine breakdowns are some of the significant reasons for supply chain disruption. Unexpected and extended downtimes can result in out of stock situations and lost revenue.
In order to avoid these situations, companies are replacing the reactive and inefficient break-fix service model with proactive maintenance approaches – predictive and preventive maintenance.
This involves using machine learning to analyze data from smart parts and sensors and predicting when a machine/part will fail and determining the right time for repairs and replacements.
This allows companies to reduce excess inventory, mitigate the costs and disruption caused due to unscheduled downtime and ultimately improve customer satisfaction and brand loyalty.
In addition, machine learning can also help understand how to extend the life of the existing assets, determine common reasons for failure and take necessary proactive steps.
Last mile logistics in supply chain management is prone to operational inefficiencies and costs upto 28 percent of the total cost of the delivery.
Some common challenges in this area include:
- Not able to find a parking spot for large delivery trucks near the customer’s destination and having to carry the package to its destination by walk
- Customers not being at home to sign the receipt of items and thus causing a delay in delivery
- Damages to the package during this last leg of delivery
In most cases, it’s very difficult for companies to identify exactly what’s going on during this last mile. This final step is commonly referred to as the “black box” of the supply chain.
In order to address such last-mile logistics operations and improve operational efficiency, a global brewing company recently worked with MIT Megacity Logistics Lab to leverage data and machine learning. In this scenario, the ML tools analyzed the historic route plans and delivery records, and helped identify customer-specific delivery challenges for thousands of customers across the globe. The company identified customers whose delivery constraints posed the most significant disruptions to its last mile logistics operations. From there, the company reconfigured its distribution services for a certain pool of customers.
4. Production Planning
Machine learning can simplify the complexities involved in developing production plans. For instance, CPG and Food and Beverage manufacturers are analyzing weather forecast data (temperature and sunshine data) with machine learning to more accurately predict the demand for certain product categories and plan production and inventory.
5. Supplier Relationship Management
Robust Supplier Relationship Management strategies are essential for improving supply chain resilience. Machine learning algorithms can help businesses analyze supplier data and provide insights into supplier compliance, performance patterns, and potential risks. Supply chain and procurement professionals can improve their supplier selection process and minimize supply chain disruptions by forecasting and identifying any new supplier risks.
If you’d like to learn more about the use cases of machine learning in supply chain management, please feel free to get in touch with one of our AI and supply chain experts for a personalized consultation.