Top 5 Use Cases of Machine Learning in Supply Chain
Increasing customer expectations in the supply chain industry have given rise to more extensive product ranges, intricate logistics, and super-fast lead times. All of this has led to unreasonably high costs throughout the supply chain network.
And minimizing the effect of these factors manually at each level is again a formula for amplified operational costs. This is where machine learning in logistics can help breathe a sigh of relief. Machine learning holds the answer to many well-known as well as emerging supply chain challenges.
The advantages of machine learning and AI can be traced in every part of the supply chain, including procurement, manufacturing, inventory management, warehousing, logistics, and customer service.
Let’s now explore the numerous uses of machine learning in logistics management, which can help drive efficiency and optimization.
Use Cases of Machine Learning in Supply Chain
The supply chain, being a heavy data reliant industry, has many machine learning applications. Let’s look at the top 5 applications of ML in this article.
- Predictive Analytics
Using machine learning solution models, companies can relish the advantage of predictive analytics for demand forecasting. There are many benefits of accurate demand forecasting in supply chain management, such as reduced carrying costs and optimal inventory levels.
Machine learning models are skillful at identifying hidden patterns in historical demand data. Machine learning can also detect issues in the supply chain way before they sabotage the business.
A robust supply chain forecasting system ensures the business is equipped with the resources and intelligence needed to respond to emerging issues and threats. Click here To learn about the proper predictive maintenance machine learning techniques; check out this guide.
2. Automated Quality Inspections For Robust Management
Logistics centers usually conduct manual quality inspections to examine industrial equipment or packages for any kind of damage during transit. The development of artificial intelligence in logistics has increased computerizing quality inspections in the supply chain lifecycle.
AI-empowered techniques and machine learning in logistics enable automated analysis of defects or damage in industrial equipment with the help of image recognition. These AI-enabled quality inspections benefit from reduced chances of delivering defective goods to customers.
3. Reduction in Forecast Errors
Machine Learning operates as a robust analytical tool to help supply chain companies process large sets of data. A report by McKinsey also demonstrates that AI and ML-based executions in the supply chain can reduce forecast errors by up to 50%.
Besides processing such enormous amounts of data, machine learning in logistics ensures that it is done with the best variety and variability. This is possible due to telematics, IoT devices, intelligent transportation systems, and other powerful technologies. This enables the supply chain companies to have superior foresight and helps them achieve accurate forecasts with reduced errors.
4. Advanced Last-Mile Tracking
Last-mile delivery is a crucial aspect of the entire supply chain as its effectiveness can directly impact numerous verticals, including customer experience and product quality. Data also proposes that the last-mile delivery in the supply chain constitutes 28% of all delivery costs.
Machine learning in logistics can offer great opportunities by accurately analyzing different data points regarding how people enter their addresses and the total time to deliver the goods to specific locations. ML can also offer valuable support in optimizing the process and providing customers with more precise information on the shipping status.
5. Fraud Prevention
Machine learning in logistics can strengthen product quality and diminish the risk of fraud or hoax. This is done by automating the inspections and auditing processes and performing real-time analysis of results to discern aberrations or deviations from regular patterns.
Furthermore, ML tools can also prevent privileged credential abuse, one of the primary causes of breaches across the global supply chain. It is estimated that 74% of data breaches start with privileged data abuse. This is where fraud detection using machine learning can come to the rescue.
Bettering the supply chain’s efficiency plays a critical role in any business. Any process advancement can substantially impact the bottom line profit by operating their businesses within rigid profit margins.
Advanced technologies such as machine learning and artificial intelligence make it easier to deal with volatility and forecast demand in global supply chains accurately. According to a study, at least 50% of all multinational companies in supply chain operations will use AI and ML-related transformational technologies by 2023. This is proof of the growing popularity of machine learning in the logistics industry.
But, to reap the total rewards of machine learning services, enterprises need to prepare for the future and start investing in AI and machine learning technologies to relish increased profitability, productivity, and better resource availability in the supply chain industry. To get started with machine learning for your business, drop us a note here. We’ll take it from there.