How ML can improve supply chain management
MACHINE LEARNING (ML) and models that are based on algorithms are great at spotting anomalies, trends, and predictive insights within massive data sets.
These powerful functions make it an ideal solution to address some of supply chain management’s biggest challenges, which primarily revolves around time, budget and resource constraints, and being able to allocate these scare elements based on robust insights, goes a long way towards addressing many of the issues.
Accordingly, ML technology has been getting a lot of traction among some of the biggest companies in the world, that boasts a huge user base.
For example, when the world largest e-commerce giant, Amazon needed to increase its operational efficiency, it turned to ML to deploy warehouse automations.
And when DHL wanted a robust tool to power the internal data analysis of its predictive network management systems, the company developed an ML s0lution.
Companies in the supply chain space that have deployed ML as part of their analytical capabilities are already benefiting from a massive improvement in demand forecasting, resource planning, cost efficiency, and on-time shipments.
Here are some of the ways in which ML is further revolutionizing supply chains to deliver enhanced operational efficiency:
#1 | Improved assets, resource utilization
ML and AI-based algorithms serve as the foundation of numerous emerging logistics and supply chains software and solution. This is because ML is capable of solving some of the critical issues that companies face in regards to logistics.
According to McKinsey, the technology is capable of providing the industry with robust and reliable insights that help them plan their resources better while accounting for anomalies and other uncertainties.
Further, the tools will be especially useful to power automation and other autonomous operations, such as the ones within Amazon’s warehouses, on top of optimizing capacity and asset utilization.
#2 | Reduced forecast errors
The advent of robust networking technology, as well as smart sensors, has enabled companies to gather a wide variety of data. But without a powerful analytical tool to process the data, the data could be rendered useless.
ML algorithm is capable of processing vast amounts of data, with the greatest variety and variability, coming from IoT devices, telematics, intelligent transportation systems, among other things. This enables companies to have better insights which and reduced uncertainty help them achieve accurate forecasts.
According to one estimate, reducing forecast errors up to 50 percent is achievable using ML.
#3 | Fraud prevention
ML powered solutions are capable of reducing the risk of fraud while at the same time enhancing the quality of products and services. This can be achieved by automating inspections and auditing processes and subsequently performing real-time analysis of results on a cloud platform.
Any form of anomalies or deviation from normal patterns could then prompt further actions. In addition to that, ML tools could also be used to prevent privileged credential abuse which is among the top causes of breaches across the global supply chain.
#4 | End-to-end visibility
ML and AI platforms are also giving unprecedented end-to-end visibility into the supply chain. The platforms combine a multi-enterprise commerce network to manage global traded and supply chain management.
With these heightened levels of visibility, companies are equipped with all the information and insights to be able to react and respond to changes faster than ever before.
- Tetra Pak gets ready to build its first digital twin warehouse in APAC
- Could AR mirrors and VR showrooms boost JD.com’s sales?
- Can digital regulatory and tax filings help businesses go digital?
- How are emerging technologies transforming the supply chain?
- China set to beat North America in 5G investments in 4 years