Home Business Insights & Advice How machine learning can improve supply chain efficiency

How machine learning can improve supply chain efficiency

by Sponsored Content
16th Nov 20 4:30 pm

A robust, resilient and diversified supply chain is vital for manufacturing businesses who produce all manner of products, including computers, RC cars and smartphones. In the current disrupted conditions, this fact has become even more evident. Fortunately, innovations such as machine learning can drastically improve the efficiency and sustainability of the process from one end to the other.

How does machine learning work?

Before gaining insights into how this technology can jump-start and improve supply chain logistics, it is necessary to understand what it is. ML uses algorithm-based data models to analyse the patterns in large swaths of data and is effective and efficient in spotting trends and patterns as well as in making predictions about inventory, safety and supplier sourcing. While it can be a costly investment, an increasing number of companies are recognising that ML can revolutionise the way they handle materials acquisition.

Use machine learning BOM tools to boost efficiency

Any bill of materials (BOM) that you create contains large amounts of data, including information about parts, their manufacturers, quantities, how and when they were purchased, vendors, maintenance and compliance data. Keeping track of this staggering amount of vital facts is a proposition that can be made infinitely easier through the use of BOM software. For instance, a ML-powered BOM tool can incorporate every one of these details and allow you to streamline your processes. By purchasing all of your materials at one time (tool available here), you can ensure that the materials you need arrive on time, in the quantities you need based on actionable data and from suppliers you can trust.

Optimise supply chain speed

Using quantitative information specific to your company, ML can examine the progress of products as they move along your supply chain. It can then compare the data to historic benchmarks and other metrics. Finally, it can make suggestions about how to improve the process.

Use demand-based intelligence to plan the movement of materials

If a company is to successfully manufacture the goods it sells, the raw materials must arrive as expected and on time. ML can assess your requirements and scrutinise your sources to determine which can give you what you are looking for in a timely fashion. Taking marketplace-wide demands into consideration is a priority best executed by this data-powered automated technology.

Optimise agreements and contracts for success

The documentation forged between companies and their suppliers provides the underpinning for mutual clear communication and maximum productivity. ML can peruse its mass stores of relevant data to learn what contract specifications and other pieces of documentation led to the best outcomes. With that in mind, it can make predictions about how a company can modify its current procedures and agreements.

Monitor and promote quality

ML can help to ensure that the materials you get from your suppliers are up to your standards. It does this by harnessing its data stores to track how the quality of the goods you receive varies over time. It can then make predictions and recommendations not only about raw materials but also concerning shipping and supplier quality.

Computers provide manufacturers with more than just access to large amounts of information gathered at a lightning pace. Today’s ML systems are programmed to analyse jaw-dropping amounts of data, making connections and predictions that are beyond the reach of any single human brain. By incorporating the insights from ML into its operations, a company can revolutionize the efficiency and resiliency of its supply chains from one end to the other.

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