When running the parts inventory in an MRO organization, the biggest challenge is keeping the right spares and materials on the shelves. Conversely, there’s a risk of having too much money tied up in slow-moving or non-moving inventory. For these kinds of problems in the supply chain, machine learning offers solutions.
Machine learning is a form of artificial intelligence that specializes in handling large data sets and finding ways to solve complex problems. In supply chain applications such as managing spare parts, machine learning offers a way to lower costs and save space while improving parts availability and reducing Mean Time to Repair.
Machine learning basics
A computer can process data extremely quickly but requires a program to tell it what operations to perform. Artificial intelligence (AI) is an alternative approach to computer programming and relies more on pattern recognition and training. Machine learning is a subset of AI that looks for patterns in very large sets of data. Most machine learning systems are trained by feeding them data that has already been labeled. This might be data on supplier performance or information on component life. Other types of systems are left unsupervised to find patterns in large datasets.
This approach is of less value to supply chain management. Another method of training is through trial and error. This has been effective for teaching computers how to play complex games such as Go, but has limited value for improving inventory management, purchasing or logistics.
Machine learning in the supply chain
Machine learning in supply chain management relates strongly to the problem of unpredictable demand and, to a lesser extent, highly variable supply or availability. One of the challenges that MRO managers face is the mix of high-usage, low-value and low-usage, high-value items they hold.
This can be illustrated by two cases. In a typical maintenance operation, consumption of lubricants and filters is reasonably predictable throughout a year and might correlate with production volumes and product mix. However, large pumps, motors and gearboxes may be needed only rarely, but when required must be available immediately to minimize production stoppages.
In both examples, machine learning can help by finding patterns that might otherwise remain hidden. In the case of lubricant and filter demand, it’s important to understand and anticipate future schedule fluctuations. This knowledge can guide inventory policies and purchasing. Likewise, failures of pumps, motors or gearboxes may also be predictable. Machine learning could perhaps conclude that a correlation exists among failure rates and a combination of product mix, demand and local weather conditions — which may influence the electrical supply quality.
Who benefits from machine learning in supply chain management?
Any manufacturer with industrial equipment and maintenance needs taking advantage of predictive maintenance can benefit from machine learning. This technology can exponentially improve prediction accuracy and grow more effective over time, providing major ROI benefits.
Typical industries include:
- Building products
- Consumer packaged goods
- Heavy equipment
- Paper and pulp
- Power distribution
- Tire and rubber
Benefits from the application of machine learning in the supply chain
Machine learning applications in the supply chain include:
- Inventory optimization: The goal of inventory optimization is to minimize the number of items held in stock while simultaneously ensuring 100% availability when they are needed. This may also relate to supplier location, stocking policies and operating hours. Machine learning contributes here by finding patterns in usage and supply. It may, for example, conclude that some parts are best held by the supplier while others should be kept on site. It might also note that some suppliers are more reliable than others and suggest appropriate replenishment levels and even prices based on analysis of historical data.
- Purchasing cost control: Purchasing costs cover more than just the price paid for an item or items. Machine learning can help identify opportunities to consolidate orders to obtain quantity discounts. It can assess the advantages of various payment terms and help reduce freight costs, for example, by saving on priority shipping.
- Asset life extension: A frequent debate among maintenance organizations relates to the merits of more expensive but longer-lasting parts rather than cheaper, short-lived components. Machine learning can sift through data from disparate sources to reach a conclusion and so extend the life of high-value assets.
- Transportation management: Machine learning also benefits transportation management by helping identify and select suppliers and optimizing delivery schedules. This could even take into account delivery logistics, comparing, for example, sea versus air freight and the relative impacts on availability and costs.
Helping clients maximize asset life and performance
With an understanding of the pain points and advantages of supply chain management, you are ready to reap the benefits of an effective strategy. ATS provides a one-stop-shop for procurement support and other MRO asset management services. We are ready to understand your needs and provide a solution. For more information, contact us.