ML use cases in Manufacturing

Machine Learning (ML) in manufacturing essentially aims to automate complex or repetitive industrial activities by providing cognitive abilities to machines. Cognitive abilities or intelligence is provided to machines by identifying patterns in the manufacturing processes or workflows, which in turn is achieved by first gathering enough data and then analyzing it to unearth the useful patterns.?

Predictive Maintenance: Predictive maintenance is one of the key use cases for ML in manufacturing because it can pre-empt the failure of vital machinery or components using algorithms. By analyzing data from previous maintenance cycles, machine learning can identify patterns that can be used to predict equipment failures and when future maintenance will be needed. This information can then be used to schedule maintenance before problems occur. This, in turn, could save manufacturers significant time and money since it allows them to tackle specific issues exactly when needed, and in a highly focused way. This benefits manufacturers by:

1. significantly reducing planned and unplanned downtime and, thus, costs.
2. providing technicians with focused inspection, repair and tool requirements
3. reducing the size of the technical team needed to make repairs.

However, even with the best algorithm, predictive quality analytics will only be as effective as the data that is used to train it. In order to be successful, manufacturers must have a well-designed data collection strategy that captures all relevant information about their process.

Logistics: A constant challenge with manufacturing is the losses from overstocking or under-stocking inventories. Overstocking often leads to wastage and lower margins. Under-stocking can translate into losses in sales, revenue, and customers. It can provide accurate demand forecasting by analyzing past data.

One example is from food manufacturer Danone Group who is using machine ML to improve their demand vs supply estimated. And they are getting promising results with a 20% decrease in forecasting errors, 30% fewer lost sales, and a high 50% reduction in demand planner’s workload.

Digital twin: A digital twin, a real-time digital representation of a physical object or, indeed, a process that can be used by manufacturers to carry out instant diagnostics, evaluate production processes, and make performance predictions. But more than this, digital twins can help manufacturers revolutionize their engineering practices while offering full design, production and operational customization. So, in other words, manufacturing companies can create a virtual representation of their products and processes, which can be used to test and optimize them before they are built. The benefits of ML-enabled digital twins in manufacturing.

Predictive quality and yield: As consumer demand grows in line with an expanding population, process-based losses are becoming harder for manufacturers to tolerate. AI and machine learning can enable businesses to get to the root cause of losses related to quality, yield, energy efficiency and so on, thereby protecting their bottom line and enabling them to remain competitive. It does so by using continuous, multivariate analysis via process-tailored ML algorithms, and also through machine learning-enabled Root Cause Analysis (RCA). ML and AI-driven RCA, in particular, is a powerful tool for tackling process-based wastage and is far more effective than manual RCA.

Energy consumption forecasting: Manufacturers can now use machine learning algorithms that process data on factors like temperature, lighting, activity levels within a facility and more to build predictive models of likely energy consumption in the future. Machine learning algorithms can analyze large data sets to identify patterns and relationships that would be difficult to find using traditional methods. They do this using:

1. Sequential data measurements.
2. Autoregressive data models that identify cyclical/seasonal trends - data scientists will often pair this approach with feature engineering, which turns raw and unordered data into “features” for algorithms to define and build predictive analytics models on.
3. Deep neural networks - which can process vast quantities of data and rapidly identify patterns.

Forecasting energy consumption is important for manufacturing for a number of reasons. First, it can help factory owners and operators plan for future energy needs. This planning is essential to ensuring that factories have the necessary resources to meet production demands. Additionally, forecasting energy consumption can help factories avoid disruptions in production due to unexpected changes in energy costs or availability.

Cognitive supply chain management: With the proliferation of IoT technologies, it’s only a matter of time before smart supply chains?completely redefine how manufacturers carry out their operations. Automation is the first rung on the ladder, but soon entire supply chains could be "cognitive". This means that they can use AI and machine learning algorithms to?perform automatic analysis?of datasets, including inbound and outbound shipments, inventory, consumer preferences, market trends, and even weather forecasts for predicting optimal shipping conditions.?

References:

?https://www.birlasoft.com/articles/17-use-cases-of-ai-in-manufacturing

?https://www.logicladder.com/ai-ml-use-cases-in-manufacturing-industries/

?https://eleks.com/blog/machine-learning-in-manufacturing/