Beyond maintenance, AI-driven analytics for quality control, downtime and operational efficiency throughout the plant floor and supply chain.
Intel is saving an estimated $656 million per year in business value using predictive analytics across the company’s sales, supply chain, factory and manufacturing operations globally, according to the company’s 2016-2017 Intel IT Annual Performance Report.
The company is also saving 50,000 employee-hours with its solution.
Predictive analytics empowers manufacturers to understand and predict accurately what is happening and what would happen shortly within their factory premises.
In fact, 50 percent of manufacturers in the US have turned to predictive analytics to tame the complexity of their routine operations.
The more data available, the more accurate these predictions become.
How predictive analytics work?
Predictive analytics collects data from machines and sensors and integrates it with real-time operator data, offline quality data and data from MES and ERP systems.
This data is then cleaned, merged, formatted and organized in the cloud.
Drawing on historical data, machine learning algorithms can identify patterns in behaviour that have previously led to problems.
If real-time activity starts to show signs of one of those problem patterns, the system can predict the potential outcome and alert factory personnel.
Once operators, engineers or plant managers have been alerted, corrective action can be taken to prevent issues from having a significant impact.
6 Reasons to invest in predictive analytics for your manufacturing business
Predictive Analytics is increasingly looked at as the panacea to address some of the toughest manufacturing pain points.
The best way to get started is to use automated machine learning (AutoML) tools.
These allow manufacturing domain experts and process technicians to automatically build, validate, and deploy predictive models at the touch of a button.
1. Understand the supply side of your manufacturing chain
Manufacturing data analytics helps you understand the cost and efficiency of every component in your production life cycle, all the way to your suppliers’ trucks.
Advanced analytics guides you to reach better decisions by visualizing how each aspect impacts the end result.
If certain components are constantly failing or are not doing exactly what is needed, analytics will spot and highlight them before they become an issue.
2. Create systems that can fix themselves
Manufacturing systems constantly operate under heavy loads and any stoppage in work can result into spiralling losses.
By leveraging big data analytics, companies can design manufacturing systems that can consistently gauge their own need for repairs.
This enables systems to fix themselves and provide early alerts for situations that are easily resolvable.
More importantly, data analytics can deliver insights into which components fail most frequently, letting you turn your reactive solutions into proactive ones.
3. Better understand your machine utilization and effectiveness
Powerful predictive analytics can help manufacturers gain real-time insight into how well their manufacturing lines are operating, both on a micro and macro level.
Understanding how downtime for a single machine can affect the chain or how different configurations may improve overall efficiency is an absolute essential.
Generating actionable data that lets you make real improvements in the overall process is a major advantage of applying analytics to manufacturing.
4. Create better demand forecasts for products
Demand forecasts are critical because they guide a production chain and can make quite a difference between strong sales or a warehouse full of unpurchased inventory.
For most companies, forecasts are based on previous years’ historic values instead of actionable forward-looking data.
When combined with predictive analytics, manufacturers can build a more precise projection of what purchasing trends will be in the months or years to follow.
These predictive insights are based not just on previous sales, but on processes and how well lines are operating, resulting in smarter risk management along with less production waste.
5. Digitalise supply chain management
The advantage of predictive analytics in supply chain management is the real-time connect.
Data collected on a real-time basis can be combined with historical data to draw and connect patterns.
Key metrics like peak times, loading and unloading times, normal and losses can be forecasted accurately with predictive analytics.
6. Accelerate engineering innovations
Enormous amounts of data and their number crunching is needed to bring a product from concept to life.
Predictive analytics helps bring together all that data under one roof to help create more customer-targeted products to market quicker than before.
It compares historical sales records to customer demographics, regional population, income levels and more to fix data-driven pricing models.
Ford relies on this technique to push its sales numbers and optimize its inventory levels at dealerships.
Factoring in the change
The evolution of IIoT and rise in operational efficiencies from big data initiatives fuel the growth of the global manufacturing predictive analytics market.
Studies suggest that predictive maintenance could enable the world’s manufacturing industry to save up to US $700 billion over the next two decades.
The real advantage of using predictive analytics lies in the real-time vantage that is much needed for forecasting and planning manufacturing operations.
It is here that predictive analytics positions itself as an insight providing digital offering.
If you’d like to take your first step towards using an intuitive predictive business analytics tool, you can book a free consultation and see what Data Semantics can do for you.
