Three Ways Predictive Analytics Add Value to Manufacturers

Analyst Viewpoint

Many manufacturers are benefiting from predictive analytics today. For years manufacturers have been collecting data from their manufacturing processes. As the internet of things (IoT) has evolved, manufacturers have even more data at their disposal. They are collecting data from the supply chain. They are collecting data from production processes. They are collecting data as the goods they produce begin their useful lives in the hands of customers. The analyses of this data provide significant bene- fits to manufacturing organizations. More than nine in 10 (94%) participants in our Next-Generation Predictive Analytics benchmark research reported that predictive analytics can have a significant positive or transformational impact on their organizations.

The availability of inexpensive data storage and increased computational capability means that organizations can use more sophisticated algorithms and explore more alternative analyses to produce models that ultimately provide more accurate predictions. With these powerful models, more than half of manufacturers reported they have achieved a competitive advantage and reduced costs through improved operational efficiencies. In addition, nearly half cited increased profitability, increased workforce productivity and new revenue opportunities.

Manufacturers are applying predictive analytics in many areas, but three key areas specific to manufacturing include predictive maintenance, production optimization and design optimization. Enhancements to these areas have the potential to reduce costs, improve operational efficiencies, increase customer satisfaction and create new revenue streams.

Predictive maintenance analyses use historical maintenance data to plan and schedule maintenance in a way that minimizes maintenance costs and maximizes uptime of the assets. For example, if the bearings in a piece of equipment used in the production process can be replaced before they fail, it may prevent additional damage to the equipment that would be costlier and take more time to repair. Sensor data such as temperature, vibrations and sound can all be potential indicators of impending failure. This is where predictive analytics comes in. Analyses of historical sensor data can produce models that can predict failure more accurately than statistical models.

Predictive maintenance analyses can also be a new source of revenue. If customers allow an organization to collect data from the items it manufactures, the organization can monitor that information to create models for predictive maintenance and then offer the monitoring or the servicing of the equipment for a fee.

Production optimization analyses help reduce the costs of manufacturing and increase the quality of the goods produced. These analyses can include a wide variety of data — sources of raw materials, inventory levels, environmental conditions, production schedules, yields, defects and workforce assignments — to identify the optimal production conditions. Supplier performance can play a big part in optimizing production. For example, predictive models can identify where goods produced using material from a particular supplier resulted in higher warranty claims. Or the models can identify if particular suppliers are more likely to meet or miss delivery schedules.

Finally, design optimization offers a way to improve the design process, particularly in made-to-order manufacturing. Organizations can use predictive technology to simulate more scenarios, then capture the data from those scenarios and feed them back into simulations. In addition, organizations can collect real world data from customers and feed those into the predictive models as well.

Predictive analytics offers significant benefits to manufacturers. In addition to reducing costs and improving efficiencies, several of these analyses offer ways for an organization to create ongoing relationships with its customers. Maintaining and improving the customer relationships will also help improve the bottom line due to increased customer loyalty and repeat business. If your manufacturing organization is not employing predictive analytics today, you should consider it. If you have already embraced predictive analytics, consider ways you can further leverage its benefits.