3 Strategies for Applying Predictive Analytics in Manufacturing


Predictive analytics in manufacturing is a hot trend right now.

In this post, we’ll take a look at why that is and detail three strategies for using predictive analytics in your organization. 

1. Collect Quality Data

We’ve already mentioned the vast amounts of data that are generated every minute. With more than 65,000 Google searches every minute, to use just one example, the last thing we really need to do is to collect more data we won’t analyze.

In fact, most data is ignored. According to Forbes contributor Mary Meehan, enterprises only use between 27 percent and 40 percent of the analytics they collect. (Full disclosure: The link she cites as a source from Forrester is no longer available, but the statistic has been cited in other, reliable publications.)

Manufacturers looking to apply predictive analytics should focus on collecting quality data for true key performance indicators. Doing this will result in quicker aggregation and cleansing, and it will require less storage space.


2. Forecasting Demand

Timing production is a science in the manufacturing industry.

Traditionally, demand forecasts have relied on using past data to determine future needs. For example, analyzing several years’ worth of data might reveal that an organization sees an upward trend in production in the spring. As a result, that organization prepares for production spike by ordering goods in the winter.

With predictive analytics, however, manufacturers can see trends unfolding in real time to better pinpoint when that busy season might hit.

For example, a Fortune 200 food and beverage chain predicted demand based on what the weather was like in their area, resulting in new marketing campaigns that helped them achieve their goals.

Manufacturers could use predictive analytics to improve their ordering processes.


3. Improving The Supply Chain

It’s difficult for a supply chain to work when its pieces break down.

Predictive analytics aid with predictive maintenance, which estimates when the servicing of equipment is required. This reduces costs in two ways. For one, parts can stay in use throughout their full lifecycle. Two, parts are replaced before a breakdown backs up the entire production process.

When medical equipment manufacturer Medivators implemented predictive analytics, they increased their “average time taken for a technician to file a [service] report following an incident from around two weeks to two days.”

This translated to an increase in cash flow since the organization reduced their delay in billing clients for a service event.

Overall, predictive analytics is a boon to any organization because they force you to consider only quality data, they help with forecasting demand, and they help improve your supply chain.

If you’re interested in getting started with predictive analytics, please contact us today.

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