This article is published by Skoda Minotti’s Manufacturing & Distribution Group.
As any manufacturer will tell you, challenges lurk everywhere. Yet one of the top challenges facing manufacturers today is the demand to make quicker, smarter decisions about industry specific needs – like forecasting downtime in production and reducing wasted resources.
This reality is stressing U.S. manufacturers, as the industry is currently growing at 1.1% domestically – far behind manufacturing growth in countries like China, India, Germany and the UK. Using big data (i.e., extremely large data sets that may be analyzed computationally to reveal patterns, trends and associations) in combination with predictive analytics can help you reveal where potential weaknesses exist in your supply chain, which is highly actionable information. Ultimately, results from the data can potentially improve your company’s revenue and streamline its manufacturing process, resulting in a leaner operation.
Three areas in particular shed light on how manufacturing operations are impacted by predicative analytics and big data.
- The Industrial Internet of Things (IIoT) Unleashes the Power of Information
Perhaps you’ve heard of the Internet of Things (IoT), the interconnection via the internet of computing devices embedded in everyday objects, enabling them to send and receive data. Well, the IIoT operates in much the same fashion. Insights gained from analyzing data can dramatically improve manufacturing performance in your company at many points along the way. Among the top results reported by executives are reductions in equipment breakdowns and unscheduled downtime. Other notable outcomes include drops in unscheduled maintenance, supply chain management issues, reported safety incidents, off-spec products, near-miss safety issues and inadequate staffing.
- Supply Chain Management is Enhanced by Data
As the name suggests, predictive analytics can guide manufacturers to make better forecasting decisions based on what their customers are asking for now, and what they’ll ask for in the future. The data also can indicate product failures before customers experience them. With this type of intelligence, manufacturers are better able to make well-informed choices in real-time. They can optimize operating costs by reducing wasted resources and also predict the potential for experiencing downtime. But that’s not all. Big data analytics, combined with the IIoT, can:
- Increase the accuracy of maintenance and repair schedules
- Find quality problems and defects in work products
- Predict workloads
- Analytics are Imperative Today and Tomorrow
Improving operational performance yields multiple benefits, so it should be every manufacturer’s goal. That’s why leveraging big data is critical. In particular, big data leads manufacturers to be more efficient with how they use and manage resources. Take product failure, for example. Some companies actually utilize data analytics to predict product failures before customers experience them. In many cases, actual production is handled by offshore contract manufacturers, so analytics help them zero in on where the problems surfaced without being in direct proximity to them.
Data analysis can point to when a product is most likely to fail based on factors that include:
- Production line
- Batch size
- Day and month when it was made
- Number of engineering changes
- Consumer usage patterns
In warranty costs alone, money could be recaptured by making adjustments.
The urgency to adopt these new technologies has never been greater. If manufacturers fail to ramp up their usage of big data and predictive analytics, they will struggle to remain competitive and will likely be unable to deliver orders that are accurate and on-time. The key is to take data and act meaningfully on its message. Customer demand for service anywhere and anytime won’t change, so we challenge you as a manufacturer to keep up with the healthy demand—and reap the rewards as a result.