Deploying the Factory of the Future to solve production problems of the present

Any manufacturing enterprise planning to become the “Factory of the Future” should start by thinking of the myriad challenges makers and producers face today. While a host of innovative technologies at present promise nothing less than a revolution in the business of fabricating goods, a clear understanding of fresh or novel mechanisms is needed to allow their intelligent integration into current manufacturing practices.

Ultimately, however, new technologies like augmented and virtual reality, robotics, artificial intelligence, and the Cloud will succeed or fail depending on their ability to address the pain points and challenges now confronting manufacturers. These challenges include retaining skills and talent, improving productivity and reducing downtime, ensuring worker safety, and bolstering manufacturing flexibility and speed-to-market.

The sections that follow discuss these challenges in greater detail, along with their possible resolution through the Factory of the Future.


Challenge 1: Skills and people retention

Advanced technologies like robotics and artificial intelligence have raised concerns that machines could potentially replace the human workforce. For the short term, however, there is little apparent need to worry. With US manufacturing unemployment rates currently low at 3.0%, according to the US Bureau of Labor Statistics, the bigger challenge in industry isn’t the potential fallout from a layoff or workplace reduction, but in finding enough workers and in attracting new blood to replace retiring baby boomers.

The cost of a retiring workforce goes far beyond finding a replacement. According to the Mellon Financial Corporation, between 1.0% and 2.5% of the total revenues of a company are lost because of decreased productivity stemming from new hires and the learning curve involved.

The Factory of the Future solution:

Augmented Reality (AR) can help address the challenge of knowledge-sharing within an organisation and of supporting less skilled workers by providing on-the-job support, training, and even remote assistance. Mixed Reality (MR) solutions could also be helpful by combining Augmented and Virtual Reality to let users navigate both real and artificial spaces at the same time.

Through AR, engineers fixing equipment can access user manuals through a head-up display and follow step-by-step procedures, freeing their hands for more important work than to turn pages. Workers can also be equipped with specialized glasses incorporating an audio link and a camera allowing live streaming, all the while enjoying real-time support from off-site engineers.

In the case of Lockheed Martin, the Maryland-based aerospace firm has trialled AR solutions to produce its F-35 fighter planes. Company engineers were equipped with educational software hosted on AR glasses that showed not only how the fighter plane’s different parts fit but also the part numbers associated with the plane’s components. The result was a 30% improvement in productivity, enabling engineers to work faster with just minimal training.

Challenge 2: Increasing productivity and reducing downtime

While many new technologies can be introduced in the name of boosting efficiency, cost remains king and will ultimately dictate purchasing decisions and a company’s bottom line. Such decisions are often based on a short payback window spanning less than a year in time, rather than on the total cost of ownership (TCO).

Machine and equipment failures can be especially costly with unplanned downtime, running into the tens of thousands of dollars per minute. Other aggravating factors—the cost of repairing the failed part, or the damage caused to associated pieces of equipment—could add to the total financial tally. As a result, technologies capable of reducing unplanned downtime can provide a quick and tangible payback.

The Factory of the Future solution:

Predictive maintenance and condition monitoring applications can be of immense value. Supported by the increased access to data collected from manufacturing equipment, they enable the monitoring of a machine’s performance to determine if it is deviating from accepted readings in parameters like vibration and temperature. As values stray from the norm, algorithms can be used to analyse the risk of failure and even the time frame in which a failure may occur. And with relevant data stored onsite as well as in the Cloud, comparisons can be made against previous asset failures to improve the accuracy of identifying imminent faults.

Advanced notification of an asset’s potential failure not only provides companies with the tools to reduce unplanned downtime, the information can also be used to create smarter maintenance schedules, enabling maintenance teams to focus on the degraded equipment alone. Smarter repair and replacement can lower expenditures, with analytics deployed intelligently to identify which parts to replace and which ones to keep.

Utilizing predictive maintenance to monitor the health of assets is clearly a positive application—the low-hanging fruit, so to speak—of the industrial IoT, with a quick payback on investment that can be expected.

Predictive maintenance is, in fact, the top IoT-related application among organizations in their use of the Cloud, as shown in the chart below.

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