Introduction to Print Services:
The printing industry has experienced peaks of innovation followed by years of continuous improvement. The convergence of modern technologies can lead to breakthroughs that were previously inconceivable, and at a faster pace than before. Keypoint Intelligence has identified five technology areas (cloud computing, big data and analytics, artificial intelligence, robotics, and augmented reality) that will ultimately lead to largely autonomous print production.
This might seem like a science fiction movie, but this transition will most likely occur by the end of this decade since each of these technologies has a compounding beneficial effect on the others. Large amounts of data have been shown to improve the accuracy and capabilities of artificial intelligence (AI) by building the fundamental building blocks for machine learning and deep learning. Technology can improve each other simply by being applied to it.
In many cases, the terms “smart factory” and “Industry 4.0” are used as umbrella terms for these technologies, but they fail to emphasize the connections and dependencies between technology, processes, and people. For the printing industry, we prefer to call the future Smart Print Manufacturing (SPM), which combines advanced technologies, such as artificial intelligence, with effective manufacturing processes to achieve semi-to-fully autonomous print production. Our five core technologies for SPM are discussed in this article as well as their current status and future implications.
These are the Top Core SPM Technologies
Cloud computing’s primary value proposition–accessibility–was able to be recognized during the height of the pandemic. Businesses that had already migrated to the cloud were able to ease the transition to work from anywhere with the help of cloud computing and software as service offerings. Our North American Software Investment Outlook research found that cloud-enabled software usage increased by as much as 94% year-over-year. Web-to-print (W2P) software that relies heavily on cloud technology experienced a 10% increase as more PSPs began offering online ordering. Despite the pandemic certainly accelerating the adoption of cloud computing, this trend is here to stay.
Big Data and Analytics
As much as ink and paper manufacturing companies, your mind may not immediately go to technology companies like Apple, Google, and Facebook when someone mentions big data. Under the surface of any printing operation, however, you’ll find that a lot of data is being generated.
Print shops may have hundreds, if not thousands, of contacts and important information about their customers stored in their customer relationship management (CRM) system. To manage and streamline production, the print shop’s management system keeps track of quotes, jobs, and stock levels.
The biggest generator of data is probably the equipment on the shop floor that could collect information on the jobs, machine usage, and environmental conditions, as well as analyze output to ensure quality.
Reports are traditionally generated from print shop data by key staff and management to monitor key operational and financial metrics. The last few years have seen a big shift in data analytics offered by print management information system (MIS) providers and equipment manufacturers. They can be built on top of well-known data analysis platforms such as Microsoft BI, Sisense, or Izenda, which gather data from a wide range of sources. The downside of these platforms is that they often require professional services for the integration of multiple data sources.
For the most part, manufacturers have been capturing data locally or in the cloud from Internet-connected equipment (assuming customers opt-in) to use in their own data analytics tools. PSPs need a more comprehensive view of their entire operations, specifically ink consumption and equipment uptime. Original equipment manufacturers (OEMs) can provide insights related to equipment uptime, overall equipment effectiveness (OEE), and ink consumption.
By implementing data and interchange standards, along with the growth of industry technology platforms, we believe that today’s data analytics options will become less siloed in the future.
In the printing industry, we are just beginning to make use of the vast amounts of machine data generated to improve the quality, efficiency, and autonomy of the process. An algorithm can create machines that efficiently accomplish a particular task by using a large amount of data. The machine learning component of AI is responsible for this.
In the last year, a few machine learning application cases have been marketed. Visual inspection systems and machine learning are being used by HP and Ricoh to identify, classify, and correct problems in print output.
This type of printing infrastructure solution uses algorithms to locate print defects and, in some cases, uses user feedback to enhance its accuracy and speed. Corrective action can be taken depending on the issue, such as compensating for a clogged printhead or requesting a reprint if necessary. With Artificial Intelligence, operators need to be less skilled, but the quality is still assured.
With PredictPrint Media Manager, users can scan a paper ream’s barcode when others at the company scan the system’s barcode to access up-to-date media settings. The solution requires only scanning the barcode, loading the paper in the printer, and then making wizard-driven selections as needed to automate size, type, color, coating, and weight.