Smart print manufacturing: the future of production printing
Keypoint Intelligence has identified five basic technologies (cloud computing, big data and analysis, artificial intelligence, robotics and augmented reality) which will ultimately lead to mostly, if not completely autonomous print production. This article explores the current state and future implications of our five basic technologies for GPS.
- When the pandemic forced employers to switch to a work-from-anywhere model, cloud computing and software-as-a-service solutions eased the transition.
- A print shop’s customer relationship management system can contain hundreds or even thousands of contact records and critical customer information.
- Printing can further tie the digital advantages of augmented reality to the physical world by providing the trigger to initiate the experience via quick response code or other techniques.
By Ryan McAbee
The pace of change in the printing industry has been marked by spikes in innovation followed by years of continuous improvement. The convergence of modern technologies can enable breakthroughs that were not possible before, and at a faster rate than before. At Keypoint Intelligence, we have identified five basic technologies (cloud computing, big data and analysis, artificial intelligence, robotics and augmented reality) that will ultimately lead to print production that is mostly, if not completely, autonomous. It may sound like a distant sci-fi movie, but that transition will likely happen by the end of this decade, as each of these technologies has a cumulative beneficial effect on the others. For example, large amounts of data are the fundamental building blocks for machine and deep learning that improves the accuracy and capabilities of artificial intelligence (AI). Simply put, one technology improves the other.
Although the terms âsmart factoryâ and âIndustry 4.0â are commonly used as generic terms for these technologies, they often do not sufficiently emphasize the intersection and dependencies between technology, processes and people. We prefer to describe the way forward for the printing industry as Smart Print Manufacturing (SPM), which combines advanced technologies, such as AI, with efficient manufacturing processes to achieve the goal of ‘a semi-fully autonomous printing production. This article explores the current state and future implications of our five basic technologies for GPS.
The five basic SPM technologies
At the height of the pandemic, we were able to recognize one of the main value propositions of cloud computing: accessibility. When employers were forced to move to a work-from-anywhere model, cloud computing and software-as-a-service solutions eased the transition for companies that had already adopted cloud computing services. Our North American Software Investment Outlook research has found that the use of cloud-enabled software has increased by up to 94% year-over-year. Even cloud-heavy Web-to-Print (W2P) software saw a 10% gain as more PSPs needed to deliver social distance online orders. While the pandemic has certainly accelerated the adoption of cloud computing, this trend has endured.
Figure 1. Year-over-year increase in cloud deployment
N = Varies; Base: respondents who currently own these software products
Source: NA Software Investment Outlook, Keypoint Intelligence 2020/2021
Big Data and Analytics
When someone mentions big data, the printing industry with its manufacturing base may not come to mind as much as tech companies like Google, Facebook, and Apple. However, if you look under the surface of any printing operation, you will find that a lot of data is being generated. A print shop’s customer relationship management (CRM) system can contain hundreds, if not thousands, of contact records and critical customer information. The Print Shop Management System maintains quotes, jobs and shop floor data to manage and streamline production. Probably the biggest data generator is shop floor equipment that could collect information about jobs, machine usage, and environmental conditions, while also analyzing the output to ensure quality.
Traditionally, data within the print shop has been transformed into reports pulled by key staff and management to monitor key operational and financial metrics. In recent years, print management information system (MIS) vendors and equipment manufacturers have started offering data analytics. Some of them are built on well-established data analysis platforms such as Microsoft BI, Sisense or Izenda, where data can be collected from many sources. The downside is that these platforms often require professional services to integrate multiple data sources.
Manufacturers have primarily focused on capturing data locally or in the cloud from internet-connected devices (assuming customers accept it) for use in their own data analysis tools. While these Original Equipment Manufacturer (OEM) tools can provide information on equipment availability, overall equipment efficiency (OEE), ink consumption, and other metrics, PSPs need to ” a more complete view of all of their operations.
Going forward, we believe exchange and data standards along with the growth of industry technology platforms will ease the siled state of today’s data analytics options.
Our industry is in the early stages of using the large amount of machine data generated to improve the operation, quality and autonomy of the printing process. By using large amounts of data, algorithms can effectively train machines to perform a particular task. It’s a part of AI known as machine learning.
There are a few machine learning use cases that have come to market over the past year. HP and Ricoh use visual inspection systems and machine learning to identify, categorize and correct print output problems. This type of solution uses algorithms and, in some cases, user feedback to improve the accuracy and speed of print defect detection. Depending on the problem, the software may take corrective actions, such as compensating for a clogged printhead, or queuing a reprint if necessary. Due to AI, less skilled operators are needed even if the quality is assured. In a different use case, Xerox PredictPrint Media Manager uses AI to correlate and share up-to-date settings for different media as other users scan the barcode on the ream of paper. The solution automates size, type, color, coating and weight settings with the simplicity of scanning the barcode, loading the paper into the printer, and then completing the process with wizard-guided selections as required.
According to the International Federation of Robotics, the installation of industrial robots more than tripled worldwide between 2010 and 2019, bringing the total installed base to more than two billion. There are several trends that are lowering old barriers to adoption and making robotics more accessible. The most impactful change has been a constant decrease in costs as well as a constant increase in variation and capacities.
To date, use cases for production printing are more limited and OEM programs are still ongoing, with some offering robotic automation for material movement. State-of-the-art PSPs with large or complex fulfillment operations have started adopting Automated Guided Vehicles (AGVs) for their warehouses. We expect adoption to accelerate over the decade, driven by a desire to increase productivity while minimizing or shifting labor costs to higher value-added tasks.
Augmented Reality (AR) is an enhanced version of reality created by overlaying a digital layer, primarily for educational or entertainment purposes. Printing can further tie the digital advantages of AR to the physical world by providing the trigger to initiate the experience via a quick response code (QR) or other techniques. There are many use cases, from how-to videos on critical customer documents to more creative AR that allows customers to experience a product.
For SPM, we focus on AR which enables or supports print production, not AR which is part of the printed product. The most popular use case is AR to diagnose and maintain printing equipment that can be used by the OEM service technician or the end user. The experience allows the end user to identify the parts and can provide instructions on how to repair or replace the items.
Going forward, we expect AR to play a key role in bringing the physical and digital worlds closer together by using digital twins to visually model different scenarios of the print manufacturing process. AR could help model a new physical layout of the print shop or predict bottlenecks throughout production processes based on print volumes and the mix of varying applications.
The bottom line
Keypoint Intelligence believes the printing industry is reaching another peak of innovation, akin to past moments of creative destruction that lead to new opportunities. During times of creative destruction, the new replaces (or displaces) the old while creating new opportunities.
The impact of SPM technologies is likely to happen faster and be more disruptive than those that came before it, as each can magnify the effect of the others. Rather than dreading this transition, it is more productive to focus on some positive effects that have already occurred. Cloud computing removes the need for local IT infrastructure and administration, but opens up access to the growing hybrid and remote workforce. Big data and AI will disrupt the creative and analytical tasks people now do, but allow us to better use our time for higher value work. The key for PSPs is to always plan for the future by identifying and adopting transformative innovations to successfully transition from the old to the new.
Ryan McAbee is the Director of Workflow Consulting Services at Keypoint Intelligence, which focuses on providing technology, business and market information to clients in the digital marketing and media and workflow markets. In this role, he is responsible for conducting market research, market analysis and forecasting, content development, industry training, and consulting with print service providers.