Keypoint Intelligence has identified five core technologies (cloud computing, big data and analytics, artificial intelligence, robotics and augmented reality) that will ultimately lead to largely – if not completely – autonomous print production. This article examines the current state and future impact of our five core technologies for SPM.
- As the pandemic forced employers to switch to a work from anywhere model, cloud computing and software-as-a-service solutions made the transition easier.
- A print shop’s customer relationship management system can contain hundreds, if not thousands, of contact records and critical information about customers.
- Print can further link the digital benefits of augmented reality with the physical world by providing the trigger for the initiation of the experience through a quick response code or other techniques.
By Ryan McAbee
The pace of change in the printing industry is characterized by peaks in innovation, followed by years of continuous improvement. The convergence of modern technologies can enable breakthroughs that were not possible before, and faster than before. At Keypoint Intelligence, we have identified five core technologies (cloud computing, big data and analytics, artificial intelligence, robotics and augmented reality) that will ultimately lead to largely – if not completely – autonomous print production. This may seem like a distant science fiction film, but that transition is likely to happen by the end of this decade as each of these technologies has a positive impact on the other. For example, large amounts of data are the basic building blocks for machine and deep learning that improve the accuracy and capabilities of artificial intelligence (AI). Simply put, one technology improves the other.
While the terms “Smart Factory” and “Industry 4.0” are often used as umbrella terms for these technologies, the overlaps and dependencies between technology, processes and people are often not sufficiently emphasized. We prefer to refer to the way forward for the printing industry as Smart Print Manufacturing (SPM), which combines advanced technologies such as AI with effective manufacturing processes in order to achieve the goal of semi to fully autonomous print production. This article examines the current state and future impact of our five core technologies for SPM.
The five core technologies of SPM
At the height of the pandemic, we were able to spot one of cloud computing’s most important value propositions – accessibility. When employers were forced to move to a work-from-anywhere model, cloud computing and software-as-a-service solutions made the transition easier for companies that had already adopted cloud services. Our study of the North American Software Investment Outlook found that cloud-enabled software usage increased by up to 94% year over year. Even cloud-heavy web-to-print (W2P) software saw a 10% increase as more PSPs were needed for socially distant online orders. Although the pandemic has certainly accelerated the adoption of cloud computing, this trend continues.
Figure 1. Year-over-year increase in cloud delivery
N = varies; Basis: respondents who currently own these software products
Source: NA Software Investment Outlook, Keypoint Intelligence 2020/2021
Big data and analytics
When someone talks about big data, they might not think of the printing industry and its manufacturing base as much as technology companies like Google, Facebook and Apple. However, if you look beneath the surface of a printing process, you will find that a lot of data is being generated. A print shop’s customer relationship management (CRM) system can hold hundreds, if not thousands, of contact records and critical information about customers. The print shop’s management system stores quotes, orders and workshop data to manage and streamline production. The biggest generator of data is likely the equipment in the shop floor, which gathers information about the jobs, machine usage, and environmental conditions, while analyzing the output to ensure quality.
Traditionally, within the print shop, the data is converted into reports that are accessed by key employees and management to monitor key operational and financial metrics. In recent years, Print Management Information System (MIS) providers and device manufacturers have begun to offer data analytics. Some of them are based on established data analysis platforms such as Microsoft BI, Sisense or Izenda, on which data can be collected from many sources. The disadvantage is that these platforms often require professional services to integrate multiple data sources.
Manufacturers have mainly focused on collecting data locally or in the cloud from internet-connected devices (provided customers agree) for use in their own data analysis tools. While these original equipment manufacturer (OEM) tools can provide insights into device availability, Overall Equipment Effectiveness (OEE), ink usage, and other indicators, PSPs need a broader view of their overall operations.
We believe that data and exchange standards, along with the growth of industrial technology platforms, will reduce the isolated state of today’s data analysis options.
Our industry is in the early stages of using the huge amount of machine data generated to improve the operation, quality and autonomy of the printing process. With the help of large amounts of data, algorithms can effectively train machines to perform a specific task. This is a part of AI known as machine learning.
There are a few machine learning use cases that have hit the market over the past year. HP and Ricoh use visual inspection systems and machine learning to identify, classify, and correct problems in print output. This type of solution uses algorithms and in some cases user feedback to improve the accuracy and speed of detection of printing defects. Depending on the problem, the software can take corrective action, such as: B. compensate for a clogged printhead or queue a reprint if necessary. Because of the AI, less experienced operators are required even if the quality is guaranteed. In another use case, Xerox PredictPrint Media Manager uses AI to correlate and exchange current settings for different media when other users scan the giant paper’s barcode. The solution automates the settings for size, type, color, coating and weight by simply scanning the barcode, loading the paper into the printer and then completing the process with wizard-driven selections as required.
According to the International Federation of Robotics, the installation of industrial robots worldwide more than tripled from 2010 to 2019, which corresponds to a total installation base of over two billion. There are several trends that reduce previous barriers to adoption and make robotics more accessible. The most influential change was a steady decline in costs and a steady increase in variants and capabilities.
To date, the use cases for production printing are more limited and programs from OEMs are still in the works, with some offering robotic automation for material movement. Technology-oriented PSPs with extensive or complex fulfillment operations have started to introduce automated guided vehicles (AGVs) for their warehouses. We anticipate adoption will accelerate over the decade, driven by the desire to increase productivity while minimizing labor costs or shifting to higher-value tasks.
Augmented Reality (AR) is an augmented version of reality created by overlaying a digital layer, primarily for educational or entertainment purposes. Print can further link the digital benefits of AR with the physical world by providing the trigger for initiating the experience through a quick response code (QR) or other techniques. There are many use cases, from video explainer on critical customer documents to more creative AR that enables customers to experience a product.
For SPM, we focus on AR that enables or supports print production, not AR that is part of the printed product. The most common use case is AR for diagnosing and maintaining pressure equipment that can be used by the OEM service technician or by the end user. The experience enables the end user to identify parts and can provide instructions on how to repair or replace items.
We expect that AR will play a key role in bridging the physical and digital worlds in the future by using digital twins to visually model different scenarios of the print making process. AR could help to model a new physical layout of the print shop or to predict bottlenecks in the entire production process based on different print volumes and application mix.
The bottom line
Keypoint Intelligence believes that the printing industry is reaching another peak of innovation, similar to previous moments of creative destruction that led to new opportunities. In moments of creative destruction, the new replaces (or supplants) the old and creates new possibilities.
The effects of SPM technologies are likely to come in faster and more disruptive than the previous ones, as each can amplify the effects of the other. Instead of dreading this transition, it is more productive to focus on some positive effects that have already occurred. Cloud computing replaces the need for local IT infrastructure and administration, but opens up access to the growing hybrid and remote employees. Big data and AI will disrupt creative and analytical tasks performed by humans today, but it will allow us to better use our time for higher quality work. The key for PSPs is to always plan for the future by identifying and applying transformative innovations to successfully move from the old to the new.
Ryan McAbee is the director of Keypoint Intelligence’s production workflow consulting service, focused on providing technology, business and market insights to customers in the digital marketing and media and production workflows markets. In this role he is responsible for carrying out market research, market analysis and forecasting, content development, industry training and advising print service providers.