Today’s savvy print operations are leveraging smart automation because every touchpoint in the flow from when a new job enters the business up to the point of delivery and invoicing, brings potential delays and costs. In the mix of carefully orchestrated and structured activities along with the less predictable adhoc events, the smart printer is aiming to automate specific tasks while pursuing a plan for continuous improvement. The use of data and analytics creates a more effective business environment.
Most printers have analytics in place to help run the business. They ingest data and produce charts and graphs based on generic algorithms that suit how the business operates. These static algorithms build an administrative baseline, but they don’t learn and adapt as the company grows, and they don’t reach into the prepress area. Consequently printers can miss the insights derived from the business data which has been input from production and might change how resources are used and where investments are made.
Kodak Prinergy on Demand business solution expands these options by implementing comprehensive automation that uses the power of artificial intelligence (AI), including Machine Learning (ML), to collect more data and provide deeper insight. This style of automation does more than program repetitive tasks; it learns over time by watching for patterns and adjusting to compensate for changing needs.
By linking the business and prepress data in the same analytics platform, data is assessed consistently, and so provides a more comprehensive range of input to every business decision made. Business and production insights become more relevant because the algorithms detect more precise patterns about what is going on in the business. As bottlenecks begin to appear in production, this real-time analysis enables dynamic adjustments to keep work moving smoothly.
A common bottleneck occurs around the proof and approval functions. Knowing how long these cycles are for each job, not just the aggregated averages, can be revealing. Customer-specific or product-specific friction might require new rules to ensure smooth processing but writing those rules can take time. Now imagine a solution that sees the friction and adds appropriate rules based on existing workflow patterns. Optimising these processes is how AI/ML becomes a differentiator.
When the concept of the Internet of Things (IoT) emerged, the promise was an expanded ability to capture more data, analyse more data, and then use that data to guide operations more efficiently. That is precisely what Prinergy on Demand does and so fulfills the promise of the Internet of Things. Data guides incremental adjustments so drives more efficiency from each manufacturing process by eliminating mundane tasks and using feedbacks to refine processes incrementally.
When the job is ready for proof approval in a typical workflow, the system may generate an email for the client. The schedule may call for approval within 48 hours, but if that approval doesn’t happen, the project sits idle until it does. There may be rules to send follow-up emails until there is a response, but it is rare to see the data behind those delays captured and analysed.
In the analytics-supported data-rich workflow, there is more insight into delays. Instead of identifying simply that a delay happened, the new process captures more discrete data, including the average delay by customer or product type, the number of delays by customer, and even the increase in costs because of each delay. For example, reviewing and approving changes made to production files by internal and external users can be time-consuming. Furthermore, mistakes made during this critical step in production can cause spoilage, rework and or downtime on press. Prinergy On Demand business solutions allows managers to identify how much time and effort operators are applying towards this critical task. Likewise, analysis of production activities across the team in real-time can identify operators that may need extra training or processes that may need refining.
Automating data collection and analysis will also expose rules that are mismatched to project requirements. But that automation can do much more. It can show where pricing models are out of sync with the real production costs and which products should be retired. With constant testing of each data point, bottlenecks surface before they become costly.
The goal of modern print manufacturing is to operate by the numbers. This can be a significant change for organisations that have until now relied on their team of experts to guide business and production decisions. The proof is in the results, however. Implementing AI-managed ML-enabled data capture and analysis puts the data hiding in your processes to work, freeing time, and resources, providing more opportunities to grow the business.
