• Written by Sonali Subhadarsini, Product Manager

A shift supervisor in a printing and packaging facility is likely to start their day by checking the production schedule, looking for any jobs that are running behind or have been delayed due to equipment issues or material shortages, and wondering what questions they can ask of their own data to get ahead of potential problems. The operations team in such a facility is responsible for ensuring that jobs are completed on time and to the required quality standards, which can be a challenging task given the job-shop economics of the industry, with heavy setup costs and frequent changes to production schedules.

The operations questions that currently wait on a report

The operations team in a printing and packaging facility typically has to wait for a report to be generated before they can answer questions such as which jobs are currently running behind schedule, or which machines are experiencing the most downtime. This can be frustrating, as the team may have to wait for hours or even days for the report to be generated, by which time the situation may have changed. The team may also have to rely on manual data collection and analysis, which can be time-consuming and prone to errors. By using AI for operations in printing and packaging, the team can ask questions of their own data in real time, without having to wait for a report to be generated.

For example, a shift supervisor can ask what jobs are currently running behind schedule, and receive an immediate answer, allowing them to take corrective action to get the jobs back on track. This can be particularly useful in a job-shop environment, where production schedules are frequently changed and unexpected delays can have a significant impact on the overall production schedule.

The data behind them: process and exception records, project status, budget consumption, supplier performance

The data that underlies the operations questions in a printing and packaging facility is typically stored in a variety of systems, including process control systems, enterprise resource planning (ERP) systems, and customer relationship management (CRM) systems. This data includes process and exception records, project status, budget consumption, and supplier performance metrics. By integrating this data and using AI for operations in printing and packaging, the operations team can gain a more complete understanding of the production process and identify areas for improvement.

The category manager, for example, can use this data to compare the performance of different suppliers, and identify areas where improvements can be made. This can help to reduce costs and improve the overall efficiency of the production process. The data can also be used to identify trends and patterns in the production process, which can help the operations team to anticipate and prevent problems, rather than simply reacting to them.

Sector constraints: setup and makeready losses and quoting accuracy

The printing and packaging industry is subject to a number of sector-specific constraints, including setup and makeready losses, and quoting accuracy. Setup and makeready losses refer to the time and materials wasted during the setup and makeready process, which can be significant in a job-shop environment. Quoting accuracy refers to the accuracy of the quotes provided to customers, which can have a significant impact on the profitability of the production process. By using AI for operations in printing and packaging, the operations team can better understand these constraints and identify areas for improvement.

For example, the operations team can use AI to analyze the setup and makeready process, and identify areas where improvements can be made. This can help to reduce waste and improve the overall efficiency of the production process. The team can also use AI to analyze the quoting process, and identify areas where improvements can be made to improve quoting accuracy.

What improvement looks like: on-time delivery, exception rate, expediting cost

Improvement in the printing and packaging industry can be measured in a number of ways, including on-time delivery, exception rate, and expediting cost. On-time delivery refers to the percentage of jobs that are delivered on time, which is a key metric for the operations team. Exception rate refers to the percentage of jobs that experience exceptions, such as delays or defects, which can have a significant impact on the overall production schedule. Expediting cost refers to the cost of expediting jobs that are running behind schedule, which can be significant in a job-shop environment. By using AI for operations in printing and packaging, the operations team can improve these metrics and achieve better outcomes.

For example, the operations team can use AI to analyze the production schedule and identify areas where improvements can be made to improve on-time delivery. The team can also use AI to analyze the exception rate, and identify areas where improvements can be made to reduce exceptions. This can help to reduce the expediting cost, and improve the overall efficiency of the production process. To learn more about how AI can be used to improve operations in printing and packaging, visit our operations page.

What this does not do, or when it is the wrong choice

While AI for operations in printing and packaging can be a powerful tool for improving the production process, it is not a solution to every problem. For example, AI is not a replacement for human judgment and expertise, and it should not be relied upon to make critical decisions without human oversight. Additionally, AI may not be the best solution for very small facilities, or for facilities with very simple production processes. In these cases, the costs and complexity of implementing AI may outweigh the benefits. If you are unsure whether AI for operations in printing and packaging is right for your facility, contact us to discuss your options.

It is also worth considering that most people get wrong about AI for operations in printing and packaging is that it is a magic bullet that can solve all problems, which is not the case. AI is a tool that can be used to improve the production process, but it requires careful planning, implementation, and ongoing maintenance to achieve the desired results. By understanding the limitations and potential benefits of AI for operations in printing and packaging, facilities can make informed decisions about whether and how to implement this technology.