• Written by Muhammed Abdulla NC, CEO

The production manager of a printing and packaging firm checks the schedule for the day, looking at the mix of short and long runs, each with its own setup and makeready costs, and wonders how to make the most of the new enterprise AI system that has been installed to help with querying the company's data.

Challenges in printing and packaging

The printing and packaging industry presents unique challenges that can make it harder to adopt new systems, including enterprise AI. Setup and makeready losses, quoting accuracy, and substrate waste are all significant concerns. These challenges are exacerbated by the job-shop economics that govern the industry, where heavy setup costs can make short runs unprofitable. The production manager must balance these competing demands to ensure the smooth operation of the plant.

For example, a shift supervisor checks the maintenance log to ensure that all equipment is in good working order, and that any downtime is minimised. This attention to detail is crucial in an industry where small errors can result in significant waste and lost productivity. The supervisor must also be able to quote accurately, taking into account the costs of setup, materials, and labour, in order to ensure that the company remains competitive.

Building on existing routines

One of the key factors in successful enterprise AI adoption is attaching new capability to a routine that already exists. This means identifying areas where the new system can augment and support existing processes, rather than trying to replace them entirely. By building on what already works, the production manager can ensure a smoother transition and reduce the risk of disruption to the business.

A category manager compares two suppliers, looking at their prices, lead times, and quality, and uses the enterprise AI system to analyse the data and make a recommendation. This is an example of how the new system can be used to support and enhance existing decision-making processes, rather than trying to replace the judgement and experience of the category manager.

Enabling the right questions

It is not just a matter of who needs the answer, but also who needs to be able to ask the question. In a printing and packaging firm, this might include production managers, shift supervisors, and category managers, all of whom need to be able to query the system in order to do their jobs effectively. By enabling these users to ask questions, the enterprise AI system can provide valuable insights and support decision-making at all levels of the organisation.

Most people get wrong the idea that enterprise AI systems are only for technical experts, and that they require a high level of technical knowledge to use. This is wrong, as the systems are designed to be used by anyone who needs to query the data, regardless of their technical background. The production manager can use the system to analyse production schedules, while the shift supervisor can use it to check maintenance records.

Rebuilding trust after errors

The first wrong answer from an enterprise AI system can be damaging, and can undermine trust in the system. However, this does not mean that the system is flawed, but rather that it is still learning and improving. The key is to rebuild trust by identifying and correcting the error, and by ensuring that the system is continually updated and refined. By doing so, the production manager can ensure that the system remains a valuable tool for the business.

For example, if the system provides an incorrect quote for a job, the category manager can investigate and correct the error, and then use the system to re-run the analysis and provide a revised quote. This process of correction and refinement is an essential part of building trust in the system, and ensuring that it continues to provide valuable insights and support decision-making.

To find out more about how AsscherAi can support your printing and packaging business, visit our website at www.asscherai.com.

Limitations and wrong choices

Enterprise AI adoption is not a panacea, and there are times when it is the wrong choice. For example, in situations where the data is incomplete or inaccurate, the system may not be able to provide reliable insights. Similarly, in situations where the business is undergoing significant change or upheaval, the system may not be able to keep pace with the evolving needs of the organisation. In these cases, other solutions may be more appropriate, such as consulting with an expert to identify the best approach.

The production manager must carefully consider the needs of the business, and determine whether enterprise AI adoption is the right choice. By doing so, they can ensure that the system is used effectively, and that it provides real value to the organisation. This requires a deep understanding of the business, and the ability to identify areas where the system can add real value.