• Written by Muhammed Abdulla NC, CEO

Every week, a production manager in a printing and packaging plant sits through meetings where the latest cost optimisation initiative is unveiled, only to see it falter when it meets the reality of job-shop economics. The problem is not the intention, but the failure to account for the specific challenges of printing and packaging, where setup and makeready losses, quoting accuracy, and substrate waste all conspire against easy cost savings.

Printing and packaging complexities

Setup and makeready losses are a major hurdle in printing and packaging, as each new job requires significant time and resources to set up the machinery. This makes it difficult to achieve economies of scale, as the cost of setup can outweigh the benefits of larger production runs. Quoting accuracy is also a challenge, as the complexity of printing and packaging jobs makes it hard to predict costs with certainty. And then there is substrate waste, which can be a significant cost factor, especially when working with expensive materials.

Most people get wrong the idea that these challenges can be easily overcome with a bit of process tweaking. They are wrong, because the underlying economics of job-shop production are fundamentally at odds with the idea of easy cost savings. The reality is that printing and packaging is a complex, high-variety, low-volume business, where every job is different and every customer has unique requirements.

Unit cost vs total cost

A common mistake in cost optimisation is to focus on unit cost, without considering the total cost of the workflow. In printing and packaging, the total cost includes not just the cost of materials and labour, but also the cost of setup, makeready, and waste. AI cost optimisation can help to identify areas where total cost can be reduced, but it requires a deep understanding of the workflow and the specific challenges of printing and packaging.

A shift supervisor at a printing plant, for example, might use AsscherAi to analyse the production log and identify areas where setup times can be reduced, or where substrate waste can be minimised. By looking at the total cost of the workflow, rather than just the unit cost, the supervisor can make more informed decisions about how to optimise production.

Real levers for cost optimisation

So what are the real levers for cost optimisation in printing and packaging? There are three: caching, batching, and model routing. Caching involves storing frequently-used materials or setups to reduce the time and cost of setup. Batching involves grouping similar jobs together to reduce the number of setups required. Model routing involves optimising the flow of jobs through the production process to reduce waste and minimise setup times.

These levers can be used in combination to achieve significant cost savings. For example, a category manager might use caching to store frequently-used packaging materials, and then use batching to group similar jobs together and reduce the number of setups required. By optimising the flow of jobs through the production process, the manager can also reduce waste and minimise setup times.

Token cost is not the dominant line

Token cost - the cost of a single unit of production - is often seen as the key driver of cost optimisation. But in printing and packaging, token cost is rarely the dominant line. Instead, it is the total cost of the workflow that matters, including the cost of setup, makeready, and waste. AI cost optimisation can help to identify areas where total cost can be reduced, but it requires a deep understanding of the workflow and the specific challenges of printing and packaging.

In fact, focusing too closely on token cost can be counterproductive, as it can lead to decisions that increase the total cost of the workflow. For example, a production manager might decide to reduce token cost by using cheaper materials, only to find that the resulting increase in waste and setup times outweighs any savings.

When AI cost optimisation is not the answer

AI cost optimisation is not a silver bullet, and there are times when it is the wrong choice. If the production process is already highly optimised, for example, then AI cost optimisation may not be able to identify significant areas for improvement. Similarly, if the workflow is highly variable and subject to frequent changes, then AI cost optimisation may struggle to keep up. In these cases, it may be better to focus on other areas, such as process improvement or staff training. If you are unsure whether AI cost optimisation is right for your organisation, get in touch to discuss your options.