• Written by Muhammed Abdulla NC, CEO

A production manager's dashboard often gathers dust, its carefully crafted charts and graphs a reminder of the cost optimisation efforts that have failed to yield lasting results in the manufacturing process, particularly when it comes to AI cost optimisation in manufacturing.

Manufacturing complexities

Manufacturing introduces unique challenges to cost optimisation, particularly in the context of AI cost optimisation in manufacturing. Changeover losses, where production lines are halted to switch between products, can quickly erode savings from other areas. Multi-plant comparability is also a hurdle, as different facilities may have distinct processes, equipment, and labour costs, making it difficult to identify and implement effective cost reduction strategies across the organisation.

An ageing asset base can further complicate matters, as older equipment may be less efficient or more prone to breakdowns, leading to increased maintenance costs and decreased productivity, which can impact the overall effectiveness of AI cost optimisation in manufacturing. These factors must be carefully considered when evaluating cost optimisation opportunities, and can be addressed through the use of AI cost optimisation in manufacturing.

Unit cost vs total cost

A common mistake in cost optimisation is focusing solely on unit cost, without considering the total cost of a process or workflow. In the context of AI workflows, this can be particularly problematic, as the cost of implementing and maintaining the workflow may outweigh any savings from reduced unit costs, and can be mitigated through the use of AI cost optimisation in manufacturing.

For example, a shift supervisor may implement a new AI-powered quality control process that reduces the unit cost of inspections, but increases the total cost of the workflow due to the need for additional training and equipment, which can be addressed through AI cost optimisation in manufacturing. By considering the total cost of the workflow, manufacturers can make more informed decisions about where to focus their cost optimisation efforts, and can use AI cost optimisation in manufacturing to identify areas for improvement.

Real levers for cost optimisation

When it comes to cost optimisation in AI workflows, there are three key levers that can be used to reduce costs: caching, batching, and model routing. Caching involves storing frequently-used data or results to reduce the need for redundant calculations, which can help reduce the cost of AI workflows. Batching involves processing multiple requests or tasks together, to reduce the overhead of individual transactions, and can be used to optimise AI cost optimisation in manufacturing.

Model routing involves directing requests or tasks to the most efficient or cost-effective model or process, based on factors such as workload, resource availability, and priority. By carefully managing these levers, manufacturers can reduce the cost of their AI workflows and improve overall efficiency, and can use AI cost optimisation in manufacturing to identify areas for improvement. For more information on how to implement these levers, visit our resource centre.

Token cost is not the dominant line

Token cost, or the cost of individual units of processing or storage, is often touted as a key factor in cost optimisation. However, in many cases, token cost is not the dominant line item in the overall cost of an AI workflow. Other factors, such as labour costs, equipment costs, and maintenance costs, can have a much greater impact on the total cost of the workflow, and can be addressed through AI cost optimisation in manufacturing.

For example, a category manager may compare two suppliers of AI-powered predictive maintenance software, and find that one supplier offers a lower token cost per prediction. However, if the other supplier's software requires less labour to implement and maintain, it may be the more cost-effective option overall, and can be identified through the use of AI cost optimisation in manufacturing. By considering the full range of costs associated with an AI workflow, manufacturers can make more informed decisions about where to focus their cost optimisation efforts, and can use AI cost optimisation in manufacturing to drive business success.

Limitations and wrong choices

While AI cost optimisation in manufacturing can be a powerful tool for reducing costs and improving efficiency, it is not a panacea. There are certain situations where AI cost optimisation in manufacturing may not be the best choice, such as where the costs of implementation and maintenance outweigh the potential savings, or where the workflow is highly variable or unpredictable. In these cases, other approaches to cost optimisation may be more effective, and can be discussed with our team by visiting our contact page.

Additionally, AI cost optimisation in manufacturing may not be suitable for all types of manufacturing processes, such as those with highly complex or nuanced workflows. In these cases, a more tailored approach to cost optimisation may be necessary, and can be developed through the use of AI cost optimisation in manufacturing. By carefully considering the limitations and potential wrong choices, manufacturers can ensure that they are using AI cost optimisation in manufacturing in the most effective way possible.