Cost optimisation in pharmaceuticals
AI cost optimisation in pharmaceuticals streamlines production by identifying inefficiencies in validated processes and strict documentation rules, allowing a quality control manager to query the system for deviations from standard protocols and adjust workflows accordingly to reduce administrative costs.
A pharmaceutical company's production dashboard, once a symbol of efficiency, now gathers dust in a corner of the factory floor, a reminder of the failed attempts to optimise costs in a highly regulated industry. The culprit behind this failure is not the lack of data or technology, but rather the unique constraints of pharmaceutical production, where validated processes and strict documentation rules reign supreme.
Pharmaceuticals' unique challenges
The pharmaceutical industry is notorious for its stringent regulations and complex production processes, making cost optimisation a daunting task. Change control overhead, batch record review, and deviation investigation time are just a few of the hurdles that hinder the implementation of cost-saving measures. For instance, a shift supervisor at a pharmaceutical plant must carefully document every deviation from the standard process, no matter how minor, which can lead to a significant increase in administrative costs. This is in stark contrast to other industries, where changes to production processes can be made with relative ease.
Most people get wrong the idea that cost optimisation in pharmaceuticals can be achieved through simple tweaks to existing processes. They are wrong, as the reality is that every change, no matter how small, requires a thorough review and validation, which can be a time-consuming and costly endeavour. This is why many cost optimisation initiatives in pharmaceuticals fail to gain traction, as they often underestimate the complexity of the production process.
Unit cost vs total cost of an AI workflow
When it comes to implementing AI cost optimisation in pharmaceuticals, it is essential to distinguish between unit cost and total cost of an AI workflow. While the unit cost of an AI model may be low, the total cost of implementing and maintaining the workflow can be substantial. This is because the workflow often requires significant upfront investment in data preparation, model training, and integration with existing systems. A category manager at a pharmaceutical company, for example, may need to compare the costs of different AI models and workflows to determine which one will yield the best results.
The difference between unit cost and total cost is crucial, as it can make or break the business case for AI cost optimisation in pharmaceuticals. By focusing solely on unit cost, companies may overlook the broader implications of implementing an AI workflow, including the need for ongoing maintenance and support. This is why it is essential to consider the total cost of ownership when evaluating AI cost optimisation initiatives.
The three real levers of cost optimisation
So, what are the real levers of cost optimisation in pharmaceuticals? In our experience, there are three key areas to focus on: caching, batching, and model routing. Caching involves storing frequently-used data in a readily accessible location, reducing the need for costly database queries. Batching, on the other hand, involves grouping similar tasks together to reduce the overhead of individual transactions. Model routing, meanwhile, involves optimising the flow of data through the AI workflow to minimize unnecessary processing steps. By focusing on these three areas, companies can achieve significant cost savings without compromising the integrity of their production processes.
For more information on how to implement these levers, please visit our resource centre, which provides detailed guides and case studies on AI cost optimisation in pharmaceuticals.
Token cost is rarely the dominant line
Token cost, or the cost of individual AI model executions, is often touted as a key driver of cost optimisation in pharmaceuticals. However, in reality, token cost is rarely the dominant line item in the overall cost of an AI workflow. This is because the cost of data preparation, model training, and integration with existing systems often dwarfs the cost of individual model executions. Furthermore, the cost of ongoing maintenance and support, including the need for regular model updates and retraining, can also be substantial.
By focusing too narrowly on token cost, companies may overlook the broader cost implications of their AI workflows. This is why it is essential to take a holistic view of the cost of AI cost optimisation, considering all the factors that contribute to the overall cost of ownership. If you would like to discuss your specific cost optimisation challenges with one of our experts, please get in touch.
When AI cost optimisation is not the answer
Finally, it is essential to recognise that AI cost optimisation is not a silver bullet for every cost challenge in pharmaceuticals. In some cases, the costs and complexities of implementing an AI workflow may outweigh the potential benefits. For example, in situations where production processes are highly variable or subject to frequent changes, the cost of retraining and updating AI models may be prohibitively expensive. In such cases, more traditional cost optimisation approaches, such as process reengineering or outsourcing, may be more effective.
Frequently asked questions
What are the unique challenges of cost optimisation in pharmaceuticals?
Stringent regulations and complex production processes hinder cost-saving measures, requiring thorough review and validation of every change.
How can AI cost optimisation be effectively implemented in pharmaceuticals?
By distinguishing between unit cost and total cost of an AI workflow, considering data preparation, model training, and integration with existing systems.
What are the key areas to focus on for cost optimisation in pharmaceuticals?
Caching, batching, and model routing can help companies achieve significant cost savings without compromising production process integrity.
When is AI cost optimisation not the best solution for pharmaceutical companies?
When production processes are highly variable or subject to frequent changes, traditional cost optimisation approaches may be more effective due to high AI model retraining costs.