While AI in the pharma cold chain promises exciting possibilities, achieving these benefits is not a sprint but a marathon. The journey towards fully integrating AI begins with a critical first step: data standardisation.
Imagine a world where AI seamlessly optimises the pharma cold chain. Predictive analytics prevent temperature excursions before they occur and real-time adjustments guarantee timely delivery of life-saving medications. Such capabilities would dramatically improve operations and patient care. But these advancements hinge on establishing consistent and reliable data practices .
Currently, the adoption of AI in the pharma cold chain remains more aspirational than operational. Without uniform data practices, AI technologies cannot effectively learn from past incidents or accurately predict future challenges.
The essential role of data standardisation
To turn the promise of AI applications into reality, addressing the data issue must be the top priority. Failure to do so risks undermining the entire AI integration process and jeopardising patient safety.
For example, imagine a scenario where different pharmaceutical distribution centres record temperature excursions in varying ways. Some centres might log that an excursion occurred without specifying the degree of deviation, while others provide precise temperature details. Additionally, the duration of these excursions might be recorded inconsistently, sometimes in hours, minutes or even through text descriptions.
As a result, the AI system may struggle to accurately assess the risk of product spoilage, potentially leading to inaccurate risk evaluations. When the impact of a temperature deviation on the quality of the medication isn’t clear, products may be discarded unnecessarily, causing avoidable waste and increased cost. The absence of uniform data can also complicate the attempts to identify the root causes of these excursions, making it harder to implement effective preventative measures.
By establishing a standardised framework for data across the industry, these inconsistencies can be eliminated, paving the way for AI systems to function effectively. This means developing clear guidelines for data terminology, measurement units and data recording practices. Once these standards are in place, AI can begin to analyse and predict with greater accuracy, providing better outcomes for the entire supply chain.
To maintain the quality and reliability of the data, all stakeholders in the supply chain must rigorously validate their data, maintain transparency in AI decision-making, and conduct regular audits. These steps will help build a foundation of trust in AI systems, which is crucial for their successful adoption and operation.
Adopting AI as a long-term strategy
Effective AI adoption will be a gradual process focusing on long-term gains rather than immediate results. The goal is to make sure that each phase of implementation is grounded on a solid foundation of reliable data. Viewing AI adoption as a long-term journey means we can make steady improvements over time. Each step forward builds on previous progress, leading to significant benefits in the long run.
For instance, initial phases might focus on collecting and standardising data. Later phases could involve implementing basic AI systems for monitoring, gradually advancing to more sophisticated predictive analytics and real-time adjustments. This continuous improvement requires collaboration among pharmaceutical companies, logistics providers and technology partners to coordinate and effectively integrate all efforts.
The benefits of AI in pharma cold chain for all stakeholders
The advantages of integrating AI into the pharma cold chain are extensive and impact everyone involved. AI-driven insights will optimise routes, reduce waste and lower costs. This means pharmaceutical companies can streamline their logistics, distributors can better achieve timely deliveries and patients receive their medications reliably.
Real-world examples illustrate these benefits vividly. Take a pharmaceutical manufacturer that has adopted AI-driven route optimisation. By analysing historical data and predicting traffic patterns, AI can suggest the most efficient delivery routes, reducing fuel consumption and supporting timely deliveries. This not only cuts costs but also minimises the environmental impact.
But the future of AI in the pharma cold chain depends heavily on collaboration and partnership. Sharing data and insights among stakeholders can enhance the entire supply chain. For instance, logistics partners can share real-time data on transportation conditions, such as temperature and time spent in each location, with pharmaceutical companies, helping them understand and mitigate risks in the supply chain. Pharmaceutical companies can use AI to analyse this data and make informed decisions about their shipping strategies and packaging choices.
Collaborative efforts can lead to innovative solutions that no single player could achieve alone. By working together, stakeholders can develop reliable and effective AI systems, improve data quality and ultimately ensure the safe and efficient delivery of vital medications.
Building trust in AI systems
The journey towards fully integrating AI in the pharma cold chain begins with data that is fed to the systems. Without high-quality data that flows as the lifeblood of AI systems, we cannot achieve the accurate and reliable predictions needed to instil trust in the technology.
To achieve the required level of standardisation, pharmaceutical companies, logistics providers, and technology partners must work together to implement rigorous data validation processes, maintain transparency in AI decision-making, and conduct regular audits. By focusing on these long-term strategies and fostering a collaborative environment, the industry can achieve significant advancements in efficiency, reliability and patient care.
As technology continues to evolve, the strategic implementation of AI over time will be crucial in meeting the growing demands of the pharmaceutical industry. Embracing this marathon with a clear vision and cooperative spirit will lead to a brighter, more efficient future for all involved.
By Otto Dyberg, Chief Information Officer at Envirotainer
