The journey to AI in supply chain and logistics
AI data transformation can be focused on creating a safer, more efficient, and sustainable supply chain and logistics. At Anaeko, we work with transport and logistics companies to help them analyse and share data effectively.
From a safety perspective, the goal is to reduce incidents and accidents by integrating AI technology into trucks and other technologies. This AI can detect hazards and raise awareness, support assisted driving, and eventually lead to autonomous driving. Compliance is another key aspect, where understanding and meeting training needs are essential to protect drivers and the environment.
Efficiency can also be improved is achieved through route optimisation, ensuring that logistics operations are as productive as possible. In supply chain management, predictive maintenance is a significant focus. By analysing past data, AI can help predict and prevent future maintenance issues, increasing the utilisation of vehicles and equipment across the UK.
Currently, 40% of freight journeys are made with empty trucks. Optimising routes and planning can greatly improve vehicle utilisation and better manage drivers, who are a scarce commodity. This efficiency drive is crucial for the industry.
From a sustainability standpoint, analysis should ultimately benefit the environment. Reducing carbon emissions and optimising fuel usage not only cut costs but also help create a greener and cleaner environment.
When implementing AI within an organisation, there are two key components. Firstly, AI needs to be integrated into existing systems and platforms. Secondly, to drive specific AI processes, it is crucial to raise awareness within the organisation and among staff about what AI is and isn’t. Experimentation is necessary to prove the value of AI. Often, we implement pilot projects where measurable impacts are demonstrated before scaling operations across fleets, regions, and supply chain functions.
Transport and logistics firms are transforming their use of data from historical reporting to more advanced analytics.
Initially, organisations focus on descriptive analytics, which report on what happened. For example, performance indicators might be tracked weekly, monthly, quarterly, or annually to assess and improve safety targets. This involves looking back at historical data to identify trends and areas for improvement.
As organisations progress, they move towards diagnostic analytics, which aim to understand why something happened. This stage often involves machine learning and AI to analyse much more data than a human could manage. By surfacing insights, these diagnostics help organisations make better decisions.
The next stage is predictive analytics, where organisations look at trends to forecast future outcomes. For instance, correlating checks performed and defects found during predictive maintenance can help identify patterns and predict future issues.
Finally, organisations can move towards prescriptive analytics, which recommend solutions based on the data. This is particularly relevant for sustainability in the supply chain. For example, EV replacement strategies can benefit from economic life cycle analysis of vehicles. By understanding the right time to replace vehicles and which vehicles to choose, organisations can make data-driven decisions. This analysis can be enhanced with data from blockchain technology, which provides detailed information about vehicle efficiency and other metrics.
Finally than, wWith great power in AI comes great responsibility. It’s essential to ensure that AI-driven decisions adhere to ethical principles, laws, regulations, and maintain integrity. As AI’s popularity increases, so does its power consumption, which necessitates sustainable practices. Cloud providers like Amazon Web Services and Microsoft are advancing greener energy consumption to mitigate this impact. While there may be concerns about AI taking jobs, in reality, it’s about augmenting and cooperating with humans.
To deliver impactful AI solutions in business, you need an architecture built on trusted data. This involves transitioning from historical reporting to prescriptive analytics that make and recommend decisions. Expert skills in AI, data engineering, and data science are vital for this sophisticated work. In my view the solution is for tTransport and logistics firms to should focus on their core business, offloading the heavy lifting to AI specialists.
