A guide to beat logistics challenges in the age of AI
The transport and logistics industry is facing several complex challenges, including predicting capacity and driver shortages. These challenges are being compounded by factors such as increasing demand, logistics sector growth, and technological advancements that are changing the way transport and logistics companies operate nowadays.
Thus, artificial intelligence showed up as a powerful tool to help logistics managers overcome these challenges. AI can be used to forecast capacity needs, optimize fleet management, and predict future risks. This information can be used to make better decisions for resource allocation and route planning, and – therefore – overcoming most common challenges within transport and logistics.
Challenges with implementing AI
- High implementation costs
AI solutions can be expensive to develop and implement.
- Lack of resources
Many logistics companies lack the internal resources to develop and implement AI solutions.
- Privacy and security concerns
There are concerns about the privacy and security of data used by AI solutions.
- Concerns about being replaced
Some employees are concerned that AI will replace them.
Five AI benefits for the capacity challenge
There are a variety of possible benefits that may be gained from adopting AI to handle the problem of logistical capacity, despite the challenges involved. Among these advantages are:
- Predicting needed resources
AI can predict capacity needs, using previous and present data. This data can improve resource allocation and planning. AI can also forecast product and service demands to assure enough resources to fulfil client demand.
- Real-time data analysis
AI can analyse real-time data to provide insights into current capacity levels and needs. This information can be used to make agile decisions about resource allocation and routing. For example, AI can be used to identify bottlenecks in the supply chain and re-route shipments accordingly. This can help improve efficiency and reduce delivery delays.
- Dynamic planning
AI solutions with dynamic planning feature models that take into account real-time data, and changing conditions can help optimize fleet utilization and reduce operations and fuel costs. For example, AI can be used to predict the number of trucks needed to meet demand for a specific day or week. This information can be used to ensure that enough trucks are available to meet customer demand without overspending.
- Analysing historical data
AI can analyse historical data to identify patterns and predict future risks. This information can be used to take preventative measures before a risk becomes a reality. For example, AI can be used to predict when a truck is likely to break down. By scheduling preventive maintenance with this data, it is possible to avoid a financial loss.
- Gaining a competitive edge in the logistics market
Companies that can use AI to effectively manage their capacity needs will have a competitive advantage in the logistics market. They will be able to meet customer demand more efficiently and cost-effectively. They will also be able to better adapt to changing conditions and disruptions. This will increase their chances of achieving long-term success.
In conclusion
As AI continues to develop, it is likely to play a greater role in the transport and logistics sector, as it can help logistics managers overcome the challenges they face today. By providing real-time data insights, predicting needed capacity, analysing historical data, and helping gain a competitive edge, AI is supporting logistics companies be more efficient, responsive, and reliable.
Norma LIVE is an example of an AI-powered solution that can help transport and logistics businesses overcome the challenges they face, such as predicting their needed capacity of resources and driver shortages, as it uses real-time data to provide accurate and up-to-date forecasts. These features help logistics managers optimize their route planning in an efficient way, reducing operational costs.
