Despite big data and AI technologies continuing to transform many major industries across the globe, recent reports suggest that the UK’s transport and logistics industry may be falling behind other essential sectors. According to a recent multinational YouGov survey, only 50% of UK-based transport and logistics professionals utilise basic data analytics to enhance core operations, with only 25% leveraging AI technology to optimise decision-making capabilities.

When compared to figures from Germany and America, nations that the same report reveals to have achieved data analytics adoption rates of 41% and 63% respectively, it’s clear that the UK’s transport sector is facing significant issues regarding modernisation. With advanced AI systems now capable of dramatically improving essential tasks like demand forecasting, route optimisation and supply chain management, professionals must pursue transformation.

Obstacles to overcome 

The cost of adoption and a lack of in-house expertise are currently believed to be the major obstacles preventing UK businesses from developing smart solutions. The above mentioned YouGov report suggests over 25% of UK-based logistics companies lack the required funds to pursue transformation, with 13% lacking the required internal skills to develop novel tools.

As a result, over 75% of UK companies claim any proposed adoption initiatives would need to be carried out by external providers, with professionals preferring to deploy readily made solutions rather than developing bespoke in-house technologies. This requirement may lead to increased costs, while also hindering any ability for teams to create truly custom solutions.

This is of particular importance as many of the benefits associated with the adoption of AI and data analytics tools relate to gaining actionable insights into unique operations. With aid from real-time and historic information, organisations can look to optimise specific aspects of essential processes to accurately assess inefficiencies and outmanoeuvre negative impacts. 

Real-time visibility 

The main case for increasing the adoption rate of data analytics and AI in the UK’s transport sector concerns real-time visibility. By automating the collection and analysis of high-quality information pertaining to all aspects of the supply chain, companies can measurably improve efficiencies in manufacturing, warehousing, transportation and almost all aspects of logistics.

Reviewing the six big losses in manufacturing framework helps to illustrate the importance of this pursuit, with the adoption of data analytics and AI systems helping to address all primary loss-causing events. Planned stops can be timed appropriately in reference to historical data and real-time scheduling, systemic losses can be better understood, production planning can be effectively optimised and entire warehousing operations can be intelligently automated.

These same benefits equally apply to wider aspects of transport and logistics. For example, demand forecasting supported by predictive analytics helps businesses avoid delays caused by commodity unavailability, while AI and machine learning algorithms can be used to adjust route planning processes in direct response to supply chain issues and transport disruptions.

Efforts to support adoption

Supporting and promoting the adoption of AI and data analytics in the UK transport sector will likely require considerable investment from the government, alongside concerted efforts from industry professionals to recalibrate the sector as a whole. At present, the government seems committed to funding developments in AI and tangentially related technologies across most major sectors, with £1.1 billion earmarked to fund qualifications in future technologies. 

Additionally, efforts must be made to develop sustainable business practices positioned to support the initial adoption and continual growth of AI and data analytics technologies. This point was also raised in the aforementioned YouGov survey, with researchers uncovering a distinct lack of focus among UK logistics companies with regard to sustainability objectives.

The report found that 60% of UK logistics professionals believe their operations lack specific sustainability goals customised to support transport and logistics processes. Furthermore, at least 33% claim to have no current plans to define such goals. This is of particular interest as effectively adapting to the use of future technologies may act to enhance sustainability plans by helping businesses to rescue waste and carbon emissions through intelligent automation.

Finally, successful adoption initiatives will require a commitment to collaboration across all stakeholders from various backgrounds. This will include transport businesses, technology providers, industry partners and government agencies. Clear organisational structures and regulatory guidelines will need to be in place to ensure data is collected and handled in an appropriate and effective manner, with knowledge sharing and training initiatives prioritised.

Summary 

As big data and AI technologies continue to shape the future of almost all major sectors, the UK’s transport and logistics industry must make efforts to modernise existing operations. By enhancing organisational processes with support from real-time data and advanced machine learning algorithms, stakeholders can address multiple common industry-wide inefficiencies.

However, reports suggest funding and skill-based roadblocks are preventing professionals from realistically pursuing transformative processes, leaving the UK’s transport sector falling behind its international counterparts. With this in mind, collaborative efforts between the UK government, industry partners and UK transport businesses themselves must be prioritised.

By working together to help transport businesses harness the power of future technologies, leaders can ensure the industry remains competitive for many years to come. With supply chains, manufacturing operations and wider logistics processes continuing to adapt to the modern market, the importance of data analytics and AI will become increasingly apparent.
Source: Marian Domingo