Many companies already have access to what is termed ‘big data‘, providing information on almost every aspect of the supply chain. That’s great, but big data is only useful if it can be interpreted quickly and easily. Pumping big data to a team of analysts with spreadsheets is never going to leverage its maximum benefits, so analytical algorithms have been developed to support this analysis. However, algorithms must be developed and tweaked by people which takes time. The answer to this problem is now coming in the shape of artificial intelligence (AI).
Over the last few years, major logistics operations have been increasingly relying on big data analytics, as well as research teams, to provide informed real-time data to make decisions about their operations. However, the data generated can be varied and granular to understand, and despite a multitude of software applications built around complex algorithms to support this analysis, getting insights can still be a time-consuming and complex process. This is where AI is coming to the rescue, by deploying techniques to automate the creation of new algorithms, with limited human intervention, through machine learning, deep learning and natural language processing.
It is hoped that these techniques will help streamline and automate the pertinent information coming from big data in supply chains. AI effectively ‘teaches’ computers to parse data in a contextual manner, allowing logistics operators to make decisions quickly, without having a team mining and interpreting large volumes of information. As one example, this type of data interpretation will become a critical platform for supporting anticipatory logistics – where market demand triggers are read, and product is readied for despatch, before customer orders are placed. Anticipatory logistics is dependent on analysing a plethora of information on trending social signals, market events and customer purchasing behaviour, all of which need deep dive analysis to interpret how logistics should react.
AI is not the future, it’s now. Whilst the transportation and logistics sector is probably lagging other early adopting sectors, such as telecommunications and financial services, there are some companies leading the way. In fact, a study carried out by McKinsey in 2017 discovered that companies in the transportation and logistics sector who already employed a proactive AI strategy were seeing higher profit margins than those who were not – with up to 5% variance. The comparatively low early adoption of AI in logistics is probably due to the huge financial commitment required. There simply aren’t the same level of margins to trigger investment that are benefited from in other sectors. Where AI is being deployed in logistics, or at least tested, is tending to be in the 3rd party logistics sector where the investment can be supported by multiple large client organisations.
It’s not just in data analysis of demand that AI can support supply chains and logistics operation – it’s also in tangible assets such as trucks. Machine learning is being used in the development of predictive maintenance, optimisation of routes in real-time to avoid congestion and is also a key part of driverless technology. In fact, AI may already be taking driverless technology to the next level even before the technology has been deployed. A start-up company called iSee, which is a spin-off from MIT, is working on trying to incorporate cognitive science into AI to introduce a ‘common sense’ factor to automated driving.
The transportation industry will be hugely affected by advances in AI technology, from anticipatory logistics through to how vehicles are managed and driven. Companies who start thinking now about how their business can become data-driven and AI-enabled will help drive forward their competitive advantage in logistics. Whilst many companies may already have access to big data, through POS information and CRMs, without the deployment of AI they may find themselves becoming data rich but insight poor and being left behind.
With increased customer demand expectations, traditional methods of process improvement and optimisation are coming under strain and often not able to solve increasingly complex structural sales and operational planning issues. Therefore, there is increasing importance that companies start to leverage AI technologies to help drive improvement and further innovation in their logistics operations.
The supply chain consultants at Paul Trudgian are experts in all aspects of logistics and supply chain management. If you would like to discuss how we can assist your business, contact our team today on 0121 51 0008, or email [email protected]