Artificial and Augmented Intelligence in the Logistics industry

Artificial and Augmented Intelligence in the Logistics industry

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The logistics industry is transforming. The growth of big data, a shift to digital first and advances in Artificial Intelligence (AI) are supporting the sector to embrace change on a massive scale to revolutionise the way it works.

Post By Stuart Thomas -
Consolidation Director

Logistics companies are uniquely positioned to benefit from AI across almost all aspects of the supply chain. It deals with huge volumes of data, both structured and unstructured, which is the perfect source to feed into AI.

As a sector that depends on networks, both in a physical and digital sense, the logistics industry is driven by high volumes, low margins, lean asset allocation and time sensitive deadlines.  AI can support the optimisation of network orchestration to degrees of efficiency that are simply unachievable with human thinking alone.  It can help the logistics industry to redefine today’s behaviours and practices, taking operations from reactive to proactive, planning from forecast to prediction, processes from manual to autonomous, and services from standardised to personalised.

Shippers, carriers, suppliers and consumers can all expect to benefit from the logistics technology trends already emerging.  Here, we take a look at how AI can make the most impact.

Automated warehouses

AI technology is fundamentally changing many warehousing operations, including data collection and inventory processing, creating new opportunities for companies to increase revenue.  In warehouses, automation can be applied to predict the demand for particular products based on the data being received.  This means orders can be modified and in-demand items delivered to local warehouses.  By predicting demand and planning logistics well in advance, there’s also an opportunity to reduce transportation costs.

Computer vision, which is a form of visual inspection powered by AI, can help identify and organise inventory and even undertake autonomous quality control with the ability to identify damage, classify it and determine the appropriate corrective action faster than ever before.

Autonomous vehicles

Autonomous guided vehicles (AGVs) are already starting to play a bigger role in logistics operations.  Thinking about operational environments, such as warehouses, the traditional model has been very human-centric with trained operatives needed to operate materials handling equipment like fork-lifts, pallet jacks, wheeled totes and even tugging cars to move goods between locations.  With the evolution of AI, there is a very real shift towards a more automated approach using non-industrial collaborative robotics.

Out on the road, more affordable and accessible AI technology means many industry giants are starting to leverage the technology in their transportation.  Google, with its Waymo self-driving car, and Mercedes are just two of the brands making real progress in understanding the technologies that will support autonomous driving; it’s not impossible to see how this could be utilised in trucks and lorries to support the UK’s delivery networks.

Big, clean data

AI is not just about robots.  In simple terms, big data is a term that describes the large volume of data that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important, more how an understanding of the data can support better decision making and more strategic business outcomes.  This is where AI comes in, allowing logistics companies to forecast more accurate outlooks and optimise their performance like never before.  In a sector that is complex, dynamic and relies on many moving parts, big data has a critical role to play in organisational oversight.

However, getting to the point of having clean data is an important step, and a challenge for the industry.  Data is typically generated from multiple points and people, meaning efficiency gains can be difficult to measure.  Algorithms are now being developed to help analyse historical data, identify issues and improve data quality to the level where greater operating transparency is achieved and opportunities identified.  By understanding its vehicle routing data, global delivery firm UPS is saving 10million gallons of fuel annually by optimising their routes, based on an understanding that routes that primarily involve left turns are both safer and quicker.

The industry itself understands how big of a change big data will bring: according to Third Party Logistics Study[1], 81% of shippers and 86% of third-party logistics companies believe that using big data effectively will become ‘a core competency of their supply chain organizations’.

AI-powered customer experience

As in many customer-facing sectors, customer experience is key. For many consumers, their interactions with logistics companies are typically limited to sending a parcel, or as part of an online transaction. For business, it’s more complex with long-term service contracts, service level agreements and a breadth of geographical coverage all forming part of the relationship.

AI has the opportunity to transform and personalise all of these touchpoints to help increase loyalty and retention. Integration with voice agents, such as Amazon’s Alexa, can allow customers to find out about the progress of their parcel, going as far as connecting to the logistics provider if there is a problem with the shipment.

Content discovery AI can enable logistics companies to be proactive about managing their customer relationships.  Already being trialled in stock trading, this kind of AI analyses a raft of content, such as documents, market intelligence and relevant data, and uses it to make predictions and then ‘votes’ on the best course of action.  In logistics, this approach can support engagement based on the profile of customers, using data from platforms like social media to create a tailored and personalised experience.

Anticipatory Logistics takes the AI-powered logistics customer experience to the next level, delivering goods to customers before they have even ordered them or realised they needed them. Anticipatory logistics seeks to leverage the capabilities of AI to analyse and draw predictions from vast amounts of data such as browsing behaviour, purchase history and demographic norms as well as seemingly unrelated data sources such as weather data, social media chatter, and news reports to predict what customers will purchase.  Exposing these data sources to AI analysis, companies can effectively predict demand and shorten delivery times by moving inventory closer to customer locations and allocating resources and capacity to allow for previously unforeseen demand. In some cases, it would even require having non-purchased inventory constantly in transit to allow for instant delivery for an order placed while the goods are in motion

Powering the transformation

AI has the potential to fundamentally change the way the logistics industry runs.  According to the World Economic Forum[1], by 2025 the digital transformation of the sector could bring $1.5trillion of value to the industry and a further $2.4trillion-worth of benefits to society through reduced emissions, less traffic congestion and better prices.  AI is already creating an impressive shift in not just how things are done but understanding why they need to be done.  With drone-operated warehouses, self-learning systems, experimental delivery methods and even the adoption of blockchain all on the horizon, it’s clear that AI here to stay.

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