Logistics companies are facing a wave of major disruption — and artificial intelligence (AI) and industry-wide platforms are at the heart of it.
Digitalisation and the rise of new players such as Yodel, Whistl and Radial have reshaped the logistics and transport market. No-legacy logistics companies are finding it easy to embrace emerging technologies such as augmented intelligence, automation and robotic process automation (RPA). Meanwhile, long-established players risk falling behind due to dated IT systems and complex processes.
AI and machine learning (ML) can play a major role in optimising automated warehouse systems for product replenishment, avoiding costly overstocks or out-of-stock situations resulting in lost sales. But where else can the logistics industry leverage AI?
Here are a few examples:
- Predictive logistics and big data could accurately predict demand volumes based on customer behaviour, weather forecasts, trending products, etc.
- Computer vision, which provides machines with a sense of sight coupled with contextualised AI and ML, could analyse digital images, surpassing laser-based bar code scanners. This could bolster applications for inspection and maintenance problems where algorithmic rules become less precise, such as mechanical inspections, material sorting and handling, or spotting random cosmetic imperfections.
- Internet of things (IoT) will be key, feeding critical data into AI-based systems to reduce downtime and increase supply chain tracking and traceability.
- AI-powered customer experience, through voice-controlled AI interfaces (e.g., Amazon Alexa or Google Home), will further develop and boost space- and asset-sharing platforms for on-demand storage, pick-up and delivery, while eliminating communication complexities.
Some companies are focusing on initiatives that unlock value in legacy assets by migrating critical workloads to the cloud. AI and ML can then convert unused data into insights to accelerate time to market, develop new solutions, decrease downtime, and enhance customer and employee experience and safety.
This has been the aim of DB Cargo (part of Deutsche Bahn group), which has implemented a new cargo-management system to simplify logistics and data exchange with business partners’ IT systems, telematics systems and process control systems.
In retail, Ocado, the online-only supermarket giant, has invested in AI, ML, big data, simulation technology and cloud platforms for its logistical operations. The end-to-end Ocado Smart Platform (OSP) covers everything from automated warehouse facilities to ordering and delivery. Moreover, via an AI/ML-powered e-commerce, logistics and fulfilment platform, OSP can predict customer demands to ensure that food is not overstocked and wasted.
Platform sharing (e.g., Trucksonthemap or Uber Freight) allows for logistical and transportation capacity-sharing via digital platforms. These sharing platforms offer services such as real-time data insights and communication among shippers, carriers, providers and customers to gain a comprehensive overview of load capacity, idle time, traffic delays, tracking, inefficiencies, costs and payment collection.
The sharing economy is not a new concept. In the early 2000s, models began to shift from a linear approach to one of networking focused on asset sharing, software and on-demand behaviour. Today, logistics specialists can create new revenue streams by leveraging sharing business models to enhance asset and capacity utilisation.
Amazon recently introduced its digital freight brokerage platform, building on its premise of one-day and same-day deliveries, undercutting market prices from 26 to 33 per cent. This new load-sharing platform is for transporters that want Amazon’s rates for full truckload dry van freight, allowing Amazon to turn cost into revenues and opportunities for scale and innovation.
Sharing platforms make widespread use of mobile devices through software-as-a-service (SaaS) applications to provide a real-time overview of business operations for shippers and carriers, as well as seamless smart matching of loads with available volumes. Additionally, driver exchange platforms, such as DX, provide an automated platform — with reduced bureaucracy and reduced fees — for freelance drivers.
So how can logistics companies ensure they are on the right side of disruption?
- Involve IT teams and the wider business early in the adoption of augmented intelligence or the design of a sharing platform. Make sure the core team is committed and will work alongside systems integrators.
- Consider cloud-based solutions such as SaaS for scalability, increased agility, reduced time to market, cost savings, and personalised customer and employee experiences.
- Monitor process efficiency with a multimodal approach. In the initial stage, put a comprehensive plan into place; in the second stage, evaluate its effectiveness and identify areas in need of change before scaling up.
- Make use of your untapped machine-generated data. New software features, IoT and mobile devices are all generating data ready to be turned into actionable insights.
- Understand the problem before considering whether AI is the answer. Consider AI ethics and safety throughout the project, assess how the AI model will link to the wider service, evaluate data accuracy and how the data was collected, and consider regulatory compliance.