How AI and Platform Sharing will Transform Logistics

Logistics companies are facing a second wave of major disruption – and this time, artificial intelligence and industry-wide platforms will be at the heart of it.

For a long time, established players such as Royal Mail/Parcelforce Worldwide, DHL, Bishopsgate, DB Schenker, Autologic, UPS, and TNT operated within a standardized environment with limited opportunities for change to increase operational efficiencies. Digitalization and the rise of a new wave of players such as Yodel, Whistl, and Radial have greatly already greatly re-shaped the logistics and transport market. 

The new no-legacy logistical companies are finding it easy to embrace emerging technologies such as augmented intelligence, automation, and RPA. In contrast, long-established players risk falling behind due to dated IT systems and complex processes. 

AI and ML can play a major role in optimizing automated warehouse systems for product replenishment to achieve an automated sweet spot. This means that logistics specialists and suppliers can side-step costly overstocks or out-of-stock situations resulting in lost sales.

So where can the logistics industry further leverage AI and for what? Here is a few hypothetical examples:

•    Predictive logistics and Big Data will provide actionable insights such as the ability to accurately predict demand volumes based on e.g. customer behavior, weather forecasts, trending products, etc
•    Computer Vision provides machines with a sense of sight coupled with contextualized AI to analyze and understand the content of digital images through ML, surpassing the traditional laser-based barcode scanner. This AI-driven method could bolster applications, particularly for inspection and maintenance problems where algorithmic rules become less precise, such as spotting random cosmetic imperfections, mechanical inspections or material sorting and handling, when coupled to robotics
•    Internet of things (IoT) will be a key trend that feeds critical data into AI-based systems, to pre-emptively 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), that will further develop and boost space and asset sharing platforms for on-demand storage, pickup, and delivery where users could simply make use of a sharing platform with voice commands eliminating communication complexities

Some logistics companies are starting to focus on initiatives that unlock value in legacy assets by migrating critical elements and workloads onto a modern cloud environment. This allows them to gain agility and to leverage AI/ML by converting unused data into insights using advanced analytics tools. This, in turn, allows them to accelerate time-to-market, develop new solutions, decrease downtime, and dramatically increase 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’ (i.e. IT systems, telematics systems, and process control systems, etc). 

In retail, Ocado, one of the biggest online-only supermarkets, catering to over 580,000 customers each day, has heavily invested in technology for its logistical operations. The Ocado Smart Platform (OSP), an end-to-end platform, covers everything from automated warehouse facilities to ordering and delivery solutions. OSP leverages cutting-edge technologies in areas such as AI, ML, Big Data, Simulation, and Cloud Platforms. Moreover, via AI/ML-powered e-commerce, logistics and fulfillment platform, OSP can predict customers’ produce demands to ensure excess food and beverages are stocked and wasted.

Platform sharing (e.g. Trucksonthemap or Uber Freight) allows for logistical and transportation capacity sharing via digital platforms. These sharing platforms offer full or partial services such as real-time data insights and communication between shippers, carriers, providers, and customers to gain a comprehensive overview of load capacity, idle time, traffic delays, tracking, inefficiencies, costs, payment collection, etc. 

However, the sharing economy is not a new concept. During the first decade of the new millennium, businesses started to see a shift in models from a linear approach to one of networking focused on asset sharing, software, and on-demand behavior. 

Today, logistics specialists can leverage sharing business models to enhance asset and capacity utilization, creating new revenue streams. Amazon recently went live with its  digital freight brokerage platform, building on its premise of one-day and same-day deliveries, undercutting market prices from 26% to 33%. This new load sharing platform is intended for transporters who want Amazon’s rates for full truckload dry van freight, allowing Amazon to turn cost into revenue and opportunities for scale and innovation. 

Sharing platforms make widespread use of mobile devices through Software-as-a-Service (SaaS) applications where it’s possible to obtain a real-time overview of business operations for shippers and carriers, and seamless smart matching of loads with available volumes wherever and whenever required. 

Additionally, according to a recent report from the Freight Transport Association (FTA), the UK now suffers an HGV driver shortage of 59,000, as 64% of transport and storage businesses now face severe skills shortages. These increasing figures have led to driver exchange platforms, such as DX, an automated platform for freelance drivers with reduced bureaucracy for both the driver and the hirer, and reduced fees.

So what steps can logisitcs companies take to ensure that they are on the right side of the next wave of disruption? 

Involve IT teams and the wider business at an early stage of the adoption of augmented intelligence or the design of a sharing platform. Make sure that the core team is fully committed and will work alongside systems integrators and technology providers.

Consider cloud-based solutions such as SaaS which are increasingly popular in this sector, offering scalable solutions, increased agility, reduced time-to-market, cost-saving, and customized customer and employee experience over time. 

A multimodal approach offers deeper insights into process efficiency. At an initial stage, a comprehensive plan needs to be put into place, whilst a second stage evaluates the effectiveness of stage one. This approach mitigates chances for errors and identifies areas in need of further changes before scaling up.

Start making use of your untapped machine-generated data. Data volumes are increasing with the implementation of new and more complex software features, new devices such as IoT or mobile devices, and other technologies, all generate data ready to be turned into actionable insights

Understand the problem and the needs before considering AI and whether it’s the right approach for you. Consider AI ethics and safety throughout the entire project, assess how the AI model will link to the wider service, evaluate data accuracy and how the data was collected and regulatory compliance. Finally, determine what’s the most viable and cost-effective solution to solve your users’ needs.

This blog first appeared on DXC's Thrive thought leadership platform.