Today we find ourselves in another transformational era in human history. Much like the agricultural and industrial revolutions before it, the digital revolution is redefining many aspects of modern life around the world. Artificial intelligence plays an
DHL and IBM release a landmark report, Artificial Intelligence in Logistics
Global transport and delivery firm DHL and long-term computing giant IBM have issued a landmark collaborative report, Artificial Intelligence in Logistics. It is a detailed, non-academic analysis of the future of transport and logistics and how it will get there.
In this edited version, analysts look at what artificial intelligence (AI) is and how it will look to company owners and managers. The experts then examine what it can do for customers and suppliers and where it is likely to go.
DHL senior vice president and global head of innovation Matthias Heutger and IBM global industry leader for freight, logistics and rail Keith Dierkx launched the report.
“Today’s current technology, business, and societal conditions favour a paradigm shift to proactive and predictive logistics operations more than any previous time in history,” Heutger explains. “As the technological progress in the field of AI is proceeding at great pace, we see it as our duty to explore, together with our customers and employees, how AI will shape the logistics industry’s future.”
Dierkx notes that technology is changing the logistics industry’s traditional value chains, and ecosystems are reshaping enterprises, industries and economies.
“By leveraging AI into core processes, companies can invest more in strategic
growth imperatives to modernise or eliminate legacy application systems,” Dierkx says.
“This can make existing assets and infrastructure more efficient, while providing the workforce with time to enhance their skills and capabilities.”
STEPPING INTO THE FUTURE
In recent years, AI has come roaring out of research laboratories to become ubiquitous and ambient in our personal lives, so much so that many consumers do not realise they use products and applications that contain AI on a daily basis.
AI stands to greatly benefit all industries, achieving adoption leaps from consumer segments to enterprises and onward to the industrial sector. Technological progress in the fields of big data, algorithmic development, connectivity, cloud computing and processing power have made the performance, accessibility, and costs of AI more favourable than ever before. Just as the relational database found its way into core business operations around the world – providing better ways to store, retrieve, and organise information – AI is now following a similar path. It is becoming an integral part of every future software system and soon we will no longer need to call it out as AI.
Already today, AI is prevalent in consumer-facing applications, clerical enterprise functions, online and offline retail, autonomous mobility, and intelligent manufacturing.
Logistics is beginning its journey to become an AI-driven industry, but the future is still rife with challenges to overcome and opportunities to exploit. With this in mind, experts from IBM and DHL have jointly written this report to help you answer the following key questions: • What is AI, and what does it mean for my
organisation? • What best-practice examples from other
industries can be applied to logistics? • How can AI be used in logistics to reinvent back office, operational, and customer-facing activities? Looking ahead, we believe AI has the potential to significantly augment current logistics activities from end to end. As in other industries, AI will fundamentally extend human efficiency in terms of reach, quality, and speed by eliminating mundane and routine work. This will allow logistics workforces to focus on more meaningful and impactful work.
WHY LOGISTICS? WHY NOW?
There are many reasons to believe that now is the best time for the logistics industry to embrace AI. Never before has this maturing technology been so accessible and affordable. This has already made narrow forms of AI ubiquitous in the consumer realm; enterprise and industrial sectors are soon to follow.
In logistics, the network-based nature of the industry provides a natural framework for implementing and scaling AI, amplifying the human components of highly organised global
supply chains. Furthermore, companies deciding not to adopt AI run the risk of obsolescence in the long term, as competitors seize and effectively use AI in their business today.
Researchers at IBM estimate only 10 per cent of current systems, data, and interactions include elements of AI analysis and results. However, the returns on AI investments are already improving; relatively moderate outlay is generating a much larger return than ever before.
But as complexity grows – with more unstructured data, more sophisticated learning algorithms and techniques, and more high-level decision-making tasks – the cumulative nature of AI means that AI analysis and results will improve even further.
There is another indicator that now is a good time for AI to flourish – this is the state of its adoption in the world today. Innovations occur first and become mainstream in the consumer world. Once a tipping point is reached, these innovations work their way into commercial enterprises and ultimately into industrial companies.
AI is stretching beyond consumer ubiquity and into customer-focused commercial ventures. Eventually, once the value of AI is proven in the commercial context, it will arrive in the industrial setting. The specific timing of these transitions is impossible to predict, but the fact that AI is now deeply embedded in consumer markets and is experiencing explosive growth in customer-facing commercial areas clearly indicates the use of AI in industrial sectors such as logistics is quickly approaching.
Logistics companies are uniquely positioned to benefit by applying AI in almost all aspects of the supply chain. One of the most underutilised assets in the industry is the high volume of data that supply chains generate on a daily basis.
This data is both structured and unstructured, and AI will enable logistics companies to exploit it. In addition, as many logistics companies around the world embrace digital transformation, transitioning away from legacy enterprise resource planning systems to advanced analytics, increased automation, and hardware and software robotics, and mobile computing, the next obvious step in the increasingly digital supply chain is to apply AI. Furthermore, logistics companies depend on networks – both physical and increasingly digital – which must function harmoniously amid high volumes, low margins, lean asset allocation, and time-sensitive deadlines. AI offers logistics companies the ability to optimise network orchestration to degrees of efficiency that cannot be achieved with human thinking alone.
AI 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.
BACK OFFICE AI
In an increasingly complex and competitive business world, companies that operate global supply chains are under unprecedented pressure to deliver higher service levels at flat or even lower costs. At the same time, internal functions of global corporations, such as accounting, finance, human resources, legal, and information technology are plagued by large amounts of detail-oriented,
“AI technologies like natural language processing can extract critical information.”
repetitive tasks. Here, AI presents a significant opportunity to save time, reduce costs and increase productivity and accuracy with cognitive automation.
Cognitive automation refers to intelligent business process automation using a combination of AI and robotic process automation (RPA). This is the replacement of clerical labor using software robots that can be integrated into existing business applications and IT systems.
RPA is not equivalent to AI; where AI is able to learn and extract insights from unstructured data, RPA is able to execute rule-based workstreams given well-structured inputs on behalf of human workers, and cannot learn beyond its initial programming.
FINANCIAL ANOMALY DETECTION
Logistics service providers often rely on vast numbers of third parties, including common carriers, subcontracted staff, charter airlines, and other third-party vendors to operate core functions of their business. This puts an increased burden on logistics accounting teams to process millions of invoices annually from thousands of vendors, partners, or providers.
Here, AI technologies like natural language processing can extract critical information, such as billing amounts, account information, dates, addresses, and parties involved from the sea of unstructured invoice forms received by the company.
Once the data is well classified, an RPA bot can take it and input it into existing accounting software to generate an order, execute payment, and send the customer a confirmation email, all without human intervention.
Consultancy firm Ernst & Young (EY) is applying a similar approach for detection of fraudulent invoices. Using machine learning to thoroughly classify invoices from international parties and identify anomalies for expert review helps EY comply with sanctions, anti-bribery regulations, and other aspects of the US Foreign
Corrupt PracticesAct. EY’s fraud detection system achieves 97 per cent accuracy and has been rolled out to more than 50 companies. Similar logic can be applied to any business process with high-frequency repetitive tasks.
Global logistics and supply chain operators typically manage large fleets of vehicles and networks of facilities worldwide.
German real estate software-as-a-service (SaaS) firm Leverton uses AI on its platform of the same name to simplify the processing and management of real estate contracts for businesses. The system uses natural language processing to classify any contractual clauses, policy-relevant sections, and signature portions.
Paired with a human-in-the-loop to review these findings, contracts written in complex legal language – often several hundred pages in length – can be processed in a fraction of the time it would take a team of human experts.
Keeping customer information up to date is a challenge for large enterprises; up to 25 per cent of all phone numbers and email addresses stored in digital contact applications are no longer in use. In the logistics industry, keeping
address information complete and current is critical for successful delivery of shipments.
Often, large teams of data analysts are tasked with customer relationship management (CRM) clean-up activities, eliminating duplicate entries, standardising data formats, and removing outdated contacts.
American startup CircleBack has developed an AI engine to help manage contact information, continually processing billions of data points to determine whether contact information is accurate and up to date. AI tools trained in input management can use natural language processing to do some pre-processing of customer address information to ensure completeness, correctness, and consistency with global and regional address formats.
SEEING, SPEAKING AND THINKING ASSETS
AI also stands to greatly benefit the physical demands of working in modern logistics.
The use of AI-enabled robotics, computer vision systems, conversational interfaces, and autonomous vehicles is the physical embodiment of AI in logistics operations, welcoming in a new class of tools to augment the capabilities of today’s workforce. Intelligent robotic sorting is the effective high-speed sorting of letters, parcels, and even palletised shipments – one of the most critical activities of modern parcel and express operators. Every day, millions of shipments are sorted with a sophisticated array of conveyors, scanning infrastructure, manual handling equipment, and personnel. The logistics industry can draw on AI-driven robotics innovations from the recycling industry.
Finnish company SenRobotics has been developing intelligent robotic waste sorting systems since 2011. The company’s SRR2 robotic system uses a combination of computer vision and machine learning algorithms embedded in off-the-shelf robotic arms in a synchronised way to sort and pick recyclables from moving conveyor belts. The AI engine ingests real-time data from three different cameras and sensor types, and is trained to identify a wide variety of food and beverage cartons by recognising logos, labels, and 3D forms.
The result is a system consisting of two AI-powered robotic arms that can sort unstructured recyclables on a moving conveyor belt at a rate of 4,000 items per hour with high degrees of precision. This suggests a useful AI application in logistics. Similar sorting capabilities could theoretically be applied to parcel and letter-sized shipments to reduce human effort and error rates.
Autonomous guided vehicles (AGVs) are already starting to play a bigger role in logistics operations. Within any given logistics operation, it is typical to see multiple people operating material handling equipment such
“AI also stands to greatly benefit the physical demands of working in modern logistics.”
as forklifts, pallet jacks, wheeled totes, and even tugging cars to move goods between locations or vessels. To reduce this, companies are beginning to use non-industrial, collaborative robotics, including AGVs. AI is a key part of this.
GreyOrange, a Singapore-founded automation and robotics company that develops self-navigating AGVs, recently also launched GreyMatter, its next-generation software platform.
One of the company’s launch partners for both innovations is Nitori, a Japanese furniture and home furnishings chain. As the name suggests, GreyMatter makes use of AI to allow real-time collaboration between AGVs, enabling optimised navigation path planning, zoning, and speeds, as well as self-learning to improve AGV capabilities over time. When given orders to fulfil, the AGVs and the platform are aware of each item that is being transported and the routes that are taken to retrieve and deliver these items. Nitori is using this valuable data to achieve the most efficient handling routes and predict product popularity and seasonal trends – self-optimising operations that ensure ever-shortening fulfilment times as well as real-time visibility of product demand.
AI-POWERED VISUAL INSPECTION
AI-Powered Visual Inspection is another high-potential area for AI in the logistics operational environment. Advances in computer vision are allowing us to see and understand the world in new ways, and logistics operations are no exception.
IBM Watson, the computer system capable of answering questions posed in ordinary language, is using its cognitive visual recognition capabilities to do maintenance of physical assets with AI-driven visual inspection.
In industrial sectors like logistics, damage and wear to operational assets over time are simply inherent.
Using a camera bridge to photograph cargo train wagons, IBM Watson was recently able to successfully identify damage, classify the damage type, and determine the appropriate corrective action to repair these assets. First, cameras were installed along train tracks to gather images of train wagons as they drove by. The images were then automatically uploaded to an IBM Watson image store where AI image classifiers identified damaged wagon components.
The AI classifiers were trained on where to look for wagon components in a given image and how to successfully recognise wagon parts and then classify them into seven damage types. As more data was gathered and processed, Watson’s visual recognition capabilities improved to an accuracy rate of over 90 per cent in just a short period of time. The anomalies and damages discovered by Watson were sent to a workplace dashboard managed by maintenance teams. This model and process can loosely be applied to other types of logistics asset including, but not limited to aircraft, vehicles, and ocean vessels. Computer vision inventory management and execution are becoming reality today in the retail industry.
French startup Qopius is developing
computer vision-based AI to measure shelf performance, track products, and improve retail store execution. Using deep learning and fine-grained image recognition, Qopius is able to extract characteristics of items such as brand, labels, logos, price tags, as well as shelf condition – for example, out of stock, share of shelf, and on-shelf availability. In warehouse inventory management, similar use of computer vision AI offers potential for real-time inventory management at the individual piece and stock-keeping unit level.
Canadian startup TwentyBN is working on deep-learning AI that is able to decipher complex human behaviour in video streams. Previous applications of its technology include autonomous detection from video feeds alone of things like an elderly person falling, aggressive behaviour on public transport, and shoplifting in stores. Considering that many warehouses today are equipped with surveillance cameras for safety purposes, this type of AI technology can be used to optimise performance (by detecting, for example, successful pick and pack tasks) and increase operational safety (for example, with instant alerting of accidents involving workers).
Conversational interfaces are becoming increasingly common in the consumer world. Voice-based picking has been around in supply chain operations since the 1990s, but recent breakthroughs in natural language processing are bringing new conversational capabilities to the supply chain.
American startup AVRL is enabling traditional industrial IT platforms with conversational capabilities via proprietary, natural language AI. Before the parallel advancements of AI and speech recognition technologies, voice-enabled tools in the supply chain were static; they were limited to keywords and audible menus, and operated with fixed commands. Furthermore, these systems were limited in terms of interaction, supporting only a number of languages, accents and dialects. As a result, humans had to rely on relatively scripted responses to operate these rigid voice systems.
While there are myriad factors influencing the development, acceptance, and distribution of fully autonomous transportation, this section examines how AI is contributing to the progress of autonomous vehicles.
For fully autonomous vehicles to become widely accepted, they need to significantly outperform human driving capabilities. This begins by enabling the vehicle to perceive and predict changes in its environment – something that is simply impossible without AI.
Autonomy today relies on a suite of sensing technologies that work together to produce a high-definition three-dimensional map of the vehicle’s environment. Deep-learning algorithms on board the vehicle process this live stream of environmental data to identify obstacles and other cars, interpret road signs, street markings, and traffic signals, and comply with speed limits and traffic laws. Since there is no possible way to hard-program an autonomous vehicle to react to every possible driving scenario in the real world, developers must turn to the continuous knowledge acquisition of deep learning. This way they can develop autonomous vehicles that constantly improve their capabilities as they are introduced to new surroundings.
Traditional auto industry players such as BMW, Daimler, Ford, Toyota, and VW have embraced AI as a critical component in their autonomous vehicle development journeys. More famously, newer entrants like Google, Tesla, and Waymo have developed their own autonomous vehicles using proprietary AI and manufacturing
“Autonomous fleets will eventually be used in all aspects of the supply chain from end to end.”
techniques. On the other hand, automotive suppliers such as Bosch, Mobileye, Nvidia, Quanergy, and SF are making components including sensors, algorithms, and data available to support further development of autonomous vehicles. In addition, mobility platform companies Lyft and Uber are partnering with established automotive companies to offer on-demand rides autonomously.
Convenience, cost reduction, and increased efficiency in the form of lower emissions and fewer accidents are the primary drivers for autonomous vehicle use. Thanks to the falling cost of components, increasing performance of deep learning algorithms, and the growing collective body of transportation industry knowledge on the topic of autonomous driving, the supporting technology is developing rapidly.
However, full implementation – in other words, a vehicle without a driver in the legal sense – will necessitate significant regulatory changes in any country and this will take some time.
Autonomous fleets will eventually be used in all aspects of the supply chain from end to end. Early signs of this can be seen in intra-logistics, line-haul trucking, and last-mile delivery.
Truck platooning refers to the intelligent caravanning of groups of semi-trucks. With machine-to-machine communications and collaborative assisted cruise-control technology, between two to five semi-trucks can follow each other and autonomously synchronise acceleration, steering, braking and following distance. The platoon is controlled by a human driver operating the lead truck, with a backup driver in each following truck if needed.
The British Transportation Research Laboratory, together with DHL and DAF Trucks, will pilot a platooning project on UK motorways in 2019. This is one of many recent and planned platooning trials across the trucking industry.
Autonomous vehicles and trucks today actually handle freeway driving relatively well, and each platooning trial or autonomous freeway mile driven by semi-trucks adds new data for all trucks to improve their autonomous driving capability.
Over time, this capability will continue to get better until fully autonomous trucks become a reality. Experts estimate that freeway autonomy intelligence is largely complete; the main challenge is now the miles on smaller streets between freeways and destinations.
In recent years, there has been extensive experimentation with last-mile delivery with the aim of reducing cost and complexity.
One method in particular has captured consumer imagination and media headlines – delivery via autonomous unmanned aerial vehicles – but regulatory frameworks and sufficiently dense road delivery networks limit the commercial viability of this to rural areas at best. A more practical application of autonomy in the last mile comes in the form of autonomous unmanned ground vehicles (UGVs) that operate fully autonomously or in collaboration with a delivery person.
American startup Robby Technologies is developing Robby 2, an autonomous unmanned ground vehicle with advanced AI for navigation and interaction capabilities.
Given the dynamic complexity of navigating sidewalks, pedestrians, road and rail crossings, Robby’s on-board AI is called upon to continuously sense and react to novel situations and become smarter with use. Embedded conversational AI helps improve interaction between humans and the robot; if a person blocks Robby’s path, a soft voice politely asks “excuse me” and will even say “thank you” when a person makes room for the robot. With growing delivery demand in dense areas, autonomous robots like Robby can help existing last-mile delivery fleets manage increased volumes at lower cost.
Top: A full AI learning cycle
Below: IBM global travel and transportation rail leader Keith Dierkx
Above: DHL Senior vice president strategy, marketing and innovation Matthias Heutger
Below: Seeing, speaking and thinking logistics operations
Below: Deep learning in action