Global Technology Megatrends in Logistics and Manufacturing Industry in 2020 and Beyond

2020-01-07

Global technology megatrends in logistics and manufacturing 2020 and beyond

The coming decade will bring more revolutionary changes that will impact the logistics and manufacturing industries. The arrival of 2020 signals the advent of several key global trends, and new technologies will play a crucial role in them.

Overall, the year 2019 was a decade of digital transformation. And the year 2020 defines the beginning of a decade of smart transformation and intelligent automation. AI-based solutions are progressively permeating all industries. Logistics and manufacturing industries are always the first to deploy these exponential and disruptive technologies.

 
 

 

Global Technology Megatrends in Logistics and Manufacturing Industry in 2020

DIGITAL TWIN TECHNOLOGY

The technology of digital twin will continue to flourish over the next 5 years, thanks to the continued surge of global e-Commerce. Certain factors are instrumental in the expansion of digital twins, three of which are upscaling the Internet of Things (IoT) solutions, augmenting existing IoT solutions, and extending the use of Big Data and cloud platforms.

Additionally, there is bound to be an increase in the demand of the digital twin technology in the transportation industry, especially because of its intelligent supply chain management solutions. The industrial sector is not spared in the adoption of digital twin – the technology will continue to be used as an analytical and simulation tool for monitoring production assets and transfer of material and products in real-time. This is because the technology enables the creation of digital copies of production plants and material flows using historical and real data.

The deployment of digital twins will also grow due to the increasing demand for comprehensive and autonomous management of manufacturing and logistics processes. The technology is not only being deployed as an analytical and simulation tool but also as intelligent information agents for autonomous and intelligent operations management.

These agents (software avatars of physical objects – such as manufacturing and logistics equipment, tools, and materials) communicate with each other and are redistributing tasks that are subsequently performed by their physical counterparts in real space. Synchronization and coordination of individual manufacturing and logistics operations and processes, as well as the orchestration of various components (manufacturing, material, data, and human resources), can be run simultaneously in real-time owing to the cyber-physical management systems and platforms.

An example of such a process is the current Smart Industry solutions implemented to automate warehouse processes and in-plant logistical flow. Most innovative e-Commerce companies are automating their picking processes. This is done by sequential picking using a conveyor belt and Smart Industry solutions.

Thanks to its digital twin, each order-picking plastic crate on the conveyor belt “knows” what material it contains, what is yet to be picked up, and where it is headed (delivery route, a carrier, and a customer). These automated warehouse solutions improve picking performance, shorten lead times, and enhance supply management through predictive planning.

The same applies to a Milk Run tugger, its digital twin “knows” what material to bring to the production line or which finishes product pick up and deliver to the warehouse or for shipping. This next generation of internal logistics management is called dynamic Milk Run. Two of its many benefits in a manufacturing factory are the reduction of production cycles and elimination of logistical downtimes.

                  Digital Twin Technology Market Size by End Use 2014-2025

Digital twin technology, U.S. market size by end-use 2014-2025 (USD millions) (source: Grand View Research)

ARTIFICIAL INTELLIGENCE OF THINGS (AIoT)

The current Smart Industry platforms equipped with active digital twin technology (autonomous intelligent agents) have the capabilities of collective intelligence, whose use will multiply as a result of the increasing number of devices (up to 41.6 billion devices by 2025) and data (79.4 zettabytes in 2025) in the Internet of Things (IoT). Moreover, the increasing availability of artificial intelligence technologies (including in-depth learning, natural language processing, computer vision, speech recognition, and data prediction analytic) also contribute to the expansion of collective intelligence.

In practice, this means that enterprise Internet of Things (IoT) networks will interconnect diverse hardware (including manufacturing and logistics equipment), software, firmware, sensors, and embedded devices that possess some degree of intelligence and autonomous behavior. Artificial intelligence (AI) will soon become the standard equipment for all things not only in the consumer sector but also in the manufacturing and logistics industry. Like the fourth industrial revolution set off by the Internet, artificial intelligence (AI) will be the enabler and driver of the next revolution.

The combination of the Internet of Things (IoT), artificial intelligence (AI), and digital twin technology in Smart Industry systems gives rise to a new phenomenon: Artificial Intelligence of Things (AIoT).

This is a network of things (a holarchy) equipped with intelligent subsystems – holons. Strictly hierarchical and centralized systems are being replaced by decentralized systems and holarchic structures. This concept transforms the nature of digital transformation itself. Instead, smart transformation emerges. There’s no denying that broad autonomization will be one of the other significant entry of the coming era, the primary driver of which will be AIoT.

The first successful examples of AI application can be found in the logistics and warehousing industries. Managements of large-capacity warehouses are constantly trying to make the best use of warehouse spaces for optimal distribution of diverse goods. The decision-making concerning the storage position most fitting for received or incoming items is a complex task. Even more so because the chosen position must ensure optimal use of warehouse space, for efficient stocking and picking up of goods in the near future. Thus, it is a task for advanced Smart Industry systems that can employ machine learning (ML) technology in order to create the appropriate decision module.

                     Size of the Internet of Things (IoT) market worldwide in 2014 and 2020, Artificial Intelligence of Things (AIoT)

Size of the Internet of Things (IoT) market worldwide in 2014 and 2020 (in billion U.S. dollars) (source: Statista)

AUGMENTED INTELLIGENCE

Artificial intelligence (AI) is becoming a tool for enhancing employees' cognitive abilities and speeding up decision-making processes – by pre-processing large amounts of relevant (raw) data for example. The concept of augmented intelligence is based on a combination of human and machine intelligence. It also contributes to the automation of a routine knowledge work resulting in the expansion of employees’ potential to solve complex problems, emergencies, and teamwork, thereby making a smart workforce out of human resources.

The adoption of the concept of augmented intelligence will become inevitable in the coming period of the third generation of business intelligence (BI) – this is not limited to the volume of diverse data that IoT networks are generating (2.5 trillion bytes of data per day in 2019). The amount of data that businesses and companies currently possess can be mined with tools of predictive and prescriptive analytics.

In some manufacturing and logistics sectors, the concept of augmented intelligence is already being implemented. Smart Industry systems now complement some human cognitive abilities – for example by displaying  (visualizing) instructions of a work plan or work operation. In the case of the latter, the system evaluates their workload in real-time, then adapts the worker's specific work plan accordingly. The system then displays the next task, including detailed operation instructions for each job, which dramatically reduces the lead time and training period.

Another example concerns warehouse staff:  Smart Industry system creates picking lists for staff based on current requirements and circumstances and identifies routes between individual storage positions to eliminate unnecessary movements and downtime of the warehouse workforce. The augmented intelligence boosts the efficiency of picking the items. Complete dematerialization (digitalization) and automation of paperwork, including bills of the store, receipts, picking and delivery protocols, are also increasingly implemented in such warehouse solutions.

Augmented Intelligence, Augmented Analytics

                     The use of data generated by IoT solutions (source: Zebra Technologies)

ECO-LOGISTICS

Given the current climate crisis, green logistics will be one of the most debated topics of the coming decade. To this end, e-Commerce, along with wholesale and retail, will try to minimize their carbon footprint and the degree of waste they produce (zero-waste).

Sustainable logistics has already begun to manifest itself in increased recycling initiatives and the deployment of reusable packaging in the manufacturing and logistics industries. That said, the sustainable operations do not concern just e-Commerce and retail, but also manufacturing businesses. And electro-mobility is not yet a universal answer.

Besides intelligent dispatching solutions (for example, TMS systems) which are optimizing delivery or pick-up routes in real-time, these principles will also apply to intralogistics in order to boost the performance of fewer logistics vehicles (thus the trend will be better utilization of smaller in-plant fleet servicing warehouses and production lines). This also applies to the application of efficient transportation concepts currently employed by 3PL companies (“third-party logistics”) on the micro-level (in-plant) of each particular companies.

The aim is to radically eliminate transporting “air” (i.e. empty tuggers, forklifts and trucks) and empty packaging to reduce the energy consumed for these operations. The solutions based on the concept of dynamic Milk Run material distribution or pick up already allow an agile form of in-plant and inter-plant logistics with the benefit of significantly reducing the number of trips required.

The trend that is currently on the rise is ecological (green) warehouses. In addition to eliminating waste and unnecessary packaging, businesses are gradually embarking on complete digitization of their paperwork. Intelligent material flow management systems are, therefore, also becoming part of environmental measures in warehouses, including AI technology.

In fact, if material or items in the warehouse are intelligently distributed (via dynamic and intelligent picking strategies), warehouse operators are more orderly (smart lighting management). The “net-zero” concept is also a part of the environmental measures in warehouses – one in which a warehouse building generates only as much energy as it consumes.

                             Green Supply Chain Best Practices, Ecological Logistics, Eco-Logistics

                     Best practices of green supply chain (source: TATA Consultancy services)

ADVANCED INTEGRATION OF THE SUPPLY CHAIN

Supply chain integration combined with thorough digital transformation leads to the implementation of solutions with broader functionality, dynamic scalability, and extensible automation. One of these intelligent logistics solutions is a new generation of WMS systems -  WES systems (Warehouse Execution System).

As the name suggests, WES is basically MES systems for warehousing and intralogistics. WES systems bring a greater degree of process dynamics to warehouses and in-plant logistical flow. They are also closely integrated with other intelligent operation management modules or systems, such as MES, TMS, or QMS systems.

In WES systems, intelligence, dynamic scaling-up, and flexible automation are the key features for supply chains and warehouse management. WES systems treat warehouse operations as complete processes, i.e., work that needs planning, allocating, and managing. Such an approach opens up new opportunities for optimizing supply and warehouse processes regarding their advanced integration with other processes in the supply chain.

                 Biggest cost drivers in the supply chain, last-mile delivery, last mile delivery logistics

                                   Biggest cost drivers in the supply chain (source: Capgemini)

LAST MILE DELIVERY AUTOMATION

The ever-growing e-commerce industry and the employment of omnichannel distribution put bigger pressure on last-mile logistics. The last-mile delivery is the part of the supply chain that poses big economic and ecological challenges to businesses.

The current trend of delivery 'Uberization' (migration to the on-demand business model based on direct contact between consumers through mobile technologies for efficient delivery) will continue, thanks to the capabilities of IoT solutions and artificial intelligence.

However, in addition to revising delivery (forwarding) business models, last-mile logistics will be subject to greater automation. One of last-mile automation initiatives in trans-shipment and fulfilment centers and logistics hubs is the implementation of the dynamic management of "chaotic" warehouses, shipping automation, and integration with TMS systems (for example through WES systems). The last-mile delivery automation initiative also comprises advanced analytics that utilize machine learning in predictive models to simulate and forecast customer behavioral patterns and forecast material flows and logistics routes.

                Last mile tech development - new technologies in the last mile delivery logistics and supply chain

                                 Last mile logistics technology development (source: McKinsey)

5G

The advent of 5G networks will radically accelerate the transfer of voluminous data and cause major changes in IoT solutions. High speed (1Gbps), low communication latency (less than 1ms), and low power consumption of 5G networks will contribute to the transformation of Internet of Things (IoT) and provide a foundation for large IoT networks – considering it will enable data-rich communication between devices, systems, and robots.

The launch of 5G networks is one of the prerequisites of accelerated initiatives and procedures for the smart transformation of manufacturing enterprises with intelligent industrial operation systems and increased robotization. 5G networks will speed up the transfer of large volumes of data and responsiveness of devices connected to the IoT network, as well as the use and processing of real-time data and data exchange with GPS or devices with built-in cameras (e.g. autonomous vehicles, AGVs). That’s not all – the versatility and scalability of existing mobile networks for industrial use (cellular industrial Internet of Things networks) will expand.

                       Global 5G Adoption, 5G networks

                                               Global 5G adoption 2020-2025 (source: Statista)

decentralized (distributed) MANUFACTURING

Sustainability trends, the transformation from the model of delivering products to delivering services ("servitization"), and detailed customization of products and services have a major impact on the manufacturing industry. While production systems and processes (especially, serial or batch production) are currently governed by the pull principle (production of a large number of products that are later offered to potential customers), a gradual shift towards the pull production system is already occurring (on-demand production).

The transformation to a pull system is also related to new technologies that enable more efficient real-time resource management, shorter production cycles, and Just-in-Time material feeding and delivery. The pull principle is crucial to decentralized (distributed) manufacturing, a concept worth significantly developing in the coming years. Network of mini- (micro-) business will thus provide manufacturing as a service (MaaS).

The goal of a decentralized manufacturing network (a cluster of production “nodes” that are not concentrated in one place) is to manufacture customized products as close to the point of delivery (or customers). This translates into a significant reduction in logistics movements, lowering carbon footprint. The network of manufacturing “nodes” (mini- and micro-enterprises) will be managed by cyber-physical systems equipped with digital twin technology and intelligent autonomous agents (for example, Smart Industry platforms). Smart Industry platforms transform the current serial production model into a so-called “plug and play” production, making way for an agile, energy-efficient, and sustainable manufacturing operation (business).

The same way Smart Industry systems enable mass manufacturing of customized/personalized products – through reconfigurable and scalable production features – distributed manufacturing will also be modular with flexibly adjustable production capacities, parameters, and functionalities. Smart Industry systems and platforms along the new technologies foreshadow a new concept of manufacturing ("smart manufacturing"), which will be in the form of decentralized ecological operation performed as on-demand service (“Manufacturing-on-Demand”).

      Decentralized (Distributed) Manufafacturing

                         Centralized vs decentralized (distributed) manufacturing (source: Frontiers)

DISTRIBUTED LEDGER TECHNOLOGY (DLT)

Distributed ledger technology (DLT) is primarily associated with blockchain and cryptocurrencies. However, it will be gradually implemented outside the financial sector. While the possibilities of this revolutionary technology are currently being tested, it is evident that DLT will soon find its application in the logistics industry. The technology brings transparency into logistics processes and supply chains. DLT technology also ensures security, transparency, traceability, and acceleration of transactions.

Practical deployment is currently under preparation by the Canadian retail network Walmart (more than 400 outlets) with its e-shop, which is currently deploying DLT technology to track deliveries, verify transactions, and automate payments to its suppliers. Benefits of DLT technology recognized by the company are: (1) improved tracking supply movement and preventive detection of potential complications and (2) accelerated payments by consolidating real-time transactions. Other benefits include increased overall transparency among suppliers, reduced administrative costs, and accurate real-time data for enhanced analytics and predictive forecasts. Moreover, DLT-based solutions integrate and synchronize all supply chain and logistics data in real-time.

The same capabilities and features of DLT technology are applicable in the manufacturing industry as it will bring greater transparency, security, and efficiency in data sharing to manufacturing processes, leading to the emergence of new business models. Though DLT technology is currently in its early stage, its functionality and features predetermine a wide range of applications in the upcoming period.

               Digital Ledger Technology (Blockchain)

                                     Properties of digital ledger technology (DLT) (source: LPEA)

PROACTIVE CYBERSECURITY

The accelerating digital transformation and the increasing number of devices connected to the Internet of Things (IoT) lead to a greater risk of cyberattacks. Industrial espionage and data leaks are not the only factors that threaten the manufacturing and logistics industries. Cyberattacks paralyzing manufacturing facilities and compromising supply chains are becoming more frequent. With the increasing complexity of enterprise digital ecosystems (including intelligent manufacturing systems), the criticality of cybersecurity will grow, and corporate firewalls will no longer be sufficient protection.

Netscout security survey revealed that IoT devices can be hacked within 5 minutes of being connected to the network, while attacks on IoT devices protected by firewalls are surging. The advancement of sophisticated technologies is also related to the improvement of subversive practices (automated botnets) and the growth of more advanced and dangerous crimeware (malware for cybercrime automation), especially now that a separate market providing cyber-attacks services exists.

As the next decade will belong to artificial intelligence (AI), companies will need to take steps toward sophisticated cybersecurity and protection solutions for AI-equipped intelligent management systems. Automation of cyber-attacks and the deployment of artificial intelligence to execute malicious cyber-attack will also pose a new significant challenge to companies.

Therefore, cybersecurity and ecological solutions will be the top priorities of companies and industries in the coming decade.

                           Artificial Intelligence (AI) in Cybersecurity

                                           The survey of AI use in cybersecurity (source: Capgemini)

SEE ALSO

 New Infrastructure of Smart Industry ERP Smart Warehouse Header Digital Twin as Analytical Tool Smart Industry Digital Twin as Intelligent Operations Management Tool Smart Industry