Accelerating intelligent automation in logistics and e-commerce

The exponential growth of new technologies is rewriting established business models across all industries. While digital transformation is penetrating all areas, logistics and manufacturing are at the forefront of the sectors in deploying new technologies, solutions and innovative concepts into their processes.

The Internet of Things (IoT), digital twins and artificial intelligence (AI) – the essential tools of the fourth industrial revolution – have become key in accelerating large-scale innovation. The implementation of these technologies results in increased productivity, quality and process variability on an unprecedented scale.

Internet-Driven Innovation

The Internet of Things (IoT) and the Internet of Services (IoS) can be considered one of the key catalysts for the digitalization of business processes. The main benefit of the Internet of Things lies in the ability to connect physical objects and information systems with each other. A frequently used example is a computer printer connected to the Internet, which is able to automatically order new toner from a defined supplier when the toner cartridge runs out.

In the industrial environment, we can encounter examples of autonomous replenishment of supplies through smart containers, for example for small fasteners. Such a container is equipped with a built-in scale, so that when the stock drops below a set limit, it sends a signal to the control system. The control system processes the data and schedules the delivery of goods to individual workplaces or other destinations.

Such an information infrastructure, which is composed of physical objects connected to the Internet and associated software entities, including various software applications (services), is referred to as a cyber-physical system.

These systems use the Internet to connect devices, tools, materials, employees, data and information systems (such as ERP, PLM, SCM, etc.) in a digital business ecosystem, from production operations to warehouses to the management components of the company.

The speed of information transfer at the digital level, as well as the ability to process and evaluate large amounts of information in real time, predetermines such solutions for intelligent, dynamic and autonomous process management in production, supply and logistics.

This is why this concept is also used in new generation Smart Industry systems that plan, manage and control business processes in production, supply, maintenance or quality control. Process management thus becomes more agile and adaptable to changes in the external (e.g. market) or internal environment.

Operational agility of the company and processes includes, for example, flexible changes in the parameters of production equipment, revision of work procedures and methods, as well as the incorporation of individual customer requirements in the serial production mode. In the field of warehousing and logistics, agility is manifested in the dynamic management of warehouse stocks and the use of warehouse capacity, as well as in the coordination and navigation of handling equipment.

Overview of investments in warehouse automation and supply management technologies 2020: Internet of Things (72%), barcode scanning (70%), tablets and PCs (69%), big data, analytics (67%), warehouse/vehicle loading automation (64%) (source: Zebra Technologies)

Digital Twins and Growing Process Intelligence

Digital twins are among the technologies with increasing demand, and they are already being integrated into modern Smart Industry systems. The basic function of a digital twin is a virtual representation of physical objects, devices, people, but also processes in industry and the supply chain. This means that this technology, through a large number of sensors, serves as a digital model and at the same time a carrier of dynamic data and status information. The verification of concepts, hypotheses and prototypes is transferred from the physical environment to digital simulations.

The use of digital twins in complex simulation models results in accelerating decision-making processes, as they enable the rapid display of relevant data and easier identification of key patterns in production and supply operations. The amount of information obtained and the existence of a digital model are also a basic prerequisite for the successful use of predictive analytics and the preparation of forecasts for future periods.

However, the functionality of digital twins in Smart Industry systems is significantly expanded to include active interaction in the real physical environment of the operation. In this form, it is no longer just a simulation tool (although this function remains native), but digital twins that perform the function of intelligent information agents.

Such an active digital twin expands the capabilities of a physical object in a cyber-physical system by the ability to communicate, coordinate, make decisions, or perform some other cognitive functions. In a more advanced form, we can even talk about having a certain form of narrowly specialized artificial intelligence. An ordinary machine, cart, or crate thus becomes a smart, i.e., a “smart” device.

Moreover, information agents can coordinate autonomously, thanks to mutual direct communication, and thus ensure the fulfillment of a common task. This allows for more flexible management of production and material flow or their selected segments.

The management and optimization of processes using digital twins is therefore also characterized by elements of collective intelligence. With this intention, automation technologies are integrated with advanced software tools within Smart Industry systems, with the aim of building autonomous production units.

On the other hand, digital twins can also be deployed as tools that help employees directly in operations. Processing and evaluating a large range of information can help operators in the same way that online navigation already helps drivers in crowded city traffic. In such a case, the concept of so-called augmented intelligence is at stake, i.e. the cooperation of human and artificial intelligence.

A justified use of digital twins is also offered in the automation of warehouse processes and supply. Innovative companies in the e-Commerce segment are already automating the processes of picking goods and materials to a large extent. This is often done by installing automation technologies such as automatic rack stackers, vertical storage systems, or controlled conveyor belts.

The most important practices in supply chain management and warehouse automation: forecasting (61.3%), warehouse management (50%), logistics (46.8%), back-end technology (32.3%), data analyst training (21%), returns management (21%) (source: Statista)

This creates a heterogeneous environment that needs to be connected by an appropriate control system. The goal is not just to place the right shelf in the stacker or transport a crate from one place to another, but to prepare all ordered goods correctly and quickly and deliver them to the customer.

And so Smart Industry solutions, which provide a system platform for creating digital twins and for their interconnection with other information services, become a kind of nervous system of the corporate environment.

In this context, digital twins have become a relevant building block of the digital ecosystem of the company. Thanks to the digital twin, each crate on the conveyor belt recognizes what goods it contains, what still needs to be prepared in it (because it knows which order it is assigned to) and at the same time knows where it is going (to which carrier, to which customer). Warehouses managed in this way achieve more efficient picking, shorter delivery times and a radical reduction in error rates.

The ever-growing e-Commerce sector and the more frequent use of omnichannel distribution (Retail 4.0) are also putting pressure on last-mile logistics. Last-mile logistics represents an economic and ecological challenge for many suppliers. For this reason, the last mile is becoming the subject of increased automation.

The digital twin of an order is a necessary prerequisite for ensuring traceability, but it can also offer the required degree of flexibility and readiness in the event of constantly changing traffic situations or changes in the delivery address by customers. In this context, the digitalization of shipping processes and integration with TMS [Transport Management System] systems bring a whole new range of benefits.

Likewise, within the internal logistics of a manufacturing company, a supply train, i.e. its digital twin, can know what material to bring or pick up, when and where. This upcoming generation of internal logistics management is known as the so-called dynamic Milk Run (Milk Run 4.0) thanks to real-time operational management. Its main benefit is the reduction of unnecessary material transport and the elimination of logistics downtime in production.

Such solutions also include the new generation of WMS systems, the so-called WES systems [Warehouse Execution System]. These systems bring a higher level of dynamism to process management in warehouse and supply processes and are also integrated into other systems in the production or material flow, such as MES [Manufacturing Execution System], TMS or QMS [Quality Management System].

WES systems view warehouse, supply and logistics processes as integrated processes, i.e. as work that needs to be planned, scheduled and managed. This opens up new possibilities for optimizing these processes, as well as their advanced integration with other processes within the supply chain.

Warehouse automation rate according to the FMCG Industry 2019 survey (percentage automation rate): automated stocking (23%), internal transport (12%), palletizing/depalletizing (35%), crate picking (12%), item picking (7%), shipment sorting (13%), truck loading/unloading (9%) (source: GS1 Germany)

From the Internet of Things (IoT) to Artificial Intelligence of Things (AIoT)

Just as the Internet of Things (IoT) and the Internet of Services (IoS) have revolutionized business process management, artificial intelligence (AI) is expected to bring another significant wave of change. The increase in computing power and its emerging new architecture (quantum computers), the amount of available data (Big Data), the speed and volume of its transmission (the advent of 5G networks), and the expansion of cloud solutions are factors that will soon make artificial intelligence tools ubiquitous.

The combination of IoT, digital twins, and AI technologies is already enabling the transformation of ordinary business objects into “smart things.” This evolutionary shift is transforming tools, production and transportation equipment, materials, semi-finished products, and finished products into almost autonomous smart things that require little interaction with workers.

The deployment of new technologies within Smart Industry solutions leads to the emergence of dynamically scalable infrastructures (digital ecosystems) with more flexible automation options. Cyber-physical platforms, such as Smart Industry systems, are becoming an integral part of new solutions for connected operations, material flows and supply chains.

Successful examples of the deployment of artificial intelligence (AI) for warehouse and supply management can already be found today. Large-capacity warehouse operations with thousands of storage positions must constantly solve the problem of optimal distribution of goods. Deciding on which storage position to store incoming goods is a complex task.

The result of such a decision must include criteria for optimal use of warehouse capacities, an efficient storage process, but also the most efficient subsequent picking of relevant goods according to customer requirements.

This is a task for advanced Smart Industry systems that can apply machine learning (ML) technology to create the appropriate decision-making module. On a smaller scale, similar solutions control the placement of product packages in vertical storage systems or in automatic rack stackers. In a classic rack warehouse environment with a number of operators, intelligent management and distribution of work tasks to all workers is provided by a Smart Industry-based control system.

 

Warehouse automation prediction (adoption by 2030/current adoption rate): cloud computing technologies (80% / 54%), supply chain 4.0 - IoT and analytics (90% / 45%), robotics and automation (85% / 36%), artificial intelligence (75% / 11%), blockchain (45% / 8%) (source: LogisticsIQ)

In addition to evaluating information and suggesting appropriate responses in real time, the decisive feature of such systems is the simplicity and intuitiveness of the user interface. The goal is to provide all involved workers with the right information for their work or decision-making.

“Smart things” emerging within the framework of Smart Industry platforms also indicate the emergence of a new trend – artificial intelligence of things (AIoT). The result is a network of things (holarchy) with intelligent subsystems – holons.

Strictly hierarchical and centralized systems will be replaced by decentralized systems and holarchic structures. These processes will also have an impact on the concept of digital transformation, which will gradually become intelligent transformation. A part of the new form of automation will be broad autonomization, one of the main accelerators of which will be AIoT.

Digital transformation is gradually transforming into smart automation with operations, businesses and clusters of businesses organically connected within a decentralized and distributed structure through new technologies and expanding enterprise digital ecosystems (continuing vertical and horizontal integration).

This edited article was originally published in IT Systems 1-2/2020.