Digitalization of Logistics and Intelligent Management of Supply Chain

2019-10-17

Digitalization of Logistics and Intelligent Management of Supply Chain

Digital transformation of enterprises and automation of logistics are becoming the norm and necessity not solely for business development, but also for the long-term sustainability of company processes. Practice confirms that the digitalization of logistics unfolds usually in three basic areas: warehouse management, in-plant (manufacturing) logistics and shipping and distribution logistics.

Digital transformation of enterprises with respect to the digitalization of manufacturing processes are becoming a norm, not only for an enterprise’s expansion but also for its long-term sustainability regardless of the company’s size or industry. The survey conducted by PwC reported that among automotive suppliers, a third of respondents planned to implement Industry 4.0 concepts within the next five years.

For logistics, digitalization is truly a pressing issue. The dynamic environment of the logistics industry constantly generates demands for quality, flexibility, and the type of services offered. To minimize operating costs, companies are being forced to reevaluate their business models, to ensure smoothness and reliability of supply chain.

Digitalization and new technologies are fast becoming irreplaceable tools for the necessary transformation of supply chain. The new technologies are instrumental in optimizing capacities, reinforcing performance, and improving quality while ensuring the efficiency of the supply chain. More and more companies are turning to innovative solutions capable of harnessing the potentials offered by digital technologies.

Digital transformation of processes and the implementation of new technologies— collectively referred to as Industry 4.0, IoT or Smart Industry solutions—do not necessarily need to be deployed using a rapid or “all or nothing” method. The modularity of intelligent logistics solutions based on the principles of cyber-physical systems facilitate the gradual transformation of supply chain.

Logistic or transportation companies (or enterprises) innovating internal logistics processes can therefore react directly to the most pressing demands in a particular supply chain segment. Companies implement customizable solutions with which they handle expected market changes, imminent challenges, or in reaction to increased demand after variability or service personalization requests from clients.

The digital transformation strategy of logistics processes can emulate the general digitalization process in manufacturing enterprises. Enterprises can begin by implementing a solution for data collection and then the horizontal integration of enterprise processes, followed by the vertical integration of processes, and concluded with the transformation by auto-optimizing machinery and systems. From experience, logistics digitalization usually unfolds in three general areas: inventory and warehouse management, intralogistics (in-plant logistics), and outbound and distribution logistics.

Digitalization of Logistics and Intelligent Management of Supply Chain

Automation of WAREHOUSE management

The most common reasons for modernization and automation of warehouse management are usually overstocked warehouses, large volumes of different types of goods, and difficult access to relevant information (inventory records, claims and complaints, returns, expiration dates, etc.). The optimization of warehouse process management takes place in three phases.

Firstly, it is necessary to ensure the proper identification of material/goods and its movements across the warehouse and beyond it. All data are generated in real-time and can be consulted/tracked any time via the function of traceability. Besides data collection and analysis, the rules of stocking and material picking in the warehouse are also defined in this initial phase. This is useful for effective and optimal filling of warehouse positions by prioritizing warehouse operations according to implemented management or the Smart Industry system.

This leads to the transformation of an uncontrolled warehouse into a controlled one, including compact and mobile buffers positioned on workstations (outside the central warehouse). In such a solution, logistics operators communicate with implemented management or Smart Industry system through advanced human-machine interfaces (i.e. terminals or mobile devices).

The following phase of the implementation of intelligent (Smart Industry) warehouse solution is based on the interconnectivity of all objects in the warehouse through Industrial Internet of Things (IIoT) and digital twin. This phase incorporates the dynamic management of warehouse processes either based on a set of predefined rules or autonomous algorithms. The goods or material do not move through warehouse processes according to a fixed regulation (standard), instead algorithms evaluate current demands based on a host of criteria such as momentary occupancy of storage positions, goods or material turnover rate, seasonability, and many more.

All relevant data are evaluated with respect to current conditions and previous evolution of relevant indicators that led to the dynamic management of stocking and picking processes. Solution for dynamic management presents broad yet precise and comprehensive visualization possibilities to the operating staff in fulfilling tasks assigned by the Smart Industry system.

Eliminating human interference is the third phase of warehouse automation ensured by closed unmanned warehouse systems. The setup of an autonomous warehouse necessitates custom-made machinery and equipment besides automated rack stowers and robots, a fleet of automated guided vehicles (AGV), or drones. All equipment is auto-organizable and their activity is coordinated based on the principles of a multi-agent system. The system platform (Smart Industry system) presents an infrastructure through which machinery and equipment can interact in such distributed setting.

The extend of robotization in warehouse and inventory management

                                  Survey on warehouse robotization (source: eyefortransport)

Intelligent management of Intralogistics

Late and insufficient servicing of production lines or other equipment compel enterprises to optimize intralogistics (in-plant or manufacturing logistics) processes. Another use of innovative internal logistics is usually the ineffective operation of vehicles.

The first step to optimizing and automating internal logistics flows applies in the company’s Milk Run system. This consists of the standardization of intralogistics processes according to a supply plan. Operators work in line with the prescribed schedule, whereas information or Smart Industry system fulfill the primary role of a visualization tool (visualization of work operations), data collection, and data analysis.

The next stage of intelligent intralogistics is the collection of data from individual production lines and workstations to generate real-time demands for material transfers for manufacturing purposes. This Smart Industry solution processes input data. Based on the analysis of input data, Smart Industry system creates and assigns tasks to operating personnel while taking into consideration their actual and expected workload and workflow, thus eliminating idle time and non-productive activities (or activities lacking added value).

The system then monitors the fulfillment of assigned tasks. An alternative solution relies on the automation of transport processes through automated guided vehicles (AGV) operating in closed routes. Information or Smart Industry system coordinates all the tasks carried out by the vehicles while considering real-time demands and circumstances.

Complete automation is the final stage. Fleet of AGVs carry out tasks generated by the system, tasks such as processing real-time demands and demands based on the results of big data analysis and predictive models. Smart Industry system provides the infrastructure for the distributed system that enables machine-to-machine communication (M2M) and their mutual data exchange in real-time.

In such an architecture based on the principle of multi-agent system—where the machinery and equipment algorithms autonomously make decisions according to a current situation, demands, and their own data set—tasks are fulfilled through decentralized management. Also, the machinery and equipment, not just AGV, do not have assigned standardized tasks, rather they are attributed roles within the framework of internal hierarchy.

Obstacles to robotization

                                 The biggest obstacles to robotization (source: eyefortransport)

Outbound Logistics and Expedition Innovation

The low effectiveness of picking and expedition processes, poor or insufficient control of order/delivery completeness, or lacking order-based flexibility of the delivery process are among the usual impetus for digitalization of outbound logistics.

In the initial phase, the pull principle of order-based delivery process remains maintained, whereas Smart Industry system monitors and controls picking processes by generating picking lists. The system also coordinates parallel pickings in case of several storage facilities after which it verifies if orders and deliveries have been completed.

The following stage of automation is the dynamic management of order/delivery distribution. Smart Industry system assigns tasks to operating personnel to achieve adequate workload for each operator and generates picking lists.

The system evaluates and prepares picking lists according to current inventory status while also prioritizing deliveries based on the current availability. Usually, at this stage, companies integrate automated and autonomous picking technologies such as picking robots.

Predictive management of order/delivery distribution is one of the more developed stages of Smart Industry solution implementation in outbound logistics. Based on big data analysis, the system produces predictive patterns and predicts customers’ orders and then configures the inventory according to material, machinery, and human resources.

The employment of artificial intelligence (AI) in supply chain

                                           The survey on AI in supply chain (source: eyefortransport)

 

This form of Smart Industry solution uses artificial intelligence (AI) to achieve dynamic and operative management of automated delivery processes. Artificial neural networks are one of the instrumental tools of predictive management since they can distinguish relevant patterns for delivery preparation in collected data that are huge.

Additionally, neural networks can be employed for order classification based on packages (by sorting packages according to type, quantity, weight, or any other criteria) for the predictive management order preparation.

The modularity and customizability of Smart Industry solutions enable their gradual scaling. This means companies can orchestrate the digital transformation of their processes according to their budget and their manufacturing and business strategy.

Smart Industry solutions ensure the improvement of production potential and capacity, enable company’s sustainable growth, thereby reducing the period of return on investment into digitalization and optimization. This is an incentive for companies and enterprises to continue transforming their processes.

SEE ALSO

 Smart Industry Trends Header Smart Warehouse Header

 Digital Twin as Analytical Tool Smart Industry Digital Twin as Intelligent Operations Management Tool Smart Industry