The systematic recording of real requirements in internal logistics is still a relatively recent phenomenon – but this so-called intralogistics holds impressive potential for process optimization. Industry 4.0 and machine learning play a key role here. Thanks to artificial intelligence (AI), logistics processes can increasingly take place automatically and in a forward-looking way, thus boosting efficiency. Additionally, Industry 4.0 makes it possible to benefit from previously unused resources.
Intralogistics is an essential component of supply chain management and production planning. With the advent of high-bay warehouses and automated goods distribution systems in the middle of the 20th century, intralogistics for companies emerged as a new field. Based on the increased use of dedicated software, individual and non-networked activities such as transport, handling and storage of materials became complex logistical processes within the framework of professionalised production planning. Today, the topic is more important than ever. Due to the ever progressing globalization, there is a growing need to automate internal logistics by way of technical solutions and concepts, all with the goal of streamlining processes and staying ahead of the competition.
For companies, optimizing intralogistics is vital as the pressure to increase efficiency in internal logistic is omnipresent. Materials and goods need to be moved quickly and as punctually as possible in order to avoid downtimes or disruption in production or delivery. Automation provides high productivity and low personnel costs as any delays between individual divisions can be reduced to a minimum. Industry 4.0 has the potential to play a key role here: connected data and machines enable a fully networked value chain, with humans acting as captains on the "production steamer". The combination of intelligent technology and human expertise, resulting in data-based decisions, ensures increased productivity - welcome to the Smart Factory.
Pattern recognition leads to optimization
The data generated by intelligent processes can in turn be used to further improve internal operations, for example through the use of artificial intelligence. It enables so-called machine learning, through which smart, networked devices independently boost their performance by help of algorithms. In this way, systems can predict important planning data such as loading, transport or unloading times much more accurately. Pattern recognition of the collected data also makes it possible to quickly identify processes that still have potential for optimisation. In the event of unexpected disruptions, such as delayed deliveries, it is possible to intervene automatically and more quickly, thus facilitating appropriate countermeasures or adjustments. Subsequent processes can then be adapted precisely to these delays. The result: efficient, interlinked processes that increase profitability and reduce the error rate.
If you also want to know how you can optimise your intralogistics, then the #INTERNATIONAL HARDWARE FAIR 2020 trade fair is the place to be for you: On 1 March 2020, the topic will be discussed in detail in several keynotes between 11.30 and 5 p.m. Mark your calendar now!