Outlook for future developments: Increasing efficiency in intralogistics.

Intralogistics 4.0: How much can efficiency be increased with smart devices, automation and data science?

Rebecca got to the bottom of this question. She studied industrial engineering and wrote her bachelor's thesis on the topic with us:

Assessment of the increase in efficiency through the implementation of smart devices, automation and data science in our warehouse management software (WMS) SuPCIS-L8 in order picking.

Outlook for future developments.

The topic of increasing efficiency, particularly through the use of robotics, is one of the top issues in the Deutsche Post DHL Group Trend Radar 2022. The Trend Radar focuses on developments in the logistics industry. Decarbonization, robotics, big data, diversification of supply chains and alternative energy solutions in logistics play the biggest role.

‍You canread the individual results of the work here:

1. Overall results at a glance: Amazing results in increasing efficiency through smart devices, automation and data science.‍
2. Smart devices results in detail: efficiency increase of 23% with smart devices in order picking.‍
3. Automation results in detail: Automation increases efficiency by 97% (semi-automated) and 140% (fully automated) in order picking.‍
4. Data science results in detail: Reduce outsourcing routes by 20-25% and optimize efficiency through intelligent algorithms.

The work has shown that although the need to increase efficiency simplifies practical applications, it creates highly complex and heterogeneous systems in the background.

This insight brings the trend of resilience to bear. Although the topic of resilience was examined in the Trend Radar more with regard to the supply chain, it also plays a major role in intralogistics processes. Only resilient processes are efficient processes in the long term. The basis of any functioning system landscape, whether highly automated or manual and analog, is its controllability.

This raises the question: How can the jungle of technologies in intralogistics be untangled, stabilized and made comprehensible for users in order to ensure long-term controllability?

The following two suggestions relate to the software component and can be linked to each other. Standardization and simplification of interfaces to promote interdisciplinary understanding. Transparency is crucial for this.

• Interface standardization, for example according to VDA5050, can simplify a heterogeneous warehouse structure consisting of different processes, technologies and providers in the same spatial storage area. After all, all machines in the warehouse and process system must communicate with each other in some way. However, it is not only the interfaces of the technical components that need to be maintained, simplified and standardized, but also the interface between people and technology.
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• As machines become increasingly networked, people must be prevented from becoming disconnected. People must be able to understand the technology and its interrelationships. Understanding and insight can be created through a healthy degree of transparency. Data science, artificial intelligence and machine learning can be used to develop early warning systems and forecasting models by detecting anomalies and correlations and analyzing the effects in probability models.

Standardization and simplification of the system landscape can be achieved through technology. The same effect can be expected as with increasing efficiency. In order to make applications simpler, leaner and more efficient, complex processes are required in the background. Understanding these requires technology, but understanding the technology requires people.

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Notes on the work: The origin of the data and information used to investigate the use cases is partly theoretical and partly practical. The studies also refer to different use cases with different methodologies. As a result, the results are not exactly comparable with each other and are not universally valid.

In addition, efficiency in order picking depends on many factors. For example, the process characteristics, the warehouse topology, the order structure and the interaction of technologies. Efficiency is correspondingly variable depending on the application.

Nevertheless, the approach chosen for this work is still the best one to answer the leading question. This is because the respective efficiencies of picking can be analyzed and related to each other, taking into account the differences. Thus, the findings obtained are complete, valid and plausible when the underlying overall concept is taken into account.