Amazing results in increasing efficiency through smart devices, automation and data science.

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:

Evaluate efficiency gains from implementing
smart devices, automation, and data science into our
warehouse management software (WMS) SuPCIS-L8 during order picking.

We still have background info and explanations for you before we go to the results from the July 2023 bachelor thesis.

‍Why is thefocus on picking to increase efficiency?
Since picking costs account for up to 70% of total warehousing and distribution costs at many companies, efforts are made to increase efficiency here. In person-to-goods picking processes, the paths are often very long and this takes up 50 - 60 % of the picking time. This means that the route time represents the greatest optimization potential for increasing efficiency. In addition, there is always a lack of skilled workers and this can be compensated for by increasing efficiency.

With these measures, a reduction in travel time is achieved:

• technical support
• Storage strategies and a route-optimized sequence
• Simultaneous processing of multiple orders
• Increase of the gripping density in the rack
• Goods-to-Person Systems
• two-stage picking

Definition of the key figures
Two parameters were selected to determine productivity. One is the picking performance, which shows the number of items picked per hour, and the other is the picking performance, which shows the number of picks per hour.

Methods-Time-Measurement (MTM)
The key figures for efficiency development with smart devices are determined using the MTM method. MTM relates a movement to a standard time and deals with the determination of target times. The MTM method makes it possible to assess a process quantitatively and objectively.

In addition to the MTM method, a model test was also carried out with smart devices for data collection. However, the MTM method is important in order to take a differentiated look at the time shares of the various activities. This basis enables a simulation of more realistic conditions to make the results comparable with the further chapters Automation and Data Science.

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Summary of the results

1. performance (picks per hour) can be increased by 23% through the use of smart devices (in this case, a commercially available mobile data collection device).

Other benefits:‍

• Employees are introduced to the picking process more quickly. This in turn leads to more flexibility in employee scheduling, especially during peak periods.
• The intuitive user guidance of the warehouse management software on the smart devices reduces the dependence on expert knowledge.
• Picking is less stressful, resulting in higher employee satisfaction.
• Complete inventory management enables a faster response to unforeseen events.
• Stock movements become transparent and easier to control for control stations.
• The seamless employee management and the control processes on the system side lead to a significantly better error balance. During the 15 test picking runs, there were seven errors in conventional picking and only one error in picking with smart devices.

Downstream processes
If errors in picking are not detected in downstream processes, they reach the customers. This leads to dissatisfaction, returns and additional costs. Also, when errors are noticed in downstream processes, it leads to delays and extra work, which again increases costs. Minimizing errors is an important part of increasing efficiency.

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2. the integration of an automated storage system, for example an AutoStore, for partially automated picking brings about an increase in efficiency of 97 % in the application case investigated compared to manual picking with smart devices.

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3. increasing the level of automation through robotics for fully automated picking means a further increase in efficiency of almost 140% compared to the partially automated process.

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4. the implementation of a data science application in combination with smart devices makes order picking a further 11% more efficient.

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All four technologies, in conjunction with our warehouse management software, are suitable solutions for increasing picking efficiency.

The bar chart refers to the left y-axis and represents the absolute picking performance of the respective use case in picks per hour. The line chart refers to the right y-axis and shows the relative efficiency increase achieved between the use cases.

Conclusion: The implementation of smart devices, automation and data science prove a significant increase in efficiency in order picking.

‍Notes: The origin of the data and information used to study the use cases are partly theoretical and partly from practical experience. The investigations also relate to different use cases with different methodologies. As a result, the results are not exactly comparable with each other and are likewise 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.

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In the upcoming blog posts, we will go into detail about the use cases, their methodology, the practical part and the detailed results of increasing efficiency with smart devices, automation and data science.

Result: efficiency increase of 23% with smart devices in order picking.

Result: Automation increases efficiency by 97% (semi-automated) or 140% (fully automated) in order picking.

Result: Reduce retrieval paths by 20-25% and optimize efficiency through intelligent algorithms.‍

Outlook for future developments: Increasing efficiency in intralogistics.