Abstract
To support order picking assistance systems are in use, as employees depend on a constant supply of information to guide them through the work process. In addition to conventional assistance systems such as picking lists or handhelds, the first Augmented Reality-based systems are already used, which allow virtual insertions into the field of vision. Since these systems enable new forms of interaction and the choice of the forms of interaction has a significant influence on the choice of hardware, the process-based design of the optimal interaction between humans and the system must be made early in the development process.
Based on sequence analyses and process models of current picking processes, we have methodologically investigated different forms of interaction between human and assistance systems, depending on the work process. For this purpose, we simulated the use of an AR-based assistance system using the Wizard of Oz method based on a representative example process of order picking and derived suitable interaction concepts from this. Furthermore, we identified the requirements of the system users by creating personas.
In this way, we were able to make a process step-dependent selection of suitable forms of interaction, which is the basis for a requirement-based AR hardware decision. The general procedure derived from this allows a transfer to other use cases to methodically support the hardware selection for an AR-based assistance system depending on the selected interaction concept. In this way, well-founded decisions for the design of such an industrial assistance system can already be made in an early phase of the product development process without high development effort.
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Appendix
Appendix
Subtask 2 – Coarse Navigation: Information intake of storage location, route to storage location, repeated information intake.
These process steps are passed through by the picker to move to the correct picking location of the next position, e.g. a rack with picking containers. For this, the identification number of the location (e.g. aisle and shelf number) must be taken and the route to this location must be covered. Since the order picker has to remember many numbers during his shift, it becomes hard to keep those numbers in mind. Often he needs to read the same number several times while he is comparing them to the labels of shelves and boxes surrounding him.
Test Setting:
The process steps of subtask 2 take place on the way from the base to the aisle row. The test person walks this path and holds the collection container in both hands, simulating the pushing of the picking cart. The change made to the test stand for each form of interaction is described in Table 3.
Potential for Improvement Identified in the Process
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I.
Omission of unnecessary processes
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Eliminate repeated pauses for information intake
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II.
Reduction of route search time
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Support route search
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III.
Reduction of search times and parallel activities
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Enable information intake parallel to walking to the storage location
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Information Design
1. Picking information (aisle and shelf number [tested with 2 digits each])
2. Support for route search (coarse navigation)
Recommended Presentation Design of the Required Information
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Text insertion in the edge of the field of view
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Direction arrows displayed
Explanation
The acoustic transmission of the numbers creates the problem that it can only be listened to selectively and just be re-recorded by an input from the picker, which makes it more difficult to memorize. In comparison, the visual display of the identification numbers is always visible and can be directly compared in the field of vision with labels in the surroundings. The information is still taken up repeatedly, but the picker does not have to interrupt his work for this. In the case of acoustic information provision, this must be actively requested by the user at the right time.
Although the routing with direction arrows means that it is no longer necessary for the order picker to match the aisle and shelf numbers, the identification numbers should still be displayed to enable continuous checks and verification of the routing support.
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Egbert, L., Quandt, M., Thoben, KD., Freitag, M. (2020). Mobile AR-Based Assistance Systems for Order Picking – Methodical Decision Support in the Early Phases of the Product Life Cycle. In: Freitag, M., Kinra, A., Kotzab, H., Kreowski, HJ., Thoben, KD. (eds) Subject-Oriented Business Process Management. The Digital Workplace – Nucleus of Transformation. S-BPM ONE 2020. Communications in Computer and Information Science, vol 1278. Springer, Cham. https://doi.org/10.1007/978-3-030-64351-5_6
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DOI: https://doi.org/10.1007/978-3-030-64351-5_6
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