Investigating the Impact of Completion Time and Perceived Workload in Pickers-to-Parts Order-Picking Technologies: Evidence from Laboratory Experiments

Nikolaos Chondromatidis, Anastasios Gialos, Vasileios Zeimpekis () and Michael Madas
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Nikolaos Chondromatidis: Department of Financial and Management Engineering, School of Engineering, University of the Aegean, 82132 Chios, Greece
Anastasios Gialos: Department of Financial and Management Engineering, School of Engineering, University of the Aegean, 82132 Chios, Greece
Vasileios Zeimpekis: Department of Financial and Management Engineering, School of Engineering, University of the Aegean, 82132 Chios, Greece
Michael Madas: Department of Applied Informatics, School of Information Sciences, University of Macedonia, 54636 Thessaloniki, Greece

Logistics, 2024, vol. 8, issue 1, 1-15

Abstract: Background: Despite the general impression that digital order-picking supportive technologies can manage a series of emerging challenges, there is still a very limited amount of research concerning the implementation and evaluation of such technologies in manual picker-to-goods order-picking systems. Therefore, this paper aims to evaluate the performance of three alternative picker-to-goods technologies (i.e., Pick-by-Radio Frequency (RF) Scanner, Pick-to-light, and Pick-by-vision) in terms of completion time and perceived workload. Methods: The Design of Experiments (DoE) methodology is adopted to investigate order-picking technologies in terms of completion time. More specifically, a full factorial design has been used (2 3 × 3 full factorial design) for the assessment of the aforementioned order-picking technologies via laboratory testing. Furthermore, for the comparative assessment of the reviewed order-picking technologies in terms of workload, the NASA Task Load Index (NASA-TLX) is embraced by system users. Results: The results reveal that the best picker-to-goods technology in terms of order-picking completion time and perceived workload under certain laboratory setup is light picking when combined with few items per order line and many order lines per order. Conclusion: The paper successfully identified the best picker-to-goods technology, however it is important to mention that the adoption of such order-picking technology implies certain managerial implications that include training programs for employees to ensure they are proficient in using such technologies, upfront costs for purchasing and implementing the order picking system, and adjustments to existing workflows.

Keywords: order-picking; pick-by-RF scanning; pick-to-light; Pick-by-vision; design of experiments; laboratory testing (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2024
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