RFID in the Wild - Analyzing Stocktake Data to Determine Detection Probabilities of Products

Matthias Wölbitsch, Thomas Hasler, Micheal Goller, Christian Gütl, Simon Walk, Denis Helic

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

Over the course of recent years, Internet of Things (IoT) technology, and in particular Radio Frequency Identification (RFID), has seen widespread adoption across several different domains. Particularly the fashion industry has integrated RFID into their day-to-day business for accurate stock tracking and monitoring. While inventory accuracy can be increased well above 90%, perfect inventory accuracy is hard to achieve, which is often related to the inherent problems of RFID technology. Several factors can favor or adversely affect RFID reader performance, such as the materials items are made of, their placement in the store, or the location of where the RFID tag has been attached. Therefore, identifying such products, that are frequently missed during stocktakes, is crucial to reach fully accurate inventories, as they require special attention to be properly processed. In this paper, we set out to tackle this real-world problem of determining products with low detectability, based on historical stocktake data of more than 400 brick-and-mortar stores. Further, we conduct a controlled user study to evaluate and improve the detectability of frequently missed products for a total of 16 stores. Our results indicate that frequently missed products can be identified and used as a foundation to further improve stock accuracy in retail stores.
Original languageEnglish
Title of host publication 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS)
Pages251-258
Number of pages8
Edition6
DOIs
Publication statusPublished - 2019

Fingerprint

Radio frequency identification (RFID)
Retail stores
Brick
Mortar
Industry
Monitoring

Fields of Expertise

  • Information, Communication & Computing

Cite this

Wölbitsch, M., Hasler, T., Goller, M., Gütl, C., Walk, S., & Helic, D. (2019). RFID in the Wild - Analyzing Stocktake Data to Determine Detection Probabilities of Products. In 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS) (6 ed., pp. 251-258) https://doi.org/10.1109/IOTSMS48152.2019.8939247

RFID in the Wild - Analyzing Stocktake Data to Determine Detection Probabilities of Products. / Wölbitsch, Matthias; Hasler, Thomas; Goller, Micheal; Gütl, Christian; Walk, Simon; Helic, Denis.

2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS). 6. ed. 2019. p. 251-258.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Wölbitsch, M, Hasler, T, Goller, M, Gütl, C, Walk, S & Helic, D 2019, RFID in the Wild - Analyzing Stocktake Data to Determine Detection Probabilities of Products. in 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS). 6 edn, pp. 251-258. https://doi.org/10.1109/IOTSMS48152.2019.8939247
Wölbitsch M, Hasler T, Goller M, Gütl C, Walk S, Helic D. RFID in the Wild - Analyzing Stocktake Data to Determine Detection Probabilities of Products. In 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS). 6 ed. 2019. p. 251-258 https://doi.org/10.1109/IOTSMS48152.2019.8939247
Wölbitsch, Matthias ; Hasler, Thomas ; Goller, Micheal ; Gütl, Christian ; Walk, Simon ; Helic, Denis. / RFID in the Wild - Analyzing Stocktake Data to Determine Detection Probabilities of Products. 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS). 6. ed. 2019. pp. 251-258
@inproceedings{ff5e343f333049568b3d1fa4fd914bc2,
title = "RFID in the Wild - Analyzing Stocktake Data to Determine Detection Probabilities of Products",
abstract = "Over the course of recent years, Internet of Things (IoT) technology, and in particular Radio Frequency Identification (RFID), has seen widespread adoption across several different domains. Particularly the fashion industry has integrated RFID into their day-to-day business for accurate stock tracking and monitoring. While inventory accuracy can be increased well above 90{\%}, perfect inventory accuracy is hard to achieve, which is often related to the inherent problems of RFID technology. Several factors can favor or adversely affect RFID reader performance, such as the materials items are made of, their placement in the store, or the location of where the RFID tag has been attached. Therefore, identifying such products, that are frequently missed during stocktakes, is crucial to reach fully accurate inventories, as they require special attention to be properly processed. In this paper, we set out to tackle this real-world problem of determining products with low detectability, based on historical stocktake data of more than 400 brick-and-mortar stores. Further, we conduct a controlled user study to evaluate and improve the detectability of frequently missed products for a total of 16 stores. Our results indicate that frequently missed products can be identified and used as a foundation to further improve stock accuracy in retail stores.",
author = "Matthias W{\"o}lbitsch and Thomas Hasler and Micheal Goller and Christian G{\"u}tl and Simon Walk and Denis Helic",
year = "2019",
doi = "10.1109/IOTSMS48152.2019.8939247",
language = "English",
pages = "251--258",
booktitle = "2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS)",
edition = "6",

}

TY - GEN

T1 - RFID in the Wild - Analyzing Stocktake Data to Determine Detection Probabilities of Products

AU - Wölbitsch, Matthias

AU - Hasler, Thomas

AU - Goller, Micheal

AU - Gütl, Christian

AU - Walk, Simon

AU - Helic, Denis

PY - 2019

Y1 - 2019

N2 - Over the course of recent years, Internet of Things (IoT) technology, and in particular Radio Frequency Identification (RFID), has seen widespread adoption across several different domains. Particularly the fashion industry has integrated RFID into their day-to-day business for accurate stock tracking and monitoring. While inventory accuracy can be increased well above 90%, perfect inventory accuracy is hard to achieve, which is often related to the inherent problems of RFID technology. Several factors can favor or adversely affect RFID reader performance, such as the materials items are made of, their placement in the store, or the location of where the RFID tag has been attached. Therefore, identifying such products, that are frequently missed during stocktakes, is crucial to reach fully accurate inventories, as they require special attention to be properly processed. In this paper, we set out to tackle this real-world problem of determining products with low detectability, based on historical stocktake data of more than 400 brick-and-mortar stores. Further, we conduct a controlled user study to evaluate and improve the detectability of frequently missed products for a total of 16 stores. Our results indicate that frequently missed products can be identified and used as a foundation to further improve stock accuracy in retail stores.

AB - Over the course of recent years, Internet of Things (IoT) technology, and in particular Radio Frequency Identification (RFID), has seen widespread adoption across several different domains. Particularly the fashion industry has integrated RFID into their day-to-day business for accurate stock tracking and monitoring. While inventory accuracy can be increased well above 90%, perfect inventory accuracy is hard to achieve, which is often related to the inherent problems of RFID technology. Several factors can favor or adversely affect RFID reader performance, such as the materials items are made of, their placement in the store, or the location of where the RFID tag has been attached. Therefore, identifying such products, that are frequently missed during stocktakes, is crucial to reach fully accurate inventories, as they require special attention to be properly processed. In this paper, we set out to tackle this real-world problem of determining products with low detectability, based on historical stocktake data of more than 400 brick-and-mortar stores. Further, we conduct a controlled user study to evaluate and improve the detectability of frequently missed products for a total of 16 stores. Our results indicate that frequently missed products can be identified and used as a foundation to further improve stock accuracy in retail stores.

U2 - 10.1109/IOTSMS48152.2019.8939247

DO - 10.1109/IOTSMS48152.2019.8939247

M3 - Conference contribution

SP - 251

EP - 258

BT - 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS)

ER -