Improvement of Factory Transport Efficiency with Use of WiFi-Based Technique for Monitoring Industrial Vehicles
Open accessAbstract
The in-factory transport influences production rate and quality, so it is characterized by the shortest displacement time and maximum utilization of available means of transport, accompanied by their lowest operating wear. In the paper, an improvement of factory transport management is suggested, employing the traffic analysis methods of industrial vehicles. The efficiency of internal logistics was analyzed with the use of the WiFi-based technique for monitoring industrial vehicles. In the research, the in-building location system Optimatik BI was used and implemented in a company manufacturing PVC profiles for the market of building materials and interior equipment. Stopping times of forklift trucks, their routes and covered distances were estimated. On this ground, factors of their effective use were calculated. Further, it was possible to analyze the traffic of the forklift trucks and to generate heat maps that, in turn, made it possible to identify problems and develop a suitable improvement plan. A mathematical model that allows for efficiency optimization by determining the best number of employees handling the transport was developed for one of the analyzed logistics tasks.
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Integrating Poka Yoke and Pick to Light in an intelligent manufacturing system will allow the creation of a connected and efficient system that minimizes errors, increases productivity and improves production quality.
“Integrating Poka Yoke and Pick to Light in an intelligent manufacturing system will allow the creation of a connected and efficient system that minimizes errors, increases productivity and improves production quality [32].”
From Poka Yoke in Smart Production Systems with Pick-to-Light Implementation to Increase Efficiency (2023) · cites this paper as [32]
Section 4.3 (page unavailable) proposes a checkpoint to prevent incomplete orders and labels or lights to communicate the packing stage. These proposals relate to error reduction, but the paper does not discuss integrating Poka Yoke with Pick to Light or establish that such an integration increases productivity and production quality. (LLM, conf=0.78)
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Cites
Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review
Yeong, D. J., Velasco-Hernandez, G., Barry, J., & Walsh, J. L. (2021) · Sensors · doi:10.3390/s21062140 · Cited by 1,096 · see citing works
Sensor Technology in Autonomous Vehicles : A review
Campbell, S., O'Mahony, N., Krpalcova, L., Riordan, D., Walsh, J., Murphy, A., & Ryan, C. (2018) · 2018 29th Irish Signals and Systems Conference (ISSC) · doi:10.1109/issc.2018.8585340 · Cited by 241 · see citing works
An evaluation of indoor location determination technologies
Curran, K., Furey, E., Lunney, T., Santos, J., Woods, D., & McCaughey, A. (2011) · Journal of Location Based Services · doi:10.1080/17489725.2011.562927 · Cited by 187 · see citing works
Heatmap Visualization of Population Based Multi Objective Algorithms
Pryke, A., Mostaghim, S., & Nazemi, A. (2007) · Springer eBooks · doi:10.1007/978-3-540-70928-2_29 · Cited by 177 · see citing works
Ding, K., Jiang, P., & Su, S. (2017) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2017.06.009 · Cited by 128 · see citing works
IoT-based system for indoor location using bluetooth low energy
Terán, M., Aranda, J. M., Carrillo, H., Méndez, D., & Parra, C. (2017) · IEEE Colombian Conference on Communications and Computing (COLCOM) · doi:10.1109/colcomcon.2017.8088211 · Cited by 85 · see citing works
Lean Philosophy Implementation in SMEs – Study Results
Antosz, K., & Stadnicka, D. (2017) · Procedia Engineering · doi:10.1016/j.proeng.2017.03.107 · Cited by 76 · see citing works
An overview of development GPS navigation for autonomous car
Rahiman, W., & Zainal, Z. (2013) · 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea.2013.6566533 · Cited by 67 · see citing works
WiFi-based indoor positioning for multi-floor Environment
Liu, H.-H., & Yang, Y.-N. (2011) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2011.6129175 · Cited by 66 · see citing works
Gregor, T., Krajčovič, M., & Więcek, D. (2017) · Procedia Engineering · doi:10.1016/j.proeng.2017.06.046 · Cited by 63 · see citing works
tidyHeatmap: an R package for modular heatmap production based on tidy principles
Mangiola, S., & Papenfuss, A. T. (2020) · The Journal of Open Source Software · doi:10.21105/joss.02472 · Cited by 62 · see citing works
Lesani, A., & Miranda-Moreno, L. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2854895 · Cited by 61 · see citing works
Random sampling algorithm in RFID indoor location system
Bao, X., & Wang, G. (2006) · IEEE International Workshop on Electronic Design, Test and Applications (DELTA) · doi:10.1109/delta.2006.73 · Cited by 48 · see citing works
WiFi Based Indoor Localization: Application and Comparison of Machine Learning Algorithms
Sabancı, K., Yi̇ği̇t, E., Üstün, D., Toktaş, A., & Aslan, M. F. (2018) · Seminar/Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory · doi:10.1109/diped.2018.8543125 · Cited by 46 · see citing works
Hybrid WSN and RFID indoor positioning and tracking system
Xiong, Z., Song, Z., Scalera, A., Ferrera, E., Sottile, F., Brizzi, P., Tomasi, R., & Spirito, M. A. (2013) · EURASIP Journal on Embedded Systems · doi:10.1186/1687-3963-2013-6 · Cited by 44 · see citing works
WiFi Sensing System for Monitoring Public Transportation Ridership: A Case Study
Ryu, S., Park, B., & El-Tawab, S. (2020) · KSCE Journal of Civil Engineering · doi:10.1007/s12205-020-0316-7 · Cited by 41 · see citing works
WIFI-Based Indoor Positioning System
Lashkari, A. H., Parhizkar, B., & Ngan, M. N. A. (2010) · doi:10.1109/iccnt.2010.33 · Cited by 37 · see citing works
Application of Fuzzy Logic and Genetic Algorithms in Automated Works Transport Organization
Gola, A., & Kłosowski, G. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-62410-5_4 · Cited by 37 · see citing works
An Ensemble Filter for Indoor Positioning in a Retail Store Using Bluetooth Low Energy Beacons
Stavrou, V., Bardaki, C., Papakyriakopoulos, D., & Pramatari, K. C. (2019) · Sensors · doi:10.3390/s19204550 · Cited by 30 · see citing works
Tubis, A. A., & Poturaj, H. (2021) · Logforum · doi:10.17270/j.log.2021.611 · Cited by 26 · see citing works
Optimization of Chosen Transport Task by Using Generic Algorithms
Burduk, A., & Musiał, K. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45378-1_18 · Cited by 22 · see citing works
Self-citationPmA: a solution for people mobility monitoring and analysis based on WiFi probes
Uras, M., Cossu, R., & Atzori, L. (2019) · International Conference on Smart and Sustainable Technologies (SpliTech) · doi:10.23919/splitech.2019.8783040 · Cited by 21 · see citing works
Dynamic Spaghetti Diagrams. A Case Study of Pilot RTLS Implementation
Gładysz, B., Santarek, K., & Lysiak, C. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-64465-3_24 · Cited by 20 · see citing works
Artificial intelligence-based hybrid forecasting models for manufacturing systems
Rosienkiewicz, M. (2021) · Eksploatacja i Niezawodnosc - Maintenance and Reliability · doi:10.17531/ein.2021.2.6 · Cited by 18 · see citing works
Suganuma, N., & Uozumi, T. (2012) · Society of Instrument and Control Engineers of Japan · Cited by 16 · see citing works
Lean methodologies to improve assembly line efficiency: an industrial application
Kanaganayagam, K., Muthuswamy, S., & Damodaran, P. (2015) · International Journal of Industrial and Systems Engineering · doi:10.1504/ijise.2015.069000 · Cited by 15 · see citing works
Reliable and low-cost indoor localization based on bluetooth low energy
Kuxdorf-Alkirata, N., & Brückmann, D. (2016) · doi:10.1109/idaacs-sws.2016.7805794 · Cited by 13 · see citing works
Application of RFID and RTLS Technology in Supply Chain Enterprise
Zang, Y., & Wu, L. (2010) · doi:10.1109/wicom.2010.5600154 · Cited by 9 · see citing works
Röhrig, C., Hes, D., & Künemund, F. (2015) · IEEE International Conference on RFID · doi:10.1109/rfid.2015.7113081 · Cited by 7 · see citing works
Raising Awareness of SMEs for Real-Time Locating Systems (RTLS) in Intralogistics
Gutewort, D., Semm, A., & Erfurth, C. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-75004-6_15 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Dohn, K. Selected problems of logistics in production-Internal transportation processes organization. WUT J. Transp. Eng. 2009, 70, 47-58.
- Senderska, K.; Mares, A.; Vaclav, S. Spaghetti Diagram Application For Workers' Movement Analysis. UPB Sci. Bull. Ser. D 2017, 79, 1.
Cited by
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Trojanowska, J., Husár, J., Hrehova, S., & Knapčíková, L. (2023) · Applied Sciences · doi:10.3390/app132111715 · Cited by 23 · see citing works
“…an intelligent manufacturing system will the creation of a connected and efficient system that minimizes errors, They must be informed on interpreting indications, recognising errors, and taking the correct steps to repair or select items. increases productivity and improves production quality [32].”
BackgroundKochańska, J., & Burduk, A. (2023) · Applied Sciences · doi:10.3390/app13137764 · Cited by 12 · see citing works
Self-citationThe possibilities of improving the human-machine co-operation in semi-automatic production process
Łapczyńska, D. (2023) · Technologia i Automatyzacja Montażu · doi:10.7862/tiam.2023.1.4 · Cited by 8 · see citing works
Trojanowska, J., Husár, J., Hrehová, S., & Knapčíková, L. (2023) · Preprints.org · doi:10.20944/preprints202305.1829.v1 · Cited by 7 · see citing works
Dörner, A., Pirkl, G., Simon, M., & Bures, M. (2024) · Procedia CIRP · doi:10.1016/j.procir.2024.04.014 · Cited by 4 · see citing works
Siegl, L., & Bornemann, T. (2025) · Production Engineering · doi:10.1007/s11740-025-01367-6 · Cited by 3 · see citing works
Fuzzy FMEA in Risk Assessment of Human-Factor in Production Process
Łapczyńska, D. (2023) · Lecture notes in mechanical engineering · doi:10.1007/978-3-031-44282-7_51 · Cited by 3 · see citing works
Sun, X. S., & Wang, H. (2025) · Information Development · doi:10.1177/02666669251345088 · Cited by 3 · see citing works
Design of Cold Chain Logistics Monitoring System based on ZigBee and GPS Positioning Technology
Pan, H. (2023) · International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) · doi:10.1109/smartgencon60755.2023.10442413 · Cited by 3 · see citing works
Checkpoint ergonomics as a health and safety effort at work among women farmers
Restuputri, D. P., Masudin, I., Wardana, R. W., Baroto, T., & Arbiatul, A. (2023) · Journal of Community Service and Empowerment · doi:10.22219/jcse.v4i3.26264 · Cited by 2 · see citing works
Categorization of Production Losses in the Context of Assessing Production Effectiveness Indicators
Kochańska, J. (2023) · Lecture notes in mechanical engineering · doi:10.1007/978-3-031-44282-7_50 · Cited by 2 · see citing works
Self-citationImplementing Local Search Algorithms to Multi-series Production Task
Musiał, K. (2023) · Lecture notes in mechanical engineering · doi:10.1007/978-3-031-44282-7_58 · Cited by 1 · see citing works
Towards Optimal TMS Integration Scenario Based on HRM and SWOT Analysis
Rijavec, R., Marsetič, R., & Strnad, I. (2023) · Sustainability · doi:10.3390/su151310004 · Cited by 1 · see citing works
Bartolo, A. S. C., Vargas, J. S. O., Cabel–Pozo, J., & Bardalez, F. N. (2025) · IEEE International Conference on Electronics, Electrical Engineering and Computing (INTERCON) · doi:10.1109/intercon67304.2025.11244673
Jia, Y., & Li, X. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae77a5
- Authors
- Kochańska, J., Burduk, A., Markowski, M., Kłusek, A., & Wojciechowska, M.
- Affiliations
- Wrocław University of Science and Technology (PL)
- Corresponding author
- Kochańska, Joanna
- Venue
- Sustainability
- Publisher
- MDPI AG
- Published
- 2023-01-06
- Volume
- 15
- Issue
- 2
- Pages
- 1113
- ISSN
- 2071-1050 (ISSN-L)
- OpenAlex
- W4313890960
- Cited by
- 15
- References
- 30
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 1 · 2023: 10
- FWCI
- 1.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sustainability (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Advanced Manufacturing and Logistics Optimization
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Transport and Logistics Innovations
- Social Sciences › Business, Management and Accounting › Management Information Systems › Logistics and Transportation Systems
- Keywords
- forklifts, intralogistics, traffic analysis, heatmaps, stopping times, transport route optimization
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