Home / Current Issue / Paper 1705863
Enhancing Efficiency of Material Handling Equipment in Industrial Engineering Sectors
Subject area: Science,Engineering and Technology · Area of research: Industrial Engineering
Abstract
Material handling equipment (MHE) is critical in industrial engineering sectors, coordinating the smooth movement, storage, management, and protection of commodities throughout manufacturing and distribution activities. Its efficiency is critical, influencing productivity, cost management, and workplace safety. This research digs into a thorough examination of strategies and technology aimed at improving MHE efficiency in industrial settings. The study seeks to provide a detailed understanding of the multiple ways used in optimizing material handling equipment, encompassing both traditional methodologies and novel improvements. Efficiency enhancement options cover a wide range of methodologies, from classic to cutting-edge solutions. Automation and robots have emerged as key players, transforming material handling operations through their speed, precision, and dependability. The integration of IoT (Internet of Things) with data analytics enables real-time monitoring and predictive maintenance, resulting in optimal performance and minimal downtime. Ergonomic design principles not only promote smoother human-machine interactions, but they also reduce worker tiredness and injury hazards, improving operational efficiency and safety standards. Furthermore, using lean concepts and continuous improvement approaches simplifies material handling operations, resulting in waste reduction and process optimization. This study uses incisive case studies to shed light on successful implementations of efficiency enhancement measures, emphasizing the variables that contribute to their efficacy. Looking ahead, future trends and emerging technologies such as artificial intelligence and advanced robots have the potential to further transform material handling efficiency, highlighting the dynamic character of this vital area of industrial engineering. Addressing problems and embracing opportunities, this study emphasizes the importance of constantly evolving procedures to suit the changing demands of industrial material handling.
Keywords
Material Handling Equipment, Efficiency Enhancement, Industrial Engineering, Automation, Robotics
References
[1] Adams, S., & White, T. (2020). Automation in Material Handling: Trends and Benefits. Journal of Industrial Engineering and Management, 13(2), 45-59.
[2] Ahmad, R., & Kamaruddin, S. (2012). An overview of time-based and condition-based maintenance in industrial application. Computers & Industrial Engineering, 63(1), 135-149.
[3] Aiello, G., Enea, M., & Muriana, C. (2012). The facility layout problem in automated manufacturing systems: A review. Annual Reviews in Control, 36(1), 101-110.
[4] Aqlan, F., & Lam, S. S. (2015). A real-time decision-making framework for supply chain disruption management. Computers & Industrial Engineering, 87, 98-106.
[5] Bányai, T., Illés, B., & Tamás, P. (2014). Optimization of material flow in logistics systems with network algorithms. Engineering Letters, 22(4), 160-167.
[6] Bogue, R. (2016). Growth in e-commerce boosts innovation in the warehouse robot market. Industrial Robot: An International Journal, 43(5), 583-587.
[7] Boysen, N., & Emde, S. (2014). Scheduling inbound and outbound trucks at cross docking terminals. OR Spectrum, 36(2), 273-294.
[8] Boysen, N., Fliedner, M., & Scholl, A. (2010). Scheduling inbound and outbound trucks at cross docking terminals. OR Spectrum, 32(1), 135-161.
[9] Brabazon, P. G., MacCarthy, B. L., Woodcock, A., & Hawkins, R. W. (2010). Mass customisation in the automotive industry: Comparing interdealer trading and reconfiguration flexibilities in order fulfilment. Production Planning & Control, 21(5), 453-467.
[10] Caggiano, A., Segreto, T., & Teti, R. (2017). Digital factory technologies for robotic automation and enhanced shop floor planning in machining applications. Procedia CIRP, 62, 450-455.
[11] Chan, F. T., & Chan, H. K. (2005). Simulation modeling for comparative evaluation of supply chain management strategies. International Journal of Advanced Manufacturing Technology, 25(9-10), 998-1006.
[12] Chao, C. C., & Kouvelis, P. (2020). Optimization-based heuristics for dynamic facility layout problems. European Journal of Operational Research, 284(1), 72-85.
[13] Choi, T. M. (2013). Emerging risks in supply chain management: An introduction to the special issue. International Journal of Production Economics, 139(2), 187-189.
[14] Christopher, M., & Peck, H. (2004). Building the resilient supply chain. International Journal of Logistics Management, 15(2), 1-13.
[15] Cichosz, M., Goldsby, T. J., & Knemeyer, A. M. (2020). Innovation in logistics and supply chain management. International Journal of Physical Distribution & Logistics Management, 50(1), 9-32.
[16] Collignon, F., & Talluri, S. (2010). Integrating quality management and supply chain management: Perspectives and empirical evidence. International Journal of Production Research, 48(14), 4321-4335.
[17] Costantino, F., Di Gravio, G., & Tronci, M. (2014). A simulation approach to manage disruption in supply chains. International Journal of Simulation Modelling, 13(1), 16-27.
[18] Cui, L., & Hertz, S. (2011). Networks and capabilities as characteristics of logistics firms. Industrial Marketing Management, 40(6), 1004-1011.
[19] Daugherty, P. J., & Autry, C. W. (2003). B2B exchanges: The role of supply chain relational adaptations and performance. Journal of Business Logistics, 24(1), 57-85.
[20] De Koster, R., Le-Duc, T., & Roodbergen, K. J. (2007). Design and control of warehouse order picking: A literature review. European Journal of Operational Research, 182(2), 481-501.
[21] Demirel, T., & Dönmez, M. (2015). A genetic algorithm for the integrated production and distribution scheduling problem in a supply chain. Computers & Operations Research, 64, 110-116.
[22] Faccio, M., Persona, A., & Sgarbossa, F. (2013). New techniques for automated storage and retrieval systems: A case study. International Journal of Advanced Manufacturing Technology, 67(1-4), 611-623.
[23] Fahimnia, B., Tang, C. S., & Davarzani, H. (2015). Quantitative models for managing supply chain risks: A review. European Journal of Operational Research, 247(1), 1-15.
[24] Fahimnia, B., Farahani, R. Z., & Govindan, K. (2013). An overview of sustainable supply chain management: Definition, challenges, and solutions. European Journal of Operational Research, 230(1), 1-15.
[25] Frazzon, E. M., Albrecht, A., & Hurtado, P. A. (2019). Hybrid simulation and optimization approaches for the operational planning of a material handling system. IFAC-PapersOnLine, 52(13), 2547-2552.
[26] Gamberini, R., Lolli, F., & Rimini, B. (2010). A new multi-objective heuristic algorithm for solving the stochastic assembly line re-balancing problem. International Journal of Production Research, 48(9), 2485-2515.
[27] Ghandour, A., & Subramanian, N. (2012). The role of radio frequency identification (RFID) technologies in improving supply chain visibility and performance. International Journal of Engineering Business Management, 4, 20-29.
[28] Govindan, K., Azevedo, S. G., & Carvalho, H. (2015). Lean, green and resilient practices influence on supply chain performance: Interpretive structural modeling approach. International Journal of Environmental Science and Technology, 12(1), 15-34.
[29] Gu, J., Goetschalckx, M., & McGinnis, L. F. (2010). Research on warehouse operation: A comprehensive review. European Journal of Operational Research, 177(1), 1-21.
[30] Guizzo, E. (2008). Three engineers, hundreds of robots, one warehouse. IEEE Spectrum, 45(7), 26-34.
[31] Hopp, W. J., & Spearman, M. L. (2011). Factory Physics. Waveland Press.
[32] Hu, W., & De Matta, R. (2010). Inventory strategies in a two-stage supply chain with production and transportation capacity constraints. International Journal of Production Research, 48(3), 679-699.
[33] Huang, S. H., & Dismukes, J. P. (2003). Manufacturing productivity improvement using effectiveness metrics and simulation analysis. International Journal of Production Research, 41(3), 513-527.
[34] Jain, A., & Lyons, A. C. (2009). The implementation of lean manufacturing in the UK food and drink industry. International Journal of Services and Operations Management, 5(4), 548-573.
[35] Jimenez, P. R., & Martinez, J. E. (2010). Case study: The impact of RFID on warehouse operations. Journal of Theoretical and Applied Electronic Commerce Research, 5(2), 101-113.
[36] Johnson, A. W., & Thompson, G. M. (2008). Simulation of automated guided vehicle systems. Computers & Industrial Engineering, 15(1), 47-56.
[37] Kaushik, P., & Cooper, T. (2012). The role of automation in warehouse design and operations. Journal of Manufacturing Systems, 31(2), 224-230.
[38] Ke, J., & Bookbinder, J. H. (2012). A new framework for measuring inventory service level in the presence of demand variability and supply lead time. International Journal of Production Research, 50(10), 2714-2731.
[39] Keskin, B. B., & Üster, H. (2015). The integrated production and distribution scheduling problem in supply chain management. Computers & Operations Research, 55, 200-215.
[40] UVWhiwxzˆ‰Š‹�ïðñò4 5 6 7 óæÛÎÀÎÀ²ÎÀ¤“‚rr“‚rbVGh^~ h˜gCJOJQJaJh^~ CJOJQJaJhdh^~ 6�OJPJQJ]�hdhÛ.6�OJPJQJ]�!hdhd6�H*OJPJQJ]�!hdhÛ.6�H*OJPJQJ]�h<V'hÛ.H*OJPJQJh<V'hdH*OJPJQJh<V'hdH*OJPJQJh<V'hdOJPJQJh^~ h^~ OJQJh^~ h^~ CJ(OJQJhÛ.hÛ.CJ(OJQJVWŠð5 6 7 >?°±¾¿WXßõèÞÞÞÌ̴¤�ÂÂÂ$„ñÿ„‹dì¤:]„ñÿ^„‹a$gd˜g$ &Fdë¤a$gdÛ. [Some characters in this reference could not be displayed correctly — please refer to the published PDF for the full reference.]
[41] $„Ť`„Åa$gdd $¤a$gdd„ñÿ„ dð¤]„ñÿ^„ gd^~ $¤a$gdd$dð¤a$gd<V' $¤a$gd<V'7 ? A 7¼½½ [Some characters in this reference could not be displayed correctly — please refer to the published PDF for the full reference.]
How to cite this paper
@article{1705863,
author = {Mahboob Al Bashar, Md Abu Taher, Dilara Ashrafi},
title = {Enhancing Efficiency of Material Handling Equipment in Industrial Engineering Sectors},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {7},
number = {11},
pages = {595-604},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1705863.pdf},
abstract = {Material handling equipment (MHE) is critical in industrial engineering sectors, coordinating the smooth movement, storage, management, and protection of commodities throughout manufacturing and distribution activities. Its efficiency is critical, influencing productivity, cost management, and workplace safety. This research digs into a thorough examination of strategies and technology aimed at improving MHE efficiency in industrial settings. The study seeks to provide a detailed understanding of the multiple ways used in optimizing material handling equipment, encompassing both traditional methodologies and novel improvements. Efficiency enhancement options cover a wide range of methodologies, from classic to cutting-edge solutions. Automation and robots have emerged as key players, transforming material handling operations through their speed, precision, and dependability. The integration of IoT (Internet of Things) with data analytics enables real-time monitoring and predictive maintenance, resulting in optimal performance and minimal downtime. Ergonomic design principles not only promote smoother human-machine interactions, but they also reduce worker tiredness and injury hazards, improving operational efficiency and safety standards. Furthermore, using lean concepts and continuous improvement approaches simplifies material handling operations, resulting in waste reduction and process optimization. This study uses incisive case studies to shed light on successful implementations of efficiency enhancement measures, emphasizing the variables that contribute to their efficacy. Looking ahead, future trends and emerging technologies such as artificial intelligence and advanced robots have the potential to further transform material handling efficiency, highlighting the dynamic character of this vital area of industrial engineering. Addressing problems and embracing opportunities, this study emphasizes the importance of constantly evolving procedures to suit the changing demands of industrial material handling.},
keywords = {Material Handling Equipment, Efficiency Enhancement, Industrial Engineering, Automation, Robotics},
month = {May},
}