Use Case Diagram for Enhancing Warehouse Performance at PT. MDA Through the Implementation of 5S, Economic Order Quantity, Safety Stock, and Warehouse Management System

Authors

  • Michael Radius Kurniawan Bina Nusantara University
  • Hadiyanto Hadiyanto Bina Nusantara University
  • Joe Daniansyah Pahlevi Zulkarnaen Bina Nusantara University
  • Christian Harito Bina Nusantara University

DOI:

https://doi.org/10.21512/emacsjournal.v6i1.11204

Keywords:

Probabilistic Economic Order Quantity, Safety Stock, 5S, Use Case Diagram, Warehouse Management System

Abstract

An industrial water pump importing company relies on a network of distribution warehouses to efficiently manage the storage and delivery of its products to clients. This paper delves into the operational intricacies of the company, with a primary focus on sustaining a superior level of service to meet customer demands, all while attempting to minimize costs and achieve optimal inventory control. The central aspects explored in this research encompass the meticulous determination of the number of pipes needed and the optimal ordering times. To address this, the Probabilistic Economic Order Quantity (EOQ) method is used and supported by 5S concept, recognized for its ability to provide reasonably accurate estimates crucial for pivotal decision-making in inventory management. The utilization of the Probabilistic EOQ method in this context reflects the company's commitment to adopting sophisticated and proven methodologies to enhance decision-making accuracy and the warehouse area is more suitable by the 5S implementation principles. The research outcomes not only assist in refining the determination of Safety Stock levels but also contribute valuable insights into the precise quantities of goods that should be ordered. With an estimated demand for 196 units of carbon 6" in the following year, a safety stock of 13 units is required, while for carbon 4" with an estimated demand of 119 units, a safety stock of 8 units is required. These upcoming insights could encompass innovative strategies, technological implementations, or advances in supply chain optimization.

Dimensions

Plum Analytics

Author Biographies

Michael Radius Kurniawan, Bina Nusantara University

Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering 

Hadiyanto Hadiyanto, Bina Nusantara University

Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering 

Joe Daniansyah Pahlevi Zulkarnaen, Bina Nusantara University

Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering 

Christian Harito, Bina Nusantara University

Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering 

References

Aravindaraj, K. and Rajan Chinna, P. (2022) ‘A systematic literature review of integration of industry 4.0 and warehouse management to achieve Sustainable Development Goals (SDGs)’, Cleaner Logistics and Supply Chain, 5, p. 100072. doi: https://doi.org/10.1016/j.clscn.2022.100072.

Carli, R. et al. (2020) ‘A Control Strategy for Smart Energy Charging of Warehouse Material Handling Equipment’, Procedia Manufacturing, 42, pp. 503–510. doi: https://doi.org/10.1016/j.promfg.2020.02.041.

Chen, W. et al. (2024) ‘Does battery management matter? Performance evaluation and operating policies in a self-climbing robotic warehouse’, European Journal of Operational Research, 312(1), pp. 164–181. doi: https://doi.org/10.1016/j.ejor.2023.06.025.

van Geest, M., Tekinerdogan, B. and Catal, C. (2021) ‘Design of a reference architecture for developing smart warehouses in industry 4.0’, Computers in Industry, 124, p. 103343. doi: https://doi.org/10.1016/j.compind.2020.103343.

Hadj Sassi, M. S. et al. (2021) ‘Knowledge Management Process for Air Quality Systems based on Data Warehouse Specification’, Procedia Computer Science, 192, pp. 29–38. doi: https://doi.org/10.1016/j.procs.2021.08.004.

Hamdy, W., Al-Awamry, A. and Mostafa, N. (2022) ‘Warehousing 4.0: A proposed system of using node-red for applying internet of things in warehousing’, Sustainable Futures, 4, p. 100069. doi: https://doi.org/10.1016/j.sftr.2022.100069.

Jacob, F. et al. (2023) ‘Picking with a robot colleague: A systematic literature review and evaluation of technology acceptance in human–robot collaborative warehouses’, Computers & Industrial Engineering, 180, p. 109262. doi: https://doi.org/10.1016/j.cie.2023.109262.

Jiang, Z.-Z. et al. (2021) ‘Spatial and temporal optimization for smart warehouses with fast turnover’, Computers & Operations Research, 125, p. 105091. doi: https://doi.org/10.1016/j.cor.2020.105091.

Kara, K. et al. (2024) ‘A single-valued neutrosophic-based methodology for selecting warehouse management software in sustainable logistics systems’, Engineering Applications of Artificial Intelligence, 129, p. 107626. doi: https://doi.org/10.1016/j.engappai.2023.107626.

Lydia et al. (2022) ‘Automated food grain monitoring system for warehouse using IOT’, Measurement: Sensors, 24, p. 100472. doi: https://doi.org/10.1016/j.measen.2022.100472.

Lyu, Z. et al. (2020) ‘Towards Zero-Warehousing Smart Manufacturing from Zero-Inventory Just-In-Time production’, Robotics and Computer-Integrated Manufacturing, 64, p. 101932. doi: https://doi.org/10.1016/j.rcim.2020.101932.

Mahroof, K. (2019) ‘A human-centric perspective exploring the readiness towards smart warehousing: The case of a large retail distribution warehouse’, International Journal of Information Management, 45, pp. 176–190. doi: https://doi.org/10.1016/j.ijinfomgt.2018.11.008.

Najlae, A., Sedqui, A. and Lyhyaoui, A. (2021) ‘A Product Driven System to Facilitate FEFO Application in Warehouses’, Procedia Computer Science, 191, pp. 451–456. doi: https://doi.org/10.1016/j.procs.2021.07.056.

Oloruntobi, O. et al. (2023) ‘Effective technologies and practices for reducing pollution in warehouses - A review’, Cleaner Engineering and Technology, 13, p. 100622. doi: https://doi.org/10.1016/j.clet.2023.100622.

Opalic, S. M. et al. (2020) ‘ANN modelling of CO2 refrigerant cooling system COP in a smart warehouse’, Journal of Cleaner Production, 260, p. 120887. doi: https://doi.org/10.1016/j.jclepro.2020.120887.

Piardi, L. et al. (2023) ‘MAS-based Distributed Cyber-physical System in Smart Warehouse’, IFAC-PapersOnLine, 56(2), pp. 6376–6381. doi: https://doi.org/10.1016/j.ifacol.2023.10.826.

Sarkar, B. et al. (2022) ‘Optimized radio-frequency identification system for different warehouse shapes’, Knowledge-Based Systems, 258, p. 109811. doi: https://doi.org/10.1016/j.knosys.2022.109811.

Simic, V. et al. (2023) ‘Neutrosophic LOPCOW-ARAS model for prioritizing industry 4.0-based material handling technologies in smart and sustainable warehouse management systems’, Applied Soft Computing, 143, p. 110400. doi: https://doi.org/10.1016/j.asoc.2023.110400.

Tiwari, S. (2023) ‘Smart warehouse: A bibliometric analysis and future research direction’, Sustainable Manufacturing and Service Economics, 2, p. 100014. doi: https://doi.org/10.1016/j.smse.2023.100014.

Vicuna, P. et al. (2019) ‘A Generic and Flexible Geospatial Data Warehousing and Analysis Framework for Transportation Performance Measurement in Smart Connected Cities’, Procedia Computer Science, 155, pp. 226–233. doi: https://doi.org/10.1016/j.procs.2019.08.033.

Winkelhaus, S. and Grosse, E. H. (2022) ‘Chapter 3 - Smart warehouses—a sociotechnical perspective’, in MacCarthy, B. L. and Ivanov, D. B. T.-T. D. S. C. (eds). Elsevier, pp. 47–60. doi: https://doi.org/10.1016/B978-0-323-91614-1.00003-4.

Zaman, S. I. et al. (2023) ‘A grey decision-making trial and evaluation laboratory model for digital warehouse management in supply chain networks’, Decision Analytics Journal, 8, p. 100293. doi: https://doi.org/10.1016/j.dajour.2023.100293.

Zhan, X. et al. (2022) ‘Industrial internet of things and unsupervised deep learning enabled real-time occupational safety monitoring in cold storage warehouse’, Safety Science, 152, p. 105766. doi: https://doi.org/10.1016/j.ssci.2022.105766.

Zhang, D., Pee, L. G. and Cui, L. (2021) ‘Artificial intelligence in E-commerce fulfillment: A case study of resource orchestration at Alibaba’s Smart Warehouse’, International Journal of Information Management, 57, p. 102304. doi: https://doi.org/10.1016/j.ijinfomgt.2020.102304.

Downloads

Published

2024-01-31

Issue

Section

Articles
Abstract 611  .
PDF downloaded 433  .