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Computer aided assembly planning using MS Excel software – a case study

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The issue of planning assembly operations remains crucial decision-making area for many of manufacturing companies. It becomes particularly significant in case of small and medium enterprises that perform unit or small-scale production, where the option of applying specialized software is often very limited – both due to high purchase price, but also due to its applicability to single unit manufacturing, that is executed based on individual customer orders. The present article describes the possibility of applying the MS Excel spreadsheet in the planning of machine assembly processes. It emphasises, in particular, the method for using the spreadsheet in subsequent stages of the process, and the identification of possible causes that have impact on problems with the planning process. We performed our analysis on the basis of actual data from one of the machine industry enterprises that manufactures in central Poland.
Rocznik
Strony
70--89
Opis fizyczny
Bibliogr. 19 poz., fig., tab.
Twórcy
  • International Tobacco Machinery Poland, Radom, Poland
  • Lublin University of Technology, Faculty of Mechanical Engineering, Department of Production Computerisation and Robotisation, Lublin, Poland
Bibliografia
  • [1] Benjaafar, S., & El Hafsi, M. (2006). Production and inventory control of a single product assemble-to-order system with multiple customer classes. Management Science, 52(12), 1896–1912. https://doi.org/10.1287/mnsc.1060.0588
  • [2] Ciesla, B., & Mleczko, J. (2021). Practical application of fuzzy logic in production control systems of engineer to order SMEs. Applied Computer Science, 17(1), 17-25. https://doi.org/10.23743/acs-2021-02
  • [3] Danilczuk, W., & Gola, A. (2020). Computer-Aided Material Demand Planning Using ERP Systems and Business Intelligence Technology. Applied Computer Science, 16(3), 42–55. https://doi.org/10.23743/acs-2020-20
  • [4] ElHafsi, M. (2009). Optimal integrated production and inventory control of an assemble-to-order system with multiple non-unitary demand classes. European Journal of Operational Research, 194(1), 127–142. https://doi.org/10.1016/j.ejor.2007.12.007
  • [5] Gola, A. (2014). Economic Aspects of Manufacturing Systems Design. Actual Problems of Economics, 156(6), 205–212.
  • [6] Gyulai, D., & Monostori, L. (2017). Capacity management of modular assembly systems. Journal of Manufacturing Systems, 43(1), 88-99. https://doi.org/10.1016/j.jmsy.2017.02.008
  • [7] Gyulai, D., Kadar, B., & Monostori, L. (2014). Capacity planning and resource allocation in assembly systems consisting of dedicated and Reconfigurable lines. Procedia CIRP, 25, 185–191. https://doi.org/10.1016/j.procir.2014.10.028
  • [8] Ju, F., & Li, J. (2014). A Bernoulli model of selective assembly systems. IFAC Proceedings Volumes, 47(3), 1692-1697. https://doi.org/10.3182/20140824-6-ZA-1003.00525
  • [9] Ju, F., Li, J., & Deng, W. (2017). Selective assembly system with unreliable Bernoulli machines and finite buffers. IEEE Transactions on Automation Science and Engineering, 14(1), 171–184. https://doi.org/10.1109/TASE.2016.2604371
  • [10] Kamath, R., & Sarkar, E. (2020). The Engineer… No Longer a Person, but a Number of an Excel Sheet – Enterprise Resource Planning and Commoditisation of Labour. Global Labour Journal, 11(2), 103–117. https://doi.org/10.15173/glj.v11i2.4101
  • [11] Li, J., Blumenfeld, D.E, Huang, N., & Alden, J.M. (2009). Throughput analysis of production systems: Recent advances and future topics. International Journal of Production Research, 47(14), 3823–3851. https://doi.org/10.1080/00207540701829752
  • [12] Manitz, M. (2008). Queueing-model based analysis of assembly lines with finite buffers and general service times. Computers & Operations Research, 35(8), 2520-2536. https://doi.org/10.1016/j.cor.2006.12.016
  • [13] Pang, Z. (2015). Optimal control of a single-product assemble-to-order system with multiple demand classes and backordering. IEEE Transactions on Automatic Control, 60(2), 480–484. https://doi.org/10.1109/TAC.2014.2328451
  • [14] Paprocka, I., Krenczyk, D., & Burduk, A. (2021). The Method of Production Scheduling with Uncertaintes Using the Ants Colony Optimisation. Applied Sciences-Basel, 11(1), 171. https://doi.org/10.3390/app11010171
  • [15] Reiman, M.I., & Wang, Q. (2015). Asymptotically optimal inventory control for assemble-to-order system with identical lead times. Operations Research, 63(3), 489-749. https://doi.org/10.1287/opre.2015.1372
  • [16] Sobaszek, Ł., Gola, A., & Kozłowski, E. (2017), Application of survival function in robust scheduling of production jobs. In Proceedings of the 2017 Federated Conference on Computer Science and Information Systems (FEDCSIS) (pp. 575–578). ACSIS. https://doi.org/10.15439/2017F276
  • [17] Świć, A., & Gola, A. (2013). Economic Analysis of Casing Parts Production in a Flexible Manufacturing System. Actual Problems of Economics, 141(3), 526–533.
  • [18] Tarigan, Z.J.H., Siagian, H., & Jie, F. (2021). Impact of Enhanced Enterprise Resource Planning (ERP) on Firm Performance through Green Supply Chain Management. Sustainability, 13(8), 4358. https://doi.org/10.3390/su13084358
  • [19] Wikarek, J., Sitek, P., & Nielsen, P. (2019). Model of decision support for the configuration of manufacturing system. IFAC PapersOnLine, 52(13), 826–831. https://doi.org/10.1016/j.ifacol.2019.11.232
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a0f54edb-409c-40b3-ad97-eef641a6d6e9
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