JOINT EFFECT OF FORECASTING AND LOT-SIZING METHOD ON COST MINIMIZATION OBJECTIVE OF A MANUFACTURER: A CASE STUDY
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GRAPH-BASED FOG COMPUTING NETWORK MODEL
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JOINT EFFECT OF FORECASTING AND LOT-SIZING METHOD ON COST MINIMIZATION OBJECTIVE OF A MANUFACTURER: A CASE STUDY
Jack OLESEN, Carl-Emil Houmøller PEDERSEN, Markus Germann KNUDSEN, Sandra TOFT, Vladimir NEDBAILO, Johan PRISAK, Izabela Ewa NIELSEN, Subrata SAHA21-36
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Abstract
Forecasting and lot-sizing problems are key for a variety of products manufactured in a plant of finite capacity. The plant manager needs to put special emphasis on the way of selecting the right forecasting methods with a higher level of accuracy and to conduct procurement planning based on specific lot-sizing methods and associated rolling horizon. The study is conducted using real case data form the Fibertex Personal Care, and has evaluated the joint influence of forecasting procedures such as ARIMA, exponential smoothing methods; and deterministic lot-sizing methods such as the Wagner-Whitin method, modified Silver-Meal heuristic to draw insights on the effect of the appropriate method selection on minimization of operational cost. The objective is to explore their joint effect on the cost minimization goal. It is found that a proficient selection process has a considerable impact on performance. The proposed method can help a manager to save substantial operational costs.
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References
Ali, M., & Miller, L. (2017). ERP system implementation in large enterprises–a systematic literature review. Journal of Enterprise Information Management, 30(4), 666–692. https://doi.org/10.1108/JEIM-072014-0071 DOI: https://doi.org/10.1108/JEIM-07-2014-0071
Alotaibi, Y. (2016). Business process modelling challenges and solutions: a literature review. Journal of Intelligent Manufacturing, 27(4), 701–723. https://doi.org/10.1007/s10845-014-0917-4 DOI: https://doi.org/10.1007/s10845-014-0917-4
Andriolo, A., Battini, D., Grubbström, R. W., Persona, A., & Sgarbossa, F. (2014). A century of evolution from Harris’s basic lot size model: Survey and research agenda. International Journal of Production Economics, 155, 16-38. https://doi.org/10.1016/j.ijpe.2014.01.013 DOI: https://doi.org/10.1016/j.ijpe.2014.01.013
Bach, I., Bocewicz, G., Banaszak, Z. A., & Muszyński, W. (2010). Knowledge based and CP-driven approach applied to multi product small-size production flow. Control and Cybernetics, 39, 69–95.
Baker, K. R. (1989). Lot-sizing procedures and a standard data set: a reconciliation of the literature. Journal of Manufacturing and Operations Management, 2(3), 199–221.
Bocewicz, G., Nielsen, P., & Banaszak, Z. (2019). Declarative modeling of a milk-run vehicle routing problem for split and merge supply streams scheduling. Advances in Intelligent Systems and Computing, 853, 157–172. https://doi.org/10.1007/978-3-319-99996-8_15 DOI: https://doi.org/10.1007/978-3-319-99996-8_15
Bocewicz, G., Nielsen, P., Banaszak, Z., & Thibbotuwawa, A. (2018). Routing and scheduling of unmanned aerial vehicles subject to cyclic production flow constraints. In International Symposium on Distributed Computing and Artificial Intelligence (pp. 75–86). Springer, Cham. https://doi.org/10.1007/978-3-319-99608-0_9 DOI: https://doi.org/10.1007/978-3-319-99608-0_9
Box, G. E., Jenkins, G. M., & Reinsel, G. C. (2011). Time series analysis: forecasting and control (Vol. 734). John Wiley & Sons. https://doi.org/0.1111/jtsa.12194
De Bodt, M. A., Gelders, L. F., & Van Wassenhove, L. N. (1984). Lot sizing under dynamic demand conditions: A review. Engineering Costs and Production Economics, 8(3), 165–187. https://doi.org/10.1016/0167188X(84)90035-1 DOI: https://doi.org/10.1016/0167-188X(84)90035-1
Drexl, A., & Kimms, A. (1997). Lot sizing and scheduling—survey and extensions. European Journal of operational research, 99(2), 221–235. https://doi.org/10.1016/S0377-2217(97)00030-1 DOI: https://doi.org/10.1016/S0377-2217(97)00030-1
Eriksen, P. S., & Nielsen, P. (2016). Order quantity distributions: Estimating an adequate aggregation horizon. Management and Production Engineering Review, 7(3), 9–48. https://doi.org/10.1515/mper-2016-0024 DOI: https://doi.org/10.1515/mper-2016-0024
Fildes, R., Goodwin, P., Lawrence, M., & Nikolopoulos, K. (2009). Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supplychain planning. International journal of forecasting, 25(1), 3–23. https://doi.org/10.1016/j.ijforecast.2008.11.010 DOI: https://doi.org/10.1016/j.ijforecast.2008.11.010
Gola A. (2014) Economic Aspects of Manufacturing Systems Design. Actual Problems of Economics, 156(6), 205–212.
Grubbström, R. W., & Tang, O. (2012). The space of solution alternatives in the optimal lotsizing problem for general assembly systems applying MRP theory. International Journal of Production Economics, 140(2), 765777. https://doi.org/10.1016/j.ijpe.2011.01.012 DOI: https://doi.org/10.1016/j.ijpe.2011.01.012
Grubbström, R. W., Bogataj, M., & Bogataj, L. (2010). Optimal lotsizing within MRP theory. Annual Reviews in Control, 34(1), 89–100. https://doi.org/10.3182/20090603-3-RU2001.0562 DOI: https://doi.org/10.1016/j.arcontrol.2010.02.004
Heady, R. B., & Zhu, Z. (1994). An improved implementation of the Wagner-Whitin Algorithm. Production and Operations Management, 3(1), 55–63. https://doi.org/10.1111/j.1937-5956.1994.tb00109.x DOI: https://doi.org/10.1111/j.1937-5956.1994.tb00109.x
Ho, C. J., & Ireland, T. C. (2012). Mitigating forecast errors by lot-sizing rules in ERP-controlled manufacturing systems. International Journal of Production Research, 50(11), 3080–3094. https://doi.org/10.1080/00207543.2011.592156 DOI: https://doi.org/10.1080/00207543.2011.592156
Hopp, W. J., & Spearman, M. L. (2011). Factory physics. Waveland Press.
Kazan, O., Nagi, R., & Rump, C. M. (2000). New lot-sizing formulations for less nervous production schedules. Computers & Operations Research, 27(13), 1325–1345. https://doi.org/10.1016/S0305-0548(99)00076-3 DOI: https://doi.org/10.1016/S0305-0548(99)00076-3
Kian, R., Berk, E., Gürler, Ü., Rezazadeh, H., & Yazdani, B. (2020). The effect of economies-ofscale on the performance of lot-sizing heuristics in rolling horizon basis. International Journal of Production Research, 1–15. https://doi.org/10.1080/00207543.2020.1730464 DOI: https://doi.org/10.1080/00207543.2020.1730464
Kourentzes, N., Trapero, J. R., & Barrow, D. K. (2020). Optimising forecasting models for inventory planning. International Journal of Production Economics, 225, 107597. https://doi.org/10.1016/j.ijpe.2019.107597 DOI: https://doi.org/10.1016/j.ijpe.2019.107597
Li, Q., & Disney, S. M. (2017). Revisiting rescheduling: MRP nervousness and the bullwhip effect. International Journal of Production Research, 55(7), 1992–2012. https://doi.org/10.1016/j.ijpe.2019.107597 DOI: https://doi.org/10.1080/00207543.2016.1261196
Mills, T. C. (2019). Applied Time Series Analysis: A Practical Guide to Modeling and Forecasting. Academic Press.
Moon, I., Yoo, D. K., & Saha, S. (2016). The distribution-free newsboy problem with multiple discounts and upgrades. Mathematical Problems in Engineering, 2017253. https://doi.org/10.1155/2016/2017253 DOI: https://doi.org/10.1155/2016/2017253
Nielsen, P., Jiang, L., Rytter, N. G. M., & Chen, G. (2014). An investigation of forecast horizon and observation fit’s influence on an econometric rate forecast model in the liner shipping industry. Maritime Policy & Management, 41(7), 667–682. https://doi.org/10.1080/03088839.2014.960499 DOI: https://doi.org/10.1080/03088839.2014.960499
Nilakantan, J. M., Li, Z., Tang, Q., & Nielsen, P. (2017). MILP models and metaheuristic for balancing and sequencing of mixed-model two-sided assembly lines. European Journal of Industrial Engineering, 11(3), 353-379. https://doi.org/10.1504/EJIE.2017.084880 DOI: https://doi.org/10.1504/EJIE.2017.084880
Patalas-Maliszewska, J. (2012). Assessing the Impact of Erp Implementation in the small Enterprises. Foundations of management, 4(2), 51-62. https://doi.org/10.2478/fman-2013-0010 DOI: https://doi.org/10.2478/fman-2013-0010
Patalas-Maliszewska, J., & Kłos, S. (2017). A Study on Improving the Effectiveness of a Manufacturing Company in the Context of Knowledge Management–Research Results. Foundations of Management, 9(1), 149160. https://doi.org/10.1515/fman-2017-0012 DOI: https://doi.org/10.1515/fman-2017-0012
Pedersen, C. H., Nedbailo, V., Knudsen, M. G., Olesen, J., & Toft, S. (2020). Analysis and development of an operations system. P4 Semester Project, GBE4 gr. 16/2.016. Global Business Engineering, Aalborg University.
Saha, S., Das, S., & Basu, M. (2010). Optimal pricing and production lot-sizing for seasonal products over a finite horizon. International Journal of Mathematics in Operational Research, 2(5), 540–553. https://doi.org/10.1504/IJMOR.2010.03434 DOI: https://doi.org/10.1504/IJMOR.2010.034340
Silver, E. A., & Meal, H. C. (1973). A heuristic for selecting lot size quantities for the case of a deterministic time-varying demand rate and discrete opportunities for replenishment. Production and Inventory Management, 2, 64–74.
Silver, E. A., Pyke, D. F., & Thomas, D. J. (2016). Inventory and production management in supply chains. CRC Press. DOI: https://doi.org/10.1201/9781315374406
Silver, E., & Miltenburg, J. (1984). Two modifications of the SilverMeal lot sizing heuristic. INFOR: Information Systems and Operational Research, 22(1), 56–69. https://doi.org/10.1080/03155986.1984.11731912 DOI: https://doi.org/10.1080/03155986.1984.11731912
Świć, A., & Gola, A. (2013). Economic Analysis of Casing Parts Production in a Flexible Manufacturing System. Actual Problems of Economics, 141(3), 526–533.
Syntetos, A. A., Boylan, J. E., & Disney, S. M. (2009). Forecasting for inventory planning: a 50-year review. Journal of the Operational Research Society, 60, 149–S160. https://doi.org/10.1057/jors.2008.173 DOI: https://doi.org/10.1057/jors.2008.173
Syntetos, A. A., Nikolopoulos, K., & Boylan, J. E. (2010). Judging the judges through accuracyimplication metrics: The case of inventory forecasting. International Journal of Forecasting, 26(1), 134-143. https://doi.org/10.1016/j.ijforecast.2009.05.016 DOI: https://doi.org/10.1016/j.ijforecast.2009.05.016
Taneja, K., Ahmad, S., Ahmad, K., & Attri, S. D. (2016). Time series analysis of aerosol optical depth over New Delhi using Box–Jenkins ARIMA modeling approach. Atmospheric Pollution Research, 7(4), 585596. https://doi.org/10.1016/j.apr.2016.02.004 DOI: https://doi.org/10.1016/j.apr.2016.02.004
Van Den Heuvel, W., & Wagelmans, A. P. (2005). A comparison of methods for lot-sizing in a rolling horizon environment. Operations Research Letters, 33(5), 486–496. https://doi.org/10.1016/j.orl.2004.10.001 DOI: https://doi.org/10.1016/j.orl.2004.10.001
Wagner, H. M., & Whitin, T. M. (1958). Dynamic version of the economic lot size model. Management science, 5(1), 89-96. https://doi.org/10.1287/mnsc.5.1.89 DOI: https://doi.org/10.1287/mnsc.5.1.89
Xi, M. H., Wang, H. X., & Zhao, Q. H. (2012). Regression Based Integration of Demand Forecasting and Inventory Decision. Advanced Materials Research, 433, 2954–2956. https://doi.org/10.4028/www.scientific.net/AMR.433-440.2954 DOI: https://doi.org/10.4028/www.scientific.net/AMR.433-440.2954
Zabjek, D., Kovačič, A., & Štemberger, M. I. (2009). The influence of business process management and some other CSFs on successful ERP implementation. Business Process Management Journal, 15(4), 588–608. https://doi.org/10.1108/14637150910975552 DOI: https://doi.org/10.1108/14637150910975552
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