Raw material planning for tapioca flour production based on fuzzy logic approach: a case study

  • Sesar Husen Santosa IPB University
  • Agung Prayudha Hidayat IPB University
  • Ridwan Siskandar IPB University
Abstract views: 236 , PDF downloads: 5148
Keywords: Defuzzification, Fuzzy logic, Forecasting, Membership function, OEE


The availability of cassava raw materials influences tapioca flour production at small and medium industry's (SMIs) Bogor Regency. Cassava raw material is a crucial factor in producing cassava yields, affecting the amount of tapioca flour production. Planning for cassava raw material must be carried out properly because the quality and quantity of cassava must be maintained to achieve the tapioca flour production target. The results of forecasting the demand for tapioca flour in SMIs using the Multiplicative Decomposition method were 2566 kg with MAD = 173.73 and MAPE = 0.08. Based on the analysis of the effectiveness of the grinding machine, the average value of OEE for one year is 0.32 (32%). Based on the value of demand forecasting results, milling machine OEE and tapioca flour prices, an analysis of cassava raw material needs are carried out using a fuzzy logic approach. The membership set used is Triangular and Trapezoidal Membership Function and Fuzzy Rule Base as many as 81 possibilities. The defuzzification of cassava raw material requirements in SMIs is 17600 kg. Based on the results of defuzzification, the need for raw materials must be increased so that the demand for tapioca flour can be achieved.


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V. C. Costa, D. V. Babos, R. R. Gamela, F. A. C. Amorim, and E. R. Pereira-Filho, “Proposition of Sample Preparation Procedure of Cassava Flour with Diluted Acid Using Mixture Design and Evaluation of Nutrient Profiles by Multivariate Data Analysis,” Food Anal. Methods, vol. 13, no. 1, pp. 145–154, Jan. 2020, doi: https://doi.org/10.1007/s12161-019-01559-8.

E. S. Ikuemonisan, T. E. Mafimisebi, I. Ajibefun, and K. Adenegan, “Cassava production in Nigeria: trends, instability and decomposition analysis (1970–2018),” Heliyon, vol. 6, no. 10, p. e05089, Oct. 2020, doi: https://doi.org/10.1016/j.heliyon.2020.e05089.

C. de Lima Barizão et al., “Biodegradable films based on commercial κ-carrageenan and cassava starch to achieve low production costs,” Int. J. Biol. Macromol., vol. 165, pp. 582–590, Dec. 2020, doi: https://doi.org/10.1016/j.ijbiomac.2020.09.150.

E. Hermiati et al., “Improvement of microwave-assisted hydrolysis of cassava pulp and tapioca flour by addition of activated carbon,” Carbohydr. Polym., vol. 87, no. 1, pp. 939–942, Jan. 2012, doi: https://doi.org/10.1016/j.carbpol.2011.08.033.

R. S. Aleman, G. Paz, A. Morris, W. Prinyawiwatkul, M. Moncada, and J. M. King, “High protein brown rice flour, tapioca starch & potato starch in the development of gluten-free cupcakes,” LWT, vol. 152, no. August, p. 112326, Dec. 2021, doi: https://doi.org/10.1016/j.lwt.2021.112326.

M. Stevenson *, L. C. Hendry, and B. G. Kingsman †, “A review of production planning and control: the applicability of key concepts to the make-to-order industry,” Int. J. Prod. Res., vol. 43, no. 5, pp. 869–898, Mar. 2005, doi: https://doi.org/10.1080/0020754042000298520.

S. H. Santosa, S. Irawan, and I. Ardani, “Determination of Overall Equipment Efectiveness Superflex Machine Using Fuzzy Approach,” Int. J. Artif. Intell. Res., vol. 4, no. 2, pp. 135–150, Jan. 2021, doi: https://doi.org/10.29099/ijair.v4i2.142.

H. Jodlbauer and S. Strasser, “Capacity-driven production planning,” Comput. Ind., vol. 113, p. 103126, Dec. 2019, doi: https://doi.org/10.1016/j.compind.2019.103126.

G. Ramya, M. Chandrasekaran, and E. Shankar, “Case Study Analysis of Job Shop Scheduling and its Integration with Material Requirement Planning,” Mater. Today Proc., vol. 16, pp. 1034–1042, 2019, doi: https://doi.org/10.1016/j.matpr.2019.05.192.

D. Gradišar and M. Glavan, “Material Requirements Planning Using Variable-Sized Bin-Packing Problem Formulation with Due Date and Grouping Constraints,” Processes, vol. 8, no. 10, pp. 1–6, Oct. 2020, doi: https://doi.org/10.3390/pr8101246.

R. Miclo, M. Lauras, F. Fontanili, J. Lamothe, and S. A. Melnyk, “Demand Driven MRP: assessment of a new approach to materials management,” Int. J. Prod. Res., vol. 57, no. 1, pp. 166–181, Jan. 2019, doi: https://doi.org/10.1080/00207543.2018.1464230.

N. K. Dev, R. Shankar, and S. Swami, “Diffusion of green products in industry 4.0: Reverse logistics issues during design of inventory and production planning system,” Int. J. Prod. Econ., vol. 223, p. 107519, May 2020, doi: https://doi.org/10.1016/j.ijpe.2019.107519.

M. Suryaprakash, M. Gomathi Prabha, M. Yuvaraja, and R. V. Rishi Revanth, “Improvement of overall equipment effectiveness of machining centre using tpm,” Mater. Today Proc., vol. 46, pp. 9348–9353, 2021, doi: https://doi.org/10.1016/j.matpr.2020.02.820.

O. E. Oluyisola, F. Sgarbossa, and J. O. Strandhagen, “Smart Production Planning and Control: Concept, Use-Cases and Sustainability Implications,” Sustainability, vol. 12, no. 9, p. 3791, May 2020, doi: https://doi.org/10.3390/su12093791.

H. Ahmadi, M. Gholamzadeh, L. Shahmoradi, M. Nilashi, and P. Rashvand, “Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review,” Comput. Methods Programs Biomed., vol. 161, pp. 145–172, Jul. 2018, doi: https://doi.org/10.1016/j.cmpb.2018.04.013.

S. Nakajima, Introduction to TPM: total productive maintenance. Productivity Press, 1988. Available: https://books.google.co.id/books?id=XKc28H3JeUUC.

M. Nakhla, “Designing extended overall equipment effectiveness: application in healthcare operations,” Int. J. Manag. Sci. Eng. Manag., pp. 1–10, Sep. 2018, doi: https://doi.org/10.1080/17509653.2017.1373377.

P. Tsarouhas, “Improving operation of the croissant production line through overall equipment effectiveness (OEE),” Int. J. Product. Perform. Manag., vol. 68, no. 1, pp. 88–108, Jan. 2019, doi: https://doi.org/10.1108/IJPPM-02-2018-0060.

F. A. S. Piran, A. De Paris, D. P. Lacerda, L. F. R. Camargo, R. Serrano, and R. A. Cassel, “Overall Equipment Effectiveness: Required but not Enough—An Analysis Integrating Overall Equipment Effect and Data Envelopment Analysis,” Glob. J. Flex. Syst. Manag., vol. 21, no. 2, pp. 191–206, Jun. 2020, doi: https://doi.org/10.1007/s40171-020-00238-6.

R. Sharma, “Overall equipment effectiveness measurement of TPM manager model machines in flexible manufacturing environment: a case study of automobile sector,” Int. J. Product. Qual. Manag., vol. 26, no. 2, pp. 206–222, 2019, doi: https://doi.org/10.1504/IJPQM.2019.097767.

N. Mbuli, M. Mathonsi, M. Seitshiro, and J.-H. C. Pretorius, “Decomposition forecasting methods: A review of applications in power systems,” Energy Reports, vol. 6, no. 2020, pp. 298–306, Dec. 2020, doi: https://doi.org/10.1016/j.egyr.2020.11.238.

M.-L. Cheng, C.-W. Chu, and H.-L. Hsu, “A study of univariate forecasting methods for crude oil price,” Marit. Bus. Rev., pp. 1–16, Dec. 2021, doi: https://doi.org/10.1108/MABR-09-2021-0076.

Y.-C. Hu, “Constructing grey prediction models using grey relational analysis and neural networks for magnesium material demand forecasting,” Appl. Soft Comput., vol. 93, p. 106398, Aug. 2020, doi: https://doi.org/10.1016/j.asoc.2020.106398.

P. Jiang, Z. Liu, X. Niu, and L. Zhang, “A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting,” Energy, vol. 217, p. 119361, Feb. 2021, doi: https://doi.org/10.1016/j.energy.2020.119361.

S. H. Santosa, S. Sulaeman, A. P. Hidayat, and I. Ardani, “Fuzzy Logic Approach to Determine the Optimum Nugget Production Capacity,” J. Ilm. Tek. Ind., vol. 19, no. 1, pp. 70–83, Jun. 2020, doi: https://doi.org/10.23917/jiti.v19i1.10295.

G. Hu, E. Bakhtavar, K. Hewage, M. Mohseni, and R. Sadiq, “Heavy metals risk assessment in drinking water: An integrated probabilistic-fuzzy approach,” J. Environ. Manage., vol. 250, no. June, p. 109514, Nov. 2019, doi: https://doi.org/10.1016/j.jenvman.2019.109514.

A. P. Hidayat, S. H. Santosa, R. Siskandar, and R. G. Baskoro, “Evaluation of Chicken Eggs Supply With Fuzzy AHP Approach Through Development of Safea Software,” J. Logistik Indones., vol. 5, no. 2, pp. 104–110, 2021. Available: https://ojs.stiami.ac.id/index.php/logistik/article/view/1881.

Q. Zhou, W. Wu, D. Liu, K. Li, and Q. Qiao, “Estimation of corrosion failure likelihood of oil and gas pipeline based on fuzzy logic approach,” Eng. Fail. Anal., vol. 70, pp. 48–55, Dec. 2016, doi: https://doi.org/10.1016/j.engfailanal.2016.07.014.

E. Pourjavad and A. Shahin, “The Application of Mamdani Fuzzy Inference System in Evaluating Green Supply Chain Management Performance,” Int. J. Fuzzy Syst., vol. 20, no. 3, pp. 901–912, Mar. 2018, doi: https://doi.org/10.1007/s40815-017-0378-y.

M. Godichaud and L. Amodeo, “Economic order quantity for multistage disassembly systems,” Int. J. Prod. Econ., vol. 199, no. May 2017, pp. 16–25, May 2018, doi: https://doi.org/10.1016/j.ijpe.2018.02.008.

J. S. Armstrong, “Evaluating Forecasting Methods,” J. S. Armstrong, Ed. Boston, MA: Springer US, 2001, pp. 443–472, doi: https://doi.org/10.1007/978-0-306-47630-3_20.

Y. D. Polewangi, “Analysis of Supplier Ability in Providing Raw Materials Cassava and Planning the Amount and Time of Arrival Raw Materials at PT. ABC,” Budapest Int. Res. Exact Sci. J., vol. 2, no. 3, pp. 382–388, 2020. Available: https://bircu-journal.com/index.php/birex/article/view/1078.

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S. H. Santosa, A. P. Hidayat, and R. Siskandar, “Raw material planning for tapioca flour production based on fuzzy logic approach: a case study”, j. sist. manaj. ind., vol. 6, no. 1, pp. 67-76, Jun. 2022.
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