Integration models of demand forecasting and inventory control for coconut sugar using the ARIMA and EOQ modification methods

Authors

DOI:

https://doi.org/10.30656/jsmi.v7i2.6500

Keywords:

Coconut sugar, Demand forecasting, Inventory control, Modified ARIMA, Modified EOQ

Abstract

Inventory control is critical because the inability to overcome inventory problems causes unpreparedness to meet consumer demand. MSMEs Bekawan Agro Coconut Sugar, independently around 35% -70%, cannot meet consumers' demand for coconut sugar, so an inventory control model is needed. Inventory control models must integrate with demand forecasting as an inventory control input. This study aims to integrate the demand fore­casting model with the inventory control model. The method used for demand forecasting is ARIMA. The inventory control model uses a modi­fied EOQ hybrid method because coconut sugar products have a shelf life; they also use coconut sap as raw material, which must be processed to prevent fermentation. The research results show that demand forecasting for one year ahead is a total of 10,310.82 Kilograms with an economic lot size of 120 Kilograms and a reorder point when the inventory position is 30 Kilograms. Daily production of 30 kilograms requires 210 litres of coconut sap/per day. The amount of sap needed requires 105 coconut trees / per day. Arrival time of coconut sugar at the storage warehouse every five days. The resulting model can be a solution for sustainable MSMEs.

Downloads

Download data is not yet available.

References

R. Ekawati, E. Kurnia, S. Wardah, and T. Djatna, ‘Predictive Demand Analytics for Inventory Control in Refined Sugar Supply Chain Downstream’, in 2019 International Seminar on Application for Technology of Information and Communication (iSemantic), 2019, pp. 100–104, doi: https://doi.org/10.1109/ISEMANTIC.2019.8884293.

H. K. Alfares, ‘EOQ and EPQ Production-Inventory Models with Variable Holding Cost: State-of-the-Art Review’, Arabian Journal for Science and Engineering, vol. 44, no. 3. pp. 1737–1755, 2019, doi: https://doi.org/10.1007/s13369-018-3593-4.

D. M. Utama, I. Santoso, Y. Hendrawan, and W. A. P. Dania, ‘Integrated procurement-production inventory model in supply chain: A systematic review’, Oper. Res. Perspect., vol. 9, no. September 2021, p. 100221, 2022, doi: https://doi.org/10.1016/j.orp.2022.100221.

P. Maheshwari, S. Kamble, A. Pundir, A. Belhadi, N. O. Ndubisi, and S. Tiwari, ‘Internet of things for perishable inventory management systems: an application and managerial insights for micro, small and medium enterprises’, Ann. Oper. Res., pp. 1–29, Oct. 2021, doi: https://doi.org/10.1007/s10479-021-04277-9.

M. F. N. Maghfiroh and A. A. N. P. Redi, ‘Tabu search heuristic for inventory routing problem with stochastic demand and time windows’, J. Sist. dan Manaj. Ind., vol. 6, no. 2, pp. 111–120, Nov. 2022, doi: https://doi.org/10.30656/jsmi.v6i2.4813.

T. E. Goltsos, A. A. Syntetos, C. H. Glock, and G. Ioannou, ‘Inventory – forecasting: Mind the gap’, Eur. J. Oper. Res., vol. 299, no. 2, pp. 397–419, Jun. 2022, doi: https://doi.org/10.1016/j.ejor.2021.07.040.

I. Svetunkov and J. E. Boylan, ‘State-space ARIMA for supply-chain forecasting’, Int. J. Prod. Res., vol. 58, no. 3, pp. 818–827, Feb. 2020, doi: https://doi.org/10.1080/00207543.2019.1600764.

Z. Achetoui, C. Mabrouki, and A. Mousrij, ‘A review of spare parts supply chain management’, J. Sist. dan Manaj. Ind., vol. 3, no. 2, pp. 67–75, 2019, doi: https://doi.org/10.30656/jsmi.v3i2.1524.

M. Armenzoni et al., ‘An integrated approach for demand forecasting and inventory management optimisation of spare parts’, Int. J. Simul. Process Model., vol. 10, no. 3, pp. 223–240, 2015, doi: https://doi.org/10.1504/IJSPM.2015.071375.

B. Dey, B. Roy, S. Datta, and T. S. Ustun, ‘Forecasting ethanol demand in India to meet future blending targets: A comparison of ARIMA and various regression models’, Energy Reports, vol. 9, pp. 411–418, Mar. 2023, doi: https://doi.org/10.1016/j.egyr.2022.11.038.

X. Wan and N. R. Sanders, ‘The negative impact of product variety: Forecast bias, inventory levels, and the role of vertical integration’, Int. J. Prod. Econ., vol. 186, no. April, pp. 123–131, Apr. 2017, doi: https://doi.org/10.1016/j.ijpe.2017.02.002.

Z. Chen, ‘Optimization of production inventory with pricing and promotion effort for a single-vendor multi-buyer system of perishable products’, Int. J. Prod. Econ., vol. 203, no. March, pp. 333–349, Sep. 2018, doi: https://doi.org/10.1016/j.ijpe.2018.06.002.

A. S. Putri and B. I. Rosydi, ‘Analysis of raw material inventory for insecticide packaging bottle with material requirement planning: a case study’, J. Sist. dan Manaj. Ind., vol. 4, no. 2, pp. 93–98, Dec. 2020, doi: https://doi.org/10.30656/jsmi.v4i2.2765.

R. Afrizal and U. Linarti, ‘A Stochastic Integrated Inventory Model Single Supplier-Single Retailer in Periodic Review with Losing Flexibility Cost’, J. Ilm. Tek. Ind., vol. 21, no. 2, pp. 301–310, Dec. 2022, doi: https://doi.org/10.23917/jiti.v21i2.19752.

V. Gružauskas, E. Gimžauskienė, and V. Navickas, ‘Forecasting accuracy influence on logistics clusters activities: The case of the food industry’, J. Clean. Prod., vol. 240, p. 118225, Dec. 2019, doi: https://doi.org/10.1016/j.jclepro.2019.118225.

J. Huber, A. Gossmann, and H. Stuckenschmidt, ‘Cluster-based hierarchical demand forecasting for perishable goods’, Expert Syst. Appl., vol. 76, pp. 140–151, Jun. 2017, doi: https://doi.org/10.1016/j.eswa.2017.01.022.

D. Hrabec, L. M. Hvattum, and A. Hoff, ‘The value of integrated planning for production, inventory, and routing decisions: A systematic review and meta-analysis’, Int. J. Prod. Econ., vol. 248, no. September 2020, p. 108468, Jun. 2022, doi: https://doi.org/10.1016/j.ijpe.2022.108468.

A. D. Saputro, D. Van de Walle, and K. Dewettinck, ‘Palm Sap Sugar: A Review’, Sugar Tech, vol. 21, no. 6, pp. 862–867, Dec. 2019, doi: https://doi.org/10.1007/s12355-019-00743-8.

N. K. Wardati and M. ER, ‘The Impact of Social Media Usage on the Sales Process in Small and Medium Enterprises (SMEs): A Systematic Literature Review’, Procedia Comput. Sci., vol. 161, pp. 976–983, 2019, doi: https://doi.org/10.1016/j.procs.2019.11.207.

S. Wardah and M. Yani, ‘Spatial-based multicriteria decision-making model for coconut sugar agro-industry location selection: A case study at Indragiri Hilir District, Riau Province, Indonesia’, IOP Conf. Ser. Earth Environ. Sci., vol. 1063, no. 1, p. 012041, Jul. 2022, doi: https://doi.org/10.1088/1755-1315/1063/1/012041.

S. Wardah and T. Baidawi, ‘Development of Fuzzy Analytic Hierarchy Process(F-AHP) For The Selection Of Alternative New Product Development Ideas In Coconut Downstream Agroindustry’, J. Phys. Conf. Ser., vol. 1641, no. 1, p. 012024, Nov. 2020, doi: https://doi.org/10.1088/1742-6596/1641/1/012024.

J. Fattah, L. Ezzine, Z. Aman, H. El Moussami, and A. Lachhab, ‘Forecasting of demand using ARIMA model’, Int. J. Eng. Bus. Manag., vol. 10, p. 184797901880867, Jan. 2018, doi: https://doi.org/10.1177/1847979018808673.

P. Mishra et al., ‘State of the art in total pulse production in major states of India using ARIMA techniques’, Curr. Res. Food Sci., vol. 4, pp. 800–806, 2021, doi: https://doi.org/10.1016/j.crfs.2021.10.009.

H. Yu, N. Kim, S. S. Kim, C. Chu, and M. Kee, ‘Forecasting the Number of Human Immunodeficiency Virus Infections in the Korean Population Using the Autoregressive Integrated Moving Average Model’, Osong Public Heal. Res. Perspect., vol. 4, no. 6, pp. 358–362, Dec. 2013, doi: https://doi.org/10.1016/j.phrp.2013.10.009.

C. J. Lynch and R. Gore, ‘Application of one-, three-, and seven-day forecasts during early onset on the COVID-19 epidemic dataset using moving average, autoregressive, autoregressive moving average, autoregressive integrated moving average, and naïve forecasting methods’, Data Br., vol. 35, p. 106759, Apr. 2021, doi: https://doi.org/10.1016/j.dib.2021.106759.

M. Ala’raj, M. Majdalawieh, and N. Nizamuddin, ‘Modeling and forecasting of COVID-19 using a hybrid dynamic model based on SEIRD with ARIMA corrections’, Infect. Dis. Model., vol. 6, pp. 98–111, 2021, doi: https://doi.org/10.1016/j.idm.2020.11.007.

F. A. Chyon, ‘Time series analysis and predicting COVID-19 affected patients by ARIMA model using machine learning’, J. Virol. Methods, vol. 301, 2022, doi: https://doi.org/10.1016/j.jviromet.2021.114433.

J. Sun, ‘Forecasting COVID-19 pandemic in Alberta, Canada using modified ARIMA models’, Comput. Methods Programs Biomed. Updat., vol. 1, no. September, p. 100029, 2021, doi: https://doi.org/10.1016/j.cmpbup.2021.100029.

C. Çalışkan, ‘The economic order quantity model with compounding’, Omega, vol. 102, p. 102307, Jul. 2021, doi: https://doi.org/10.1016/j.omega.2020.102307.

R. Patriarca, G. Di Gravio, F. Costantino, and M. Tronci, ‘EOQ inventory model for perishable products under uncertainty’, Prod. Eng., vol. 14, no. 5–6, pp. 601–612, Dec. 2020, doi: https://doi.org/10.1007/s11740-020-00986-5.

S. Sanni, Z. Jovanoski, and H. S. Sidhu, ‘An economic order quantity model with reverse logistics program’, Oper. Res. Perspect., vol. 7, no. November 2019, p. 100133, 2020, doi: https://doi.org/10.1016/j.orp.2019.100133.

G. Dobson, ‘An EOQ model for perishable goods with age-dependent demand rate’, Eur. J. Oper. Res., vol. 257, no. 1, pp. 84–88, 2017, doi: https://doi.org/10.1016/j.ejor.2016.06.073.

R. D. S. Díaz, C. D. Paternina-Arboleda, J. L. Martínez-Flores, and M. A. Jimenez-Barros, ‘Economic order quantity for perishables with decreasing willingness to purchase during their life cycle’, Oper. Res. Perspect., vol. 7, no. February, p. 100146, 2020, doi: https://doi.org/10.1016/j.orp.2020.100146.

L. Zhao, L. Zhang, J. You, and C. Duan., ‘Optimal pricing and pre-sale policies for perishable product in an EOQ inventory model’, IEEE Access, vol. PP, no. c, pp. 1–8, 2019, doi: https://doi.org/10.1109/ACCESS.2019.2916582.

S. Zeng et al., ‘EOQ for perishable goods: Modification of wilson’s model for food retailers’, Technol. Econ. Dev. Econ., vol. 25, no. 6, pp. 1413–1432, Dec. 2019, doi: https://doi.org/10.3846/tede.2019.11330.

S. S. Sana, ‘An EOQ model for stochastic demand for limited capacity of own warehouse’, Ann. Oper. Res., vol. 233, no. 1, pp. 383–399, Oct. 2015, doi: https://doi.org/10.1007/s10479-013-1510-5.

H. Liao and L. Li, ‘Environmental sustainability EOQ model for closed-loop supply chain under market uncertainty: A case study of printer remanufacturing’, Comput. Ind. Eng., vol. 151, no. June 2019, p. 106525, Jan. 2021, doi: https://doi.org/10.1016/j.cie.2020.106525.

R. Li, H. L. Yang, Y. Shi, J. T. Teng, and K. K. Lai, ‘EOQ-based pricing and customer credit decisions under general supplier payments’, Eur. J. Oper. Res., vol. 289, no. 2, pp. 652–665, 2021, doi: https://doi.org/10.1016/j.ejor.2020.07.035.

X. Zhang, J. Wang, and Y. Gao, ‘A hybrid short-term electricity price forecasting framework: Cuckoo search-based feature selection with singular spectrum analysis and SVM’, Energy Econ., vol. 81, pp. 899–913, 2019, doi: https://doi.org/10.1016/j.eneco.2019.05.026.

A. S. Ahmar, M. Botto-Tobar, A. Rahman, and R. Hidayat, ‘Forecasting the Value of Oil and Gas Exports in Indonesia using ARIMA Box-Jenkins’, JINAV J. Inf. Vis., vol. 3, no. 1, pp. 35–42, Jul. 2022, doi: https://doi.org/10.35877/454RI.jinav260.

W. Sri Rahayu, P. Tri Juwono, and W. Soetopo, ‘Discharge prediction of Amprong river using the ARIMA (autoregressive integrated moving average) model’, IOP Conf. Ser. Earth Environ. Sci., vol. 437, no. 1, p. 012032, Feb. 2020, doi: https://doi.org/10.1088/1755-1315/437/1/012032.

R. Siddiqui, M. Azmat, S. Ahmed, and S. Kummer, ‘A hybrid demand forecasting model for greater forecasting accuracy: the case of the pharmaceutical industry’, Supply Chain Forum An Int. J., vol. 23, no. 2, pp. 124–134, Apr. 2022, doi: https://doi.org/10.1080/16258312.2021.1967081.

S. I. Alzahrani, I. A. Aljamaan, and E. A. Al-Fakih, ‘Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions’, J. Infect. Public Health, vol. 13, no. 7, pp. 914–919, Jul. 2020, doi: https://doi.org/10.1016/j.jiph.2020.06.001.

A. K. Sahai, N. Rath, V. Sood, and M. P. Singh, ‘ARIMA modelling & forecasting of COVID-19 in top five affected countries’, Diabetes Metab. Syndr. Clin. Res. Rev., vol. 14, no. 5, pp. 1419–1427, Sep. 2020, doi: https://doi.org/10.1016/j.dsx.2020.07.042.

S. A. Sarkodie, ‘Estimating Ghana’s electricity consumption by 2030: An ARIMA forecast’, Energy Sources, Part B Econ. Planning, Policy, vol. 12, no. 10, pp. 936–944, Oct. 2017, doi: https://doi.org/10.1080/15567249.2017.1327993.

S. Atique, S. Noureen, V. Roy, V. Subburaj, S. Bayne, and J. Macfie, ‘Forecasting of total daily solar energy generation using ARIMA: A case study’, in 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), Jan. 2019, no. January, pp. 0114–0119, doi: https://doi.org/10.1109/CCWC.2019.8666481.

S. Chandra Das, A. M. Zidan, A. K. Manna, A. A. Shaikh, and A. K. Bhunia, ‘An application of preservation technology in inventory control system with price dependent demand and partial backlogging’, Alexandria Eng. J., vol. 59, no. 3, pp. 1359–1369, Jun. 2020, doi: https://doi.org/10.1016/j.aej.2020.03.006.

K. Rana and S. R. Singh, ‘A sustainable production inventory model for growing items with trade credit policy under partial backlogging’, Int. J. Adv. Oper. Manag., vol. 15, no. 1, pp. 64–81, 2023, doi: https://doi.org/10.1504/IJAOM.2023.10054672.

Downloads

Published

2023-12-01

How to Cite

[1]
S. Wardah, N. . Nurhasanah, and W. . Sudarwati, “Integration models of demand forecasting and inventory control for coconut sugar using the ARIMA and EOQ modification methods”, j. sist. manaj. ind., vol. 7, no. 2, pp. 127–138, Dec. 2023.

Issue

Vol. 7 No. 2 (2023): December

Section

Research Article

License

All articles in Jurnal Sistem dan Manajemen Industri can be disseminated provided they include the identity of the article and the source of the article Jurnal Sistem dan Manajemen Industri. The publisher is not responsible for the contents of the article. The content of the article is the sole responsibility of the author

Creative Commons License

Jurnal Sistem dan Manajemen Industri is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Crossref
Scopus
Google Scholar
Europe PMC