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Abstract

The aims of this study are: (1) to identify and analyze the estimated demand for chicken and goat satay products in the MSME of Sate Madura Cak Kholil; and (2) to find out the stock safety to overcome the surge in product demand at the MSME of Sate Madura Cak Kholil. The research method used is a descriptive method with a quantitative approach. The sample was determined based on a non-probability sampling technique using the purposive sampling method. The data used in this study included primary data and secondary data, namely data on supply and demand for the MSME of Sate Madura Cak Kholil. The results of the study indicate that the application of collaborative planning, forecasting, and replenish menthasan effect on reducing the bullwhip effect in the MSME of Sate Madura Cak Kholil.

Keywords

bullwhip effect MSME CPFR

Article Details

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Sjurahudin, H., & Vikaliana, R. (2022). Implementation of Collaborative, Planning, Forecasting and Replenishment (CPFR) to Reduce the Bullwhip Effect in MSME Sate Madura Cak Kholil. Ilomata International Journal of Management, 3(1), 120-130. https://doi.org/10.52728/ijjm.v3i1.418

References

  1. Bretas, V. P. G., & Alon, I. (2021). Franchising research on emerging markets: Bibliometric and content analyses. Journal of Business Research, 133, 51–65. https://doi.org/10.1016/j.jbusres.2021.04.067
  2. Chen, T., & Romanowski, R. (2014). Forecasting the productivity of a virtual enterprise by agent-based fuzzy collaborative intelligence—With Facebook as an example. Applied Soft Computing, 24, 511–521. https://doi.org/10.1016/j.asoc.2014.08.003
  3. Dai, J., Xie, L., & Chu, Z. (2021). Developing sustainable supply chain management: The interplay of institutional pressures and sustainability capabilities. Sustainable Production and Consumption, 28, 254–268. https://doi.org/10.1016/j.spc.2021.04.017
  4. Desai, A., & Rai, S. (2016). Knowledge Management for Downstream Supply Chain Management of Indian Public Sector Oil Companies. Procedia Computer Science, 79, 1021–1028. https://doi.org/10.1016/j.procs.2016.03.129
  5. Eksoz, C., Mansouri, S. A., & Bourlakis, M. (2014). Collaborative forecasting in the food supply chain: A conceptual framework. International Journal of Production Economics, 158, 120–135. https://doi.org/10.1016/j.ijpe.2014.07.031
  6. Fildes, R., & Goodwin, P. (2021). Stability in the inefficient use of forecasting systems: A case study in a supply chain company. International Journal of Forecasting, 37(2), 1031–1046. https://doi.org/10.1016/j.ijforecast.2020.11.004
  7. Galbreth, M. R., Kurtuluş, M., & Shor, M. (2015). How collaborative forecasting can reduce forecast accuracy. Operations Research Letters, 43(4), 349–353. https://doi.org/10.1016/j.orl.2015.04.006
  8. Gao, L. (2015). Collaborative forecasting, inventory hedging and contract coordination in dynamic supply risk management. European Journal of Operational Research, 245(1), 133–145. https://doi.org/10.1016/j.ejor.2015.02.048
  9. Ghozali, I. (2016). Aplikasi Analisis Multivariete dengan Program IBM SPSS 23 (I. Ghozali (ed.); 8th ed.). Badan Penerbit Universitas Diponegoro. http://kin.perpusnas.go.id/DisplayData.aspx?pId=218217&pRegionCode=UN11MAR&pClientId=112
  10. Guritno, A. D., Fujianti, R., & Kusumasari, D. (2015). Assessment of the Supply Chain Factors and Classification of Inventory Management in Suppliers’ Level of Fresh Vegetables. Agriculture and Agricultural Science Procedia, 3, 51–55. https://doi.org/10.1016/j.aaspro.2015.01.012
  11. Hill, C. A., Zhang, G. P., & Miller, K. E. (2018). Collaborative planning, forecasting, and replenishment & firm performance: An empirical evaluation. International Journal of Production Economics, 196, 12–23. https://doi.org/10.1016/j.ijpe.2017.11.012
  12. Hoogstra-Klein, M. A., & Meijboom, K. (2021). A qualitative exploration of the wood product supply chain – investigating the possibilities and desirability of an increased demand orientation. Forest Policy and Economics, 133, 102606. https://doi.org/10.1016/j.forpol.2021.102606
  13. Jabbour, C. J. C., Fiorini, P. D. C., Ndubisi, N. O., Queiroz, M. M., & Piato, É. L. (2020). Digitally-enabled sustainable supply chains in the 21st century: A review and a research agenda. Science of The Total Environment, 725(138177), 1=14. https://doi.org/10.1016/j.scitotenv.2020.138177
  14. Karimi, M., & Zaerpour, N. (2021). Put your money where your forecast is: Supply chain collaborative forecasting with cost-function-based prediction markets. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2021.09.013
  15. Lengkey, L. M. E., Kawengian, D., & Marentek, E. (2014). Peranan Komunikasi Pemasaran Dalam Meningkatkan Minat Pengguna Iklan Di Harian Komentar Manado. Jurnal Acta Diurna Komunikasi Universitas Sam Ratulangi Manado, III(3), 1–14. https://ejournal.unsrat.ac.id/index.php/actadiurnakomunikasi/article/view/5689
  16. Makarius, E. E., & Srinivasan, M. (2017). Addressing skills mismatch: Utilizing talent supply chain management to enhance collaboration between companies and talent suppliers. Business Horizons, 60(4), 495–505. https://doi.org/10.1016/j.bushor.2017.03.007
  17. Peng, M., Peng, Y., & Chen, H. (2014). Post-seismic supply chain risk management: A system dynamics disruption analysis approach for inventory and logistics planning. Computers & Operations Research, 42, 14–24. https://doi.org/10.1016/j.cor.2013.03.003
  18. Pujawan, I. N., & Mahendrawathi. (2017). Supply Chain Managemen (3rd ed.). ANDI. https://opac.perpusnas.go.id/DetailOpac.aspx?id=1058843
  19. Ramanathan, U. (2014). Performance of supply chain collaboration – A simulation study. Expert Systems with Applications, 41(1), 210–220. https://doi.org/10.1016/j.eswa.2013.07.022
  20. Ridwan, A., Ilhami, M. A., & Emeralda, I. (2012). Analisis Nilai Indeks Bullwhip Effect Pada Sistem Supply Chain dan Rancangan Perbaikan dengan Pendekatan Simulasi (Studi Kasus di PT.XYZ). Teknika: Jurnal Sains Dan Teknologi, 8(1), 1–12. https://doi.org/10.36055/tjst.v9i1.6681
  21. Saptaria, L. (2017). Analisis Peramalan Permintaan Produk Nata De Coco Untuk Mendukung Perencanaan Dan Pengendalian Produksi Dalam Supply Chain Dengan Model CPFR (Collaborative Planning, Forecasting, and Replenishment). Jurnal Nusantara Aplikasi Manajemen Bisnis, 2(2), 130. https://doi.org/10.29407/nusamba.v2i2.924
  22. Shoukohyar, S., & Seddigh, M. R. (2020). Uncovering the dark and bright sides of implementing collaborative forecasting throughout sustainable supply chains: An exploratory approach. Technological Forecasting and Social Change, 158, 120059. https://doi.org/10.1016/j.techfore.2020.120059
  23. Sugiyono. (2019). Metode Penelitian Kuantitatif Kualitatif dan R&D (I). Alfabeta. https://cvalfabeta.com/product/metode-penelitian-kuantitatif-kualitatif-dan-rd-mpkk/
  24. Toufaily, E., Ricard, L., & Perrien, J. (2013). Customer loyalty to a commercial website: Descriptive meta-analysis of the empirical literature and proposal of an integrative model. Journal of Business Research, 66(9), 1436–1447. https://doi.org/10.1016/j.jbusres.2012.05.011
  25. Walker, A. M., Vermeulen, W. J. V., Simboli, A., & Raggi, A. (2021). Sustainability assessment in circular inter-firm networks: An integrated framework of industrial ecology and circular supply chain management approaches. Journal of Cleaner Production, 286(2), 125457. https://doi.org/10.1016/j.jclepro.2020.125457
  26. Wang, Y., Graziotin, D., Kriso, S., & Wagner, S. (2019). Communication channels in safety analysis: An industrial exploratory case study. Journal of Systems and Software, 153(3), 135–151. https://doi.org/10.1016/j.jss.2019.04.004
  27. Wong, C. W. Y., Lirn, T.-C., Yang, C.-C., & Shang, K.-C. (2020). Supply chain and external conditions under which supply chain resilience pays: An organizational information processing theorization. International Journal of Production Economics, 226(2), 107610. https://doi.org/10.1016/j.ijpe.2019.107610
  28. Yao, Y., Kohli, R., Sherer, S. A., & Cederlund, J. (2013). Learning curves in collaborative planning, forecasting, and replenishment (CPFR) information systems: An empirical analysis from a mobile phone manufacturer. Journal of Operations Management, 31(6), 285–297. https://doi.org/10.1016/j.jom.2013.07.004
  29. Yuliana, P. E., & Rahayu, S. (2019). Analisis Pengaruh Penerapan Metode DRP Terhadap Bullwhip Effect Pada Rantai Suplai. Journal of Information System,Graphics, Hospitality and Technology, 1(02), 42–46. https://doi.org/10.37823/insight.v1i02.46
  30. Zaid, A. A., Jaaron, A. A. M., & Talib Bon, A. (2018). The impact of green human resource management and green supply chain management practices on sustainable performance: An empirical study. Journal of Cleaner Production, 204, 965–979. https://doi.org/10.1016/j.jclepro.2018.09.062
  31. Zhan, J., Sun, B., & Zhang, X. (2020). PF-TOPSIS method based on CPFRS models: An application to unconventional emergency events. Computers & Industrial Engineering, 139, 106192. https://doi.org/10.1016/j.cie.2019.106192

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