mata788 PERMATA123 PERJAKA188 slot88resmi slot88resmi hati108 login Situs toto warga123 RAJAINDO permata123 Akunbos PERMATA123 jokijp slot88resmi slot88jp https://kl108gambar.com https://kl108gambar.com BMW777 Slot gacor seni108 hoki88 seni108

Vol. 2 No. 1 (2024)
Articles

Optimizing Inventory Management of MFD Studio To Reduce The High Lost Sales

PDF
  • Raka Aditya Prayoga,
  • Nur Budi Mulyono
Raka Aditya Prayoga
Institut Teknologi Bandung
Nur Budi Mulyono
Institut Teknologi Bandung
DOI: https://doi.org/10.58229/jims.v2i1.135

Published 2024-03-07

Keywords

  • ARIMA model,
  • Demand forecasting,
  • Holt's model,
  • Lost sales,
  • Winter's model

How to Cite

Raka Aditya Prayoga, & Nur Budi Mulyono. (2024). Optimizing Inventory Management of MFD Studio To Reduce The High Lost Sales. Journal Integration of Management Studies, 2(1), 49–60. https://doi.org/10.58229/jims.v2i1.135

Abstract

This research delves into optimizing inventory management at MFD Studio by implementing demand forecasting to mitigate lost sales. Notably, the company has encountered a significant loss of sales, approximately 31.26% of the revenue generated by their flagship product, Outer, which has consistently held the position of the best-seller from 2021 to 2023, contributing approximately 60% to MFD Studio's overall product line during this period. The research aims to enhance inventory management efficiency by employing demand forecasting techniques. The methodology includes a thorough literature review, analysis of root causes, and conceptual framework development. The findings underscore the substantial impact of demand forecasting on inventory management, leading to a noteworthy reduction in lost sales. The study advocates for adopting a quantitative approach to demand forecasting, explicitly endorsing the ARIMA, Holt, and Winter models. Notably, the ARIMA model stands out with the lowest error, boasting a 0.0059 RMSE value, 0.0025 MAE value, and 0.0363 MAPE value. The forecast generated by the ARIMA model is anticipated to diminish the likelihood of future lost sales to 5.5%, representing a substantial decrease from the initial 31.26%. In conclusion, this research underscores the pivotal role of demand forecasting as a crucial tool for businesses, particularly in similar industries, to enhance inventory management and curtail lost sales. The practical recommendations contribute significantly to inventory management, offering actionable insights for businesses seeking to optimize their inventory processes.

PDF

References

  1. Bertola, P., & Teunissen, J. (2018). Fashion 4.0. Innovating fashion industry through digital transformation. Research Journal of Textile and Apparel, 22(4), 352–369. https://doi.org/10.1108/rjta-03-2018-0023
  2. Bharatpur, A. (2022). A LITERATURE REVIEW ON TIME SERIES FORECASTING METHODS.
  3. Chopra, A. (2019). AI in Supply & Procurement. 2019 Amity International Conference on Artificial Intelligence (AICAI). https://doi.org/10.1109/aicai.2019.8701357
  4. Chopra, S. (2020). Supply chain management : Strategy, planning and operation (7th ed.). Pearson Education.
  5. COOPER, D., & Schilnder, P. (2021). Business Research Methods. MCGRAW-HILL US HIGHER ED.
  6. Data Industri. (2023). Pertumbuhan Industri Tekstil dan Pakaian Jadi, 2011 - 2022. Data Industri. https://www.dataindustri.com/produk/tren-data-pertumbuhan-industri-tekstil-dan-pakaian-jadi/
  7. Fairlie, R., & Fossen, F. M. (2021). The early impacts of the COVID-19 pandemic on business sales. Small Business Economics, 1(1), 1853–1864. https://doi.org/10.1007/s11187-021-00479-4
  8. Fildes, R., Ma, S., & Kolassa, S. (2019). Retail forecasting: Research and Practice. International Journal of Forecasting, 38(4). https://doi.org/10.1016/j.ijforecast.2019.06.004
  9. Heizer, J., & Render, B. (2014). O P E R A T I O N S M A N A G E M E N T Sustainability and Supply Chain Management HEIZER J A Y RENDER B A R R Y.
  10. Ibrahima, C. S., Xue, J., & Gueye, T. (2021). Inventory Management and Demand Forecasting Improvement of a Forecasting Model Based on Artificial Neural Networks. Journal of Management Science & Engineering Research, 4(2). https://doi.org/10.30564/jmser.v4i2.3242
  11. Kandampully, J., Zhang, T. (Christina), & Bilgihan, A. (2015). Customer loyalty: a review and future directions with a special focus on the hospitality industry. International Journal of Contemporary Hospitality Management, 27(3), 379–414. Emerald. https://doi.org/10.1108/ijchm-03-2014-0151
  12. Lalou, P., Ponis, S. T., & Efthymiou, O. K. (2020). Demand Forecasting of Retail Sales Using Data Analytics and Statistical Programming. Management & Marketing. Challenges for the Knowledge Society, 15(2), 186–202. https://doi.org/10.2478/mmcks-2020-0012
  13. Media, K. C. (2022, February 17). Ini Tren Fashion di 2022 yang Dipengaruhi oleh Perkembangan Teknologi. KOMPAS.com. https://www.kompas.com/parapuan/read/533146918/ini-tren-fashion-di-2022-yang-dipengaruhi-oleh-perkembangan-teknologi
  14. Minner, S., & Kiesmüller, G. P. (2012). Dynamic product acquisition in closed loop supply chains. International Journal of Production Research, 50(11), 2836–2851. https://doi.org/10.1080/00207543.2010.539280
  15. Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Ben Taieb, S., Bergmeir, C., Bessa, R. J., Bijak, J., Boylan, J. E., Browell, J., Carnevale, C., Castle, J. L., Cirillo, P., Clements, M. P., Cordeiro, C., Cyrino Oliveira, F. L., De Baets, S., Dokumentov, A., & Ellison, J. (2022). Forecasting: Theory and practice. International Journal of Forecasting, 38(3). sciencedirect. https://doi.org/10.1016/j.ijforecast.2021.11.001
  16. Raghuvanshi, J., Agrawal, R., & Ghosh, P. K. (2017). Analysis of Barriers to Women Entrepreneurship: The DEMATEL Approach. The Journal of Entrepreneurship, 26(2), 220–238. https://doi.org/10.1177/0971355717708848
  17. Santos, A., & Moustafa, G. (2016). Female entrepreneurship in developing countries -Barriers and Motivation Case Study: Egypt and Brazil. https://kth.diva-portal.org/smash/get/diva2:949759/FULLTEXT01.pdf
  18. Sarasi, V., Chaerudin, I., Nugroho, D., Satmoko, & Zahra, D. (2023). ANALYSIS OF HOLT-WINTERS AND ARIMA MODEL IN MUSLIMAH SCARF DEMAND FORECASTING. Jurnal Bisnis Dan Manajemen, 24(1), 59–69.
  19. ?en, A. (2008). The US fashion industry: A supply chain review. International Journal of Production Economics, 114(2), 571–593. https://doi.org/10.1016/j.ijpe.2007.05.022
  20. Stretton, P. (2021). Beyond root cause analysis: How variation analysis can provide a deeper understanding of causation in complex adaptive systems. Journal of Patient Safety and Risk Management, 26(2), 74–80. https://doi.org/10.1177/2516043521992908
  21. Swaminathan, K., & Venkitasubramony, R. (2023). Demand forecasting for fashion products: A systematic review. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2023.02.005
  22. Tambunan, T. (2019). Recent evidence of the development of micro, small and medium enterprises in Indonesia. Journal of Global Entrepreneurship Research, 9(1). https://doi.org/10.1186/s40497-018-0140-4
  23. Victor, V., Syarfa, N., Nathan, R., & Hanaysha, J. (2018). Use of Click and Collect E-tailing Services among Urban Consumers. Amity Journal of Marketing AJM ADMAA Amity Journal of Marketing, 3(2), 1–16. https://amity.edu/UserFiles/admaa/b9dbbPaper%201.pdf
  24. Vo, T. T. B. C., Le, P. H., Nguyen, N. T., Nguyen, T. L. T., & Do, N. H. (2021). Demand Forecasting and Inventory Prediction for Apparel Product using the ARIMA and Fuzzy EPQ Model. Journal of Engineering Science and Technology Review, 14(2), 80–89. https://doi.org/10.25103/jestr.142.11