Optimizing Production Process through Production Planning and Inventory Management in Motorcycle Chains Manufacturer


  • Shelvy Kurniawan Bina Nusantara University
  • Steven Sanjaya Raphaeli Bina Nusantara University




production process, production planning, inventory management


Based on the data, there were still shortages of production from year to year and demand were
unstable in motorcycle chains manufacturer in Indonesia. To overcome these problems, the purpose of this research was to make production planning and inventory control consisting of forecasting, aggregate planning, Master Production Schedule (MPS), and Material Requirements
Planning (MRP). Forecasting used the additive decomposition method (average of all data), multiplicative decomposition (centered on moving average), and winter method (additive and multiplicative). Aggregate planning used chase strategy, level strategy, and transportation model. Moreover, MRP used lot for lot, Economic Order Quantity (EOQ), and Periodic Order Quantity (POQ) methods. The test shows several results. First, the best forecasting is additive decomposition (average of all data) with MAD value of 3.033,57, MSE with 13.590.490,
and MAPE with 10,083%. Second, the best aggregate planning is transportation model with the total cost of Rp7.708.398.390,00. Last, the best MRP method is the lot for lot with total cost Rp7.162.567.653,00.


Plum Analytics

Author Biographies

Shelvy Kurniawan, Bina Nusantara University

Management Department

Steven Sanjaya Raphaeli, Bina Nusantara University

Management Department


Akpinar, M., & Yumusak, N. (2016). Time series decomposition of natural gas consumption. International Journal of Advances in Science Engineering and Technology, 4(2), 113-117.

Aras, S., & Gülay, E. (2017). A new consensus between the mean and median combination methods to improve forecasting accuracy. Serbian Journal of Management, 12(2), 217-236.

Chen, S. P., & Huang, W. L. (2010). A membership function approach for aggregate production planning problems in fuzzy environments. International Journal of Production Research, 48(23), 7003-7023.

Chopra, S., & Meindl, P. (2010). Supply Chain Management: Strategy, planning, and operation (4th ed.). Upper Saddle River, New Jersey: Pearson Education, Inc.

Dinesh, E. D., Arun, A. P., & Pranav, R. (2014). Material requirements planning for automobile service plant. International Journal of Innovative Research in Science, Engineering and Technology (IJIRSET), 3(3), 1171-1175.

Gharakhani, D. (2011). Optimization of material requirement planning by goal programming model. Asian Journal of Management Research, 2(1), 297-317.

Heizer, J., Render, B., & Munson, C. (2017). Operations management: Sustainability and Supply Chain Management (12th ed.) United States of America: Pearson Education, Inc.

Islam, M. S., Ripon Kumar Saha, M., & Mahbubur Rahman, A. M. (2013). Development of Material Requirements Planning (MRP) software with C language. Global Journal of Computer Science and Technology, 13(3), 13-22.

Rakićević, Z., & Vujošević, M. (2015). Focus forecasting in supply chain: The case study of fast moving consumer goods company in Serbia. Serbian Journal of Management, 10(1), 3-17.

Reid, R. D., & Sanders, N. R. (2011). Operation management (4th ed.). New York: John Willey & Sons, Inc.

Sekaran, U., & Bougie, R. (2013). Research methods for business: A skill-building approach (6th ed.). Chichester: John Wiley & Sons Ltd.

Stevenson, W. J. (2015). Operations management (12th ed.). New York: McGraw-Hill Education.






Abstract 4394  .
PDF downloaded 612  .