Proposed Improvement of Demand Forecast Accuracy using Six Sigma DMAIC in PT XYZ

I Putu Edward Narayana, Yuanita Handayati

Abstract


This research aimed to optimizing strategies for PT XYZ, specifically focusing on bridging the gaps between demand forecasts and actual market demand of lubricants product, with a strong emphasis on the Business-to-Business (B2B) segment. B2B introduces unique challenges, necessitating a specialized approach. Employing the Six Sigma DMAIC methodology, a comprehensive analysis is conducted to uncover deficiencies in current sales forecasting methods. The Holt-Winter method is then applied to offer robust remedies, effectively minimizing complexities such as seasonal patterns and market trends. Drawing on historical data from the B2B lubricants segment served by PT XYZ, the analysis establishes a solid empirical foundation. This comprehensive forecasting approach positions PT XYZ as a market leader, equipped to anticipate and pre-empt market fluctuations. The application of the Holt-Winter time series method not only significantly enhances forecasting accuracy but also mitigates substantial risks linked to inventory imbalances concerning demand. The implementation of such a robust forecasting framework stands to elevate the operational efficiency of PT XYZ, with a paramount commitment to maintaining customer satisfaction in the dynamic business landscape.


Keywords


demand forecasting; lubricants product; six sigma DMAIC, time series forecasting

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References


Acar, Y., & Gardner, E. S. (2012). Forecasting method selection in a global supply chain. In-ternational Journal of Forecasting, 28(4), 842–848. https://doi.org/10.1016/j.ijforecast.2011.11.003

Byrne, B., McDermott, O., & Noonan, J. (2021). Applying lean six sigma methodology to a pharmaceutical manufacturing facility: A case study. Processes, 9(3). https://doi.org/10.3390/pr9030550

Dou, Z., Sun, Y., Zhang, Y., Wang, T., Wu, C., & Fan, S. (2021). Regional manufacturing indus-try demand forecasting: A deep learning ap-proach. Applied Sciences (Switzerland), 11(13). https://doi.org/10.3390/app11136199

Graafmans, T., Turetken, O., Poppelaars, H., & Fahland, D. (2021). Process Mining for Six Sigma: A Guideline and Tool Support. Busi-ness and Information Systems Engineering, 63(3), 277–300. https://doi.org/10.1007/s12599-020-00649-w

Kline. (2022). MAJOR TRENDS IN LUBRICANTS: HOW THE LANDSCAPE IS SHIFTING IN FAVOR OF ILMAs.

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., Ci-rillo, P., Clements, M. P., Cordeiro, C., Cyrino Oliveira, F. L., De Baets, S., Doku-mentov, A., … Ziel, F. (2022). Forecasting: theory and practice. In International Journal of Forecasting (Vol. 38, Issue 3). https://doi.org/10.1016/j.ijforecast.2021.11.001

Rifqi, H., Zamma, A., Souda, S. B., & Hansali, M. (2021). Lean manufacturing implementation through DMAIC approach: A case study in the automotive industry. Quality Innovation Prosperity, 25(2). https://doi.org/10.12776/qip.v25i2.1576

Rožanec, J. M., Kažič, B., Škrjanc, M., Fortuna, B., & Mladenić, D. (2021). Automotive OEM de-mand forecasting: A comparative study of forecasting algorithms and strategies. Applied Sciences (Switzerland), 11(15). https://doi.org/10.3390/app11156787

Salais-Fierro, T. E., Saucedo-Martinez, J. A., Rodri-guez-Aguilar, R., & Vela-Haro, J. M. (2020). Demand prediction using a soft-computing approach: A case study of automotive indus-try. Applied Sciences (Switzerland), 10(3). https://doi.org/10.3390/app10030829




DOI: https://doi.org/10.21107/jsmb.v10i2.23120

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Copyright (c) 2023 I Putu Edward Narayana, Yuanita Handayati

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Jurnal Studi Manajemen dan Bisnis
by Universitas Trunojoyo Madura is licensed under a Creative Commons Attribution 4.0 International License.