Abstract :

Bio Farma as the only vaccine manufacturer in Indonesia, divides its marketing area for vaccine distribution throughout Indonesia, represented by marketing representatives in 34 provinces in Indonesia. Segmentation based on geography for vaccine products is divided into five regions, one of the region is Papua. The forecast method used in Bio Farma for Papua Area is still manual. Marketers order vaccines from central Bio Farma and make forecasts if product stock is empty. If the product is empty, a buffer stock will be created. With the buffer stock system that has been implemented so far, there are often problems with excess product which causes the product to expire. From the data, the total loss due to overstock is Rp. 14,161,693 in 2022. If consumer demand falls short of expectations, it will definitely have an impact on the manufacturing of high inventory value, even leading to lost opportunities for sales. That is the fundamental issue with this research. The goal of this research is to identify the possible causes for the overstock and to identify a suitable solution for those issues.

There are five things consist of root causes the problem; (1) fully manual forecasting, (2) low sales forecast accuracy, (3) sales forecasting based on sales of the last one or two months, (4) lack of employee knowledge about sales forecasting, (5) significant gap between target and actual sales. The alternate strategy recommended is to provide a forecasting technique that is suitable for the company. Different forecasting techniques were selected to be compared in order to determine which may be used to improve forecasting accuracy. The forecasting techniques that are employed are the 3-month simple moving average, the 5-month simple moving average, the 3-month weighted moving average, the 5-month weighted moving average, and exponential smoothing. By using the MAD and MAPE measurement tools, exponential smoothing showed the most acceptable accuracy result.

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Keywords :

Accuracy measurement, Demand forecast, Forecast accuracy, Sales forecasting, Time series.

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References :

1. Coccia, M. (2018). The Fishbone Diagram to Identify, Systematize and Analyze The Sources of General Purpose Technologies. https://ssrn.com/abstract=3100011Electroniccopyavailableat:https://ssrn.com/abstract=3100011Electroniccopyavailableat:https://ssrn.com/abstract=3100011
2. Dayanada, Don. (2002). Capital budgeting: Financial Appraisal of Investment Projects. Cambridge University Press.
3. Jacobs, F. R., & Chase, Richard. B. (2018). OPERATIONS AND SUPPLY CHAIN MANAGEMENT.
4. Kahn, K. B. (2014). Solving The Problems of New Product Forecasting. Business Horizons, 57(5), 607–615. https://doi.org/10.1016/j.bushor.2014.05.003
5. Kallio, H., Pietilä, A. M., Johnson, M., & Kangasniemi, M. (2016). Systematic methodological review: developing a framework for a qualitative semi-structured interview guide. In Journal of Advanced Nursing (Vol. 72, Issue 12, pp. 2954–2965). Blackwell Publishing Ltd. https://doi.org/10.1111/jan.13031
6. Martinovic, J., & Damnjanovic, V. (2006). The Sales Forecasting Techniques.
7. Mentzer, John. T., & Moon, Mark. A. (2005). Sales Forecasting Management: A Demand Management Approach. International Journal of Forecasting, 22(4), 821. https://doi.org/10.1016/j.ijforecast.2005.12.001
8. Miles, M. B., & Huberman, A. M. (1994). An Expanded Sourcebook: Qualitative Data Analysis. London: Sage Publications.
9. Narkevičius, R., & Šeškauskis, Ž. (2016). SALES FORECASTING MANAGEMENT.
10. Patel, M., & Patel, N. (2019). Exploring Research Methodology: Review Article. International Journal of Research and Review Keywords: Research, Methodology, Research Methodology, 6. www.ijrrjournal.com
11. Render, B., Stair, R. M., Hanna, M. E., & Hale, T. S. (2012). Quantitative Analysis For Management.