Forecasting in a Product Manufacturing Company

This paper documented the data cleaning and interactive dashboard development work carried out as part of a larger forecasting and trend analysis project for a product manufacturing company. The experience highlighted a fundamental truth in data analytics: no analysis, however sophisticated, can produce trustworthy results without a clean and well-structured data foundation. Through the systematic application of data profiling, cleaning, and transformation techniques using Excel, Power BI, and MySQL, the raw and fragmented data collected from multiple business units was converted into a consistent, analysis-ready dataset. The interactive dashboards built on top of this data empowered stakeholders to visualize sales trends, monitor inventory levels, and track business performance in an intuitive and dynamic manner. While the forecasting and trend analysis models were developed by senior team members, this preparatory work was indispensable to their success. It demonstrated that data cleaning and visualization are not auxiliary tasks in an analytics project - they are its most critical and time-intensive components. The insights drawn from this research underscore the importance of investing in data quality and visual reporting as preconditions for any meaningful application of artificial intelligence or machine learning in business operations.

[1] Montijn van de Ven, P. L. Machado, A. Athanasopoulou, B. Aysolmaz, and O. Turetken, "Key performance indicators for business models," Information Systems and e-Business Management, 2023. DOI: https://doi.org/10.1007/s10257-023-00650-2 [2] C. Lotfi, S. Srinivasan, M. Ertz, and I. Latrous, "Web Scraping and Its Applications in Data Analysis," SCRS Conference Proceedings on Intelligent Systems, pp. 381-394, January 2021. DOI: 10.52458/978-93-91842-08-6-38 [3] G. Shmueli and O. R. Koppius, "Predictive Analytics in Information Systems Research," MIS Quarterly, vol. 35, no. 3, pp. 553-572, 2011. [4] T. Dasu and T. Johnson, Exploratory Data Mining and Data Cleaning. Wiley-Interscience, 2003. [5] Microsoft Corporation, "Power BI Documentation," Microsoft Docs, 2024. [Online]. Available: https://docs.microsoft.com/en-us/power-bi/ [6] R. Kimball and M. Ross, The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd ed. Wiley, 2013. [7] S. Makridakis, E. Spiliotis, and V. Assimakopoulos, "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, vol. 36, no. 1, pp. 54-74, 2020. [8] H. Chen, R. H. L. Chiang, and V. C. Storey, "Business Intelligence and Analytics: From Big Data to Big Impact," MIS Quarterly, vol. 36, no. 4, pp. 1165-1188, 2012.