Time-Series Forecasting in Inventory and Sales Using SAP Analytics Cloud

Paul Praveen Kumar Ashok

Abstract: Accurate inventory and sales forecasting is critical for organizations seeking to optimize supply chain operations, reduce costs, and enhance customer satisfaction. Traditional forecasting techniques often struggle to capture complex demand patterns, seasonal variability, and market dynamics. With the advent of advanced analytics platforms, businesses are increasingly leveraging machine learning driven predictive models to improve decision-making. This article investigates the application of time series forecasting in inventory and sales management using SAP Analytics Cloud (SAC), a next generation business intelligence and predictive analytics solution. SAC provides an integrated environment that combines data visualization, planning, and predictive capabilities, enabling users to construct and evaluate forecasting models without requiring extensive programming expertise. Through automated predictive scenarios, the platform applies advanced statistical and machine learning algorithms to historical sales and inventory data, producing forecasts with enhanced accuracy and transparency. The study demonstrates how SAC can identify demand fluctuations, mitigate risks of stockouts and overstocking, and facilitate data-driven strategies for diverse industries such as retail and manufacturing. Experimental results from a case study illustrate the practical utility of SAC in aligning supply with demand, reducing forecasting errors, and supporting proactive inventory management. The findings underscore the strategic role of predictive analytics in modern enterprises, highlighting how SAC empowers organizations to achieve operational efficiency and maintain competitive advantage in dynamic market environments.

Keywords: Time-Series Forecasting, Sales Forecasting, Predictive Analytics, SAP Analytics Cloud (SAC), Business Intelligence, Forecast Accuracy

How to Cite?: Paul Praveen Kumar Ashok, "Time-Series Forecasting in Inventory and Sales Using SAP Analytics Cloud", Volume 12 Issue 4, April 2023, International Journal of Science and Research (IJSR), Pages: 1994-1998, https://www.ijsr.net/getabstract.php?paperid=SR23431091015, DOI: https://dx.doi.org/10.21275/SR23431091015