P. Lu^*

Department of Information Technology, Knorr-Bremse, Suzhou, China

Submitted on 23 April 2025; Accepted on 30 June 2025; Published on 17 July 2025

To cite this article: P. Lu, “Technological Exploration and Case Analysis of Using SAP IBP's Demand Sensing and Statistical Model for Demand Forecasting, Inventory Reduction, and Supply Chain Improvement,” Trans. Appl. Sci. Eng. Technol., vol. 1, no. 1, pp. 1-5, 2025.

Copyright: 

Abstract

This paper explores the innovative use of SAP Integrated Business Planning (IBP)'s demand sensing and statistical model to enhance demand forecasting accuracy, reduce inventory levels, and improve the overall efficiency of the supply chain. Through a real-world case study, we demonstrate the effectiveness of this approach and provide quantitative data, visualizations, and tables to support our findings. The proposed method shows significant potential in revolutionizing traditional supply chain management practices [1].

Keywords: SAP; Integrated Business Planning; demand sensing; statistical model

Abbreviations: IBP: Integrated Business Planning; POS: point-of-sale; ARIMA: autoregressive integrated moving average; RMSE: root mean square error

1. Introduction

In today's highly competitive global market, accurate demand forecasting and efficient inventory management are crucial for the success of supply chain operations. Traditional forecasting methods often fail to capture the dynamic nature of demand, leading to over- or under-stocking, increased costs, and poor customer service [2].

SAP Integrated Business Planning (IBP) offers a comprehensive solution that combines demand sensing and statistical models. Demand sensing uses real-time data from various sources to detect short-term demand patterns, while statistical models provide long-term trend analysis. By integrating these two approaches, we can achieve more accurate demand forecasts and optimize inventory levels. Data visualization and tables play vital roles in this process, as they help stakeholders quickly understand complex data patterns and make informed decisions [3].

2. USE Case: Consumer Goods Company

2.1. Company background

Company XYZ is a leading consumer goods manufacturer with a global supply chain in Europe. The company faced challenges in accurately predicting demand due to factors such as seasonality, changing consumer preferences, and sudden market fluctuations. These issues resulted in high inventory holding costs and stock-outs, affecting customer satisfaction and profitability [4].

2.2. Implementation of SAP IBP

Company XYZ implemented SAP IBP's demand sensing and statistical model to address these challenges. The implementation process involved the following steps:

1. Data integration: The company integrated data from multiple sources, including point-of-sale (POS) systems, inventory management systems, and market research data. This real-time data was used to train the demand sensing model [5].

2. Model training: The statistical model was trained using historical sales data to identify long-term trends and seasonality patterns. The demand sensing model was continuously updated with real-time data to capture short-term demand changes [6].

3. Integration with supply chain processes: The demand forecasting results were integrated into the company's supply chain planning processes, including production planning, inventory management, and procurement [7].

2.3. Results

After implementing SAP IBP, Company XYZ achieved significant improvements in demand forecasting accuracy and inventory management:

1. Demand forecasting accuracy: The demand forecasting accuracy increased by 25% compared to the previous forecasting method (Figure 1). This improvement was measured by comparing the forecasted demand with the actual sales data over a period of six months [8].

2. Inventory reduction: The average inventory level decreased by 30% (Figure 2). This reduction was achieved by aligning production and procurement with more accurate demand forecasts, reducing the need for safety stocks [9].

3. Supply chain efficiency: The fill rate, which measures the percentage of customer orders that are fulfilled on time, increased from 80% to 90%. This improvement in supply chain efficiency led to higher customer satisfaction and increased sales [10].

FIGURE 1: Comparison of forecasting accuracy.

FIGURE 2: Inventory levels before and after implementation.

3. Methodology

3.1. Demand sensing

Demand sensing is a data-driven approach that uses real-time data to detect short-term demand changes. The following steps are involved in the demand sensing process:

1. Data collection: Real-time data is collected from various sources, such as POS systems, social media, and weather data. This data provides insights into current consumer behavior and market trends [11].
2. Data preprocessing: The collected data is preprocessed to remove noise and outliers. Data normalization techniques are applied to ensure that the data is in a suitable format for analysis [12].
3. Pattern recognition: Machine learning algorithms, such as neural networks and decision trees, are used to identify short-term demand patterns in the preprocessed data. These patterns are then used to adjust the demand forecasts. During the pattern recognition phase, heat maps can be used to visualize the relationships between different variables and demand patterns. For example, a heat map can show the correlation between weather conditions and sales of certain products [9, 13].

3.2. Statistical model

The statistical model is used to analyze historical sales data and identify long-term trends and seasonality patterns. The following steps are involved in the statistical model-building process:

1. Data selection: Historical sales data for a specific period is selected. The data should cover a sufficient time frame to capture long-term trends and seasonality [14].
2. Model selection: Appropriate statistical models, such as autoregressive integrated moving average (ARIMA) or exponential smoothing, are selected based on the characteristics of the data [15].
3. Model fitting: The selected statistical model is fitted to the historical sales data. The model parameters are estimated using optimization techniques to minimize the forecasting error [16].

3.3. Integration of demand sensing and statistical model

The demand sensing and statistical model are integrated to achieve more accurate demand forecasts. The statistical model provides the baseline forecast, while the demand sensing model adjusts the forecast based on real-time data. The following steps are involved in the integration process:

1. Initial forecast: The statistical model generates the initial demand forecast based on historical data [17].
2. Real-time adjustment: The demand sensing model uses real-time data to detect short-term demand changes and adjusts the initial forecast accordingly [18].
3. Final forecast: The adjusted forecast is used as the final demand forecast for supply chain planning [19] (Table 1).

TABLE 1: Initial and final forecast comparison.

Product	Initial forecast (units)	Real-time adjustment (units)	Final forecast (units)
Product A	500	50	550
Product B	300	-30	270
Product C	400	20	420

4. Data Analysis

4.1. Data sources

The data used in this study was collected from multiple sources, including:

1. POS systems: POS data provides information on actual sales transactions, including the quantity, price, and time of sale [20].
2. Inventory management systems: Inventory data provides information on the inventory levels at different locations in the supply chain [21].
3. Market research data: Market research data provides information on consumer preferences, market trends, and competitor activities [22].

4.2. Data preprocessing

The collected data was preprocessed to ensure its quality and suitability for analysis. The following preprocessing steps were performed:

1. Data cleaning: Missing values were imputed, and outliers were removed from the data [23].
2. Data aggregation: The data was aggregated at the appropriate level, such as daily, weekly, or monthly, depending on the analysis requirements [24].
3. Data normalization: The data was normalized to ensure that all variables were on the same scale [25].

4.3. Data analysis results

The data analysis results showed that the integration of the demand sensing and the statistical model significantly improved demand forecasting accuracy. The root mean square error (RMSE) of the demand forecasts decreased by 20% compared to the traditional forecasting method. The correlation coefficient between the forecasted demand and the actual sales data increased from 0.7 to 0.9, indicating a stronger relationship between the two variables [26] (Table 2).

TABLE 2: Comparison of RMSE and correlation coefficient.

Method	RMSE	Correlation coefficient
Traditional method	150	0.7
SAP IBP method	120	0.9

4. Conclusion

This paper has presented a technological exploration and case analysis of using SAP IBP's demand sensing and statistical model for demand forecasting, inventory reduction, and supply chain improvement. Through a real-world case study, we have demonstrated the effectiveness of this approach in improving demand forecasting accuracy, reducing inventory levels, and enhancing supply chain efficiency.

The use of data visualization techniques and data tables has played a crucial role in understanding the complex data patterns and the impact of the implemented solution. It has enabled stakeholders to quickly grasp the key insights and make informed decisions [27].

The proposed method offers several advantages over traditional forecasting methods. It uses real-time data to capture short-term demand changes and combines it with long-term trend analysis provided by statistical models. This integration results in more accurate demand forecasts, which can be used to optimize inventory levels and improve supply chain performance [28].

Future research directions include further improving the demand sensing and statistical model by incorporating more advanced machine learning algorithms and real-time data sources. Additionally, the scalability and applicability of the proposed method to different industries and supply chain contexts should be investigated. Moreover, more sophisticated data visualization techniques and data table presentations can be explored to better represent the complex relationships in supply chain data [29].

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