Unlocking the Potential of Statistical and Automatic Tools in Demand Planning

Introduction

Demand planning is an essential process that aims to forecast the demand for a particular product or service. The accuracy of demand planning has a direct impact on various aspects of business operations, such as inventory management, production planning, and supply chain optimization. Despite the availability of statistical and automatic tools for demand planning, many organizations still need to rely on manual processes. This paper discusses the ongoing evolutions which are renewing the demand planning process.

Current State of Demand Planning

Demand planning typically involves collecting historical sales data and using statistical models to predict future demand. However, the accuracy of these models might be limited by the quality and quantity of available data. Additionally, demand planning can be a time-consuming process that requires significant human effort and expertise. These challenges can result in inaccurate forecasts, leading to suboptimal inventory levels, production schedules, and customer satisfaction.

Statistical and Automatic Tools for Demand Planning

To address the challenges associated with demand planning, various statistical and automatic tools have been developed. These tools use advanced algorithms to analyze large datasets and generate accurate forecasts. It means that more than producing raw data, new generations of demand planning software are constantly adjusting, following their past accuracy, and using all data streams available to help the planners to get a more accurate forecast. Some of the most commonly used tools include:

1. Forecasting Models

• Time series forecasting models - These models use historical sales data to predict future demand. They can incorporate various factors such as seasonality, trends, and outliers.
• Machine learning algorithms - These algorithms use historical data to learn patterns and make predictions. They can handle complex relationships between variables and adapt to changes in the data.
• Intermittent Time series models - These models are used when a time series includes a significant amount of zero values.?

2. Multiple Data Inputs - Rather than using only Demand History, these algorithms use all the data inputs you can provide, Open Orders, Point Of Sale Data, Inventory, Store Count, Customer Forecast, Weeks of Supply Target, Shipment Lag, Promotions, etc. This kind of input helps a lot to shape the short-term demand forecast by adding insights where they are needed and reliable.

3. Demand Planning Software - These tools automate the demand planning, from data collection to forecasting. They can generate forecasts in near-real-time and provide insights into demand patterns.

4. New Product Introduction (NPI) and End of Life (EOL)? - Using chaining and forecast tree to handle the life cycle of items with new or multiple versions.

Challenges in Adopting Statistical and Automatic Tools

Despite the benefits of statistical and automatic tools, many organizations still rely on manual processes for demand planning. There are several reasons for this:

1. Lack of expertise - Demand planning requires statistical and mathematical expertise that may not be readily available in some organizations. Implementing these tools may require additional training or the hiring of skilled personnel.
2. Resistance to change - Organizations may be resistant to change and prefer to continue using existing manual processes. This resistance can be due to cultural factors or a lack of awareness of the benefits of statistical and automatic tools.
3. Cost - Implementing statistical and automatic tools may require significant investment in software, hardware, and personnel. Some organizations may not have the financial resources to make these investments.

Why Should You Adopt These Tools

1. ROI (Return on Investment) - Each company should measure how much every percent improvement in accuracy will bring in $ value, this knowledge will help set the company's priorities.
2. Improve Accuracy and Demand Planning Process - Leveraging automated processes will help let the planners focus on specific challenges/customers/items i.e. Work smarter not harder.
3. Shaping Demand - Shape demand will help to lead the market proficiently with better anticipation of the following week’s/month's demand, and provide better control.

Conclusion

In conclusion, demand planning is a critical process affecting various business operations. While statistical and automatic tools can significantly improve the accuracy and efficiency of demand planning, many organizations still rely on manual processes. This lack of adoption is due to several challenges, such as a lack of expertise, resistance to change, and cost. However, as the benefits of these tools become more apparent, it is expected that more organizations will adopt them to improve their demand planning processes.

The Analytical Factor team specializes in providing statistical and automatic tools for the demand planning/supply chain teams, that are tailored to meet the specific needs of your industry and company. With our expert's help, you can easily implement any solution and see results in more accurate forecasts and better demand planning. If you want to learn more about how statistical and automatic tools can help your company, please contact Analytical Factor ([email protected]) today. We would be happy to discuss your specific needs and help you implement solutions in a way that meets your goals.