Demand planning software... for what?

Demand planning software… for what?

Demand planning or demand forecasting, which are usually used as synonyms, help to accurately and automatically determine consumer demand in order to increase revenue and reduce inventory. Therefore, analyzing and understanding buying patterns, seasonal patterns and other trends is of great importance.

The goal of any supply chain planner in a manufacturing company is to achieve optimal inventory balance and control overstocks and understocks. Overstocks are the result of overproduction and can lead to massive losses, dead stock and unsaleable products, causing unnecessary inventory costs or lost sales. The opposite is the so-called stock-out scenario. This is the result of underproduction, meaning the manufacturer is unable to meet market demand. The consequences are not only lost sales but also annoyed customers and increasing penalty costs due to delayed deliveries.

The core task of sophisticated demand planning software is therefore to address and solve these problems with the help of analytical tools, using machine learning algorithms that predict the optimal demand.

What are the most prominent tasks of Demand Planning Tools?

To optimize inventory levels by accurately forecasting the future demand, the following aspects should be considered in a sophisticated demand planning tool:

Product Life Cycle Management: It is important to understand the life cycle of a product. In this step, useful information can be gathered that can be helpful in forecasting sales or demand for new products with similar characteristics.

Statistical forecasting: demand planning tools use historical sales data stored as time series to forecast future demand using statistical forecasting methods that account for outliers, seasonality, and other factors. The charts can be visualized and compared to actual demand. Today, the question is whether statistical methods are even necessary when ML algorithms can be used.

Secondary Data: External data such as weather, holidays, or other data that affects the future demand of your products can be used to make supply chain planning even more accurate and robust. AI is a good technology to process data in real time.

KPIs: The final phase of demand planning involves defining KPIs to monitor and evaluate the performance of demand planning results. Some metrics include forecast accuracy, cost of goods sold, and fulfillment lead time.

Why is accurate demand forecasting important?

As we mentioned earlier, demand planning helps to effectively automate the process of inventory management, considering the dynamic consumer demand. However, looking at the status quo, it is inconceivable that 80% of small and medium enterprises (SMEs) still rely on Excel as a primary demand planning tool, considering that AI-driven demand planning software enables fully automated operation. In the following, we highlight the benefits of AI-driven demand planning and provide compelling reasons why you should stop relying on Excel:

High Predictive Accuracy: Predictive accuracy helps fine-tune and continuously improve the demand planning process. For example, if the algorithms are over-predicting for a particular product, fine-tuning can be done to improve accuracy for future predictions.

Optimize Inventory Costs: The goal of any supply chain planner or business owner is to match total market demand with supply. Sophisticated demand planning tools will make this possible and help run the business as efficiently as possible.

Efficient Production Planning: The more accurately we can predict changing customer demand, the more stable the production environment will be. The better the prediction, the higher the profits.

Reduced Labor Costs: Labor scheduling is often the most costly part in a manufacturing company. Knowing the peak and down times of production helps a company efficiently plan ahead to reduce labor costs.

Which companies should use demand planning software?

Typically, demand planning software, as the name implies, is used by demand planners who are familiar with the use of statistical analysis. Demand planners are responsible for logistical optimization. Nevertheless, demand planning software is also critical for sales and marketing reps, who need to be aware of future trends to identify where and for which product they should launch a promotion or take further marketing action. In addition, supply chain managers and purchasing agents are also frequent users of demand planning software.

Promising areas for future demand forecasting include point-of-sale data and IoT. Point-of-sale data means that accuracy would increase dramatically if the data came directly from the scan at the register, meaning that the data would be automatically integrated into algorithms and data sets for the manufacturer. IoT is also a promising area for providing real-time updates on the status of raw materials in the production process.