Businesses should predict product demand to stay ahead in this competitive market. But how can businesses do it? It should utilize various demand forecasting techniques to predict product demand.
Overstocking can increase the holding costs of a business. Out-of-stock problems can decrease brand reputation in the market. Accurately forecasting product demand can help avoid problems of overstocking and out-of-stock issues. It allows a business to keep the right inventory in the warehouse to meet customer demand. Demand forecasting methods reduce costs and optimize the entire supply chain process.
What is Demand Forecasting?
Demand forecasting is a process of forecasting customer demand for a particular product or service over a specific period. The business has to analyze historical sales data and marketing trends to predict how many products customers will buy in the near future.
Proper demand forecasting will optimize inventory management and prevent overstocking or out-of-stock problems. It can enhance brand reputation and increase customer satisfaction.
Top 8 Demand Forecasting Techniques
There are various demand forecasting techniques. Here is a list of them below.
1. Market Research
It involves the introduction of a new product in a specific geographical area or a particular customer segment. The data is then collected through surveys and interviews with the customers. It is then utilized to predict product demand on a larger scale. However, this forecasting method has certain limitations, such as being relatively expensive and time-consuming. It may not even work for other geographical regions. Hence, businesses usually utilize this technique along with other demand forecast techniques.
2. Expert Opinion
Here, the business consults with a panel of experts to learn about product demand in the future. This method is suitable when there is limited data. This method is also known as the Delphi method.
- Here, the business sends a set of questions to the forecasting experts.
- Business creates a summary of the responses and shares the responses with all the forecasting experts.
- It repeats the process again. The Delphi method is closed when the opinion of a group of forecasting experts comes to a consensus.
3. Sales Force Opinion Method
The sales team of the business connects with the customer and interacts with them about the product. The opinions of the sales force team are combined and analyzed, and then the product demand is forecasted. It identifies trends based on religion and customer-specific.
4. Time Series Analysis
It is a quantitative forecasting technique that uses historical data to identify trends and then predict product demand. It is best suited for products with stable demand.
5. Machine Learning And AI Based Forecasting Method
Machine learning and AI-based forecasting methods utilize advanced algorithms for predicting product demand in the future. It can handle large amounts of data and can identify unusual patterns instantly.
6. Barometrics
The barometric demand forecast method uses external factors like weather or economic shifts and predicts product demand accordingly. Let’s discuss this with an example. A cloth retailer uses the barometric forecasting method by analyzing weather patterns and maintaining winter clothes accordingly.
7. Econometrics Forecasting Technique
The econometric demand forecasting technique uses statistical methods along with economic theories to predict product demand. It is complex to implement as it requires knowledge of both economics and statistics. For example, a company can utilize historical sales data along with economic indicators like consumer confidence to adjust its inventories.
8. Hybrid Methods
It integrates various forecasting methods to improve accuracy in product demand. For example, a business can combine quantitative methods with the Delphi method to predict product demand.
How to Choose the Best Demand Forecasting Techniques and Methods?
Choosing the right forecasting method is crucial for business. Below is a step-by-step guide to choosing the best forecasting method.
1. Analyse The Nature Of The Product And Market Conditions
If a business launches a new product in the market, then it can use qualitative methods for demand forecasting, such as the Delphi method and market research method. But if it’s a matured product, then the business should utilize quantitative forecasting methods like time series analysis for demand forecasting.
A business has to assess the market conditions and competition in the market and then decide the type of forecasting technique.
2. Time Horizon
If a business wants to predict product demand for the next week or for a month, then it can use forecasting methods like sales force or quantitative methods like moving averages.
But if the business wants to predict product demand for a complete year, then it can apply AI and machine learning forecasting techniques or simulation models for accuracy.
3. Demand Complexity
If a business has a product with stable demand, then it can utilize simpler methods like time series analysis to forecast product demand in the future.
But if the product has only seasonal or irregular demand, then a simulation model or AI and machine learning model can be the best choice.
4. Evaluate Accuracy Needs
If you want to predict perishable product demand with high accuracy, then use a simulation or AI model to forecast product demand.
You can use simple methods like expert options or the sales force opinion method to moderate the accuracy of product demand.
5. Choose Method Based On Resources And Expertise Availability
A business should invest in machine learning or AI-based forecasting methods if it can invest a greater amount and has a team that is expert in using special tools and skills.
However, if a business is searching for a cost-effective option, then it can choose time series analysis to predict customer demand in the future.
6. Compare Methods With Business Objectives
If a business needs to optimize day-to-day operations, then it should use methods like time series analysis or exponential smoothing to forecast product demand.
But for long-term planning, businesses need to utilize AI and machine learning methods.
7. Consider Data Availability
If a business has a good history of sales data, then it should use historical methods for the prediction of product demand.
Benefits Of Demand Forecasting
Demand forecasting has various benefits, which are listed below.
1. Improves Inventory Management
Demand forecasting avoids problems of overstocking and out-of-stock problems. Businesses can keep the required inventories in the warehouse storage to meet customer demand.
2. Increases Customer Satisfaction
Nowadays customers want their ordered products to be received as soon as possible. Demand forecasting keeps required inventories in the warehouse and enables fast order fulfillment, which enhances customer satisfaction.
3. Better Production Planning
Businesses can do better production planning by analyzing market trends and past sales data. They can schedule the production cycle based on the forecasting technique and avoid the risk of excess manufacturing of products.
4. Pricing Strategies
Businesses can utilize historical sales data and identify the best pricing strategies to increase their sales and revenue.
Challenges Of Demand Forecasting
A business faces various challenges when choosing a demand forecasting method. Here is a list of them below.
- Proper demand forecasting technique depends on accurate and high-quality data. However, most businesses do not have such data, and that leads to wrong predictions.
- Market conditions can change because of changes in the preferences of customers or technological advancements.
- Various external factors, like pandemics, can change the product demand pattern.
Wrap Up
Demand forecasting plays an important role in the success of a business. Effective demand forecasting can optimize inventory management and production planning, and it can even improve the financial decision-making process of a business. There are various demand forecasting techniques like qualitative, quantitative, AI machine learning, and many more. Businesses should choose the right forecasting method based on data and resource availability, team expertise, and many other factors.
AAJ Supply Chain Management
