Last updated on April 2nd, 2024 at 05:32 am
Utilising Artificial Intelligence and machine learning helps us make better probability analyses and thus helps businesses to accurately foretell how much the goods might cost in the sales and operations planning accordingly by studying the seasonal fluctuations.
We get to see the increasing use of AI in demand forecasting. This helps significantly reduce the percentage of errors that they commit in the networks of the supply chain and makes the entire process much more efficient in saving time.
What do we understand by Demand Forecasting?
Demand forecasting refers to the assessment of the various processes of a company that helps forecast the value of multiple products in the future supply chain. Doing this entire process manually is a pretty hectic job. This process is made more efficient by utilising Artificial Intelligence.
Demand forecasting helps in optimising the inventory of a business by forecasting the supply demand of the products in the future. This process includes analysing all the records of sales data.
What are the characteristics of Demand Forecasting?
There are several benefits of Demand Forecasting. These are listed as follows:
- Demand Forecasting significantly cuts down the percentage of risk by making economic financial decisions. This in turn helps in managing cash flow, resource allocation, profit margins and many more. Overall it helps in preparing the budget of the company
- Demand Forecasting helps in forecasting the demands of the customer and helps in aligning the production rate according to the demand rate and hence avoiding overflooding of inventory.
- Demand Forecasting manages the dynamic pricing of the products based on the consumers’ demand. It carefully studies the market and reads for potential opportunities which can help companies proliferate by incorporating the right marketing tactics.
What are the various types of Demand Forecasting techniques?
The various types of Demand Forecasting techniques are as follows:
- Macro-level forecasting: This only considers the broader external factors affecting the business and helps keep aware of the opportunities centring the expansion of the portfolio and other things.
- Micro-level forecasting: This type of forecasting generally focuses on a specific industry or business
- Short-term forecasting: This involves forecasting that involves a time span of almost 12 months.
- Long-term forecasting: This type of forecasting involves a time span of more than a year. Doing so, it analyses the seasonal demands by studying the annual patterns and the potential of production and expansion covering a long period. This helps in strategising long-term business plans.
What are the various advantages of using AI-based Demand Forecasting?
The various advantages of AI-based Demand Forecasting are as follows:
- AI-driven forecasting is time efficient: There is a big time constraint while working on demand forecasting manually. AI-driven forecasting on the other hand is quite fast. Saving time for critical analysis, the primary data are efficiently arranged and analysed in no time.
- Accuracy at forecasting: As it has been mentioned earlier, making informed decisions is greatly aided by the accurate forecasting driven by AI. What gives AI an even better edge over manual forecasting is that the information is continually updated which makes the information that it provides so accurate. The rise in accuracy in forecasting is found to be directly proportional to the rise in sales.
- Coherence between external and internal data: The AI-generated data follows a close look out for seasonal variations and fluctuations in the external data. It specifically studies the demand patterns and then accordingly aligns the sales forecasting. Manually maintaining coherence between this external and internal data would have been quite difficult and prone to errors.
- Battling rapid fluctuations: In the case of events which is often commonly called the Black Swan events, the sales are heavily hampered. To cope with such a downfall of sales due to failure of predictions and immediate follow-up with good planning, AI models provide a solution by giving prompt analysis with appropriate planning that follows the analysis. This helps tide over such trying times.
- Early predictions: One of the biggest benefits of using AI-generated forecasting over manual forecasting is that AI can foretell fluctuations beforehand and makes room for appropriate adjustments and arrangements by laying out the future steps to be taken for the supply chain design.
Conclusion
In this article, we got to learn about demand forecasting and how the integration of AI helped make the entire process more efficient. Secondly, we got to know that AI-driven forecasting is much more accurate than manual forecasting. Moreover, AI ensures sound coherence between the external and internal data and keeps a continuous check on the rise and fall of demand rate in the market controlling tightly the flow of the sales.
Check out Professional Certification in Supply Chain Management & Analytics provided by Imarticus if you want to give a boost to your career in this field. This supply chain analytics course is provided in collaboration with IIT Roorkee. One needs to have basic knowledge of programming and operations management to be eligible to enrol in this course.
