Demand Planning
Supply Chain Management
In a nutshell
Demand planning is the process of forecasting future customer demand for products or services. The goal is to predict as accurately as possible what quantities will be needed and when, in order to optimally manage production, procurement, inventory, and logistics.
In many companies, demand planning serves as the foundation for supply planning, S&OP, and overall supply chain management.
Why is demand planning important?
The quality of demand planning has a direct impact on a company’s success. A forecast that is too low often leads to supply shortages, lost revenue, and dissatisfied customers. A forecast that is too high, on the other hand, results in unnecessary inventory, tied-up capital, and increased depreciation risks.
The challenge is to forecast future demand as accurately as possible despite fluctuating markets, seasonal effects, marketing campaigns, price changes, or external factors.
In many companies, demand planning is still carried out using Excel spreadsheets, individual estimates, or isolated planning processes. This leads to discrepancies in the numbers and debates over which forecast is actually the correct one.
In contrast, modern demand planning processes combine historical data, market information, sales expertise, and statistical forecasting methods to produce a single forecast. This forecast then serves as the basis for purchasing, production, logistics, and financial planning.
Demand planning is therefore one of the most important components of a successful Sales & Operations Planning (S&OP) process and is often the starting point for integrated xP&A initiatives.
Furthermore, the topic is becoming increasingly important due to AI and machine learning. Modern planning platforms are now able to automatically analyze large amounts of data and continuously improve forecasts. As a result, forecasts become more accurate and planning cycles are significantly faster.
How does celver AG provide support?
For many years, celver AG has been helping companies optimize their demand planning processes and implement modern supply chain planning solutions.
In doing so, we consider not only the software used, but the entire planning process. Often, the greatest potential for improvement lies not in the forecasting method itself, but in a lack of accountability, insufficient transparency, or inadequate integration between sales, the supply chain, and finance.
Typical questions from our customers include:
- How accurate is our current sales forecast?
- Which forecasting methods are best suited for our products?
- How can sales and the supply chain work together more effectively?
- What role do AI and machine learning play?
- How can forecasts be automated?
- How can demand planning, supply planning, and financial planning be integrated?
- Which platform is best suited to our needs?
celver AG supports both the professional development of the demand planning process and its technical implementation with solutions such as SAP IBP, SAP Analytics Cloud, Board, Kinaxis, and Anaplan.
Our goal is to develop a demand planning process that is not only statistically accurate but also actually enables better decisions throughout the entire supply chain.
Related Terms
Frequently Asked Questions
Forecasting refers to the actual prediction of future demand.
Demand planning also encompasses the entire process of coordinating, validating, analyzing, and utilizing these forecasts within the company.
Common causes include:
- poor data quality
- Lack of master data maintenance
- Lack of coordination between sales and the supply chain
- significant market changes
- Inappropriate forecasting methods
AI and machine learning can analyze large amounts of data, identify patterns, and continuously improve forecasting models. This often leads to significant improvements in forecast accuracy and planning speed.
Demand planning provides the demand forecast that serves as the foundation for the entire S&OP process. Without coordinated demand planning, supply, capacity, and inventory cannot be planned effectively.
