Terms such as demand forecasting, bullwhips or “AI powered demand planning” are part of the vocabulary of the modern supply chain — i.e. supply chain management in new German.
That is why we first clarify what terms such as demand forecasting, demand planning, demand forecasting or demand planning mean. Demand forecasting or demand forecast is the English word for demand forecast. Demand planning is the translation for demand planning. Let's take a closer look.
Demand forecasting
Demand forecasting is the process of estimating future demand by analyzing data. In very simple terms, it means Predict how interested consumers are in buying a product. These forecasts are based on historical sales data, current market trends, and other external factors.
Demand Planning
Demand planning or demand planning is a higher-level process that not only uses demand forecasting forecasts, but also includes planning and managing inventory quantities, production, and procurement strategies. It is about making sure that The right product is available in the right place at the right timeto meet predicted demand. In a separate article, we will go into more detail about the different requirements for the two concepts mentioned.
This article addresses the question of the impact of the application of artificial intelligence in demand forecasting. Also known as “Predictive Analytics for Demand Forecasting”.
You could believe that supply chains have been professionalized from start to finish for some time as prime examples of demand forecasting and general demand planning. Not at all.
Status Quo Supply Chain Management
A McKinsey report from 2021 revealed that almost 75% of supply chain executives use manual methods for supply chain planning, primarily spreadsheets. In other words, Excel. It's hard to believe. This dependency naturally increases the risk of human error and results in time-consuming data collection and processing, which often results in outdated information. In addition, companies usually work in isolated environments, which complicates the process, as technical systems such as artificial intelligence must collect data from various independent sources.
(Figure 1: Tables remain the most used method for supply chain planning.
Simply networking data by breaking down data silos provides an enormous performance boost. In combination with artificial intelligence, the industry is proverbially reaching the next level here, which, according to the report,”Global AI in Supply Chain Management (SCM) 2023-2028“from Research and Markets looks as follows:
Artificial intelligence (AI) is used in the area of SCM 17.5 billion US dollars worldwide by 2028 will achieve, with AI-powered supply chains thanks to lower risks and lower overall costs To over 67% more effective are.
That is good news. So let's look at how artificial intelligence reduces both risks and costs to make supply chain management more effective.
The key benefits of AI for demand forecasting
The integration of artificial intelligence in demand forecasting is, unsurprisingly, a major step forward in supply chain management. This is because AI enables highly accurate forecasting of future demand and thus overcomes the limits of conventional forecasting methods, which often rely heavily on past patterns and simplified assumptions. By using machine learning, AI algorithms are able to huge amounts of data both in the past as well as in real time toward analyze, in order complex patterns and trends toward recognizethat could easily miss human analytics. This results in more accurate and detailed forecasts, which are crucial for various industries such as retail, life sciences (life sciences), the semiconductor industry or fintech (financial technology).
Let's look at it in detail:
Efficiency in data processing
AI is ideal for structuring and analyzing extensive data sets quickly and accurately. In contrast to traditional methods that require extensive manual input (such as using spreadsheets), AI streamlines the process of data processing. The result is a more efficient demand planning process that frees up time for strategic decisions and reduces the risk of human error.
Due to the variety of data, shipping, for example, according to Lloyd's Register (Maritime Performance Services) Only 10% of ship data is taken into account, while AI models can now view almost 100% of ship data and process it immediately.
Accurate and dynamic forecasts
AI offers a sophisticated approach to forecasting by being able to independently select and combine suitable methods for different products and levels. This granular approach results in higher accuracy of forecasts, which is crucial for well-founded decisions in the supply chain.
Comprehensive data integration
Particularly noteworthy is the ability of artificial intelligence to incorporate both internal and external data into forecasts. It can identify causal relationships between various factors, such as market trends and economic data, that influence demand. This level of integration provides a holistic view of the market that traditional methods may overlook.
Quick responsiveness
Algorithms adapt quickly to market fluctuations — a key advantage in today's volatile environment. This adaptability is particularly valuable when it comes to responding to unexpected events or “black swan” events, such as the COVID-19 pandemic, which are significantly influencing demand patterns.
Improved decision-making through clarity
Artificial intelligence is also a big step towards clear decision-making. By accurately identifying outliers, AI not only shows potential fluctuations, but also provides a comprehensive understanding of their impact on the business. This early recognition enables timely and effective intervention and ensures that decisions are not only reactive but also strategically oriented.
Better supply chain relationships
The so-called bullwhip effect describes the intensification of demand fluctuations along the supply chain, in which small fluctuations in demand at the end of the supply chain lead to disproportionately large fluctuations in order quantities and inventories among suppliers.
Accurately predicting AI minimizes the bullwhip effect and leads to better inventory levels. This in turn leads to more efficient planning by stakeholders in the supply chain, reduces stress and improves collaboration.
Democratizing data science
AI also enables people without extensive knowledge of data science to make complex demand forecasts. The self-learning systems create and adapt the models as needed, so team members can focus on strategic tasks while leaving the quantitative work to AI.
Improving forecasts over time
With every iteration of data analysis, AI in demand forecasting continues to evolve and improve. Through machine learning, AI systems are able to learn from new data and refine their predictive models over time. This continuous improvement is leading to ever more accurate forecasts. The more scenarios the system plays through and the more feedback it incorporates, the better it can identify subtle market trends and demand shifts. This dynamic learning process ensures that companies are not only equipped with the currently best forecasting models, but are also able to adapt to changing market dynamics and develop further in order to maintain a competitive advantage in demand planning.
Overall, the trend is obvious — these benefits show how artificial intelligence transforms demand forecasting from a labor-intensive, error-prone process into a strategic, data-driven approach that improves efficiency, accuracy, and responsiveness in supply chain management.
The challenges of AI in demand forecasting
There is no light without shadow. Although AI offers significant advantages in predicting market trends and optimizing inventory management, its use is not without hurdles. These challenges range from technical and data-related issues to more general organizational and market-related factors. These challenges must be understood and overcome in order to exploit the full potential of AI to improve demand forecasting. Here's a closer look at some of the key barriers companies can face:
Data quality and data integrity
AI models require high-quality, comprehensive data. It becomes problematic when the data is inconsistent, incomplete, or distorted, leading to inaccurate forecasts. As always, the well-known IT mantra counts: “garbage in, garbage out.”
Integration into existing systems
The integration of technology into existing business processes and systems is often associated with technical challenges. This integration requires, in part, overcoming cultural resistance and ensuring compatibility with legacy systems.
Complexity of AI models
Developing, interpreting, and managing AI models requires specialized knowledge. This complexity requires investing in qualified personnel or working with providers of AI solutions.
Lack of transparency
The opacity (“black box”) of some AI models can lead to transparency problems that make it difficult for managers to understand and trust the AI decision-making process. Furthermore, misjudged AI decisions can cause all sorts of damage.
Global uncertainties
Unforeseen events such as pandemics or economic fluctuations can question the predictive accuracy of AI. AI models must be quickly retrained or adapted to adapt to new market conditions.
Continuous model management
Even regardless of unpredictability, market dynamics require constant monitoring and updating of AI models to maintain their relevance and accuracy, which can be resource-intensive.
In summary, artificial intelligence has many benefits for forecasting demand. However, their successful implementation requires overcoming challenges in data quality, system integration, and ongoing management. Companies can use the power of artificial intelligence to improve their supply chain management in the areas of costs, risk, and efficiency.
Frequently asked questions (FAQs)
What is AI-driven demand forecasting?
AI-powered demand forecasting is a method that uses artificial intelligence and machine learning to predict future demand for products or services. This method is more accurate than traditional methods that rely on historical sales data and simple statistical models.
How does AI help with demand forecasting for new products?
It uses existing product data to model demand as long as there is a sales history.
How can AI improve traditional methods of demand forecasting?
AI speeds up data processing, uncovers hidden problems, and creates more detailed forecasts in contrast to traditional methods.
Can AI help with workforce planning and customer satisfaction?
Yes, AI helps predict employee demand and ensures product availability, resulting in higher customer satisfaction.
What types of AI demand forecasting are there?
AI demand forecasting includes short-term, long-term, active, passive, internal, and external types, each suited to different business needs.
What are the limits of traditional methods in demand forecasting?
Traditional methods often rely on historical data and assume that past patterns will persist. They find it difficult to incorporate external factors such as market trends and unexpected events, resulting in inaccurate forecasts.
How does AI improve demand forecasting?
AI analyzes vast amounts of data, including external factors, to identify complex patterns. This results in more accurate and comprehensive forecasts, streamlines inventory management, and reduces waste.
Can AI-based demand forecasting adapt to market changes and uncertainties?
Yes, AI can be particularly effective in adapting to rapid market changes and global uncertainties such as economic downturns or pandemics. It analyses real-time data to adjust forecasts accordingly. However, errors can also occur here.
What are the overall benefits of AI in demand forecasting for supply chain management?
AI-driven demand forecasting increases supply chain efficiency and profitability. It helps companies overcome the limits of traditional forecasting and complex relationships and adapt to changing market conditions.