Was sind die Vorteile und Herausforderungen des Einsatzes von KI für die Bestandsbedarfsprognose?

Qualitative methods of demand forecasting may be the only option available in certain situations, but it is important to remember that these methods are not fool proof. They rely heavily on human judgment and opinions, which can be unreliable and can lead to inaccurate predictions. It is important to take the time to carefully review the data and consider the potential biases of the sources before making any decisions. Additionally, it is wise to combine qualitative methods with quantitative data, when possible, to improve the accuracy of the forecast.

I think every company is beginning their planning mechanism with a qualitative methods of demand forecasting. When i talk to startups they all began in that way because it works in the beginning. The bigger the company grows and the complex the processes develop the more you need other ways to plan more detailed. If we look on the cost structure of warehouses the qualitative planing is not enough and too expensive in the long term

From experience the benefits using ai would be the broad range of memories it can use to reference against in it's thought process versus looking up data and making spred sheets to analyze to get your average sales projection from past years to current application and ai can do all this much faster and possibly more accurate.

For Ai to be optimized across an entire networked community of consumers to suppliers, the system must process huge volumes of data very quickly. Large community supply chains can have millions if not hundreds of millions of stocking locations. AI solutions must be able to make smart decisions, fast, and on a massive scale. Data in a multi-party, real-time network is always changing. Variability and latency is a recurring problem, and execution efficiency varies constantly. The AI system must be looking at the problem continuously, not just periodically, and should learn as it goes to set its own policies & fine-tune its abilities. Part of the learning process is to measure the effectiveness “analytics,” then apply what it has learned.

Retail organizations need an accurate way to predict future sales using historical data so that they know how much product to produce or stock. Using these Demand Forecast solutions, businesses can implement machine learning in their demand forecasts, helping businesses to improve production lead times and increase operational efficiency. One way you could do that is with the help of machine learning

Data in a multi-party, real-time network is always changing. Variability and latency is a recurring problem, and execution efficiency varies constantly. The AI system must be looking at the problem continuously, not just periodically, and should learn as it goes on how to best set its own policies to fine-tune its abilities. Part of the learning process is to measure the effectiveness “analytics,” then apply what it has learned.

Significant value can only be achieved if the algorithm can not only make intelligent decisions but can also execute them. Furthermore, they need to execute not just within the enterprise but where appropriate, across trading partners. This requires your AI system and the underlying execution system to support multi-party execution workflows. Ai Supplier RESULTS • Improved customer order fill rate to 99+% • Decreased inventory by 27% • Invested in strategic projects with 2 year payback Ai Retailer RESULTS • Improved store in-stocks to 99+% • Decreased inventory for Del Monte products by 30+% • Achieved negative working capital

AI is revolutionizing the way businesses approach demand forecasting and inventory management. Leveraging AI technologies such as predictive analytics, machine learning, and natural language processing, businesses can gain insights into customer demand and behaviors, optimize their inventory levels, pricing, and promotions, and reduce forecasting errors and uncertainties. AI-driven forecasting and inventory management can also help save time and resources, while also providing actionable insights and recommendations to support decision making and planning. Ultimately, AI can help businesses become more efficient, productive, and customer-centric.

If you consider to use AI to optimize your demand forecasting also think of the right choice of the LLM. We all know public LLM´s with which we played around and were impressed what that offers us BUT we should also consider that in demand forecasting we use sensitive data. Halluzinations or random responses could cost us a lot of money. In that UseCase we cannot accept that and above everything we do not want to train a public model. This sensitive data should always stay private and you should definitely pick a model which is specialized on that UseCase.

New AI systems must eliminate the stale data problem. Most supply chains today attempt to execute plans using data that is days old, but this results in poor decision-making that sub-optimizes the supply chain or requires manual user intervention to address. Without real-time information, an AI tool is just making bad decisions faster. Second Ai needs access to data outside of the enterprise. Unless the AI tool can see the forward-most demand and downstream supply, and all relevant constraints and capacities in the supply chain, the results will be no better than that of a traditional planning system. A lack of visibility & access to real-time, community data is the norm in over 99 percent of all companies.

For the AI solution to offer optimal value in the supply chain, it important to ensure the following fundamentals: 1. Access to Real-Time Data 2. Access to Community (Multi-Party) Data 3. Support for Network-Wide Objective Functions 4. Decision Process Must Be Incremental and Consider the Cost of Change 5. Decision Process Must Be Continuous, Self-Learning and Self-Monitoring 6. AI Engines Must Be Autonomous Decision-Making Engines 7. AI Engines Must Be Highly Scalable 8. Must-Have a Way for Users to Engage with the System