Using artificial intelligence to analyse businesses in agriculture industry

Artificial intelligence is largely used in many technical applications and allows you to provide various solutions in problem estimation, regression, or optimization. Artificial intelligence, specifically artificial neural networks, extend to the area of economics and finance. They are used primarily for operations that can´t be identified analytically. Neural networks are suitable for modelling very complex strategic decisions, for large sets of data, and so on. The main advantage is the ability to learn and then to capture hidden and strongly non-linear dependencies. In this paper they are used for the analysis of agricultural businesses. The aim is to analyse the state of the agricultural sector through the use of Kohonen networks and then to assess its future development. On the basis of the analysis, significant and large clusters of businesses are depicted, and the most significant clusters are analysed. It is possible to estimate the number of businesses that will be successful, those that will stagnate and those that will fail in the following period. Application of Kohonen networks is rather complex, but they have great potential and the results are very interesting

Adaptive tactical pricing in multi-agent supply chain markets using economic regimes

In today's complex and dynamic supply chain markets, information systems are essential for effective supply chain management. Complex decision making processes on strategic, tactical, and operational levels require substantial timely support in order to contribute to organizations' agility. Consequently, there is a need for sophisticated dynamic product pricing mechanisms that can adapt quickly to changing market conditions and competitors' strategies. We propose a two-layered machine learning approach to compute tactical pricing decisions in real time. The first layer estimates prevailing economic conditions—economic regimes—identifying and predicting current and future market conditions. In the second layer, we train a neural network for each regime to estimate price distributions in real time using available information. The neural networks compute offer acceptance probabilities from a tactical perspective to meet desired sales quotas. We validate our approach in the trading agent competition for supply chain management. When competing against the world's leading agents, the performance of our system significantly improves compared to using only economic regimes to predict prices. Profits increase significantly even though the prices and sales volume do not change significantly. Instead, tactical pricing results in a more efficient sales strategy by reducing both finished goods and components inventory costs

Adaptive tactical pricing in multi-agent supply chain markets using economic regimes

In today's complex and dynamic supply chain markets, information systems are essential for effective supply chain management. Complex decision making processes on strategic, tactical, and operational levels require substantial timely support in order to contribute to organizations' agility. Consequently, there is a need for sophisticated dynamic product pricing mechanisms that can adapt quickly to changing market conditions and competitors' strategies. We propose a two-layered machine learning approach to compute tactical pricing decisions in real time. The first layer estimates prevailing economic conditions—economic regimes—identifying and predicting current and future market conditions. In the second layer, we train a neural network for each regime to estimate price distributions in real time using available information. The neural networks compute offer acceptance probabilities from a tactical perspective to meet desired sales quotas. We validate our approach in the trading agent competition for supply chain management. When competing against the world's leading agents, the performance of our system significantly improves compared to using only economic regimes to predict prices. Profits increase significantly even though the prices and sales volume do not change significantly. Instead, tactical pricing results in a more efficient sales strategy by reducing both finished goods and components inventory costs