According to Gartner, the level of automation in supply chain processes will double in the next five years. At the same time, global spending on IIoT platforms is expected to grow from $1.67 billion in 2018 to $12.44 billion in 2024, reaching a 40% CAGR in seven years, according to a recent study. As global supply chains become increasingly complex, the margin for error is shrinking rapidly. With increasing competition in a connected digital world, it is becoming even more critical to maximize efficiency by reducing all kinds of uncertainty. Increasing expectations for speed and efficiency between suppliers and business partners of all kinds further underscore the need for the industry to leverage the capabilities of artificial intelligence (AI) in supply chains and logistics.
The benefits of using artificial intelligence in the supply chain:
This article was written thanks to the funds from the European Union’s co-financing of the Operational Program Intelligent Development 2014-2020, a project implemented under the competition of the National Center for Research and Development: under the “Fast Track” competition for micro, small and medium-sized entrepreneurs – competition for projects from less developed regions under Measure 1.1: R&D projects of enterprises Sub-measure 1.1.1 Industrial research and development work carried out by enterprises. Project title: “Developing software to improve forecast accuracy and inventory optimization from the perspective of customer and supplier collaborating in the supply chain using fuzzy deep neural networks.
The main feature of the model based on the so-called. “ordering point,” also known as an information-level ordering system or continuous review , is a condition […]
Based on fuzzy sets, a fuzzy inference system can be built. In such a system, fuzzy rules are implemented for modeling, which in turn make it possible to carry out the process of fuzzy inference.
Uncertainty is present in all areas of human activity, it is an inherent part of reality. This means that any attempt to describe this reality should take into account the presence of uncertainty. It is in the need to model uncertainty that the impetus for the development of fuzzy set theory and its subsequent generalizations should be seen.