Application of Artificial Intelligence in Automation of Supply Chain Management

Abstract

A well-functioning supply chain is a key to success for every business entity. Having an accurate projection on inventory offers a substantial competitive advantage. There are many internal factors like product introductions, distribution network expansion; and external factors such as weather, extreme seasonality, and changes in customer perception or media coverage that affects the performance of the supply chain. In recent years Artificial Intelligence (AI) has been proved to become an extension of our brain, expanding our cognitive abilities to levels that we never thought would be possible. Though many believe AI will replace humans, it is not true, rather it will help us to unleash our true strategic and creative potential. AI consists of a set of computational technologies developed to sense, learn, reason, and act appropriately. With the technological advancement in mobile computing, the capacity to store huge data on the internet, cloud-based machine learning and information processing algorithms etc. AI has been integrated into many sectors of business and been proved to reduce costs, increase revenue, and enhance asset utilization. AI is helping businesses to get almost 100% accurate projection and forecast the customer demand, optimizing their R&D and increase manufacturing with lower cost and higher quality, helping them in the promotion (identifying target customers, demography, defining the price, and designing the right message, etc.) and providing their customers a better experience. These four areas of value creation are extremely important for gaining competitive advantage. Supply-chain leaders use AI-powered technologies to a) make efficient designs to eliminate waste b) real-time monitoring and error-free production and c) facilitate lower process cycle times. These processes are crucial in bringing Innovation faster to the market.

The Prediction of Essential Medicines Demand: A Machine Learning Approach Using Consumption Data in Rwanda

“…In the management of health supply chains and pharmaceutical commodities, timeseries models are used the most (52 percent) and causal models are used the least (24 percent), according to a 2002 study by Jain, while judgmental models account for 19 percent and mixed or combination models account for 5% [30]. Furthermore, leveraging AI technology such as ML applications to improve the accuracy in forecasting and demand predictions for essential medicines might potentially improve their availability and reshape their well-ordered distribution [31,32].…”

A Aplicação Da Inteligência Artificial Na Indústria De Cosméticos: Cenário Atual E Oportunidades Para O Futuro

“…Cadeia produtiva de cosméticos envolve diferentes etapas e agentes econômicos, tais como: matérias-primas; indústria química; fabricação; embalagem; distribuição; varejo e consumidores. Abrange desde a pesquisa e Assim, a IA também é capaz de entender os dados por trás dos hábitos de compra, o que pode ser usado para criar experiências virtuais de varejo em plataformas digitais (DASH et al, 2019). Além disso, as empresas de dos consumidores e ajustando a produção de acordo (HOLZINGER et al, 2023).…”

Exploring the Role of Artificial Intelligence in Internet of Things (IOT)

“…AI can maximize information with gaining proper recycling effects of major business implementation. There are various features of AI which are based on the algorithmic details and large data set application [10]. The algorithmic usage is subset with the major importance of the featured model which is mainly based on Zero correction among the features of that model.…”