Search results

The usage of AI-powered chatbots and virtual assistants facilitates seamless communication, offering instant responses to inquiries and enhancing customer satisfaction. In Japan, the hospitality industry is at the forefront of this AI-driven transformation and through collaborations with technology companies, hotels are deploying AI-powered concierge services, smart room automation, and language translation systems to cater to diverse guest needs. The integration of AI in Japan's tourism sector not only enhances operational efficiency but also showcases the country's commitment to innovation and delivering exceptional customer experiences. As Japan embraces AI in its hospitality industry, it navigates the delicate balance between leveraging technology and preserving human interaction and by combining the efficiency and accuracy of AI with the warmth and personal touch of human hospitality, Japan aims to redefine the future of tourism. Moreover, AI streamlines operations by automating repetitive tasks, optimising resource allocation, and improving efficiency in areas such as reservation management, inventory control, and demand forecasting. However, along with these benefits, there are significant challenges to consider. Privacy concerns arise as AI systems collect and process personal data, necessitating robust security measures to protect sensitive information. Ethical considerations must also be addressed, as the use of AI raises questions about transparency, bias, and accountability. Furthermore, while AI enhances efficiency, there is a concern about losing the human touch that has long been a hallmark of the hospitality industry. Balancing the benefits of AI with maintaining personalised and authentic guest experiences becomes a crucial challenge.

In particular, it is worth mentoring new and more efficient solutions that can meet the increasingly specific needs of each company, especially in food management. A business intelligence (BI) solution can help your food company better understand and manage business processes more effectively. Management information is essential for all levels of an organisation to make quick and correct decisions. However, what exactly is BI, and what can it mean for a food company?

The PRISMA stands for (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and content analysis strategy used the SLR (systematic literature review) methodology to examine 151 papers published in peer-reviewed academic journals and industry reports between 2016 and 2023.

The findings show that artificial intelligence and digitalisation are linked to the UN 2030 Agenda. BI management ranks first (66%), followed by crop and land mapping systems (40%), agricultural machinery monitoring tools (39%) and decision support systems (31%). The road to digital transformation remains extended, with the main impediments being more compatibility between enterprise systems and a shortage of expertise.

The section relating to methodological perspective adopts the PRISMA methodology for systematic review. Interoperability is easily managed by assigning qualified teams to projects. The added value of a consulting firm with extensive project management experience in the food industry is closely related to the results achieved.

BI: What exactly is it, and why a data-driven culture is essential in the food and beverage industry?

This chapter reviews possible regulatory updates needed to address the four general challenges arising from digitalization of financial services, regardless of the business models of the financial services providers. These challenges are customers' data rights, artificial intelligence (AI) ethics, cybersecurity and financial exclusion.

The supply chain is undergoing a significant digital transformation to adapt to the increasingly digitalized and globalized business environment. To remain competitive in this evolving market, businesses must seamlessly integrate digital technologies throughout the supply chain, spanning all stages from procurement to distribution. This chapter delves into models and methodologies critical to digital supply chain (DSC) transformation, with a focus on advanced techniques such as the Internet of Things (IoT), artificial intelligence (AI), blockchain, and data analytics to boost the resilience and agility of supply chain operations. By leveraging practical examples and case studies, the chapter highlights the myriad enhancements digital transformation can introduce across diverse supply chain stages, including sourcing and after-sales service. Additionally, the chapter examines the complexities of cybersecurity, data integrity, and change management within the digital transformation framework, proposing strategies to address these challenges. The insights offered in this chapter will serve as a thorough guide for both practitioners and scholars in the supply chain field, equipping them to adeptly navigate the multifaceted arena of digital transformation.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.