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The purpose of the chapter is to focus on the global industrial robotics market and trends of its development. In the framework of this chapter, the authors made the forecast of industrial robots' market future values in this chapter with the linear regression method and an econometric model. This analysis has provided a conclusive answer to the question about the prospects of the industrial robotics market and the leading countries. The completed forecast showed that the global robotics market will continue to grow, thanks to the wider adoption of industrial robots, which will be used in new industries, the development of contactless user interfaces, which, among other things, will be implemented in the automotive applications, the focus on predictive maintenance and remote monitoring of equipment, as well as the transition of a large number of enterprises to digital management and full automation of existing equipment to improve the quality and productivity of processes. The authors show that in 2020 the global robotics market volume decreased due to the COVID-19 pandemic and major shift in production value chains, but in 2021 the indicator will grow again, but not so rapidly, at a more moderate pace. By 2025, the global industrial robotics market may exceed $61.4 billion, with a growth rate of 8.5%.

The purpose of this paper is to review the dramatic entry of collaborative robotics into applications. It also examines the current state of the art for collaborative robotics, factors driving their entry and their outlook for the future.

The paper includes discussions with key managers of robot companies. Attendance at the International Federation for Robotics round table discussion on collaboration and another industry round table meeting on collaborative robotics. Attendance at the CIRP technical conference on automation. Attendance at the Robotics Industry Association International Collaborative Robots Workshop.

Collaborative robotics are addressing many previously unmet applications while saving money, improving productivity, simplifying programming and speeding the time to return investment. It is forecast that collaborative robotics systems can address almost 100 million assembly and logistics tasks not previously addressable with traditional robotics technology.

The paper implies a major examination of collaborative robot technology now and in the future. Readers may be very excited to learn the many new tasks that collaborative robots are addressing, the many tools that have been developed to aid in selecting, designing and gaining worker acceptance and the many unique benefits that are provided, as well as the systems already available.

The paper implies a major examination of collaborative robot technology now and in the future. Readers may be very excited to learn the many new tasks that collaborative robots are addressing, the many tools that have been developed to aid in selecting, designing and gaining worker acceptance and the many unique benefits that are provided, as well as the systems already available.

Purpose: The goal of the chapter is to define customer attitudes towards robots in travel, tourism and hospitality (TTH) and to analyse their most significant characteristics.

Design/methodology/approach: The book chapter develops a conceptual framework of attitudes towards robots in travel, tourism and hospitality, based on critical analysis of relevant publications.

Findings: The chapter provides a definition and discussion of the characteristics of customer attitudes towards robots in TTH. It elaborates the structural elements of attitudes towards robots, and the links and interactions between the elements.

Research limitations: Research limitations stem from the small number of studies on customer attitudes towards robots in TTH.

Practical implications: The theoretical analysis can be used as a starting point for empirical studies of customer attitudes towards robots in travel, tourism and hospitality.

Social implication: Combined services, based on human employee-service robot collaboration, are the optimal decision for forming favourable customer attitudes towards robotisation and automation in tourism and hospitality. In that way clients’ needs of high technological convenience, interpersonal communication and socialisation are met simultaneously.

Originality/value: This research is among the few publications that study customer attitudes towards robots in travel, tourism and hospitality. The authors develop a matrix of users’ attitudes and behaviours when using robots in travel, tourism and hospitality.

The purpose of this study is to select the appropriate forecasting model at the retail stage for selected vegetables on the basis of performance analysis.

Various forecasting models such as the Box–Jenkins-based auto-regressive integrated moving average model and machine learning-based algorithms such as long short-term memory (LSTM) networks, support vector regression (SVR), random forest regression, gradient boosting regression (GBR) and extreme GBR (XGBoost/XGBR) were proposed and applied (i.e. modeling, training, testing and predicting) at the retail stage for selected vegetables to forecast demand. The performance analysis (i.e. forecasting error analysis) was carried out to select the appropriate forecasting model at the retail stage for selected vegetables.

From the obtained results for a case environment, it was observed that the machine learning algorithms, namely LSTM and SVR, produced the better results in comparison with other different demand forecasting models.

The results obtained from the case environment cannot be generalized. However, it may be used for forecasting of different agriculture produces at the retail stage, capturing their demand environment.

The implementation of LSTM and SVR for the case situation at the retail stage will reduce the forecast error, daily retail inventory and fresh produce wastage and will increase the daily revenue.

The demand forecasting model selection for agriculture produce at the retail stage on the basis of performance analysis is a unique study where both traditional and non-traditional models were analyzed and compared.

This paper aims to illustrate the potential of high-frequency data for tourism and hospitality analysis, through two research objectives: First, this study describes and test a novel high-frequency forecasting methodology applied on big data characterized by fine-grained time and spatial resolution; Second, this paper elaborates on those estimates’ usefulness for visitors and tourism public and private stakeholders, whose decisions are increasingly focusing on short-time horizons.

This study uses the technical communications between mobile devices and WiFi networks to build a high frequency and precise geolocation of big data. The empirical section compares the forecasting accuracy of several artificial intelligence and time series models.

The results robustly indicate the long short-term memory networks model superiority, both for in-sample and out-of-sample forecasting. Hence, the proposed methodology provides estimates which are remarkably better than making short-time decision considering the current number of residents and visitors (Naïve I model).

A discussion section exemplifies how high-frequency forecasts can be incorporated into tourism information and management tools to improve visitors’ experience and tourism stakeholders’ decision-making. Particularly, the paper details its applicability to managing overtourism and Covid-19 mitigating measures.

High-frequency forecast is new in tourism studies and the discussion sheds light on the relevance of this time horizon for dealing with some current tourism challenges. For many tourism-related issues, what to do next is not anymore what to do tomorrow or the next week.

This research initiates high-frequency forecasting in tourism and hospitality studies. Additionally, we detail several examples of how anticipating urban crowdedness requires high-frequency data and can improve visitors’ experience and public and private decision-making.

The aim of this paper is to suggest a new approach to the problem of sales forecasting for improving forecast accuracy. The proposed method is capable of combining, by means of appropriate weights, both the responses supplied by the best-performing conventional algorithms, which base their output on historical data, and the insights of company’s forecasters which should take account future events that are impossible to predict with traditional mathematical methods.

The authors propose a six-step methodology using multiple forecasting sources. Each of these forecasts, to consider the uncertainty of the variables involved, is expressed in the form of suitable probability density function. A proper use of the Monte Carlo Simulation allows obtaining the best fit among these different sources and to obtain a value of forecast accompanied by a probability of error known a priori.

The proposed approach allows the company’s demand forecasters to provide timely response to market dynamics and make a choice of weights, gradually ever more accurate, triggering a continuous process of forecast improvement. The application on a real business case proves the validity and the practical utilization of the methodology.

Forecast definition is normally entrusted to the company’s demand forecasters who often may radically modify the information suggested by the conventional prediction algorithms or, contrarily, can be too influenced by their output. This issue is the origin of the methodological approach proposed that aims to improve the forecast accuracy merging, with appropriate weights and taking into account the stochasticity involved, the outputs of sales forecast algorithms with the contributions of the company’s forecasters.

This paper aims to provide details of a major new European robotic research programme and of a recent survey concerning the attitudes of Europeans to robotic technology.

Following an introduction, this paper briefly summarises Europe’s position within the global robotics industry and then discusses the SPARC project. It then examines the finding of a European survey of public attitudes towards robots and concludes with a short discussion.

This shows that the European Union (EU) is a significant force within the global robotics business, and that it is about to embark on its largest ever robotics R&D programme. An EU-wide survey of public attitudes towards robots showed generally positive views but great resistance to the use of robots to care for children, the elderly and the disabled. There was also widespread concern that growing numbers of robots will take jobs.

This paper summarises Europe’s position within the global robotics industry, provides details of the SPARC project and analyses the finding of a European survey into public attitudes towards robots.