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This paper aims to identify and determine regional tourism axes in Hormozgan Province, Iran, as a region with significant potential

The research method is quantitative and uses the fuzzy accreditation tool and TOPSIS model; the identification, determination and ranking of regional tourism axes have been performed by analyzing the spatial distribution of tourism attractions in the GIS environment.

The results show that given the capacities of Hormozgan Province, at least 15 axes are recognizable. This paper highlights regional tourism planning as a tool for urban and rural socio-economic development in potential provinces such Hormozgan.

This study provides a number of practical implications for regional tourism development as follows: it identifies some of the most important potential axes in Hormozgan Province, which can be considered as investment areas in the national and regional tourism development strategy. The spatial results of this study could be embedded in all urban and rural developmental plans in the province. Tourism investment should shift its spatial concentration from the spot approach, especially islands and cities, to the axis approach while equipping those axes as comprehensive spatial strategic regional tourism plans. Sectoral tourism in each sector including sports, economy and nature could be planned as if sectoral institutions and organizations are going to develop their own tourism goals.

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.

This study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into off-season and peak-season, with the former characterized by longer lead times and higher supply uncertainty. In contrast, the latter incurs higher acquisition costs but ensures certain supply, with the retailer's purchase volume aligning with the acquired volume. Retailers can replenish in both phases, receiving goods before the sales season. This paper focuses on the impact of the retailer's demand forecasting bias on their sales period profits for both phases.

This study adopts a data-driven research approach by drawing inspiration from real data provided by a cooperating enterprise to address research problems. Mathematical modeling is employed to solve the problems, and the resulting optimal strategies are tested and validated in real-world scenarios. Furthermore, the applicability of the optimal strategies is enhanced by incorporating numerical simulations under other general distributions.

The study's findings reveal that a greater disparity between predicted and actual demand distributions can significantly reduce the profits that a retailer-supplier system can earn, with the optimal purchase volume also being affected. Moreover, the paper shows that the mean of the forecasting error has a more substantial impact on system revenue than the variance of the forecasting error. Specifically, the larger the absolute difference between the predicted and actual means, the lower the system revenue. As a result, managers should focus on improving the quality of demand forecasting, especially the accuracy of mean forecasting, when making replenishment decisions.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

The purpose of this paper is to illustrate a profitable and original index options trading strategy.

The methodology is based on auto regressive integrated moving average (ARIMA) forecasting of the S&P 500 index and the strategy is tested on a large database of S&P 500 Composite index options and benchmarked to the generalized auto regressive conditional heteroscedastic (GARCH) model. The forecasts validate a set of criteria as follows: the first criterion checks if the forecasted index is greater or lower than the option strike price and the second criterion if the option premium is underpriced or overpriced. A buy or sell and hold strategy is finally implemented.

The paper demonstrates the valuable contribution of this option trading strategy when trading call and put index options. It especially demonstrates that the ARIMA forecasting method is a valid method for forecasting the S&P 500 Composite index and is superior to the GARCH model in the context of an application to index options trading.

The strategy was applied in the aftermath of the 2008 credit crisis over 60 months when the volatility index (VIX) was experiencing a downtrend. The strategy was successful with puts and calls traded on the USA market. The strategy may have a different outcome in a different economic and regional context.

The study aims to investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns.

This work evaluates various AM-enabled supply chain configurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network.

This research assesses the preferred AM-enabled supply chain configuration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly affects the preferred network configuration. The relationship between the demand volume and relative AM production capacity affects the regions of superior network configuration performance.

This research makes several simplifying assumptions regarding AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity and post-processing requirements.

This research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling.

The purpose of this paper is to identify actions and guidelines for enabling and fostering the Industry 4.0 adoption, as well as to understand the role of three ecosystem actors in these actions (i.e. companies, educational organizations and regional policy makers).

52 semi-structured expert interviews in the Tyrol-Veneto cross-border macro-region were carried out and interpreted using the innovation ecosystem concept. In particular, drawing from this latter, six ecosystem building blocks were identified and used to analyze the interviews' content.

The findings allow not only to build a comprehensive framework for action to support Industry 4.0 adoption, but also to confirm the importance of exploring Industry 4.0 through the lens of the ecosystem concept. Indeed, the authors show that R&D activities should be complemented with interorganizational actions, such as training and networking, and that all ecosystem actors should be involved in the Industry 4.0 adoption.

This is among the few studies that adopt the innovation ecosystem perspective to explore best practices for Industry 4.0 adoption, thus overcoming the weakness of existing papers based on a firm-level perspective. It also complements previous ecosystem-based research on Industry 4.0 by exploring the technology adoption side, rather than the technology provision one, and by considering the adoption of a wide set of technologies.

The link between confidence and economic decisions has been widely covered in the economic literature, yet it is still an unexplored field in tourism. The purpose of this paper is to address this gap, and investigate benefits in forecast accuracy that can be achieved by combining the UNWTO Tourism Confidence Index (TCI) with statistical forecasts.

Research is conducted in a real-life setting, using UNWTO unique data sets of tourism indicators. UNWTO TCI is pooled with statistical forecasts using three distinct approaches. Forecasts efficiency is assessed in terms of accuracy gains and capability to predict turning points in alternative scenarios, including one of the hardest crises the tourism sector ever experienced.

Results suggest that the TCI provides meaningful indications about the sign of future growth in international tourist arrivals, and point to an improvement of forecast accuracy, when the index is used in combination with statistical forecasts. Still, accuracy gains vary greatly across regions and can hardly be generalised. Findings provide meaningful directions to tourism practitioners on the use opportunity cost to produce short-term forecasts using both approaches.

Empirical evidence suggests that a confidence index should not be collected as input to improve their forecasts. It remains a valuable instrument to supplement official statistics, over which it has the advantage of being more frequently compiled and more rapidly accessible. It is also of particular importance to predict changes in the business climate and capture turning points in a timely fashion, which makes it an extremely valuable input for operational and strategic decisions.

The use of sentiment indexes as input to forecasting is an unexplored field in the tourism literature.

The tourist industry has to adopt a big data-driven foresight approach to enhance decision-making in a post-COVID international landscape still marked by significant uncertainty and in which some megatrends have the potential to reshape society in the next decades. This paper, considering the opportunity offered by the application of the quantitative analysis on internet new data sources, proposes a prediction method using Google Trends data based on an estimated transfer function model.

The paper uses the time-series methods to model and predict Google Trends data. A transfer function model is used to transform the prediction of Google Trends data into predictions of tourist arrivals. It predicts the United States tourism demand in Italy.

The results highlight the potential expressed by the use of big data-driven foresight approach. Applying a transfer function model on internet search data, timely forecasts of tourism flows are obtained. The two scenarios emerged can be used in tourism stakeholders’ decision-making process. In a future perspective, the methodological path could be applied to other tourism origin markets, to other internet search engine or other socioeconomic and environmental contexts.

The study raises awareness of foresight literacy in the tourism sector. Secondly, it complements the research on tourism demand forecasting by evaluating the performance of quantitative forecasting techniques on new data sources. Thirdly, it is the first paper that makes the United States arrival predictions in Italy. Finally, the findings provide immediate valuable information to tourism stakeholders that could be used to make decisions.

This research aims to shed new lights on the most shared constructs developed on Innovation Ecosystems, Entrepreneurial Ecosystems and Technology Transfer Ecosystem proposing an additional stand-alone ecosystem.

This research is built upon a qual-quantitative analysis of an empirical case. The latter analysis is performed through a single case study methodology on the San Giovanni Hub of the Federico II University of Naples.

Evidences show how a technological hub orchestrates three main ecosystems for the knowledge exploitation: the technology transfer ecosystem, devoted to gather knowledge form universities' labs towards industries; the innovation ecosystem, able to manage the exploration and exploitation of new knowledge and techniques; the entrepreneurial ecosystem, that supports startup/spinoff creation process.

Limitations mainly concern the fact that it is centred on just one case study.

Practical implications imply new opportunities of collaboration involving different stakeholders as university administrators, researchers, businesses and policymakers, creating a supportive environment for innovation.

The research offers a new vision about the role of Universities as creators and enablers of ecosystems pursuing diverse value propositions. The Academic Innovation Ecosystem is a new conceptualization of this role played by a university, and it can convey innovation and entrepreneurial attitude within its ecosystem leveraging on the transfer of university knowledge and technology.

The literature on economic forecasting, is showing an increase in criticism, of the inaccuracy of forecasts, with major implications for economic, and fiscal policymaking. Forecasts are subject to the systemic uncertainty of human systems, considerable event-driven uncertainty, and show biases towards optimistic growth paths. The purpose of this study is to consider approaches to improve economic foresight.

This study describes the practice of economic foresight as evolving in two separate, non-overlapping branches, short-term economic forecasting, and long-term scenario analysis of development, the latter found in studies of climate change and sustainability. The unique case of Ireland is considered, a country that has experienced both steep growth and deep troughs, with uncertainty that has confounded forecasting. The challenges facing forecasts are discussed, with brief review of the drivers of growth, and of long-term economic scenarios in the global literature.

Economic forecasting seeks to manage uncertainty by improving the accuracy of quantitative point forecasts, and related models. Yet, systematic forecast failures remain, and the economy defies prediction, even in the near-term. In contrast, long-term scenario analysis eschews forecasts in favour of a set of plausible or possible alternative scenarios. Using alternative scenarios is a response to the irreducible uncertainty of complex systems, with sophisticated approaches employed to integrate qualitative and quantitative insights.

To support economic and fiscal policymaking, it is necessary support advancement in approaches to economic foresight, to improve handling of uncertainty and related risk.

While European Union Regulation (EC) 1466/97 mandates pursuit of improved accuracy, in short-term economic forecasts, there is now a case for implementing advanced foresight approaches, for improved analysis, and more robust decision-making.

Building economic resilience and adaptability, as part of a sustainable future, requires both long-term strategic planning, and short-term policy. A 21st century policymaking process can be better supported by analysis of alternative scenarios.

To the best of the authors’ knowledge, the article is original in considering the application of scenario foresight approaches, in economic forecasting. The study has value in improving the baseline forecast methods, that are fundamental to contemporary economics, and in bringing the field of economics into the heart of foresight.