Taiwan Machinery Industry Research Articles

A parallel-series hybridization of seasonal intelligent based statistical model for demand forecasting

PurposeThe purpose of this paper, because of the complexity of demand time series and the need to construct a more accurate hybrid model that can model all relationships in data, is to propose a parallel-series hybridization of seasonal neural networks and statistical models for demand time series forecasting.Design/methodology/approachThe main idea of proposed model is centered around combining parallel and series hybrid methodologies to use the benefit of unique advantages of both hybrid strategies as well as intelligent and classic seasonal time series models simultaneously for achieving results that are more accurate for the first time. In the proposed model, in contrast of traditional parallel and series hybrid strategies, it can be generally shown that the performance of the proposed model will not be worse than components.FindingsEmpirical results of forecasting two well-known seasonal time series data sets, including the total production value of the Taiwan machinery industry and the sales volume of soft drinks, indicate that the proposed model can effectively improve the forecasting accuracy achieved by either of their components used in isolation. In addition, the proposed model can achieve more accurate results than parallel and series hybrid model with same components. Therefore, the proposed model can be used as an appropriate alternative model for seasonal time series forecasting, especially when higher forecasting accuracy is needed.Originality/valueTo the best of the authors’ knowledge, the proposed model, for first time and in contrast of traditional parallel and series hybrid strategies, is developed.

10 000 km drogi w poszukiwaniu obrabiarek – relacja z Międzynarodowych Targów Obrabiarek na Tajwanie

26th Taipei International Machine Tool Show – TIMTOS 2017 was held from 7 to 12 of March 2017. The keynote of this year show was “Smart Manufacturing”. The schedule included press conferences. Visitors were encouraged to get acquainted with the offers from production companies and to speak to the leading persons in Taiwan machinery industry.

Did international production/distribution networks mitigate the effect of the global financial crisis? Evidence from Taiwan machinery industry

This study employs survival analysis to investigate how the trade relationships of international production/distribution networks mitigated the negative effects on exports during the global financial crisis of 2008–2009. Using monthly trade data at the most disaggregated level, this study employs January 2009 as the base, which was the worst month of the crisis, to identify whether there were active or inactive trade relationships. We reveal that even in the worst month of the crisis, the country-product pairs had high survival rates for active trade relationships, implying they were major drivers of the recovery of trade values after the crisis.

Corporate motivation and performance in R&D alliances

In the face of the global challenges of rapid transitions in technologies and markets, R&D activity has become one of the main ways for companies to engage in innovation. In addition, minimizing transaction cost is no longer sufficient to ensure a company's survival; therefore, companies must investigate and acquire resources to facilitate innovation within the organization. This study investigates corporate motivation and the performance of R&D alliances among machinery manufacturers in Taiwan. To explore the relationships between motivation and performance, this study adopts two distinct but complementary perspectives on R&D alliances: transaction-cost economics (TCE) and resource-based theory (RBT). This study includes the administration of a survey to explore the issues of motivation of companies participating in R&D alliances, types of governance structure in alliances, relationships between governance structure and performance, and relationships between motivation and performance of an R&D alliance in Taiwan's machinery industry (the TMI). The results in this study assert that corporate motivation as derived from both TCE and RBT perspectives has a significant positive relationship with the performance of R&D alliances; however, the other moderating variables, such as types of governance structure and corporate attributes, do not have a significant impact on the performance of R&D alliances in the TMI.

Forecast of development trends in Taiwan's machinery industry

Industry transformation oriented toward high technology is being expected in the next 10 years by targeting Taiwan becoming an island of the technology. The machinery industry, which has been playing a vital role in Taiwan's economic development in the past, should be continuously emphasized during the course of the future development. This paper presents a detailed study on the future development of Taiwan's machinery industry along with the valuable proposals to the government policy and the investment strategy to the private sectors. The 10-year forecasting survey based on the strength, weakness, opportunity and threats (SWOT) analysis was made through an integrated professional team using the Delphi method. The derived results of market growth forecasting and the projected high potential products are further elaborated in the paper.

A fuzzy seasonal ARIMA model for forecasting

This paper proposes a fuzzy seasonal ARIMA (FSARIMA) forecasting model, which combines the advantages of the seasonal time series ARIMA (SARIMA) model and the fuzzy regression model. It is used to forecast two seasonal time series data of the total production value of the Taiwan machinery industry and the soft drink time series. The intention of this paper is to provide business which are affected by diversified management with a new method to conduct short-term forecasting. This model includes both interval models with interval parameters and the possible distribution of future value. Based on the results of practical application, it can be shown that this model makes good forecasts and is realistic. Furthermore, this model makes it possible for decision makers to forecast the best and worst estimates based on fewer observations than the SARIMA model.

Combining neural network model with seasonal time series ARIMA model

This paper proposes a hybrid forecasting model, which combines the seasonal time series ARIMA (SARIMA) and the neural network back propagation (BP) models, known as SARIMABP. This model was used to forecast two seasonal time series data of total production value for Taiwan machinery industry and the soft drink time series. The forecasting performance was compared among four models, i.e., the SARIMABP and SARIMA models and the two neural network models with differenced and deseasonalized data, respectively. Among these methods, the mean square error (MSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) of the SARIMABP model were the lowest. The SARIMABP model was also able to forecast certain significant turning points of the test time series.

Applied Hybrid Grey Model to Forecast Seasonal Time Series

The grey forecasting model has been successfully applied to finance, physical control, engineering, economics, etc. However, no seasonal time series forecast has been tested. The authors of this paper proved that GM(1,1) grey forecasting model is insufficient for forecasting time series with seasonality. This paper proposes a hybrid method that combines the GM(1,1) grey forecasting model and the ratio-to-moving-average deseasonalization method to forecast time series with seasonality characteristics. Three criteria, i.e., the mean squares error (MSE), the mean absolute error (MAE), and mean absolute percentage error (MAPE) were used to compare the performance of the hybrid model against other four models, i.e., the seasonal time series ARIMA model (SARIMA), the neural network back-propagation model combined with grey relation, the GM(1,1) grey model with raw data, the GM(1,N) grey model combined with grey relation. The time series data of the total production value of Taiwan's machinery industry (January 1994 to December 1997) and the sales volume of soft drink reported from Montgomery's book were used as test data sets. Except for the out-of-sample error of the Taiwan machinery production value time series, the MSE, the MAE, and the MAPE of the hybrid model were the lowest.