The impressive growth of the Vietnam textile and apparel industry has led to some concerns due to its insufficient value chain and fierce competition within the industry. Thus, performance evaluation is significant issue to estimate the values of companies over time. Firstly, the grey prediction is used to forecast future data for 20 largest enterprises in six years (2016–2021) based on actual indicators. Then, the paper uses Malmquist productivity index and its decomposition into efficiency and technical change to measure the past productivity growth. Finally, window analysis is applied to significantly detect the trends of performance in 12 years (2010–2021) from large number of inputs and outputs. The results are found that how technology changes is the determinant for productivity growth and undeveloped technology causes a huge barrier to industry. In addition, the results are illustrated that textile companies are predicted to be more stable due to the importance of supplying materials for the entire industry. This paper aims to prove in-depth analysis can be conducted through combined models and also provide some recommendations for enhancing the sustainability performance of the industry.

Coffee is the most traded commodity after petroleum. The Vietnamese coffee bean industry has raised concerns lately over an inefficient coffee value chain; bets on coffee price uncertainty are increasing worldwide in the current. Accurate optimization of coffee bean prices helps manufacturers to control an unpredictable market and upgrade cooperativeness in sustainable agriculture. The authors proposed a forecasting method to deal with demand volatility and uncertainty in volumes and coffee bean prices. In this paper, we applied the forecasting nonlinear grey Bernoulli model (NGBM) (1,1). NGBM (1,1), which is based on the parameter optimization algorithm, can increase the precision of predictions. NGBM (1,1) was integrated with Fourier residual modification model to forecast coffee bean price, which was a crucial factor in the Vietnamese coffee bean supply chain. The price of coffee beans was calculated using a differential equation in an uncertain system, along with actual data collected over the past six years. The results of this study demonstrate that an integrated forecasting model is an effective forecasting method. This research can help companies to control risks that come with uncertain coffee prices and reduce risks in the sustainable agriculture supply chain.

The Japanese automobile industry has been hit sharply by the economic downturn of recent decades. The rise in costs and decline in sales have led to serious problems in the auto industry. In order to address these issues, most companies engage in downsizing and redesigning production operations. It is crucial to investigate the time wasted by replacing assembly boards occurring in manufacturing lines. Therefore, the aim of this study was to provide an integrated approach, Teoriya Resheniya Izobreatatelskih Zadatch (TRIZ), to providing efficient solutions for the automobile industry. The first step of this methodology is to detail the technical problems using the Function and Attribute Analysis (FAA) model. Secondly, a contradiction matrix and the inventive principle were applied to find the solutions. In this study, an auto part supplier named Sumi-Hanel located in Hanoi, Vietnam, was taken as a case study; the empirical results showed that waste time had been reduced to 67%, nearly 8400 square meters was saved, and a 20% cost reduction was achieved by reusing old frames. The research proves that the combination of TRIZ and lean manufacturing successfully increases production performance and reduces waste due to technological advancements.

Vietnam has emerged as a highly promising destination for foreign direct investment (FDI) projects, experiencing a commendable and consistent influx of FDI in recent years. However, this surge is not uniformly distributed across provinces. This study applies the data envelopment analysis (DEA) and Malmquist index measurement to evaluate FDI attractiveness of provinces in Vietnam from 2012 to 2020. Based on DEA, the analysis takes the provinces’ five indicators (Labor Force, Gross Regional Domestic Product, Provincial Competitiveness Index, FDI by capital, and FDI by cases) into account in order to identify provinces ripe for targeted improvement as well as those that can serve as exemplars for others. Next, Malmquist indexes are derived from DEA assessment to observe the evolution of each province during 2012 to 2020, and then decomposed to uncover the cause of performance change. We find that only four provinces are able to achieve perfect efficiency in attracting FDI, including three provinces from South East region: Vung Tau, Binh Duong, Ho Chi Minh, and Ha Tinh. Nevertheless, nearly 80% of the provinces improve their performance. The methodology and findings in this paper can provide significant insights for policymakers to devise mechanisms to attract FDI in their locality.

In Vietnam, foreign direct investment (FDI) is an important capital flow for sustainable socio-economic growth and international economic integration, contributing to the supplementation of capital, modern technology, management and business capacity, and the ability to organize and participate in the global supply chain. In this paper, a novel hybrid model combining simple average combination of SMA (Simple Moving Average), IFTS (Improved Fuzzy Time Series), and DEA window analysis is proposed to evaluate FDI attractiveness in Vietnam. Five crucial indicators, including labor force (LF), gross regional domestic product (GDP), the Provincial Competitiveness Index (PCI), FDI by capital, and FDI by cases, were employed to explore their impacts on the efficiency of attracting FDI into provinces, for sustainable economic growth. First, the future values of indicators for 2021–2022 were predicted based on collected historical data from 2012 to 2020. Then, the DEA window was employed to evaluate the efficiency of the provinces in terms of their FDI attractiveness during the period 2012–2022. From the results, Binh Duong, Ho Chi Minh, and Vung Tau were found to be the most efficient localities at attracting FDI, while An Giang, Tuyen Quang, and Can Tho had the lowest FDI attractiveness indexes. The proposed model was proven to be effective in identifying both the provinces which should be targeted for further improvement, and the provinces which should serve as role models for other provinces. In this direction, this paper can serve as a significant guideline for decision-makers and the Government to improve FDI attractiveness, with solutions to attract FDI in a sustainable way.

This study applies an assessment model to measure the efficiency of companies in Vietnam garment industry based on Grey theory (GM (1, 1)) and data envelopment analysis model. The objective of this research is to provide an effective method to find the right strategic partners. Total 11 companies of garment industry are chosen with realistic data collected from financial statements of Vietnam published stock market during period 2007 to 2010. Firstly, the GM (1, 1) is employed to predict the inputs and outputs factor. Secondly, Slack based measure of supper efficiency (Super-SBM) model is applied to help the target company to find the right partners. The empirical results show that companies who have better efficiencies do not guarantee them to get the good performance after alliances, companies with low-mid efficiencies may have the chance to put in efforts for alliances because they may get some benefits for both candidate and target companies. This study could give companies some recommendations about how to improve productivity as well.