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This study presents the development of a supply chain (SC) observatory, which is a benchmarking solution to support companies within the same industry in understanding their positioning in terms of SC performance.

A case study is used to demonstrate the set-up of the observatory. Twelve experts on automatic equipment for the wrapping and packaging industry were asked to select a set of performance criteria taken from the literature and evaluate their importance for the chosen industry using multi-criteria decision-making (MCDM) techniques. To handle the high number of criteria without requiring a high amount of time-consuming effort from decision-makers (DMs), five subjective, parsimonious methods for criteria weighting are applied and compared.

A benchmarking methodology is presented and discussed, aimed at DMs in the considered industry. Ten companies were ranked with regard to SC performance. The ranking solution of the companies was on average robust since the general structure of the ranking was very similar for all five weighting methodologies, though simplified-analytic hierarchy process (AHP) was the method with the greatest ability to discriminate between the criteria of importance and was considered faster to carry out and more quickly understood by the decision-makers.

Developing an SC observatory usually requires managing a large number of alternatives and criteria. The developed methodology uses parsimonious weighting methods, providing DMs with an easy-to-use and time-saving tool. A future research step will be to complete the methodology by defining the minimum variation required for one or more criteria to reach a specific position in the ranking through the implementation of a post-fact analysis.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

Background/Objectives: Protein-based meal replacements (MR) with viscous soluble fibre are known aids for weight loss. This study aims to compare the effects of new whey and vegan MR containing different amounts of PGX (PolyGlycopleX) on weight loss over 12 weeks, along with a calorie-restricted diet.

Subjects/Methods: Sixty-eight healthy adults of both sexes (53 women; 15 men; average age 47.1 years; BMI 31 ± 7.1 kg/m² and weight 85.05 ± 23.3 kg) were recruited. Participants consumed a whey or vegan MR twice/d (5–10 g/day PGX) with a low-energy diet (1,200 kcal/day), over 12 weeks. Weight, height, waist and hip circumference were recorded (four time periods).

Results: Forty-four participants completed the study. Results showed significant reductions in average body weight and at week 12, whey group was [−7.7 kg ± 0.9 (8.3%), p < 0.001] and vegan group was [−4.5 kg ± 0.8 (6.2%), p < 0.001)]. All participants (n = 44; BMI 27 to 33 kg/m²) achieved significant reductions in body measurements from baseline to week 12; p < 0.001. Conclusions: Supplementation of protein-based MR with PGX and a balanced, low-energy diet, appears to be an effective approach for short-term weight loss.

As the authors were evaluating if the MR as a whole (i.e. with PGX) caused weight loss from baseline over the 12 weeks, no comparators, i.e. just the MR without PGX, were used. Formulation of these new MRs resulted in a whey product with 5 g PGX and a vegan product with 2.5 g PGX. Only 2.5 g PGX could be formulated with the vegan protein due to taste and viscosity limitations. Study participants were not randomized and no control groups (e.g. no MR or MR without PGX but with energy restricted diet) were used. Furthermore, it is not clear whether the sort of protein alone or the combination with a higher amount of PGX (whey with 5 g PGX/serving vs vegan with 2.5 g PGX/serving) has contributed to these significant greater weight-loss effects. This was something the authors were testing, i.e. could only 2.5 g PGX/serving have an effect on weight loss for a vegan MR. These limitations would be somethings to evaluate in a subsequent randomized controlled study. Hence, the results of this study may serve as a good starting point for further sophisticated randomized controlled trials that can demonstrate causality – which the authors acknowledge as one of the fundamental limitations of an observational study design. Participants tracked their calories but adherence and compliance were self-assessed and they were encouraged to keep their exercise routine consistent throughout the study. Hence, these are further limitations. No control group was used in this study to observe the effect of the dietary intervention and/or physical activity on weight loss alone. However, a goal of the authors was to keep this study as close to a real-life situation as possible, where people would not be doing any of these measurements, to see if with minimal supervision or intervention, people can still lose weight and alter their body composition. Furthermore, differences in gender and the corresponding weight loss effects in response to MR-protein-based treatments could be evaluated in follow-up studies.

This study indicates that the consumption of protein-based (animal, whey or plant, pea protein) MR incorporating the highly soluble viscous PGX is beneficial for weight loss when combined with a healthy-balanced, calorie-restricted diet. MRs at either 2.5 g or 5 g per serving (RealEasy^TM with PGX) proved to be a highly effective as a short-term solution for weight loss. The observed results are encouraging, however, further long-term studies (i.e. randomized clinical trials RCT) are needed to confirm the clinical relevance. RCTs should focus on the individual effects of PGX and/or the different protein sources used in MRs, on weight loss and the maintenance of the reduced body weight, and should measure detailed blood parameters (lipid profiles, glucose etc.) as well as collect detailed exercise and food consumption diaries.

To the authors’ knowledge, this is the first study comparing a whey versus vegan, (as pea) protein-based MR that is supplemented with fibre PGX; thus, this work adds information to the already existing literature on fibre (such as PGX) and MRs regarding their combined weight loss effects. The purpose of this study was to observe if the novel protein-based (either whey or vegan versions) MR RealEasy^TM with PGX at 2.5 or 5 g in addition to a calorie-restricted diet (total of 1,200 kcal/day) would aid in weight loss in individuals over a 12-weeks period. Adding increasing amounts of whey protein and soluble fibre can help reduce subsequent ad libitum energy intake which could help adherence to energy restricted diets, but whether similar effects are seen with vegan protein is unclear – this study does aim to address this.

This study aims to apply new modifications by changing the nonlinear logarithmic calculation steps in the method based on the removal effects of criteria (MEREC) method. Geometric and harmonic mean from multiplicative functions is used for the modifications made while extracting the effects of the criteria on the overall performance one by one. Instead of the nonlinear logarithmic measure used in the MEREC method, it is desired to obtain results that are closer to the mean and have a lower standard deviation.

The MEREC method is based on the removal effects of the criteria on the overall performance. The method uses a logarithmic measure with a nonlinear function. MEREC-G using geometric mean and MEREC-H using harmonic mean are introduced in this study. The authors compared the MEREC method, its modifications and some other objective weight determination methods.

MEREC-G and MEREC-H variants, which are modifications of the MEREC method, are shown to be effective in determining the objective weights of the criteria. Findings of the MEREC-G and MEREC-H variants are more convenient, simpler, more reasonable, closer to the mean and have fewer deviations. It was determined that the MEREC-G variant gave more compatible findings with the entropy method.

Decision-making can occur at any time in any area of life. There are various criteria and alternatives for decision-making. In multi-criteria decision-making (MCDM) models, it is a very important distinction to determine the criteria weights for the selection/ranking of the alternatives. The MEREC method can be used to find more reasonable or average results than other weight determination methods such as entropy. It can be expected that the MEREC method will be more used in daily life problems and various areas.

Objective weight determination methods evaluate the weights of the criteria according to the scores of the determined alternatives. In this study, the MEREC method, which is an objective weight determination method, has been expanded. Although a nonlinear measurement model is used in the literature, the contribution was made in this study by using multiplicative functions. As an important originality, the authors demonstrated the effect of removing criteria in the MEREC method in a sensitivity analysis by actually removing the alternatives one by one from the model.

This research investigates when and how brands influence attribute importance weights. Most past studies modelling consumer decision processes treated the brand of a product as an attribute parallel to the price, color or size of a product, and as a result, those studies assigned an equal (i.e. non-contingent) importance weight across brands for each attribute. In contrast, this study introduces a brand-contingent attribute-weighting process, in which brand is a higher-order construct that influences attribute importance.

This study presents a multi-level choice model in which the importance weight of an attribute can vary across brands. This study then estimates the model using real purchase data and survey data from an airline industry.

This study finds that attribute importance weights are contingent upon two aspects of a brand – the perceived relative position of the brand and consumers’ brand usage experiences. Specifically, when consumers perceive a brand to be inferior to its competitors in a given attribute, they generally place greater weight on that attribute for that brand. In contrast, when consumers perceive a brand to be superior to its competitors in a given attribute, only consumers with extensive brand usage experiences place greater weight on that attribute for that brand.

The findings provide managerial insights on brand positioning and segmentation strategies using consumers’ brand usage experiences.

This study advances the literature on consumer decision processes by modeling an attribute-weighting process that is contingent upon brands. The present study models this process based on consumer behavior theories and estimates the model using real market data.

The shipper selects a suitable shipping route and plans for a voyage in order to import and export cargo on the basis of published sailing schedules. The reliability of the sailing schedule will influence the shipper’s logistics expense, which means that the logistics costs will depend on the reliability of schedules published by container shipping companies. Therefore, it is important to consider factors which can cause delays would for container ships sailing on sea routes. The reliability of published sailing schedules can be affected by a number of different factors. This study adopts the multi-criteria decision making (MCDM) method to estimate the importance of the delaying factors in a sailing schedule. In addition, the consistent fuzzy preference relations (CFPR) method is applied to identify the subjective importance (weights) of the delaying factors. The entropy weight method combined with the actual performance of the container shipping company are both used when estimating the objective importance (weights) of the delaying factors. According to the analysis results, the criteria can be divided into four quadrants with different management implications, which indicate that instructions for chase strategy, sailing schedule control, fleet allocation, transship operation arrangement and planning for ports in routes are often ignored by container shipping companies. Container shipping companies should consider adjusting their operational strategies, which would greatly improve their operational performance.

This study explores the characteristics of high-speed rail (HSR) and air transportation networks in China based on the weighted complex network approach. Previous related studies have largely implemented unweighted (binary) network analysis, or have constructed a weighted network, limited by unweighted centrality measures. This study applies weighted centrality measures (mean association [MA], triangle betweenness centrality [TBC], and weighted harmonic centrality [WHC]) to represent traffic dynamics in HSR and air transportation weighted networks, where nodes represent cities and links represent passenger traffic. The spatial distribution of centrality results is visualized by using ArcGIS 10.2. Moreover, we analyze the network robustness of HSR, air transportation, and multimodal networks by measuring weighted efficiency (WE) subjected to the highest weighted centrality node attacks. In the HSR network, centrality results show that cities with a higher MA are concentrated in the Yangtze River Delta and the Pearl River Delta; cities with a higher TBC are mostly provincial capitals or regional centers; and cities with a higher WHC are grouped in eastern and central regions. Furthermore, spatial differentiation of centrality results is found between HSR and air transportation networks. There is a little bit of difference in eastern cities; cities in the central region have complementary roles in HSR and air transportation networks, but air transport is still dominant in western cities. The robustness analysis results show that the multimodal network, which includes both airports and high-speed rail stations, has the best connectivity and shows robustness.

This study aims to analyze the development direction of train speed, density and weight in China.

The development of China's railway in the past 40 years can be divided into 3 stages. At the stage of potential tapping and capacity expansion, it is important to improve the train weight and density by upgrading the existing lines, and improving transportation capacity rapidly. At the stage of railway speed increase, the first priority is to increase train speed, reduce the travel time of passenger train, and synchronously take into account the increase of train density and weight. At the stage of developing high-speed railway, train speed, density and weight are co-developing on demand.

The train speed of high-speed railway will be 400 km h⁻¹, the interval time of train tracking will be 3 min, and the traffic density will be more than 190 pairs per day. The running speed of high-speed freight EMU will reach 200 km h⁻¹ and above. The maximum speed of passenger train on mixed passenger and freight railway can reach 200 km h⁻¹. The minimum interval time of train tracking can be compressed to 5 min. The freight train weight of 850 m series arrival-departure track railway can be increased to 4,500–5,000 t and that of 1,050 m series to 5,500–6,400 t. EMU trains should gradually replace ordinary passenger trains to improve the quality of railway passenger service. Small formation trains will operate more in intercity railway, suburban railway and short-distance passenger transportation.

The research can provide new connotations and requirements of railway train speed, density and weight in the new railway stage.

The purpose of this paper is to analyze and compare the performances of novel roadable personal air vehicle (PAV) concepts that meet established operational requirements with different types of engines.

The vehicle configuration was devised considering the dimensions and operational restrictions of the roads, runways and parking lots in South Korea. A folding wing design was adopted for road operations and parking. The propulsion designs considered herein use gasoline, diesel and hybrid architectures for longer-range missions. The sizing point of the roadable PAV that minimizes the wing area was selected, and the rate of climb, ground roll distance, cruise speed and service ceiling requirements were met. For various engine types and mission profiles, the performances of differently sized PAVs were compared with respect to the MTOW, wing area, wing span, thrust-to-weight ratio, wing loading, power-to-weight ratio, brake horsepower and fuel efficiency.

Unlike automobiles, the weight penalty of the hybrid system because of the additional electrical components reduced the fuel efficiency considerably. When the four engine types were compared, matching the total engine system weight, the internal combustion (IC) engine PAVs had better fuel efficiency rates than the hybrid powered PAVs. Finally, a gasoline-powered PAV configuration was selected as the final design because it had the lowest MTOW, despite its slightly worse fuel efficiency compared to that of the diesel-powered engine.

Although an electric aircraft powered only by batteries most capitalizes on the operating cost, noise and emissions benefits of electric propulsion, it also is most hampered by range limitations. Air traffic integration or any safety, and noise issues were not accounted in this study.

Aircraft sizing is a critical aspect of a system-level study because it is a prerequisite for most design and analysis activities, including those related to the internal layout as well as cost and system effectiveness analyses. The results of this study can be implemented to design a PAV.

This study can contribute to the establishment of innovative PAV concepts that can alleviate today’s transportation problems.

This study compared the sizing results of PAVs with hybrid engines with those having IC engines.

The purpose of this paper is to summarize different methods of constructing the financial conditions index (FCI) and analyze current studies on constructing FCI for China. Due to shifts of China’s financial mechanisms in the post-crisis era, conventional ways of FCI construction have their limitations.

The paper suggests improvements in two aspects, i.e. using time-varying weights and introducing non-financial variables. In the empirical study, the author first develops an FCI with fixed weights for comparison, constructs a post-crisis FCI based on time-varying parameter vector autoregressive model and finally examines the FCI with time-varying weights concerning its explanatory and predictive power for inflation.

Results suggest that the FCI with time-varying weights performs better than one with fixed weights and the former better reflects China’s financial conditions. Furthermore, introduction of credit availability improves the FCI.

FCI constructed in this paper goes ahead of inflation by about 11 months, and it has strong explanatory and predictive power for inflation. Constructing an appropriate FCI is important for improving the effectiveness and predictive power of the post-crisis monetary policy and foe achieving both economic and financial stability.