Editorial

Citation

Moutinho, L. and Huang, K.H. (2014), "Editorial", Journal of Modelling in Management, Vol. 9 No. 1. https://doi.org/10.1108/JM2-12-2013-0066

Editorial

Article Type: Editorial From: Journal of Modelling in Management, Volume 9, Issue 1

Welcome to volume 9. Time passes so quickly and here we are in our ninth year. This is an interesting mixed feature issue for the start of the ninth volume because it encompasses novel and traditional methodologies, the perennial financial issues of cash flow, the resurgence of optimisation modelling, traditional contextual domain of banking and the surprising element of dealing with military supply chain (SC) measures.

According to Sucky’s literature research and conversations with apparel manufacturers’ specialists, there is not any common analytic method for demand forecasting in apparel industry and to our knowledge, there is not adequate number of study in literature to forecast the demand with adaptive-network-based fuzzy inference system (ANFIS) for apparel manufacturers. The purpose of this paper is constructing an effective demand forecasting system for apparel manufacturers. The ANFIS is used forecasting the demand for apparel manufacturers.

The results of the Sucky’s study showed that an ANFIS-based demand forecasting system can help apparel manufacturers to forecast demand accurately, effectively and simply.

ANFIS is a new technique for demand forecasting, combines the learning capability of the neural networks and the generalization capability of the fuzzy logic. In this study, the demand is forecasted in terms of apparel manufacturers by using ANFIS. The input and output criteria are determined based on apparel manufacturers’ requirements and via literature research and the forecasting horizon is about one month. The study includes the real life application of the proposed system and the proposed system is tested by using real demand values for apparel manufacturers.

The purpose of Lu’s paper is to examine the effect of financial constraints on firm growth considering six types of ownership structure. According to the theory of financial management and asymmetric information theory, external funds are costly for small firms. However, some ownership structures may alleviate cash flow-growth sensitivity. We consider different types of ownership structure to study cash flow-growth relation and its sensitivity.

Results are drawn from a dynamic panel data model under our two specific empirical models. Those designs can capture important empirical meanings.

The sensitivity of growth to cash flow decreases significantly when managers control larger proportions of a firm’s stock and when a firm belongs to a conglomerate. The findings also show that small and young firms grow faster. R&D and advertising expenditures also motivate a firm’s growth, as do profitability and abundant cash flow.

Lu’s paper uses a dynamic panel data model to investigate the effect of cash flow on firms’ growth under six types of ownership structure. The sensitivity analysis of growth to cash flow provides new results for traditional literature. In fact, different ownership structures lead to distinct cash flow-growth sensitivity.

In Udechukwu’s outline paper, he explains his process: analyse and compare the performances of portfolio optimisation models including Markowitz’s mean-variance model (MV model), Konno and Yamazaki’s mean-absolute deviation portfolio optimisation model (MAD model), Young’s minimax portfolio model and the VaR model.

Historical data on 43 constituent shares listed on the Hong Kong Hang Seng Index (HIS) covering a four-year period are obtained. We then test the performance of each model under different scenarios and against different sets of historical data.

We find that:

* Different levels of required annual returns impact on portfolio composition.

* Historical data have a major impact on the determination of portfolio composition.

* The level of required annual return impacts on how optimisation models perform.

We posit that with a comprehensive understanding of the performance of each of these performance optimisation models, investors may be able to develop a better understanding of how to adjust investment risk strategies, thus preventing serious losses.

There are two major points of value to this paper. In the first place, the paper presents an original review of portfolio optimisation models. Second, using “real” data, we utilise five different scenarios to test the performance of each model under different situations.

Tsolas’s paper aims to assess two distinct aspects of performance in terms of technical (sales) efficiency and efficiency in market value generation of a sample of Greek metallurgical firms listed on the Athens Exchange by using data envelopment analysis (DEA). Both aspects of performance are measured by employing the DEA BCC model, combined with bootstrap and generalized proportional distance function (GPDF). Statistical analysis is performed to investigate whether there is a positive link between the two examined performance dimensions. Inefficiency is uncovered in both performance dimensions, but there is a lower level of performance in market value generation than in technical efficiency. Correlation analysis results do not point out positive links between performance measures for the sample firms.

The derived performance measures allow firm managers to set their own priorities and to seek out improvements along the two dimensions of performance; moreover, they may contribute to the reduction of information asymmetry among investors.

This paper is one of a few that investigate the link between DEA-based sales performance and performance in market value generation. It contributes methodologically through the adoption of fundamental analysis principles in estimating efficiency in the two performance dimensions and the development of a DEA efficiency model in the presence of negative data.

So far we lack a comprehensive definition of military SC flexibility, as well as performance measures to evaluate it. Sokri’s paper aims to address these gaps. It seeks to develop performance measures to assess the flexibility of a military SC. Building on the flexibility literature, novel performance measures were developed to assess the volume flexibility (the ability to change the level of moved products) and delivery flexibility (the ability to meet short-lead times).

This study characterizes the behaviour of a military SC by focusing on the volume and delivery sides. Efficiency, for example, is not within the scope of this analysis.

The results of this paper could serve as a means to compare between SCs with drastically different sizes.

Sokri’s paper presents a novel ways to examine the flexibility of a military distribution process. The developed measures of flexibility are relevant, simple, dimensionless, and action oriented.

Garg’s paper aims to measure customer experience in Indian banks. This study examines the 14 factors of customer experience and identifies their impact on customer satisfaction. In Garg’s study, psychometric scale development procedure is followed comprising with the steps of item generation and selection, scale refinement and scale validation. A one-way ANOVA test is applied to identify the relationship between 14 experience factors and demographics of respondents.

The findings of the study present a 41-item 14 factor reliable and valid customer experience scale among which “convenience” appears as the most significant among all the factors. This study concentrates on a sector-specific scale whereas a generalized scale which can be applied in other service sectors should be developed. In comparison with previous studies, the results of the current study provide a more absolute coverage and understanding of various touch points used in measuring customer experience in banks.

By this reliable and valid scale bank managers can identify the current and expected experiences of the customers and can build up effective strategies for the utmost satisfaction of the customers.

To the best of our knowledge, this study represents the foremost studies for developing a validated tool to measure the experiences of banks’ customers.

I hope you enjoy the issue. Lets hope the ninth year brings an increase in submissions but above all more challenging topics of research, more interest in application of different modelling approaches and novel methods of analysis. All that will make a contribution to further mind stretch and thought provoke our readers.

Thanks for your continued support.

Luiz Moutinho, Kun Huarng Huang