Journal of Modelling in Management

Deshmukh, Arun Kumar; Mohan, Ashutosh

2017 Journal of Modelling in Management

doi: 10.1108/JM2-12-2015-0101

PurposeThe study aims to present demand chain management (DCM) modeling of Indian apparel retailers. This will result in a structured model presenting contextual interrelationship among DCM variables so that retailers can proactively manage their demand chain.Design/methodology/approachThe research follows an exploratory research design. It initially involves identification and analysis of influential factors of the implementation of DCM practices through the review of literature. Then, these variables were analyzed using total interpretive structural modeling or TISM followed by a statistical verification and case-based validation of the model.FindingsThe major findings of the paper are: top-management commitment and support, information management and supply chain agility in supply chain are the most significant enablers with the highest driving power. The other apparel retail specific significant variables are assortment planning, category management and marketing orientation. The model also indicates that the firms that implement customer-centric DCM practices do well in terms of organizational performance and thereby achieve differential advantage over their competitors.Research limitations/implicationsBecause the literature on DCM is still in nascent stage, the study bases itself on interpretive method; that is, TISM of analysis with a limited number of experts. Future studies may consider larger sample with more advanced statistical tools such as structural equation modeling for further validation of the findings.Originality/valueThe novelty of the paper lies in the study of an emerging supply chain philosophy; that is, DCM and its key practices per se. It has rarely been studied from the theory building perspective hitherto. Moreover, TISM-based approach is applied for the first time to study the DCM practices and its drivers vis-à-vis dependents.

Rehman, Saif Ur; Qingren, Cao; Weiming, Gao

2017 Journal of Modelling in Management

doi: 10.1108/JM2-09-2015-0076

PurposeThe aim of this paper is to develop a model for presenting level of trust and analyzing the contribution of various trust-building measures undertaken by an organization.Design/methodology/approachThe conceptual framework for the model is based on previous research and the concept of trust and its implications in business environment. This model includes various stages of trust measured against time and trust building measures (TBM). This trust model relates the trustee’s position with the trustor at any point in time and describes its impact on trustee’s position in terms of trustworthiness (sum of “trust deficit” and “trust gain”). 10;Vectors and linear Algebra equations are used to construct the model supplemented with an example from real-life business environment for better understanding of the model. 10.FindingsA trust framework, elaborating level of trust between two parties is explained with the help of a mathematical model. The model includes various stages of trust measured against time and TBM.Research limitations/implicationsIn the practical application of the model, the authors adopted an existing scale to measure trust levels, which can have its limitations and shortcomings. It is however suggested to choose as specific scale for the industry as possible.Practical implicationsThe model can be applied in any situation, person or environment specially to determine the current situation of organizational trust in business which can be helpful in making decisions.Originality/valueThe concept of making trust a part of strategy and a tool for decision-making is novel and applicable in all sectors and situations. By providing a real-time view of the level of trust and impact of TBM will help predict future levels of trust and make it an essential part of decision-making process.

Godinho, Pedro; Moutinho, Luiz; Pagani, Margherita

2017 Journal of Modelling in Management

doi: 10.1108/JM2-10-2015-0078

PurposeThe purpose of this study is to propose a measure for earned attention and a model and procedure for the maximization of earned attention by a company over a period of time.Design/methodology/approachUtility functions are used as the base of the earned attention measure. An evolutionary algorithm – a memetic algorithm – is applied to identify strategies that aim to maximize earned attention. Computational analysis is performed resorting to simulated data, and the memetic algorithm is assessed through the comparison with a standard steepest ascent heuristic.FindingsThe shape of the utility functions considered in the model has a huge impact on the characteristics of the best strategies, with actions focused on increasing a single variable being preferred in case of constant marginal utility, and more balanced strategies having a better performance in the case of decreasing marginal utility. The memetic algorithm is shown to have a much better performance that the steepest ascent procedure.Originality/valueA new mathematical model for earned attention is proposed, and an approach that has few applications in business problems – a memetic algorithm – is tailored to the model and applied to identify solutions.

Jothimani, Dhanya; Shankar, Ravi; Yadav, Surendra S.

2017 Journal of Modelling in Management

doi: 10.1108/JM2-09-2015-0073

PurposePortfolio optimization is the process of making an investment decision on a set of assets to realize high returns with low risk. It has three major stages: asset selection, asset weighting and asset management. Asset selection is an important phase because it influences asset allocation and ultimately affects the returns of a portfolio. Today, there is an increase in the number of listings on a stock exchange. Therefore, it is important for an investor to screen and select stocks for investment. This study focuses on the first stage of the portfolio optimization problem, namely, asset selection. The purpose of this study is to evaluate and select profitable stocks quoted on National Stock Exchange (NSE) for portfolio optimization.Design/methodology/approachFinancial ratios are considered as the input and output parameters for evaluating the financial performance of the firms. This study adopts a hybrid principal component analysis (PCA) and data envelopment analysis (DEA) approach to evaluate the efficiency of the firms. Based on the efficiency scores, the firms are selected for the investment process.FindingsThe model helps to determine the relative efficiencies of the firms. The efficient firms are considered to be the potential stocks for investment. It helps the investors to screen the stocks from a large number of stocks quoted on NSE.Research limitations/implicationsOne of the limitations of the standard DEA model is that it fails to discriminate the firms when the number of input and output parameters are larger than the number of firms. To overcome this problem, either a parameter can be ignored or weight-restricted DEA can be applied. When an input/output parameter is dropped, the information in that variable is lost. Weight-restricted DEA model uses expert opinion for measuring the relative importance of input and output parameters. Expert opinion is subjective and might be biased. The PCA-DEA model helps to identify the efficient firms by improving the discriminatory power of standard DEA without any loss of information and without the need for expert opinion, which might be biased.Practical implicationsAsset selection is an important stage in the investment process. Selection of stocks based on the efficiency score is an easier option available to the investors. But the misclassification of firms either due to biased expert opinion or discrimination inability of DEA can be costly to an investor. The PCA-DEA model overcomes both these limitations. Investors can select the potential candidates for asset allocation based on the efficiency scores obtained using the PCA-DEA model. Further, the relative efficiencies obtained can help the firms to benchmark their performance against the best performing firms within their industry.Originality/valueThis paper is one of few papers to adopt the PCA-DEA framework to select stocks in the Indian stock market.

Park, Joonwook; Rajagopal, Priyali; Dillon, William; Chaiy, Seoil; DeSarbo, Wayne

2017 Journal of Modelling in Management

doi: 10.1108/JM2-12-2015-0100

PurposeJoint space multidimensional scaling (MDS) maps are often utilized for positioning analyses and are estimated with survey data of consumer preferences, choices, considerations, intentions, etc. so as to provide a parsimonious spatial depiction of the competitive landscape. However, little attention has been given to the possibility that consumers may display heterogeneity in their information usage (Bettman et al., 1998) and the possible impact this may have on the corresponding estimated joint space maps. This paper aims to address this important issue and proposes a new Bayesian multidimensional unfolding model for the analysis of two or three-way dominance (e.g. preference) data. The authors’ new MDS model explicitly accommodates dimension selection and preference heterogeneity simultaneously in a unified framework.Design/methodology/approachThis manuscript introduces a new Bayesian hierarchical spatial MDS model with accompanying Markov chain Monte Carlo algorithm for estimation that explicitly places constraints on a set of scale parameters in such a way as to model a consumer using or not using each latent dimension in forming his/her preferences while at the same time permitting consumers to differentially weigh each utilized latent dimension. In this manner, both preference heterogeneity and dimensionality selection heterogeneity are modeled simultaneously.FindingsThe superiority of this model over existing spatial models is demonstrated in both the case of simulated data, where the structure of the data is known in advance, as well as in an empirical application/illustration relating to the positioning of digital cameras. In the empirical application/illustration, the policy implications of accounting for the presence of dimensionality selection heterogeneity is shown to be derived from the Bayesian spatial analyses conducted. The results demonstrate that a model that incorporates dimensionality selection heterogeneity outperforms models that cannot recognize that consumers may be selective in the product information that they choose to process. Such results also show that a marketing manager may encounter biased parameter estimates and distorted market structures if he/she ignores such dimensionality selection heterogeneity.Research limitations/implicationsThe proposed Bayesian spatial model provides information regarding how individual consumers utilize each dimension and how the relationship with behavioral variables can help marketers understand the underlying reasons for selective dimensional usage. Further, the proposed approach helps a marketing manager to identify major dimension(s) that could maximize the effect of a change of brand positioning, and thus identify potential opportunities/threats that existing MDS methods cannot provides.Originality/valueTo date, no existent spatial model utilized for brand positioning can accommodate the various forms of heterogeneity exhibited by real consumers mentioned above. The end result can be very inaccurate and biased portrayals of competitive market structure whose strategy implications may be wrong and non-optimal. Given the role of such spatial models in the classical segmentation-targeting-positioning paradigm which forms the basis of all marketing strategy, the value of such research can be dramatic in many marketing applications, as illustrated in the manuscript via analyses of both synthetic and actual data.

Kasie, Fentahun Moges; Bright, Glen; Walker, Anthony

2017 Journal of Modelling in Management

doi: 10.1108/JM2-02-2016-0015

PurposeThis paper aims to propose a theoretical decision support framework, which integrates artificial intelligence (AI), discrete-event simulation (DES) and database management technologies so as to determine the steady state flow of items (e.g. fixtures, jigs, tools, etc.) in manufacturing.Design/methodology/approachThe existing literature was carefully reviewed to address the state of the arts in decision support systems (DSS), the shortcomings of pure simulation-based and pure AI-based DSS. A conceptual example is illustrated to show the integrated application of AI, simulation and database components of the proposed DSS framework.FindingsRecent DSS studies have revealed the limitations of pure simulation-based and pure AI-based DSS. A new DSS framework is required in manufacturing to address these limitations, taking into account the problems of flowing items.Research limitations/implicationsThe theoretical DSS framework is proposed using simple rules and equations. This implies that it is not complex for software development and implementation. Practical data are not presented in this paper. A real DSS will be developed using the proposed theoretical framework and realistic results will be presented in the near future.Originality/valueThe proposed theoretical framework reveals how the integrated components of DSS can work together in manufacturing in order to determine the stable flow of items in a specific production period. Especially, the integrated performance of case-based reasoning (CBR) and DES is conceptually illustrated.

Tang, Wangmei; Ang, Sheng

2017 Journal of Modelling in Management

doi: 10.1108/JM2-03-2016-0025

PurposeThe paper aims to study a strategy of advance selling with part payment (ADP) in which pre-ordering consumers are required to pay a portion of advance price first and then pay the rest in the spot period to complete the order. The authors compare the ADP strategy with strategies of advance selling with full payment (ADF) and no advance selling (NA) from the perspective of sellers.Design/methodology/approachThe paper proposes a two-period pricing model with price-off promotion in the first period for a market consisting of consumers and a single seller. For each strategy (i.e. NA, ADF and ADP), solutions to the seller’s optimal order quantity in the spot period, optimal advance price and prepayment in the advance period are derived by backward conduction. Numerical study is also used to obtain straightforward insights.FindingsAdvance price of ADF is lower than that of ADP. Order quantity of ADF is higher than that of ADP. ADP brings more profit than the other two selling strategies, i.e. NA and ADF, when ADP’s implementing conditions are satisfied. While ADF is effective only when unit cost is low, ADP is applicable irrespective of whether the cost is low.Originality/valueExisting researchers on advance selling mainly focus on the ADF strategy. The paper pays attention to different payment mechanisms in advance selling and steps further to propose a new form of advance selling, i.e. the ADP strategy. The effects of ADP on consumer’s purchasing behavior and seller’s marketing decisions are analyzed.

Gomes, Carlos Francisco Simões; Costa, Helder Gomes; de Barros, Alexandre P.

2017 Journal of Modelling in Management

doi: 10.1108/JM2-01-2016-0005

PurposeThe purpose of this paper is to present a hybrid modelling that combines concepts and techniques for scenario building together with a Multi-criteria Decision Aid (MCDA) outranking approach. The paper presents a case to illustrate the proposed methodology.Design/methodology/approachThe research method is a qualitative and quantitative mixture and it is presented as a study case. Bibliographic research is used to construct the theoretical framework. There are a number of studies that develop a sensibility analysis in MCDA modelling; however, none of them explore the robustness of the MCDA solution with use of scenarios variation.FindingsThe methodology allows the criteria that must be taken into account, according to the decision makers’ values and preferences. It is interesting to note that, depending on the scenario, different weights were applied for each criterion, and the performances of alternatives under each criterion has changed as well.Practical implicationsThis need arises in decision problems that are susceptible to the influence of scenario variation.Originality/valueThis proposal was applied to a real case that has taken into account six alternatives, with a prospective analysis of three scenarios, evaluated by four criteria. The authors use prospective scenarios to choose the criterion weights and alternatives evaluation.

Kumar, Divesh; Rahman, Zillur

2017 Journal of Modelling in Management

doi: 10.1108/JM2-02-2016-0013

PurposeThis paper aims to intend to help focal firms which are keen to develop a sustainable supply chain by identifying enablers, in knowing the interrelationships involved and in ranking the enablers.Design/methodology/approachInterpretive structural modeling and fuzzy MICMAC were used for the modeling and clustering of the enablers and fuzzy analytical hierarchy process has been used for the ranking purpose.FindingsAwareness about sustainability incentives, pressure from stakeholders, support from supply chain partners and demand from customer for sustainable products were found very important for developing a sustainable supply chain.Research limitations/implicationsThis research will help practitioners to appreciate the importance of the enablers to focus on the making sustainability adoption feasible across the supply chain. This would also facilitate focal firm management to develop a sustainability culture across the supply chain.Originality/valueSimilar work has not been carried before in which interaction among enablers and their priorities were analyzed using hybrid methodologies in developing country context.

Abedi, Mehdi; Seidgar, Hany; Fazlollahtabar, Hamed

2017 Journal of Modelling in Management

doi: 10.1108/JM2-02-2016-0011

PurposeThe purpose of this paper is to present a new mathematical model for the unrelated parallel machine scheduling problem with aging effects and multi-maintenance activities.Design/methodology/approachThe authors assume that each machine may be subject to several maintenance activities over the scheduling horizon and a machine turn into its initial condition after maintenance activity and the aging effects start anew. The objective is to minimize the weighted sum of early/tardy times of jobs and maintenance costs.FindingsAs this problem is proven to be non-deterministic polynomial-time hard (NP-hard), the authors employed imperialist competitive algorithm (ICA) and genetic algorithm (GA) as solution approaches, and the parameters of the proposed algorithms are calibrated by a novel parameter tuning tool called Artificial Neural Network (ANN). The computational results clarify that GA performs better than ICA in quality of solutions and computational time.Originality/valuePredictive maintenance (PM) activities carry out the operations on machines and tools before the breakdown takes place and it helps to prevent failures before they happen.