Search results

The purpose of this paper is to provide and illustrate a framework for the role of governance mechanisms in information sharing among supply chain members. The importance of trust in governing interorganizational relationships is emphasized.

Trust, bargaining power, and contract are three key constructs supporting the governance of information sharing and material flow coordination in supply chains. A conceptual framework showing how these governance mechanisms affect coordination and ultimately, supply chain performance is presented. Four types of trust – calculative, competence, integrity, and predictability – are thought to play an important role in determining the efficacy of information sharing. Three research questions are posed on the relationships among trust, bargaining power, contracts, and information sharing in supply chain coordination. These governance issues are shown to be key factors in the supply chain business model, as illustrated in a case study from the retail distribution industry in the USA where collaborative planning, forecasting and replenishment is used to exchange supply and demand forecasts.

An example from the retail distribution industry shows that the three constructs of the governance framework are intertwined. Trust as a governance mechanism plays a crucial role in sharing information among business partners.

The proposed framework is illustrated with a single case. It will need to be tested empirically for supply chains across different industries.

The paper presents a governance mechanism framework for supply chain information sharing. Knowledge of the role of governance mechanisms in information sharing coordination will help chain members to realign business relationships and contribute to improved overall operational performance of the chain.

The purpose of this paper is to investigate the impact of demand information sharing on products’ greening levels with downstream competition. Specifically, this study examine two types of green products, “development-intensive” (DI) and “marginal-cost intensive” (MI), in a two-echelon supply chain where the manufacturer produces substitutable products, and competing retailers operate in a market with uncertain demand.

The authors adopt the manufacturer-led Stackelberg game-theoretic framework and consider a multistage game. This study consider how retailers receive private signals about uncertain demand and decide whether to share this information with the manufacturer, who then decides whether to acquire this information at a certain given cost. This paper considers backward induction and Bayesian Nash equilibrium to solve the model.

The authors find that in the absence of competition, information sharing is the only equilibrium and improves the greening level under DI, whereas no-information sharing is the only equilibrium and improves the greening level under MI, an increase in downstream competition drives higher investment in greening efforts by the manufacturer in both DI and MI and the manufacturer needs to offer a payment to the retailers to obtain demand information under both simultaneous and sequential contract schemes.

This paper contributes to the literature by examining how the nature of products (margin intensive green product or development intensive green product) influences green supply chain decisions under information asymmetry and downstream competition.

The purpose of this paper is to examine the impact of remittance inflows (remittances) on electricity consumption and electric power losses in Jamaica.

The authors use annual data from 1976 to 2014 and apply vector error correction modelling, Granger causality testing and impulse response analysis.

First, the authors find that there is co-integration between remittances and the energy variables, namely electricity consumption and electric power losses. Second, short-run Granger causality exists between the energy variables and remittances. This causality is bidirectional between the energy variables and positive changes in remittances, but it is unidirectional running from the energy variables to negative movements in remittances. Third, the authors find that in the long-run remittances have a negative relationship with electric power losses and a positive relationship with the consumption of electricity.

Findings from this paper will help to elucidate the relationship between electricity consumption, and electric power losses, and remittances.

The problem of electric power losses is acute in Jamaica and it is mostly due to theft. At the same time, Jamaica receives significant remittances. Social policy could have a role to encourage the use of remittances to help stem the theft of electricity.

This is the first study that examines the relationships between remittances, electricity consumption and electric power losses.

The study aims to explore the relationship between financial literacy and general life satisfaction. The study further investigates the mediating role of financial self-efficacy in this relationship in the context of Indian rural households.

Households belonging to the rural area of the Koraput district of Odisha were taken as the sample unit of this study. A structured questionnaire was framed to collect primary data using multi-stage and convenience sampling; 299 responses were received. Data analyses were performed using partial least square-structure equation modelling through SmartPLS 4.0.

The results of the study connoted that financial literacy has a noteworthy impact on the overall life satisfaction of households with lower incomes, both directly and indirectly. Moreover, the study identified financial self-efficacy as a significant complementary partial mediator in the relationship between financial literacy and overall satisfaction with life.

The findings of the study can be used by financial regulatory authorities and policymakers to seed the financial concepts’ understanding among the rural community to enhance their financial status and thereby overall satisfaction with life.

To the best of the authors’ knowledge, the exploration study of life satisfaction of rural households is yet to be discovered in the context of previous research frameworks despite rural households being an intricate part of the Indian economy. The study adds to the existing literature on life satisfaction, necessitating financial literacy expertise in rural households for achieving financial self-efficacy.

This paper aims to present a new mathematical model for laminated rhombic conoids with reasonable thickness and depth. The presented model does not require the shear correction factor, as transverse strain variation through the thickness was assumed as a parabolic function. The zero transverse shear stress provision at the bottom and the top of rhombic conoids was enforced in the model. The presented model implemented a C⁰ finite element (FE) model, eliminating C¹ continuity requirement in the mathematical model. The proposed model was validated with analytical, experimental and other methods derived from the literature.

A novel mathematical model for laminated composite skew conoidal shells has been proposed. Parabolic transverse shear strain deformation across thickness is considered. FE coding of the proposed novel mathematical model was done. Slope continuity requirement associated with present FE coding has been suitably avoided. No shear correction factor is required in the present formulation.

This is the first attempt to study the bending response of laminated rhombic conoids with reasonable thickness and depth. After comparisons, the parametric study was performed by varying the skew angles, boundary conditions, thickness ratios and the minimum rise to maximum rise (hl/hh) ratio.

The novelty of the presented model is reflected by the simultaneous addition of twist curvature in the strain field as well as the curvature in the displacement field allowing the accurate analysis of reasonably thick and deep laminated composite rhombic conoids. The behavior of conoids differs from that of usual shells such as cylindrical and spherical due to the conoid’s inherent twist curvature with its complex geometry and different location of maximum deflection.

The purpose of this paper is to investigate the volatility linkage between global oil market and major South Asian equity markets.

In order to serve the purpose, the authors employ a recently developed vector autoregressive-generalized autoregressive conditional heteroskedastic model to examine whether shocks and volatility spill over from the oil market to various equity markets under consideration.

The findings of the empirical analysis suggest that all the markets studied do receive volatility from the oil market. Not surprisingly, the authors do not find any significant evidence of volatility transmission from the equity markets to the global oil market. Additionally, while computing the optimal portfolio weights and hedge ratios, the authors document that inclusion of oil in the portfolio of stocks tends to reduce the risk of the resultant portfolio.

Fully original.

The learning outcome of this case study is to help students identify issues of the electric two-wheeler industry in India, revisiting conventional business models and transitioning toward sustainable business models. Eventually, this case study will enhance students’ analytical, qualitative analysis, multidisciplinary approach and strategic decision-making skills.

This case study can be used to discuss Michael Porter’s five forces model, TOWS matrix and Michael Porter’s generic strategies for competitive advantage.

Bounce was established in 2014 by Vivekananda Halkere, Anil G. and Varun Agni. The startup was an on-demand service provider of scooters. It also claimed to be the world’s fastest-growing scooter rental startup. As of March 2020, Bounce operated in 12 Indian cities, namely, Bengaluru, Jaipur, Hassan, Kolar, Mysore, Bhuj, Udaipur, Belgavi, Hyderabad, Ahmadabad, Hampi and Delhi. Bounce’s revenue grew to INR 1,000m in the fiscal year (FY) 2020 compared to INR 160m in FY 2019. Halkere was happy and proud of what his friends and he had achieved in the past two years. However, he was concerned about competition. What plan of action was needed to help thwart competition. What would be the best strategy to achieve growth and monetize operations? and How would Bounce address these major challenges to capture market share?

This case study can be taught in advanced undergraduate, MBA or executive-level programs dealing with strategic management. This case study helps students in dealing with issues pertaining to a given market sector where a firm is operating and the strategies to thwart competition.

Teaching notes are available for educators only.

CSS11: Strategy.

The purpose of this paper is to build a monitoring scheme in order to detect and subsequently eliminate abnormal behavior of the concerned casting process so as to produce worm wheels with good quality characteristics.

In this a study, a process monitoring strategy has been devised for a centrifugal casting process using data-based multivariate statistical technique, namely, partial least squares regression (PLSR).

Based on a case study, the PLSR model constructed for this study seems to mimic the actual process quite well which is evident from the various performance criteria (predicted and analysis of variance results).

The practical implication of the study involves development of a software application with a back-end database which would be interfaced with a computer program based on PLSR algorithm for estimation of model parameters and the control limit for the monitoring chart. It would help in easy and real-time detection of faults.

This study concerns the application of a PLSR-based monitoring strategy to a centrifugal casting process engaged in the production of worm wheel.

In recent years, determining the effective and suitable supplier in the supply chain management (SCM) has become a key strategic consideration to the success of any manufacturing organization in terms of business intelligence (BI), as many quantitative and qualitative critical factors are measured from big data. In today’s competitive business scenario, the main purpose of this study is to determine suitable and sustainable suppliers during supplier selection process is to reduce the risk of investment along with maximize overall value to the customer and develop closeness and long-term relationships between customers and suppliers to build a resilient SCM to mitigate uncertainty for automotive organizations.

As these types of decisions generally involve more than a few criteria and often necessary to compromise among possibly conflicting factors, the multiple-criteria decision-making becomes a useful approach to solve this kind of problem. Considering both tangible and intangible criteria, the aim of this paper is the presentation of a new integrated fuzzy analytic hierarchy process and fuzzy additive ratio assessment method with fuzzy entropy using linguistic values to solve the supplier selection problem to build the resilient SCM under uncertain data. Fuzzy entropy is used to obtain the entropy weights of the criteria.

Organizations gather massive amounts of information known as BD on the basis of historical records of uncertainties from several internal and external sources to manage uncertainty to improve the overall performance of organizations using BI strategy for analyzing and making effective decision to support the managements of automotive manufacturing organizations in an information system.

Although this study tries to represent a full analysis on suitable and resilient global supplier selection under various types of uncertainty, still there are some improvements that can be made in the future by developing a more refined and more sophisticated approach to further enhance the performance of the proposed scheme to calculate overall rating scores of the alternatives.

The novelty of this paper is to propose a framework of BI in SCM to determine a suitable and resilient global supplier where all the meaningful information, relevant knowledge and visualization retrieved by analyzing the huge and complex set of data or data streams, i.e. BD based on decision-making, to develop any manufacturing organizational performance worldwide.

Conventional risk prioritization methods rely on crisp inputs but struggle with imprecise data and hesitancy, resulting in inaccurate assessments that affect service and information quality and performance monitoring. This study proposes a fuzzy data-driven risk prioritization model for service quality under imprecise information.

Enterprise risk management is crucial for service quality management, ensuring effective identification, assessment and mitigation of risks impacting service delivery and customer satisfaction. This paper proposes a fuzzy data-driven multi-criteria model for risk prioritization involving multiple decision-makers. It introduces a hybrid method combining intuitionistic and hesitant fuzzy group decision-making to assess better and prioritize risks based on decision-maker preferences.

The proposed hybrid fuzzy model improves service quality in business operations by efficiently representing uncertain information in traditional frameworks. It helps identify potential risks in advance and enhances control over business operations, enabling organizations to benchmark service quality and identify best practices. Accordingly, organizations acquire information and background knowledge to benchmark their service quality. This, in turn, improves service quality under performance management.

Despite the advantages of fuzzy models in risk prioritization, such as mimicking human reasoning more accurately, their complexity can hinder adoption. The intricate computational steps may deter shop-floor managers who prefer the more straightforward conventional crisp RPN approach, which is easier to understand and implement. However, while developing a hybrid fuzzy risk prioritization model may require more effort, its benefits become apparent over time. Once developed, the model can be integrated into software applications, allowing decision-makers to use it easily. This integration simplifies fuzzy computations and enhances risk prioritization, leading to more informed decision-making and improved risk management in the long term.

The proposed robust fuzzy framework improves risk management by integrating uncertain information and multiple decision-makers expertise, leading to more reliable outputs that enhance strategic decisions and operational efficiency.

We validate the proposed approach at an integrated steel plant’s risk management process, covering broad areas of the service quality domain. To the best of our knowledge, no study exists in existing literature attempting to explore the efficacy of the proposed hybrid fuzzy approach in risk management practices at prime sectors like steel. The study’s novelty is backed by this validation experiment, which indicates that the effectiveness of the results obtained from the proposed multi-attribute hybrid fuzzy methodology is more practical. The model’s outcome substantially adds value to the current risk assessment and prioritization literature that significantly affects service quality.