Search results

The study aims to establish an impact of supplier relationship and information and communication technology through purchasing practices on firm performance.

Review of relevant literature resulted in constructs, namely, supplier relationships, information and communication technology, purchasing practices and firm performance. A survey of 179 manufacturing companies through structured questionnaire was conducted. The responses were analysed through structural equation modelling using the partial least squares method.

It is observed that the firm performance is directly influenced by purchasing practices and indirectly by supplier relationships and information technology. The use of information technology in materials management affects supplier relationships and purchasing practices both.

The study provides a model for purchasing practitioners by highlighting the importance of supplier relationship management. Though the firms are running after improving technology, it can only affect firm performance through proper purchasing practices.

The study provides empirical evidence to the practical notions that exist in purchasing practitioners.

The purpose of this research paper is to understand the major factors considered before choosing the mode of transportation for the freight movement in India by different stakeholders and look into the future prospects on each of these sectors, i.e. railways, roadways and inland waterways.

This paper collected the primary data from the various stakeholders in the transportation sector and the secondary data through websites and various ministries of each of the sectors. The various factors are then determined by thoroughly analysing the responses by performing factor analysis in SPSS.

Earlier railways were the preferred medium of transportation, but the dynamics shifted during the 90’s to roadways, and now, it is responsible for nearly 60% of the freight traffic with waterways slowing increasing its share of the pie. Also, there are a lot of factors which stakeholders consider, but the major factors that came out are cost, sustainability, timing, government initiatives, visibility and performance.

The result of this study implies that sectors should create a robust network for easy reach of the customers and try working in conjunction to create an efficient, affordable and highly connected network. This study will also help in taking vital decisions regarding the future planning of transportation sector.

The findings help in improving the transportation network and help in better decision-making by various stakeholders while choosing the mode of transportation.

The purpose of this paper is to design, develop, implement and validate a multi-criteria decision model for measuring supplier satisfaction through a case study.

A three-stage methodology was used to develop a framework to measure supplier satisfaction. The framework involved factors and Key Performance Indicators (KPIs) from literature and exploratory study. Further, using the framework, a multi-dimensional decision model to calculate Supplier Satisfaction Index was developed. The proposed decision framework was implemented as a real-world case study in an Indian manufacturing organization.

The study makes two major contributions: first, it develops a framework to measure supplier satisfaction using factors and KPIs suitable to the buyer organization; second, the model developed to calculate supplier satisfaction helps in understanding overall satisfaction of suppliers along with the level of satisfaction of each supplier. The model can also be used to suggest improvements to buyer organizations on specific factors and KPIs under each factor.

Supplier satisfaction plays an important role in multinational companies (MNCs), so the sample of practitioners considered in this study is relevant. However, it is likely that the small sample size of only suppliers and companies selected solely from the Indian manufacturing MNCs may have introduced some bias.

A comprehensive framework for enhancing the relationship with suppliers will be instrumental in deciding, managing and improving the level of supplier satisfaction.

This approach provides purchase managers with the flexibility of selecting factors and KPIs at every level of analysis and also a single index to establish supplier’s satisfaction with a buyer company.

Increasing health-care costs are a major concern, especially in the USA. The purpose of this paper is to predict the hospital charges of a patient before being admitted. This will help a patient who is getting admitted: “electively” can plan his/her finance. Also, this can be used as a tool by payers (insurance companies) to better forecast the amount that a patient might claim.

This research method involves secondary data collected from New York state’s patient discharges of 2017. A stratified sampling technique is used to sample the data from the population, feature engineering is done on categorical variables. Different regression techniques are being used to predict the target value “total charges.”

Total cost varies linearly with the length of stay. Among all the machine learning algorithms considered, namely, random forest, stochastic gradient descent (SGD) regressor, K nearest neighbors regressor, extreme gradient boosting regressor and gradient boosting regressor, random forest regressor had the best accuracy with R² value 0.7753. “Age group” was the most important predictor among all the features.

This model can be helpful for patients who want to compare the cost at different hospitals and can plan their finances accordingly in case of “elective” admission. Insurance companies can predict how much a patient with a particular medical condition might claim by getting admitted to the hospital.

Health care can be a costly affair if not planned properly. This research gives patients and insurance companies a better prediction of the total cost that they might incur.

Longitudinal facial cracks (LFC) are one of the major defects occurring in the continuous-casting stage of thin slab caster using funnel molds. Longitudinal cracks occur mainly owing to non-uniform cooling, varying thermal conductivity along mold length and use of high superheat during casting, improper casting powder characteristics. These defects are difficult to capture and are visible only in the final stages of a process or even at the customer end. Besides, there is a seasonality associated with this defect where defect intensity increases during the winter season. To address the issue, a model-based on data analytics is developed.

Around six-month data of steel manufacturing process is taken and around 60 data collection point is analyzed. The model uses different classification machine learning algorithms such as logistic regression, decision tree, ensemble methods of a decision tree, support vector machine and Naïve Bays (for different cut off level) to investigate data.

Proposed research framework shows that most of models give good results between cut off level 0.6–0.8 and random forest, gradient boosting for decision trees and support vector machine model performs better compared to other model.

Based on predictions of model steel manufacturing companies can identify the optimal operating range where this defect can be reduced.

An analytical approach to identify LFC defects provides objective models for reduction of LFC defects. By reducing LFC defects, quality of steel can be improved.

The purpose of this paper is to determine, rank and form composite relational factors that impact the millennial consumer’s mind-set when they opt for an access-based subscription of an over-the-top (OTT) platform service. In the competitive rising Indian market of OTT platforms, there is a need to understand what factors drive the subscription of a service for a company strategizing to build up on their customer base or for a company seeking to retain its customers.

The approach includes determining factors that impact the buying behavior of the consumer and have them ranked by the survey participants in order of their importance as a factor in considering a subscription of an OTT platform service. Questionnaire as a method is used for primary data collection in this research. Using “purposive sampling,” participants of the survey were determined based on their age group and current or historic consumption of at least one OTT platform service. The survey was conducted for the millennial viewership from Tier I and Tier II cities that have good internet connectivity over their mobile phones.

The result of this research is a ranking of factors based on their importance as perceived by the millennial consumers and then form composite factors, which have similarities in responses.

This research enables the consumers of the information to dwell on the factors that prove to be of comparative importance to the consumer and plan/forecast their strategies and further research studies accordingly.

A research along similar lines has been conducted for US-based OTT platforms. However, this research is specific for Indian consumers and platforms and holds significance because of growth in the Indian OTT market.