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This paper deals with total quality management (TQM) with an emphasis on developing suitable strategies for improving quality and productivity in manufacturing systems. In recent years, TQM has been seen as an important strategy for achieving success in business both in terms of quality and productivity. However, there seem to be no clear strategic framework and guide‐lines for implementing TQM in manufacturing in the light of available advanced production concepts and technologies. A review of previous implementation approaches of TQM in practice has been presented in order to gain further insights into the implementation aspects of TQM. The main objective of this paper is to present a general framework for the development of TQM in manufacturing organizations considering the recent developments in production concepts and technologies and competitiveness among firms to utilize quality as a competitive weapon.

The paper is presented in two parts. The first is concerned with the methodology of the competing transformation models and the second details the results of the empirical tests. In particular, it concentrates on empirical testing of the stability and cross‐sectional invariance of the factor patterns underlying the arbitrage pricing models of two neighbouring security markets. In previous studies, the method of transformation analysis has been used to address these issues at the individual asset level. In the present study three alternative transformation analysis models are used to study the stability and invariance problems. The tests are carried out at an aggregated level, such that subsets of asset returns are combined in equally weighted portfolios in the spirit of Fama and MacBeth. Portfolio formation is motivated by the observed anomalies of individual asset return series. Even if some anomalies may be present on the aggregated level also, their impact will be weaker. The amount of different issues of the same company in the database is varied in order to study the impact of parallel issues on the empirical results.

Provides evidence on the power of transfer function noise modelling in explaining the empirical connection between endogenous and exogenous (control) variables in linear regression type input‐output systems. The multiple input transfer function noise model – of specific value when the input variables are collinear – is used to demonstrate the connection between macroeconomic forces and stock market pricing on a thin security market. Shows that the transfer function approach provides new evidence partly in conflict with previous results obtained by ordinary least squares methodology. Previous empirical evidence suggests that money supply, inflation, the level of industrial production and the psychological impact of the general index of the Stockholm Stock Exchange affects Finnish stock pricing. The problem of selecting relevant economic state variables is tackled by regressing each of the five factor time series obtained from testing the arbitrage pricing theory (see Östermark, circa 1989) on the set of tentative state variables. The economic state variables are significant explanators of stock pricing, both at the market and at the individual asset level. Only nine individual stocks are tested. Comprehensive testing of all individual stocks is left for future research.

Presents a mathematical model for determining Economic Production Quantity (EPQ) in a multistage flow‐shop production system for the case where the demand for items per unit time is deterministic and the planning horizon is finite. Solves an example problem to illustrate the model.

In any organization, the information relevant to the problem area is essential in making decisions. The top management makes decisions like expansion of the plant, automation, recruitment of senior personnel and diversification of products, etc. There are many kinds of tools or methods available to analyse these problem areas. Among these, turnover rate is useful for many purposes, especially in evaluating the performance of an organization. Here, the turnover rate is considered in order to identify the effect of the number of types of products or product mix on the performance of a manufacturing system. It can be defined as the ratio of average demand to average inventory level. The model presented discusses the effect of the number of products/product mix on the turnover rate of a production system when the appropriate market value of the products is considered. The turnover rate discussed is based on the economic production quantity (EPQ) and the manufacturing cycle time of each product. An example is presented to explain the application of the model.

Interest in the field of entrepreneurship has significantly increased among academics, practitioners and government officials in the past decade both in the USA and in Europe. The increased interest is reflected in the increased number of courses, majors and minors at colleges and universities throughout the world; the increased number of endowed chairs; the increased number of journals in the field; the increased coverage of the field by the media; and the increased interest in the provision of government support. In light of this significant increased interest, it is important to understand the state of research in the field in Europe in the last few years, the focus of this article.

Considers the modelling of dynamic systems using biased regression and spectral methods. Provides evidence on the power of transfer function modelling for unravelling the empirical connection between endogenous and exogenous (control) variables in both regression type and spectral input‐output systems. The Multiple Input Transfer Function Noise Model – of specific value when the input variables are collinear – has previously been used to demonstrate the connection between macroeconomic forces and stock market pricing on a thin security market. Compares the adequacy of representative time and frequency domain algorithms for modelling observed data series. The estimations are done with the combined Transfer Function and Cartesian ARIMA Search algorithm of Östermark and Höglund and the CAPM/APM programs of Östermark.

While traditionally it was believed that shadow banking undercuts business from traditional commercial banks, the time has now arrived to examine the various innovative practices used by various shadow banks and non-banking finance companies (NBFCs) to explore various collaboration and competition possibilities. The parallel existence of the traditional and shadow banking systems creates a market environment where both the entities are inter-dependent for growth and development with their edge of advantages and snags. This study aims to investigate the development and growth of deposits in NBFCs and scheduled commercial banks (SCBs) and, through the adoption of innovative practices, highlights possible growth opportunities for both ahead.

This study uses yearly bank deposit data from 1998 to 2019. This study incorporates univariate autoregressive integrated moving average modeling to predict the future deposit growth of SCBs and NBFCs in India.

This study concludes that both the entities, i.e. NBFCs and SCBs, will experience deposit growth; however, the proportionate growth of deposits in SCBs will be higher than NBFCs.

This study concludes that the NBFCs will exhibit higher growth in the future. Thus, a strengthened regulatory framework will boost the growth of the NBFCs, providing a safe environment to the investor. Further, as this study primarily considers only deposit-taking NBFCs and commercial banks and a single variable – “deposit” to predict its future growth, it offers a scope for future research to consider and include other kinds of NBFCs like non-deposit taking NBFCs, housing finance companies, micro-finance Institutions and infrastructure finance companies.

A competently regulated financial system of an emerging economy confers tremendous growth opportunities to the financial institutions functioning in the system. Deposits are a significant parameter for the performance of the financial institution; thus, by keeping it as the underlying premise, this study forecasts the future growth in deposits for both the commercial banks and NBFCs. This forecasted growth in deposits for both entities, if analyzed and acted upon appropriately, can, apart from other opportunities for investment, be used to point at directional growth of the economy and the gross domestic product, considering that credit growth is a barometer for economic growth. The scope of this study is limited to NBFCs and SCBs of India and considers only a single variable, i.e. deposit for data analysis and growth forecasting.