Over the past few decades, Quality Management System (QMS) and High Performance Work System (HPWS) have emerged as key concepts to enhance organizational effectiveness. All over the globe, majority of contemporary manufacturing and services organizations have applied at least one of these development strategies or even both. This study proposes an integrated framework of QMS and HPWS and empirically investigates the relationship between QMS and HPWS practices and their direct and indirect effects on organizational effectiveness using structural equation modelling (SEM). This research makes a number of significant contributions: (1) The black box of the conjoint implementation is opened up for better appreciation of the interplay of QMS and HPWS practices and their influence on organizational effectiveness (2) The key QMS practices recognized as contributing factor of performance have been classified and examined at two distinct levels i.e. QMS Top Level practices and QMS Core practices (3) The mediating and interaction effects of QMS Core practices and HPWS practices on the relationship of QMS-Top Level practices and organizational effectiveness have been thoroughly investigated. The proposed framework is tested through cross-sectional data from 90 Technical Services Organizations (TSO) operating in Pakistan. The research hypotheses are supported by the test results of the SEM. The findings and implications are discussed along with limitations and future research guidelines.

The 'system perspective' concept has intensely been supported by strategic human resource management researchers , who have so far been fairly successful in producing substantial evidence to prove that synergistic and mutually reinforcing human resource (HR) practices bonded together in 'bundles' , can significantly affect the performance of organizations at multiple-levels . To understand how HR activities interrelate to generate positive (negative) synergistic effects and affect performance across multiple levels, it is necessary to comprehend the pattern of relationships within HR activities. However, limited efforts have been made to explore the nature of the 'Internal-Fit'of HR elements within the construct of HPWS. This research study aims (1) to develop a multi-leveltheoretical framework of HPWS to illustrate how aligned and interrelated HR activities can generate synergistic effects that may affect performance at multiple levels and (2) to clarify the nature of 'Internal-Fit' of HR elements in both horizontal and vertical dimensions within HPWS.

This research work empirically assesses the relationship between the Quality Culture (QC) practices and core practices of Quality Management System (QMS) and investigates their direct and indirect influences on organizational performance. Data for this research work is collected from 80 Technical Services Organizations of Pakistan through mail survey and the proposed framework and hypotheses have been examined through Structural Equation Modelling. The results of hypotheses show that synergies among QCPractices have a positive impact on QMS-Core practices as well as organizational performance. Moreover, QMSCore practices mediate the relationship between QC-Practices and organizational performance. This empirically validated model can be used as a benchmark by future researchers for further examinations in other industries sectors, especially in manufacturing.

Inherent uncertainties in demand and supply make it problematic for supply chains to accomplish optimum inventory replenishment, resulting in loss of sales or keeping excessive inventories. To cope with erratic demands, organizations have to maintain excessive inventory levels, sometimes taking up to one-third of an organization’s annual budget. The two most pressing concerns to handle in inventory management are: how much to order and when to order. Therefore, an organization ought to make the correct and timely decisions based on precise demand information to avoid excessive inventory accumulation resulting in enhanced competitive advantage. Owing to the significance of inventory control and analysis, this paper reports on developing and successfully implementing a hybrid framework for optimum level inventory forecasting in Technical Services Organizations. The proposed framework is based on a case study of one of Pakistan’s leading Technical Services Organization. The paper presents a statistical analysis of historical data and a comprehensive fault trend analysis. Both these analyses set a solid foundation for the formulation of a comparative analysis matrix based upon price and quantity based analysis of inventory. Finally, a decision criterion (Forecasting Model) is proposed using three primary forecasting techniques with minimum error calculations. The study’s finding shows a forecast error of 142.5 million rupees in the last five years, resulting in the accumulation of more than 25 thousand excessive inventory stock. Application of price and quantity based analysis identifies that 65% of the annual budget is significantly dependent upon only 9% (in terms of quantity) of "High Price and Small Quantity" Items (HS). These HS items are forecasted through three different forecasting methods, i.e., Weighted Moving Average, Exponential Smoothing, and Trend Projection, with Minimum Absolute Deviation to significantly reduce the forecasting error while predicting the future required quantity. The research work aims to contribute to the inventory management literature in three ways. First, a new comparative analysis matrix concept for identifying the most critical items is introduced. Second, a Multi-Criteria Forecasting Model is developed to capture a wide range of operations. Third, the paper suggests how these forecasting criteria can be integrated into a single interactive DSS to maintain optimum inventory level stock. Even though the DSS framework is based on data from a single organization, the application is expected to manage inventory stock in a wide range of manufacturing and services industries. This study’s proposed hybrid framework is the first of its kind that encapsulates all four dimensions of inventory classification criteria, forming a multi-criteria hybrid model within a DSS framework.