Effective application of tools and techniques is critical for successful Total Quality Management (TQM) implementation. The influence of organization profile on the application of TQM tools and techniques and outcomes is investigated in Indian organizations. Correlation analysis and ANalysis Of VAriance (ANOVA) are utilized for the research. Tools and techniques are found having significant relation to the outcomes of implementation. Organization’s age, size, and turnover significantly influenced the TQM results. Organization’s size and turnover significantly influenced the application of tools and techniques. The study suggests taking the organization profile into consideration for effective implementation. Further research can be conducted in other countries.

The purpose of this study is to analyze the relationships between supply chain leadership and reverse supply chain performance, government policy and reverse supply chain performance, and finally, government policy and supply chain leadership. The research methodology is analytical, namely survey research that aims to collect, compile, analyze, interpret, and finally draw conclusions. The approach used is quantitative, which includes the development of an empirical model and its measurement based on theoretical studies. Research respondents are managers who are responsible to manage the reverse supply chain operations such as supply chain, warehouse, transportation/distribution, production, planning and control of production and inventory planning and control, procurement, and marketing in manufacturing companies. The research data was obtained by distributing online questionnaires to 560 supply chain managers of manufacturing companies who were determined using the simple random sampling method and the questionnaires were designed using a Likert scale. Data analysis used structural equation modeling (SEM) with SmartPLS 3.0 software tools. The stages of data analysis are validity test, reliability test and hypothesis testing. The results indicate that supply chain leadership had a significant effect on reverse supply chain performance, government policy had a significant effect on reverse supply chain performance and, finally, government policy had a significant effect on supply chain leadership.

The purpose of this study is to analyze the effects of the e-procurement on supply chain performance and supply chain innovation. The study also investigates the effect of supply chain innovation on supply chain performance. The research method is a quantitative survey, and the research data is obtained by distributing online questionnaires on a scale from 1 to 7 distributed via social media. Respondents in this study are 250 managers of manufacturing organizations in Indonesia determined by simple random sampling method. The model used in this study is the causality model and to test the hypotheses proposed in this study, the analytical technique used is Structural Equation Modeling (SEM) with SmartPLS software as a data analysis tool. The independent variable of this research is e-procurement implementation, supply chain innovation and the dependent variable is supply chain performance. The stages of data analysis are validity test, reliability test and hypothesis testing. The results of this study indicate that the application of e-procurement had a positive and significant effect on supply chain performance, the application of e-procurement had a positive and significant effect on supply chain innovation, supply chain innovation had a positive and significant effect on supply chain performance and supply chain innovation was able to mediate the effect of e-procurement on supply chain performance.

Manufacturing, in general, creates a finished good from a set of simpler supplied parts. Supplied parts are installed into higher assemblies, higher assemblies move into even higher assemblies, and eventually this terminates at the finished good. Delays or variation during the manufacturing process ripple all the way to the finished good, possibly from different branches of the build and possibly magnifying any individual effect. There is extensive literature regarding Lean Manufacturing and it provides strategies and business philosophy to deal with variation, however it offers little in the way of quantitative analysis on the effects of that variation upon the whole. Digital Twins and discrete event simulations can and have been used to model the impact of variation in its totality. Various papers on Digital Twins have explored how to model manufacturing, but very little on generalized behavior. (i.e. How schedule slips at the subassemblies impacts the delivery dates / quantities at the finished good level). This paper explores the analytical quantitative effects of input/sales variation through the manufacturing cycle and the resultant effect on the finished good manufacturing schedule/cycle. We demonstrate that even small random variations/interruptions propagate up the build chain, get reduced in magnitude and end up producing predictable reductions in the average build rate of the final product. Additionally, it is shown that the more supplied parts that comprise a finished good the greater the expected reduction in average build rate.

Recently, Lean Six Sigma (LSS) has attracted much attention from both researchers and practitioners. In fact, many published papers used a quantitative research methodology to build and test theory in the field of LSS implementation in manufacturing area. Very few studies have been reported about the critical success factors influencing of six sigma implementations. The aim of this study is to present the results, from an empirical evidence, of Six Sigma implementation in Tunisian manufacturing. Therefore, fourteen main factors have been identified through literature review and questionnaire survey. Interpretive structural modeling (ISM) has been used to develop a hierarchical structure for analyzing the interactions among the key factors. By using the MICMAC technique, the barriers were classified into four groups according to their driving power and dependence power. Our finding provides important insight to managers and practitioners about factors in the LSS implementation process.

Employees and financial well-being are two aspects that are closely related to each other, and have been deeply studied by researchers. Not only can financial well-being directly affect an individual, but it can also indirectly affect his/her organization as well as employer. Any level of financial employees’ well-being, either low or high, will change their job performance. Thus, the purpose of this study is to determine the level of financial well-being among manufacturing employees in Batu Pahat, as well as to test the relationships between determinants and financial well-being among manufacturing employees in Batu Pahat. In this study, seven research hypotheses were developed to examine seven determinants, including age, income, gender, education, current job position, in-come, and marital status which influence employees‘ financial well-being. In this study, 220 employees at the production level were selected randomly from a manufacturing company in Batu Pahat, Johor, Malaysia. Then, a questionnaire was distributed to the employees. The data obtained were analyzed quantitatively using SPSS version 22.0. The results of this study revealed that the level of financial well-being was moderate and all of the determinants were positively related to financial well-being among the manufacturing employees. This quantitative study is important to the manufacturing industry in Malaysia in order to gain insight on the correlation between financial well-being and its determinants.

Determining how a new production cell will function is problematic and can lead to disastrous results if done incorrectly. Discrete-event simulation can provide information on how a line will function before, during, and after the line is in operation. A simulation model can also provide a visual animation of the line to see how product will flow through the line. This paper discusses the development and analysis of a simulation model of a new manufacturing line. The manufacturing cell is a new motor assembly cell. An analysis of the capability of the line for varying demand levels was conducted for the two main motor types produced on the line. An ARENA® simulation model was developed, verified, and validated to determine the daily production and potential problem areas for the various demand levels. The results show that at all but one demand level, the line is capable of producing to within one unit of customer demand if the required number of workers is present. At the highest demand level, the simulation results suggest that the line is not capable of meeting demand. Additional analysis indicates that multiple workstations could prove problematic with minor fluctuations in demand. Problematic workstations were identified for each assembly area and for the line as a whole.

A state of the art instrumented Drop Weight Impact Tester Machine was developed in Iran University of Science and Technology which measures the energy absorption of composite materials under impact load. The output of the machine is used to draw load- time graph and calculate the amount of energy absorbed by the specimens. The machine was equipped with various sensor systems to measure the velocity of the impactor just before it contacts the specimen and the amount of contact force, and with a data acquisition system to record the force and time history. Capability of testing according to many different types of standards and capability of studying behaviour of the specimen after impact are two important characteristics of this machine. This designed system, after manufacturing and calibration, was installed and successfully utilized.