Study and measurement of the productivity in the printing industry in Villahermosa Tabasco to develop an improvement proposal

Productivity is one of the paths that a company must take to achieve its objectives, goals and purposes, working effectively and efficiently. To achieve a culture of measurement, it is important to perform a productivity analysis, which will help to obtain adequate information to create development recommendations in the near future. This work provides information on the results obtained from a productivity measurement study in a sample of companies in the printing industry, in Villahermosa Tabasco, which helps to understand and adapt business needs within the cultural, environmental, technological, economic and political variables. To carry out this study, a measurement instrument has been used that obtains specific information on the elements that an organization must have for the development of productivity, this tool is called Comprehensive Productivity Assessment Technique (TIEP), the which will give us results that serve to generate a model for improving productivity, which will be of great help for the growth of this business sector with positive results that bring benefits to the region and society.

Digital Resources Integration and Performance Evaluation Under the Knowledge Management Model in Academic Libraries

Leveraging a knowledge management model (KMM), organizing digital resources, and measuring performance beyond digital libraries continue to be hot topics for many organizations. Given its growing importance, the aim of this study was to design a robust integrated research model based on social cognitive theory and KMM to organize resources and improve the productivity of academic librarians to make services effective. Using a quantitative method based on key data collected by librarians at Pakistani universities, the researchers reviewed previously published literature and sought expert opinions to develop a research tool. The authors distributed 350 printed questionnaires to professionals from 190 universities, of which 339 were returned. These were analyzed to test the hypotheses and assess the accuracy of the measurement and structural models. After undergoing rigorous statistical analysis, the seven constructs using Social Cognitive Theory and the Knowledge Management Model have proven to be suitable for digital asset integration, employee productivity measurement, and high-quality academic services. In addition, the model based on social cognitive theory supports the application of KMM to promote knowledge culture, resource organization, and successful integration and innovation of academic services by enhancing the technical and professional skills of staff in academic institutions.

LABOR PRODUCTIVITY RATING MODEL FOR LIGHT BRICK WALL INSTALLATION IN RESIDENTIAL PROJECTS

<p>Management on a construction project is something that cannot be ignored, because without construction management a project will be difficult to run according to expectations in the form of cost, time, quality and the success or failure of a construction project depends on the effectiveness of resource management. One way that can be done to achieve project objectives is to increase work productivity. This study aims to determine the productivity of labor in the installation of lightweight brick walls based on facts in housing projects. Labor productivity by analyzing the LUR value and productivity rating then becomes the regression analysis data to produce the model. The data search was carried out by direct observation to the field and conducting unstructured interviews with workers in the field. The resource persons are workers as artisans and workers who are experts in the work of installing light brick walls. This productivity measurement uses the productivity rating method, where worker activities are classified into 3 things, namely Essential contributory work, Effective work, and not useful. The results of the analysis show that the productivity of light brick wall installation workers based on the fact has higher productivity than the SNI labor coefficient. To calculate the labor productivity of installing lightweight brick walls, then the model from the results of this study can be used. The results of the evaluation of the model to predict labor productivity for installing lightweight brick walls have an average accuracy of 99.34%, so the model can be declared accurate.</p>

Returns to Scale, Productivity Measurement, and Trends in U.S. Manufacturing Misallocation

Aggregate productivity suffers when workers and machines are not matched with their most productive uses. This paper builds a model that features industry-specific markups, industry-specific returns to scale, and establishment-specific distortions, and uses it to measure the extent of this misallocation in the economy. Applying the model to restricted U.S. Census microdata on the manufacturing sector suggests that misallocation declined by 13% between 1982 and 2007. The finding of declining misallocation starkly contrasts with the 29% increase implied by the widely used assumptions that all establishments charge the same markup and have constant returns to scale.

Tool for Measuring Productivity in Software Development Teams

Despite efforts to define productivity, there is no consensus in the software industry regarding what the term productivity means and, instead of having only one metric or factor that describes productivity, it is defined by a set of aspects. Our objective is to develop a tool that supports the productivity measurement of software development teams according to the factors found in the literature. We divided these factors into four groups: People, Product, Organization, and Open Source Software Projects. We developed a web system containing the factors that influence productivity identified in this work, called Productive, to support software development teams in measuring their productivity. After developed the tool, we monitored its use over eight weeks with two small software development teams. From the results, we found that software development companies can use the system to support monitoring team productivity. The results also point to an improvement in productivity while using the system, and a survey applied to users demonstrates the users’ positive perception regarding the results obtained. In future work, we will monitor the use of the tool and investigate the users’ perceptions in other project contexts.

Essential Indicators of Quality in Primary Care Settings: An Evidence-Based, Structured, Expert Approach

Backgrond and Objective: Primary health care has a central role in the workings of the health care system and health of the American public. Thus, a high-performing, high-quality primary care system is essential. As a result, measurement frameworks are needed to assess the quality of the infrastructure, workforce configurations, and processes available in primary care practices due to the complexity of primary care. As part of a larger project supported by AHRQ (grant no. 1 R01 HS 025982), our research team reports the use of an evidence-based approach to compile a targeted set of existing care measures. These measures are prioritized according to their overall contribution and value to primary care. Within this paper, we describe the process by which the performance measures were selected and present the final set of measures resulting from the process. Methods and Results: We adapted The Productivity Measurement and Enhancement System, or ProMES, to select and rank existing primary care measures according to value to the primary care clinic. ProMES is a comprehensive performance measure development approach firmly grounded in motivational theory and performance measurement that uses expert elicitation to identify objectives of performance, corresponding measures and priority rankings. A design team of Nine subject matter experts (SMEs) identified three fundamental objectives for delivery of high-quality primary care, themed around access, patient-healthcare team partnerships, and technical quality. The SMEs also selected sixteen performance indicators from the 44 pre-vetted measures that already exist in three different data sources for primary care. One indicator, Team 2 Day Post Discharge Contact Ratio, was selected as an indicator for both Objective 2 and 3. Indicators were prioritized according to value using the contingency functions developed by the design team. Finally, the SMEs proposed nine new indicators of quality not currently in existence to address critical gaps in measuring the primary care domain. Implications and Future Directions: Performance measures selected as part of our modified-ProMES process assist in the implementation of targeted care quality measures prioritized in accordance to their value in primary care . Our measure set provides an actionable catalogue of measures that can serve as a first step toward interoperability of electronic health record systems. Future work toward this goal should address both logistical considerations (e.g., data capture, common data/programming language) and lingering measurement challenges, such as the best way to operationalize these measures for teams working in complex and shifting situations (e.g., rotating team members).

Productivity Measurement: A Study of Selected Pharmaceutical Companies in India

Purpose: This study aims to measure, analyze, and compare the productivity performance of selected pharmaceutical companies in India. Approach/Methodology/Design: This study is based on secondary data. In this study, seven pharmaceutical companies are selected as a sample based on paid-up capital of the year 2019-20. The seven companies are selected for their higher paid-up capital, and the study period is seven years from 2013-14 to 2019-20. In this study ratio analysis is used as an accounting tool in which four productivity ratios are employed. The one-way ANOVA technique of parametric test is used as a statistical tool to identify the difference among sample means. Findings: The major findings of the study indicate that in all the selected companies the performance of material productivity, labor productivity and overhead productivity show fluctuating trend. The overall productivity performance of all the companies is very close to each other during the study period of seven years. The result of the statistical tests revealed that in all productivity ratios drawn null hypotheses are not accepted. This means there is a significant difference in different productivity ratios among selected pharmaceutical companies during the study period. Practical Implications: In this study, productivity analysis is carried out which is helpful to measure the productivity performance. The results would help investors to make the right choice of investment in selected pharmaceutical companies. Given the present situation of COVID-19, productivity analysis will be helpful to identify the existing production capacity in concern with pharmaceutical products and services. Originality/value: The significant contribution of this study is to measure the various productivity performances of pharmaceutical companies. Further, the average productivity performance is compared among the seven selected pharmaceutical companies, which shows that average productivity performance is different among the selected companies.