Purpose: The objective of this study was to analyze workplace productivity through employee sentiment analysis using machine learning. Theoretical framework: A lot of literature is already published on employee productivity and sentiment analysis as a tool, but the study here is intended to address the issues in employee productivity post-COVID’19. Design/methodology/approach: The authors have studied the relationship between sentiments and workplace productivity post-COVID- 19. Sentiments were captured from the text inputs given by seventy-two survey respondents from a mid-sized consultancy firm and correlated against the productivity scores. A machine learning model was developed using Python to calculate the sentiment score. Findings: 98.6% of the respondents had a high productivity score, whereas 88.9% showed positive sentiments. The majority of the responses showed a positive correlation between positive sentiments and high productivity levels. Research, Practical and Social Implications: The study paves way for identification of action plan for productivity enhancement through sentiment analysis. Originality/Value: No previous work on employee productivity using sentiment analysis is done till now.
Research methodology One of the major reasons of layout-related difficulties faced by manufacturing industries is non-value-adding and redundant work. Plant layout study aims at economic production with larger volumes and variety as well. Method studies focus on the effectiveness with efficiency by a systematic critical scrutiny of work being done. The intention is to identify logical sequence of activities highlighting and eliminating the unnecessary mudas. Time and motion study is a combination of time study and motion study analysing and eliminating any unnecessary movement for productivity optimization of that job or process. Thus, through the elimination of unnecessary motions, times for performing the processes may be reduced and productivity increased. The intention is to subdivide the different operations of a job or process into measurable elements. Hence this case has been developed based on the primary data. The primary data was collected using Industrial Engineering Studies like layout study, method study and time and motion study. This case has been classroom tested with MBA students in their Lean Management Course. Case overview/synopsis Arin Synthetics Ltd. (ASL) though had installed modern machinery in its facility, process efficiency and optimization were a concern. Top Brass at ASL believed that ASL was overstaffed and its processes had creep as far as efficiency is concerned. This case focuses on ways to improve the process efficiency to rationalize the manpower at ASL. Presence in large growing global markets put cost pressure on ASL, thus mandating improvement in the efficiency of its processes through manpower rationalization. This case, therefore, discusses one of the highly staffed process of waste collection. Could ASL achieve reduction in the manpower in waste reduction without affecting the overall process? Was there a strategic mistake in the thought process of disposing of the waste generated by the manufacturing complex? Complexity academic level Operations management, Productivity and performance, Quality management, Lean management.
Day after day, individuals all across the world come up with fresh ideas for improving the future. Several intriguing discoveries and ideas paved the way for a new age of electronics, telecommunications, business, and medicinal innovation. Using less resources, greater changes in these domains can be achieved. As improving efficiency and productivity allow exponential development in some areas of the global economy, artificial intelligence (AI) and machine learning (ML) is being adopted by a growing number of individuals, corporations, and governments. Since real-world scenarios influence imprecise and unpredictable situations, fuzzy systems have become an inescapable machine learning aspect. Thus, this research presents a qualitative analysis of the significance of fuzzy machine learning systems like fuzzy support vector machines (FSVM) in various physical domains. Based on this analysis, this research extends with the proposal of a fuzzy machine learning-based framework for two different physical domains: (1) intelligent transportation and (2) ecological risk handling. Thereby, this state-of-the-art approach presents fault detection using FSVM (FD-FSVM) model in the intelligent transportation domain. In ecological risk handling, this study proposes an improved FSVM for risk level classification (FSVM-RLC) approach, which uses the persistent organic pollutants data for training and validation. These two domains are chosen randomly to evaluate the classification performance of the fuzzy machine learning algorithms based on their mean absolute error, accuracy, precision, recall, and F1 score. Apart from this, the mean square error and mean absolute error are measured. Compared to existing machine learning models, the individual results of these two approaches show the highest performance. Furthermore, this fuzzy integrated machine learning technique kept consistency in both domains by giving 98.2% and 97.89% accuracy levels for FD-FSVM and FSVM-RLC, respectively.
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