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Globalization, advancement of technologies, and increment in the demand of the customer change the way of doing business in the companies. To overcome these barriers, the six-sigma define-measure-analyze-improvecontrol (DMAIC) method is most popular and useful. This method helps to trim down the wastes and generating the potential ways of improvement in the process as well as service industries. In the current research, the DMAIC method was used for decreasing the process variations of bead splice causing wastage of material. This six-sigma DMAIC research was initiated by problem identification through voice of customer in the define step. The subsequent step constitutes of gathering the specification data of existing tire bead. This step was followed by the analysis and improvement steps, where the six-sigma quality tools such as cause-effect diagram, statistical process control, and substantial analysis of existing system were implemented for root cause identification and reduction in process variation. The process control charts were used for systematic observation and control the process. Utilizing DMAIC methodology, the standard deviation was decreased from 2.17 to 1.69. The process capability index (C p ) value was enhanced from 1.65 to 2.95 and the process performance capability index (C pk ) value was enhanced from 0.94 to 2.66. A DMAIC methodology was established that can play a key role for reducing defects in the tiremanufacturing process in India.

The current study is aimed at recognizing the risk factors for major work-related issues (ie, musculoskeletal disorders [MSDs]) among 138 manual-working farmers. Data were collected using modified Standardized Nordic Questionnaire and the Rapid Upper Limb Assessment score sheet. Different factors influencing MSDs were detected using logistic regression methods. The highest occurrence rate for MSDs was observed in the lower back (71.4%), followed by fingers (62.1%), shoulders (56.4%), and hands/wrists (55%). Factors such as age, gender, daily working hours, hand dominance, perceived fatigue, and work experience were found to be associated with MSDs in one or more upper body regions. The outcome of multinomial regression showed that gender is the most influencing factor for MSDs in all upper body regions except the shoulders. The outcomes of study indicate high occurrence of MSDs among manual-working farmers and highlight the significance of individual and work-related factors.

Background Our objective was to examine occupational risk factors for musculoskeletal disorders of the neck, back, and knee among railroad maintenance‐of‐way (MOW) workers. Methods Four thousand eight‐hundred sixteen active, retired, and disabled members of the Brotherhood of Maintenance of Way Employes Division (BMWED) completed a survey. We computed adjusted prevalence ratios (aPRs) using Poisson regression for neck, back, and knee musculoskeletal symptoms by work exposures, adjusted for age, region, race/ethnicity, smoking, and potential second job and spare time vehicle vibration exposure. Results Among active male BMWED members, we found associations between use of high‐vibration vehicles and neck pain (aPR = 1.47, 95% confidence interval (CI): 1.07‐2.03) and knee pain (aPR = 1.38, 95% CI: 1.04‐1.82) for more than 1.9 years (vs 0) of full‐time equivalent use; but not back pain. Back pain radiating below the knee (sciatica indicator) was associated with high‐vibration vehicle use greater than 0.4 and less than 1.9 years (aPR = 1.58, 95% CI: 1.15‐2.18). We also found significant associations between often or always lifting, pushing, pulling, or bending on the job (vs seldom or never) and neck pain (aPR = 2.43, 95% CI: 1.20‐4.90), back pain (aPR = 1.94, 95% CI: 1.24‐3.03), the sciatica indicator (aPR = 5.18, 95% CI: 1.28‐20.95), and knee pain (aPR = 2.84, 95% CI: 1.47‐5.51), along with positive gradients in the outcome by exposure time. Conclusions Biomechanical work exposures, including force and nonneutral postures, were associated with neck, lower back, and knee pain. Whole‐body vibration, as measured by the duration of use of high‐vibration vehicles, was associated with neck pain, knee pain, and sciatica. Prevention programs should address occupational risk factors for musculoskeletal disorders among MOW workers.

Manual harvesting is a physically demanding occupation with several work-related issues in which musculoskeletal disorders (MSDs) happen most commonly. The risk factors for MSDs among manual harvesting farmers are not investigated properly in low and low-middle-income nations. Therefore, a study among 140 farmers of Rajasthan, India was carried out through the usage of Nordic Musculoskeletal Questionnaire and the Rapid Upper Limb Assessment (RULA) technique to identify ergonomic risks. χ2 analysis was used to find the relationship between the MSDs and various factors. Also, logistic regression methodology was applied to get the most influencing factor for MSDs in different body regions. The lower-back, fingers, shoulders and wrists/hands were the body parts in which more than 50% workers reported MSDs. MSDs in one or more body regions were found to be associated with age, daily working in farms, farming experience, gender, hand dominance and perceived work fatigue. The age was majorly associated with MSDs in all body regions except the shoulder and neck as per the outcome of logistic regression. The outcome of RULA grand score had been found higher than or equal to 5 in 92% of the farmers which give directions for further research and changes.

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Literature shows that hand tool improvement studies have been given less importance in low-income and lower-middle-income countries. However, some work of significance is reported in the agriculture sectors of these countries. Hence, it is concluded that ergonomic intervention in hand tools is much needed for those industries which employ traditional methods of working.

In work-from-home (WFH) situation due to coronavirus (COVID-19) pandemic, the handheld device (HHD) users work in awkward postures for longer hours because of unavailability of ergonomically designed workstations. This problem results in different type of musculoskeletal disorders (MSDs) among the HHD users. An integrated multi-criteria decision-making approach was offered for identifying the risk level of MSDs among HHD users. A case example implemented the proposed approach in which, firstly, the best–worst method (BWM) technique was used to prioritize and determine the relative importance (weightage) of the risk factors. The weightages of the risk factors further used to rank the seven alternatives (HHD users) using Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) technique. The outcomes of the BWM investigation showed that the three most significant risk factors responsible for MSDs are duration of working, poor working posture and un-ergonomic design. The outcome of the VIKOR technique exhibited that computer professionals were at the highest risk among all users. The risk factor priority must be used for designing a working strategy for the WFH situation which will help to mitigate the risks of MSDs.