China
Africa
Explore chapters and articles related to this topic
Management and worker factors
Published in Lincoln H. Forbes, Syed M. Ahmed, Lean Project Delivery and Integrated Practices in Modern Construction, 2020
Lincoln H. Forbes, Syed M. Ahmed
Work sampling is a useful method for conducting work measurement studies. It can estimate the proportions of the total time spent on a task in terms of its various components. It is particularly useful in distinguishing between direct and indirect work in situations where stopwatch time study is not practical or appropriate. Work sampling involves taking a small portion or sample of the occurrences in an activity being studied in order to make inferences about the frequency of occurrences in the overall activity. In construction, for example, certain tasks have high variability – installing drywall panels may involve taking an increasing number of steps to take them from a stack to the point of installation. Also, the panels are often not installed as whole sheets – workers may need to cut holes in the panels for electrical outlets and other devices. On the other hand, it is useful to know what percentage of the time workers are actively working instead of being idle, and work sampling can provide that information.
Work Measurement (WM) Techniques
Published in Munyai Thomas, Mbonyane Boysana, Mbohwa Charles, Productivity Improvement in Manufacturing SMEs, 2017
Munyai Thomas, Mbonyane Boysana, Mbohwa Charles
Dinis-Carvalho et al. (2015:239) consider work sampling as a technique that focuses on how employees spend time working in various manufacturing processes. This technique helps management to identify non-value-added activities and costs incurred with the aim of eliminating waste in manufacturing SMEs. As reported by Czumanski and Lödding (2016:2934), work sampling comprises observations made of functions or departmental sections, groups of employees in engaging in their traditional work activities, and old-fashioned machine operations in the manufacturing environment. The aim of the technique in manufacturing SMEs is to target detailed analyses of worker and machine operations in work systems of different production lines in order to improve manufacturing processes.
General introduction
Published in Adedeji B. Badiru, Handbook of Industrial and Systems Engineering, 2013
Work sampling is based on the laws of probability and is used to determine the proportions of the total time devoted to the various components of a task. The probability of an event occurring or not occurring is obtained from the statistical binomial distribution. When the number of observations is large, the binomial distribution can be approximated to normal distribution. The binomial probability of x occurrences is calculated as follows: b(x;n,p)=nCxpxq(n-x)
Associations between observed time sitting at work and musculoskeletal symptoms: a repeated-measures study of manufacturing workers
Published in International Journal of Occupational Safety and Ergonomics, 2023
Jennifer L. Garza, J. M. Cavallari, M. G. Cherniack
Posture, activity, tools and handling (PATH) observational analysis was used to characterize occupational job exposures including time sitting at work, head posture, trunk posture, hand grip and load. PATH is a work sampling-based approach that was developed to characterize the ergonomic hazards of non-repetitive work. The posture codes in the PATH method are based on the Ovako work posture analysing system (OWAS), with other codes included for describing worker activity, tool use, loads handled and grasp type. PATH has been used previously in research among other working populations in North America [21]. A researcher observed each participant at 10 time points each on 1–2 days and noted when participants were sitting, had neutral head, trunk and hand grip postures or handled loads. We calculated the percent time in each job exposure across the 10 observation points.
Learning to see value-adding and non-value-adding work time in renovation production systems
Published in Production Planning & Control, 2022
Hasse Neve, Søren Wandahl, Søren Lindhard, Jochen Teizer, Jon Lerche
Three cases of renovation projects were chosen. The cases are summarised in Table 1. The cases were chosen based on two criteria: (1) they had to be renovation projects, and (2) they needed to be alike to compare production system behaviours. The chosen cases’ original building structure and floor plan was very similar, and they were planned to go through comparable deep renovations including the building envelope, interior and installations. All cases were social housing renovation projects consisting of apartments (number for each is outlined in Table 1). All cases were located in alike cities in the western part of Denmark. Five trades were selected in each case for the planned work sampling study. The number of trades for each case was chosen to represent the majority of work in progress, so the production system behaviour of each case could be analysed and compared. Furthermore, the trades had to include traditional renovation work such as carpentry, painting, masonry, so forth, which would occur on any renovation project. The trades are outlined in Table 1. All trades were followed within the same period, which was crucial for understanding how the different trades affected each other. Furthermore, all data were collected at times during the projects that were not close to neither project start-up nor completion, thus representing normal production conditions.
Use of vision and sound to classify feller-buncher operational state
Published in International Journal of Forest Engineering, 2022
Pengmin Pan, Timothy McDonald, Mathew Smidt, Rafael Dias
Inter-arrival times of cutting and piling events were tabulated from the work sampling and model-derived results. Distributions of the observed values were plotted in Figure 5. The Kolmogorov–Smirnov test, a nonparametric means of evaluating if samples of two continuous independent random variables were drawn from the same distribution (Baumgartner et al. 1998), was used to evaluate statistically the similarity of the results. Values for the statistic were calculated using the scipy.stats.ks_2samp function (The SciPy Community, 2020) in Python and results were summarized in Table 4. All P-values for the tests were greater than α = 0.1 meaning the null hypothesis could not be rejected and the underlying distributions were indistinguishable. The model-based automated work sampling procedure, therefore, produced results that were not statistically different from the human-derived version.