Applying Subject Matter Expertise (SME) Elicitation Techniques to TRAC Studies

Teter, Michael D.

doi:10.21236/ada616463

Cited by 2 publications

(1 citation statement)

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“…Considering this feature of mental work, direct observations to estimate the task durations by employees can be severely limited. At the same time, using expert estimates may lead to a significant deviation from the actual task duration (Teter, 2014;Roy et al, 2013;Christenfeld, 2008, 2007;Goswami and Urminsky, 2014).…”

Section: Theoretical Contributionmentioning

confidence: 99%

A workload measurement framework for determining staffing needs

Salimgereyev

2023

Materials of the International Scientific-Practical Conference “The Paradigm of Sustainable Economic Development in the Context

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Determining workforce demand is an integral part of the workforce planning process -determining workforce size, composition, organization, and deployment. It analyzes the current and forecasts the future levels of workload required to fulfill the organization's mission and the factors that influence the workload. But the lack of reliable sources of actual task durations is the most significant difficulty in determining the workload. This paper proposes a workload measurement framework. Its purpose is to measure the workloads of tasks managerial, professional, and technical employees perform at the workplace.Our approach suggests a detailed description of work processes and an elementwise time study using a specially designed web application.The proposed workload measurement framework was successfully implemented in the study to determine staffing needs for local government employees. More than 1800 state employees responded to the time study. The proposed workload measurement framework provided quality data for workload analytics.The proposed workload measurement framework has two benefits. Firstly, it provides the workload model developers with the actual task duration data, thus making the workload models more accurate and less dependent on subjective inputs. Secondly, it decreases the labor-intensity of the data collection and analysis processes, thus reducing the workload model development lead times.

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Section: Theoretical Contributionmentioning

confidence: 99%