Simulation in Production and Logistics 2015 Markus Rabe & Uwe Clausen (eds.) Fraunhofer IRB Verlag, Stuttgart 2015

Integration of Ergonomic Analysis into Simulation Modelling of Manual Operations

Integration der ergonomischen Analyse in die Simulationsmodellierung von manuellen Operationen

Alireza Golabchi, SangUk Han, Simaan AbouRizk, University of Alberta, Edmonton (Canada), alireza1@ualberta.ca, sanguk@ualberta.ca,

abourizk@ualberta.ca

Abstract: Considering the prevalence of simulation modelling in production design, integrating ergonomic safety considerations into simulation models can help produc-tion planners identify and prevent ergonomic risks before workers encounter unsafe conditions, in addition to productivity improvements. This study proposes integra-ting ergonomic analysis into micro-motion-level simulation modelling by develo-ping a simulation modelling template that enables simultaneous estimation of the standard duration of manual activities, as well as biomechanical risk factors inherent in the production design. The proposed approach enables modelling of manual acti-vities at a human motion level in order to provide standard task durations in addition to initial insight into the level of ergonomic risks associated with a planned produc-tion scenario. As a case study, the proposed methodology has been implemented in a construction steel fabrication company and the results confirm the validity of the approach in identifying ergonomic risks associated with a production design.

1 Introduction Simulation modelling is an effective tool for design and planning of construction operations. A digitalised model representing a physical system allows prediction of the system performance in a real world application, as well as understanding of the system behaviour under various conditions. In construction, advances in simulation modelling techniques have been made to estimate the duration of projects and the productivity of different operational systems. However, it still remains challenging to analyse manual operations due to the uncertainties in modelling human move-ments and estimating the cycle time of manual tasks. Furthermore, the impact of the designed workplace and workflow on human capacity (e.g., durations and frequen-cies of physical tasks) for ergonomic safety analysis has not been thoroughly considered. In this regard, integrating ergonomic considerations into simulation models can help production planners identify and prevent ergonomic risk factors

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Integration of Ergonomic Analysis into Simulation of Manual Operations 493

are provided with a standard duration in which a worker is expected to complete an action, as well as a comparison of the efficiency of different alternatives of performing the operations (ILO 1986). Researchers have studied large samples of various manual activities in order to develop PMTS methods that can provide the standard time required to carry out a manual task. Currently, the most widely used PMTSs include Methods-Time Measurement (MTM) (Maynard et al. 1948), Modular Arrangement of Predetermined Time Standards (MODAPTS) (Heyde 1966), and Maynard Operation Sequence Technique (MOST) (Zandin 1980). This study uses the MODAPTS method for modelling worker motions due to its simplicity and quick application. However, the other methods can also be similarly applied using the same approach.

MODAPTS classifies the complexity of manual actions (e.g., get, move, put) by the amount of sensory feedback required to carry out the action rather than the geometrical properties of the material to be handled. It was developed in an attempt to reduce the time and cost required to apply prior PMTS methods and create a system that could be memorised, is simple to learn and apply and yields consistent results (Heyde 1989). MODAPTS assumes that the duration of any body motion can be expressed as a multiple of the time required to move a single finger, called a MOD. The MOD unit, equal to 0.129 second, is used assuming that the movements are carried out with minimal energy expenditure and that the movement duration is proportional to the fifth root of the moment of inertia of the body part moved (Minter 1983). Figure 2 shows the moving distance of the different body parts and the corresponding MOD values for MODAPTS.

Body Part Finger Hand Arm Whole arm Extended arm Trunk

MOD 1 2 3 4 5 7

Distance Moved 2.5 cm 5.0 cm 15 cm 30 cm 45 cm 75 cm

Figure 2: Basic movements as defined by MODAPTS

In order to analyse a manual task using MODAPTS, the task has to be broken down into basic motions, known as modules, that can be described using MODAPTS clas-ses, and for each motion, a MOD value has to be assigned. By adding the MOD values and converting the sum of MODs to seconds, the total amount of time required to complete the task is obtained. MODAPTS codes are used to represent different classes of movements which describe the type of motion. Some basic

494 Golabchi, Alireza; Han, SangUk; Abourizk, Simaan

classes include: Get (G), Move (M), Put (P) and Use (U). For example, a worker moving his hand 15 centimetres to pick up a wrench is represented by the MODAPTS code M3G3. M3 represents the hand movement motion that takes 3 MODs, and G3 represents performing an impeded grasp which also takes 3 MODs. For the purpose of this study, the MODAPTS method is integrated into discrete-event simulation in order to enable the modelling of manual activities in simulation models of construction operations.

2.2 Ergonomic Assessment

Various ergonomic assessment methods have been developed in order to evaluate the risks associated with manual tasks and identify unsafe actions that can lead to Work-related Musculoskeletal Disorders (WMSDs). These methods consider human posture as well as external risk factors (e.g., frequency and duration) to provide a global risk assessment of a motion, which is used to take required corrective actions (Golabchi et al. 2015b). Different methods prioritise different factors and focus on various body parts in order to assess different cases of manual handling activities. The cube model (Kadefors 1994) is one of these ergonomic assessment tools which provides an ergonomic evaluation of a manual task by inputting basic design data. This study uses the cube model for ergonomic evaluation due to its simplicity and efficiency, which make it suitable for incorporating into simulation modelling.

The cube model was developed based on the premise that the risk of acquiring WMSDs is mainly dependent on three interrelated variables (Laring et al. 2002). These variables include: working posture, muscular force, and time. For each of these factors, three levels of demands are defined based on severity, including low demand, medium demand, and high demand. These demand levels can be assigned using scientific evidence (e.g., Snook and Ciriello’s (1991) tables for maximum acceptable weight limits), or by using a consensus approach to identify the severity of each demand (Kadefors 1993). The three factors (i.e., posture, force and time) are considered as axes of a cube, yielding 27 sub cubes, which is the number of possible combinations. For each combination, a score is calculated by multiplying the scores of each of the three demands, assuming 1, 2, and 3 as low, medium, and high de-mand, respectively (Kadefors 1997). A manual task is considered ergonomically acceptable for a final score of less than 5, conditionally acceptable for a score between 5 and 10, and ergonomically unacceptable for a score of higher than 10.

2.3 Integrated Micro Motion Level Simulation

The authors use Simphony (Hajjar and AbouRizk 1999) as the platform for simulation modelling as it enables development of special purpose templates that can encompass elements that work in conjunction with the general template. For the purpose of this study, an integrated micro-motion-level simulation template is developed that consists of elements that enable applying the MODAPTS standard in the simulation environment, without requiring knowledge about the details of implementing MODAPTS, and also provide insight into the level of ergonomic risks associated with manual tasks of the model, based on the principles of the cube model. The modelling elements developed include one primary element, named MHE (Manual Handling and Ergonomics), and six secondary elements. The MHE element requires basic design data pertaining to a manual task as input and

Integration of Ergonomic Analysis into Simulation of Manual Operations 495

calculates the corresponding MODAPTS duration for the task and uses it for simu-lation purposes, and also reports on the level of ergonomic risks for each manual task. Figure 3 shows the design inputs required for the MHE element. As shown in Figure 3, the required inputs include design information that a production planner has when designing or redesigning a process.

Figure 3: Inputs of the MHE element

The six secondary elements represent lower level manual activities that are designed based on the MODAPTS classes and include: Move, Get, Put, Walk, BendAndArise and SitAndStand. For example, the Move element can represent the motion of a worker moving his hand to grasp or put down an object, with the required input being the distance between the worker’s hand and the object. These elements are designed and implemented to provide more flexibility in designing detail-level manual motions, in cases where a designer intends to investigate the effect of changing the attributes of one motion on the simulation results (Golabchi et al. 2015c). Furthermore, these models are used when the model requires placing one lower-level motion between non-manual elements. Figure 4 shows the Simphony environment and the elements of the special purpose template.

By using the developed elements, designers will have the opportunity to use reliable estimates of the duration required to perform a manual task within the simulation environment, without requiring prior knowledge about the details of predetermined motion time systems. Furthermore, designers are provided with an assessment of the level of ergonomic risks associated with each manual activity, which enables them to prevent WMSDs during the workplace design phase.

Inputs required for MODAPTS calculation

Inputs required for ergonomic assessment

496 Golabchi, Alireza; Han, SangUk; Abourizk, Simaan

Figure 4: Simphony modelling environment

3 Implementation: Case Study In order to implement and validate the proposed approach, the integrated micro motion level simulation has been used to model a steel plate handling task in a steel fabrication and construction service provider company in Canada (Golabchi et al. 2015c). Due to the nature of manual tasks in the construction industry, workers are frequently involved in physically challenging activities. This makes the integrated simulation approach suitable for modelling these manual tasks since both a standard duration for worker actions and a feedback on the level of ergonomic risks associated with the action is obtained. The steel plate handling task investigated as the case study includes a worker removing a steel plate from a drilling machine after the drilling is done, carrying the plate to designated bins, and placing it in the appropriate spot inside the bin. This cycle can be modelled for simulation purposes using the developed MHE element. The drilled plates have various dimensions and weights with an average size of 40 cm x 40 cm x 5 cm and an average weight of 20 kilogrammes. The task has been observed at the jobsite in order to collect the required inputs for the MHE element. Table 1 shows the inputs required and the description of each input for modelling the plate handling task. Although the proposed approach can be highly effective in designing new processes, an existing manual task has been selected for the case study in order to firstly enable studying the validity of the approach and secondly demonstrate its functionality in redesigning existing operations, besides designing new processes. It should be noted that the MHE element modelling the steel plate handling task will be used as part of a larger simulation model in conjunction with other simulation modelling elements, representing the whole steel fabrication operation. The steel plate handling activity can also be broken down into lower level motions and be modelled using the secondary elements.

Integration of Ergonomic Analysis into Simulation of Manual Operations 497

Table 1: Inputs required for modelling the steel plate handling task

Input Description for steel plate handling Alternatives

Distance The distance that the worker carries the plate.

-

EndPosition The precision required for placing the plate at the destination.

GeneralLocation, WithTidiness, ExactLocation

Frequency The frequency of the task per day. LessThanOneHour, BetweenOneAndFour, MoreThanFourHours

Posture The optimality of the worker’s posture while handling the plate.

Optimal, NearOptimal, OutsideOptimal

RetrievalEnd/ RetrievalStart

The distance between the plate and the worker’s hands when retrieving/placing the plate.

OneInch, TwoInches, SixInches, TwelveInches, EighteenInches, ThirtyInches

StartGrasp The ease of grasping the plate. SimpleGrasp, ImpededGrasp

Weight The weight of the plate. -

Validating the proposed approach using the case study involves two steps: (1) comparing the MODAPTS duration calculated by the model with actual time data collected from the jobsite, and (2) investigating the reliability of the ergonomic assessment provided by the simulation model. In order to perform the first part of the validation study, the actual duration for the worker to complete the task is also recorded for each instance, besides collecting the data related to the inputs shown in Table 1. Table 2 shows the results of running the simulation model using the MHE element for 10 instances of the plate handling task as an example. The MODAPTS code and duration are also calculated manually to ensure that the simulation model is calculating the durations accurately, and the results are consistent with the durations calculated by the simulation model, as shown in Table 2. A correlation analysis has been carried out and a Pearson correlation coefficient of 0.956 and a Spearman correlation coefficient of 0.913 is calculated between the actual time and the simulation MODAPTS time, which indicates very strong association between the two data sets.

In order to investigate the reliability of the ergonomic assessment results from the developed simulation template, for each instance of the steel plate handling task, the level of ergonomic risks associated with the instance is analysed using the Rapid Upper Limb Assessment (RULA) (McAtamney and Corlett 1993) method. RULA is a widely-used approach to ergonomic assessment which focuses on human posture and also considers the load involved as well as the frequency of the task (Golabchi et al. 2015a). The result of a RULA analysis is a total score between 1 and 7, with 1 and 2 indicating acceptable posture, 3 and 4 indicating that changes may be needed, 5 and 6 indicating that changes are required soon, and 7 indicating that immediate

498 Golabchi, Alireza; Han, SangUk; Abourizk, Simaan

investigation and modification is required. Since the result of a cube model analysis is represented in one of the three assigned categories (i.e., acceptable, conditionally acceptable, and unacceptable), the RULA scores are also categorised as 1 and 2 representing safe motion, 3 and 4 indicating further investigation required, and 5, 6 and 7 specifying unsafe motion, to enable comparison of the results. Table 3 shows the result of the ergonomic analysis for 10 instances of the steel plate handling task. The results indicate consistency between the output of the ergonomic assessment from the simulation model and the RULA analysis, which confirms the reliability of the approach in evaluating the ergonomic risks of manual activities.

Table 2: Comparison between actual time and simulation time for case study

Instance Duration (Sec) MODAPTS Code

(Manual) MODAPTS Duration (Manual) Actual Simulation

1 8.2 7.095 M2G1L3W47M2P0 7.095 (=55*0.129)

2 6.7 6.966 M3G3L3W39M4P2 6.966 (=54*0.129)

3 6.2 6.450 M3G3L3W35M4P2 6.45 (=50*0.129)

4 6.8 6.450 M2G1L3W39M5P0 6.45 (=50*0.129)

5 5.4 5.934 M2G1L3W35M5P0 5.934 (=46*0.129)

6 5.6 6.063 M3G1L3W35M5P0 6.063 (=47*0.129)

7 6.9 6.321 M3G1L3W35M5P2 6.321 (=49*0.129)

8 4.1 4.386 M3G3L3W23M2P0 4.386 (=34*0.129)

9 6.1 5.418 M2G3L3W31M1P2 5.418 (=42*0.129)

10 5.8 5.676 M3G3L3W31M2P2 5.676 (=44*0.129)

Table 3: Comparison between results of ergonomic assessment and RULA

Instance Cube model result RULA

Score Interpretation

1 Conditionally acceptable 3 Further investigation 2 Unacceptable 6 Unsafe

3 Unacceptable 6 Unsafe

4 Conditionally acceptable 3 Further investigation 5 Unacceptable 6 Unsafe

6 Unacceptable 6 Unsafe

7 Conditionally acceptable 3 Further investigation 8 Conditionally acceptable 3 Further investigation 9 Unacceptable 6 Unsafe

10 Unacceptable 6 Unsafe

Integration of Ergonomic Analysis into Simulation of Manual Operations 499

The results of the case study analysis indicate that the integrated micro motion level simulation approach can be effectively used by process designers and production planners to provide firstly a standard duration for manual tasks and secondly an insight into the level of ergonomic risks associated with a design. The correlation between the actual and simulation time can be used as a benchmark for worker’s performance which enables evaluating the efficiency of operations for work process design and redesign purposes.

In future work, the authors will consider linking human motion data to the elements of the developed special purpose simulation template in order to enable more accurate ergonomic evaluation and mitigation of ergonomic risks while redesigning ongoing operations. Furthermore, more data from jobsites will be collected in order to formulate the conversion of standard MODAPTS time to realistic jobsite time by accounting for inefficiencies in carrying out manual tasks. The result can be used to develop efficiency factors for various types of manual activities.

4 Conclusion Simulation modelling is an effective tool for design and planning of operations since a digitalised model representing a physical system allows for prediction of the system performance in the real world, as well as for understanding system behaviour under various conditions. In construction, advances in simulation modelling tech-niques have been made to estimate the duration of different types of projects and the productivity of various operational systems. However, it still remains challenging to analyse manual operations due to the uncertainties in modelling human movements and estimating the cycle time of manual tasks. Furthermore, although production planners intend to take ergonomic considerations into account while planning opera-tions, there is a lack of tools to enable ergonomic safety assessment during work-place design. In this regard, this study proposes integrating ergonomic analysis into micro-motion-level simulation modelling, which enables simultaneous estimation of the standard duration of manual activities and ergonomic risks inherent in production design. A special purpose simulation template is developed that enables modifying a workplace design in order to achieve both ergonomically safe and productive operation systems and potentially allows for the in-depth understanding of the trade-off between productivity and safety. This study provides industry practitioners with the opportunity to incorporate reliable representations of manual activities into simulation models of operations and take advantage of standard PMTS methods as well as ergonomic assessment tools, without requiring prior knowledge about these systems.

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