Managing Risk Dormancy in Multi-Team Work: Application of

Time-Dependent Success-and-Safety Assurance Methodology

Farag Emad, Ingman Dov, Suhir Ephraim

E-mail:uj326@iec.co.il; Tel:972-52-3995774 ; Fax: 972-4-8183683

Faculty of Industrial Engineering and Management, Technion – Israel Institute of

Technology, Haifa 32000, Israel

Abstract

The success and safety of many to-day’s industrial activities, such as, e.g., constructing power

plants, transmission lines, and civil engineering objects, is often influenced by situations, when

successful and safe work completion is associated with the implementation of various more or less

complex multi-team layouts. Multi-team effort is characterized by the presence and interaction of

numerous, not necessarily concurrent time-and-space constrained interfering activities, as well as

by dormant risks. Such risks might threaten the fulfillment of the general task carried out by the

main team and/or by other teams on the construction site. This analysis addresses the role of the

dormant risks during the fulfillment of a non-simultaneous multi-team work. The objective of the

analysis is to suggest an effective and physically meaningful probabilistic predictive model. The

model is aimed at the understanding, quantification and effective managing the dynamics of the

system of interest. The emphasis is on the role of possible dormancies. The study is an extension

of the authors' earlier research on spatial and time dimensions in the addressed problem. The study

extends the risk management approach to a holistic level.

1. Introduction

Modern infrastructure and industrial activities are typically executed by several different on-site

teams: Sasoua, K., and Reason, J., (1999) "most human work is performed by teams rather than

by individuals". Each team performs distinct and designated activities over time. At the same

time the today’s technologies require better understanding and increasingly higher quality than

in the past. Sophisticated work methods, tools, and equipment have been developed for, and

became available to, the workers in multi-team systems. Such methods are crucial to achieve the

optimum and safe outcome of a particular project. Tight schedules are implemented today to

meet customers' requirements, and to ensure the demands for increased productivity. This

imposes additional pressure on workers. Handling of hazardous materials and energy sources,

such as electricity or radiation, requires the use of highly qualified labor, as well as customized

safety procedures and equipment. Dynamic physical conditions, such as noise, vibrations and

working at heights, contribute also to the likelihood of hazardous situations.

Teams often operate independently, with no explicit functional linkage between them. In other

cases, more or less close professional collaboration is required to perform a particular task. For

example, repair work in an electrical utility typically requires involvement of several teams.

One team of electricians disconnects the power, another team repairs the damage, and a third

team is deployed outside the secluded site, often on a standby basis, to assist, if necessary, the

workers inside the worksite. In this example three different teams perform various aspects of the

work aimed at a particular mutual goal. A mistake, error or a failure in one team's actions

affects other teams involved in sequential large-scale activity, and has a potential to create a

hazard. The hazard might remain dormant for some time, but eventually can become or generate

a risk. This risk might have an immediate impact on the team member who caused it, and/or

threaten another team continuing the job at the given site. This scenario can be identified as risk

dormancy (RD). When occurring in a multi-team situation, it becomes a multi-team risk

dormancy (MTRD). It is this type of risk dormancy that is the main concern and the main

subject of this analysis.

Several researchers have addressed the MTRD lately. Mitropoulos, P., Howell, G. A., and

Abdelhamid, S.T., (2005) stated that “errors by one crew may create unpredictable conditions

for a following crew” and suggested that "future research should focus on better understanding

the effect of task unpredictability and on developing error management strategies". Reason,

J.T., (2000) wrote: “Different actors’ decisions and actions can produce latent conditions or

pathogens in a system. These might lie dormant for a time until they combine with local

circumstances and active failure and penetrate the system's many layers of defenses, and an

accident occurs.” Despite the recognized importance of the MTRD, there are no studies that

suggest effective solutions to the MTRD problems.

The existing studies suggesting various safety models employ quite a few of diverse

approaches. Some models focus on actions or processes and examine the time and space of the

occurred accidents that led to personal injury or to damage to some assets. These are s.c. active

failures. Other models are system oriented, such, e.g., organization models related to the

management policy, actions and decision making Rasmussen, J., Pejtersen, A. M., and

Goodstein, L. P. p-149 (1994). Reason, J.T., (1997), Interdisciplinary models La Coze, J.,

(2005) "focus on cause-effect relationships close in time and space to the accident sequences".

Akinci, B., Fischen M., Levitt R.; and Carlson R., (2002) examined the time-space management

aspect of accident prevention. He indicated that "lack of management of activity space

requirements during planning and scheduling results in time-space conflicts in which an

activity’s space requirements interfere with another activity’s space requirements or work-in-

place." Rosenfeld, Y., Rozenfeld, O., Sacks, R., and Baum, H., (2006). Rozenfeld, O., Sacks,

R., and Rosenfeld, Y., (2009) addressed this situation by expanding the safety model to MT

problems that involve mutual risk exposure of two or more teams sharing the same time–space

domain.

In the analysis that follows a rather general holistic approach is used to address dormant risks

encountered during consecutive MT work activities. This approach treats the problems of

interest from a rather general point of view, regardless of a specific nature of a particular risk or

a possible outcome. Our approach provides a probabilistic assessment of the management's

dynamic response at the organizational level. Here are several typical examples.

Example 1: A scaffold is required for certain tasks performed on a public utility construction

site by teams working at heights, such as, say, plasterers and painters Figure (1).

Scaffold builder team

Figure 1a Prior of lean scheduling

Figure 1(b) Lean Scheduling Time-Space Dependent Model (Rozenfeld, 2009)

Project Schedule

Project Schedule

Plumber

Bricklayer team,

Plasterer team team

Plumber

Scaffold builder team

Bricklayer team,

Plasterer team

Figure 1 Illustration of the scaffold example

Painter team

Painter team

A scaffold building team erects the scaffold, while a plumbing team is scheduled to

subsequently lay sewer pipes. In the meantime the scaffold is being used by other teams. In

such a situation, the time-space sharing is implemented as illustrated in Figure (1a). In the

safety assessment of the project in question risk exposure in team activities performed with

time-space overlapping is not considered. All the teams—the plasterers, the painters and the

plumbers—worked on the site simultaneously. The Lean Scheduling Time and Space

Dependent Model Rozenfeld, O., Sacks, R., and Rosenfeld, Y., (2009) illustrate the time

segregation.

This means that the plumbing team's work is rescheduled to avoid the risk of dealing with

falling objects or tools from the plastering or bricklaying teams. In space segregation, on the

other hand, the plumbers would be assigned to work at the other side of the site, where no teams

would be working above them. The risk posed by the scaffolding team is eliminated, as

illustrated in Figure (1b). The planner's instructions indicate, however, that the plumbing team

must lay the pipes in trenches at the foot of the building, where the scaffold is assembled.

Although the plastering team could be temporarily repositioned and the excavation could be

rescheduled, this still might destabilize the scaffold and increase the probability of its collapse

at a later time Figure (2). Rozenfeld's time-space dependent model does not address this aspect

of scaffold destabilization risk. The risk leads to an additional risk, namely, to the risk

dormancy. In this example, other scaffold users, such as the painting team, are exposed to an

underestimated risk, which, however, has been identified beforehand. This example illustrates

the risk dormancy (RD) phenomenon, i.e., a risk that is dormant, while awaiting for other teams

Figure 2 RD Exposure in MT Example- Risk of Scaffold Collapse, threat to painter team

Project schedule

Excavation for sewer

Plastering team Scaffold building Painting

team

Underestimation of the identified RD in MT work

or misidentification of RD

that might use the hazardous scaffold. Fig. 2 emphasizes the progress that could be achieved by

developing tools and methods to deal with such underestimated or misidentified risks that stem

from the dynamic changing of the site (in this case, the excavation).

Example 2: Power grid works are inherently created serially. A utility lineman team replaces an

insulator on a high-voltage power line after obtaining permission from the responsible

electrician. The electrician operates according to a written checklist issued by the engineering

department. The professional teams work in a serial manner with respect to both time and space:

first, the engineering department writes the instruction checklist; then the responsible electrician

shuts down the power at a circuit box mounted near the work site (along the power line) and

linemen replace the insulator further down the line (not even necessarily at the same site). In

such situations several teams are active at the site, and communication and coordination of their

interactions might be quite complex. Any error in these activities could create RD, thereby

increasing the likelihood of an electrical shock accident. Examples of this type of error are

failure to check for current, misidentification of the appropriate line connector, and/or the

specification of the wrong transformer or pole number.

The above examples (scaffold collapse or electrician's error) represent dormant risks caused by

MT activities that have no time or space overlapping. This means that accidents caused by the

MTRD activities regardless of their simultaneity and space have not been addressed. MT

activities create dynamic work sites (environments) with constant changes depending on the

needs for the complex system, in/for which the work is being performed. Such complex

systems require a tight control, i.e., appropriate risk management, which should be the main

component of a safety management system (SMS).

The term “risk management” includes the notion of mitigating risks to an adequate and

achievable level acceptable by the organization. This can be done by the application of two

major methods:

1) Appropriate and effective risk control and

2) The use of the most suitable accident causation models.

The obvious challenge in the assurance of the occupational safety is the development of the

ability to effectively control problematic situations, identify hazards and assess and control

risks. The actual down-to-earth and practical on-site work is more complicated, however, than

the models that attempt to predict and simulate the effort. Proactive approaches, such as risk

assessment and control, can never be entirely accurate, complete, and successful when applied

alone. Reason, J.T., (2000) "Effective risk management depends crucially on establishing a

reporting culture. Without a detailed analysis of mishaps, incidents, near misses, and 'free

lessons,' we have no way of uncovering recurrent error traps or of knowing where the edge is

until we fall over it". Risk management should be therefore implemented both proactively and

reactively, and its complete success necessitates reactive approaches such as accident

investigation/causation. A description of the process of investigating accidents in the context of

the concept presented in this paper, namely, the underlying cause - risk dormancy (RD), is set

forth below.

The general strategy of pursuing risk management approach in the problem in question includes

the following major items:

Hazard identification: Before quantifying the probability of failure, hazards related to the

system operation must be identified. Several identification techniques are available Carter, G.,

and Smith, S., some of which are based on brainstorming among people familiar with the

installations at a work site, while other techniques are of a more systematic nature. According to

Cuny, X., and Lejeune, M. (2003), recognizing hazards can be a rather complicated task. Many

different kinds of uncertainty factors contribute to the challenge of recognizing hazards and a

clear-cut determination of the source and level of the risk for each hazard is next-to-impossible.

Furthermore, many observations are needed to accurately estimate the likelihood of an accident,

particularly, if it is of rare occurrence. One of the paradigms in the Accident Root Causes

Tracing Model Abdelhamid, S. T., and Everett, J. G. (2000) reveals that workers, more often

than not, fail to identify hazardous situations. This leads to the conclusion that the hazard

identification process only partially covers the hazards that should be identified on site. Multi-

team hazards (MTH) are even more difficult to identify.

Risk assessment: Hazards become a problem only when they could possibly result in an

accident whose occurrence is preceded by a sequence of events that may cause a hazardous

situation. After a hazard is identified, all possible sequences of events that can be triggered by

that hazard must be studied and checked to determine whether or not they might lead to an

accident. With the relevant scenarios in hand, it is possible to calculate the two elements of a

risk: the probability of the events occurring, and their consequences. Reason, J.T., (1997)

presents three models for safety management: the Pearson model, the Engineering model, and

the Organizational model. Each of these models has a different perspective on human error.

“Workplaces and organizations are easier to manage than the minds of individual workers. You

cannot change the human condition, but you can change the conditions under which people

work. In short, the solutions to most human performance problems are technical rather than

psychological.” This concept can be better understood by considering the work of Papadopoulos

et al., who concluded that "risk assessment must be conducted for each task and for each

worker. This risk assessment must consider all hazards and their interactions and must be

revised when changes occur". In addition they wrote: "However, frequent changes regarding

workforce, working hours and working conditions, as well as time pressure, result in

insufficient time for conducting a complete and effective risk assessment, determining training

needs, setting up, applying and monitoring the corresponding OSH measures. Furthermore, the

methodological tools used in risk assessment up to now are not sufficient for this complex

situation". In an earlier paper on this subject, Drivas and Papadopoulos (2004) pointed that risk

assessment need to be considering all hazards and their collaborations and must be reviewed

when changes occur. Risk assessment will be even more difficult to identify for risks arising

from MT work, which is characterized by difficulty in identifying or the lack of the researcher

capacity to evaluate risks.

Risk control: Risk management is basically a control problem Rasmussen, J., and Svedung, I.,

(2000). The review focuses on the most suitable methods of risk control:

)a) Control Hierarchy is achieved by applying four main levels of action: 1) the hazard is

eliminated; 2) a physical barrier is erected between the hazard and the performer (worker); 3)

personal protective equipment is used; and 4) workers comply with written safety instructions.

)b) Root Cause Analysis addresses accident causation according to four basic categories:

management factors (safety and risk control), intermediate factors (procedures, work design,

training), performance (behavioral and technical) factors, and external (environmental) factors.

)c) Griffel, A. (1999), Comparative Analysis consists of measurements for risk prevention

using the following four dimensions: effectiveness, applicability, efficiency, and influence.

Moreover, since most serious accidents are apparently caused by the operation of hazardous

systems outside the design envelope, the basic challenge in the development of improved risk

management strategies is essentially to ensure improved interaction between the decision

making and planning strategies at the various levels of the organization Rasmussen, J., and

Svedung, I., (2000).

Thus, despite the currently implemented risk management strategy, the control of MTRD

appears to be unsolved yet.

Accident causation: Accident investigation/causation analyses enable one to better understand

the factors and processes leading to accidents. Our analysis that follows explores further

accident causation or aggravating factors, i.e., risk dormancy in multi-team work.

This paper's primary objective is to develop a holistic safety model, in which both management

and labor respond to various dormant MT risk situations. Management is responsible for

making decisions concerning the handling of such risks and accident causation in accordance

with a SMS. Disorder might result in a potential for unsafe actions. Such actions are

characterized by the following major attributes:

Deviation of a process from its planned time schedule, requiring corrective action to

remove the source of nonconformity in order to prevent recurrence. The corrective

action is aimed at ensuring that the existing potentially hazardous situations do not

lead to accidents.

Time lags in the sequenced work of the various teams that require communication and

mutual reporting.

Continuing random changes to the physical sites, thereby necessitating continuous risk

assessment.

Uncontrolled multiple degrees of freedom, instead of a tight and narrow path to a

successful outcome.

The lack of quality methodology principles being applied to monitor safe performance,

so as to reduce or even prevent accidents, especially those with casualties.

These attributes can create hazards that might be difficult to identify properly.

Two major problems with MT risk management have been identified:

(1) RD identified in MT work is often underestimated. For example, after excavating

trenches intended for power or communication lines or for drainage pits, the installation of the

cables of pipes is often delayed and the trenches and pits are marked using yellow caution tape

only. Despite the identification of an open trench or a pit as a hazard, such trenches and pits are

sometimes left open for days and even weeks, constituting a threat to other teams working at the

site.

(2) The potential threat of risk situations in an MT work is often misidentified. This leads

to a dynamic risk situation. For example, a maintenance technician places a rag on the floor to

absorb the condensation from a faulty office air conditioner and leaves to get his tools. A

secretary inadvertently steps on the wet rag, slips and falls, and injures her ankle.

The analysis that follows addresses the occupational accident data of the integrated electrical

public utility (PU) in the State of Israel between 2004 and 2011. As of 2011, the PU company

employed 12,687 workers and maintained and operated several power station sites with an

aggregate installed generating capacity of 13,133 MW, supplied to customers via a national grid

transmission and distribution (T&D) system. The following segmentation of the employee

roster into five operational divisions reflects the company’s main areas of activity relevant to

this study.

The works and expertise of the first and the second divisions, the North and South District ones,

are the T&D of electrical power. The third division is engaged in building power plants and

substations. The fourth division is in charge of logistics, and provides transportation services,

cranes, heavy vehicles and workshop works to the other divisions. The fifth division is the

Generation Division, which operates the power stations. The number of the employees in these

five divisions is shown in Table 1.

Table 1 Number of employees by division

Division

North

District

South

District Logistic Generation

Construction

Number of

employees 800 1300 700 2100

1700

The reported accidents of five PU’s divisions during 2004-2011 were compiled by its safety

department. The data presented in this paper were collected in accordance with the Israel

Institute for Occupational Safety and Hygiene (IIOSH) classification system. Each of the

accidents was examined to determine its causation in the context of the RD in MT work. The

results were subdivided into categories using two main factors: risk dormancy (RD) and non-

risk dormancy (NRD). The criteria described in section 1.3 were applied. The results, as they

N.MTRD.A - Non MTRD Accidents

MTRD.A - MTRD Accidents

Table 2 RD and Non- RD accidents in MT work

relate to the above five PU divisions, are shown in Table 2. These results reveal as much as

9.85% RD related accident rate for all the accidents.

2. Analyses

2.1 Risk dormancy analysis

2.1.1 Multi-team risk dormancy

Risk dormancy is the time delay between the occurrence of a failure (hazard event) in the

action of one team (Team A) that affects another team (Team B) involved in the process (Fig.3).

Such a failure has the potential to produce a hazard that is underestimated or undetected by

Team B and eventually becomes or generates a risk that lies dormant for a period of time (risk

dormancy). This risk has no immediate effect on the Team A member who caused it, but might

be a threat to another team (referred to as Team B) that will later continue the same job or will

Division

North

District

South

District Logistic Generation

Construction

N.MTRD.A 783 860 474 1391 715

MTRD.A. 45 81 64 134 133

TOTAL 828 941 538 1525 848

%

MTRD.A 5.4 % 8.6 % 11.9 % 8.78 %

9.85 %

be engaged in a different job at the site. This situation is referred to as risk dormancy in MT

work.

An analysis of the RD time path reveals the following stages leading to an accident (Fig.3):

T0 - Beginning of Team A activity that could possibly generate a hazard event that

becomes a risk and could threaten the Team B work

TR- Hazard event time

Td- Risk dormancy time, which is equal to the time interval from the hazard event caused

by the Team A activity until the accident occurs, injuring the next team. The time is estimated

by the professional safety officer teams, based on their experience and knowledge.

Tacc - The time the accident occurred.

This paper addresses the time aspects of RD in MT work and proposes a model for risk

evaluation and management based on time-dependent probability (TDP) methodology. In this

context, classification criteria for RD are related to risks generated by MT activities.

2.1.2 Probabilistic analysis of risk dormancy time

The analysis of RD in MT work requires collecting information regarding an accident and

establishing the sequence of events that led to the accident. This includes identifying the team

affected by the accident and the accident occurrence time, as well as the team that most

probably generated the risk that ultimately materialized (risk-causing team) and the time when

Beginning of activity that most

likely generated a hazard event that

constitutes a risk for Team B

Risk dormancy time

Figure 3 MT risk dormancy pathway

Team A: hazard

Team activities

Team B: underestimates/fails to detect

hazard created by Team A

Team's "A" Hazard

Hazard event

Accident

Time

the risk was actually generated. The affected team and the time, Tacc, of accident occurrence are

easily determined in most cases since accidents are usually investigated and documented. It is,

however, often quite difficult to identify the risk-causing team and determine the time at which

the risk was generated Figure (3).

Still, identifying the team that generated the risk is quite complicated and requires efforts

of a team of professional analysts, such as, e.g., the safety officer's team, which is supposed to

be familiar with the stages and layout of the work. The accident investigation determines not

only the active causes leading to the accident, but also includes the attributes of the schedule

itself, as well as tools, places, personnel, etc. involved in the risk creation. As a result, the safety

officers can determine with high confidence the commencing time T0 of the Team A activity

that most likely created a hazard event TR, which became a risky one and threatened the Team

B. Finally, a team of safety experts analyzes all actions that preceded the occurrence of the

accident, since the range of the risk dormancy time uncertainties is basically the risk creation

time until its materialization.

One can therefore calculate the statistics of the risk dormancy duration from its creation

TR until accident occurrence Tacc regardless of the teams involved in the hazard generation. RD

time is a random variable; hence a probabilistic analysis should apply. Actually, Tacc could be

established rather accurately due to the time recording of an accident’s occurrence, while T0 and

TR should be considered as are best estimates made by the professional safety officers.

RD is clearly a positive value, and its range is between the RD generating point TR on one

side and the time of the accident occurrence Tacc where RD terminates at the other.

The risk dormancy time Td is a random variable extending between the beginning of

Team A activity T0, which could possibly generate a hazard event TR, and the accident time

Tacc. When TR is close to T0, then the time Td reaches its maximum value. Similarly, when the

time TR is close to Tacc, then Td approaches zero. Thus, the entire range of RD time uncertainty

can be expressed as

(1)

In accordance with the maximum entropy principle, we choose a uniform distribution for

RD time Figure (4) regardless of which particular team caused it.

The probability density function (pdf) is therefore a constant, as expressed by the

equation:

(2)

Here is the Heaviside step function:

(3)

f (Td) - Normalized pdf of RD time (Normalization of pdf by Tacc to achieve an integral equal

to 1) and Td is the random RD time uniformly distributed

Our observations are based on the distribution of RD time generated by a number of

accident occurrences and not only on a single event. Thus the distribution should be averaged

for all events. The average is the arithmetic mean of all the RD times:

Figure 4 Risk dormancy time distribution

Here - Average of risk dormancy time of each division, i - Index of time to accident

on each division, n - RD accidents number on each division, k - Division index.

The RD time probability of all accidents at the PU (in the five divisions, to be precise)

expressed in equation (5):

Here - Average probability of RD time of all divisions

Fexp – experiment cdf of RD time of all divisions

3. Results

As shown in equation (7), the CDF fit function is specified the five divisions of the PU. This fit

function positively predicts the experimental CDF function Fexp equation (6) of RD time for

MTRD accidents data:

(7)

F (Td) – CDF fit function

θ1 - Scale parameter, Short-term expected time

θ2 - Scale parameter, Long-term expected time

β1 - Shape parameter, Short-term expected time

β2 - Shape parameter, Long-term expected time

α – Partition parameter, dividing the data into short α part and long (1-α) content

The RD time distribution parameters for the five PU divisions are shown in Table (3).

Each one is also subdivided by two distinctive populations. Sub-data are well described by

Weibull distribution. Accordingly, each data subset is characterized by a vector of three

parameters as shown in the following table:

Table 3 Distribution characteristic parameters

Table (3) distinguishes between two different groups. First, the three divisions: North,

South and Logistics, showing a clear distinction between short and long term. Expectedly, the

fit function of this group shows a good fit to RD time data see Figures (5a) (5b) (5c). However,

the second group of the two other divisions, Generation and Construction, has a majority of RD

time appearances of short-term character, about 0.9 and 0.8 respectively, compared to a smaller

long-term RD appearance. Because of that we ignore the long term for this group, whose sub-

data are well described by Weibull distribution. Each subset data are characterized by a vector

of two parameters (Table 4).

Table 4 Distribution characteristic parameters

Division/

Parameter

α θ1 θ2 β1 β2

North

District

0.47 3.98 570 0.92 1.4

South

District

0.68 4 539 0.92 0.96

Logistics 0.69 19 622 0.67 1.32

*

Generation

0.9 20 660 0.66 1.55

*Construct

ion

0.8 51 750 0.49 2.32

Division / Parameter θ1 β1

Generation 26 0.4

Construction 121 0.45

Accordingly, a modified CDF fit function of the Weibull type is specified:

The fit function shows a sufficient fitness to RD time data see Figures (5d) (5e). Monte-Carlo

simulation is used to generate random points from the domain RD time distribution data to

determine the validity of the five divisions' parameters - a kind of bootstrap simulation.

The simulation data show rather poor correlation between α, β and θ parameters. Consequently

we are considering them as independent parameters.

Hazard function: To confirm the results of the effect of RD one could examine the

impact of these parameters by employing the hazard function for each division:

The hazard function has resulted in the same groups of CDF functions with respect to RD time:

Group One: North, South and Logistics divisions characterized by two shape parameter β1 and

β2 as follows: 0.92, 0.92, 0.67 and 1.4, 0.96, 1.32 respectively (see Tables (3) (4). The results

for the β value were found to be close to 1, demonstrating almost constant failure rate in time

0 200 400 600 800 1 103

0

0.01

0.02

0.03

0.04

trace 1

Figure 7-a Hazard function of North district divisions

Dormancy time

Haza

rd f

un

ctio

n

see Figures (7a) (7b) (7c).

However, we are unable to explain the volatility behavior of the two models presented in

equation 7, which brings one to the three choices of Weibull. Similarly, one could question

why these parameter values were obtained. These questions will be addressed in the future

work.

0 200 400 600 8000

0.01

0.02

0.03

0.04

trace 1

Figure 7-b Hazard function of South distric t divisions

Dormancy time

Haz

ard f

unct

ion

0 200 400 600 8000

0.01

0.02

0.03

0.04

trace 1

Figure 7-c Hazard function of Logistic division

Dormancy time

Haz

ard f

unct

ion

Group Two: Generation and Construction Divisions characterized by single-shape

parameter β1 of 0.4 and 0.45 respectively see Table 4. The results of β were found to be less

than 1 demonstrating a decreasing failure rate in time as shown in Figures (7d) (7e).

4. Discussion

The type of organizations considered in this study are quite complicated, as they are

characterized by significant and strong interdependence between the management and MT

professional labor, in addition to the effect of interaction among themselves, regardless of

0 200 400 600 800 1 103

0

0.01

0.02

0.03

0.04

trace 1

Figure 7-d Hazard function of Generation division

Dormancy time

Haz

ard f

unct

ion

0 200 400 600 800 1 103

0

0.01

0.02

0.03

0.04

trace 1

Figure 7-e Hazard function of Construc tion division

Dormancy time

Haz

ard f

unct

ion

simultaneity. This complexity could lead to problematic aspects in internal company behavior,

sometimes causing safety problems.

The scope of this paper extends the risk management approach from the well-known

management models, with the addition of the important aspect of the MT safety perspective

beyond simultaneous situations, i.e., RD - risk dormancy. The research provided here extends

the existing approaches to a holistic level.

The obtained data on RD accidents indicate that the risk dormancy time distribution is

characterized by Weibull parameters: θ1, β1 - short term, θ2, β2 -long term respectively and

partition parameter α as shown in Tables (3) (4) above, for situations, in which the very nature

of the work dictates the dormancy time, as supported in the following data discussion:

First, θ1, β1 short term expected RD time and α partition parameters:

(1) North and South divisions - T&D districts

Scale parameter θ1 , whose RD time is 3.9 and 4 hours, respectively. An examination of

the accident investigation data shows that tasks in the T&D districts have the following

characteristics:

i. Most tasks are scheduled and completed in one day, because the nature of T&D work

requires power supply resumption to customers as quickly as possible. As a result, the short

term of risk dormancy time lasts a few hours.

ii. Tasks are performed sequentially by a professional MT.

iii. Subtasks are performed sequentially.

iv. Similar field of activity. E.g., lineman-teams of differing proficiency levels are

necessary for executing complementary parts of power line work due to the complexity of the

work and the existence of risk factors such as electricity, and, as a consequence of that, have

high safety level requirements. For example, erecting a transformer requires at least two

different teams: an electricians’ team to de-energize transformer connections to the power lines

and install grounds and an overhead line-work team to install the transformer. Thus, the work

teams require the necessary expertise for each phase of the work.

Shape parameter β1 of 0.92 for both districts:

These β1 values are close to 1, indicating an almost constant accident rate regarding RD

time. This happens if there is maximum entropy, characterized by exponential distribution

process. Remarkably, hazards and risky situations analyzed in this paper, and which are more

likely to cause accidents at a constant rate, are related to electrical supply divisions (South and

North divisions as shown in Figures (7a) and (7b).

Partition parameter α - divides the South and North divisions’ risk dormancy time

appearances into two separate categories in which the short term is 0.47, 0.68 respectively.

Case study - 1: A lines work team of PU North division performed an underground

cable connection to an overhead line. According to the safety instructions, the cable to be

worked on must be positively identified by tags and must be isolated from the electric supply

sources. Furthermore, tests must be performed to verify that the cable is de-energized, and

grounds of an approved type must be applied to protect workers from all the energy sources.

Earlier in the morning of the same day, an authorized clearance team should be assigned to

identify and de-energize the underground cable, in accordance with an authorization provided

and documented by a system operator. The clearance team is supposed to install the protective

short-circuiting and grounding equipment required for the protection of the team working on

cable connection. The permission to start working was given at the work site. While the cable

cutting work was in progress, an explosion occurred. The team cut an energized cable. The

authorized clearance team misidentified the correct cable and gave the work authorization for

the wrong cable. Workers were injured due to electrical arc flash.

In this case the above-mentioned scale parameter characteristics apply about 2 hours of

short-term dormancy time.

(2) Logistic division

Scale parameter θ1, whose risk dormancy time is 19 hours. The present results revealed a

prominent attribute related to work course duration. The large majority of appearances of these

RD accident occurrences are at the end of a working day or a shift. The following theme is the

main characteristic of the risk dormancy causation: in the Logistic division the main MT

activities occurring at the beginning or at the end of the working day/shift were the loading and

unloading of trucks.

Shape parameter β1 - a shape parameter value of β1 = 0.67 1 indicates that the accident

rates decrease over time. This happens if significant hazards or risky situations are generated

result in an accident at a decreasing rate over time.

Partition parameter α, which is dividing the Logistic division risk dormancy time

appearances into two substantial populations' 0.69, 0.31 of short and long term, respectively.

Case study - 2: A workshop employee was on his way to repair a metal processing

machine. A stack of iron bars, delivered the previous day, was still on the workshop floor,

protruding into the employee’s pathway. He stumbled as he passed the stack and injured his leg.

Material deliveries are usually made in the morning and materials are unloaded from trucks at

the workshop yard close to where the machines are placed, pending transfer to storerooms.

These irons bars were unloaded a day before the accident. A 24 dormancy time was estimated

by the safety officer.

Case study - 3: The PU owns a rather big truck fleet, used for truck-mounted work

platforms and truck-mounted cranes, which are used for loading/unloading and uplifting

workers to heights. In this case one of the trucks was sent back to duty from in-house periodic

maintenance service on the morning of that day, the truck driver opened the engine hood during

a routine cleaning and checking procedure at the end of the shift and was injured while trying to

remove a "piece of rubber" that was inadvertently left there by a garage worker. An eight-hour

RD time was estimated by safety department.

(3) Generation and Construction divisions with significant short term expected

RD time

It's obvious from Table (3) that partition parameter α of the magnitude of 0.9 and 0.8,

respectively, indicates the dominant short term RD time appearances in these divisions.

Therefore, we neglected/ignored the long term appearance in those two divisions as shown in

Table (4).

Scale parameter θ1, whose risk dormancy time is 26 and 121 hours, respectively. Task

substitute time, i.e., first task completion and transition to the next task by MT at generation,

construction and logistic divisions are longer than in T&D districts.

Shape parameter β1 of 0.4 and 0.45 again a value of β1 1 indicates that the accidents

rate decreases over time. This happens if there are significant hazards or risky situations

generated early and leading to an accident in a decreasing rate over time.

Case study - 4: To carry out a maintenance job in a turbine building of a power station, a

scaffold was erected and placed on the route of an overhead bridge crane. The crane consists of

parallel runways with a traveling bridge spanning the gap and equipped with hoist that travels

along the bridge. These cranes are electrically operated from ground level by a control pendant.

Three days later a team from another department used the crane for lifting heavy valves as part

of a job. The crane hit the scaffold, causing extensive damage. In this case the above-mentioned

generation scale parameter characteristics apply a risk dormancy time of about 72 hours.

Second, θ2, β2 long term expected RD time and α partition parameters:

(1) North, South and Logistic divisions

Scale parameter θ2 whose RD time is 570, 539 and 622 hours, respectively, the long

term RD accidents are likely to affect teams with no professional linkage between them. There

is no significant difference observed in the results for all the divisions as seen in Table (3) and

(4).

Case study – 5: A working team of the Southern PU district was sent for carrying out

maintenance work on electric supply line of low voltage network.An employee whose job

included activities installing, constructing, adjusting, repairing, etc. climbed on a metal pole and

started repair work. An electrical current flow as a result of contact between transformer cables

and the pole causing electrical shake to worker. Investigation found that a month before another

working team of the same district performed different work on the same transformer, causing

faulty cables connection of the transformer. As a result, loose connection of the cable made

contact with the conductive metal pole and electrical flash of short circuit injured the team.

In this case, different teams, namely maintenance and operation teams of the same

district, performed different tasks without professional affiliation or linkage between them. The

scale parameter θ2 characteristic applied a risk dormancy time of about 720 hours.

(2) Generation and Construction divisions

Negligible long term expected RD time

5. Conclusions

A novel probabilistic risk management model has been introduced to characterize the risk

dormancy phenomenon to be considered as a proper way of taking in to account MT aspects in

the occupational safety research. Furthermore, a holistic perception of MT functional

complexity allows for a generalized view of MT mutual interaction instead of focusing in the

single team behavior.

The following conclusions can be drawn from the carried out analysis.

The model is innovative in two major ways: Firstly, the identification of risk dormancy in

sequential multi-team work, i.e., Multi-team Risk Dormancy – MTRD. Though, unidentified

dormant risks or the underestimation of identified dormant risks are a "ticking bomb": each such

risk represents an unsafe/hazardous event that is certain to happen in the foreseeable future and

which threatens other teams continuing the same or a different job on site. Indeed, the current

time-space approach does not address or offer solutions to such risks. Therefore, we have

developed an RD approach that offers a solution for predicting TDP. Secondly, the PU accident

database enables us to evaluate and determine the above-mentioned significant risk aspect

tendencies. Accordingly, the proposed model defines risk dormancy, a new facet of risks

generated by multi-team work in modern industrial and infrastructure organizations, regardless

of the time frame involved.

Acknowledgments

True friendship is the willingness to sacrifice one's self for the other. This study is

dedicated to my friend, the late Dr. Majdi Latif, who inspired and encouraged me.

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