Current World Environment

Introduction

Human being has afallible naturewhose mistakesare inevitable. Thereby, from ancient timespeopleare looking forwaysto reducetheirriskoferrors.In other words,  by improving workplace conditions,  well-designing the equipment, and adopting appropriate strategies, the risk-generating factors should be minimize as much as possible(Ahmadi 2005b). Prior to 1931, safety experts tend to focus on physical  strategies such as machine guards, regardless of discipline and grooming considerations.

They believed that accidents are caused by inappropriate physical conditions.Preventive actions such as awareness programs by posters, brochures, training workshops,  etc.  could also be effective factor in mitigating risky activities. According to which, the individuals at risk will learn how to control incidences. Heinrich offered a total of ten items  including theories and concepts  of industrial safety rules(Lees Frank 2004 ; Whittingham 2004; Jahangiri 2004). Following Heinrich’s thoughts, attentions have paid to the importance of unsafe activities as a main risk-generating factor in industries (Ahmadi 2005a).

In 1960s,  there was emphasized on preventive engineering on reduced technical defect and increased equipment reliability and safety barriers. After plane crashes in 1974, and1980  as well as Three Miles Island in 1979, attention has drawn towards other issues of human fallibility, better education, improvement of human interactions - machinery, and support systems for the  respond to the reduction and prevention of the spread of "human error"(Reason 2000; Ghalenoy 2006; Grozdanović2006).

Possibility index of human error is a quantitative and dynamic approach for taking into account human factors in risk assessment. This index is a method for identifying, evaluating and mitigating the risks associated with human errorsduring emergencyconditions(DiMattia et al. 2005, Khan et al. 2006). Carlos Conte et al. (2011) presented a generalized utility model for the diagnosis and prediction of accidents among the Spanish workforce with the aim of managing automatically work-related accidents at a national level.Jasch and Lavicka in 2006 was done a research on health and safety risk management of the Styrian automobile cluster in Austria. They prepared a management plan which is useful for small and medium sized companies as a starting point to shape their (EHS) system.

The present study aims atassessing biological risk center of Saipa Company as the second largest Iranian auto manufacturer.

Method:

The present study lasted for  12 mounts  in Saipa Company as the second largest auto-manufacturer  in Iran. . Accordingly, three environmental experts spent  6 mounts to detect  biological risk centers in all parts of the company such as Production Unit including Painting Unit No. 1, Painting Unit No. 2, Body Unit No. 1,Body Unit No. 2,Assembly Unit No. 1, Assembly Unit No. 2, Press and non-production parts such as Power Post ,Gas Central, Power House , fuel tanks , polymer, C.M.M ,chemistry, metallurgy and mechanic, chemical material, Etka,Shirazi land, Saipa3, Seico, Sale, Saipa5,office,clinic, triple salon , maintenance ,technical affairs, restaurant, public places, road test, treatment plant, waste, underground canals,  andstreets. The collected data were  recorded, separately.

The famous PDCA Cycle suggested by Dr. Demingwere used to detectand record risky processes. After identifying  risky centers, appropriate strategies were presented to combat the risks.

Using Cocran formula, the  number of visiting was  calculated for each month  in separation of different unities in the company.

According to available data and Cocaran formula, the number of visiting  was 29 for each month. Using the Excel Software, a total of 29 - 46 places were selected to be visited in each month.

After detecting  all risky centers during  the study period of 6 months, some weekly meetings were held in which  HSE (Health, Safety and Environment) managers, maintenance and repairs, technical and energy services managers  were participated in order to find proper mitigation measures. Finally, a corrective action planrelated to manufacturing and administrative units was prepared containing managerial strategies on maintenance and repairs, as well astechnical and energy services.

Corrective actions  could be categorize in two types, one is related to equipments and another is associated with personnel behavior resolved by  different ways (culture-buildingtrainings,  brochures, pamphlets, etc). Subsequently, Procedure number PJ-01-001 was used to evaluate risk levels of each risky centers based upon criteria occurrence probability, occurrence probability and intensity.This method includes those requirements recommended by the standard OHSAS 18001, 2007. Table 1 is a sample checklist of PJ-01-001 Procedure derived from FMEA Method by which risky factors are evaluated based on a three-point qualitative scale (high, medium and low).
 

Table1- a sample checklist of PJ-01-001 Procedure to classify biological risky centers in Saipa Company. Click here to View table

 
For determination risk identified rate according to appendix form 1, at first compute intense and probability of occurrence risk from below table then determination risk rate according to evaluating matrix.

In order to classify risky centers, the occurrence probability and intensity were initially be determined using Tables 2 and 3. Afterwards, they were ranked using the evaluating matrix. Occurrence intensity: The occurrence intensity was measured based on economic damages and losses to the personnel.

Table 2-Intense rating determination

Human	rating
Death of more than 1 person	7
Death of 1 person	6
Disability over 60%	5
Disability over  30% up to 60%
Disability 10% up to 30%
Disability less than 10%
Medical break of 7 days up to 1month	4
Medical break of 2days up to 7	3
1 day Medical break	2
First aid	1

 
Occurrence probability was determined as follows using the coefficients in Tables 3, 4 and 5.

1. Probability outcome (coefficient 6) (Table 3)
2. Degree of exposure (coefficient 8) (Table 3)
3. human and engineering control actions (coefficient 10) (Table 4)
4. Ability to discover (coefficient 4) (Table 5)

The average of the mentioned coefficients was calculated using the following equation:

P-(4*ability to discovery + 6* outcome frequency + 8*degree of exposure + 10* control action)/28       ... (1)

Each of the factors was also calculated according to Tables 2,3 and4. If the probability result is a decisional number, the value will be roundedto one decimal place using the Equation 2.


Table 3- Outcome frequency and exposure to risky factors

Coefficient	Outcome frequency	Coefficient	Exposure to risk
			Safty	Occupation Health	General Health Person/Place
10	Every day happening	10	Every day exposure	> 480	More than 2000  people
9	One day among	9	Every other day	420-479	1001-1999
8	Once a week	8	Once a week	360-419	501-1000
7	mid-week	7	mid- week	300-359	301-500
6	Once a month	6	Once a month	240-299	201-300
5	Every other 3 months	5	Every other 3 months	180-239	101-200
4	Every other 6 months	4	Every other 6 months	120-179	51-100
3	Once a year	3	Once a year	60-119	11-50
2	Once in 1-5 years	2	Once in 5 years	30-59	2-10
1	Once in more than 10 years	1	Once in more than 10 years	< 29	1

 
Table 4- Rating for the scope of control actions required

Human	Rate	Engineering	Rate
Lack of awareness	2	Lack of control action	8-10
Lack of personal protective equipment	2	Low effectiveness of control action	8- 6
Lack of job satisfaction	2	Medium effectiveness of control action	4- 6
Lack of job skills	2	Good effectiveness of control action	2-4
Lack of health	2	Full effectiveness of control action	0-2
10                     Overall rating		10                                                     Rating

 
Table 5-  prioritization of risks

Discovery	Rating	Example
Quite obvious risk	1Ùˆ2	The risk is easily recognized from a distance
It is understood by all five senses	4Ùˆ3	Noise / light and ... Etc. has created
Only one of the five senses can detect	6Ùˆ5	Just seen or heard or ...
Should be used for the diagnosis of simple tools	8Ùˆ7	The phase meter, thermometer conventional
Advanced tools are required to use	9Ùˆ10	Sensor or a digital tool

 
Results: The studies showed that the biological risky centers detected during the six month of 2010 (September 23, 2010 to March 21, 2011) was 55 and in the next four months of 2011 correction action for all of the risky centers was issued. As a result, at the end of the fourth month the number of risky centers was reduced from 55 to 22 in the company; 60% reduction in this less period in a large industrial company is a really good success.

Figure 1 The reduction of biological risky centers in Saipa Company in duration of four month Click here to View figure

 
Biological risky centers differ according to personnel function in production and administrative halls. A brief description is given in table 6. However, it should be mentioned that some of these biological risky centers are common halls because as they have been detected in the WC (health services).

Table 6- The couple of examples of biological risky centers in company

Production halls (place)	Administrative halls (place)	Others places and common halls (place)
Small distance between the personnel	Desks surfaces and keyboards being contaminated (polluted or dirty)	The WC Being contaminated (polluted or dirty)
Eating food on the floor and dirty cartons	Being ill and carriers servitor	No trash door
Of contaminated protective devices(earmuff)	Glasses being contaminated(dirty glasses)	Water tap damage in WC (bathroom)

 
The result of evaluation on biological risky centers in production halls is shown in table 7 and tables 8 -13 shows the evaluation of biological risky centers in non-production parts of the company.

Table 7- The results of evaluation of biological risky centers in production halls.

Place		Total	High	Medium	Low
Production	Body 1	33	2	13	18
	Body 2	33	2	13	18
	Press	33	0	13	20
	Die tools	33	0	10	23
	paint 1	32	2	13	17
	paint 2	33	0	14	19
	Assembly 1	33	2	13	18
	Assembly 2	34	2	18	14

As you can see in table 7 amount of risk in high, medium and low are up to 2, 18 and 23 cases respectively in production halls.

Table 8- The results of evaluation of biological risky centers in buildings

Place		Total	High	Medium	Low
Buildings	office	34	0	2	32
	Clinic	33	0	2	31

 
As shown in table 8, there is no high risk case in buildings and there are 2 and 32 cases for medium and low risk respectively.

Table 9- The results of evaluation of biological risky centers in energy resources buildings

Place		Total	High	Medium	Low
energy resources	Power post	2	0	0	2
	Gas central	2	0	0	2
	powerhouse	29	0	3	26
	Fuel tanks	2	0	0	2

 
As shown in table 9, there is no high risk case in energy resources buildings and there are 3 cases with medium risk and for low risk cases there are 26 cases in power house and 2 cases in other places (Fuel tanks,gas centraland power posts).

Table 10- The results of evaluation of biological risky centers in laboratories

Place		Total	High	Medium	Low
Laboratories	polymer	12	0	1	11
	C.M.M	33	0	2	31
	chemistry	12	0	1	11
	power	12	0	1	11
	Metallurgy & mechanic	12	0	1	11

As shown in table 10, there is no high-risk case in laboratories but there are 2 and 11 cases in medium risk and low risk, respectively, just in C.M.M there is 2 cases with medium risk and 31 cases with low risk.

Table 11 -The results of evaluation of biological risky centers in storages

Place		Total	High	Medium	Low
Warehouse	Receipt of goods	30	0	2	28
	Chemical material	30	0	2	28
	Non-production	30	0	4	26
	production	30	0	7	23

As shown in table 11, there is no high-risk case in storages but there are 7 and 28 cases with medium and low risk respectively.

Table 12-The results of evaluation of biological risky centers in out of company

Place		Total	High	Medium	Low
Out of company	Etka	27	0	4	23
	Shirazi land	14	0	0	14
	Saipa 3	33	0	2	31
	Seico	27	0	4	23
	Sale	33	0	2	31
	Saipa 5	33	0	2	31

 
Out of company are places that do some of the business companies in abroad. As shown in Table 12, the subsidiary centers have no high-risk cases and there are 4 and 31 cases with medium and low risk respectively.

Table 13-The results of evaluation of biological risky centers in workshops

Place		Total	High	Medium	Low
Workshops	Technical affairs	29	0	4	25
	Triple salon	29	0	5	24
	Maintenance	29	0	4	25

 
The variation of job (thing) in workshops usually is the same, for this reason the number of biological risky centers in all of them is the same - 29 cases. As shown in Table 13, workshops (production halls) have no high-risk cases and there are 5 and 25 cases with medium and low risk respectively.

Table 14 -The results of evaluation of biological risky centers in site

Place		Total	High	Medium	Low
site	Restaurant	41	2	32	7
	Public places	20	0	6	14
	Road test	14	0	1	13
	Treatment plant	33	0	2	31
	Waste	8	0	3	5
	Underground canals	2	0	2	0
	Conex	14	0	1	13
	streets	10	1	6	3

 
As shown in table 14, there are 2 cases with high risk in restaurant, 6 and 32 cases with medium risk in public places and restaurant respectively and 14 and 31 cases with low risk in public places and treatment plant respectively.

Conclusion

Today's world has undergone a variety of pollution of noise, water, soil, biological, etc. caused by human recklessness. Risk centers in hygiene, immunity, and environment are different and have independent and separated identification. According to their extent of operating; these centers are less or more. In this investigation; tried to recording the huge part of biological risk centers in Saipa company. For this reason; the present research was done to identify biological risk centers in SAIPA. After recognizing the biological risk centers using the method PJ-01-001, it was found that there are a total of 33 biological risks in the restaurant. Treatment plant (with a total of 33 risks), public places (with a total of 20 risks), and Road Test Unit (with a total of 14 risks) are the main risk generating centers in the company.Moreover, public places, Road TestUnit, Treatment Plant, Waste Disposal, underground canals, and Conex has no high-risk biological centers. Based upon the obtained results, it was revealed that mitigation measures should be focused on two places restaurantand treatment plant.

References
 

1. Ahmadi, A., 2005a. Safety Management inOil, Gas & Petrochemical Industries. Vol.1: Engineering Approach to Human Errors.First Edition.Pourshad
2. Ahmadi, A., 2005b. Safety Management inOil, Gas & Petrochemical Industries. Vol.2: Preventing Methods from OccurringHuman Errors in Oil related Industries .FirstEdition.SepehrFatemi
3. DiMattia, D.G., Khan, F.I. Amyotte, P.R.,2005. Determination of human errorprobabilities for offshore platform musters,Journal of Loss Prevention in the ProcessIndustries. 18, pp. 488–501
4. Ghalenoy, M., 2006. Safety Analysis for Control Room Operators Human Error in one of Petrochemical Industries using HEART Technique. MSC thesis.TarbiatModarres University (TMU)
5. Grozdanović, M. and Stojiljković E., 2006.Framework for Human Error Quantification, Philosophy, Sociology and Psychology. 5, pp. 18-23
6. Jahangiri, M., 2004. Human ErrorsIdentification And Analysis in TehranRefinery Isomax Unit with PHEA technique.MSC thesis. Occupational Health Department,School of Public Health, Tehran University ofMedical Science
7. Khan, F.I., Amyotte, P.R. and DiMattia, D.G., 2006. HEPI: A new tool for human error probability calculation for offshore operation. Safety Science. 44, pp. 313–334
8. Lees Frank, P., 2004. Lees Loss Prevention in the Process Industries. Vol.1. Third edition
9. Whittingham, R.B., 2004. The Blame Machine:Why Human Error Causes Accidents, ElsevierButterworth-Heinemann
10. Carlos Conte J., Rubio E., Isabel García A., Cano F. (2011). Occupational accidents model based on risk–injury affinity groups. Safety Science, Volume 49, Issue 2, February 2011, Pages 306-314.
11. Jasch Ch., Lavicka A. (2006). Pilot project on sustainability management accounting with the Styrian automobile cluster. Journal of Cleaner Production, Volume 14, Issue 14, 2006, Pages 1214-1227.