Introduction to Industrial Risk Management

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Introduction to
Industrial Risk Management

José Durán

About the author:
José Durán is an Electrical Engineer with a Masters Degree in Maintenance Engineering.  In the last few years he has been working as a Consultant Engineer in Operational Reliability Improvement process for oil companies in Venezuela and Colombia, consulting, training, and facilitating activities like: Reliability Centered Maintenance, Industrial Risk Management, Maintenance Optimization,  Maintenance Project Management, etc. It is covering different aspects of the oil industry: extraction, pumping, refining. Also services: electric power generation, gas injection, etc.
More information: please contact  him:
Fax: 58-74-639673

All of us have some ideas about what "risk" is, not to insure the car “is a risk”, playing the lottery “is a risk” a little bit different, to risk on an investment, etc. All these gave us some ideas about a probability that something may happen, a probably win or loss event. In fact almost constantly we are living in a world driven by risk, although sometimes we are not aware that we are using this concept in the process of making important decisions.

In the mathematic world we may explain the risk like probability (frequency) of event times its consequences. As we can see in engineering the risk is associated with negative consequences

R = P X C
R = F X C

Both equations are equivalent and will have value units divided by time units ($/year, $/month, etc.). About the differences of both equations: We must not talk about frequencies of events that have never happened,  in these cases we use the "probability" concept.

The majority of industrial and engineering operations are in the risk world. Almost everything done involves probabilities and consequences. The future never will be predicted in a 100% and there are a lot of factors that contribute with uncertainty, for example:

>  External influences (oil prices, political changes, etc.)

>  Equipment characteristics (reliability, availability)

>  Materials, contractors performance and suppliers

>  Organization complexity

>  Human errors and bad communications

>  ...     Etc.

Fortunately it is possible to transfer some calculation methods in the fields of probability and risk to the industrial field.

Risk Acceptance

What risk value is acceptable? This is a million dollars question, it is not simple but it is approachable. Starting with logic rules and company vision direction. What is really true is that risk must be evaluated and controlled under accepted levels. Tools like criticality and lost opportunity studies give us a quick evaluation of the risk factors that are currently affecting us. For big potential risks we must develop deeper analysis like HAZOP studies, for example.

Let us see how to calculate a risk exposure:

If we have a big event probability (fire, explosion) estimated in one every 100-50 years and the consequences valued between 10 and 200 million dollars, then the risk exposure would be between:
 R = $100,000/year   ($10 millions and 100 years)


 R = $4,000,000/year  ($200 million and 50 years)

How to estimate the event frequencies?

Information options will allow us to see some possibilities:

a.- Computer system: When we have it and it is well data provided. It may be a really valuable information supply, although many times its information is too general and does not allow us to distinguish clearly some data like event causes.

b.- Worker’s experiences: They tend to know their equipment really well but the human capacity to estimate is not efficient enough when we start to deal with extreme and non common quantities.  Another conflictive point is the different opinions between the workers.

c.- Available databases: Other option is to use databases gathered by third parties for example OREDA (Off Shore Reliability Data), IEEE (Institute of Electrical and Electronics Engineers). They can give us approximations to some factors like failure rates; normally those data may be referenced values or benchmarks to do primarily evaluations. The fact is that different operational conditions make those data really dispersed .


The event consequences in the operational point of view could be summarized in:

I-  Equipment failures: They are caused by  reliability problems. This includes repair costs, production losses, etc.

II- Falling Performance. The production capacity (quality, quantitative) is affected by the event, so the net sales and the profit margin are compromised. Typical examples are capacity deterioration caused by fouling process in filters, compressor blades, etc.

III- Rise of production cost: The event consequence that triggers a production cost rise that may be caused by an efficiency loss (more energy consumption, materials, etc.), maybe you require more people to do the same tasks, etc.

IV- Effects on life cycle cost or capital investment: In these cases the consequences are because of the equipment life cycle is affected by the events occurrence, for example the overhaul cycles of equipment may be affected by the stopping and starting and by the maintenance cycles. The capital investment (for example the buy of new equipment) also may be affected by these events.

V- Legal requirements: In these cases the event may be associated with the breaking of a safety or environment requirements. This may unchain big impacts to the environment or workers' health with big economic penalties.

VI- Shine factor. It includes the other factors of a company's daily life that are difficult to evaluate like welfare, external appearance, community relationship, etc.

Evaluating the Risk

There are several methods to calculate the risk; in this case we will only see a really simple way to do initial calculations. The risk may be evaluated under this scheme:

I- List the bad events.

II- Investigate the frequencies/probabilities of the events above.

III- Estimate the event consequences of each event.

IV- Calculate the risks associated with each event.

V- Identify the bigger risky events and to prepare strategies to their reduction.

VI- You can calculate the total risk using this equation (you must use the same time base)

Total Risk = ?Fi X Ci

Changing the risk.

Starting with the equation we can see that the risk may be modified cutting the frequencies or consequences or both of them.

Risk Analysis in an Operational Reliability project

In a project of Operational Reliability
improvement of the risk analysis is always present, when we make criticality or lost opportunities we are doing risk evaluations of systems or situations under risk. And when we propose new operation, maintenance or security  strategies we must be capable to estimate their future impact in the company.
So the risk concepts we use allow us to take decisions based in more than suppositions.

Cost Risk Analysis

As we saw before, the risk may be modified. One of the most powerful tools to decrease the industrial risk is the Reliability Centered Maintenance. This suggests maintenance strategies based on operational conditions of the company and the impact of the possible failures in security, environment, production and repair costs. Whatever the philosophy used to decrease the risk we must to be able to evaluate the benefits of the results and a really good way to determine what are the risk concepts.

How can we do a Cost Risk Analysis?

A risk analysis of this type (non-optimization) may be faced calculating the risk before and after the new strategy implementation and the costs' change . Deeper analysis must be done when we are studying critical processes or big investment (there is cost risk software in the market). Some processes do not seem to be in this category because they do not have a big probability, but they could produce big consequences in any of the categories defined above.

It has some advantages and disadvantages. Among the advantages we can see that it is a probabilistic concept leaving a movement range.  The disadvantage: this is not a really easy concept “to sell” in the whole organization and its different levels.

Data Management

When we are treating this evaluation a weak point in any analysis may result in the difficulty to get reliable data. The management uncertainty  is a big research field. Having absolute reliable data is extremely difficult. How can we assure about the average “life” of some components?

Is the uncertainly bad?

In fact the only bad thing is the incapacity to work with uncertainty, it may bring analysis paralysis; when the will to find exact results convert people in perfect researchers without decision making capacity.

Managing the uncertainly

A good way to face the uncertainty is to use these cases: More likely, best case and worst case and to do sensibility tests trying to make more reliable prognostics. Other possible choice is to manage numbers with tolerance ranges (80 +/- 10 %). The fact of presenting decision ranges make our studies with a good security margin and at the same time, give them a bigger credibility than trying to be perfect calculators generating “exact” values using uncertainly data. Let us see some advices to research data:

1. Show how the information will be used, what it is needed for, and what is the precision required.

2. Ask without commitments

3. Test highest and lowest values without influence of your opinion.

4. Use indirect ways to indicate facts that may not be real.

Software tools:

Now there are in the market some tools to evaluate engineering decisions based on cost/risk concepts, the usefulness of these tools has been demonstrated with many million dollars saved for many companies around the world using them for these typical cases:

1. Optimize maintenance intervals.
2. Optimize inspection intervals.
3. Project evaluation.
4. Optimize Spare part stockholding.
5. Optimize Shutdown strategies.


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This page last updated on

08/01/08 17:31











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