Data collection system

The rapid development of microelectronics has enabled many similar measurements to be made with data collecting systems and then stored electronically. The raw data can then be downloaded to the data processing installation, where they can be plotted and evaluated at any time [1]. This applies particularly to monitoring measurements on pipelines for intensive measurements, see Section 3.7. Figure 3-1 shows an example of a computer-aided data storage system. 

The traditional approach, because it sees the major causes of errors and accidents as being attributable to individual factors, does not encourage a consideration of the underlying causes or mechanisms of error. Thus, accident data-collection systems focus on the characteristics of the individual who has the accident rather than other potential contributory system causes such as inadequate procedures, inadequate task design, and communication failures. 

The critical incident technique was first described by Flanagan (1954) and was used during World War II to analyze "near-miss incidents." The war time studies of "pilot errors" by Fitts and Jones (1947) are the classic studies using this technique. The technique can be applied in different ways. The most common application is to ask individuals to describe situations involving errors made by themselves or their colleagues. Another, more systematic approach is to get them to fill in reports on critical incidents on a weekly basis. One recent development of the technique has been used in the aviation world, to solicit reports from aircraft crews in an anonjmrous or confidential way, on incidents in aircraft operations. Such data collection systems will be discussed more thoroughly in Chapter 6. 

This section provides an overall structure within which the different aspects of data collechon and incident analysis methods can be integrated. The importance of effective data collection systems as part of the continuous improvement process in Total Quality Management. 

The major categories of data collection systems are described. These include ... 

Quantitative human reliability data collection systems for generating human error probabilities for use in quantitative risk assessment. 

Setting up a Data Collection System in a Chemical Plant (6.10)... 

This section sets out a step-by-step procedure for setting up a data collection system, including the important issues of gaining workforce acceptance and management support. 

The function of this section is to provide an overall framework within which to describe the important aspects of data collection systems in the CPI. As mentioned in the introduction, the emphasis in this chapter will be on methods for identifying the causes of errors that have led to accidents or significant near misses. This information is used to prevent reoccurrence of similar accidents, and to identify the underlying causes that may give rise to new types of accidents in the future. Data collection thus has a proactive accident prevention function, even though it is retrospective in the sense that it is usually carried out after an accident or near miss has already occurred. 

FIGURE 6.1. Overall Structure of Data Collection System... 

The model of human error held by management and the plant culture constitutes the environment in which the data collection system operates. Within this environment, all data collection systems need to address the topics listed in Figure 6.1. These topics, from the types of data collected, to the feedback systems that need to be in place, will be addressed in subsequent sections of this chapter. 

Many data collection systems place the primary emphasis on the technical causes of accidents. There is usually a very detailed description of the chemical process in which the accident occurred, together with an in-depth analysis of the technical failures that are seen as the major causes. The human or system failures that may have contributed to the accident are usually treated in a cursory manner. Technically oriented reporting systems are very common in the CPI, where engineers who may be unfamiliar with human factors princi-... 

Quantitative Human Reliability Data Collection Systems... 

The requirements for the development of a CPI-specific quantitative human reliability data collection system are as follows ... 

The discussion of alternative types of data collection systems serves to emphasize the fact that the design of such systems needs to have very clear objectives. Although a range of data collection systems have been described as if they... 

The first area focuses on the cultural and organizational factors that will have a major influence on the effectiveness of a human error data collection system and how well the information derived from such a system is translated into successful error reduction strategies. Regardless of how effectively the technical issues are dealt with, the system will not be successful imless there is a culture in the organization which provides support for the data gathering process. No data collection system aimed at identifying human error causes of accidents will be workable without the active cooperation of the workforce. 

Emphasis for prevention will be on changing individual behavior by symbolic or tangible rewards based on statistical evidence from the data collection system. "Hard" performance indicators such as lost time incidents will therefore be preferred to "softer" data such as near-miss reports. Accident prevention will also emphasize motivational campaigns designed to enhance the awareness of hazards and adherence to rules. If a severe accident occurs, it is likely that disciplinary sanctions will be applied. 

Because of the emphasis on modeling accident causation, data collection systems based on the system-induced error approach are likely to modify their data collection strategies over time. Thus, as evidence accumulates that the existing causal categories are inadequate to accoimt for the accidents and near misses that are reported, the data collection philosophy will be modified, and a new accident causation model developed. This, in turn, will be modified on the basis of subsequent evidence. 

Cultural Aspects of Data Collection System Design... 

A company s culture can make or break even a well-designed data collection system. Essential requirements are minimal use of blame, freedom from fear of reprisals, and feedback which indicates that the information being generated is being used to make changes that will be beneficial to everybody. All three factors are vital for the success of a data collection system and are all, to a certain extent, under the control of management. To illustrate the effect of the absence of such factors, here is an extract from the report into the Challenger space shuttle disaster ... 

Such examples illustrate the fundamental need to provide guarantees of anonymity and freedom from sanctions in any data collection system which relies on volimtary reporting. Such guarantees will not be forthcoming in organizations which hold a traditional view of accident causation. 

The following types of information are collected in most CPI safety-related data collection systems ... 

The overall conclusion that can be drawn from a survey of CPI data collection systems is that the better systems do attempt to address the causes of human error. However, because of the lack of knowledge about the factors which influence errors, the causal information that is collected may not be very useful in developing remedial strategies. General information in areas such as severity, work control aspects and the technical details of the incident will be required in all data collection systems. However, in almost all cases a structured process for causal analysis is lacking. Some of the requirements for causal analysis are set out in the following sections. 

As discussed earlier, most data collection systems in the CPI place considerable emphasis on the "what," but provide little support for the "how" or "why." Causal analysis methods can be broadly divided into techniques which emphasize the structure of an accident and those which focus on causes. Structural techniques provide information on the "what" and "how," and the causal techniques enable the "why" to be investigated. 

This is in contrast to many data collection systems, in which considerable efforts are expended in developing a "definitive" data collection philosophy. However, once the system is in place, there is little attempt to modify this on the basis of operational feedback. 

The fact that the model connecting error types with their causes may change as a result of gaining further experience with the data collection system means that the informahon gathered on the PIFs in a situation may also change. For example, if incident data indicates the neglect of safety procedures because of production pressures, then the questions relating to this area wUl need to be extended. 

In a data collection system that was developed in the transportation sector, the application of these principles generated the following format for a data collection form ... 

Workforce Support for Data Collection and Incident Analysis Systems Few of the incident investigation and data collection systems reviewed provide any guidelines with regard to how these systems are to be introduced into an organization. Section 6.10 addresses this issue primarily from the perspective of incident reporting systems. However, gaining the support and ownership of the workforce is equally important for root cause analysis systems. Unless the culture and climate in a plant is such that personnel can be frank about the errors that may have contributed to an incident, and the factors which influenced these errors, then it is unlikely that the investigation will be very effective. 

SETTING UP A DATA COLLECTION SYSTEM IN A CHEMICAL PLANT... 

In previous sections of this chapter, the required characteristics of effective causally based data collection systems to reduce human errors and accidents have been described. In this final section, the stages of setting up such a system in a plant will be described. 

Implement Pilot Data Collection Exercise in Supportive Culture In order to ensure that the data collection system has been thoroughly checked and tested prior to its laimch, it is advisable to test it in a plant or plant area where there is likely to be a supportive culture. This will allow the effectiveness of the system to be addressed prior to a larger-scale implementation in a less controlled environment. 

Since the resources for data collection systems will be provided by senior management it is essential that information from the system is fed back to policy makers at this level. It is also important that the system indicates the problem areas as well as the successes. Many organizations have drifted to a state where safety standards have fallen to below acceptable levels over time as a result of suppression of information feedback to senior managers. This may be carried out with good intentions, but its long-term effect can be disastrous. 

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