Manage Data Collection

Skill management Data collection Material movement... 

EP.3 Manage data collection EP.8 Manage regulatory requirements/compliance... 

Rg,6- Histogram of major management data collected in the dismantling activities along with the major activities and project milestones... 

Rg.9 Management data collected from the dismantling activities in the reactor building... 

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. 

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 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. 

The type of data collected on human error and the ways in which these data are used for accident prevention will vary depending upon the model of error and accident causation held by the management of an organization. This model will also influence the culture in the plant and the willingness of personnel to participate in data collection activities. In Chapters 1 and 2 a number of alternative viewpoints or models of human error were described. These models will now be briefly reviewed and their implications for the treatment of human error in the process industry will be discussed. 

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 ... 

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. 

Three major themes have been emphasized in this chapter. The first is that an effective data collection system is one of the most powerful tools available to minimize human error. Second, data collection systems must adequately address underlying causes. Merely tabulating accidents in terms of their surface similarities, or using inadequate causal descriptions such as "process worker failed to follow procedures" is not sufficient to develop effective remedial strategies. Finally, a successful data collection and incident investigation system requires an enlightened, systems oriented view of human error to be held by management, and participation and commitment from the workforce. 

The level of effort estimates given in Exhibit 4-1 are for time used by the project team. Design time is an estimate of the number of person days that will be needed to develop a fully defined system program/element or management process starting with the data collected during the assessment phase. 

As mentioned earlier some measures will be chosen because improvements in these areas were part of the project justification. It is most likely that these will be efficiency measures. Calculation of these measures generally requires analysis of data or specific data collection exercises. There is a relatively high cost associated with preparing these measures so they should be used prudently. In choosing efficiency measures, you should use only those where you have comparative data about the current management systems. For example, if there is no information on the number of hours dedicated to PSM and ESH, don t use this to try to demonstrate the improvement in efficiency. 

EuReDatA Project Report No. 3, Guide to Reliability Data Collection and Management. B. Stevens, Commission of the European Communities, Joint Research Centre, Ispra Establishment, S.P./1.05.E3.86.20... 

The Swedish Thermal Power Reliability Data System (ATV) is maintained and managed by the Swedish State Power Board at Stockholm, Sweden. Engineering and reliability data have been collected from both nuclear and nonnuclear power generating plants. Nuclear data collection began in 1973. Collection of reliability data began in 1976. Over 30,000 events have been recorded in the data base. 

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