Using Digital Data Management Systems to Streamline Work Processes

Introduction

All industrial complexes, from oil refineries, petrochemical, and gas-processing plants to medical manufacturing complexes, require periodic inspections, audits of programs, data analysis and assessments of equipment. The types of equipment being assessed can range from piping, pressure vessels, tanks and rotating equipment, to smaller, ancillary items such as hoses, safety harnesses and fire extinguishers.

However, despite generational advances in technology, a large percentage of today’s asset inspections are vulnerable to “dirty data” collection and retention, which is then incorporated into facility documentation and management systems.

Dirty data is defined as data acquired by taking NDE results or visual inspections, which might be flawed, and recording that data onto paper files or stand-alone spreadsheets. This data can contain inaccurate information, duplicated data, incomplete data, or data that was initially accurate but is later inaccurately transcribed into a digital documentation system. Basically, inspection data can lose accuracy during the process of handling data, i.e. getting that data from the field to the operator’s files and database systems.

In fact, according to recent reports, approximately 75% of industrial organizations have identified significant costs stemming from the use of dirty data and have reported that more than 40% of all maintenance errors were traced to dirty data documentation as a causal factor. In worst-case scenarios, such data errors can become the proximate cause for unscheduled shutdowns, environmental incidents, accidents, and even fatalities.

In response to the challenge of widespread data inaccuracies, Digital Data Management Systems (DDMS) were created to optimize and improve the integration of reliable, efficient, and consistent data required to carry out sustainable data management programs. DDMS technology is an actionable intelligence software system that is designed to provide a step-change improvement for data management via specific data digitization and analyzation processes (illustrated in Figure 1).

Streamlined Workflow

As depicted in the following graphic (Figure 2), a typical workflow process used to collect, document and analyze inspection data requires a significant number of workers who hand off data from one person to another. Initially, office workers will schedule the activity required for an inspection. The actions will typically include preparations such as gathering drawings, gathering previous reports by looking through files, typing up what must happen, and then pushing all of that to the people performing the activity or the inspection. The inspections are then undertaken and documented.  The paperwork flows through human interactions, where mistakes often occur.