In many on-the-job situations, data loggers have increased productivity 20-30 percent by assessing and evaluating the progress of maintenance and project work. The following scenarios describe the way data loggers provided timely and detailed information managers used to improve product-specification decisions and technician tasks.
In some cases, utility companies plan outages to repair, rebuild and upgrade generating and transmission units. Managers schedule projects within a tight time frame, so high productivity is crucial for meeting the cost and scheduling goals while maintaining a safe environment for several thousand electricians, mechanics, pipe fitters, general maintenance workers, supervisors and engineers at several sites.
Recently, in several of these outages, managers employed work-measurement specialists to work on site observing around-the-clock activities, identifying delays, classifying them by cause, evaluating and ranking delays by amount of time they took, and recommending specific, actionable improvements that would allow technicians to complete the project on time.
Since U.S. utilities operate at close to capacity, a successful outcome was essential, one that helped the utility maintain a power supply sufficient to help meet the huge power demand, contribute to greater energy independence, and allow more oil to be converted to gasoline, which would lower the cost. Managers also could apply the lessons learned to other new utility projects or upgrades.
First, specialists mapped routes throughout the facility covering support areas, furnace settings, reactors, and turbine and generator spaces to ensure good coverage of all the workers on site. Then they selected start times on random routes using statistical techniques to ensure good time-of-observation coverage over all three shifts.
Observers immediately began monitoring activity, classifying observations into categories — direct work, planning, material delays, equipment delays, and breaks — and recording observations on the hand-held, pre-programmed data loggers. They recorded the observations on the data logger’s touch screen with a stylus, first indicating the route or area, then the craft, and finally, the observed activity.
The data logger automatically applied a time stamp to each observation, showing the date and hour of the day in minutes and seconds. As observers completed each day’s sampling, they uploaded the data to a PC and added it to the rapidly expanding database. In one week, they recorded several thousand observations, providing managers with a clearer picture of the distribution — by time, skill, and area — of the observed activities.
Statistically, a direct correlation exists between the number of observations and the time spent doing an activity. For example, if 20 percent of the observations were classified as planning — checking prints, marking records, looking up data — the conclusion was that 20 percent of the time was spent planning. So the total number of observations dictated the accuracy of the study.
If data showed materials were not available in a particular area at a certain time, the organization improved material staging by material-support personnel in that area to eliminate this delay. If technicians lost time waiting for crews, tools, or equipment, supervisors tried to better anticipate crew needs, or they relocated toolboxes to better serve the crew’s requirements as they move among job sites.
If certain products, such as scaffolding or power tools, were in short supply and delayed phases of the job, supervisors purchased these items and staged them to allow the work to flow more smoothly, saving time and enabling the job to progress more rapidly. Comparisons showed productivity could be tripled when mechanics had easy access to power wrenches to replace ratchet wrenches for fastening thousands of hex-head cap screws during equipment assembly. The cost of the tools would be so small compared to the labor savings that the tools would pay for themselves in just a few days.
As a result of the data collection and analysis using data loggers, the recommended improvements in products and crew utilization have more than paid for the cost of the work-sampling studies. The projects also have a greater likelihood of on-time completion within budget.
Other data logger uses, such as power-quality monitoring, sub-metering, and power-factor monitoring, also have delivered benefits, resulting in major cost savings, energy savings and productivity improvement.
With all the advantages derived from data loggers, maintenance and engineering managers can add many more of these devices to technicians’ toolboxes. They also will be able to reap generous rewards in both function and cost, far exceeding the modest expense, proving the well-established principle that measurement is indeed essential for control.
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