How often do we notice a technician or operator summoned to a machine by some type of alarm system? Is the alarm visual, audible or a combination of both, and once summoned, what does the technician or operator do? Flip the switch, restart the process, and go on about his or her business? If by chance you are shorthanded or very busy on a given shift, do these alarms ever get shut off entirely in an effort to make a technician’s job easier? Stop and think for a moment. Do these alarms really change people’s behavior and drive us to a corrective or preventive understanding of the problem or are your key resources just going through the motions?
Don’t misunderstand; these questions are not to say audible and visual alarms are bad. Quite the contrary, Andon lights and sound alarms are good systems to employ in automated and semi-automated processes. They are very effective where employees are running multiple pieces of equipment and have a range of cross functional responsibilities.
The common error lies in what gets done with the alarm information—we often fail to learn from what the system is telling us as to the condition or cause of the alarm. The time wasted by skilled people in response only to these alarms could be better used in more value added and proactive efforts. The alarm should prompt us to use our problem-solving techniques to begin a disciplined approach of detection, data collection, data analysis, corrective actions and ultimately preventive actions. Using this type of approach will start you on the journey to a more robust system of manufacturing.
We all need to invest in the measurement of downtime on our equipment, dividing the information into pertinent categories for your business, and driving down to the details will aid in awareness and education. Resetting of general faults and alarms does not educate our operators or technicians to drive continuous improvement. We get stuck in the mode of “I can get my job done, but all I ever do is reset the machines,” but if we measure the actual faults, frequency and cause where available, the data would tell us that a very high percentage of these alarms could be eliminated and managed by the operators. This will allow your technicians more time spent on crucial jobs and root cause analysis.
Collecting data from a certain machine or process is sometimes one of the first steps in response to a high scrap rate or part of the root cause analysis undertaken in response to a customer complaint for bad product. Broadening the data collection effort to represent groups or families of causes, outside of those product quality issues and across the spectrum of manufacturing processes and machines, will lead to the identification of continuous improvement opportunities. Merely creating a simple log of the high level causes of these alarms can be the start of learning more about your process capabilities, machine capabilities and robustness of product design. Use this information as a foundation for your continuous improvement actions and initiatives.
Begin by having shift technicians and/or operators create a tally list of causes for each event that requires a response to a machine or process. After a number of occurrences have been compiled, a Pareto analysis can summarize these into like causes. The Pareto can be developed using a number of occurrences, minutes of response or dollars based on machine rate. With a complete Pareto, communicate with your team; conduct a series of brainstorming discussions, work to identify root causes and create an action register to corrective actions. Many times, broader issues such as product design, product specification, process standardization, automation design, tool or machine functionality can be identified through this analysis.
To sustain these efforts, a disciplined PDCA (Plan, Do, Check, Act) cycle must be implemented and maintained. This will ultimately improve information flow and educate the workforce, so that workers and technicians operate in sync.
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