The modern manufacturing environment presents new challenges for engineering, operation, maintenance, IT, and other support teams. Without a solid knowledge-based support system, the process suffers from some key limitations that result in manufacturing delays, poor quality, high maintenance costs, and the like, which can cause a huge waste of money. Some of the challenges are listed below.
Failures repair time and equipment downtime is too often much longer than anticipated
Too often failures' repair time is much longer than it should if best-practices-based repair procedures were used. Failures repair time variation among support team members is huge. Sometimes it takes hours for one engineer, while another one can repair the same failure in a few minutes. Longer repair time usually yields to long equipment downtime, scrap materials, and a lot of waste.
Failure diagnostics consumes most of the repair time
The time spent by support users on failure diagnosis consumes most of the overall repair time, with an average of 60-80% of the total repair time. As the manufacturing system becomes more complex, this portion increased.
More than one way to detect failure root-cause
A support team member can follow several testing procedures until the root cause is identified. Too often the sequence of the testing/ diagnostics steps is not optimal. Carrying it out in the right order can dramatically reduce the diagnostics process and as a result, decrease the overall repair time. Moreover, in many incidents, the support worker doesn't gather all the required information on the failure, which can narrow down the possible root cause list. Reducing this list from 9 possible reasons to 3 for example can reduce the diagnostics time by 66%.
Reinvent the wheel again and again
Most system failures have already occurred in the past, and there is already some experience within the organization on how to effectively deal with them. Knowledge sharing between support team members is not performed effectively, resulting in many cases of "reinventing the wheel", each time a support worker tackles a failure that he specifically didn't resolve in the past, or doesn't remember how to handle effectively. Exposing support users to other team members' experiences can help them to provide best-practice treatment. In a highly automated distribution center, which serves one of the biggest pharmaceutical corporations, a specific automation equipment failure caused more than 3 hours of downtime. A "well-cooked" diagnostics procedure was documented in Kiami's smart solution, and the repair time of the same failure was reduced to a few minutes.
Higher support level members deal with simple failures
Many times higher support level workers (such as maintenance or engineering) are called to handle simple failures that can be easily detected and resolved by the operational team.
In an electronics assembly plant, production line operators used to call maintenance to fix machine placement failures, caused by a bent vacuum placement nozzle. Since maintenance team members were occasionally occupied dealing with other failures, or not on-site during the night shift, repairing such failures took a long time, which lead to production downtime. A simple documented testing and repair procedure enabled line operators to detect and fix such failures very quickly, avoiding unnecessary downtime.
Knowledge sharing climate/ culture
Although knowledge sharing between support team members is essential, in reality, this is not always the case. Too often there are team members who retain their unique knowledge and experience to themselves in order to retain a "competitive advantage" over others. It is very difficult to encourage all team members to openly share their experiences to the benefit of all. Even when the motivation for knowledge sharing exists, it is often not done effectively, leaving "silos of knowledge" trapped in the heads of a few. Only a systematic knowledge-sharing methodical approach, with proper incentive to do so, can improve the organization's knowledge-sharing climate and leverage support teams to a higher level of performance.
The experienced and skilled worker that leaves the company
Sometimes a very experienced, skilled support worker, who retains a great deal of knowledge in his mind, leaves the company. His experience "goes" with him, leaving knowledge "holes" in the team. Sometimes it takes months to recover from such losses, and the related costs are high. Systematic documentation of best practice failures treatment ensures that such accumulated knowledge remains in the company, thereby minimizing the loss.
New Support Team Member Learning Curve is Long
It takes months until a new support team member can effectively handle system failures. Too often, new team members acquire sufficient skills when they gain this experience by themselves. Documentation of best-practice failures treatment enables team members to learn from other team member experience, whilst reducing the learning curve of new team members and increasing all members' professionalism and skills.
How can smart AI-based knowledge management software boost support teams' effectiveness?
Such software can boost manufacturing support teams' effectiveness by enabling a much more efficient failures repair process and a systematic knowledge collaboration and sharing platform across the company. Some of the essential benefits are:
Cuts-down failures repair time. In some complex failures from a few hours to a few minutes, resulting in reduced downtimes and manufacturing delays.
Improved manufacturing efficiency and quality, reduced material waste due to long downtimes or returning failures, and increased yield, throughput, and utilization.
It enables to preserve organization accumulated knowledge and spread best-practice diagnostics and repair procedures inside teams, between teams, and manufacturing sites.
It enables operational teams to resolve by themselves a wider range of failures, which previously have been resolved by maintenance, engineering, or IT. It is extremely useful when support team members are not available or not on shift, allowing a quick resolution.
It enables to shorten new workers' learning curve, minimize the loss as a result of experienced worker that leaves the team and raises professionalism level of all.
It enables to shorten ramp-up and reduce the risk while introducing a new process, equipment, information system, or product.
KIAMI provides smart AI-based knowledge management for efficient industrial troubleshooting, maintenance management, and quality management software.
Learn more in: www.kiami-solutions.com