How Smart AI-based Knowledge Management Software can boost industrial companies
Updated: Mar 29, 2020
Modern manufacturing environments present 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 results in manufacturing delays, low product quality, high maintenance and support costs, low system availability and the like, that 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 exactly the same failure in 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
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, that can narrow-down the possible root cause list. Reducing this list from 9 possible reasons to 3 for example, can cut-down 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 it effectively. Exposing support users to other team members' experience 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 downtime. A "well cooked" diagnostics procedure was documented in Kiami's smart solution, and the repair time of exactly the same failure was reduced to few minutes.
Higher support level members deals 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 failure 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" on others. It is very difficult to encourage all team members to openly share their experience to the benefit of all. Even when 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 knowledge sharing climate and leverage support teams to a higher level of performance.
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 few hours to few minutes, resulting in reduced down times and manufacturing delays.
Improved manufacturing efficiency and quality, reduced material waste due to long down times 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 lost 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.
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