Leveraging Modern Tech to Find the Most Important Troubleshooting Info

Every maintenance team responsible for responding to operational disruptions can attest to the nightmare of sorting through documents to find the most appropriate information related to a downtime event. 

In many cases, the problem isn’t that the information doesn’t exist, but rather it is difficult to sort through years or decades of historical maintenance records and documents to locate similar symptoms or to track down any other information closely related to the problem at hand.

What does this challenge cause? Frustration over the wasted downtime and money lost when the information was there all along but simply hard to find.

Unplanned downtime itself is costly. Being unable to locate the information to promptly correct the problem causes the losses to keep climbing. Image used courtesy of Canva

The solution presented by eschbach provides a way to leverage those years of experience, tribal knowledge, and documentation by using state-of-the-art digital technology to identify the best key pieces of data to solve problems faster.

Shiftconnector’s AI Smart Search Technology

Shiftconnector by eschbach is a digital platform used to track team communications, primarily to save and store information from shift changeovers, work order and other asset documents, and communication across functional teams. As these digital records are kept, they serve as the foundation of knowledge about what has happened, what steps have been taken, what actions led to success and failure on past operations, and other information that can be difficult to track without dedicated staff members who manually upload these details into an online database.

The Shiftconnector platform has been paired with eschbach’s AI-driven Smart Search technology, which is far more than just a search engine. It’s a search algorithm which can scan even the smallest details in a disruption scenario to select the most appropriate fixes and solutions. 

eschbach’s Smart Search uses AI to filter and find the best information for a particular event based on current and past event data. Image used courtesy of eschbach

How Does AI Help Solve Problems Faster?

Artificial intelligence (AI) is a term that has seen countless areas of application in the past few years. In effort to create a decision-making process that considers details on the peripheral of an event, the algorithm must be more complex than simply ‘if this, then this’ programming, which dominated past programming. By bringing in this peripheral information, the computer is more likely to connect event details in the subtle yet critical ways that a more experienced technician might predict.

It is a common situation when a machine failure occurs to have the more junior technicians performing standard reset and troubleshooting operations to no avail. When the experienced staff arrives, that knowledge from the past provides context—“the last time it was freezing cold outside, the fluid line froze and stopped the machine”. This, of course, is only one example, but the ability of that mechanic to connect dots which led to a correct solution is the ultimate goal of this ‘AI’ concept.

How does eschbach’s Smart Search Save Information?

Locating useful solutions to failures is only one example of the use of AI in this Smart Search application. In addition to filtering through stored information, AI is also able to process incoming communication, as well as catalog and index topics, keywords, phrases, and much more.

In any team, there is diversity. Diversity in processes, languages, and even communication skills. Basic ‘if this, then this’ programs might be unable to handle information inflow that does not match an exact format with prescribed form fields of data entry, but the goal of these smarter technologies is to parse information to provide more thorough inputs. Of course, standardized processes and workflows are essential, but solving problems with reliable data is an even more critical ultimate goal.

Any time machine maintenance is performed, there is new data that might be used to help analyze and repair future breakdowns. Image used courtesy of Canva

Using AI to Solve Downtime Disruptions

No matter how carefully a team plans and manages communication and operational workflows, there will always be disruptions. Changing workforce with outgoing tribal knowledge, environmental and external factors, and the evolution of technology itself will always provide challenges for even the highest performing teams.

Companies like eschbach understand these challenges and are working hard to develop the tech that will allow streamlined cataloging and searching of past knowledge and experience to apply the greatest minds to approach tomorrow’s problems.