Overcoming Upgrade Challenges for EAMs: Barriers to Quality Data

Manufacturers are increasingly considering ways to leverage new technologies driven by artificial intelligence (AI) and automation in their current or future environments. However, the quality and quantity of data that can be provided to utilize these capabilities optimally are often missing.

In the concluding part of this series, based on an SAPinsider webinar by Robert Hancock, Global Vice President of Sales at Sigga, we look at how organizations can overcome data challenges while upgrading their SAP Plant Maintenance (PM) or enterprise asset management (EAM) environments from paper-based systems to mobile maintenance units.

The three barriers to data

Hancock highlighted the challenges with data with an example. A large oil and gas company in South Africa wanted to understand why a significant number of work orders in the organization’s maintenance department were not completed in the first attempt. They used data automation to overcome the hurdle but were still unable to resolve the issue. When Sigga analyzed the data for an insight into the problem, they found that over 60% of their failed data referenced parts without more details. Since the legacy system did not allow free texting, the data came across as vague.

This is an example of unclean data, one of the three data challenges faced by an organization. The ERP system was not configured to allow free-text reasons for incomplete jobs, which led to vague or unclean data. Other data challenges include:

• Insufficient data: AI needs a certain amount of data to provide accurate insights.
• Disorganized data: This type of data is spread across different systems, making it hard to leverage it with a single tool.

Solutions to data challenges

So, how can manufacturing organizations overcome data challenges? According to Hancock, the first step is conducting data analysis and gathering requirements. This step is essential for understanding the data needed to migrate to mobile environments. “Organizations must ask, ‘What are the pieces of data I need to make this thing run?’” Hancock noted. He added that once that data was gathered, it must be analyzed and tested to ensure it is clean and organized to be utilized effectively in the modernized system. Organizations can also use data cleansing tools. This is an important next step, especially for those moving to modernized SAP EAM and PM.

Finally, an organization must be able to adjust data capture requirements. Hancock elaborated on this step with the example of a Sigga customer deploying a mobile work order solution. “The solution includes creating SAP notifications for their production personnel to inform maintenance of any breakdowns,” he said. “The maintenance crew then analyzes the information in that notification and creates a work order.”

However, Sigga realized that though the maintenance technicians were using the mobile work order, they continued to get paper-based notifications from production personnel. This was because the notification system was set up to ask too many questions instead of a simple notification mentioning the piece of equipment giving the problem and asking the technician to come and fix it. Sigga changed the user interface so that the production personnel could send a short notification on the issue to the technicians. “So sometimes you have to adjust your data requirements to meet business needs,” he said.

Hancock concluded that together, user adoption, the lowest common denominator, and proper data management are important for manufacturers that want to successfully modernize with SAP mobile asset management solutions for EAM and PM.