Maintenance Gets a Makeover

Asset management software is spurring more proactivity and greater efficiency.

By Seán Ottewell, Editor at Large

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Until recently Nova Chemicals, Calgary, AB, was struggling to process upwards of 20,000 maintenance work orders per year at each of its 11 chemicals and plastics resins manufacturing facilities.

The company was running SAP's enterprise asset management (EAM) software but not using its full functionality. Plant maintenance processes weren't integrated with related EAM processes, leading to inefficiency and a lack of transparency. Reporting and analytics activities were insufficient. What's more, Nova's existing maintenance system couldn't provide a complete view of current and scheduled work, materials and dependencies categorized by plant and department — adding to the challenges of capacity evaluation and prioritization of work orders.

"We needed a solution to help make sure that repair work on our equipment and plants is done correctly and as quickly as possible — at the lowest possible cost," explains Ron Chow, a maintenance and reliability leader at Nova.

Today the situation has changed: the SAP EAM software provides a complete and consolidated view of scheduled maintenance at Nova Chemicals. It facilitates maintenance scheduling, work execution and material availability processes. All key stakeholders can access information relevant to them — for instance, business users can get a daily and weekly view of work scheduled, priorities, resources required, scheduling conflicts and project status — and gain a better understanding of the wide-ranging effects of maintenance operations.

The software has spurred better coordination and integration of maintenance scheduling, enhanced communication between maintenance and operations, and cut the number of unplanned equipment outages. It has directly impacted the bottom line, e.g., reducing by 47% the time spent on reactive, emergency work; increasing by 61% the time spent on proactive, preventative maintenance; and improving maintenance schedule compliance by 22% — in a year-long process that also included a pilot program.

Nova currently is focused on its next step — the integration of data from other processes such as capacity evaluation, additional forecasting and spending analytics, and maintenance cost budgeting.

DATA ISSUES
Retrieving and integrating ever more data into EAM software is posing some increasingly subtle challenges to the broader chemical community.

"The whole chemical industry is focused on data-mining. The Tier 1 companies in particular believe that somewhere inside all that operations data there has got to be information that will help them to better run their business. There is decades-worth of this information available to the larger companies," says John Harrison, Toronto-based senior solutions specialist for SAP. He is responsible for the chemical industry worldwide and has a particular focus on plant maintenance.

However, identifying the most relevant and accurate data on which to build maintenance and repair strategies is a far less straightforward task than it might appear initially — particularly when it comes to smaller chemical companies. "The quality of what they are recording and for how long they have been recording it is a moot point because they might not even be recording their data properly," Harrison notes. This can make a nonsense of the key performance indicators (KPIs) that companies are using, he cautions.

"I have a real concern in general about where data comes from and the calculations being carried out using it. I constantly have to deal with these two questions: 'Are you gathering enough data to generate them?' and 'Are you collecting the correct data?' To me, the bigger problem here is inconsistency in data definitions because these lead to inconsistent and wrong solutions."

He believes the issue of KPI benchmarking and the master definitions needed to do it should have been fixed decades ago. In the meantime, SAP is working with its KPI group, which includes Dow, Nova Chemicals, Celanese and Chevron Orinite, to try to agree on cross-industry definitions.

So far, there's a consensus on definitions for the top 30–40 KPIs such as mean time between failures (MTBF) and mean time to failure (MTTF). However, trying to define "failure" itself triggers huge discussions at maintenance meetings, he says.

Despite this challenge, the integration of business KPIs such as payroll and attendance with maintenance KPIs such as MTBF, MTTF, hour utilization by trade, and equipment vibration trends allows chemical companies to look more deeply into issues of concern. For example, it may explain why one shift experiences more shutdowns than another.

From this sort of work, a third, very important layer is emerging. "Here we are starting to see multiple disciplines of information coming together which can show up, for example, if there are higher failure rates either before or after maintenance staff vacations. Do patterns emerge? This is the sort of information that is now becoming accessible."

At the heart of SAP's efforts in this area is its HANA in-memory computing platform — which the company describes as a breakthrough technology that helps users to dramatically accelerate analytics, business processes and predictive capabilities.

"HANA allows us to analyze large amounts of information from multiple data sources at the same time and this is the future. My belief is that as we have access to more and more business data we can begin to ask some quite odd questions," notes Harrison.

Examples he cites include: Do high (or low) summer temperatures have an impact on plant shutdowns? Does buck fever (deer hunting season) lead to a decrease in production? Why is more electrical tape used by plant technicians in Canada in winter?

"This last turns out to be due to pilfering for use on ice hockey sticks and a company might decide that this is irrelevant, but when you start blending these questions together it gives you a much greater sense of how a plant is operating over time. Of course, you do need a very big database to achieve this."

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