by Mike Hetzel, Industry Solutions Manager
Today’s industrial sector is marked by never-before-seen disruption, fueled by the Internet of Things (IoT) and Big Data. According to Digitalist Magazine, one-third of manufacturing plants alone have already implemented IoT technologies into their processes. What’s more, a PricewaterhouseCoopers survey reveals that over 70% of manufacturers expect to be “digitally advanced” by the year 2020.
So what does all this mean for the industrial space at large? Well, it means that industrial organizations, no matter what product or widget they produce in their facilities, face the universal challenge of being confronted by a deluge of data and the question of how to make use of it.
Asking the Right Questions
Often, deciding how to best capture and utilize data generated across your industrial network starts with taking a closer look at both your primary and secondary assets. From there, we encourage plant managers to ask three questions:
1. What’s the value at stake?
Primary, production-oriented assets, like the distillation units in an oil refinery for example, often get the most attention when considering investments like asset management and predictive analytics solutions, and rightly so. This is front-line, mission-critical equipment that, should it fail, can lead to catastrophic downtime costs. Clearly, the value of proactively monitoring the health and performance of these assets is huge. But what about your power generation assets like boilers, turbines and generators?
Primary assets are often proprietary, can require higher levels of investment to design and adapt solutions and carry a higher risk of project failure. Lower-hanging fruit may be hiding in the secondary assets, such as power generation and water treatment, where “off the shelf” solutions have cut their teeth and matured in their respective industries. Starting here may very well provide impressive returns with lower investments and provide insights on how to replicate or adapt solutions across the broader spectrum of assets.
We encourage decision makers to go a level deeper and consider the value of leveraging data associated with their power generation assets. Issues like asset failure, inefficiencies and degradation may not directly impact production, but directly impact the bottom line. For this reason, we urge plant managers to consider and understand what’s at stake by investing (and NOT investing) in their power generation assets.
2. Do we have the right domain expertise?
If you do find value in a solution that helps centralize asset data and management functions, you may also want to consider whether your team has the expertise to implement and manage this type of solution. To work effectively, analytics solutions must follow a particular sequence of events: data leads to insight, insight leads to knowledge and knowledge leads to action. This means you must have expertise at each phase of the sequence.
IT teams are a natural starting point for many advanced analytics and asset management investments. Unfortunately, while IT is an excellent resource for integrating new technology, they may lack the ability to interpret meaning from a flood of asset data. Data science is the trendy new practice that many industrial plants are investing in to make use of their asset data, but not every company is equipped to green light a data science team. And even if you have a team of data scientists who can analyze and interpret your asset data all day long, do they have the functional knowledge of what that data actually means to the inner workings of your plant? For these reasons, it’s essential for teams to augment their existing IT or data science staffs with the domain expertise to realize the full potential of their analytics solution.
3. How do we get the right resources in place at the right time?
Of course, identifying skillset and knowledge gaps is only part of the puzzle. The other is finding ways to fill those gaps—and fast.
Naturally, speed to value is what stakeholders will care about most with any analytics investment. This forces you to answer the question of how to enable a solution to bring the most value in the shortest period of time.
When faced with the “speed-to-value dilemma,” consider these questions:
- How much initial investment are you willing to spend during the ramp-up period?
- Do you need to start building a data science team or monitoring & diagnostics team?
- Do you need to cross-train your engineers on certain technologies and process?
- Do you have resources in-house or should you consider an outsourcing model?
Investing in an industrial analytics solution is a multi-faceted decision that may require you to approach and evaluate your asset needs in a whole new way. But doing so proactively, with the right collaborators involved, sets you up to evaluate potential analytics vendors in the most prepared fashion.
Check back soon for part 2 in this series!