IoT Agenda

Dec 6 2018   10:46AM GMT

5 questions to ask before piloting AI for IIoT analytics

Prateek Joshi Profile: Prateek Joshi

Tags:
ai
APM
Artificial intelligence
asset performance management
Data Science
Data scientist
IIoT
IIoT analytics
IIoT data
Industrial IoT
Internet of Things
iot
IoT data

A new industrial age is being propelled by companies wanting their assets to generate more revenue without further investment or infrastructure upgrades. Artificial intelligence and IIoT can make this a reality. With a system intelligently assessing the conditions that affect manufacturing processes, humans can learn and make decisions based on that information.

This allows operations to improve with little or no manual analysis from personnel, leading to lower costs and downtime, and the ability to produce faster, as well as a slew of other benefits.

Sounds good, but there’s more to this.

Large data sets are too time-consuming for a system to process, especially if attempted manually. AI is used to find correlations and the root cause to specific events. Add in a capable asset performance management system and AI algorithms can offer advanced analytics that deliver a clear view of business outcomes, and even what the future may hold.

It’s an exciting time and a lot of companies are ready to rush right in. But if AI was simple and success guaranteed, everybody would already be on board. It’s an evolving field, and if not done right, people might walk away with the impression that AI can’t do much or that it’s not effective. Before turning these new technologies loose, consider the following questions.

What are we trying to solve?

Not identifying key business pain points to solve is a reason many AI pilots flounder. The thing is, even when these initiatives appear successful, they will stall at some point. You have to know what you’re trying to achieve and, most importantly, make sure that the leadership is fully aware of it. This will enable you to continue working on it despite encountering obstacles. Here are a few examples that grab executives’ attention and commitment:

  • Minimize unplanned downtime: Forecast performance metrics and schedule maintenance to keep operations up and running.
  • Reduce energy costs: Get to the root cause of energy spikes faster. Take advantage of off-peak energy prices.
  • Reduce chemical costs: Purchase and use resources more cost-effectively, such as lowering chemical dosing amounts.
  • Increase efficiency of work orders: Manage dispatches optimally by predicting the performance of assets in advance.

What improvements will be reached?

When pilots succeed but don’t progress, it’s often because results weren’t as powerful as anticipated. The fact is that results are still positive even when performance improvements weren’t obtained but a clear reason is determined as to why it didn’t happen.

The challenge is to find a project with which everyone feels comfortable. Getting some kind of pilot off the ground just to get an evaluation started is actually reasonable. This is where concrete, meaningful improvement goals become important. Your solution provider should lead this charge since they know what’s possible.

What access to data will you have?

When it comes to data, three key aspects make up the backbone of an AI project: quantity, quality and access. AI projects use historical data in order to train algorithms to predict future outcomes. The more data the better. It may not all come into play, but data scientists will want to tease out any and all correlations and look for causal effects, so having access to this data is crucial.

Even so, while less data poses challenges, project goals can still be met. Even gaps in data — such as a lack of one or more sensor inputs — can be overcome. It’s important to know what you have to work with, so bring in a data science team to conduct an investigation before beginning.

Do we have data scientists and subject matter experts?

It’s important to have strong collaboration between data scientists and subject matter experts (SMEs) who understand the process to be optimized. Without this, the project will likely fail. Some solution providers have good AI expertise, others have SMEs. These types of projects require a combination of both.

How do we proceed?

There are a lot of approaches you can take to evaluate and execute a plan. Do you involve an analytics company if you have your own SME? Should a consulting engineering firm organize the project? Do you get a one-stop solution provider to do the whole thing?

All of these are viable options. The key is to know that the analysis can be done, and access to historical and near real-time data is crucial.

Data analysis should be completed and vetted up front. Your team or provider must be able to tell you, within certain limits, that you’ll get the prescriptive recommendations necessary to meet your project goals. If a significant payment is needed before any analysis occurs, you could be funding someone else’s learning curve.

Improving your workflows is a process. The key is to be realistic, patient and persistent.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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