In the previous blog post, we talked about how to measure the success of an asset performance monitoring solution. With the buzz of AI and machine learning out there, we at Plutoshift hear questions about what exactly machine learning analytics can actually do. The quick answer is a lot, but the longer and more important answers will be considered here in blog post #2 of this series. What factors should you consider when you’re implementing advanced analytics for industrial processes?
When thinking about introducing these new technologies to your company, here are the 5 considerations that will help:
1. What are the specific business goals that AI can solve?
This may sound obvious, but not identifying a key business pain point to solve is frequently the reason pilots do not progress. Even when they appear successful, they will stall at some point. Exploring new technologies and how to improve your business is the sign of a vibrant company.
However, when a pilot flies under the radar of executive’s awareness, the hurdle to take a pilot to the next level is difficult. A business objective that’s stated from the outset will improve your odds greatly. This quote from a savvy Utilities Manager is spot on:
Well, I guess it’s good to know if I needed to know it.
Some examples of business pain points that can get the right attention from the outset are:
- Reduce unplanned downtime: You can forecast performance metrics and schedule maintenance to reduce downtime
- Reduce energy costs: You can take advantage of off-peak energy prices
- Reduce production material cost: You can lower chemical dosing amounts
2. What improvement in process will be attained?
When pilots succeed but don’t progress, it’s because the results were not very exciting. This doesn’t mean that the results must be a slam dunk. In fact, some of the most exciting results are when performance improvements weren’t obtained but a clear reason is determined as to why it didn’t happen. Identifying where to invest with reasonable certainty of improved results is an outstanding thing to learn.
Typically, new technology investigations have a champion at the company. Since you’re reading this article, perhaps that’s you! Your vision is vital to a successful enterprise.
The challenge is to find a project with which everyone is comfortable. The idea of getting some kind of pilot just to get an evaluation started seems reasonable. Yet, in these situations, buy-in is hard to come by. Pilots take up people’s time and goodwill runs short. You as the champion get tired of carrying the project alone. When a pilot is complete most of us are happy to be done with it. We are not all that excited to dive back in unless there is something to really entice us.
This is where concrete meaningful goals become important. Without the expectation of a real payoff, it’s hard to progress. This is certainly true with AI solutions but generally true with any project. Your vendor should be leading this improvement charge. If they can’t, consider this before making a commitment. As one old pool player, who also happens to be a Director of Plant Operations, said to me:
Call your shots! If you don’t, it really doesn’t matter whether you make it or not.
3. What access to data do you have to support the considered project?
This is specifically an AI project concern. As far as data is concerned, there are three key aspects that form the backbone of an AI project — quantity, quality, and access. AI projects use historical data to train algorithms that can predict future outcomes.
More data is always better. It may not all be used, but data scientists will want to tease out any correlations and look for causal effects. Lack of data certainly makes it challenging, but it does not mean that the project goals cannot be met.
Gaps in data can be overcome. Lacking one or more sensor inputs may be overcome. This is the type of initial investigation a data scientist team can do for you. More on this in blog #3 of this series.
4. Do you have a combination of data scientists and subject matter experts for the project proposed?
I spoke to the role of data scientists in this process. Equally important is the strong collaboration between data scientists and the SME who understands the process to be optimized. Without this, the project will likely not be successful.
This is also important because it is rare. Several solutions are available that have good AI expertise and others that have subject matter expertise. These types of projects, at least for the next couple of years, will require both of these. Both should be equally held responsible for the successful outcome.
5. How to assess a potential solution provider?
After you’ve checked all the points above, there’s still the need to evaluate the plan and execute the project. Is it to find a pure analytics company when you have your own subject matter expertise? Relying on a consulting engineering firm to organize the project? Getting a one-stop vendor to do the whole thing? All of these are viable options.
The key is to know that the analysis can be done. This is not guaranteed because historical data is crucial. Also, access to data is required in near real-time.
This means that the data analysis should at least be completed and vetted initially. Can your team or your provider tell you within certain limits that this analysis will yield prescriptive recommendations that will meet the goals of the project?
However, you combine the resources to execute this project. This initial analysis should have little to no cost. You can call it the Phase Zero of data analysis. If a sizable payment must be made before any data analysis occurs, it would mean that you’re funding the learning curve for whomever required the purchase order.