The Value Chain of Industrial Intelligence

I have spent my career connecting data to decisions. Every company is looking to maximize the value of their investment in data within the constraints defined by their value levers, whether that is Safety, Carbon Footprint, Productivity, Efficiency, or Quality.

Fundamentally, companies are looking to reduce variability in their decision-making so their actions at every level — from strategic to tactical and operational, including the control of the industrial assets — allow them to better predict their outcomes.

When I talk with clients in these industries such as mining, oil, and gas, I use this slide. I think it does a good job of communicating our shared goals.

What we’re doing at Uptake with industrial intelligence is not so different from their daily routine, albeit at the frontline of their site — a refinery, mine, assembly line, power plant, or some other industrial setting. We extract, transport, process, store, and distribute valuable assets.

Like our industrial customers, we take raw materials and refine them. In our case, that just so happens to be data. It’s a comparison our Executive Chairman has made before — one between data science and shale production.

We blend data, raw materials, with other catalyst materials (our Asset Strategy Library® or other knowledge and information). We process them using advanced analytics (empirical and statistical using first principles and AI/ML) and produce a refined product (our insights and recommendations) and byproducts (patterns, trends, and metadata for machine learning models — ultimately improving predictions for equipment performance).

Data doubles as a mirror for industrial activity, but it has the good fortune of time (and math and physics) to give customers a dynamic view. Our customers get advanced visibility of future performance and an enhanced perspective of current and past behavior.

Take pumps an example. In the oil sands industry, pumps are present in bitumen extraction, diluted bitumen product tanks, high-conversion refineries, upgraders, and simple refineries.

A pump can be used for condensate extraction. For this application, the pump system removes steam from the feed system by generating enough pressure. It then delivers the excess system to a deaerator, helping to dewater the working environment and keep personnel safe and the operation productive. However, the same type of pump can be used for steam recovery for power generation or for water treatment at a refinery.

The environment in which operators use equipment shapes its performance. The pump manufacturer may recommend a course of preventive maintenance, but since the pump was intended for use across industries — including at indoor sites — the operating context is often a missing or only partial aspect of the maintenance approach.

This context is also an important consideration for predictive maintenance and analytics. Uptake leverages its Asset Strategy Library to add this context to the blend of field sensor, work order and maintenance data. Based on the stressors that are present in the operating context of the pump, Uptake can predict risk and future failures as degradation starts to happen.

Pumps represent just one source of challenge and opportunity for maintenance and reliability professionals. Still, they can pose significant and costly problems. In an Uptake customer survey, 70% of industrial organizations identified pump systems as being a top risk to productivity.

Short-handed maintenance and reliability engineers have to maintain asset types in addition to pumps. Companies need a way to prioritize maintenance work activities to ensure lower risk and higher reliability of their pump systems.

In many cases, multiple original equipment manufacturers (OEMs) for pumps complicate monitoring the performance of the asset or its components at a system, site, and fleet levels. That makes managing various pump systems challenging — because of different suppliers, operating systems and conditions, maintenance practices, and spare parts availability (and especially so with supply chain disruptions.)

And expensive, too: pumps can consume around 30% of the maintenance budget for process-intensive operations.

The theory of constraints has been used across industries as a methodology for identifying the most important limiting factor that stands in the way of achieving a goal and then systematically improving that constraint until it is no longer a limiting factor.

It's a relevant approach to pumps, as well as other industrial assets. Companies have a large number of critical assets and a limited number of resources in capital and personnel.

By scaling insights on ancillary (or balance of plant) systems, including pumps, using software provided by Uptake, your maintenance and reliability teams have the support to make more holistic decisions about operations. Address both the complexity of their most critical production systems and overall site conditions.

There is no one-size-fits-all approach to asset performance management. The value chain of industrial intelligence, and the theory of constraints approach, sees to that.

We build up a buffer of high-quality insights to help your team focus on the right priorities to maintain consistent production and a steady operation. Thus, we can liberate and maximize the allocation of a team's time to higher-value activities. Through industrial intelligence, maintenance and reliability professionals can validate and optimize their approach to analytics.

This risk-based approach considers safety, environmental, productivity, efficiency, and quality conditions to better balance the key value levers. It truly reflects the bottom-line value.

Pressed for time and resources, companies need decision support — industrial intelligence — that caters to the industrial assets under management. Instead of drilling for oil or mining copper, industrial intelligence mines for data that enables and delivers insights, per a company’s requirements, so that they can produce higher-quality products faster.

Ready to put your data to work?