Predict. Prevent. Perform.

Predictive Analytics for the Internet of Things

1807/2016

A predictive maintenance example

By |July 18th, 2016|Categories: Uncategorized|

A prediction doesn’t mean that something will happen! A prediction merely says something may happen. Obviously, the more accurate that prediction gets, the closer it comes to determining something will happen. Yet, we often misinterpret accuracy or confidence in a prediction; when something has 20% chance of failing or 90% chance of failing, we often mistake the result of the failure for the chance of failing. In both cases, when the failure occurs, the result […]

2906/2016

The next efficiency frontier?

By |June 29th, 2016|Categories: Uncategorized|

Mountains of consulting dollars have been invested in business process optimisation, manufacturing process optimisation, supply chain optimisation, etc. Now’s the time to bring everything together and with all these processes optimised, our whole production apparatus utilisation rate becomes ever higher. When all goes well, this means more gets done per invested dollar, making CFO and investors happy through better ROA (Return On Assets). However efficient, this increasing load on the machine park comes at a […]

106/2016

Predictive Maintenance – a framework for executives

By |June 1st, 2016|Categories: Uncategorized|

We typically expect statements like “there’s a 20% chance of part A failing over the coming two weeks” from a predictive analytics solution. More important than the prediction though, is the interpretation of that statement and what it means to operations, maintenance, etc.
Predictions are at the core of predictive maintenance applications. Understanding and by extension, applying predictions is not a given. The four-axis framework laid out in this blog should allow any executive to not […]

2305/2016

One prediction, many users

By |May 23rd, 2016|Categories: Uncategorized|

“Houston, we have a problem” must have carried a different meaning depending on whether you were an astronaut on board Apollo XIII, the astronaut’s family, in mission control or a rocket engineer on the Saturn project. While the example seems obvious, many people have a vague idea on where to apply predictive maintenance in their business. When we ask about whose jobs will be impacted we very often don’t get beyond “the maintenance engineer”. And […]

1005/2016

The steps to predictive maintenance

By |May 10th, 2016|Categories: Uncategorized|

Predictive maintenance is almost all about data, software, etc. and is therefore for many maintenance departments very far from their natural habitat. This scares off many, but in reality it shouldn’t. Modern businesses can no longer function with hard walls between departments. What’s important is that every department knows how what they are doing influences other departments’ work. This also means that CIO’s have to be more business experts than ICT experts. Here’s a quick […]

2804/2016

Your data might not be your data.

By |April 28th, 2016|Categories: Uncategorized|

Previous Predikto bloggers have emphasized the centrality of data in the successful adoption of predictive maintenance initiatives.

Leveraging the full range of equipment operation, environmental and maintenance data, operators can better align the “right” resources to the appropriate mission requirements and/or environmental conditions, and more easily transition to effective implementation of condition-based maintenance programs, which can reduce the financial impact of scheduled maintenance as well as minimize the “fire drills” associated with an unplanned performance degradation […]

2704/2016

A scarce resource

By |April 27th, 2016|Categories: Uncategorized|

McKinsey, in this study – http://www.mckinsey.com/business-functions/business-technology/our-insights/big-data-the-next-frontier-for-innovation – foresees data science jobs in the United States to exceed 490,000 by 2018. However, only 200,000 data scientists are projected to be available by then… Globally, by the same time this demand is projected to exceed supply by more than 50 percent.
At the same time, 99% of CEO’s indicate that big data analytics is important to their strategies (KPMG study). Beyond big data analytics, the rise of predictive analytics (PdA) creates the need […]

2004/2016

What about unplanned?

By |April 20th, 2016|Categories: Industries, Internet Of Things (IoT), Predictive Analytics, Predictive Maintenance, Uncategorized|

Everybody’s looking at process inefficiencies to improve maintenance but there’s lower hanging – and bigger – fruit to focus on first: unplanned events!
Maintenance has pretty simple goals; guarantee and increase equipment uptime and do so at the lowest possible cost. Let’s take a quick look at how unplanned events influence these three conditions.
Guarantee uptime
When production went through the evolutions of JIT (Just In Time), Lean,… and other optimisation schemes, schedules got ever tighter and deviations […]

404/2016

What’s happening to my train (and by extension, any equipment)?

By |April 4th, 2016|Categories: Uncategorized|

At regular intervals, company managers are asked to provide their forecast for the next period(s). While some dread this exercise – and it is a tough ask for them to put together this forecast – others can almost pull the numbers on request. Why? Because they have good visibility on current and past performance, on current and past conditions and are well informed on forecasted evolutions in the market. Why is that so difficult for […]

2103/2016

Context is King to Operationalize Predictive Analytics

By |March 21st, 2016|Categories: Predictive Analytics|Tags: , |

Companies have invested significantly in Big Data solutions or capabilities. They usually start with adding more sensors on their equipment or perhaps bringing all of their historical data into a Big Data repository like Hadoop.  They have taken the first step towards a “Big Data” driven solution. The challenge is that “tackling” the data does not bring any tangible value.  This is why Predikto focuses so much of our R&D and technology in the “Action” […]