Predict. Prevent. Perform.

Predictive Analytics for the Internet of Things

1108/2016

Today’s focus area for operations: increase uptime!

By |August 11th, 2016|Categories: Uncategorized|

As other domains such as procurement, supply chain, production planning, etc. get increasingly lean, attention focuses on the few remaining areas where large gains are expected from increasing efficiency. Fleet uptime or machine park uptime is thé focus area today. Indeed, investors increasingly look at asset utilisation to determine whether an operation is run efficiently or not. As we know, in the past many mistakes have been made by focusing on acquisition cost at the […]

2107/2016

What did the Coffee Pot say to the Toaster?

By |July 21st, 2016|Categories: Internet Of Things (IoT), Technology & Engineering|

The Internet of Things (IoT) is at the precipice of the Gartner Hype cycle and there is no shortage of the “answers to everything” being promised. Many executives are just now beginning to find their feet after the storm wave that was the transition from on-premise to cloud solutions and are now being faced with an even faster paced paradigm shift. The transformative tidal wave that is IoT is crashing through CEO, CTO, and CIO’s […]

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 is […]

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 […]