Are you ready for Data Science?

Data science is big business. It’s about leveraging a company’s data to optimise operations or profitability. It’s putting the big into big data and providing insight into operations that only years before seemed like something that was out of science fiction. Promises of predictive technologies that can see into the future have companies wanting more but when do you decide that your organisation is ready to take the plunge?

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Budgeting for “Smart” projects

images-2Budgeting for industrial “Smart” projects

Why go online?

Industrial (internet of things) IoT projects, for providing hands-on visibility and control capabilities for remote equipment, require a larger initial investment then portable or localised monitoring solutions. The advantage is obviously the 24 / 7, anywhere, anytime, control that cannot be matched. Governments, farms, factories and other early adopters are jumping on the IoT train as it heads out of the station and into the wilderness.

Budgeting Traps

The major trap that many of these early adopters stumble onto is simply not allocating the right resources to ensure it’s success. Sure, Industrial application of IoT provide multiples of a return on the investment but projects that try to save a few dollars by lets say, using substandard hardware, or not providing appropriate user training, are going to provide a lower return on investment.

“Considering that an online monitoring system should have a 10-25 year lifespan, is it really worth saving a dollar today to cost 10 down the track? “

With this in mind, it’s important to understand the true cost of implementation. In this way projects are scoped properly, perform better, and the total cost of ownership over the life cycle can be reduced.

Calculating the Total Project cost

To accurately assess the Total Cost of implementation of an industrial IoT project, the user must consider the cost of data acquisition and analysis equipment plus installation.

images-1To accurately assess the Total Cost of Ownership, asset owners must consider all of the following investments, in no particular order:

  • System Engineering and installation
  • Field instrumentation (Sensors and Cabling, WiFi)
  • Monitoring and Diagnostic System (Including Hardware and Software)
  • User Training and Customer Support (if Required)
  • System Maintenance (Sensor replacement, software updates)
  • Necessary external expertise and support.

Industrial IoT projects typically have a finite life cycle, which will extend anywhere from 10 to 25 years and beyond. The maintenance and support costs should be considered for this period to ensure that all costs are adequately assessed for the total cost of ownership.

Project Cycle Expenditure

The amount of expenditure is broken into the following parts for a new installation however this may vary dependent on the industry and system being deployed.

  1. Feasibility and Pre-Engineering – 10%
  2. Procurement – 40%
  3. Installation – 30%
  4. Commissioning – 20%

Following the project deployment, consideration must be given to the ongoing maintenance and support costs that have been mentioned previously. The total cost of ownership is the combination of these costs. Many industrial IoT systems can send thousands of messages a day, meaning that consumption can be quite high, leading to high ongoing data processing and storage costs. This can range from a few dollars to thousands of dollars a day.

If you would like to help calculate the cost of IoT projects please contact M-innovation on 1300 916 082


Why IoT & Big Data are the future of Farming

Farming Situation

future of farming

Data generated by the average farm per day, Year 2000 to 2034. Data by BI Intelligence

Farming is one of the predicted growth industries for Australia. Global predictions suggest this trend will continue for the next few decades. The future of farming lays certain with IoT and big data analytics playing an essential role.

Analysts at the UN Food and Agriculture Organisation have determined that the world will need to produce 70% more food in 2050 than it did in 2006 to keep the growing population fed. To meet this demand agricultural companies are turning to the Internet of Things and Big Data to improve their efficiency and capacity.

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Calculating Risk Avoidance with Online Monitoring Systems

Calculating Risk Avoidance with Online Monitoring Systems 


Risk is typically the product of two factors, Consequence and probability. These factors are often combined to develop a financial justification in monetary units. Risk avoidance is the process of reducing risk based on reducing the cost of consequence and likelihood of occurrence.

The role of an online monitoring system is to ensure that the risk is avoided as much as practicable. This is achieved by reducing the cost of consequence, i.e. avoiding a catastrophic failure and consequential costs associated with lost productivity, labour and spare parts and reducing the likelihood through

For example: If a high criticality pump fails catastrophically, a potential consequence is $200,000 in lost productivity, labour and spare parts and other incidentals.

Consider this event occurring at a frequency of once every ten years. This represents an annual risk of $20,000 per annum. Now consider the same failure with the probability of one failure every two (2) years. The annual risk has now increased to $50,000 per annum.

$200,000 Failure Costs

Failure Every 10 Years             = $200,000 / 10 years             = $20,000 / annum

Failure Every 2 Years              = $200,000 / 2 years               = $100,000 / annum Read More