It’s easy to say that Predictive Maintenance (PdM) is going to save your business money, however proving it in advance is difficult and scepticism about the benefits of Predictive Maintenance come from the inability to measure the ROI of investments in it. 

With so many variables and the unique nature of your business, its operations and assets, coming up with precise figures on the total benefit (or cost) to your organization to be able to get to an ROI figure is problematic (we’ve included some generic ones at the end of this article here).

What we will say at the outset is that Predictive Maintenance solutions do not need to cost millions of dollars, nor do they need to be massive, all-encompassing projects that risk disrupting your operations. Our approach to Predictive Maintenance (and all IoT projects) and what we would recommend anyone looking to get started with it is to start small, utilize industry knowledge and preconfigured solutions to quickly and affordably deploy a solution that enables you to test the waters and see for yourself what the impact on your business will be. 

Any new technology investment always takes a leap of faith to a degree, but it doesn’t need to be paralysing or into the complete unknown.

The 7 steps to building a business case for Predictive Maintenance:

  1. Identify one common point of difficulty within your operations – it’s tempting to roll lots of things into a project, but it’s important to have a clearly defined scope and goals to the projects, so pick a particular machine, asset-class or technology. If there’s something that commonly fails, or a particular asset that’s expensive and time consuming to maintain, then that is clearly somewhere where you can start to build a business case.
  2. Measure the assets output and downtime – how many hours was it operational? How long was the planned downtime? How long was the unexpected downtime? What is its baseline productivity (per-hour) to your organization? Can you compare it with other similar/same assets that you have? At the end of this, you want to know how long it’s running and what its output is.
  3. Determine all the inputs that go into the assets operation – here, we’re looking to factor in all the costs that go into running the machine so that you can work out more baseline numbers, such as cost-per-hour to run, cost-per-hour when not functioning, annual cost to maintain, what’s the cost in lost production/parts/man hours for each of event etc. Make sure that you factor in energy costs, human costs (both maintenance and operational), HVAC, current spend on parts etc.
  4. Help! I don’t have the measures you need’ – in some scenarios you might be able to fill some blanks by finding industry or asset benchmarks, or be able to look into commercial numbers to try and extrapolate out figures. In other situations, you might need to speak to people that work in this space to get comparisons from other projects they’ve worked on.
  5. Determine what’s possible – if your machine is breaking down every 14 days, how long can you get that out to? If you can get utilization up to 22 hours a day rather than 20, what’s the impact on productivity?
  6. Scope – with an understanding of what the current situation is costing the business and knowledge of what you’re going to need to measure, it’s time to start engaging with someone who can give you some ballpark figures on costs for sensors, integration with existing systems, IoT platform configuration and data science that’s needed to get a solution operational. Depending on complexity and scale, this should be in tens of thousands of dollars and be measured in weeks, not months.
  7. Calculate ROI and indirect benefits – you should have all the numbers to calculate the likely ROI from the project now, but do also to remember to include other figures that aren’t directly measurable. The health and safety angle and preventing workplace accidents is always a key priority, so be sure to factor that in (read more about the benefits of Predictive Maintenance in our blog: What Predictive Maintenance (PdM) is really about and the benefits of it)

You should now have a view on how you can make the business case and scoping a pilot Predictive Maintenance programme. It’s always worth remembering that Predictive Maintenance programmes need data and time to operate, before you can then fine tune and find the maximum benefit that can be derived from them, so prepare for scaling, but factor in enough time for optimization and use case proving.

Rayven has experience building Predictive Maintenance IoT solutions with organizations in manufacturing, facility & asset management, trucking & logistics, agriculture, energy & utilities, and infrastructure & construction – implementing them both quickly and affordably.

Speak to us today to find out more.

 

Want to know more about Predictive Maintenance? Then read our other blogs!

 

P.S. Here are some general handy stats that you might want to use in your business case:

  • ‘Poor maintenance strategies can reduce the overall productive capacity of a plant by 5 to 20 percent’ – IoT Slashes Downtime with Predictive Maintenance, Gary Wollenhaupt, ptc.com, March 2016
  • ‘Predicting failures via advanced analytics can increase equipment uptime by up to 20%’. – Predictive Maintenance: Taking proactive measures based on advanced data analytics to predict and avoid machine failure – Analytics Institute and Deloitte
  • ‘On average, Predictive Maintenance increases productivity by 25%, reduces breakdowns by 70% and lowers maintenance costs by 25%’- Predictive Maintenance: Taking proactive measures based on advanced data analytics to predict and avoid machine failure – Analytics Institute and Deloitte
  • ‘Predictive maintenance increases equipment uptime by 10 to 20% while reducing overall maintenance costs by 5 to 10% and maintenance planning time by 20 to 50%’ – Predictive Maintenance: Taking proactive measures based on advanced data analytics to predict and avoid machine failure – Analytics Institute and Deloitte
  • ‘Manufacturers’ savings from Predictive Maintenance could globally total between $240 and $630bn by 2025’ – The Internet of Things: Mapping the Value Beyond the Hype, McKinsey Global Institute
  • ‘The global Predictive Maintenance market is expected to grow to $6.3 billion by 2022’ – Global Predictive Maintenance (PdM) Market Research Report, Market Research Future
  • ‘Predictive maintenance in factories could reduce cost by 12%, improve uptime by 9%, reduce safety, health, environment, and quality risks by 14%, and extend the lifetime of an aging asset by 20%’ – Predictive Maintenance 4.0 – Beyond the hype: PdM 4.0 delivers results, PwC
  • ‘91 percent of Predictive Maintenance manufacturers’ see a reduction of repair time and unplanned downtime, and 93% see improvement of aging industrial infrastructure’ – Digital Industrial Revolution with Predictive Maintenance Report, The CXP Group 
  • ‘By 2022, spending on IoT-based Predictive Maintenance will increase to $12.9bn, up from $3.4bn in 2018’ – Gartner