Advancing from Pilot to Enterprise Rollout
10 June 2020

Check out our helpful tips that will see the implementation of Industrial Analytics advance beyond the proof of concept phase.

A common cause of frustration of clients is a difficulty in advancing from a successful industrial analytics pilot project to a full enterprise rollout.

AI Predictive Analytics has the power to improve how industrial businesses operate, by revealing insights from their data which highlights improvements that can be made to processes, and transforms the way critical infrastructure is maintained. However this benefit is only obtained once all the data is unleashed and the solution is applied enterprise wide.

So we've come up with some helpful tips to get you advancing;

  • Go BIG for your pilot project

  • Highlight your wins

  • Plan to scale quickly

  • Breakdown silos

  • Navigate the change

 

Go BIG for your pilot project

When it comes to selecting the problem statement you want to address in your Proof of Concept, we recommend picking an area that is costing the business a significant amount of money and has data already recorded. AI Industrial Analytics is at its most powerful when it has historical and live data to analyse, ideally 12 months of historical data is sufficient.  If you have an issue but no data, we suggest starting to record the data now and addressing that issue in the future.
Most companies will give AI one shot, and therefore you want to make sure the stakes are high enough to get the buy-in from the C-suite, and to show-off the true benefits of predictive analytics.
 

Highlight your wins

During the pilot and as you rollout your solution enterprise wide, it is important to highlight your successes. These could be successful catch of the days and the savings from those incidents, number of days without a major equipment failure,  number of people logging into the system, improvements to productivity, new teams implementing the solution, and general maintenance savings.  No matter the size of the success, make sure it is highlighted. It is important to remain highly visible. This ensures on-going support from upper management.
 

Plan to scale quickly

After a successful pilot it is important to scale quickly in order to see true organisational digital transformation. A projects momentum can be lost if there is a delay in rolling it out company wide.  We've even see companies suddenly change their strategy as something more urgent comes along, halting the implementation altogether.  The goal is to embed the change into the companies framework so it becomes business as usual.  It may require a change in processes by operational and maintenance teams, if so, this needs to be planned and communicated clearly. Most of the time these teams will appreciate having technology that provides insights into why their equipment is failing, however they may be afraid of it replacing their jobs and so push-back may be experienced.
 

Break down the silos

Companies that breakdown internal silos, and remove competitiveness between divisions, position themselves for success. AI Industrial Analytics is most powerful when working with data from un-seemingly related processes, as it identifies unexpected root cause of failures.  However for this to be effective, teams need to communicate freely and work together to optimise their processes.  We encourage companies to look at the way they incentivise staff to promote team work and the adoption of the technology.
 

Navigate the change

We have all become more accustomed to change as a result of the 2020 Pandemic, and just like that, advancing industrial analytics will take some navigating with your team.  By promoting the wins, increasing communication and reassuring the team of the purpose behind the change, you’ll be able to navigate any issues that arise and embed the technology within your organisation. Be prepared this may take time, however any worthwhile endeavour always does.
 

We’d love to hear how you are advancing AI Predictive Analytics in your workplace, and if you need any assistance or would like to know more about implementing AI Predictive Analytics please contact our team.
 
 

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