Updating the customer experience and optimizing plant operations using machine learning are two of the top priorities for manufacturers today. But despite so many organizations being eager to make this happen, most simply don’t have the equipment or the talent to do so.
Fortunately, this situation is starting to change. Most recently, Amazon announced several investments it’s making in Amazon Web Services (AWS) to help accelerate cloud adoption in manufacturing.
These innovations are a great start to removing the obstacles most manufacturers have faced in achieving their goals. However, technology alone will not solve problems. Organizations need to pair that technology with the right processes, talent and inputs to ensure it delivers as intended. In manufacturing, one of the most important prerequisites for a successful move to the cloud is making data actionable. Let’s take a look at exactly what that entails.
Getting Past Core Challenges to Recognize Data as an Enabler
Long term success in manufacturing requires continuous innovation. Your competitors are always just around the corner looking to do things better, faster, cheaper, and if your organization doesn’t look to do the same, it’s easy to fall behind.
The best way to improve operations in pursuit of these goals comes down to using data effectively. Historically that’s been easier said than done for manufacturers, though.
The problem isn’t a lack of data. Far from it: Every manufacturer — no matter what your current operations look like — is sitting on a wealth of data. Rather, the problem lies in effectively accessing and using that data. Two of the biggest challenges manufacturers face when it comes to data include the cost prohibitive nature of accessing that data and difficulty acting on any data that is accessible.
In terms of data access, common challenges include:
- Effectively accessing data from remote systems, many of which are decades old and run in part on “tribal knowledge” from on-site experts
- Combining data from various, disconnected systems, many of which operate and collect data in very different ways
- Enriching all of that data with any necessary context to make it insightful and actionable
Meanwhile, common data usage challenges include:
- Finding opportunities to use data that allow for teams to start small, experiment and easily refine their approach based on results
- Making it easy for different teams to regularly view incoming data and take action accordingly based on key goals
- Tracking and managing data to understand new trends and measure the impact of ongoing efforts
The latest cloud innovations coming out of AWS are poised to help manufacturers overcome these challenges and start using data as an enabler for innovation. Doing so comes with several notable benefits that can’t be overstated: Increasing agility, scalability and overall business resiliency, reducing IT costs and allowing teams to prioritize innovation over break-fixes. Of course achieving these goals also requires the proper data strategy.
3 Considerations for Making Manufacturing Data Accessible and Actionable in the Cloud
What does the best path for moving your manufacturing data to the cloud to drive digital transformation and improve operations look like?
There’s no single “right” answer that will work for every organization. It will depend on your data and your goals, among many other points. That said, applying the following considerations can help make this move a success for any organization:
1) Pick an entry point
First, you need to pick an area to start. The amount of data and opportunities to use it can get very overwhelming fast, so it’s important to define a pilot project. This project should have a very specific scope so that everyone stays aligned on the purpose of the initiative and the overall plan for using data.
Beyond giving your team a clear place to start and keeping everyone focused, picking this entry point also helps create a proof of concept of sorts. Specifically, you should view this pilot project as an opportunity to get a win from using data. You can then use this win to build momentum for future projects.
2) Focus on plant level data that has been challenging to access
Second, it’s important to focus on any plant level data that has historically been difficult to access so you can begin working on plans to change that. As you do so, think about what elements of that data you want based on how you plan to use it to achieve certain goals.
As you develop the plan to start accessing that data, it’s also important to think through how you’ll make it actionable. This may require enriching it with other data and/or bringing it into certain systems that make it easy for your team to take action. Planning for all of this upfront will help ensure you extract the data in the right way for how you plan to use it.
3) Set clear KPIs
Finally, you need to set clear key performance indicators (KPIs) that you can use to measure success and communicate those results to the business. Setting these KPIs upfront is a must so that you remain true to your original purpose and keep everyone aligned while working toward those outcomes.
Importantly, you should track regular progress toward these KPIs and use them to determine whether or not your efforts are working as intended. These should also be the metrics you use to communicate with stakeholders about progress, as this helps maintain alignment on what defines success.
Bringing Manufacturing Data to the Cloud with AWS
When done correctly, bringing manufacturing data to the cloud with AWS can deliver enormous benefits. It can help your team increase productivity and deliver better, more innovative results while positioning your organization well for continued growth and a high level of agility to respond quickly to changing situations.
Following the considerations outlined here can help make sure you bring your data to the cloud successfully in a way that makes it highly actionable for future use.
Once you accomplish that move, what happens next? Check back for part two of this blog post for tips on how to take advantage of well managed data in the cloud to enable predictive maintenance.