Industry 4.0 creates new possibilities for leveraging data to increase production automation, throughput, quality and efficiencies. While many companies focus on data creation and collection, the true value—and a lot of it—comes from putting the data to use to solve concrete business problems.
Early-adopting factories are implementing such technologies. A McKinsey report published last year presents the impact of Industry 4.0, including factory output increases of up to 200 percent, product cost reduction of up to 40 percent and reduction of up to 90 percent in time to market.
In order to successfully leverage this new wave of technology, one must start with defining the problem they are trying to solve (e.g., “reduce rework”), then define the technology and solutions (software and/or hardware, AI, robotics etc.) and only then decide which data is needed and how to get it.
Considering this framework, if one began collecting data just for the sake of having data, that would be like putting the carriage before the horse.
Now, with a specific business problem to solve, we can also set key performance indicators (KPIs) to measure the success of the project or, simply put, its ROI. To demonstrate this approach, let’s look at one example: the problem they were looking to solve, the approach taken, and the ROI measured.
Example: An Aerospace Tier 1 Supplier
Project goals: Improve on-time delivery, increase throughput and reduce material scrap (mainly composite materials, expensive and time-sensitive).
Approach taken: Applying AI-based software to provide users with actionable analytics: predictive alerts on delays, bottlenecks and quality problems, and specific recommendations on how to address them. This would be done by collecting relevant data, learning historical production patterns and understanding the current and expected production situation to drive these analytics. The data collected comes from current systems (ERP, MES) for planned schedules and work-order progress, as well as sensors tracking raw materials and parts throughout the facility in real-time.
Given this data, AI was used to: monitor raw material, predict and alert for expiration; predict production delays or bottlenecks, alert production staff and reroute/expedite jobs as needed, and optimally assign specific materials to jobs, ensuring the fabrication process is complete before material expires.
Having data alone does not address the problem at hand, yet certainly AI must have some data to work. However, defining the problem first and then the data required to solve it massively reduces the challenges of data collection and integration.
KPIs measured and ROI: The project team focused on three KPIs:
- Improved on-time delivery, measured at roughly 4 percent.
- Improved first-time quality, with an estimated impact of 2.8 percent reduction of re-work.
- Reduction of raw material scrap estimated at 6.5 percent.
To summarize, AI and Industry 4.0 allow us to go far beyond reports and dashboards to address the challenges manufacturers face today. The focus of implementing Industry 4.0 solutions must be on the business value. This will guide you to what is the right solution for your company’s needs and drive the correct selection of technologies. Both top- and bottom-line benefits are realized. Quantifying these benefits must be diligently approached just as with any other investment decision and technology adoption.