Data is a crucial component of digital transformation in the manufacturing sector. In fact, it lies at the foundation of every digital initiative. However, data in itself is not a value driver. It must be put to use.
In general, manufacturing operations collect more than enough data to form a basis for the initial stages of transformation. The challenge is that many may not have a clear idea of how to use it or cannot easily access it because it is locked in legacy systems. And simply accessing that data is not enough – organizations must be able to monetize it. With the right combination of technology and creative thinking, this is a realistic goal. One multinational aircraft manufacturer, for example, was able to create a whole new revenue stream by sharing existing aircraft maintenance and event data with airlines.
Here are three examples of how industrial manufacturers can monetize data to increase efficiency:
R&D and Engineering
In order to optimize product design, a data-centric approach is required. Engineering teams should have access to data from a broad spectrum of sources. Sales and marketing data provide engineers with insights into customer needs and preferences that ultimately translate into increased revenue when their products reach the market and generate sales. Service and support data help teams to design for both reliability and serviceability, leading to reduced breakdowns and faster maintenance. Procurement data enables the design of components suitable for manufacture by suppliers with the most optimal production processes and/or materials, resulting in lower costs. All in all, a data-centric approach enables decisions that directly or indirectly affect the bottom line in numerous ways.
Today, most data sets reside in siloed applications (e.g., CPQ and CRM for sales and marketing, SRM for suppliers, and ERP for end-to-end manufacturing data). These data silos are a significant impediment in leveraging the power of analytics to enhance critical facets of R&D and Engineering. This is evident from a recent report we published wherein only 8% analytics usage was reported for the R&D function. An IT architecture with data at its core that integrates these applications via cloud-based Product Data Management (PDM) or Product Lifecycle Management (PLM) can boost engineer efficiency, which leads to optimized product design, and shorter time to market, allowing for a competitive sales advantage.
Shop Floor Operations
The shop floor is a prime example of how large amounts of data are being captured and stored, but not adequately monetized. IT/OT integration can change that. For example, rather than basing machine maintenance schedules on averages, which is often the case, maintenance requirements can be precisely calculated in real-time based on heat, pressure, vibration, and other data fed into a predictive analytics system. This results in reduced downtime – both planned and unplanned, which can have a significant financial impact. An aerospace client of ours was able to enhance Overall Equipment Efficiency by 20% using data insights obtained via better IT/OT integration.
Service and Support
Data can often be monetized in service and support organizations simply by delivering it in new and more efficient ways. For example, technicians who wear smart glasses that display service instructions for complex production machines can often accomplish repairs faster because they can rapidly access repair information on-site. This kind of support also enables less experienced (and therefore less costly) employees to carry out advanced repairs successfully. Finally, transmitting the right data to customers enables them to carry out repairs on-site themselves, with no need for a truck roll. Since the true cost of a truck roll (including labor, vehicle-associated costs and opportunity costs) can be upwards of $1,000 per incident, reducing their numbers can save larger companies millions of dollars annually.
For most companies, all of these possibilities are within reach now with data that is already being captured. In simple terms, it’s not the data; it’s what you do with it.