Tooling Digitization

Digitizing tooling management offers clear benefits to OEMs. This articles discusses why manual management options do not work and how leveraging new technologies, like IoT and AI, can help. With 92% of all OEMs using spreadsheets to collect, review and share tooling data, it’s time for manufacturers to get into the 8% by digitalizing their molds, dies and other tooling.

As global organizations adopt more automation and implement technology that replaces their manual business processes, tooling remains an area that’s largely left up to chance. In fact, 92% of original equipment manufacturers (OEMs) still rely on spreadsheets to collect, review and share tooling data.

This creates massive inefficiencies, holds up production and throttles throughput for manufacturers that are under extreme pressure to get their products to market on time.

To avoid these and other problems, the dies, molds, jigs, gauges, cutting equipment and other tooling must be in the right place at the right time and fully operational.

When any of these boxes are left unchecked, the end result will be improper or dysfunctional tooling that directly impacts output capacity, quality, product lifecycle and costs.


Spreadsheets don’t work for businesses. Not only do they take time (and manual effort) to populate, manage and share, but spreadsheets don’t store historical data, are hard to analyze and too easy to change (sometimes incorrectly, thus increasing the risk of error).

The small organization with a single location and few external partners may get away with using spreadsheets to manage specific aspects of its business, but the global manufacturer with multiple locations, business partners and customers needs a more streamlined, automated and reliable way to manage its tooling.

“The manufacturing business model used to be organized and operated on a regional basis, but it has since expanded into a global relationship, where the production change moves between countries,” says Dr. Mason Lee, senior data scientist at eMoldino, developer of a cloud-based solution that connects tooling via an artificial intelligence-driven analytics platform and that provides real-time, global visibility on tooling status.

“The situation puts OEMS in a position to be more exposed to limitations in the supply chain, including manual information collection and a lack of global communication,” Lee continues. In the past, for example, companies collected data manually without too much of a problem because their focus was likely on regional production.

Now that these OEMs are operating on a global scale, speed of operations has become critical and manual data collection is completely inefficient. When the same OEMs digitalize their tooling, they get real-time tooling data, clear visibility and better collaboration within manufacturing processes and across the entire supply chain. Other key benefits include major efficiency gains, enhanced data accuracy and optimized procurement practices.


The emergence of the Internet of Things (loT) and artificial intelligence (Al) has driven increased application of technology in production management, where the push is on to get processes digitized and automated. Both OEMs and suppliers are using real-time loT technologies to gain visibility and agility, for example. “This is emerging as a new paradigm for manufacturing value chains,” says Lee.

As evidenced by the 92% of OEMs still using spreadsheets to manage their tooling data, the path to establishing a digitalized system for collecting and sharing production data isn’t always clear. There are many prerequisites to maintain and accomplish this feat, says Lee, which requires a mix of devices, loT sensors, support, installation services and implementation at individual production sites.

“Implementation can be difficult and a potential barrier,” Lee points out. Despite these perceived obstacles, the benefits of digitalizing tooling data are very clear. At a fundamental level, production efficiency suffers when the data collection process is based on manual methods of checking and entering the tool mold status and production status into a computer.

This not only creates the potential for human error, but also the risk of documenting inaccurate data about assets and production patterns. “Since the data isn’t collected automatically,” says Lee, “there’s a lack of visibility and potential for inaccurate production management.”

Using the “garbage in/garbage out” idea-whereby computers fed incorrect or poor-quality inputs always produce faulty outputs-as an example, Lee says no matter how digitalized a system is, if the data is incorrect, everything will be wrong. Knowing this, he says digitalized methods of transmitting and pooling production data is a key issue for all OEMs right now.

“Using loT sensors and Al, manufacturers can prevent data contamination,” says Lee. “And because the data is collected and analyzed in real-time, OEMs can check it and take immediate action when a tooling problem arises.” This, in turn, speeds up the production process, enables faster time to market and allows for quick attention to quality concerns.

This Making the Case explores the key challenges that OEMs face when they run their tooling operations on spreadsheets or other manual processes, showcases tooling digitalization in a real-life “before and after” case and outlines the top benefits that manufacturers get when they decide to join the 8% of OEMs that have made the switch to digitalized tooling processes..

This article was from Supply Chain Management Review and was legally licensed through the Industry Dive Content Marketplace. Please direct all licensing questions to