• eMoldino

The Problems of Manual Mold Data Collection

Updated: Jun 19, 2020

Industrial digitalization is a promise. It is a promise of cyber-physical systems: smart machines, storage systems, and production facilities capable of exchanging information, triggering actions, and controlling each other autonomously.

IIoT is the glue that enables this paradigm. Applied to manufacturing molds, IIoT becomes the foundation for solutions such as digital mold counters. These solutions optimize OEM mold management by phasing out outdated methods of data processing.

And by “outdated”, we refer specifically to manual data collection.

Learn how to maximize the use out of your molds with automated data processing.

Why Should We Care About Manual Data Collection?

Let’s be clear. OEMs should be using accurate, current data to inform their mold activities. Molds are costly; about one-third of a company’s manufacturing investments are bound up in these assets. It is thus imperative that tooling-related management decisions be driven by thorough, data-backed diagnostics.

The problem is the reliability of said data. Tooling managers yet rely on manually collected data to inform their decisions. In 2018, McKinsey reported that 92% of OEMs were still using spreadsheets to process tooling data. Even worse, roughly 91% of molds were not being monitored in real time.

This means that the vast majority of OEMs are tracking important metrics (i.e.: lead times, mold utilization rates, etc.) periodically, painstakingly, and with a high chance of human error. Without ready ways of locating them, molds may even be lost over time.

"...92% of OEMs were still using spreadsheets to process tooling data."

Manual data collection isn’t just an issue of accuracy, it is one of efficiency; the process creates unnecessary production downtimes and inefficient uses of manpower. Tools in operation must be stopped for data retrieval, while personnel must be assigned to the recording, organization, validation, and transmission of said data.

Simply put, pre-digitized methods of data collection is a poor optimizer of mold asset management.

The Core Issues of Manual Data Collection

"Manual data collection isn't just an issue of accuracy; it is one of efficiency."

Fundamentally, the problems of manually collected mold data is best summed up in three points.

Data Timeliness

Manually collected data is rarely timely enough to be helpful. Information of this kind is updated perhaps once a month or once every quarter, and will quickly become obsolete. At best, this will provide rough estimations that can only partially inform decisions.

Data Integrity

With manual data comes the issue of data integrity, or the lack thereof. The risk of human error is inherent to manual data collection. As such, the possibility of data manipulation, whether accidental or deliberate, cannot be ruled out. In the worst case, the poor level of data integrity will hamper or even misguide tooling managers.

Data Volume

It is also impossible for manual methods to cope with the volume of tooling data OEMs require. OEMs can possess thousands of molds, each working at different paces, spread amongst suppliers around the world.

Humans simply cannot record nor analyze the sheer volume of data in a timely fashion. And yet, OEMs need such analyses to identify trends and to create rigorous manufacturing analytics.

Digitalizing Mold Data Collection

Already, digital mold solutions exist in the market, offering to help companies transition to mold digitalization. In mold digitalization, manual data entry will be among the first things to be phased out in favor of real-time, IIoT data collection.

Focusing primarily on the toolings themselves, mold digitalization hopes to bring tooling and tooling data into real time. Molds themselves can be tracked by sensors and IoT applications, tackling the job of data processing in a way no human would be able.

Optimize Mold Management with Digital Mold Solutions

AI and machine learning algorithms can be brought into play as powerful new ICT systems collect mold data rigorously, accurately, and in bulk. Such systems can and will be able to provide the kinds of descriptive, diagnostic, predictive, and prescriptive analytics you need to optimize your mold management as per the idealized standards of Industry 4.0.

The best mold solutions have exactly these values in mind. Get started with these solutions by planning with experts; understand how you might use digitalization to improve your mold management.

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