In Digital Manufacturing

What is the ideal operating speed for your stamping press to deliver the highest productivity?

What’s the optimum clean-in-place interval for the vats in your beverage production line?

Your operators are running an asset hard. What’s the consequence of the machine’s future health?

You have a 12% scrap rate. How do you improve that?

These are familiar manufacturing optimization challenges. While each has a unique and different solution, all would be discovered by going to the same source: massive quantities of data. Empirical information about everything happening inside production, enabling you to track and analyze every factor, parameter, and measurement, understand how they correlate and interact, and arrive at actionable insights.  

In the old days, optimization efforts were largely based on hunches and guesswork, because manufacturers didn’t have a lot of experiential data to work with. IoT and other innovations have changed that. Today’s shop floors are teeming with sensors and other devices that acquire and transmit real-time readings on every aspect of production: temperatures, pressures, tolerances, ingredient mixes, the arrival of new parts, and so on. Input is abundant. But unfortunately, answers remain few and far between. Why is that still so?

Data, Data Everywhere, but Not a Drop of Insight  

This is an exaggeration, but not by much. The quantity and diversity of manufacturing data have grown exponentially, but the ability to make it useful has not. Manufacturers lack a unified system to orchestrate enormous information inflows — that is, to take in, transform, and combine diverse data streams into a detailed matrix-like view.

This is disheartening, but it shouldn’t be surprising. Traditional business software was never built for an assignment like this. At the core, these systems are structured around workflows, not data. Think about ERP, for example.  Business logic is embedded in sequences of predetermined screens that walk users through defined processes. Data for specific cases is manually entered in designated fields, and the business logic is duly applied to it. The paradigm is well-suited for automating business processes. But for rationalizing factory data, it’s a very poor fit.

Making Manufacturing Data Make Sense: Data-Driven Software Architectures

What manufacturers need is software structured around data rather than workflows — the kind of algorithms and platforms pioneered, proven, and refined over the past decade by leaders like Google, Netflix, and Amazon. These applications bring together AI, machine learning, and massive cloud storage to ingest, aggregate, and transform all types and sources of data into a universally usable format. Then they apply machine learning and other analysis techniques to model complex dependencies and interrelationships, uncover hidden patterns, and produce actionable insights. From the ground up, these “data-first” platforms are engineered to do everything that old-school software can’t:

  • Process and reconcile a dizzying array of data types and formats in real time
  • Incorporate fast, highly scalable data-centric architectures
  • Utilize huge storage repositories that can grow dynamically as needed
  • Autonomously map incoming information streams to existing data models

These are precisely the capabilities required for the kind of “data orchestration platform” mentioned earlier, the absence of which has hobbled manufacturing analytics until now. With such a platform you can create an operational digital twin of your entire production process: an end-to-end representation of all your machines, materials, environmental inputs, and the complex relationships among them.  

The data models inside the digital twin are broadly applicable because they’re built on your entire universe of production-related data — including continuous streams of real-time input — rather than just a limited information set, narrow retrospective view, or single use case. So once you’ve configured your operational digital twin, it will scale to address all the manufacturing challenges we opened with, and just about any other.

This is how you unleash the full potential of manufacturing analytics. And it all starts with data.

To learn more about the Data First concept, read VC firm Wing’s inaugural The Wing Data-First 50.

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