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Why Data Comes First in Manufacturing Analytics

Manufacturers are taking huge advantage of advanced analytics and are reducing process flaws, thus saving time and money.

Data analytics

In the previous 20 years, many manufacturers have been able to reduce waste and variability in their production processes and hugely impacted product quality and its yield by utilizing lean and Six Sigma programs.

However, in certain environments - pharma, chemicals or mining for instance - huge swings in variability are happening day to day, even if lean techniques are applied. Given the number and complexity of production activities that influence the output in these and other industries, manufacturers need a more granular perspective to diagnose and correct process flaws.

Advanced data analytics provides just that.

Data everywhere, but not a drop of insight

This well may be an exaggeration, but not by far. The quantity and diversity of manufacturing data have grown exponentially through time, but the ability to make it useful on the other hand has not. Manufacturers are lacking a unified system to organize enormous information inflows - that is, to transform and combine diverse data streams into a detailed similar to matrix-like view.

This is sadly disheartening, but it shouldn't come as a surprise. Traditional business software was never created for an assignment like this. At the foundation, these systems are structured around workflows, not the actual data.

Let's think about ERP, for instance. Business logic is built-in sequences of predetermined screens that guide users through defined processes. Data for specific situations is manually entered in designated fields, and the logic of the business is duly applied to it. The paradigm is suited excellently for automated business processes. But for rationalizing factory data, it's a lacking fit.

Data-Driven Software Architectures

What most of the manufacturers need is a software that is structured around data rather than workflows and utilize real-time manufacturing analytics. Bringing together AI, machine learning and massive cloud storage to safeguard and transform all types of data into a universally usable format. Then they apply machine learning and other technical analysis techniques to create complex dependencies and interrelationships, uncover covered patterns and produce meaningful insights. From the bottom up, these "data-first" platforms are designed to do everything that old-school software can't:

  • Process a confusing array of data types and formats in real-time.
  • Incorporate quick, highly scalable data-centered architectures.
  • Utilize massive storage repositories that are able to grow dynamically as needed.
  • Autonomously map incoming data streams to existing models.

These are the precise capabilities that are mandatory for the kind of "data orchestration platform" referred earlier, the absence of which has confused manufacturing analytics until now.

With such a platform you can create an operation digital twin of your complete production process.

The data models inside the digital twin are broadly applicable because they're created on your entire universe of production-related data including multiple streams of real-time input - rather than just poor information set or single-use case.

Capitalize on big data

The crucial first step for manufacturers that want to use advanced analytics to boost yield is to consider how much data the company has on its hands. Most companies collect huge amounts of process data but typically use them only for tracking purposes, not as a foundation for improving operations.

For these players a big challenge is to invest in the systems and skill sets that will allow them to optimize their usage of existing process information - for example, centralizing or indexing data from various sources so they can be looked closely more easily or hiring data analyst who is trained in spotting patterns and drawing actionable insights from given information.


The big data era has only just emerged, but the practices of advanced analytics are chained in years of mathematical research. It can be a crucial tool for realizing improvements in yield, particularly in any manufacturing surroundings in which process complexity and its variability are present.

Yes, companies that successfully create their capabilities in conducting quantitative assessments can set themselves apart from other competitors.

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

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