27 Jun
2023

Going Beyond the Numbers: Maximizing ROI with Data Analytics in Manufacturing

Technology has given rise to data – reams of it. In fact, in today’s digital environment there is more data available to manufacturers than in all of history combined. Yet for many manufacturers big data is a big problem.

Manufacturing Analytics
Manufacturing Data
Data Intelligence
Going Beyond the Numbers: Maximizing ROI with Data Analytics in Manufacturing
Manufacturing Analytics
Manufacturing Analytics
Manufacturing Data
Manufacturing Data
Data Intelligence
Data Intelligence
Consumer Product Goods
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Food & Beverages Processing
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Machining Center
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Material Building & Construction
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Packaging & Co-manufacturing
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Pharmaceuticals & Supplements
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Original Equipment Manufacturer (OEM)
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Technology has given rise to data – reams of it. In fact, in today’s digital environment there is more data available to manufacturers than in all of history combined. Yet for many manufacturers big data is a big problem.

The issue is not with the volume or quality of data – but rather with deriving its value. The challenge for many manufacturers lies in tapping into this information, putting it into context, and gaining direction from it. Manufacturing data is useless without the means to translate it into manufacturing analytics that support decisions.

The Big Data Dilemma

Data intelligence is derived from raw numbers. This information must be analyzed and translated into actions that impact the business. But with data accumulating faster than it can be transformed into manufacturing analytics, companies are missing opportunities.

This missing link between raw numbers and manufacturing analytics is very real and prevalent across industries. A recent report by Wakefield Research reveals that while 96% of data executives use the information to create new revenue streams, only 20% are able to use all captured data. This means that fully 80% of companies are operating partially in the dark and failing to capitalize on opportunities and investments.

Accordingto the study, 78% of those interviewed confirm that data is growing faster than their ability to derive value from it. This not only creates missed opportunities but is severely restricting ROI. Leading reasons for the data analysis problem include a lack of technical skills (43%) and poor or outdated data infrastructure (34%).

A leading cause for this inability to fully leverage data intelligence is that information is largely inaccessible. A staggering 98% of data executives say that data silos exist in their companies, and 69% report that their data is trapped and unable to be fully used. These factors are traced to the time required to retrieve the data (46%), the risk of disruption (44%), and the expense associated with accessing it (44%).

Yannick Desmarais, founder, and CEO of Worximity, says that this dilemma is blocking improvement opportunities and suppressing significant growth.

“Management must have actionable data without relying on a team of analysts to provide it. And for those overseeing multiple plants, the sheer volume of data can be overwhelming.”

Desmarais further explained that decision-makers must have the ability to project the impact of decisions across the entire company.

“Imagine that a manufacturer cuts its costs to win a large contract or to keep from losing a customer. On the surface, this might sound like a winning proposition. But all actions have consequences, and the manager must be able to accurately forecast the impact of winning or losing a contract throughout the company and how that translates into the bottom line. Without manufacturing analytics, this is impossible or subject to human error.”

From Data Acquisition to Data Intelligence

Data obtained from monitoring machines, operations, and systems provides manufacturers with the opportunity to exploit these statistics toward enhancing production. An article on indeed.com mentions that, in addition to increasing production and reducing downtime, this information can be leveraged toward improvement goals that might include improving workflows and increasing product quality.

The article calls out aggregation as aproven method to help implement manufacturing analytics. Compiling large amounts of raw information from multiple sources into a single database helps companies to analyze the data and extract insights while mitigating manual steps.

A growing number of companies taking advantage of manufacturing analytics are turning to Worximity to capture operating data from production machines, aggregate the data into a data base, then use the information to address inefficiencies, identify improvement opportunities, determine investment, and quantify the return on that investment.

Prolifik is a leading supplier of high-quality galvanized steel ventilation ducts. The company’s Director of Continuous Improvement, Veronic Chenier, leveraged analytics from Worximity’s Manufacturing Digital Performance Manager to make improvements on a production line. Once stoppages were reducedand throughput increased on that line, Chenier was able to real locate labor essentially gaining half of a resource.

For Prolifik and the 20% of companies who are fully tapping into their data, analytics are guiding decisions that are translating into measurable bottom-line improvements.

Breaking Down Your Data

It’s no secret that data is the fuel that powers business. It provides actionable insights, predicts future outcomes, drives improvement, and provides confidence for critical decision-making. What seems to be a secret is how to unlock and extract its value.

A 2022 article from World Economic Forum references a 2021 study of 1300+ manufacturing executives. The findings revealed that just 39% had successfully scaled data-driven use cases beyond the production process of a single product and thus achieved a clearly positive business case.

On the surface, deriving manufacturing analytics from raw data can appear intimidating. The key is to break the process down into smaller manageable objectives and digestible content.

Begin by answering these questions:

·     Why is this data needed?

·     What information is needed?

·     Where can we access that information?

·     With whom should that data be shared?

Worximity works with manufacturers of all types and sizes to collect, analyze and translate production data into manufacturing analytics that drive the right actions. Don’t be intimidated or overwhelmed by big data.  We’ll provide the tools and resources to guide you through the process every step of the way.  Learn just how.

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