15 Sep
2020

Why It’s Time to Prioritize Your Manufacturing Data Collection

Manufacturing data collection is an essential part of factory productivity and improvement. Read about why you should prioritize collection.

Big Data
OEE in Manufacturing Industry
Why It’s Time to Prioritize Your Manufacturing Data Collection
Big Data
Big Data
OEE in Manufacturing Industry
OEE in Manufacturing Industry
Food & Beverages Processing
Text Link
Consumer Product Goods
Text Link
Material Building & Construction
Text Link
Original Equipment Manufacturer (OEM)
Text Link
Pharmaceuticals & Supplements
Text Link
Packaging & Co-manufacturing
Text Link
Text Link

In a factory environment, manufacturing data collection is a vital practice. Without such data collection, facility managers are “flying blind” when it comes to looking for areas in which to improve, and even when it comes to identifying problem areas in production. Without high-quality manufacturing data collection systems in place, simple tasks such as forecasting become impossible, and it is difficult to compete at the highest level by taking advantage of advanced manufacturing improvement methodologies such as lean manufacturing.

Most manufacturing businesses compete in low-margin environments, which means that optimizing production operations and managing production costs is essential. Collecting manufacturing data allows for the analysis and calculation of essential manufacturing performance metrics, including downtime, OEE, and throughput, while also calling attention to problems such as equipment malfunctions and stoppages.

 

In the short term, data collection can help to identify problems; in the long term, it can help to identify the best ways to implement continuous improvement practices within a company.

Manufacturing data collection is obviously impactful in improving manufacturing operations. So why are so few companies excelling at it? Historically, manufacturing businesses have collected data manually, through shop floor paper-based systems. This approach is slow to produce insights because data must pass through many hands and be manually processed—or, at best, processed with a spreadsheet—to produce valuable conclusions. It’s also error-prone and subject to human bias because accurate reporting is sometimes at odds with internal incentives. 

The Role of Industry 4.0

Industry 4.0 solutions, like Worximity, enable accurate, comprehensive data collection with insights available on demand through customized dashboards. Companies that implement solutions like Worximity see rapid paybacks with easy implementations. 

Unfortunately, a surprisingly small percentage of manufacturing companies are implementing Industry 4.0 data collection systems across their operations, according to McKinsey & Company’s recent report, Industry 4.0: Capturing Value at Scale in Discrete Manufacturing. The report shows that, although 68 percent of companies see Industry 4.0 as a top strategic priority, “only about 30 percent of companies are capturing value from Industry 4.0 solutions at scale today.” McKinsey & Company’s research indicates that manufacturing data collection is still a relatively small-scale endeavor for most manufacturers.

McKinsey & Company also identifies areas of focus for manufacturers to get their data collection up to the scale of most operations. It recommends that specific types of manufacturing companies turn their attention to narrowly focused value drivers first. For small-lot manufacturing companies (e.g., machine tool builders), a primary focus should be data-driven OEE optimization. Mass-customized production (e.g., automotive manufacturing) operations should focus first on high throughput and consistent product quality. High-volume manufacturing (e.g., consumer products) production should focus on automation and maximizing OEE.

McKinsey & Company recommends that you think of where value is added first, not where you can add technology first. Like Worximity, the company believes that having your people highly engaged will be essential for your success and that it is important to have a business leadership mindset, not an IT process mindset. Your manufacturing data collection infrastructure should enable local operations to deliver value before being scaled across your operations.

Methods for Manufacturing Data Collection

Facilities have several options for data collection methods. We’ll expand on two of the most common ones below.

Manual Data Collection

This method has been used extensively in the past and involves line workers or dedicated employees circulating through a facility to collect various production data. This data is given to analysts, who process the data and calculate various metrics, then report them to a supervisor. 

Unfortunately, manual data collection is quite flawed and outdated for a number of reasons. For one, this method is labor-intensive: At least two employees must work together to complete it—and more than that at larger facilities. Also, this method can be quite inaccurate because of the potential for human error to impact the data collection and calculation. 

This method is also limited to collecting fairly elementary measurements, and usually only a small amount of data points for those measurements. A final issue with this method is the fact that, by the time the data is delivered and metrics are calculated, they are likely outdated, which could lead supervisors to make decisions that do more damage to efficiency than good.

Automated Data Collection

Automated manufacturing data collection rectifies the vast majority of issues encountered when using manual data collection. When data collection is automated, large amounts of data can be collected; the data is more accurate, because human error is not an issue; the data can be reported and metrics calculated instantaneously; and this data can be presented to managers in real time. 

Using this method, when issues arise in production, managers can take action immediately to mitigate problems. The detailed data available with automated data collection also gives managers an accurate idea of where problem areas are in production, and what steps can be taken to improve efficiency. 

Manufacturing Metrics to Measure

Following the McKinsey & Company model, Worximity recommends that you focus on the following value-driving manufacturing metrics:

  • Overall equipment effectiveness
  • Throughput
  • Manufacturing cycle time
  • Time to make changeovers
  • Capacity utilization
  • Schedule or production attainment
  • Percentage planned versus emergency maintenance work orders
  • Availability
  • Yield
  • Customer rejects/returns
  • Supplier quality incoming
  • Customer fill rate, on-time delivery, and perfect order percentage

Without the metrics and data listed above, it will be impossible for a company to implement manufacturing best practices, because it will be trying to fix problems it hasn’t yet diagnosed. To improve efficiency, managers must know where to look to find problems. Collecting manufacturing data is the first step in identifying room for improvement and implementing continuous improvement practices.


The goal of every manufacturer today should be to optimize OEE. OEE is the overarching production efficiency measure that compiles a number of vital manufacturing KPIs into one objective measure of manufacturing success. With accurate and timely manufacturing data in hand, manufacturers can make better decisions that will drive OEE up and, as a result, increase business profitability.

Manufacturing has dabbled in Industry 4.0 technologies such as automated manufacturing data collection, but the industry at large is failing to take advantage of the vast opportunities in front of it, including improving the all-important OEE metric. It’s time to prioritize these efforts and move forward to realize these economic gains. 

Worximity enables manufacturers to easily implement pilot projects that are scalable across your organization. An ideal pilot project is an OEE assessment. Worximity’s 30-Day OEE Assessment offer enables you to implement a manufacturing data collection effort with a clear objective in mind that is directly related to fast ROI. Reach out and let’s get your assessment started!

Related articles

Back to the blog
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
23
Oct 2019

How To Know If You Are Receiving Reliable Data from Your Production Line

Reliable data is critical for managing production line operations. We can deliver reliable, real-time production measurement system for your operations.

English
30
Dec 2020

What’s Affecting Your Performance with Real-Time Data and KPI Tracking

Having real-time data tracking can help you further understand what is affecting your production performance.

English
19
Apr 2021

Learn How to Improve Raw Materials Yield by Connecting Your Scales and Checkweighers

Here’s how connecting your scales and checkweighers leads to a quick ROI and better control on your raw materials.

English

Related articles

Back to the blog
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
13
Mar 2024

Moteurs d’efficacité manufacturière : Monitoring des machines et du TRG

Le monitoring des machines et du TRG (taux de rendement global) sont efficaces pour améliorer l'efficacité globale, mais quelle est la différence entre eux et qu'est-ce qui est le mieux adapté à vos opérations?

French
13
Mar 2024

Engines of Manufacturing Efficiency: Machine Monitoring and OEE

Machine Monitoring and OEE (Overall Equipment Effectiveness) are effective at boosting overall efficiency, but what is the difference between them and what is best for your operations?

English
20
Feb 2024

Comment les entreprises ont mis en oeuvre les 14 points de Deming dans le secteur manufacturier

Les 14 points de gestion de Deming ainsi que la suite d'outils de performance de Worximity stimulent l'amélioration et l'innovation dans le secteur manufacturier.

French
16
Feb 2024

Principales différences entre la fabrication discrète et la production par processus

Découvrez le rôle essentiel que joue votre logiciel dans la fabrication discrète et dans la production par processus.

French