15 Sep

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

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!

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