From chaos to clarity: How Danfoss automated data collection and improved quality with Q-DAS
Adam Břicháček, Application Engineer, Hexagon’s Manufacturing Intelligence division
Driving efficiency in manufacturing: Danfoss's journey with Q-DAS automation
Dive into how Danfoss successfully optimised and automated its data collection and quality evaluation process with Hexagon's Q-DAS solutions, transforming the company's quality loops and significantly enhancing its manufacturing operations.
Founded in 1933 by Mads Clausen in Nordborg, Denmark, Danfoss has become a global leader in innovative, energy-efficient solutions. Their focus areas include refrigeration, air conditioning, heating, and more, all of which target increased machine productivity, reduced emissions, and lower energy consumption.
Danfoss engineers don’t just develop these solutions; they apply them across a wide range of industries. This includes industrial machinery, automotive, marine, and off- and onhighway equipment, as well as advancements in renewable energy like solar and wind power. They’re also involved in district-energy infrastructure for cities.
With a rich history of innovation dating back to its founding, Danfoss remains a family- and foundation-owned company. Today, it employs over 42,000 people and serves customers in over 100 countries through a global network of 95 factories.
Some manufacturers try to sidestep the issue by manually tracking measurement data on Excel spreadsheets and via other paper-based methods. However, this approach is time-consuming, difficult to share and collaborate on, and introduces concerns about data accuracy. Moreover, other application-based solutions have their own limitations.
Additionally, previous manual methods don’t provide a secure, statistically reliable way to work with the data, increasing the opportunity for data loss or employees inadvertently recording inaccurate data results. Ultimately, employees must spend hours each day collecting and processing data and then making adjustments based on their analysis.
Engineering solutions provider Danfoss faced similar challenges. The company had numerous systems providing data but needed a way for process and quality engineers (and management) to collect and evaluate that data.
“Many of our systems weren’t integrated, making it difficult and time-consuming for us to evaluate effectively,” explained Robert Knoll, Design & Deliver Group, Delivering Manager (IT) at Danfoss. “We lacked statistical process control (SPC) tools. We wanted to automate this data collection and enable more robust reporting solutions so we could improve internal and external quality.”
With Q-DAS, Danfoss has been able to standardise its configuration and catalogues across the company and plants, building a process encompassing everything from the measurement request to data evaluation. The company used many of Q-DAS’s standard settings and customised several K-fields used by dynamic data filters for reporting.
“Using Q-DAS has improved our statistical process control and measurement system analysis, helping audits go much more smoothly,” said Knoll. “One of the most common advantages is replacing the reactive managerial module with a predictive one in terms of data visualisation and following investigation of the trend facts.”
Danfoss has appreciated Hexagon’s support. “Sometimes, we need something unique for a specific client — like a customised catalogue and special settings. In this situation, we connect with Hexagon’s support team and get their assistance in creating the necessary catalogues. And because Hexagon offers local support in our area, they can react quickly — and with no language barrier. Hexagon’s support has been excellent — if we have a critical question or issue, they often get us the solution the next work day. It also saves me time and allows me to handle other critical IT tasks.” Knoll explained.
The team has been able to expand the utility of the Q-DAS solution by adding custom utilities. “I can write utilities or programmes we can use with devices that don’t directly support Q-DAS. Because Q-DAS is supplier agnostic, it can support other suppliers’ equipment — not just Hexagon’s. We export data from those systems to Q-DAS and can monitor all of the systems on our shop floor — not just the machining area, but also in the assembly line and testing,” Knoll said.
Danfoss engineers don’t just develop these solutions; they apply them across a wide range of industries. This includes industrial machinery, automotive, marine, and off- and onhighway equipment, as well as advancements in renewable energy like solar and wind power. They’re also involved in district-energy infrastructure for cities.
With a rich history of innovation dating back to its founding, Danfoss remains a family- and foundation-owned company. Today, it employs over 42,000 people and serves customers in over 100 countries through a global network of 95 factories.
The challenge: Siloed data hinders quality control
In many manufacturing organisations, engineering, production, and quality assurance departments work together with the common goal of making high-quality products. But how do these departments effectively close the data quality loop when data is locked into a specific machine or device, making the process of capturing, sharing, and acting on that data difficult, if not impossible?Some manufacturers try to sidestep the issue by manually tracking measurement data on Excel spreadsheets and via other paper-based methods. However, this approach is time-consuming, difficult to share and collaborate on, and introduces concerns about data accuracy. Moreover, other application-based solutions have their own limitations.
Additionally, previous manual methods don’t provide a secure, statistically reliable way to work with the data, increasing the opportunity for data loss or employees inadvertently recording inaccurate data results. Ultimately, employees must spend hours each day collecting and processing data and then making adjustments based on their analysis.
Engineering solutions provider Danfoss faced similar challenges. The company had numerous systems providing data but needed a way for process and quality engineers (and management) to collect and evaluate that data.
“Many of our systems weren’t integrated, making it difficult and time-consuming for us to evaluate effectively,” explained Robert Knoll, Design & Deliver Group, Delivering Manager (IT) at Danfoss. “We lacked statistical process control (SPC) tools. We wanted to automate this data collection and enable more robust reporting solutions so we could improve internal and external quality.”
The Q-DAS solution: Streamlining data collection and analysis
Knoll and his team explored potential solutions, ultimately deciding to adopt Q-DAS. “We were able to more or less perform a self-installation and setup when we originally deployed Q-DAS back in 2005 — it was a really easy installation,” said Knoll. Based on the lessons learned so far, the team feels competitive regarding improvement activities and proposals as part of the process approach.With Q-DAS, Danfoss has been able to standardise its configuration and catalogues across the company and plants, building a process encompassing everything from the measurement request to data evaluation. The company used many of Q-DAS’s standard settings and customised several K-fields used by dynamic data filters for reporting.
“Using Q-DAS has improved our statistical process control and measurement system analysis, helping audits go much more smoothly,” said Knoll. “One of the most common advantages is replacing the reactive managerial module with a predictive one in terms of data visualisation and following investigation of the trend facts.”
Danfoss has appreciated Hexagon’s support. “Sometimes, we need something unique for a specific client — like a customised catalogue and special settings. In this situation, we connect with Hexagon’s support team and get their assistance in creating the necessary catalogues. And because Hexagon offers local support in our area, they can react quickly — and with no language barrier. Hexagon’s support has been excellent — if we have a critical question or issue, they often get us the solution the next work day. It also saves me time and allows me to handle other critical IT tasks.” Knoll explained.
The team has been able to expand the utility of the Q-DAS solution by adding custom utilities. “I can write utilities or programmes we can use with devices that don’t directly support Q-DAS. Because Q-DAS is supplier agnostic, it can support other suppliers’ equipment — not just Hexagon’s. We export data from those systems to Q-DAS and can monitor all of the systems on our shop floor — not just the machining area, but also in the assembly line and testing,” Knoll said.
Figure 1: Danfoss’ operations feature seven distinct but interrelated quality loops, each with a different purpose and goal.
Beyond automation: Building quality loops
Q-DAS has helped Danfoss create an integrated system of quality loops. These different quality loops allow Danfoss to leverage its data differently for other processes throughout the organisation.Quality loop 1: Metrology lab
The first loop is in the metrology laboratory. CMM operators measure the pieces coming from the production according to priority and reason for measurement. After measuring each piece, they can see its result and historical data in the Q-DAS module’s O-QIS MCA/CMM reporting. The operator can decide to accept or reject the measurement, typically only rejecting when they find a problem during measurement. After they accept the measurement, the data may move in several different directions. For example, in the case of “standard” or “setup” measurement, the data proceeds to quality loop 2. In the case of a special measurement reason – like measurement system analysis (MSA) or production part approval process (PPAP) – the data goes straight to centralised upload processing in preparation for loop number 4.
Quality loop 2: Production
In production, personnel have a computer—enabled with an O-QIS MCA/CMM reporting module—next to the CNC machines. The CMM reporting module waits for the measured data from the metrology lab, and once it arrives, the machine operator can see the results and some historical data. If something is “wrong” in the process, the screen displays alarms, alerting the operator to decide if and what intervention is necessary. This feature allows operators to react immediately to potential process problems without unnecessary delays. Then, data continues to the central database to be saved.
Quality loop 3: Production+Metrology
The third quality loop also takes place in production, but no one is waiting for the metrology lab’s results. Pieces are measured on local measurement stations with hand gauges, and each station has a computer with a Q-DAS module and an O-QIS procella. In the procella, operators can load the test plan and perform measurements according to the instructions and pictures.
Similar to MCA/CMM reporting, operators can see historical data and receive alarms in the case of process problems like stability or control limit violations. This context allows operators to react quickly to process changes and problems. Similar to the previous loops, measurement data is directly saved in the central database.
Quality loop 4: Engineer measurement request
The fourth loop begins with an engineer making a measurement request—typically for an MSA or PPAP. Once the measurement is done, it goes to the central database for analysis. The engineer can open the measurements based on filters and analyse anything they need.
Quality loop 5: Assembly + special test strands
The fifth loop occurs in the assembly area equipped with special test stands, with testing occurring on parts of the finished product. In quality loop 5, pressure, temperature, and other special characteristics are measured, and results are automatically converted to Q-DAS format and sent to the central database for later evaluation.
Quality loop 6: Organisation
Though not currently in place, the sixth loop will eventually encompass the organisation. Nowadays, vision, asap reality, hopefully. M-QIS reporting will be installed on the server module, and thanks to this, it will be able to create and distribute reports automatically based on the data saved in the central database. These reports will be issued periodically, and each will have different recipients. For example, process engineers will receive e-mails every Monday morning with reports from the processes for which they are responsible.
The report will only visualise the previous week, evaluating it from a process capability point of view, with the “worst” results shown at the top of the report. Similarly, the production manager will receive a report every Friday afternoon. This report will display overall results across the production process, highlighting the ratio between in-tolerance and out-of-tolerance measurements for each machine.
Quality loop 7: Organisation + PC qs-STAT modules
The last loop encompasses the entire organisation and includes qs-STAT modules installed on numerous PCs. Quality and process engineers can load data onto these PCs from the central database and then make any desired evaluations and special reports that M-QIS doesn’t handle.
Results: Fully automated data collection and evaluation
Working with Q-DAS, Danfoss has fully automated its data collection and evaluation — meaning everyone can work with the results, but no one has to work to get the results. Its quality loops enhance the company’s data security, providing near real-time results across the process and allowing its engineers and operators to make fast, informed decisions on addressing process problems. That leads to significant time savings, optimises processes over time, and leads to less scrap.
With Q-DAS and quality loops in place, Danfoss can leverage its data as much as possible, with minimal effort required from staff. “We collect thousands of data per day—and it would be impossible for us to capture, record, analyse, and evaluate it manually. With Q-DAS running everything automatically, we can perform measurements, analysis, and process adjustments in moments. Q-DAS has allowed us to reduce the number of conflicts in the system,” Knoll concluded.
