Episode Overview
“Show me that you were able to get the right data at the time in enough time to fix it.”
Dick Willis is a Industrial Digitization Leader at Trane Technologies. He is a skilled veteran in the digitized factory industry and an expert in data collection.
In this episode of Zen and the Art of Manufacturing Podcast, Bryan Sapot and Dick sit down to talk about how and when manufacturers should move to automatic data collection. A solid understanding of collecting and reporting data is essential in manufacturing, but data collection is hard. And, it’s hard to do it well.
There are quite a few hurdles to getting that structure just right. Two of those hurdles are a reliance on pen and paper using Excel reporting methods and meaningless measurements. Controversial, right? These are barriers to efficient problem solving and valuable insight. In this episode of Zen and the Art of Manufacturing, learn when the best time to move to automatic data collection is and what that looks like for your plant.
Key Takeaways
Originally broadcast on March 30, 2021
The focus of today’s discussion is on the role of technology in continuous improvement in manufacturing. While AI can be useful for identifying obscure patterns, most tasks in manufacturing are human-driven and require quick responses to changes. Many companies have historically used paper, Excel, and whiteboards to drive improvement, but there comes a point when these methods may no longer be sufficient.
Manual vs. Automatic Data Collection
Moving away from manual tracking methods like paper and whiteboards to more real-time systems requires a shift in culture and leadership. Centralizing data and implementing accountability systems are crucial for effective problem-solving and decision-making. The transition to electronic systems should be driven by the need for quicker responses and more reliable data.
Many companies have historically had success managing the factory floor using paper, Excel and white boards. The challenge is what to do next when the company to improve their process. Having an automated system to detect a problem can be faster and more reliable than asking someone to watch for problems on top of their other duties.
Should production data start with daily or hourly updates? It depends on how fast the engineering department is ready to respond. If they can respond immediately to changing conditions, then manufacturers should just straight to hourly data updates. “If you’re looking at a system once a day, then that is as fast of you can respond. But if you are looking at the system once an hour then you might be able to spot a pattern.”
Cost-Benefit Analysis
Deciding what to measure automatically versus manually involves a cost-benefit analysis. While machines can provide data on faults and performance, operators often have valuable insights that machines may not capture accurately. Retrofit kits and PLC data access can enhance data collection, but the accuracy and cost-effectiveness of these solutions must be carefully evaluated.
The challenge lies in validating data collected from machines and operators. While machines can provide fault codes, operators may have a deeper understanding of the root causes of issues. Balancing the use of machine-generated data with operator input is essential for accurate and actionable insights. Experimentation with different data collection methods is ongoing to determine the most cost-effective and reliable approach.
What are we actually solving? How many times did we think we had a problem and we didn’t? That’s part of the maturity model. It’s not just about fixing problems, but also about identifying the right problems to fix.
How to evaluate when it’s time to move away from manual reporting
1. How often is the manual system looked at?
Evaluate how well the written system is working. Ask operators to write down detailed notes for one week, then ask how often a supervisor responded to one of those notes. If the response was more than 3 days, then something needs to change and a centralized system is needed to aggregate the performance data and feedback from the operators.
2. Is the data being used in the daily production meetings?
Evaluate how reliably information from the operators is bubbling up to the supervisors during the daily production meeting. The data should be pulled up for discussion so problems can be solved. “OEE isn’t worth as much if you don’t have an accountability system to go with it.”
3. Is the culture and leadership set up to promote data tracking?
The leadership is responsible for setting up a culture where operators submit notes, those notes are looked at on a frequent basis, and responded to in a timely manner. “That accountability system can be paper, but frequently once you get the Pareto into a centralized system, you want to have an accountability system that goes along with it that interacts with that and that makes it easy.”
Gaining Operator Buy-In
The operators are the first line of defense on the factory floor. “They’re going to notice something is wrong quicker than a system will since they’ve been doing this for long enough that they can hear the machines acting up.” With a traditional manual reporting system, this information might be passed along to a supervisor and noted on the board for review tomorrow. But that’s an additional step as they are trying to do their job and clear the jam. Stepping away to find the right person and tell them the right thing is counterproductive to meeting their production goals.
It’s important to consider the operator’s perspective when implementing data entry requirements. Operators are judged on their ability to make parts and hitting their numbers, so adding additional data entry tasks can be frustrating. However, it’s crucial to communicate the benefits of providing this data for problem-solving and improving throughput. Operators need to understand that by entering data, they are enabling the team to fix issues and make their work easier in the long run.
One challenge faced in data entry is ensuring the accuracy of the information provided. For example, if an operator is asked to explain why they didn’t hit a certain target, they may provide a reason even if there was nothing wrong with the machine. This highlights the importance of setting accurate targets and metrics to avoid misleading data. It’s essential to align data entry requirements with the actual goals of improving performance and addressing real issues.
Implementing OEE
When it comes to OEE metrics, there is a need to differentiate between bean counter OEE, which focuses on running machines continuously at maximum speed, and a more practical approach that considers scheduled OEE. By aligning OEE metrics with actual machine capabilities and production schedules, operators can better understand their performance and work towards improving efficiency. This approach also allows for a more realistic assessment of hidden capacity and operational improvements.
Implementing an effective OEE system involves not just tracking metrics, but also using the data to drive problem-solving and decision-making. By focusing on scheduled OEE and setting achievable targets, operators can monitor their progress, adjust their performance, and work towards meeting production goals. This approach provides a clear framework for operators to understand their performance and make informed decisions to improve efficiency.
Building Continuous Improvement Into The Company Culture
In order to drive continuous improvement and address issues effectively, it’s essential to establish a structured problem-solving process. This includes tracking downtime, identifying root causes, and ensuring that fixes are implemented and monitored. By integrating an issue management system and aligning it with maintenance practices, teams can ensure that problems are addressed promptly and effectively. This approach helps in maintaining accountability and driving a culture of problem-solving and improvement.
The Maturity Model
The maturity model serves as a framework for assessing progress in human capital development, system implementation, and process improvement. By evaluating factors such as reaction time to problems, data quality, and problem resolution, organizations can gauge their level of maturity in addressing issues and driving improvements. The model focuses on identifying potential areas for enhancement and guiding investments in people, systems, and processes to achieve better outcomes.
Ultimately, the goal of the maturity model is to enable organizations to maximize their potential for improvement by focusing on key areas such as data quality, problem resolution, and process efficiency. By continuously assessing and refining their practices, teams can drive cultural change, enhance problem-solving capabilities, and achieve better operational outcomes. The model provides a structured approach to measuring progress and guiding investments in areas that will yield the greatest benefits.
The process of mapping out the steps through the model involves identifying the need for specific data, such as false positives and false negatives, to ensure the accuracy of the signals being received. This process requires a team effort, including shop floor team leaders, engineers, maintenance personnel, and operational technology experts, to manage and implement the necessary changes.
Trusting the Data
Implementing software tools can greatly benefit smaller companies by simplifying data collection and analysis. These tools can automate deviation detection and provide real-time progress updates to operators, allowing for more efficient decision-making and problem-solving. While automation can reduce the need for manual intervention, it is still essential to have individuals with the necessary skills to interpret and act on the data.
Maintaining trust in the data collected is crucial for the success of any system. Identifying and eliminating false signals, even if it requires additional effort, is necessary to ensure the reliability of the information being used for decision-making. By involving a diverse team in this process, companies can create a culture of accountability and continuous improvement.
The Value of Your People
The importance of respecting people’s time and being punctual was a valuable lesson learned from childhood. This principle of being early to be on time reflects a deeper respect for others and their schedules. Additionally, recommendations for books like “The Prophet” and “Let’s Get Real” highlight the importance of spiritual and business growth through meaningful conversations and practical advice.
The discussion emphasizes the significance of focusing on actionable data and avoiding unnecessary data collection. While initial data gathering may be broad to identify key metrics, refining the data collection process over time is essential for efficiency and effectiveness. Prioritizing data that directly impacts decision-making and problem-solving is key to driving continuous improvement.
The conversation also touched on the interconnected nature of various methodologies like OEE, lean, and theory of constraints. While some may argue for strict adherence to specific methodologies, the speaker emphasizes the importance of flexibility and adaptability in problem-solving. Ultimately, the goal is to motivate people, solve problems efficiently, and drive continuous improvement through a combination of tools and approaches.
The role of industrial digitization in improving manufacturing processes is highlighted, with a focus on coaching teams to enhance efficiency and flow. The speaker’s portfolio includes working on digitization initiatives beyond OEE, indicating a broader scope of projects aimed at optimizing operations. By leveraging digital tools and strategies, companies can streamline processes and drive innovation in manufacturing.
Connect with Dick Willis on LinkedIn.
