Profound Book Club August 11, 2023 (Morning Session)
Summary of the Book Club Discussion
Here are a few key points from the conversation:
They discussed the Hawthorne experiments at the Hawthorne Works and how it may have influenced Deming's thinking. Hawthorne was an early example of trying to improve factory conditions and create a better community for workers outside of work.
Deming likely learned from the statistical control techniques used at Hawthorne, pioneered by Walter Shewhart. Control charts help distinguish between common cause and special cause variation.
There was some confusion clarified around Shewhart's terminology of "chance" vs "assignable" causes of variation, which Deming renamed to "common" and "special" causes. Common cause variation comes from the system, while special causes are anomalies to investigate.
Understanding patterns in data is key rather than just reacting to outliers. Control charts make the patterns visible and help determine when a process is stable or becoming unstable.
The overall emphasis was on appreciating the history of ideas influencing Deming rather than viewing him as a lone genius. The group shared stories and insights about how statistical thinking developed and why it matters.
FAQs from this morning's conversation
What was the main topic discussed? The main topic was W. Edwards Deming and concepts like the Hawthorne experiments, statistical control, and understanding variation.
What is the Hawthorne effect? The Hawthorne effect refers to how worker behavior and productivity can change when they know they are being studied.
Who conducted experiments at the Hawthorne Works? Walter Shewhart and others conducted experiments and pioneered statistical quality control techniques there.
What are two types of variation Shewhart identified? He called them chance cause and assignable cause variation. Deming later renamed their common cause and special cause.
What is special cause variation? Special cause variation refers to anomalies or events outside the normal pattern.
What do control charts help determine? Control charts help determine if a process is stable or becoming unstable by revealing patterns in the data.
What was Botchagalup's key quote about variation? "Misunderstanding variation is the root of all evil."
What was the overall emphasis of the discussion? Appreciating the history of ideas and people like Shewhart that influenced Deming's thinking on statistical quality control.
Here is a more detailed summary of this morning’s conversation
The group discussed W. Edwards Deming and his influences, focusing on concepts originating from the Hawthorne Works experiments in the 1920s-1930s. Walter Shewhart worked at Hawthorne and pioneered statistical quality control techniques like control charts to distinguish between chance cause and assignable cause variation.
Deming later renamed chance cause as "common cause" - routine, inherent variation from the system itself. Assignable cause became "special cause" - anomalies or events outside the normal pattern. Understanding these differences and the patterns revealed in data is crucial. As Deming said, "Misunderstanding variation is the root of all evil."
The conversation explored how the innovative employee programs at Hawthorne aimed to improve work life and retain workers. This likely exposed Deming to early ideas about company culture. Hawthorne also allowed Shewhart to develop techniques like control charts using actual production data.
Control charts visualize data over time to detect stability or instability in processes. The group discussed how patterns within the control limits reveal issues like the increased variation that prompt investigation into root causes. Examples like merging data from multiple sites can alter variation.
Overall, the emphasis was on Deming as a synthesizer of ideas, not a lone genius. The participants shared insights into the evolution of statistical thinking, including stories about Deming's work in Japan. They see understanding history as an essential context for Deming's profound knowledge and systems view.
The key lessons were about appreciating the interplay of ideas from pragmatism, empiricism, and other streams that influenced Shewhart, Deming, and the creation of statistical quality control. This foundation allowed Japan to rebuild after WWII and transform management thinking.
Hawthorne Works
Hawthorne Works, a Western Electric manufacturing facility in Cicero, Illinois, operated in the early 20th century. Some key facts about Hawthorne Works:
It opened in 1905 initially as a warehouse for Western Electric, which was the manufacturing arm of the Bell Telephone Company.
It expanded over the years into a large manufacturing complex that employed tens of thousands of workers to produce telephone equipment and parts.
Hawthorne became well known as the site of a series of experiments on workers that took place between 1924-1932.
The Hawthorne experiments studied how changes like lighting and break times impacted worker productivity. This research gave rise to the "Hawthorne effect."
The studies found that worker productivity seemed to improve when any change was made, suggesting their performance was influenced simply by the fact they were being observed and studied.
Walter Shewhart pioneered statistical quality control techniques at Hawthorne in the 1920s-30s using data from the factory. His work laid the foundations for the field.
Hawthorne provided early insights into industrial labor relations and influenced management thinking about the workplace environment, employee communication, and other social factors.
The facility gradually declined after WWII as manufacturing decentralized and eventually closed in the 1980s. But its legacy continued to impact the evolution of quality control.
Walter Shewhart
Walter Shewhart (1891–1967) was an American physicist, engineer, and statistician often called the father of statistical quality control. He is best known for his groundbreaking work in quality management and statistical process control (SPC). Shewhart's ideas laid the foundation for modern concepts of quality assurance and process improvement that are widely used in industries today.
Shewhart introduced the concept of control charts as a tool to monitor and maintain the stability of manufacturing processes. Control charts allow organizations to identify process variations and take corrective actions to prevent defects and improve overall quality. His work emphasized the importance of understanding and controlling sources of variation in production processes, leading to better product consistency and reliability.
In addition to his work on control charts, Shewhart contributed significantly to developing statistical methods and their application to industrial processes. His ideas profoundly influenced the field of quality management, and his work became the basis for quality improvement methodologies like Lean.
Walter Shewhart's contributions have had a lasting impact on various industries, helping them achieve higher levels of efficiency, consistency, and quality in their products and processes.
