Holistic Thinking In Management

Trends Towards Holistic Thinking In Management

Trends towards holistic thinking in "QUALITY MANAGEMENT"

(in Manufacturing Sector)

Quality as a concept has been widely used for the improvement in the performance of organizations. In its initial stages it was applied only to the manufacturing sector, but subsequently it spread to the services and other sectors. Over the years the definition of quality has been revised from being applied only to products; subsequently quality initiatives have evolved to encompass focus on customer satisfaction, continuous improvement, people involvement, empowerment of the employees, team work, data-driven decision making etc.

This study shall start by taking a look at the history of the quality initiatives and milestones over the years. There is ample evidence of attention to quality in the pre-industrial revolution era, as evinced in the legacy of the Egyptian civilization and other civilizations of that age. But it was the industrial revolution which brought into prominence "Quality" in managerial thought.

We begin with Eli Whitney's invention of technique of producing interchangeable parts as the first recorded initiative in quality management.

1798: Eli Whitney, Mass Production and Interchangeable Parts

Best known for his invention of the cotton gin in 1787, Eli Whitney had a greater impact on modern manufacturing with the introduction of his revolutionary uniformity system. In 1798, Whitney was awarded a government contract to produce 10,000 muskets. He proved it was possible to produce interchangeable parts that were similar enough in fit and function to allow for random selection of parts in the assembly of the muskets. Throughout the next century, quality involved defining ways to objectively verify the new parts would match the original parts or design. Exact replication was not always necessary, practical, cost effective or measurable. Objective methods of measuring and assuring dimensional consistency evolved in the mid-1800s with the introduction and use of go gages that verified the minimum dimension of the new part. Correct replication of the maximum dimension was assured by using the no go gages that were introduced about 30 years later. Minimum and maximum tolerance limits, as measured by the use of these gages, provided some of the first objective measures of similarity among parts. These measures eventually evolved into specifications.

1913: Henry Ford and the Moving Assembly Line

With the introduction of Henry Ford's moving automobile assembly line in 1913, the need for redetermined part consistency became more acute. It was critical that only good parts be available for use so the production assembly line would not be forced to slow down or stop while a worker sorted through piles of parts to find one that fit. With the Industrial Revolution in full swing, ever increasing production volumes required different methods of testing and assurance to provide reasonable certainty the new product would be similar to the original product or design. It was no longer practical to test each piece against go and no go gages. Such testing was cost prohibitive, unacceptably time consuming and, in some cases, impossible, especially if the test adversely affected the functionality of the output. Therefore, methods to monitor the consistency of the process that produced the parts and the use of sampling, rather than 100% inspection, were becoming necessities.

1924: Walter Shewhart and Control Charts

The Western Electric manufacturing plant in Hawthorne, Illinois, is noteworthy because it was the breeding ground for many quality leaders, including Joseph M. Juran, W. Edwards Deming and Walter A.Shewhart. Shewhart introduced a new data collection, display and analysis form. It contained the first known example of a process control chart and signaled the beginning of the age of statistical quality control. The original control chart form allowed an inspector to document the percentage of defective product in both a tabular and a time ordered graphic format. As data collection progressed, statistically computed limits were drawn to identify the expected range of defective products. This helped alert the operator to changes in the process. The ability to use statistically based control charts changed the role of the quality inspector from one of identifying and sorting defective product to one of monitoring the stability of the process and identifying when it had changed. Early detection by the inspector or worker helped identify the causes of the change and target improvements. Improved product quality resulted through planning and timely, appropriate corrective action. As production lots grew larger and more complex throughout the remainder of the 1920s and 1930s, the need for sophisticated quality assurance and control gave birth to large quality control functions. Quality control departments came to include inspectors, chief inspectors, supervisors, engineers and managers. The use of statistics grew, and in 1950, the U.S. government required statistically based levels of product quality from its vendors. When World War II ended, consumer affluence in the United States provided constantly increasing demand. Fortunately, consumers had a tolerance for marginal quality. They readily absorbed the additional cost of inspection and sorting, thereby allowing manufacturing operations to continue to focus on volume and output without a need to focus on quality improvement or cost reduction.

1945: The Japanese Quality Movement Begins

Japan was crippled by the World War II. For supporting Japanese industries in rebuilding U.S sent Deming, learned statistician and Homer Sarasohn from M.I.T. Deming reinforced the value of viewing data against computed statistics to quantify variation and predict future process performance. This allowed timely identification of the sources of problems and promoted the opportunity for continuous improvement. Throughout the years, Deming promoted the use of the plan-do-check-act (PDCA) cycle of continuous improvement and later changed it to the plan-do-study- act or PDSA cycle. The level of quality awareness and the use of statistical methods grew rapidly, but the statisticians became isolated and were seen as a separate layer of experts. Managers weren't able to dedicate the time or effort to fully understand the statistical theories and applications, and the operators were afraid of the statisticians, in part, because they feared measuring devices

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