What is The Shainin System?
The Shainin System™ (SS) is defined as a problem-solving system designed for medium- to high-volume processes where data are cheaply available, statistical methods are widely used, and intervention into the process is difficult. It has been mostly applied in parts and assembly operations facilities.
The Shainin System, developed by Dorian Shainin, is a structured method for solving complex problems. Technical problems are addressed using Red X Problem Solving to drill down to the hidden source of the problem. Business process problems are addressed using TransaXional, a function-based approach to reveal where the process is breaking down. Red X Problem Solving recognizes that while variation in system output could come from a long list of potential sources, including interactions, the Pareto principle means that one source is stronger than all the others.
That’s the Red X.
Red X Problem Solving combines engineering insight with statistical thinking and follows a roadmap called FACTUAL.
Focus is a management function to provide the problem-solving team with a well-defined project statement.
Approach evaluates strategies based on the nature of the problem. There are standard strategies for:
- Features: problems related to part size or shape.
- Properties: problems related to maintaining material properties.
- Defects: problems related to localized imperfections.
- Malfunction events: performance problems.
- Destructive events: parts that have been damaged or broken.
Converge executes the strategies by applying statistical tools in a series of tactical cycles where each cycle eliminates portions of the system that cannot contain the Red X. Each cycle requires disciplined planning, execution, analysis, and documentation that leads to the next tactical cycle depending on the outcome.
Test confirms the identity of the root cause. This is often done with the application of a simple statistical test to demonstrate that the problem can be turned on or off by controlling the input level of the Red X.
Understand captures the relationship between the source of the problem and the system output and establishes a tolerance on the Red X that accounts for the range of product performance that will result in customer enthusiasm and the variation coming from all other system factors.
Apply uses the knowledge captured and an understanding of engineering alternatives to develop and implement a sustainable corrective action. This phase requires updating
documentation of standard work and may involve a statistical test to confirm the effectiveness of the proposed corrective action.
Leverage extends the project value by: providing insights gained to engineering for consideration in future product and process designs; applying the investigative strategies to similar technical problems; and applying the lessons learned to other processes.
Red X Problem Solving can be applied from the point in product development where samples are available, through product launch and into ongoing production. It can be applied to problems in the supply chain, system assembly, field performance and product reliability.
TransaXional is applied to business process problem solving and optimization. It is consistent with Shainin principles of converging on the source of a problem using simple tools.
TransaXional follows a roadmap called DETAIL
Define Project is a management function to provide the problemsolving team with a focused project statement.
Execute Function Model is an analysis of the system functions that must be performed correctly to achieve the system goals.
Talk to the Occurrences investigates failed transactions to understand which functions were not performed properly.
Assign Priorities identifies foundational function failures and establishes the order to address corrective actions.
Identify Actions defines process improvements and demonstrates improved system performance.
Leverage Results ensures that new knowledge has been communicated across the organization and procedural
documentation has been updated.
TransaXional can be applied to logistics, inventory management, system implementation, purchasing, product development, and finance.
Shainin System™ Resources
Statistical Engineering: Six Decades Of Improved Process And Systems Performance (Quality Engineering) This article describes the existing Shainin-defined statistical engineering discipline and evaluates its effectiveness based on the standards proposed by Snee and Hoerl. The methods are illustrated with an investigation of a performance problem in a complex electromechanical system.
Diagnostic Quality Problem Solving: A Conceptual Framework And Six Strategies (Quality Management Journal) This paper contributes a conceptual framework for the generic process of diagnosis in quality problem solving by identifying its activities and how they are related. It then presents six strategies that structure the diagnostic process by suggesting a certain sequence of actions and techniques.
Lighting The Way (Six Sigma Forum Magazine) Six Sigma is a tool that can aid in process improvement using statistical methods and design of experiments (DoE). One such DoE method is the Shainin method, which breaks down process variation to three or four causing factors. In a case study of the manufacturing of compact fluorescent lamps, both Six Sigma and the Shainin method are used to determine process capabilities and reduce process variation.
Update provided by Richard D. Shainin - www.shainin.com