How to solve problems effectively?

Problem solving can be challenging if the root cause is unknown and the problem needs to be solved right away. Everyone involved with the problem looks at it in different ways.

Listed below are some key perspectives as viewed by different people.
- The Problem Solver's view: Get to the root cause as quickly as possible and implement corrective actions to fix the problem.
- Management's view: The business is losing money or reputation because of the problem. Why is it taking so long to fix it?
- Process owner's view: We don't want to supply bad or suspect product or service to the customer, so we continue to be extra careful. Additional inspection levels, 100% inspection, quality audits are some of the ways to prevent poor quality product or service leaving the facility.

Because of the difference of opinion about the problem, most problems don't get fixed permanently. Usually the first symptom cause is considered the root cause and quickly fixed. That is why the problem re-appears after a while.

So how do we solve problems effectively?
1. Use structured problem solving method like the DMAIC approach
2. Solve problems that make business sense i.e. that will benefit the business
3. Use data to prove root cause and validate solutions!

Lean Six Sigma uses the DMAIC approach and tools for process/data analysis. DMAIC or Define-Measure-Analyze-Improve-Control are the phases of a Six Sigma project. Statistical tools at each phase of the project allow problem solvers to use process data to prove root causes and validate the solutions will fix the problem.

LSS projects focus on problems that make a business case and bring bottom line improvements.
The figure below shows key elements of an LSS project.

The clearly defined structure of LSS allows practitioners to solve problems effectively.

p, % and ppm for discrete data

Discrete or attribute data deals with counts. There are two types of attributes - defectives and defects.
Defectives are number of parts that are non conforming to a standard. Defective counts are parts that fail to meet a specific criteria set by the customer.
Defects on the other hand are non conformities or problems/issues within parts. A single part can have multiple defects i.e. the defect count can be greater than the total number of parts inspected.

It is important to understand the fundamental difference between defects and defectives because the statistics, tools are different for each. Here we will look at the stats/charts for defective data.

Defective data can be presented in several ways. There are 3 ways to calculate and chart defective data.
1. p or proportion
2. % or percent
3. ppm or parts per million

Most continuous improvement practitioners get confused between the 3 ways and this causes misinterpretation of the information presented. To clearly understand the difference, we will use sample data and perform the calculations.
For a set of 250 samples, quality inspection found 12 bad parts i.e. 12 defectives.