Intro to Basic QA Statistics Series (Part 1)
REFLECTION: FOR STUDENTS: Don’t throw your stat’s books away. You will use them one day, and unlike most other textbooks, old statistics textbooks are usually just as valid as they were years ago.
FOR ACADEMICS: Yes, teach your students to use R and Minitab, but be sure they can do the calculations by hand
FOR PROFESSIONALS/PRACTITIONERS: I know you likely are old hands at this, but thank you for any advice you can provide those entering the QA field who have questions. Stats sometimes seems mystifying, so be sure to share the knowledge with mentorship.
Foundation
Most in the industrial world understand that tracking and analysis of data is a requirement to know if a process is producing waste or if your process is stable and everything is running as it should. The problem that arises from time to time is the “craftsman attitude” of a process owner, or perhaps even that same attitude in a plant manager.
What I am describing with the term -“craftsman attitude”- is the acquired belief of a knowledgeable Subject Matter Expert or Expert Operator, which leads a person to decide of their own accord they are wise enough to make a critical decision without proper Data Analysis.
At this point in any conversation with a person trying to argue they do not need Data, I would quote Deming- “In God we trust, all others must bring DATA.”
This phenomenon is likely a combination of two primary things:
Resistance to Change and a natural aversion (at least in Western Society) to developing a deep understanding of mathematics. Resistance to Change is inherent in many human beings and very hard to overcome. Awareness of the potential Resistance and how it may influence your decisions can help mitigate how you react to Quality Improvement issues.
Mathematics is something you must pursue on your own unless you were gifted with a natural aptitude (as the school systems are not very effective when it comes to producing mathematically educated high school graduates). Fear not.
Lack of mathematical prowess means very little in this day and age. You need only understand the basic concepts of statistics and how to apply them, and most stats software will walk you through the rest.
This post will be the first of a few on basic stats to help those who may feel like it is beyond them to help show that it is not that complicated.
I will be keeping the stats on a basic level, and breaking the posts up into digestible bits, so please continue reading 😊
Basic Statistical Terms
The first terms that should be understood first are Population, Sample, Statistic, and Parameter.
-A Population considers every member/unit of a group
-A Sample would be a random surveillance study of a portion of the Population
-A Parameter is Derived from Analysis of the Population
-A Statistic is Derived from Analysis of the Sample of the Population
Conclusion
A Parameter is a description of an entire group (Population). A Statistic is the description of a sample of the Population. Understanding the difference is critical. A Parameter indicates that the whole population has been evaluated in some way to obtain the result.
A Statistic depends upon randomness and sample size to adequately estimate the outcome of the population parameter.
The larger the random sample, the better!
If you have the time and resources, always choose a larger sample size!
-Next Time: Basic Measures of Central Tendency
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Lean Supply Chain Vs. Cutting Costs to the Bottom Line
REFLECTION: FOR STUDENTS: Study Deming’s philosophy, and and observe how critical Culture is to how Toyota dominates long term.
FOR ACADEMICS: Teach how interconnected organizations are more resilient than silo shaped organizations
FOR PROFESSIONALS/PRACTITIONERS: If Top Management is using Lean incorrectly for short term financial savings, demonstrate using COPQ how the Hidden Factory is damaging the company under the disguise of “Lean”. If they still will not react strategically, search for other employment, as the more valuable you are to the company, the more easily you are converted into short term savings. Cutting the experienced and hiring those fresh out of school with little to no experience is a quick way to claim “savings”. Replacing the years of lost experience by training the new employees and paying for all their errors is a massive expense.
The Goal of Lean Management
Some (especially top management and supply chain) misinterpret lean as the ability to do more with less by eliminating waste and decreasing costs.
This idea of Lean often leads to two critical issues-
1. Companies cutting labor that is no longer “needed” to “reduce waste” so they can “do more with less.” It is a fancy way of telling the rest of the company employees to work harder to take up the slack for all the people I just fired and is very damaging to buy-in. Companies lose tribal knowledge that the company was unable to capture, and new learning costs are incurred when the costliest employees are cut to save money (and it is a frequent occurrence when Lean is used for cost-cutting).
2. Supply Chain chooses suppliers based upon financials, and OTD, not supplier quality. Lots of promises and preliminary approvals that drag on and on. You get what you pay for. Low cost frequently equates to low quality. If a competitor knows their product is better, they will raise the price because others will want to buy it. Cheap suppliers look good for the first quarter, but when the hidden costs of rework and recalls hit, somebody has to be held accountable.
Was it Quality’s fault? Supply Chain’s fault? No. It was the Administration was accountable for allowing the hidden factory back into the culture. Supply Chain especially should be able to understand when the QM tells the board that we need to invest in better suppliers. The goal of Lean management is to maximize customer value while minimizing waste. Allowing rework into the factory through the supplier is a total waste of resources, and it can cost a lot of money, time, and, frequently, loss of reputation in the eyes of potential customers. Investment in your company brings long term gains, not slashing people and resources.
It is also essential to set up an APQP/PPAP process that matches the needs of all the stakeholders. Two main factors to consider are the expected Volume of orders for the product or service and the Variety of products / and services. With products of high levels of variety (such as frequent custom orders) with relatively low product volume, Agile Project-type planning control is most effective. Low variability (not much change customer to customer), but substantial orders, would bring about a need for lean planning.
Conclusion
Lean is not a tool to be used like a Machete to “cut the fat” quarter to quarter. Eventually, while you are hacking at the perceived “weeds and vines” you will cut your company’s foot off and some of your foundation will be lost. Add value and eliminate waste carefully by categorizing value and waste as a team.
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Quality Concepts Matter 