A business hypothesis is a statement or assumption made by a business or entrepreneur about a particular aspect of their business. It is typically formulated as an if-then statement and serves as a starting point for testing and validating ideas.
Let’s consider an example to illustrate this concept:
Hypothesis: If we offer a discount on our product, then we will attract more customers and increase sales.
In this example, the business is hypothesizing that by providing a discount on their product, they can achieve two specific outcomes: attracting more customers and increasing sales. This hypothesis is based on the assumption that a lower price point will incentivize potential customers to purchase the product.
To test this hypothesis, the business might run a limited-time promotion where they offer a discount on their product. During the promotion period, they would closely monitor and analyze key metrics such as customer traffic, conversion rates, and sales volume.
After the promotion ends, the business would assess the data collected to determine if their hypothesis was accurate. If the results show a noticeable increase in customer traffic and sales during the discount period, it would support their hypothesis. On the other hand, if the results indicate no significant change or even a decline in sales, it would suggest that the hypothesis was incorrect.
Based on the findings, the business can make informed decisions about whether to continue offering discounts, adjust their pricing strategy, or explore alternative approaches to achieve their desired goals.
In summary, a business hypothesis is an educated guess about a particular aspect of a business that is formulated to guide experiments and data analysis, ultimately leading to informed business decisions.
As a business analyst you are often expected to act as a bridge between a functional domain and the business stakeholders. Business analysts must be great verbal and written communicators, tactful diplomats, problem solvers, thinkers and analyzers. Though you have been extensive training in project management and related areas, using systematic business and management tools such as graphical analysis, data distribution & visualization, statistical discovery, etc are considered to be difficult by many Business Analysts.
Fortunately Lean Six Sigma, which is process improvement methodology provides many of the tools that can be handy for Business Analysts at one place. It comprises of statistical tools and techniques along with visualization tools. There are many tools such as Visual Analysis & Data Discovery tools like Fish-bone, 5 why, in scope-Out scope, Box plots and analytical tools like MSA, Descriptive Statistics, Variation, Correlation and Regression. They are explained in brief as under:
There are many tools which a business analyst will learn from Lean Six Sigma Green Belt Certification. We’ll talk about few Visual analysis tools from Lean Six Sigma in brief as under:
Here are few examples of data discovery analytical tools that a Business Analyst will learn from Six Sigma.
A business analyst having Green Belt Certification shall have a comprehensive understanding of Lean six sigma and shall be able to apply its tenets to their daily work. The principles of Six Sigma are so widely applicable that employees getting trained are highly valued and aggressively sought after. Lean Six Sigma Certification will be a stepping stone for professionals to a higher level as you avail expertise in different problem solving tools and techniques of Lean Six Sigma.
If you are new to Lean Six Sigma then Y=f(X) is one amongst many jargons that you will have to familiarize yourself.
The objective of Lean Six Sigma philosophy and DMAIC improvement methodology is to identify the root causes to any problem and control/manage them so that the problem can be alleviated.
Six Sigma is process oriented approach which considers every task as a process. Even the simplest of the tasks, such as performing your morning workout or getting ready to office is considered as a process. The implication of such a view point is to identify what is the output of that process, its desired level of performance and what inputs are needed to produce the desired results.
Y is usually used to denote the Output and X for the inputs.
Y is also known as dependent variable as it is dependent on the Xs. Usually Y represents the symptoms or the effect of any problem.
On the other hand, X is known as independent variable as it is not dependent on Y or any other X. Usually Xs represents the problem itself or the cause.
As you will agree that any process will have at least one output but most likely to have several inputs. As managers, we all are expected to deliver results and achieve a new level of performance of the process such as Service Levels, Production Levels, Quality Levels, etc., or sustain the current level of performance.
In order to achieve this objective, we focus our efforts on the output performance measure. However a smart process manager will focus on identifying Xs that impact the output performance measure in order to achieve the desired level of performance.
How does one identify the input performance measures or Xs?
Six Sigma DMAIC methodology aims to identify the inputs(Xs) that have significant impact on output (Y). After that the strength and nature of the relationship between Y and Xs are also established.
Six Sigma uses a variety of qualitative and quantitative tools & techniques listed below to identify the statistical validation of the inputs (or root causes), their strength and nature of relationship with Y:
What does f in Y= f(X) mean?
‘f’ represents the nature and strength of the relationship that exists between Y and X. On one hand, this equation can be used for a generic interpretation that symbolizes the fact that Y is impacted by X and nature of relationship can be quantified. On the other hand, such a mathematical expression can be created provided we have sufficient data using the above mentioned analytical tools such as regression and other hypothesis tests.
The mathematical expression that we obtain is nothing but an equation such as:
TAT = 13.3 – 7.4*Waiting Time + 1.8*No. of Counters – 24*Time to Approve
Once such an equation is established, it can be easily used to proactively identify the Y for various values of X. Thus Y= f(X) is the basis for predictive modeling. All the newer analytical concepts such as Big Data, etc are based on this foundation principles.
For each of the root causes identified in the Analyze phase, the Lean Six Sigma Team uses an apt structured or unstructured brainstorming method to generate various alternatives to overcome the problem. These techniques may include Channeling, Anti-solutions, Analogy, Wishful thinking, Random word stimulus methods, etc.
SCAMPER is another popular method which can be used by the Six Sigma Green Belt to systematically improve the current process using any of the following methods: Simplify or Substitute, Combine, Adapt, Modify, Put to different use, Eliminate & Reduce.
If there are too many options that the team has identified, then a variety of solution screening methods can be used to select the best solution for implementation. These screening methods include NGT (Nominal Group Technique), N/3 Voting, Criteria Based Matrix (CBT), etc.
Proposed solutions can be a new process, technology change, policy changes, alterations of inputs, measurement system refinement, customer, employee or vendor education, etc. In such cases, either revised process map, future state value stream mapping, etc., may need to be proceeded.
The solution that the team has selected should directly impact the CTQ of the project. Six Sigma Green Belt should validate this.
Before implementing solutions, the Six Sigma Green Belt needs to ensure that the proposed solutions are complete and well refined. This will ensure that there are no delays, rework during implementation, and the full impact on CTQ is derived. In order to do this, a tool called Failure Modes Effect Analysis (FMEA) is used. The main purpose of this tool is to assess all the risks involved with a solution, and how to mitigate them by refining the solution before implementation. Risk Priority Number (RPN) derived from FMEA helps in prioritizing the risks and acting on them in a systematic manner.
In Lean Six Sigma Projects, it is an important step to statistically validate the impact on CTQ (before implementation & after Implementation). Hypothesis tests like 2-t test, ANOVA, Chi-square tests, etc., are used to perform this statistical validation. These tests help to identify if the improvement is significant or marginal in nature. Six Sigma Green Belt should be able to select and perform appropriate tests using statistical softwares.
On successful completion of these deliverable and formal Improve tollgate review, the Lean Six Sigma project team is ready to move to the Control phase. Next >>>