Sensitivity Analysis

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From the course by University at Buffalo

Advanced Manufacturing Process Analysis

85 ratings

University at Buffalo

Advanced Manufacturing Process Analysis

85 ratings

Course 4 of 9 in the Specialization Digital Manufacturing & Design Technology

Variability is a fact of life in manufacturing environments, impacting product quality and yield. Through this course, students will learn why performing advanced analysis of manufacturing processes is integral for diagnosing and correcting operational flaws in order to improve yields and reduce costs. Gain insights into the best ways to collect, prepare and analyze data, as well as computational platforms that can be leveraged to collect and process data over sustained periods of time. Become better prepared to participate as a member of an advanced analysis team and share valuable inputs on effective implementation. Main concepts of this course will be delivered through lectures, readings, discussions and various videos. This is the fourth course in the Digital Manufacturing & Design Technology specialization that explores the many facets of manufacturing’s “Fourth Revolution,” aka Industry 4.0, and features a culminating project involving creation of a roadmap to achieve a self-established DMD-related professional goal. To learn more about the Digital Manufacturing and Design Technology specialization, please watch the overview video by copying and pasting the following link into your web browser: https://youtu.be/wETK1O9c-CA

From the lesson

Data Analysis: Computational Techniques and Platforms

The purpose of this module is to introduce various techniques used in advanced analysis, like Determination of Significant Variables/Factors, Data Visualization, and Anomaly Detection. Also, this module will introduce various computational platforms (HPC, Cloud computing techniques) that exist for carrying out advanced analysis.

Sensitivity Analysis4:35

Meet the Instructors

Rahul Rai
Associate Professor
Mechanical and Aerospace Engineering

This lesson will provide

a description of how to identify

significant factors in the data through the use of Sensitivity Analysis.

Let us consider the decision-making scenario,

in which you have to make a decision on selecting

a mode of transportation for a trip from Miami to Buffalo.

In this scenario, you can use two modes of transportation, drive or fly.

The selection of the mode of transportation can depend

a lot on different factors that can change between now,

and the time of actual trip.

In the trip decision-making example,

some factors that might occur between now and final trip date,

that can impact your final decisions are;

increase in gas prices,

competition increases between airlines leading to drop in flight ticket prices,

drop in hotel prices,

or change in rental car rates.

Let's say, your overall travel budget for this trip, is around $1,000.

Some of the above variables can impact

your overall vacation costs significantly than others.

For example, if the hotel room prices are $50 a night for a three-day trip,

hotel room price factor might not be as impactful as the flight rate.

That is the concept of significant and insignificant variables.

One would say, our flight rates are significant variables and the hotel rates are

insignificant due to the amount of impact they have on the final trip cost.

In the real world, any process, activity,

or behavior is inherently mixed with uncertainty.

Sensitivity Analysis, sometimes called What-If Analysis,

is used to determine how uncertainties in different scenarios affect the overall outcome.

If some of my input changes,

what is the impact on my output?

Techniques such as Sensitivity Analysis,

aids in identifying and weighing the source of these uncertainties.

It also provides the user with a probable glimpse of future.

How do we do Sensitivity Analysis?

For simple scenarios, we could get

experienced subject matter experts together and do

a brainstorming to understand what factors could change,

and assign a likelihood for each change and assess the impact on the output.

On the other hand, for more complicated problems,

we may have to rely on simulation exercises that replicate

the scenarios to help understand what changes could occur,

and assess the impact of each change on output.

These simulations can be physical simulations or computer simulations.

There is additional reading material in the resource section of the course.

It has some very useful information on how

simple exercises can be used to create simulation models.

We are sharing a video with you.

In this video, we will see how

the Siemens Technomatic Simulation Software is used by

process designers to design the factory process layout.

There are factors that have higher significance on the output than others.

This is used to decide,

what changes need to be made to the process layout prior to implementation.

As you can imagine,

redesigning the process layout with this simulation prior to process implementation can

save significant resources compared to making these changes after implementation.

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