Experiencing MIS
Experiencing MIS
7th Edition
ISBN: 9780134380421
Author: KROENKE
Publisher: PEARSON
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Chapter 1, Problem 5CE

A)

Explanation of Solution

Systems Thinking:

System thinking is a management discipline which involves identifying and understanding the components of a system and modeling the system by connecting the input and output of the components.

Importance of Systems thinking to business professionals:

  • Business people often face situati...

B)

Explanation of Solution

Systems thinking and Moore’s law:

Moore’s Law:

Moore’s Law is coined by Gordon Moore, the co-founder of Intel Corporation, which states that the since the integral circuit has been invented, the number of transistors per square inch in it has been doubled every two years. As the computers are getting exponentially faster, the cost of data processing is approaching zero.

System thinking to explain the reasons for which the farmer digs up a field of pulpwood trees:

  • In the production of paper the input are the pulpwood trees...

C)

Explanation of Solution

Systems thinking with regard to consequences of Bell’s Law, Moore’s law or Metcalf’s Law:

Bell’s Law:

Bell’s Law is coined by Gordon Bell in 1972, which states that in every decade a new computer class is formed which establishes a new industry.

  • As said by Gordon Bell digital devices will be evolved very quickly and this will enable new platforms and industries in every 10 years, one can use system thinking to deduce that currently the market is run by products of smart phones, tablets and smart watches.
  • In next 10 years new wearable technological devices with implementation of augmented reality and virtual reality may rule the market.

Moore’s Law:

Moore’s Law is coined by Gordon Moore, the co-founder of Intel Corporation, which states that the since the integral circuit has been invented, the number of transistors per square inch in it has been doubled every two years. As the computers are getting exponentially faster, the cost of data processing is approaching zero...

D)

Explanation of Solution

Jennifer’s failure to display system thinking:

  • Jenifer failed to display system thinking skills as for the firms supply chain Flextime she was unable to create a model which could have been beneficial for her to understand about the operations ...

E)

Explanation of Solution

Ways by which people can improve their system thinking skills:

Yes”, system thinking can be improved as system thinking skills are correlated with IQ which is dependent on visual and auditory skills...

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Here is a clear background and explanation of the full method, including what each part is doing and why. Background & Motivation Missing values: Some input features (sensor channels) are missing for some samples due to sensor failure or corruption. Missing labels: Not all samples have a ground-truth RUL value. For example, data collected during normal operation is often unlabeled. Most traditional deep learning models require complete data and full labels. But in our case, both are incomplete. If we try to train a model directly, it will either fail to learn properly or discard valuable data. What We Are Doing: Overview We solve this using a Teacher–Student knowledge distillation framework: We train a Teacher model on a clean and complete dataset where both inputs and labels are available. We then use that Teacher to teach two separate Student models:  Student A learns from incomplete input (some sensor values missing). Student B learns from incomplete labels (RUL labels missing…
here is a diagram code : graph LR subgraph Inputs [Inputs] A[Input C (Complete Data)] --> TeacherModel B[Input M (Missing Data)] --> StudentA A --> StudentB end subgraph TeacherModel [Teacher Model (Pretrained)] C[Transformer Encoder T] --> D{Teacher Prediction y_t} C --> E[Internal Features f_t] end subgraph StudentA [Student Model A (Trainable - Handles Missing Input)] F[Transformer Encoder S_A] --> G{Student A Prediction y_s^A} B --> F end subgraph StudentB [Student Model B (Trainable - Handles Missing Labels)] H[Transformer Encoder S_B] --> I{Student B Prediction y_s^B} A --> H end subgraph GroundTruth [Ground Truth RUL (Partial Labels)] J[RUL Labels] end subgraph KnowledgeDistillationA [Knowledge Distillation Block for Student A] K[Prediction Distillation Loss (y_s^A vs y_t)] L[Feature Alignment Loss (f_s^A vs f_t)] D -- Prediction Guidance --> K E -- Feature Guidance --> L G --> K F --> L J -- Supervised Guidance (if available) --> G K…
details explanation and background   We solve this using a Teacher–Student knowledge distillation framework: We train a Teacher model on a clean and complete dataset where both inputs and labels are available. We then use that Teacher to teach two separate Student models:  Student A learns from incomplete input (some sensor values missing). Student B learns from incomplete labels (RUL labels missing for some samples). We use knowledge distillation to guide both students, even when labels are missing. Why We Use Two Students Student A handles Missing Input Features: It receives input with some features masked out. Since it cannot see the full input, we help it by transferring internal features (feature distillation) and predictions from the teacher. Student B handles Missing RUL Labels: It receives full input but does not always have a ground-truth RUL label. We guide it using the predictions of the teacher model (prediction distillation). Using two students allows each to specialize in…
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