联系方式

  • QQ:99515681
  • 邮箱:99515681@qq.com
  • 工作时间:8:00-23:00
  • 微信:codinghelp

您当前位置:首页 >> Java编程Java编程

日期:2021-04-20 10:37

CRANFIELD UNIVERSITY

CRANFIELD SCHOOL OF MANAGEMENT

MSC LOGISTICS AND SUPPLY CHAIN MANAGEMENT

MSC PROCUREMENT AND SUPPLY CHAIN MANAGEMENT

BIG DATA ANALYTICS

ASSIGNMENT

Date Set: 29 March 2021

Date Due: 19 April 2021

2 of 7

Introduction

In this Big Data Analytics Assignment, you will be assessed in two areas: theory and practice.

In the theory area, we expect you to critique recent publications in the big data analytics

domain. In the practice area, we present you with datasets on which we expect you to perform

appropriate analysis and draw managerial conclusions. Each of these areas will comprise 45%

of your marks. As is the case in many other assignments, 10% of the marks are attributable

to style and presentation.

The word limit for this assignment is 1,500. It is an upper limit, not a target. Please report the

number of words and do not exceed this limit (this includes references). Although theory

and practice questions are equally weighted, we anticipate you will use more words in the

theory part, roughly 2/3 of your upper limit.

Please use the discussion board for any questions you might have about this assignment. We

will answer all questions that are asked on or before 15 April 2021 -12 pm to minimise the lastminute

stress and encourage timely attention to the assignment.

It has been an absolute pleasure to teach you big data analytics. We hope you enjoy this

assignment and use the techniques in the future.

Dr Abhijeet Ghadge, Prof Emel Aktas

March 2021, Cranfield University, UK.

Disclaimer: Although the problems presented in this assignment are informed by real

events, the names, characters, businesses, places, events, locales, and incidents are the

products of the authors’ imagination. Any resemblance to actual persons, actual businesses,

or actual events is coincidental.

3 of 7

Q1 Theory [45 marks]

Identify three recent academic papers- two on predictive analytics and one on prescriptive

analytics within logistics and supply chain problem context.

Critically discuss the application of predictive and prescriptive analytical techniques

for improving supply chain performance.

Using your favourite research database (e.g. Scopus), search and select three papers that

explain the application of different predictive and prescriptive analytical techniques to solve

logistics/supply chain problem. Please do not use a literature review paper in your selection.

We are looking for applied research papers. Don’t forget to include selected three papers in

the list of references. Please highlight them separately (in bold) to other references that you

may use.

Q2 Practice [45 marks]

For the practical part of the assignment, we have prepared datasets for you to replicate the

analyses we have done in the module to a larger extent. For this purpose, we have an affinity

analysis exercise, where we have done some of the preprocessing on the company’s data to

assist you. Affinity analysis helps us determine which products are likely to be ordered

together. It relies on the number of occurrences of instances and presents a confidence score

for the likelihood of two products being bought together to inform warehouse layout decisions

(e.g. using the output of your analysis, the manager may decide to put the products bought

together in closeby locations to reduce the picking time). The confidence score is the number

of instances Product 1 and Product 2 were bought together over total instances Product 1 was

bought. Product 1 in this case is the first product mentioned in the rule.

In the “affinity_data_and_product_names.zip” file, you will find two text files:

1. affinity_data.txt

2. affinity_productnames.txt

“affinity_data.txt” has the orders placed with a company over a four-month period in 2019.

Each row indicates an order and each column is the products held in stock by the company.

The data is made of 0s and 1s where 1 indicates that the product was in the given order. This

data file comprises 67844 orders of 21447 products, so it will take a while to load to memory

depending on your computer.

4 of 7

“affinity_productnames.txt” has the names of the products that you will need to use when

reporting your findings.

Please report the descriptive statistics for the number of products in an order (min, Q1,

median, Q3, max, mean, standard deviation). Please note that we are asking for the

descriptive statistics of one variable: the number of products in an order, which you will have

to calculate before you produce the descriptive statistics.

Please perform an affinity analysis exercise on this data to draw the top five rules with the

highest support for the occurrence of two products together. In your response, we expect

you to present the support and the confidence in the rule with the corresponding product

names.

Please add your script as an appendix with appropriate comments.

Style and Presentation [10 marks]

You will receive marks for the style and presentation of your assignment. Please pay

attention to

1. Writing grammatically correctly and concisely.

2. Providing captions for your tables and figures and citing them in the text.

3. Using an appropriate level of accuracy (no need for 5+ figures after the decimal

point. We recommend 3).

4. Presenting your assignment in a report structure, so we can map your answers to the

questions we have asked.

5. Citing the references you used in preparing your answers.

Marking Criteria

Marks will be awarded for the level of understanding demonstrated in the concepts and accuracy of your answers.

ASSESSMENT

CRITERIA

Very poor (0-

39%)

Poor (40-49%) Satisfactory

(50-59%)

Good(60-69%) Very good

(70-79%)

Excellent

(80-100%)

Theory [45]

Publication time

of papers [5]

Papers

missing.

Papers

selected were

published more

than a decade

ago.

Papers selected

are published

within the last

decade.

Papers

selected are

published within

the last five years.

Papers

selected are

published within

the last three

years.

Papers

selected are

recent (2020-

2021).

Relevance of

papers [10]

Papers

missing.

Papers are not

relevant to

logistics & supply

chain

management.

Papers have

questionable

relevance to for

logistics & supply

chain

management.

Papers are

somewhat

relevant to

logistics & supply

chain

management.

Papers are

mostly relevant to

logistics & supply

chain

management.

Papers are

relevant to

logistics & supply

chain

management.

Comparison

[20]

Papers not

compared.

Comparison

arguments are not

valid at times.

Comparison

criteria identified

have questionable

appropriateness to

the task at hand

and evaluation of

the papers is

questionable.

Somewhat

appropriate

comparison

criteria are

identified and

applied somewhat

competently.

Mostly

appropriate

comparison

criteria are

identified and

applied mostly

competently.

Appropriate

comparison

criteria are

identified and

applied

competently.

Reflection [10] There are no

reflections.

Reflections are

not specific to the

area identified.

Reflections are

general and basic.

Reflections

lack sufficient

detail at times.

Reflections are

detailed.

Reflections are

detailed and

referenced.

6 of 7

ASSESSMENT

CRITERIA

Very poor (0-

39%)

Poor (40-49%) Satisfactory

(50-59%)

Good(60-69%) Very good

(70-79%)

Excellent

(80-100%)

Practice [45]

Descriptive

Statistics [10]

Descriptive

Statistics missing.

Descriptive

statistics

produced but has

some errors.

Descriptive

statistics is

produced and

presented without

any commentary.

Descriptive

statistics is

produced and

presented with

some

commentary.

Descriptive

statistics is

produced and

presented clearly

with key

conclusions.

Descriptive

statistics is

produced and

presented clearly

with all applicable

conclusions.

Model Build

and Test [25]

No model built. Model build

steps and choices

made by the

author not

explained.

Main steps to

build the model

are presented

without any

explanation.

The main

steps to build the

model are

explained.

The steps to

build the model

explained without

any justification.

The steps to

build the model

explained well and

the model choice

justified.

Insights [10] No

recommendations

produced.

The link

between

recommendations

and the results is

missing.

Few

recommendations

are produced for

the company

without

justification.

Few

recommendations

are produced for

the company and

justified by

somewhat

relevant

examples.

Several

meaningful

recommendations

produced for the

company and

justified by

relevant

examples.

Data-driven,

meaningful

recommendations

are produced for

the company and

justified by

references and

relevant

examples.

7 of 7

ASSESSMENT

CRITERIA

Very poor (0-

39%)

Poor (40-49%) Satisfactory

(50-59%)

Good(60-69%) Very good

(70-79%)

Excellent

(80-100%)

Style and

Presentation

[10]

Coherence and

conciseness of

writing. Good

grammar, no

spelling errors,

and use of

appropriate

vocabulary.

Language far

from fluent,

meaning unclear,

grammar and/or

spelling poor.

Language far

from fluent but

understandable,

grammar and/or

spelling poor.

Language

understandable,

meaning apparent

but not explicit,

grammar and/or

spelling poor.

Language

mainly fluent,

minor spelling

and/or grammar

and/or

punctuation

errors.

Language

fluent thoughts

and ideas clearly

expressed.

Exceptionally

fluent structure,

and clarity of

expression.

Relevant and

accurate

referencing

Incoherent

and/or absent

referencing.

Inconsistent,

incoherent

referencing.

Inconsistent,

referencing with

some errors.

Referencing,

relevant but with

several errors.

Referencing,

relevant with few

errors.

Referencing

clear, relevant and

consistent.

Virtually errorfree.

Effective and

well-presented

figures and

tables

Table and

figure captions

are missing.

Tables and

Figures not cited

in the text.

Table and

figure captions

missing or

inconsistent.

Tables and

Figures cited

wrongly in the

text.

Table and

figure captions

with minimal

information.

Tables and

Figures are not

always cited in the

text and

sometimes spilling

over the margins.

Table and

figure captions

are sometimes

meaningful.

Tables and

Figures are

sometimes cited

in the text.

Table and

figure captions

are meaningful.

Tables and

Figures are

consistently cited

in the text.

Table and

figure captions are

meaningful.

Tables and

Figures cited in

the text. No

spillage on the

page margins.


版权所有:留学生编程辅导网 2020 All Rights Reserved 联系方式:QQ:99515681 微信:codinghelp 电子信箱:99515681@qq.com
免责声明:本站部分内容从网络整理而来,只供参考!如有版权问题可联系本站删除。 站长地图

python代写
微信客服:codinghelp