ABC Trading Ltd. (ABC) is a garment manufacturing company headquartered in Hong Kong. Its manufacturing facilities are spread all over Asia, in countries such as China, Malaysia, Thailand, the Philippines, and Vietnam. It produces over 30 million garments

data mining

Description

ABC Trading Ltd. (ABC) is a garment manufacturing company headquartered in Hong Kong. Its manufacturing facilities are spread all over Asia, in countries such as China, Malaysia, Thailand, the Philippines, and Vietnam. It produces over 30 million garments, mostly ladies’ apparel, annually. 

 

The garment industry is one of the most labour-intensive industries. A number of human factors, such as workmanship and human errors, can easily affect product quality. To assure the quality of products, ABC provides training for operators to enhance their job skills in different operations such as fabric spreading, cutting, sewing, and finishing. It also tracks operators’ performance, which is used to determine salary increment, bonuses and promotions. 

 

Currently, ABC does not have a systematic approach in reviewing the effectiveness of the training programme. In addition, it is found that the number of product quality issues is increasing, causing a large amount of product rework cost. ABC would like to use data mining to examine factors associated with good operator performance so that its training resources can be more effectively allocated for quality improvement. The dataset – ABC.csv, relates to the training programme offered by ABC and the description of the attributes is listed in Table 1.

                                                     Table 1. Description of ABC.csv

Attribute Name

Description

Labels / Values

OpID

Operator identity

Unique number

Gender

Gender

0 = “male”

1 = “female”

Edu

Education level

0 = “secondary & below”

1 = “above secondary”

Tedu

Type of education

0 = “academic”

1 = “vocational”

Wexp

Prior work experience

0 = “2 years or less”

1 = “more than 2 years”

Twexp

Type of prior work experience

0 = “office work”

1 = “factory work”

Trg

Completion of operator training

0 = “no”

1 = “yes”

Age

Age

Integer measurement

Trgper

Training performance index

Integer measurement

Qual

Qualitative skills index

Integer measurement

Quant

Quantitative skills index

Integer measurement

Wkrate

Weekly rate

Integer measurement

Mcdays

Average days of sick leave taken per year

Integer measurement

Yrsco

Years with company

Integer measurement

Opper

Operator performance

 0 = “not-so-good”

 1 = “good”

 

 

 

 

Question:

(a) With reference to the pre-modelling, modelling, and post-modelling stages of data mining, explain the key tasks to be performed when ABC applies data mining to achieve its business objective. Your answer must be less than 300 words.  (30 marks)

 

(b) For this task, you are required to analyse variables that are worth exploring in order to detect interesting patterns in the data. With the aid of diagrams, perform the following data exploration tasks using appropriate data visualisation techniques (one technique for each task):

 (i) Univariate data exploration (10 marks)

 (ii) Bivariate data exploration (10 marks)

 (iii) Multivariate data exploration  (10 marks)

 

(c) Discuss the findings of each data exploration task you performed in part (b). (24 marks) 

 

(d) Identify one (1) variable that contains missing values. As part of your data preparation consideration, what is your suggestion for handling the missing values of this variable?  Justify your suggestion. (6 marks)

 

Answers should not exceed 1800 words or 10 pages (including all the relevant graphs and tables)

Please use IBM SPSS Modeler

Instruction Files

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