Module Details

Module Code: STAT7004
Title: Process Improvement
Long Title: Process Improvement - Quantitative Metho
NFQ Level: Intermediate
Valid From: Semester 1 - 2014/15 ( September 2014 )
Duration: 1 Semester
Credits: 5
Field of Study: 4620 - Statistics
Module Delivered in: 1 programme(s)
Module Description: This module covers mathematical and statistical techniques used in process improvement.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Develop, plot and analyse both variable and attribute statistical process control charts.
LO2 Explain and perform financial calculations relating to investment decisions.
LO3 Differentiate various functions by rule, and apply differentiation to the solution of optimization problems.
LO4 Evaluate anti-derivatives and definite integrals using table look-up and the method of substitution. Apply integration to problems relevant to the student's discipline.
LO5 Plan, conduct and analyse multi-factorial industrial experiments.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
8599 MATH6009 Mathematics for Manufacturing
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Control Charts
Statistical Process Control, Variable control charts, for example, Mean chart, R chart. Attribute control charts, for example, p-chart, np-chart, c-chart, u-chart. Construction and interpretation of control charts.
Process Capability
Interpretation of the process capability indices Cp and Cpk. Calculation of process capability indices using both X and R method as well as using probability plots.
Financial Calculations
Payback time, Time value of money, Loan repayment costs.
Differentiation
Definition and interpretation of a derivative. Differentiation of various functions using table look-up. Product, quotient and chain rules. Applications: curve sketching, max-min problems. Applications in industry.
Integration
Integration as anti-differentiation. Standard integrals. Integration by substitution. Integration as summation. Definite integral and its significance. Applications of definite integral to elementary first order differential equations.
Experimental Design
Context of experimental design in pharmaceutical sector. Planning and conducting industrial experiments, replication, randomisation, introduction to Analysis of Variance. Calculation of main effects, interaction effects and regression model representation.
Laboratory Programme
Use of a relevant software package (e.g. Microsoft Excel) to support delivery of the theoretical content of this module.
Module Content & Assessment
Assessment Breakdown%
Coursework40.00%
End of Module Formal Examination60.00%

Assessments

Coursework
Assessment Type Short Answer Questions % of Total Mark 20
Timing Week 9 Learning Outcomes 3,4
Assessment Description
Differentiation and Integration
Assessment Type Short Answer Questions % of Total Mark 20
Timing Week 5 Learning Outcomes 3,4
Assessment Description
SPC, financial calculations - this assessment may be partly/fully lab-based.
End of Module Formal Examination
Assessment Type Formal Exam % of Total Mark 60
Timing End-of-Semester Learning Outcomes 1,2,3,4,5
Assessment Description
End-of-Semester Final Examination
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Lecture Every Week 3.00 3
Tutorial Contact Tutorial sheets, problem solving Every Second Week 0.50 1
Lab Contact Mathematics/Statistics Laboratory Every Second Week 0.50 1
Independent & Directed Learning (Non-contact) Non Contact Review of lecture material; completion of exercise sheets Every Week 3.00 3
Total Hours 8.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Lecture Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Student study Every Week 4.00 4
Tutorial Contact Tutorial sheets, problem solving Every Second Week 0.50 1
Lab Contact Mathematics/Statistics Laboratory Every Second Week 0.50 1
Total Hours 8.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
 
Module Resources
Recommended Book Resources
  • Douglas C. Montgomery. (2012), Design and Analysis of Experiments, 8th. Hoboken : Wiley-Blackwell, [ISBN: 9781118097939].
  • Douglas C. Montgomery. (2009), Introduction to statistical quality control, John Wiley & Sons, Hoboken, N.J., [ISBN: 9780470233979].
  • Bird John O. (2007), Engineering Mathematics, ebook 6th edition. Dawson Books, [ISBN: 9780080965635].
Supplementary Book Resources
  • (2002), Practical reliability engineering, Wiley, Chichester, [ISBN: 0470844620].
  • Andre Francis. (2004), Business Mathematics and Statistics, Thompson, London, [ISBN: 9781844801282].
  • K A Stroud. (2013), Engineering Mathematics, 7th. Hampshire : Palgrave Macmillan, [ISBN: 9781137031204].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_SGMPR_7 Bachelor of Science in Good Manufacturing Practice and Technology 1 Mandatory