Inventory Management
PostExperience Module Outline
Inventory Management (IMA)
Introduction
This module provides an understanding of the fundamental concepts and techniques underlying modern inventory management and control; structured exercises and case studies provide application experience which is reinforced by a company based postmodule assignment.
Objectives: On completion, the participant will be able to

Understand the basic inventory classes and their characteristics

Understand the differences between inventory management and inventory control

Understand classical inventory theory and its basic assumptions

Understand mechanisms for coping with uncertainty

Analyse a classical inventory control situation

Understand the impact of data availability and accuracy on inventory

Appreciate the basic issues in physical stock control

Interpret common inventory performance measures

Understand what works and does not in inventory management and control
Contents:

Inventory definitions, types and functions

Inventory management decisions  allocations and tradeoffs

Inventory control  How much to order? When to order?

Classical inventory theory and its basic assumptions

The nature of demand  independent or dependent

Coping with uncertainty  the safety stock and its use

Data requirements for successful inventory management and control

Physical control of stocks

Special cases in inventory management

Inventory management within a JIT, MRP or MRP II environment

Performance measures for inventory systems

Lessons from practice  what works and what doesn't
Duration: 2½ Days
Assessment: Post Module Assignment
Prerequisite Knowledge
Arithmetic:
You should be able to do basic arithmetic and to use formulae.
Statistics:
You should have a basic understanding of descriptive statistics. The minimum statistics knowledge should include: an understanding of averages, probability, frequency distributions including the normal distribution, standard deviation, sample and population. You will need enough knowledge to be able to explain these terms to others.
(Sources of support are identified on a separate page attached to this outline)
This module is approved by BAE SYSTEMS as a Manufacturing Developing You (MDY) programme “Core Module”.
RECOMMENDED PREREADING TO IMPROVE KNOWLEDGE PRIOR TO MODULE ATTENDANCE
Two books, listed below, have been identified to help you develop your skills and knowledge. These books may be obtained from book shops, local libraries or through company learning resource centres where they exist. Use the readings according to your needs: skim them when revising the material, but read in detail and try some of the examples when the material is new.
IMPROVING ARITHMETIC SKILLS AND KNOWLEDGE
In Improve Your Maths (2^{nd} edition) by G Bancroft and M Fletcher, AddisonWesley, 1998, ISBN 0201331306, you should focus on:
Chapter 1 Arithmetic operations: pp 311
Introduction, whole numbers, negative numbers, rounding of numbers
Chapter 2 Fractions and decimals: pp 1526
Introduction, fractions, decimal numbers
Chapter 3 Percentages and ratios: pp 2837
Introduction, percentages, ratios
Chapter 13 Simple algebra: pp 153161
Introduction, terminology, simplification
IMPROVING STATISTICAL SKILLS AND KNOWLEDGE
In Statistics (4^{th} edition) by F Owen and R Jones, Longman Group, 1994,
ISBN 0273603205, you should focus on:
Chapter 1 The organisation of data: pp123
Data types, statistical tabulations, grouped frequency distribution, the stem and leaf diagram, the time series
Chapter 2 The presentation of data l: pp 2846
The bar chart, the pie chart, plotting the frequency distribution – the histogram, plotting the time series, the strata graph
Chapter 5 Averaging of data: pp 93109
Arithmetic mean, arithmetic mean of a frequency distribution, limitations on the use of the arithmetic mean, the median, the mode
Chapter 9 Dispersion: pp 181191
Measures of the range, measures of average deviation, relative dispersion
Chapter 10 The normal distribution: pp 203210
Standard scores, standard normal distribution, applications of the normal distribution
Chapter 11 Probability: pp 216227
Some definitions, how we measure probability, three approaches to probability, the laws of probability, applications of the laws of probability
Chapter 15 Sample design: pp 302310
The sampling frame, systematic sampling, stratified sampling, multistage sampling, cluster sampling, quota sampling
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