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Inventory Management

Post-Experience 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 post-module assignment.


Objectives: On completion, the participant will be able to


  1. Understand the basic inventory classes and their characteristics

  2. Understand the differences between inventory management and inventory control

  3. Understand classical inventory theory and its basic assumptions

  4. Understand mechanisms for coping with uncertainty

  5. Analyse a classical inventory control situation

  6. Understand the impact of data availability and accuracy on inventory

  7. Appreciate the basic issues in physical stock control

  8. Interpret common inventory performance measures

  9. Understand what works and does not in inventory management and control


Contents:


  1. Inventory definitions, types and functions

  2. Inventory management decisions - allocations and trade-offs

  3. Inventory control - How much to order? When to order?

  4. Classical inventory theory and its basic assumptions

  5. The nature of demand - independent or dependent

  6. Coping with uncertainty - the safety stock and its use

  7. Data requirements for successful inventory management and control

  8. Physical control of stocks

  9. Special cases in inventory management

  10. Inventory management within a JIT, MRP or MRP II environment

  11. Performance measures for inventory systems

  12. Lessons from practice - what works and what doesn't


Duration: 2½ Days

Assessment: Post Module Assignment



Pre-requisite 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 PRE-READING 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 (2nd edition) by G Bancroft and M Fletcher, Addison-Wesley, 1998, ISBN 0-201-331306, you should focus on:



Chapter 1 Arithmetic operations: pp 3-11

Introduction, whole numbers, negative numbers, rounding of numbers

Chapter 2 Fractions and decimals: pp 15-26

Introduction, fractions, decimal numbers

Chapter 3 Percentages and ratios: pp 28-37

Introduction, percentages, ratios

Chapter 13 Simple algebra: pp 153-161

Introduction, terminology, simplification



IMPROVING STATISTICAL SKILLS AND KNOWLEDGE



In Statistics (4th edition) by F Owen and R Jones, Longman Group, 1994,

ISBN 0-273-60320-5, you should focus on:



Chapter 1 The organisation of data: pp1-23

Data types, statistical tabulations, grouped frequency distribution, the stem and leaf diagram, the time series

Chapter 2 The presentation of data l: pp 28-46

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 93-109

Arithmetic mean, arithmetic mean of a frequency distribution, limitations on the use of the arithmetic mean, the median, the mode

Chapter 9 Dispersion: pp 181-191

Measures of the range, measures of average deviation, relative dispersion

Chapter 10 The normal distribution: pp 203-210

Standard scores, standard normal distribution, applications of the normal distribution

Chapter 11 Probability: pp 216-227

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 302-310

The sampling frame, systematic sampling, stratified sampling, multi-stage sampling, cluster sampling, quota sampling



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