Industrial Engineering

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AE 476 Industrial Engineering
Introduction to Industrial Engineering
Functional areas of Management
Definition and Significance of Industrial Engg.
Objectives and Competitive strategies of any manufacturing industry.
How Industrial Engg. concepts helps to achieve this?
Sub-systems of Operations and its improvement through Industrial Engineering
Syllabus.
Textbooks and references.
Course Faculty: Dr. B. S. GIRISH

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Functional
Areas of
Management

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PRODUCTION/OPERATIONS
MANAGEMENT

4. Functional Areas of Management 1-4

5. Corporate Objectives
 Objectives - Increasing Profitability:
 Increasing Profits
 Growth: Increase market share, Global outreach
 Return on Investment as early as possible
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6. Competitive Strategies
 Today’s manufacturing and service industries face
fierce competition
 Customers demands are rising
 Challenge for industries to produce goods of right
quantity, quality, in time and at minimum cost.
 Functional Strategies
 Marketing strategies
 Financial strategies
 Personnel strategies
 Production/Manufacturing strategies
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7. Production/Manufacturing strategy
 P/M function aims to provide products/services to its
customers by using following strategies:
 Timely delivery of products/services.
 Flexibility in meeting customers demand in terms of change
in customer demand and change in production volume
 Quality of products/services to meet customer specifications
 Cost effectiveness in terms of low price for its
products/services relative to that of its competitors.
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8. Operations subgoals
 To be effective in manpower cost control
 Effective material utilization and its cost control
 Effective facility utilization and its cost control
 Other objectives achieved:
 Improves the economy of the nation (GDP)
 Improves employment opportunities
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9. What is GDP?
 Gross domestic product (GDP) is the total monetary or market
value of all the finished goods and services produced within a
country’s borders in a specific time period. As a broad measure
of overall domestic production, it functions as a comprehensive
scorecard of a given country’s economic health.
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10. Operations subgoals
 Achieving highest efficiency at all operational
subsystems

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• Gearing up the operation subsystems to meet delivery commitments

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Definition of Industrial Engg.
 American Institute of Industrial Engineers (AIIE)
defines Industrial Engineering as follows:
Industrial Engineering is concerned with the design,
improvement and installation of integrated system of
men, materials and equipment. It draws upon
specialized knowledge and skills in the mathematical,
physical sciences together with the principles and
methods of engineering analysis and design to
specify, predict and evaluate the results to be
obtained from such system.

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Definition
The prime objective of industrial engineering is:
1- To increase the productivity.
2- Eliminating waste and non-value added activities.
3- Improving the effective utilization of resources.

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Industrial Engineering Approach
 In carrying out various activities, the
industrial engineer:
 Gathers and analyses facts.
 Prepares the alternative solutions taking in to
consideration all the constraints both internal and
external.
 Selects the best solution for implementation.

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Objectives of Industrial
Engineering
 The basic objectives of Industrial
Engineering departments are:
1- To establish methods for improving the operations
and controlling the production costs.
2- To develop programmes for reducing these costs.

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Techniques of Industrial
Engineering
 Following tools and techniques are used to improve productivity
of the organization by optimum utilization of resources.
1- Value Analysis.
2- Production, Planning and Control.
3- Inventory Control.
4- Job Evaluation.
5- Material Handling Analysis.
6-Ergonomics (Human Engineering).
7- Operations Research Techniques.
8- Method Study.
9- Time Study (Work Measurement).

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1.9 Place of Industrial
Engineering in Organization

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Industrial Engineering in Service Sector
 Large number of industrial engineers are in demand
and attracted to careers in exciting, challenging and
rewarding new fields.
 The various service industries are:
1- Health Service.
2- Government Organizations.
3- Banking.

19. Industrial Engineering (Elective)
SYLLABUS
Introduction - operations strategy, competitiveness and productivity - product design and
process selection – value analysis and value engineering – forecasting - characteristics of
production systems - plant location and layout – capacity planning – production planning
and control – materials requirement planning (MRP) – inventory control – Introduction to
work study, time and motion study - Introduction to total quality management (TQM) -
Mass Production Management-Line Balancing Techniques.
Textbooks:
 Buffa, E.S. And Sarin, R.K., Modern production /Operations Management, John Wiley &
Sons, (1994).
 I. L. O., Introduction to Work Study: Indian Adaptation, Third (Revised Edition) Oxford &
IBH Publishing Co. Pvt. Ltd. New Delhi, (1997).
References:
 Muhlemann, A., Oakland, J. O., and Lockyer, K., Productions and Operations Management,
Macmillan (1992).
 Narasimhan, S. L., McLeavey D. W., and Billington, P. J., Production Planning and Inventory
Control, Prentice Hall (1977)
 Barnes, R. M.: Motion and Time Study: Design and Measurement of Work, 7e,John Wiley
and sons, New York.(1980)
 …….. 19

20. Continuous Assessment
 Quiz-1 (15 marks)
 Quiz-2 (15 marks)
 Assignment (20 marks)
 End Semester Exam (50 marks)
 Assignments
 Class Test (MCQ based) – 4 marks
 Tutorial assignments - 6 marks
 Case study– 10 marks
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21. Productivity
 Crucial to the welfare of the industrial firm as well as for the
economic progress of the country.
 High Productivity: refers to doing work in shortest possible time
with least expenditure on inputs without sacrificing quality and
with minimum wastage of resources.
 Definition: Productivity is the quantitative relation between what
we produce and what we use as a resource to produce them.
 It is arithmetic ratio of amount produced (output) to the
amount of resources (input).
 Productivity=output/Input
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22. Productivity
 European Productivity Agency (EPA) has defined productivity as
“Productivity is an attitude of mind. It is the mentality of
progress, of the constant improvements of that which exists. It
is the certainty of being able to do better today than yesterday
and continuously. It is the constant adaptation of economic and
social life to changing conditions. It is the continual effort to
apply new techniques and methods. It is the faith in human
progress”.
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23. Productivity
 Productivity can be increased:
 Production is increased without increase in inputs.
 Same production with decrease in inputs.
 Production is increased with decrease in inputs.
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24. Productivity Measures
 Partial Productivity=Total Output/Individual Input
 Labour Productivity=Total Output/Labour Input
 Capital …
 Material …
 Energy ….
 Total productivity=Total tangible output/total tangible input
 Total tangible output=Value of finished goods produced +value
of partial units produced+dividends from securities+other
income
 Total tangible input=value
of(human+material+capital+energy+other inputs)
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25. Expectations from Productivity
 Management and Entrepreneurs: High return on
investment (ROI), High market share and corporate image
 Managers and Workers: Higher salaries and wages, safer
work environment, increased quality of work life.
 Suppliers: Prompt payment, continuous order.
 Customers: Lower cost, quality, reliability, safety and
timeliness of delivery.
 Government: Economic development, employment
generation, more exports.
 Share Holders: Higher dividends
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26. Dynamics of Productivity Change
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Reduction in
Product cost
More profits
More Savings
Increase in demand
of goods and services
Better machines
Higher investments
More skilled labour
More output

27. DEMAND FORECASTING
 Plays a crucial role in the development of plans for
the future.
 A fundamental activity of management
 Forecasting as defined by American Marketing
Association
“An estimate of sales in physical units (or monetary
value) for a specified future period under proposed
marketing plan or programme and under the
assumed set of economic and other forces outside
the organization for which the forecast is made”.
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28. Demand Forecasting
 Forecasting is not a guess work.
 looked upon as a projection based on past data.
 based on large volume of data on past performance.
 Long, Medium and short term forecasts
 Short term: covers period of less than one year.
 Inventory control, loading and scheduling, budgeting
 Medium term: one year or five years
 Aggregate planning- Manpower planning, Subcontracting planning
 Long term: covers period of five years or more
 Product diversification
 Capacity and Investment planning
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29. Classification of forecasting techniques
 Judgemental : art of human judgement.
 Time series methods: based on past data-uses
statistical and management science techniques
 Causal Techniques: tries to establish cause and
effect relationship between sales and some other
parameters related to sales. e.g. sales-growth of
industry, sales-cultural changes, sales-environmental
impacts, sales-economic status of the country. Uses
coefficient and regression analysis.
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30. Classification of forecasting techniques
 Simulation:
 Imitate customer choices that give rise to demand to arrive
at a forecast.
 Companies simulate customer buying behaviour under
dynamic and stochastic environment.
 Combines time series with causal methods to find answers:
 What will be the impact of price promotion be?
 What will be the impact of a competitor opening a store nearby?
 What will be the impact of price increase?
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31. Judgemental techniques
 Market Research: extensive survey and statistical analysis. To get details
about location, buyer occupation, prices, quantity, quality, consumer
income, etc.
 Customer and distributor surveys: survey through questionaires given
along with guarantee cards. Sales personnel and retails outlets surveys
also taken.
 Marketing trials: applicable to new products. Usually cosmetics and
toothpastes. Decision made based on controlled experiments
 Executive opinion method: non-scientific, biased and subjective.
 Delphi technique: a panel of experts are asked sequential questionaire in
which response to questionaire is used to produce next questionaire. The
responses are sent to another experts panel. Through a series of
exchanged views a reliable consensus is reached. 31

32. Delphi Technique
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33. Time Series Analysis
 Does not study the factors that influence the demand.
 All factors that shape the demand are grouped in one factor-time
 Consists of determining the trend underlying the demand and
extrapolate the future trend.
 Methods:
 Moving average method
 Weighted moving average method
 Exponential smoothing method
 Holts Model and Winters Model
 Measure of forecast accuracy
 Mean Absolute Deviation (MAD)
 Mean Square Error (MSE)
 Mean Forecast Error (MFE)
 Mean Absolute Percent Error (MAPE)
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34. Comparison of forecasting methods
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35. Measures of forecast error
 Mean Forecast Error (MFE)
 MFE=
1
𝑁 𝑡=1
𝑁
𝐸𝑡 where 𝐸𝑡 = 𝐷𝑡 − 𝐹𝑡
 Best suited in industries where inventory and backlog costs are
minimum and at the end of the Nth period the total error is the
least
 Mean Average Deviation (MAD)
 MAD=
1
𝑁 𝑡=1
𝑁
𝐸𝑡 where 𝐸𝑡 = 𝐷𝑡 − 𝐹𝑡
 Best suited in industries where inventory and backlog costs are
higher and any deviation from error is to be penalised
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36. Measures of forecast error
 Mean Square Error (MSE)
 MSE=
1
𝑁 𝑡=1
𝑁
𝐸𝑡
2
where 𝐸𝑡 = 𝐷𝑡 − 𝐹𝑡
 Similar to MAD, but penalizes larger errors
 Mean Absolute Percentage Error (MAPE)
 MAPE=
1
𝑁 𝑡=1
𝑁 𝐸𝑡
𝐷𝑡
. 100 where 𝐸𝑡 = 𝐷𝑡 − 𝐹𝑡
 MAPE gives decision maker an idea of how much the forecast is off
as a percentage of demand
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37. Measures of forecast error
 Bias and Tracking Signal(TS)
 𝑏𝑖𝑎𝑠𝑡 = 𝑖=1
𝑡
𝐸𝑖
 𝑇𝑆𝑡 =
𝑏𝑖𝑎𝑠𝑡
𝑀𝐴𝐷𝑡
 Bias determines whether a forecasting method consistently over- or
under-estimates demand
 The bias will fluctuate around 0 if the error is truly random and not
biased one way or the other. Ideally if we plot all the errors, the
slope of the best fit straight line passing through should be zero
 If the TS at any period is outside the range ±6, this is a signal that
the forecast is biased and is either underforecasting (<-6) or
overforecasting (+6).
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38. Methods of forecast
 Moving Average Method
 Weighted moving average method
 Exponential smoothing method
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39. Methods of forecast
 Holts Model (trend corrected
exponential smoothing)
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40. Subsequent Classes
 Characteristics of Production Systems
 Production Planning and Control
 …..
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