production and operations management · 2 production and operations management module contents unit...

1

Master of Business Administration (MBA)

GBS 660

Production and Operations

Management

Course Lecturer

Prof Levy Siaminwe, Phd

Email: [email protected]

mailto:[email protected]

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Production and Operations Management Module

Contents

Unit 1: Insight into Production and Operations Management

Unit 2: Demand Forecasting

Unit 3: Inventory Management

Unit 4: Aggregate Planning

Unit 5: Master Production Schedule

Unit 6: Material Requirement Planning

Unit 7: Shop-Floor Planning and Control

Unit 8: Total Quality Management

Unit 9: Capacity Requirement Planning

Learning Objectives

Upon completing this module, students should be able

to:

understand the strategic role of operations

management in organisations

understand key concepts and issues of operations

management in both manufacturing and service

organisations

understand the interdependence of the operations

function with the other key functional areas of an

organisation

apply analytical skills and problem-solving tools to

the planning and control of production operations

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Recommended Textbook

Norman Gaither (1992) Production and

Operations Management, Sixth Edition, The

Drysen Press; ISBN: 0-03-097561-1 or latest

version

R. Dan Reid and Nada R. Sanders (2010)

Operations Management: An Integrated

Approach, International Student Version,

Wiley; ISBN: 978-0-470-52458-9

Unit 1

Insight into Production and Operations

Management

1.1 What is Operations Management?

1.2 The Role of Operations Management in Business

1.3 Decisions in Operations Management

1.4 Issues in Operations Management

1.5 What is Production and Operations

Management?

4

1.1 What is Operations Management?

The business function responsible for

planning, coordinating, and controlling

the resources needed to produce products

and services for a company.

Operations Management is in every

organization….

Manufacturing Products

Providing Insurance Cover

Providing Healthcare

Etc....

5

Managing Operations in

Manufacturing Products

Receiving requirements

Procuring raw materials

Recruiting and retaining staff

Planning resources

Improving quality, cost and delivery

Defining policies and procedures

Making products

Storing and distributing products

Etc…


Providing Insurance Cover

Receiving requests for cover

Assessing/reassess risk

Answering customer enquiries

Processing claims

Making payments


Planning resources

Improving quality, cost and delivery

Customer care process

Accredit repairers/Arrange repairs

Forecast demand–Process applications–Process

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Providing Healthcare Obtaining finance


Sourcing and procuring supplies

Responding to emergencies

Scheduling patient operations

Capacity planning (bed/theatre management)

Quality control/track and trace

Dispatch ambulance

Contact theatre team

Prepare resource (theatre)–Kitting for operations–

Resource planning (Ward)–Plan diagnostics

resources–Liaise with other departments

Operations Management Activities (1/3)

Forecast Demand

• Market Product

• Adapt to comply with customer demand

• Understand what the customer wants

• Understand how much the customer wants

• Know product demand

Sourcing and Procurement

• Order Stationery

• Check delivery with order

• Order materials

• Schedule suppliers

• Managing stock (getting it in the right place at the

right time)

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Creation of Output

• Arrange for necessary equipment

• Schedule material/staff/equipment to produce

goods and services

• Plan ‘work order’

• Produce goods

• Quality control

Delivery

• Deliver finished products

• Consider logistics/delivery

• Dispatching the goods or service to the customer

• Arrange packaging/presentation


Manage People • Employ people

• Train people

• Outsource

• Delegation

• Managing people

• Recruit and train staff

• Schedule labour

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Operations Management is:

A management function

An organisation’s core function

Core Functional Areas of the Organisation

Operations

Finance

Marketing

Manages cash flow, current assets

and capital investments

Manages

customer

demands;

Generates

sales for

goods and

services.

Manages

people,

equip.,

tech.,

materials

and info.

to

produce

goods

and/or

services

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Organising to Produce Goods and

Services Production activities are dependent mutually and

connected tightly

Marketing

MIS Engineering

HRM

QA

Accounting

Sales

Finance

POM

1.2 The Role of Operations

Management in Business

Operations Management Transforms

inputs to outputs

Inputs are resources such as

People, Material, and Money

Outputs are goods and services

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Operations Management’s

Transformation Process

Inputs

• Human

Resources

• Facilities and

Processes

• Technologies

• Materials

The

Transformation

Process

Outputs

• Goods

• Services

Customer Feedback

Performance Information

Operations Management’s

Transformation Role

To add value

Increase product value at each stage

Value added is the net increase between

output product value and input material

value

Provide an efficient transformation

Efficiency – means performing activities

well for least possible cost

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Manufacturers and Service

Organizations

Both use technology

Both have quality, productivity, & response

issues

Both must forecast demand

Both can have capacity, layout, and location

issues

Both have customers, suppliers, scheduling

and staffing issues

1.3 Decisions in Operations

Management

Strategic Decisions – set the direction for the

entire company; they are broad in scope and

long-term in nature.

Tactical and Operational Decisions – focus

on specific day-to-day issues like resource

needs, schedules, and quantities to produce.

Strategic and Tactical decisions must align.

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Strategic Decisions

Responsible for, and decisions about:

What to make (product development)

How to make it (process and layout

decisions) –or should we buy it

Where to make it (site location)

How much is needed (high level capacity

decisions)

Tactical Decisions

Address material and labour resourcing

within strategy constraints, for example:

How many workers are needed and when

(labour planning)

What level of stock is required and when

should it be delivered (inventory and

replenishment planning)

How many shifts to work. Whether

overtime or subcontractors are required

(detailed capacity planning)

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Operational Decisions

Detailed lower-level

(daily/weekly/monthly) planning,

execution and control decisions, for

example:

What to process and when (scheduling),

The order to process requirements

(sequencing)

How work is put on resources (loading)

Who does the work (assignments)

1.4 Issues in Operations Management

Environmental sustainability, recycling , reuse

Customers demand better quality, greater speed, and lower

costs

Globalisation of supply and demand

Achieving and sustaining high quality while controlling cost

Integrating new technologies and control systems into

existing processes

Obtaining, training, and keeping qualified workers and

managers

Increased cross-functional decision making

Integrating production and service activities at multiple sites

in decentralized organizations

Recognized need to better manage information using ERP

and CRM systems

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1.5 What is Production and Operations

Management?

The creation of goods

and services by turning

inputs into outputs,

which are products

and services

Production

Management of the

production process

Operations

Management

Production and operations management

(POM) is the management of an organization’s

production system

A production system takes inputs and converts

them into outputs

The conversion process is the predominant

activity of a production system

The primary concern of an operations manager is

the activities of the conversion process

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Operations Strategy

Marketplace

Corporate Mission

Operations Strategy

Operations management

Marketing Strategy Finance Strategy

People Plants Parts Processes

Planning & control systems

Production System

Inputs:

Materials

Customers

Outputs:

Products

Services

Unit 2

Demand Forecasting

2.1 Forecasting Definition and Fundamental

Rules

2.2 Types of Forecasting Methods

2.3 Quantitative Forecasting Methods

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2.1 Forecasting Definition and Rules

Forecasting is the prediction of future events

on the basis of either:

historical data

Opinions

trend of events, or

known future variables

Demand forecasting is estimating the future

demand for products and services and the

resources necessary to produce these outputs

It is the first step in planning in any business

Forecasting in Business

Forecasts provide information that assist

managers in guiding future activities toward

organizational goals

Forecasting is critical to management of all

organizational functional areas :

Marketing relies on forecasting to predict demand

and future sales

Finance forecasts stock prices, financial performance,

capital investment needs

Information systems provides ability to share

databases and information

Human resources forecasts future hiring requirements

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General Characteristics of Forecasts

Forecasts are seldom perfect

The prediction does not take account of all

factors; The environment is complex and

subject to rapid change

Forecasts are more accurate for groups or

families of items

Forecasts are more accurate for shorter time

periods; Long term forecasting is problematic

Every forecast should include an error estimate

Elements of a Good Forecast

The forecast should be timely

The forecast should be accurate

The forecast should be reliable

The forecast should be expressed in

meaningful units

The forecasting technique should be simple

to understand and use

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Steps in the Forecasting Process

1. Determine the purpose of the forecast (what are the

objectives of forecasting?)

2. Select the Items for which forecasts are needed

(single product or group of products)

3. Determine the Time Horizon for the forecast (short-

term, medium-term or long-term)

4. Select the Forecasting Model (Qualitative technique

or Quantitative technique)

5. Gather information to be used in forecasting

6. Generate the forecast

7. Monitor forecast accuracy over time

Choosing the Forecasting Model

The greater the ability to react, the less accurate

the forecast has to be

A tradeoff between the cost of doing the forecast

and the opportunity cost of proceeding with

misleading numbers

Factors to consider:

1. Length of forecast horizon

2. The amount and type of available data

3. Degree of accuracy required

4. Presence of data patterns

5. Availability of qualified personnel

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2.2 Types of Forecasting Methods

Qualitative Methods

Rely on subjective

opinions from one

or more experts.

Quantitative Methods

Rely on data and

analytical

techniques.

Qualitative Forecasting Methods

Usually based on judgments about causal

factors that underlie the demand of particular

products or services

Do not require a demand history for the

product or service, therefore are useful for

new products/services

Approaches vary in sophistication from

scientifically conducted surveys to intuitive

hunches about future events

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Examples of Qualitative Forecasting

Methods

Grass Roots: deriving future demand by asking

the person closest to the customer.

Market Research: trying to identify customer

habits; new product ideas.

Panel Consensus: deriving future estimations

from the synergy of a panel of experts in the area.

Historical Analogy: identifying another similar

market.

Delphi Method: similar to the panel consensus

but with concealed identities.

2.3 Quantitative Forecasting Methods

Quantitative forecasting methods are

mathematical models based on historical data

They are based on the assumption that the

“forces” that generated the past demand will

generate the future demand, i.e., history will

tend to repeat itself

Analysis of the past demand pattern provides

a good basis for forecasting future demand

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Quantitative Forecasting Methods

Categories

Time Series Models:

Assumes information needed to generate a forecast

is contained in a time series of data

Assumes the future will follow same patterns as

the past

Causal Models or Associative Models

Explores cause-and-effect relationships

Uses leading indicators to predict the future

Majority of quantitative forecasting methods fall

in the category of time series analysis

Quantitative Forecasting Methods

Simple Moving Average (Time Series Analysis)

Weighted Moving Average (Time Series Analysis)

Exponential Smoothing (exponentially weighted moving average) (Time Series Analysis)

Exponential Smoothing with Trend (double smoothing) (Time Series Analysis)

Linear Regression (Causal or Associative Method)

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Historical Demand Data Patterns

1. Trend is the gradual upward or downward

movement of the data overtime. Trends are noted

by an upward or downward sloping line (T)

2. Seasonality is a data pattern that repeats itself

over the period of one year or less (days, weeks,

months, or quarters) (S)

3. Cycle is a data pattern that repeats itself... may

take years (C)

4. Random fluctuations are “blips” in the data

caused by chance or random variation or

unexplained causes (R)

Data Patterns in a Time Series

1 2 3 4

x

x x x

x x

x x x

x x x x x

x x x x x x x x

x x

x x x x

x x

x x

x

x x

x x

x x

x

x x

x x

x

x

x

Year

Sale

s

Seasonal variation

Trend

Level

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Short Range Forecasts

In cases in which the time series is fairly stable and has no significant trend, seasonal, or cyclical effects, one can use smoothing methods to average out the irregular components of the time series

Three common smoothing methods are:

Simple moving average

Weighted moving average

Exponential smoothing

Simple Moving Average

Used if demand is not growing nor declining

rapidly

Used often for smoothing, that is removing random

fluctuations in the data

Equation

where:

Ft = forecast for period t,

n = number of periods to be averaged (AP)

At-1 = actual demand realized in the past period

for up to n periods

n

A+...+A +A +A =F n-t3-t2-t1-t

t

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Simple Moving Average Example 1

Historical demand for a product is as shown in the Table on the

left. Using a simple four month moving average, calculate a

for cast for October. The table on the right shows the solution.

Month Actual

Demand

April 60

May 55

June 75

July 60

Aug 80

Sept 75

Oct ?

Month

Actual

Demand

Forecast

(AP = 4

Month)

April 60

May 55

June 75

July 60

Aug 80 62.5

Sept 75 67.5

Oct - 72.5

Simple Moving Average Example 2

During the past ten weeks, sales of cases of Comfort

brand headache medicine at Robert’s Drugs Store have

been as follows:

Week Sales Week Sales

1 110 6 120

2 115 7 130

3 125 8 115

4 120 9 110

5 125 10 130

Forecast the sales in period 11 using a three period

moving average.

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Simple Moving Average Example 2 Solution

Solution performed in Microsoft Excel software

Ft is the forecast for week t.

F4 (forecast for week 4)=116.7

F11 (forecast for week 11)=118.3

Thus we would forecast the sales for Week 11 to be 118.3

Robert's Drug

n=3

Week (t ) At Ft

1 110

2 115 #N/A

3 125 #N/A

4 120 116.7

5 125 120.0

6 120 123.3

7 130 121.7

8 115 125.0

9 110 121.7

10 130 118.3

11 118.3

Weighted Moving Average

This is a variation on the simple moving

average where instead of the weights used to

compute the average being equal, they are not

equal

This allows more recent demand data to have

a greater effect on the moving average,

therefore the forecast

The weights must add to 1.0 and generally

decrease in value with the age of the data

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Weighted Moving Average

Allows different weights to be assigned to

past observations

Older data usually less important

Weights based on experience, trial-and-error

Equation

F = w A + w A + w A +...+w At 1 t-1 2 t-2 3 t-3 n t- n

1=wn

1=t

t wt = weight given to time period “t”

occurrence (weights must add to one)

Robert’s Drug Sales Forecast using Weighted

Moving Average

Use a 3 period weighted moving average to forecast the

sales for week 11 giving a weight of 0.6 to the most recent

period, 0.3 to the second most recent period, and 0.1 to the

third most recent period.

F11 = (0.6)*130 + (0.3)*110 + (0.1)* 115= 122.5

Thus we would forecast the sales for week 11 to be 122.5.

Sales for the

most recent

period

Sales for 2nd

most recent

period

Sales for 3rd

most recent

period

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Disadvantages of Moving Average

Methods

o Increasing n (number of periods averaged)

makes forecast less sensitive to real changes in

the data

oThey do not forecast trends well. They lag the

actual values

oRequire sufficient historical data

Exponential Smoothing Method

Premise: The most recent observations might have the

highest predictive value.

Therefore, we should give more weight to the more

recent time periods when forecasting

Requires smoothing constant ()

Which ranges from 0 to 1, and

Subjectively chosen

The method involves little record keeping of past data

It is the most used of short range forecasting

techniques because it is fairly accurate, models easily

formulated, easily understood, little computation

required, and easy to test

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Exponential Smoothing Equation

The equation used to compute the forecast is...

Ft = Ft-1 + (At-1 - Ft-1), or

Ft = ·At-1 + (1- ) Ft-1

where...

Ft = forecast demand

At = actual demand realized

= smoothing constant

Note that every observation is included in equation, but

their weights get smaller.

Robert’s Drug Sales Forecast using

Exponential Smoothing Method

F11 = 0.1 * A10 + 0.9 F10

= 0.1 *130 + 0.9 * 115.4

= 116.9

Robert's Drugs

α=0.1

Week (t ) Salest Ft

1 110 #N/A

2 115 110.0

3 125 110.5

4 120 112.0

5 125 112.8

6 120 114.0

7 130 114.6

8 115 116.1

9 110 116.0

10 130 115.4

11

Thus we would forecast

sales for week 11 to be

116.9

Forecast Robert’s drug

sales for period 11

using Exponential

Smoothing with α= 0.1

29

Responsiveness with Different Values

3000

2500

2000

1500

1000

1 2 3 4 5 6 7 8 9 10 11 12

Actual demand

alpha = .1

alpha = .5

alpha = .9

Questions that You should be Asking

For the Moving Average technique, how do I determine

the best value of AP (n) to use for forecasting?

For Exponential Smoothing, how do I determine the

best value of α to use?

If I realize that a smoothing technique should be

employed, how do you know which smoothing

technique is best?

In order to answer the above questions, we need a

criteria for judging the accuracy of a forecasting

technique. Once we select a criterion, the method (or

parameter) which provides the best value for our

criterion is the best method (or parameter) to use for

forecasting our scenario.

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Forecast Accuracy

Accuracy is the typical criterion for judging

the performance of a forecasting approach

Accuracy is how well the forecasted values

match the actual values

Monitoring Forecast Accuracy

Accuracy of a forecasting approach needs to be

monitored to assess the confidence you can

have in its forecasts and changes in the market

may require reevaluation of the approach

Accuracy can be measured in several ways,

two of which are:

Mean Absolute Deviation (MAD)

Mean Squared Error (MSE)

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Mean Absolute Deviation (MAD)

The mean of the absolute values of all forecast errors

is calculated, and the forecasting method or

parameter(s) which minimize this measure is selected.

MAD =

A - F

n

t tt=1

n

n

demandForecast -demand Actual

=MAD

n

1=i

i

Mean Squared Error (MSE)

MSE = (Syx)2

Small value for Syx means data points tightly

grouped around the line and error range is small.

The smaller the standard error the more accurate

the forecast.

MSE = 1.25(MAD)

when the forecast errors are normally distributed

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Selecting the Smoothing Technique for

Robert’s Drugs Sales Forecasting

Determine the smoothing technique that is best for

forecasting Robert’s Drug sales: A two period

moving average, a three period moving average,

exponential smoothing (α=0.1), or exponential

smoothing (α=0.2)

Realistically we should have experimented with more

values of n for the moving average, and α for

exponential smoothing to determine the absolute best

parameters to use for our technique

We randomly chose to use the MSE criterion to judge

the best technique

MSE for Moving Average with AP = 2

Robert's Drug

Sales n=2 Error

Week (t ) At Ft (At - Ft) (At - Ft)2

1 110

2 115 #N/A

3 125 112.5 12.5 156.25

4 120 120.0 0.0 0.00

5 125 122.5 2.5 6.25

6 120 122.5 -2.5 6.25

7 130 122.5 7.5 56.25

8 115 125.0 -10.0 100.00

9 110 122.5 -12.5 156.25

10 130 112.5 17.5 306.25

11 120.0

MSE 98.44

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MSE for Moving Average with AP = 3 Robert's Drug

Sales n=3 Error


1 110

2 115 #N/A

3 125 #N/A

4 120 116.7 3.3 11.11

5 125 120.0 5.0 25.00

6 120 123.3 -3.3 11.11

7 130 121.7 8.3 69.44

8 115 125.0 -10.0 100.00

9 110 121.7 -11.7 136.11

10 130 118.3 11.7 136.11

11 118.3

MSE 69.84

MSE for Exponential Smoothing with = 0.1

Sales α=0.1 Error


1 110 #N/A

2 115 110.0 5.0 25.00

3 125 110.5 14.5 210.25

4 120 112.0 8.1 64.80

5 125 112.8 12.2 149.94

6 120 114.0 6.0 36.25

7 130 114.6 15.4 237.73

8 115 116.1 -1.1 1.26

9 110 116.0 -6.0 36.13

10 130 115.4 14.6 212.87

11

MSE 108.25

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MSE for Exponential Smoothing with = 0.2

Sales α=0.2 Error


1 110 #N/A

2 115 110.0 5.0 25.00

3 125 111.0 14.0 196.00

4 120 113.8 6.2 38.44

5 125 115.0 10.0 99.20

6 120 117.0 3.0 8.81

7 130 117.6 12.4 153.13

8 115 120.1 -5.1 26.01

9 110 119.1 -9.1 82.45

10 130 117.3 12.7 162.20

11

MSE 87.92

Since the three period moving average

technique (MA3) provides the lowest MSE

value, this is the best smoothing technique to

use for forecasting Robert’s Drug Sales

Selecting the Smoothing Technique for

Robert’s Drugs Sales Forecasting

35

Exponential Smoothing with Trend

Attempts to correct (somewhat) the lag in the

exponential smoothing method

Trend equation with a smoothing constant,

(delta)

Formulae…

FITt = Forecast including trend

FITt = Ft + Tt

Ft = FITt-1 + (At-1 - FITt-1)

Tt = Tt-1 + (Ft - FITt-1)

Trend-Adjusted Forecasting

Three steps to compute a Trend-Adjusted

Forecast:

Step 1: Compute Ft , the exponentially

smoothed forecast for period t

Step 2: Compute the smoothened trend, Tt

Step 3: Calculate the forecast including

trend, FITt = Ft + Tt

36

Trend-Adjusted Forecasting Exercise

A large cement manufacturer uses exponential smoothing to forecast demand for a piece of pollution-control equipment. It appears that an increasing trend is present. Month At Month At 1 700 5 713 2 685 6 728 3 648 7 754 4 717 8 762 If the initial forecast for month 1 was 650 units and the trend over that period was 0 units, calculate FIT for the 9-month period. Use smoothing constants, = 0.1 and = 0.2.

Linear Regression in Forecasting

Linear regression is based on

1. Fitting a straight line to data

2. Explaining the change in one variable

through changes in other variables.

dependent variable = a + b (independent

variable)

By using linear regression, we are trying to explore

which independent variables affect the dependent

variable

37

Linear Regression Model

Regression models are used to test if a

relationship exists between variables; that is, to

use one variable to predict another

Equation is of the form: Y = a + bX+ error

where:

Y = dependent variable (response)

X = independent variable (predictor)

a = intercept (value of Y when X = 0)

b = slope of the regression line

error = random error

Simple Linear Regression

The predicted line is: Ŷ = a + bX

Used to predict Y for some future value of X

Sample data are used to estimate the true

values for the intercept and slope (a and b

values, respectively)

Error ( = Y – Ŷ), difference between the

actual value of Y and the predicted value

The Least Squares Method of Linear

Regression minimises the sum of squared

errors

38

Least Squares Regression Equations

Ŷ = a + bX

22

2

XXn

XYXYXa

22

XXn

YXXYnb

Developing a Linear Regression Equation

Step 1: Collect the historical data required for

analysis.

Step 2: Identify the X and Y values for each

observation.

Step 3: Put the data in tabular form and make

necessary column calculations.

Step 4: Compute the Y intercept (a) and the

slope (b) using least squares regression

equations.

Step 5: Formulate the estimating equation.

39

Manufacturing Example

Step 1: Collect the historical data required for analysis

Step 2 and 3: Put the data in Tabular form.

X = manufacturing direct labour hours in hundreds of hours

(00’s)

Y = manufacturing overhead in thousands of dollars ($000’s)

40

Step 5: Formulate the Estimating Equation

Ŷ = a + bX

Ŷ = 5.8269 + 5.6322X

Where:

Ŷ = Manufacturing Overhead ($000’s)

X = Manufacturing Direct Labour Hours (00’s)

Estimate manufacturing overhead given an estimate

for manufacturing direct labour hours of 2,100:

Ŷ = 5.8269 + 5.6322X

Ŷ = 5.8269 + 5.6322(21)

Ŷ = 5.8269 + 118.2762

Ŷ = 124.1031 thousand dollars

Rounded to the nearest dollar, the estimate

would be $ 124,103.

41

Measuring the Fit of the Regression Model

Correlation Coefficient

r – strength of the relationship between Y

and X variables

Coefficient of Determination

r2 - proportion of explained variation

To understand how well the model predicts the

response variable, we evaluate the following:

Correlation Coefficient

The correlation coefficient (r) measures the

strength of the linear relationship.

Note: -1 < r < 1

2222 YYnXXn

YXXYnr

42

Correlation Coefficient Interpretation

Coefficient of Determination

The coefficient of determination (r2 ) is the

proportion of the variability in Y that is

explained by the regression equation.

Note: 0 < r2 < 1

43

Manufacturing Example Model Fit

This means that approximately 94% of the

variation in manufacturing overhead (Y) can

be explained by its relationship with

manufacturing direct labour hours (X).

r2 = 0.94 r = 0.97

Forecasts with Seasonality

Seasonal indices can be used to make

adjustments in the forecast for seasonality.

A seasonal index indicates how a particular

season compares with an average season.

The seasonal index can be found by

dividing the average value for a particular

season by the average of all the data.

44

Calculating Seasonal Indices

Month Demand Yr 1 Demand Yr 2 2-Yr Avge Monthly Avge Seasonal Index

Jan 80 100 90 94 0.957

Feb 75 85 80 94 0.851

Mar 80 90 85 94 0.904

Apr 90 110 100 94 1.064

May 115 131 123 94 1.309

Jun 110 120 115 94 1.223

Jul 102 108 105 94 1.117

Aug 88 102 100 94 1.064

Sep 85 95 90 94 0.957

Oct 77 83 80 94 0.851

Nov 75 85 80 94 0.851

Dec 82 78 80 94 0.851

1,128 (1,128/12) (2Yr-Ag/M-Ag)

Decomposition Method with Trend and

Seasonal Components in historical data

Decomposition is the process of isolating linear trend and seasonal factors to develop more accurate forecasts.

There are five steps to decomposition:

1. Compute the seasonal index for each season.

2. Deseasonalize the data by dividing each number by its seasonal index.

3. Compute a trend line with the deseasonalized data.

4. Use the trend line to forecast.

5. Multiply the forecasts by the seasonal index.

45

Decomposition Method Example

Given three years of quarterly data, determine the

seasonal Indices

Demand Data:

Qtr Year 1 Year 2 Year 3 Total Index

1 6 8 7

2 12 13 14

3 9 11 10

4 15 17 18

Qtr 1 SI = 0.5998

Qtr 2 SI = 1.1140

Qtr 3 SI = 0.8569

Qtr 4 SI = 1.4284


Deseasonalize the actual demand data by dividing by

the appropriate seasonal factor:

Year Quarter Period Demand DeSeas.

1 1 1 6 10.0

1 2 2 12 10.8

1 3 3 9 10.5

1 4 4 15 10.5

2 1 5 8 13.3

2 2 6 13 11.7

2 3 7 11 12.8

2 4 8 17 11.9

3 1 9 7 11.7

3 2 10 14 12.6

3 3 11 10 11.7

3 4 12 18 12.6

46


• Then perform a linear regression, least squares

approximation of the relationship between

quarter (X) and deseasonalized sales (Y)

• Form of a Linear Equation:

Y = a + b X

• From the equations:

a = 10.44

b = 0.1899

X Y XY XX

1 10.0 10.0 1

2 10.8 21.6 4

3 10.5 31.5 9

4 10.5 42.0 16

5 13.3 66.5 25

6 11.7 70.2 36

7 12.8 89.6 49

8 11.9 95.2 64

9 11.7 105.3 81

10 12.6 126.0 100

11 11.7 128.7 121

12 12.6 151.2 144

78 140.1 937.8 650

47


Project the trend using the predictive equation for

each quarter of year 4:

Quarter 13: F13 = 10.44 + 0.1899 (13) = 12.9

Quarter 14: F14 = 10.44 + 0.1899 (14) = 13.1

Quarter 15: F15 = 10.44 + 0.1899 (15) = 13.3

Quarter 16: F16 = 10.44 + 0.1899 (16) = 13.5


Adjust for seasonality by multiplying by the

seasonal factors for the appropriate quarters:

Qtr Proj. ReSeas.

13 12.9 7.7

14 13.1 14.6

15 13.3 11.4

16 13.5 19.2

Therefore, year four forecasts are:

Qtr 1 = 7.7

Qtr 2 = 14.6

Qtr 3 = 11.4

Qtr 4 = 19.2

48

The graph shows that the forecasts have not lagged actual

demand, and has captured both trend and seasonality

0

5

10

15

20

25

0 4 8 12 16 20

Quarter

De

ma

nd Demand

Proj.

ReSeas.

Unit 3

Inventory Management

3.1 Inventory and Independent Demand Inventory

Management Models

3.2 Determining Inventory Order Quantities

3.3 Determining Inventory Order Points

3.4 Inventory ABC Classification

49

3.1 Inventory and Independent Demand

Inventory Management Models

Inventory is the stock of any item or

resource used in an organisation

Inventory in the system is the result of

imperfection in Demand forecasting,

production, and supplier delivery

Inventory is the most expensive and the most

important asset for an organisation

Inventory Types

• Raw materials and purchased parts from

outside suppliers

• Components: subassemblies that are awaiting

final assembly

• Work in process: all materials or components on

the production floor in various stages of

production

• Finished goods: final products waiting for

purchase or to be sent to customers

• Supplies: all items needed but that are not part of

the finished product

50

Independent Demand versus Dependent

Demand

A

B(4) C(2)

D(2) E(1) D(3) F(2)

Dependent Demand:

Raw Materials,

Component parts,

Sub-assemblies, etc.

Independent Demand:

Finished Goods/Parts

Product Tree

Why Do We Want to hold Inventory (1/3)

Finished Goods Inventory:

– Essential produce to stock positioning, of strategic

importance

– Necessary in level aggregate capacity plans

– Products can be displayed to customers

In-Process (Work-in-Process (WIP)):

– Necessary in process-focused production, uncouples

the states of production, increases flexibility

– Producing and transporting larger batches of

products creates more inventory but may reduce

materials-handling and production costs

51


Raw Materials Inventory:

– Suppliers produce and ship raw materials in batches

– Larger purchases result in more inventory, but

quantity discounts and reduced freight and

materials-handling costs may result

– Raw material sourcing has long and variable lead

times

Improve customer service: meet or exceed

customer’s expectations of product availability


Reduce certain costs such as:

– ordering costs (processing the purchase order,

expediting, record keeping and receiving the order

into the warehouse)

– stockout costs (lost sales, dissatisfied customers,

disruptions to production)

– acquisition costs (quantity discount, lower

transportation and handling costs)

– start-up quality costs (learning curve, less

changeovers and less scrap)

Contribute to the efficient and effective operation

of the production system (decoupling)

52

Why We Do not Want to hold Inventory

Certain costs increase such as:

– carrying costs (interest on debt, interest income foregone,

warehouse rent, lighting, security, receiving, insurance, etc)

– cost of customer responsiveness (large WIP clog production

systems)

– cost of coordinating production (large WIP lead to schedule

coordination problems)

– cost of diluted return on investment (large inventories

reduce ROI and adds to the finance costs by increasing

interest rates on debt and reducing stock prices)

– reduced-capacity costs (Inventory is some form of waste!)

– large-lot quality cost (defects would lead to large batch

losses)

– cost of production problems (large WIP camouflage

underlying production problems!)

Common Inventory Problems to Avoid

Not stocking products customers expect to be

in stock

Too many stockouts of products that are

stocked

Excess inventory and dead stock of other

products

Product proliferation

Lower margins resulting in fewer dollars

available to invest in inventory

53

Use Inventory Policies to solve the

common Inventory Problems

Inventory Policies specify decision rules with

respect to the point in time when a

replenishment of the inventory should be

initiated, as well as to the replenishment

quantity that should be ordered from the

supplying node in the supply network

Independent Demand Inventory

Management Models

1. Fixed Order Quantity System: this

involves placing orders for the same quantity

of the item each time that item reaches a pre-

set minimum stocking level, or reorder point

2. Fixed Order Period System: this involves

review of inventory at fixed time intervals,

and orders are placed for enough items to

bring its inventory levels back up to some

predetermined level

54

Inventory Control Decisions

Two fundamental decisions in controlling

inventory:

1. How much to order

2. When to order

Overall goal is to minimize total

inventory cost

Inventory Costs

Cost of the Items (Acquisition costs)

Cost of Ordering or Setup

Cost of Carrying or Holding inventory

Cost of safety stock

Cost of stockouts

55

Inventory Carrying Costs

Capital Costs: based on inventory

investment

Inventory Service Costs: relate to insurance

and taxes paid

Storage Space Costs: relate to warehousing

of inventory

Inventory Risk Costs: arising from

obsolescence, damages, pilferage and

relocation costs

3.2 Determining Inventory Order

Quantities

The procedure of determining inventory

order quantities would depend on the

inventory management system

Fixed Order Quantity System rely on the

behaviour of inventory costs to identify

the quantities with minimum total

inventory stocking costs

This section uses the Fixed Order

Quantity System

56

Inventory Costs Behaviour

Costs associated with ordering too much (represented by carrying costs)

Costs associated with ordering too little (represented by ordering costs)

These costs are opposing costs, that is, as one increases the other decreases

The sum of the two costs is the Total Stocking Cost (TSC)

When plotted against order quantity, the TSC decreases to a minimum cost and then increases

This cost behaviour is the basis for answering the first fundamental question: how much to order?

Inventory Cost Behaviour Plot

Annual Cost ($)

Order Quantity

Minimum

Total Annual

Stocking Costs

Annual Carrying Costs

Annual Ordering Costs

Total Annual Stocking Costs

Smaller Larger

Lo

we

r H

igh

er

EOQ

57

Fixed-Order Quantity Models

Basic EOQ

EOQ for Production Lots

EOQ with Quantity Discounts

Fixed-Order Quantity Models Assumptions

Demand for the product is constant and uniform

throughout the period

Lead time (time from ordering to receipt) is

constant

Price per unit of product is constant

Inventory holding cost is based on average

inventory.

Ordering or setup costs are constant.

All demands for the product will be satisfied (No

back orders are allowed)

58

Variables used in EOQ Equations

ac = Cost of purchasing or producing one unit of a material

or product

C = Cost of carrying one unit in inventory for one year

D = Annual demand for a material

d = Demand rate or usage rate

EOQ = Optimum number of pieces per order

p = Supply rate or production rate

Q = Quantity of material ordered at each order point

S = Average cost of completing an order for a material

TMC = Total of annual acquisition cost and total annual

stocking cost for a material

TSC = Total annual stocking costs for a material

Model I: Basic EOQ

Typical assumptions made:

– annual demand (D), carrying cost (C) and ordering cost (S) can be estimated

– average inventory level is the fixed order quantity (Q) divided by 2 which implies:

• no safety stock

• orders are received all at once

• demand occurs at a uniform rate

• no inventory when an order arrives

– Stockout, customer responsiveness, and other costs are inconsequential

– acquisition cost is fixed, i.e., no quantity discounts

59

Steps in finding the Optimum Order

Quantity

Develop an expression for the ordering cost.

Develop an expression for the carrying cost.

Set the ordering cost equal to the carrying

cost.

Solve this equation for the optimal order

quantity, EOQ.


Quantity Annual Ordering Cost:

Annual Carrying or Holding Cost:

SQ

D

orderper units ofNumber

cost Ordering x demand Annual

C2

Q

YearPer Cost Carryingx Inventory Average

60


Quantity

C

2

Q S

Q

D =

Q2

C

2 S D =

EOQ =

C

2 S D

Per Unit versus Percentage Carrying

Cost

• Typically, carrying cost, C, is stated in

– per unit $ cost

– per year

• Sometimes, an annual Interest rate, I, is cited

and C must be calculated

– I multiplied by ac (unit cost) or acquisition cost

• I(ac) then replaces C

61

Per Unit versus Percentage Carrying

Cost

EOQ =

Per Unit Carrying Cost:

Percentage Carrying Cost:

C

2DS

EOQ = I(ac)

2DS

Denominator

Change

Calculating other Parameters

Total Stocking Cost:

C2

QS

Q

D TSC

Expected Number of Orders:

EOQ

D

N

QuantityOrder

Demand

Expected Time between Orders:

N

Days

T

Yearper Orders ofNumber

Yearper Days WorkingofNumber

62

Basic EOQ Exercise

NCZ produces fertilizer to sell to wholesalers.

One raw material – calcium nitrate – is purchased

from a nearby supplier at $22.50 per ton. NCZ

estimates it will need 5,750,000 tons of calcium

nitrate next year. The annual carrying cost for this

material is 40% of the acquisition cost, and the

ordering cost is $595.

a) What is the most economical order quantity?

b) How many orders will be placed per year?

c) How much time will elapse between orders?

Model II: EOQ for Production Lots

Used to determine the order size, production

lot, if an item is produced at one stage of

production, stored in inventory, and then sent

to the next stage or the customer

Differs from Model I because orders are

assumed to be supplied or produced at a

uniform rate (p) rather than the order being

received all at once

It is also assumed that the supply rate, p, is

greater than the demand rate, d

63

Production and Demand Cycles

Inv

ento

ry L

evel

Demand Portion of Cycle

Demand Portion of Cycle

Maximum

Inventory

Level

Time

Production Portion of Cycle (t) = Q/p

Q

Developing the Production Order

Quantity Annual Ordering Cost:

Annual Carrying or Holding Cost:

SQ

D

orderper units ofNumber

cost Ordering x demand Annual

C]p

d)-(p[

2

Q

YearPer Cost Carryingx Inventory Average

64

Setting the Equations equal to Solve for

EOQ

C]p

d)-(p[

2

Q S

Q

D

]d)-(p

p[

C

2DS EOQ

Note the similarities with Model I equation

EOQ for Production Lots Exercise

A Power Company buys coal from a Coal mine to

generate electricity in rural areas. The Coal mine

can supply coal at the rate of 3,500 tons per day for

$10.50 per ton. The Power Company uses the coal

at a rate of 800 tons per day and operates 365 days

per year. The annual carrying cost for coal is 20%

of the acquisition cost, and the ordering cost is

$5,000.

a) What is the economical production lot size?

b) What is the Power Company’s maximum

inventory level for coal?

65

Model III: EOQ with Quantity Discounts

Under quantity discounts, a supplier offers a lower

unit price if larger quantities are ordered at one time

This is presented as a price or discount schedule,

that is, a certain unit price over a certain order

quantity range

This means this model differs from Model I

because the acquisition cost (ac) may vary with the

quantity ordered, that is, it is not necessarily

constant

Under this condition, acquisition cost becomes an

incremental cost and must be considered in the

determination of the EOQ

Model III: EOQ with Quantity Discounts

To evaluate the most economical quantity to

Order, use the

Total annual material costs (TMC) = Total

annual stocking costs (TSC) + Annual

acquisition cost

Total Annual Material Cost:

acD)(C2

QS

Q

D TMC

66

EOQ with Quantity Discounts Steps

To find the EOQ, the following procedure is used:

1. Compute the EOQ using the lowest acquisition

cost.

– If the resulting EOQ is feasible (the quantity

can be purchased at the acquisition cost

used), this quantity is optimal and you are

finished.

– If the resulting EOQ is not feasible, go to

Step 2

2. Identify the next higher acquisition cost.

EOQ with Quantity Discounts Steps

3. Compute the EOQ using the acquisition cost from

Step 2.

– If the resulting EOQ is feasible, go to Step 4.

– Otherwise, go to Step 2.

4. Compute the TMC for the feasible EOQ (just found

in Step 3) and its corresponding acquisition cost.

5. Compute the TMC for each of the lower acquisition

costs using the minimum allowed order quantity for

each cost.

6. The quantity with the lowest TMC is optimal.

67

EOQ with Quantity Discounts Exercise

A Motor Vehicle parts supplier has a regional engine

oil warehouse in Lusaka. One popular engine oil,

Castrol GTX, has estimated demand of 25,000 next

year. It costs the supplier $100 to place an order for

this oil, and the annual carrying cost is 30% of the

acquisition cost. Determine the optimal order quantity

if the supplier quotes these prices for the oil:

Q ac

1 – 499 $21.60

500 – 999 20.95

1,000 + 20.90

3.3 Determining Inventory Order

Points

Basis for Setting the Order Point

Demand During Lead Time (DDLT)

Distributions

Setting Order Points

68


In the fixed order quantity system, the ordering process

is triggered when the inventory level drops to a critical

point, the order point

This starts the lead time for the item.

Lead time is the time to complete all activities associated

with placing, filling and receiving the order

During the lead time, customers continue to draw down

the inventory

It is during this period that the inventory is vulnerable to

stockout (run out of inventory)

Customer service level is the probability that a stockout

will not occur during the lead time


Thus, the order point is set based on

the demand during lead time (DDLT) and

the desired customer service level

The degree of uncertainty in the DDLT and the

customer service level desired determines the

amount of safety stock needed

Order point (OP) = Expected demand during

lead time (EDDLT) + Safety stock (SS)

69

Demand During Lead Time (DDLT)

Distributions

If there is variability in the DDLT, the DDLT is

expressed as a distribution

discrete

continuous

In a discrete DDLT distribution, values

(demands) can only be integers

A continuous DDLT distribution is appropriate

when the demand is very high

Setting Order Point for a Discrete DDLT

Distribution

1. Assume a probability distribution of actual

DDLTs is given or can be developed from a

frequency distribution

2. Starting with the lowest DDLT, accumulate the

probabilities. These are the service levels for the

DDLTs

3. Select the DDLT that will provide the desired

customer service level as the order point

70


Distribution Example

One of Emerging Technologies’ inventory items

is being analyzed to determine an appropriate

level of safety stock. The manager wants an 80%

service level during lead time. The item’s

historical DDLT is:

DDLT (cases) Occurrences

3 8

4 6

5 4

6 2



Construct a Cumulative DDLT Distribution

Probability Probability of

DDLT (cases) of DDLT DDLT or Less

2 0 0

3 .4 .4

4 .3 .7

5 .2 .9

6 .1 1.0

To provide 80% service level, OP = 5 cases

71



Safety Stock (SS):

OP = EDDLT + SS

SS = OP - EDDLT

EDDLT = .4(3) + .3(4) + .2(5) + .1(6) = 4.0

SS = 5 – 4 = 1

Setting OP for a Discrete DDLT Distribution

with known Stockout Costs Exercise

Lusaka Eye Hospital has determined that its

reorder point for spectacle frames is 50 units. Its

carrying cost per frame per year is $5, and

stockout (or lost sale) cost is $40 per frame. The

store has experienced the probability distribution

for inventory demand during the reorder period as

shown on the next slide. The optimum number of

orders per year is six.

How much safety stock should Lusaka Eye

Hospital keep on hand?

72

Setting OP for a Discrete DDLT Distribution

with known Stockout Costs Exercise

Initial calculations:

OP = 50 (d*LT)

Ch = $5 /unit per year

Css = $40/ unit (stockout cost)

D/Q = 6 times per year

Setting Order Point for a Continuous DDLT

Distribution

Assume that the lead time (LT) is constant

Assume that the demand per day is normally

distributed with the mean ( d ) and the standard

deviation (σd )

The DDLT distribution is developed by “adding”

together the daily demand distributions across the

lead time

222

21 )(......)()( LDDLT

73


Distribution

The resulting DDLT distribution is a normal

distribution with the following parameters:

EDDLT = LT(d )

2)( dDDLT LT


Distribution

The customer service level is converted into a

Z value using the normal distribution table

The safety stock is computed by multiplying

the Z value by σDDLT.

The order point is set using OP = EDDLT +

SS, or by substitution:

2

dOP = LT(d) + z LT(σ )

74



A SME supplies lubricants including a popular

motor oil SAE 30. When the stock of this oil drops

to 20 gallons, a replenishment order is placed. The

store manager is concerned that sales are being lost

due to stockouts while waiting for an order. It has

been determined that lead time demand is normally

distributed with a mean of 15 gallons and a standard

deviation of 6 gallons.

The manager would like to know the probability of a

stockout during lead time.



• EDDLT = 15 gallons

• σDDLT = 6 gallons

OP = EDDLT + Z(σDDLT )

20 = 15 + Z(6)

5 = Z(6)

Z = 5/6

Z = .833

75



Standard Normal Distribution

0 .833

Area = .2967

Area = .5

Area = .2033

z

The probability of a stockout during lead time is .2033.


Distribution Exercise Daily demand for product EPD101 is normally

distributed with a mean of 50 units and a standard

deviation of 5. Shipping is usually certain with a lead

time of 6 days. The cost of placing an order is $8 and

annual carrying costs are 20% of unit price of $1.20. A

95% service level is desired for the customers who

place orders during the reorder period. Backorders are

not allowed. Once stocks are depleted, orders are filled

as soon as stocks arrive. No stockout costs. Assume

sales made over the entire year.

What is the reorder point? What is the cost of carrying

safety stocks?.

76

3.4 Inventory ABC Classification

Start with the inventoried items ranked by dollar

value in inventory in descending order

Plot the cumulative dollar value in inventory versus

the cumulative items in inventory

Typical observations

– A small percentage of the items (Class A) make up a

large percentage of the inventory value

– A large percentage of the items (Class C) make up a

small percentage of the inventory value

These classifications determine how much attention

should be given to controlling the inventory of

different items

ABC Classification

Items kept in inventory are not of equal

importance in terms of:

– dollars invested

– profit potential

– sales or usage volume

– stock-out penalties

0

30

60

30

60

A B

C

% of

$ Value

% of

Use

77

ABC Classification

Group A Items - Critical

Group B Items - Important

Group C Items - Not That Important

Inventory

Group

Dollar

Usage (%)

Inventory

Items (%)

Are Complex

Quantitative

Control

Techniques

Used?

A

B

C

70

20

10

10

20

70

Yes

In some cases

No

ABC Classification

100

90

80

70

60

50

40

30

20

10

0

Percent of Inventory Items

Per

cen

t of

An

nu

al

Doll

ar

Usa

ge

1 2 3 4 5 6 7 8 9 10

A

Items

B

Items C

Items

78

ABC Classification and Inventory Policy

Greater expenditure on supplier development

for A items than for B items or C items

Tighter physical control on A items than on B

items or on C items

Greater expenditure on forecasting A items

than on B items or on C items

Unit 4

Aggregate Planning

4.1 Production Planning Hierarchy and Aggregate

Planning

4.2 Role of Aggregate Planning in Production

Management

4.3 The Aggregate Planning Problem

4.4 Aggregate Planning Strategies

79

4.1 Production Planning Hierarchy and

Aggregate Planning

Master Production Scheduling

Production Planning and Control Systems

Pond Draining

Systems

Aggregate Planning

Push

Systems

Pull

Systems

Focusing on

Bottlenecks

Long-Range Capacity Planning Long-Range

(years)

Medium-Range

(3-18 months)

Short-Range

(weeks)

Very-Short-Range

(hours - days)

Production Planning Hierarchy

Units of Measure

Master Production Scheduling

Production Planning and Control Systems

Pond Draining

Systems

Aggregate Planning

Push

Systems

Pull

Systems

Focusing on

Bottlenecks

Long-Range Capacity Planning Entire

Product Line

Product

Family

Specific

Product Model

Labour, Materials,

Machines

80

Role of Aggregate Planning in

Production Management

Given

– Capacity is limited and has cost

– Lead times are greater than zero

Aggregate planning is:

– The process by which a company determines

levels of capacity, production, subcontracting,

inventory, stock-outs, and pricing over a

specified time horizon

– Where the goal is to….

maximize profit

What is Aggregate Planning?

Aggregate Planning is the intermediate

planning method used by a firm to seek the

most optimal resource inputs in order to meet

anticipated demand for product families.

“Intermediate” in this case means anywhere

from 3 to 18 months depending on the

company and its industry, types of products,

etc.

81

Aggregate Planning Scope

Decisions are usually made at a product family (not

Stock Keeping Unit (SKU)) level

– SKUs within product families tend to use same

capacities, have similar costs

– Avoids too much detail- there might be 10 product

families for 1500 SKUs

The time frame is generally 3 to 18 months

– Too early to schedule by SKU

– Too late to make strategic, long term plans (“build

another plant”)

– Answers question of “How can a firm best use the

facilities it has?” with possibly “Do we need to outsource

or subcontract?”

Aggregate Planning Problem

Given the demand forecast for each period in the

planning horizon, determine the production

level, inventory level, and the capacity level for

each period that maximizes the firm’s profit over

the planning horizon

Specify the planning horizon

Specify the duration of each period (time

bucket) typically 1 month

Specify key information required to develop an

aggregate plan

82

Medium-Term Capacity Adjustments

Workforce level

– Hire or layoff full-time workers

– Hire or layoff part-time workers

– Hire or layoff contract workers

Utilization of the work force

– Overtime

– Idle time (under time)

– Reduce hours worked

Inventory level

– Finished goods inventory

– Backorders/lost sales

Subcontract

Information Needed for an Aggregate Plan

Demand forecast in each period

Production costs – Machine costs

– labour costs, regular time ($/hr) and overtime ($/hr)

– subcontracting costs ($/hr or $/unit)

– cost of changing capacity: hiring or layoff ($/worker) and cost

of adding or reducing machine capacity ($/machine)

Labour/machine hours required per unit

Material requirements per unit, material cost and availability

Inventory holding cost ($/unit/period)

Stock-out or backlog cost ($/unit/period)

Yield rates, if applicable (% loss in production or inventory)

Constraints: physical or policy limits on overtime, layoffs,

capital available, warehousing, stock-outs and backlogs

83

Aggregate Planning Goals

Specify the optimal combination of:

– production rate (units completed per unit of time)

– workforce level (number of workers)

– inventory on hand (inventory carried from previous

period)

Meet demand (Sales Forecast)

Use capacity efficiently

Satisfy inventory policy

Minimize cost (Labour, Inventory, Subcontract,

Plant and Equipment)

Aggregate Plan Outputs

Production quantity from regular time, overtime,

and subcontracted time: used to determine

number of workers and supplier purchase levels

Inventory held: used to determine how much

warehouse space and working capital is needed

Backlog/stock-out quantity: used to determine

what customer service levels will be

Machine capacity increase/decrease: used to

determine if new production equipment needs to

be purchased or capacities need to be rededicated

84

Why Aggregate Planning is Necessary

Fully load facilities and minimize overloading

and underloading

Make sure enough capacity available to satisfy

expected demand

Plan for the orderly and systematic change of

production capacity to meet the peaks and

valleys of expected customer demand

Get the most output for the amount of

resources available

Aggregate Planning Strategies

1. Chase strategy: match production rate to production

requirements by varying the workforce (no inventory

buildup or shortage allowed)

2. Level strategy: keep a constant workforce who work at

maximum capacity (inventory will vary from period to

period); workforce level chosen such that the total

requirement over the planning horizon can be exactly met

3. Stable workforce: keep a constant workforce who work at

maximum capacity; outsource in order to match

production and requirements (no inventory buildup or

shortage allowed); workforce level chosen such that they

can exactly satisfy the requirements in the period with the

minimum requirement level

85

Aggregate Planning Inputs

A forecast of aggregate demand covering the

selected planning horizon (3-18 months)

The alternative means available to adjust short-

to medium-term capacity, to what extent each

alternative could impact capacity and the related

costs

The current status of the system in terms of

workforce level, inventory level and production

rate

Aggregate Planning Production Plans

A production plan: aggregate decisions for

each period in the planning horizon about

– workforce level;

– inventory level;

– Backorders/Lost sales;

– production rate; and

– Units subcontracted/Outsourced

Projected costs if the production plan was

implemented

86

Aggregate Planning Methods

Informal or Trial-and-Error Approach (Cut

and Try Approach)

Mathematically Optimal Approaches

– Linear Programming

– Linear Decision Rules

Computer Search

General Steps in Cut and Try Method

1. Convert demand forecasts into production

requirements

2. Identify pertinent company policies

3. Develop alternative production plans for the

company (pure or mixed strategies?)

4. Calculate the cost of each plan

5. Choose the best plan that fits (minimal costs)

87

Aggregate Planning Variables

Wt = Workforce size in period t

Ht = Number of workers hired at start of period t

Lt = Number of workers laid off at start of period t

Pt = Production in period t

EIt = Inventory at the end of period t

Dt = Demand in period t

Ct = Number of litres subcontracted for period t

Ot = Number of overtime hours worked in period t

Example: CA&J Company…

JAN FEB MAR APR MAY JUN Total

Demand Forecast 1,800 1,500 1,100 900 1,100 1,600 8,000

Working Days 22 19 21 21 22 20 125

Inventory holding $1.50/unit/month

Backorders $5.00/unit/month

Hiring and training $200.00/worker

Layoff $250.00/worker

Labour time required 0.20 units/hour

Straight time cost (8 hours) $4.00/hour

Outsourcing $20.00/unit

Costs

Beginning Inventory 400 units

Inventory

Labour

Beginning Labour 40 workers

88

First step: Analyze the requirements…

JAN FEB MAR APR MAY JUN

Beginning Inventory 400

Demand Forecast 1,800 1,500 1,100 900 1,100 1,600

Production requirement

Ending Inventory


Beginning Inventory 400 0 0 0 0 0

Demand Forecast 1,800 1,500 1,100 900 1,100 1,600

Production requirement 1,400 1,500 1,100 900 1,100 1,600

Ending Inventory 0 0 0 0 0 0

First step: Analyze the requirements…

89



Production hours

required

Days per month 22 19 21 21 22 20

Worker hours per month

Workers required

Workers hired

Hiring cost

Workers laid off

Layoff cost

Labour cost

Plan 1: Chase strategy (variable workforce)



Production hours

required

7,000 7,500 5,500 4,500 5,500 8,000

Days per month 22 19 21 21 22 20

Worker hours per month 176 152 168 168 176 160

Workers required 40 49 33 27 31 50

Workers hired 0 9 0 0 4 19

Hiring cost 0 1800 0 0 800 3,800

Workers laid off 0 0 16 6 0 0

Layoff cost 0 0 4,000 1,500 0 0

Labour cost 28,000 30,000 22,000 18,000 22,000 32,000

Plan 1: Chase strategy

90

Hiring cost 6,400

Layoff cost 5,500

Labour cost 152,000

Total Cost 163,900

Plan 1: Chase strategy

Plan 2: Level strategy (Level Capacity)


Beginning inventory 400

Working days per month 22 19 21 21 22 20

Production hours available

Monthly production level

Demand Forecast 1,800 1,500 1,100 900 1,100 1,600

Ending Inventory

Shortage Cost

Inventory cost

Labour cost

91

Plan 2: Level strategy


Beginning inventory 400 -62 -407 -230 147 385


Production hours available 6688 5776 6384 6,384 6,688 6,080

Monthly production level 1,338 1,155 1,277 1,277 1,338 1,216

Demand Forecast 1,800 1,500 1,100 900 1,100 1,600

Ending Inventory -62 -407 -230 147 385 1

Shortage Cost 310 2035 1150 0 0 0

Inventory cost 0 0 0 220.5 577.5 1.5

Labour cost 26752 23104 25536 25536 26752 24320

Number of workers required

= Total hours required over planning horizon/(8*total days)

= 38,000/(8*125) = 38. This is the no. of workers for each month

Layoff cost 500

Shortage

cost 3,495

Inventory

cost 799.50

Labour cost 152,000

Total Cost 156,794.50

Plan 2: Level strategy

92

Plan 3: Stable strategy with outsourcing




Monthly production

hours

Monthly production

level

Monthly outsourcing

level

Monthly outsourcing

cost

Monthly labour cost





Monthly production

hours

4,752 4,104 4,536 4,536 4,752 4,320

Monthly production level 950 821 907 907 950 864

Monthly outsourcing

level

450 679 193 - 150 736

Monthly outsourcing cost 9,000 13,580 3,860 0 3,000 14,720

Monthly labour cost 19,008 16 416 18,144 18,144 19,008 17,280

Number of workers

= enough workers to cover requirements in April

= 900*5/(21*8)

= 27 workers (this is the no. of workers for each month)

93

Layoff Cost 3,250

Outsourcing

Cost 44,160

Labour Cost 108,000

Total Cost 155,410


Comparison

Hiring cost 6,400

Layoff cost 5,500

Labour cost 152,000

Total Cost 163,900

Chase Level Stable

Layoff cost 500

Shortage

cost 3,495

Inventory

cost 799.50

Labour cost 152,000

Total Cost 156,794.50

Layoff Cost 3,250

Outsourcing

Cost 44,160

Labour cost 108,000

Total Cost 155,410

94

The cost of each option

Work environment harmony (management-

union relations)

Ergonomics aspects during increased overtime

durations (fatigue, morale, productivity)

Impact on product quality due to overworking

(excessive overtime)

Flexibility of increasing or decreasing

unplanned production levels

Factors important in the choice of the option

Unit 5

Master Production Schedule (MPS)

5.1 Master Production Schedule (MPS)

5.2 Time Fences in MPS

5.3 Developing an MPS

5.4 Rough-Cut Capacity Planning

95

5.1 Master Production Schedule (MPS)

A Master Production Schedule (MPS) is a

realistic, detailed, manufacturing plan for

which all possible demands upon the

manufacturing facilities (such as available

personnel, working hours, management policy

and goals) have been considered and are

visualized

The MPS is a statement of what the company

expects to produce and purchase expressed in

selected items, specific quantities and dates

Objectives of MPS

Determine the quantity and timing of

completion of end items over a short-range

planning horizon

Schedule end items (finished goods and parts

shipped as end items) to be completed

promptly and when promised to the customer

Avoid overloading or underloading the

production facility so that production

capacity is efficiently utilized and low

production costs result

96

Effective MPS…

Give management the information to control

the manufacturing operation

Tie overall business planning and forecasting

to detail operations

Enable marketing to make legitimate delivery

commitments to warehouses and customers

Greatly increase the efficiency and accuracy of

a company's manufacturing as it drives detailed

material and production requirements in

Material Requirements Planning (MRP) phase

5.2 Time Fences in MPS

The rules for scheduling

No Change

+/- 5%

Change

+/- 10%

Change

+/- 20%

Change Frozen

Firm

Full Open

1-2

weeks

2-4

weeks

4-6

weeks

6+

weeks

97

The Rules of Scheduling

Do not change orders in the frozen zone

Do not exceed the agreed on percentage

changes when modifying orders in the other

zones

Try to level load as much as possible

Do not exceed the capacity of the system when

promising orders

If an order must be pulled into level load, pull

it into the earliest possible week without

missing the promise

5.3 Developing an MPS

Using input information:

– Customer orders (end items quantity, due dates)

– Forecasts (end items quantity, due dates)

– Inventory status (balances, planned receipts)

– Production capacity (output rates, planned

downtime)

Schedulers place orders in the earliest

available open slot of the MPS

98

Developing and MPS

Schedulers must:

estimate the total demand for products from

all sources

assign orders to production slots

make delivery promises to customers, and

make the detailed calculations for the MPS

Developing an MPS Example

Arizona Instruments produces bar code scanners

for consumers and other manufacturers on a

produce-to-stock basis. The production planner

is developing an MPS for scanners for the next 6

weeks.

The minimum lot size is 1,500 scanners, and the

safety stock level is 400 scanners. There are

currently 1,120 scanners in inventory. The

estimates of demand for scanners in the next 6

weeks are shown on the next slide.

99


Demand Estimates

CUSTOMERS

BRANCH WAREHOUSES

MARKET RESEARCH

PRODUCTION RESEARCH

500

200

0

10

1

0

50

300

1000

0

0

500

400

2 3 4

200

0 0 0

300 500

0 10 0

700

6 5

1000

200

WEEK


Computations

CUSTOMERS

BRANCH WAREHOUSES

MARKET RESEARCH

PRODUCTION RESEARCH

500

200

0

10

1

0

50

300

1000

0

0

500

400

2 3 4

200

0 0 0

300 500

0 10 0

700

6 5

1000

200

WEEK

TOTAL DEMAND

BEGINNING INVENTORY

REQUIRED PRODUCTION

ENDING INVENTORY

710

1120

0

410 560

1500

410

1350

1160

1500

900

560

700

1250 950 460

460 1160

1500 1500 0

1010 1200

950

100


MPS for Bar Code Scanners

SCANNER PRODUCTION 0 1500 1500 1500 1500 0

1 2 3 4 6 5

WEEK

5.4 Rough-Cut Capacity Planning

As orders are slotted in the MPS, the effects

on the production work centers are checked

Rough-Cut Capacity Planning (RCCP)

identifies underloading or overloading of

capacity

Rough-Cut Capacity plans are used only to

determine if sufficient capacity exists over

broad time frames such as a month or a quarter

RCCP is the validation of MPS with respect to

capacity

101

Rough-Cut Capacity Planning Example

Emerging Technologies makes a line of computer

printers on a produce-to-stock basis for other

computer manufacturers. Each printer requires an

average of 24 labour-hours. The plant uses a

backlog of orders to allow a level-capacity

aggregate plan. This plan provides a weekly

capacity of 5,000 labour-hours.

Emerging Technologies’ rough-draft of an MPS

for its printers is shown on the next slide. Does

enough capacity exist to execute the MPS? If not,

what changes do you recommend?


Rough-Cut Capacity Analysis

PRODUCTION 100 200 200 280 250

1 2 3 4 5

WEEK

TOTAL

1030

LOAD 2400 4800 4800 6720 6000 24720

CAPACITY 5000 5000 5000 5000 5000 25000

UNDER or (OVER) LOAD 2600 200 200 (1720) (1000) 280

102


Rough-Cut Capacity Analysis:

The plant is underloaded in the first 3 weeks

(primarily week 1) and it is overloaded in the

last 2 weeks of the schedule.

Some of the production scheduled for week

4 and 5 should be moved to week 1.

Rough-Cut Capacity Planning Example 2

A firm produces two products, A and B, on a produce-to-

stock basis. The safety stock for A is 30 and for B it is 40.

The fixed lot size for A is 50 and for B it is 60. The

beginning inventory for A is 70 and for B it is 50. Prepare

an MPS for these two products for the next 6 weeks using

the demand estimates given on the next slide.

Suppose that the final assembly for the two products is

done on the same line, determine if the MPS developed is

underloaded or overloaded given that the final assembly

line has a weekly capacity of 100 hours available, while

each Product A requires 0.9 hours and each Product B

requires 1.6 hours of final assembly capacity.

103


INTRA-COMPANY

BRANCH WAREHOUSES

R&D

CUSTOMER DEMAND

0

0

0

20

1

20

0

0

0

20

10

0

20

2 3 4

20

20 20 20

0 0

0 0 10

10

6 5

10

0

WEEK

INTRA-COMPANY

BRANCH WAREHOUSES

R&D

CUSTOMER DEMAND

0

0

0

30

1

30

0

0

0

30

0

10

0

2 3 4

0

20 20 20

0 20

10 10 0

10

6 5

0

0

WEEK

Product A

Product B


TOTAL DEMAND

BEGINNING INVENTORY

REQUIRED PRODUCTION

ENDING INVENTORY

20

70

0

50

1

30

0

50

20

30

50

50

30

2 3 4

50

70 50 30

30 30

50 50 50

30

6 5

30

50

WEEK

TOTAL DEMAND

BEGINNING INVENTORY

REQUIRED PRODUCTION

ENDING INVENTORY

30

50

60

80

1

50

0

80

30

70

60

40

50

2 3 4

40

80 50 90

90 70

60 0 60

40

6 5

30

50

WEEK

Product A

Product B

104


MPS for Products A and B

PRODUCT A 0 0 50 50 50 50

1 2 3 4 6 5

WEEK

PRODUCT B 60 0 60 60 0 60

Rough-Cut Capacity Planning Steps

Compute the actual final assembly hours

required at the plant each week and the total 6

weeks to produce the MPS (this is the Load)

Compare the load to the labour-hours capacity

in each week and for the total 6 weeks (this is

the rough-cut capacity planning)

Assess if enough production capacity exist to

produce the MPS, and recommend any

changes to the MPS if necessary.

105



Product A assembly hours 0 0 45 45 45

1 2 3 4 5

WEEK

TOTAL

180

TOTAL LOAD (Hours) 96 0 141 45 141 564

CAPACITY (Hours) 100 100 100 100 100 600

UNDER or (OVER) LOAD 4 100 (41) 55 (41) 36

Product B assembly hours 96 0 96 0 96 384

45

6

141

100

(41)

96



The final assembly line is underloaded in

weeks 1, 2, and 5, and it is overloaded in

weeks 3, 4, and 6 of the schedule.

A better balance of weekly final assembly

capacity is possible if some of the production

lots are moved into earlier weeks of

production schedule. Move lots of Product A

from weeks 4 and 6 into weeks 3 and 5, and

move the lot of Product B from week 3 into

week 2.

106


Revised Rough-Cut Capacity Analysis

Product A assembly hours 0 0 90 90 0

1 2 3 4 5

WEEK

TOTAL

180

TOTAL LOAD (Hours) 96 96 90 90 96 564

CAPACITY (Hours) 100 100 100 100 100 600

UNDER or (OVER) LOAD 4 4 10 10 4 36

Product B assembly hours 96 96 0 0 96 384

0

6

96

100

4

96

Demand Management

Review customer orders and promise shipment

of orders as close to request date as possible

Update MPS at least weekly.... work with

Marketing to understand shifts in demand

patterns

Produce to order..... focus on incoming

customer orders

Produce to stock ..... focus on maintaining

finished goods levels

Planning horizon must be as long as the longest

lead time item

107

Unit 6

Material Requirement Planning (MRP)

6.1 Material Requirement Planning (MRP)

6.2 MRP System

6.3 MRP: Typical Procedures

6.4 Lot-Sizing in MRP

6.1 Material Requirement Planning(MRP)

Computer-based system for determining the

quantity and timing for the acquisition of

dependent demand items needed to satisfy the

MPS requirements

Explodes Master Schedule (MPS) into required

amounts of raw materials and subassemblies to

support MPS

Nets against current orders and inventories to

develop production and purchased material

ordering schedules

108

Relationship of MRP and other Plans

Firm orders

from known

customers

Forecast

of demand

from random

customers

Aggregate

Product Plan

Master

Production

Schedule

(MPS)

Material

Requirements

Planning

(MRP)

Engineering

design changes

Bill of

Materials

(BOM) file

Inventory

transactions

Inventory

records

file

Reports

7/4/2016

6.2 MRP System

Order Changes

Planning Reports

MRP

System

Inventory

Transaction Data

Bill of

Materials File

Master

Production

Schedule

Inventory

Status File

Service-Parts

Orders and

Forecasts

Performance Reports

Inputs Outputs

Planned Order

Schedule

Exception Reports

109

Inventory Status File

Includes information on the status of each

item by time period

– Gross requirements

– Scheduled receipts

– Amount on hand

– Lead times

– Lot sizes

– and more …

MRP Logic Terminology (1/6)

Gross Requirements

– These requirements are typically forecast for

independent demand items

– Assumes no on-hand inventory

110


On-hand inventory

– The inventory physically present in the

facility

Allocated inventory

– The inventory physically present in the

facility but allocated to a particular work

order or purchase order


Net requirements

– A quantity of an item that must be purchased

or manufactured in order to be able to fully

deliver independent demand requirements in a

timely fashion

– Presence of positive net requirements signals

that an order must be planned to be received in

a given period

111


Planned order receipts

– Quantities that must be planned to be

received in some future periods in order to

meet the requirements

Planned order released

– Quantities that must be planned to be

released in some future periods in order to

meet the requirements


Scheduled receipts

– Quantities that will be received in some

future periods as their corresponding orders

have been released in the past

• Planned order receipts become scheduled

receipts at the time when they are released to

the shop or to suppliers

112


Gross to Net logic:

Net Requirements = Gross Requirements

+ Allocated Inventory

+ Safety Stock

- Inventory On Hand

+ Backorders

6.3 MRP: Typical Procedures

1. Develop a Bill of Materials (BOM). The BOM

identifies

– The components

– Component descriptions

– Amount required to produce 1 unit of final product

2. Develop a Material Structure Tree

– The tree has several levels depending on the depth of

subcomponent required

– Parents and components are identified 1. Items above any level are parents

2. Items below any level are components

– The tree shows how many units are needed at each

level of production

113

3. Determine the Gross Material Requirements

– Once the materials structure tree is done, construct

a gross material requirements plan.

– This is a time schedule that shows when an item

must be ordered

1. when there is no inventory on hand, or

2. when the production of an item must be started in

order to satisfy the demand for the finished product

at a particular date.

4. Determine the Net Material Requirements Plan

– A net material requirements plan is constructed

using

the gross materials requirements plan

and the inventory on-hand information

– This plan includes, for each item:

Gross requirements,

On-hand inventory,

Net requirements,

Planned-order receipts (Scheduled receipts), and

Planned-order releases

114

Material Structure Tree Example

Assume demand for product A is 50 units. Each

unit of A requires

1. 2 units of B, which in turn requires

1. 2 units of D

2. 3 units of E

2. 3 units of C, which in turn requires

1. 1 unit of E

2. 2 units of F

Material Structure Tree: An Example

115

This structure tree has 3 levels:

• 0, 1, and 2

There are 3 parents:

• A, B, C

There are 5 components:

• B, C, D, E, F

B and C are parents and components

Numbers in parentheses next to the levels indicate

the amounts needed for 1 unit of final product of A

For example, B(2) indicates that it takes 2 units of

B to make 1 unit of A

Component Calculations to meet Demand of 50

units of Product A:

Part B: 2×# of A = 2×50 = 100

Part C: 3×# of A = 3×50 = 150

Part D: 2×# of B = 2×100 = 200

Part E: 3×# of B + 1×# of C = 3×100 + 1×150 =

450

Part F: 2×# of C = 2×150 = 300

116

A Material Requirement Plan (MRP) for

Awesome Speakers is shown as an example,

starting with the product tree. Fifty Awesome

speakers are required.

7/4/2016

Product Structure for “Awesome” A

117

Component Calculations:

Part B: 2×# of A = 2×50 = 100

Part C: 3×# of A = 3×50 = 150

Part D: 2×# of B + 2×# of F = 2×100 + 2×300 =

800

Part E: 2×# of B + 2×# of C = 2×100 + 2×150 =

500

Part F: 2×# of C = 2×150 = 300

Part G: 1×# of F = 1×300 = 300

Requirements for 50 “Awesome” A

Lead Times for “Awesome” Speaker Kits (As)

COMPONENT LEAD TIME (Weeks)

A 1

B 2

C 1

D 1

E 2

F 3

G 2

118

Using Lead Times a Time-Phased Product Structure can be

constructed. Note: this is not necessary if you know the

delivery date

1 2 3 4 5 6 7 8

3 weeks

F

2 weeks E

A

1 week 1 week

C G 2 weeks

D

1 week

E

2 weeks

Start production

of D

D 1 week

B

2 weeks to produce

Must have D and E

completed here so

production can begin on B

7/4/2016

Gross Materials Requirements Plan for 50 Speaker Kits A

WEEK LT

1 2 3 4 5 6 7 8

A Required date

Order release date

50

50 1 wk

B Required date

Order release date

100

100 2 wks

C Required date

Order release date

150

150 1 wk

D Required date

Order release date

200

200 1 wk

E Required date

Order release date

200

300

200 300 2 wks

F Required date

Order release date

300

300 3 wks

D Required date

Order release date

600

600 1 wk

G Required date

Order release date

300

300 2 wks

119

Inventory for “Awesome” Speaker Kits (As)

ITEM ON-HAND

INVENTORY

A 10

B 15

C 20

D 10

E 10

F 5

G 0

Net Requirements Plan – Item A

GROSS REQUIREMENTS

SCHEDULED RECEIPTS

AVAILABLE (PROJ. ON-HAND = 10)

NET REQUIREMENTS

PLANNED ORDER RELEASES

ITEM: A Lead Time: 1 week

LOT SIZE: Lot For Lot

10

1

10 10

2 3 4

10 10 10 10

6

40

5 7

WEEK

40

50

40

8

120

Net Requirements Plan – Item B

GROSS REQUIREMENTS

SCHEDULED RECEIPTS


NET REQUIREMENTS


ITEM: B Lead Time: 2 weeks


15

1

15 15

2 3 4

65

15 15 15

65

6 5

80A

65

7

WEEK

8

Net Requirements Plan – Item C

GROSS REQUIREMENTS

SCHEDULED RECEIPTS


NET REQUIREMENTS


ITEM: C Lead Time: 1 week


20

1

20 20

2 3 4

100

20 20 20

100

6 5

120A

100

7

WEEK

8

121

Net Requirements Plan – Item E

GROSS REQUIREMENTS

SCHEDULED RECEIPTS


NET REQUIREMENTS


ITEM: E Lead Time: 2 weeks


10

1

10

120

10

2 3 4

200 120

200

120

10

130B

6 5

200C

200

7

WEEK

8

Net Requirements Plan – Item F

GROSS REQUIREMENTS

SCHEDULED RECEIPTS


NET REQUIREMENTS


ITEM: F Lead Time: 3 weeks


5

1

5

195

5

2 3 4

195

5 5

6 5

200C

195

7

WEEK

8

122

Net Requirements Plan – Item D

GROSS REQUIREMENTS

SCHEDULED RECEIPTS


NET REQUIREMENTS


ITEM: D Lead Time: 1 week


10

1

380

10

380

390F

380

2 3 4

130

130

130B

6 5 7

WEEK

8

130

Net Requirements Plan – Item G

GROSS REQUIREMENTS

SCHEDULED RECEIPTS


NET REQUIREMENTS


ITEM: G Lead Time: 2 weeks


0

195

1

0

195

195F

195

2 3 4 6 5 7

WEEK

8

123

6.4 Lot-Sizing in MRP

Lot-size is the quantity ordered/produced at one

time

Large lots are preferred because:

– Changeovers cost less and capacity greater

– Annual cost of purchase orders less

– Price breaks and transportation breaks can be utilized

Small lots are preferred because:

– Lower inventory carrying cost

– Reduced risk of obsolescence

– Shorter cycle time to produce customer order

The best method, resulting in least cost,

depends on cost and demand patterns.

Lot-Sizing Techniques

• Economic Order Quantity (EOQ)

– does not consider quantity discounts

– does not always provide the most economical approach with lumpy demand

• Lot-for-Lot (LFL)

– accommodates lumpy demand

• Period Order Quantity (POQ)

124

Lot-Sizing for Speaker Kits

Speaker Kits Inc., wants to compute its ordering and carrying cost of inventory on lot-for-lot criteria. Speaker Kits has determined that, for the 12-inch speaker/booster assembly, setup cost is $100 and holding cost is $1 per period.

The production schedule, as reflected in net requirements for assemblies, is shown on the next slide. Lead time is 1 week.

What is the total cost?

Lot-Sizing Techniques: Lot-for-Lot

1 2 3 4 5 6 7 8 9 10

Gross Requirements 35 30 40 0 10 40 30 0 30 55

Scheduled Receipts 30 40 10 40 30 30 55

Projected on Hand 35 0 0 0 0 0 0 0 0 0 0

Net Requirements 0 30 40 0 10 40 30 0 30 55

Planned Order Releases 30 40 10 40 30 30 55

125

Lot-Sizing Techniques: Economic Order

Quantity

1 2 3 4 5 6 7 8 9 10


Scheduled Receipts 73 73 73 73



Planned Order Releases 73 73 73 73

Lot-Sizing Techniques: Part Period Balancing

1 2 3 4 5 6 7 8 9 10


Scheduled Receipts 80 100 55



Planned Order Releases 80 100 55

126

Example: Bill of Material – Product A &

Product B

A

C (3)

D(2) Other

Subcomponents

Other

Components

B

C (2)

D(2) Other

Subcomponents

Other

Components

Demand and other information for Products A and B

Month

Product 1 2 3 4 5 6 7 8 9 10 11 12

A 0 0 80 0 0 80 0 0 80 0 0 80

B 0 50 0 50 0 50 0 50 0 50 0 50

It takes two months to produce a unit of C and one month to produce a unit

of D. At the beginning of month 1, there are 150 units of C and 600 units of

D in stock from previously planned manufacturing or purchasing orders,

while 50 units of C and 100 units of D are scheduled to be received at the

beginning of month 2.

Develop MRP using the Lot-for-Lot, Period Order Quantity (POQ) with

period (P) = 3, Economic Order Quantity (EOQ) and Part-Period Balancing

(PPB) lot sizing strategies. For the EOQ assume that the average demand for

component C is 225 units per month, the set up cost of producing C during a

month is $225 and the cost of holding one unit of C in inventory for one

month is $0.5.

127

Equations to use in developing the MRP

NRt (C)=[(GRt (C ) – SRt (C ) –OHIt-1 (C)]+

OHIt (C)=[SRt (C) + OHIt-1 (C ) –GRt (C)]+

Where:

NRt : Net Requirements in period t

GRt : Gross Requirements in period t

SRt : Scheduled Receipts in period t

OHIt : On-Hand Inventory in period t

Net Requirements Plan – Item C

1 2 3 4 5 6 7 8 9 10 11 12

GR 0 100 240 100 0 340 0 100 240 100 0 340

OHI

(150) 150 100 0 0 0 0 0 0 0 0 0 0

SR 0 50 0 0 0 0 0 0 0 0 0 0

NR 0 0 140 100 0 340 0 100 240 100 0 340

PP ? ? ? ? ? ? ? ? ? ? ? ?

Month

128

Lot-for-Lot with 2 Months Lead Time-Item C

1 2 3 4 5 6 7 8 9 10 11 12

GR 0 100 240 100 0 340 0 100 240 100 0 340

OHI

(150) 150 100 0 0 0 0 0 0 0 0 0 0

SR 0 50 140 100 0 340 0 100 240 100 0 340

NR 0 0 140 100 0 340 0 100 240 100 0 340

PP 140 100 0 340 0 100 240 100 0 340 X X

Month

Lot-for-Lot with 1 Months Lead Time-Item D

1 2 3 4 5 6 7 8 9 10 11 12

GR 280 200 0 680 0 200 480 200 0 680 X X

OHI

(600) 320 220 220 0 0 0 0 0 0 0 0 0

SR 0 100 0 460 0 200 480 200 0 680 X X

NR 0 0 0 460 0 200 480 200 0 680 X X

PP 0 0 460 0 200 480 200 0 680 X X X

Month

129

MRP Record – Item C (POQ; P = 3)

1 2 3 4 5 6 7 8 9 10 11 12

GR 0 100 240 100 0 340 0 100 240 100 0 340

OHI

(150) 150 100 440 340 340 0 0 340 100 0 0 0

SR 0 50 580 0 0 0 0 440 0 0 0 340

NR 0 0 140 100 0 340 0 100 240 100 0 340

PP 580 0 0 0 0 440 0 0 0 340 X X

Month

MRP Record – Item C (EOQ)

1 2 3 4 5 6 7 8 9 10 11 12

GR 0 100 240 100 0 340 0 100 240 100 0 340

OHI

(150) 150 100 310 210 210 320 320 220 430 330 330 440

SR 0 50 450 0 0 450 0 0 450 0 0 450

NR 0 0 140 100 0 340 0 100 240 100 0 340

PP 450 0 0 450 0 0 450 0 0 450 X X

Month

130

Net Requirements Plan – Item C (PPB)

1 2 3 4 5 6 7 8 9 10 11 12

GR 0 100 240 100 0 340 0 100 240 100 0 340

OHI

(150) 150 100 100 0 0 100 100 0 100 0 0 0

SR 0 50 240 0 0 440 0 0 340 0 0 340

NR 0 0 140 100 0 340 0 100 240 100 0 340

PP 240 0 0 440 0 0 340 0 0 340 X X

Month

Choosing the Lot Sizing Strategy for Component C

The following are the cost for each of the last three lot sizing approaches:

POQ Method:

Holding cost = 0.5(150+100+440+340+340+100) = $905

Setup cost = 225(3) = $675

Total cost = $1,580

EOQ Lot-sizing Method:

Holding cost = 0.5(150+100+310+210+210+320+320+

220+430+330+330+440) = $1,685

Setup cost = 225(4) = $900

Total cost = $2,585

Part-Period Balancing Method:

Holding cost = 0.5(150+100+100+100+100+100) = $325

Setup cost = 225(4) = $900

Total cost = $1,225

Use the PPB which gives the lowest cost in this case.

131

Unit 7

Shop-Floor Planning and Control

7.1 Scheduling Process-Focused Manufacturing

7.2 Input-Output Control

7.3 Order-Sequencing Rules

7.4 Minimising Total production Time

7.1 Scheduling Process-Focused

Manufacturing

Process-focused factories are often called job

shops.

A job shop’s work centers are organized around

similar types of equipment or operations.

Workers and machines are flexible and can be

assigned to and reassigned to many different

orders.

Job shops are complex to schedule.

132

Job Shops use a FUNCTIONAL LAYOUT

– In the functional layout, like processors are

grouped into departments. As products visit the

department they are placed on one of the

processors of the group.

Flow Shops use a PRODUCT LAYOUT

– In the product layout the required processors for a

product are identified and arranged using a linked

station-to-station alignment

Job Shop vs Flow Shop

Job Shop

133

Flow Shop

Design the product in customer order

Plan the operations the product must pass

through ..... this is the routing plan

Work moves between operations on a move

ticket

Pre-Production Planning

134

Assigning a priority to each order. This aids in

setting the sequence of producing orders at

work centers.

Issuing dispatching lists to each work centre

Tracking work-in-progress (WIP) and keeping

the system updated

Controlling input-output on all work centers

Measuring efficiency, utilization, and

productivity of workers and machines at each

work centre.

Common Shop Floor Control Activities

7.2 Input-Output Control

Input-output control identifies problems such as

insufficient or excessive capacity, bottlenecks or

any issues that prevents the order from being

completed on time

Planned input should never exceed planned

output

Input Output Work

Center

135

Input-Output Control Report (Week -1)

Week: -1 1 2 3 4

Planned input: labor-hrs 100 50 40 100

Actual input: labor-hrs

Cumulative deviation

Planned output: labor-hrs 120 70 50 100

Actual output: labor-hrs

Cumulative deviation

Planned ending WIP: l-h 50 30 20 20

Actual ending WIP: l-h 70

Input-Output Control Report (Week 1)

Week: -1 1 2 3 4


Actual input: labor-hrs 50

Cumulative deviation -50


Actual output: labor-hrs 110

Cumulative deviation -10


Actual ending WIP: l-h 70 10

136

Input-Output Control Report (Week 4)

Week: -1 1 2 3 4


Actual input: labor-hrs 50 40 30 80

Cumulative deviation -50 -60 -70 -90


Actual output: labor-hrs 110 50 20 70

Cumulative deviation -10 -30 -60 -90


Actual ending WIP: l-h 70 10 0 10 20

Infinite loading

– Assigns jobs to work centers without regard to capacity

– Unless excessive capacity exists, long queues occur

Finite loading

– Uses work center capacity to schedule orders

– Popular scheduling approach

– Integral part of Capacity Requirement Planning (CRP)

Assigning Jobs to Work Centers:

How Many Jobs/Day/Work Center

137

Forward scheduling

– Jobs are given earliest available time slot in

operation

– excessive WIP usually results

Backward scheduling

– Start with promise date and work backward through

operations reviewing lead times to determine when a

job has to pass through each operation

– Less WIP but must have accurate lead times

Assigning Jobs to Work Centers:

Which Job Gets Built First?

Today Due Date

B E

Forward Scheduling

Today Due Date

B E

Backward Scheduling

138

We want to determine the sequence in which

we will process a group of waiting orders at a

work center

Many different sequencing rules can be

followed in setting the priorities among

orders

There are numerous criteria for evaluating the

effectiveness of the sequencing rules

7.3 Order-Sequencing Rules

First-Come First-Served (FCFS)

Next job to process is the one that arrived first

among the waiting jobs

Shortest Processing Time (SPT)

Next job to process is the one with the shortest

processing time among the waiting jobs

Earliest Due Date (EDD)

Next job to process is the one with the earliest due

(promised finished) date among the waiting jobs

Sequencing Rules (1/2)

139

Least Slack (LS)

Next job to process is the one with the least [time to

due date minus total remaining processing time]

among the waiting jobs

Critical Ratio (CR)

Next job to process is the one with the least [time to

due date divided by total remaining processing

time] among the waiting jobs

Least Changeover Cost (LCC)

Sequence the waiting jobs such that total machine

changeover cost is minimized

Sequencing Rules (2/2)

Average flow time - average amount of time

jobs spend in shop

Average number of jobs in system

Average job lateness - average amount of time

job’s completion date exceeds its promised

delivery date

Changeover cost - total cost of making

machine changeovers for group of jobs

Evaluating Sequencing Rules

140

Example: Evaluating Sequencing Rules

Use the FCFS, SPT, and Critical Ratio rules to sequence

the five jobs below. Evaluate the rules on the bases of

average flow time, average number of jobs in the system,

and average job lateness.

Job Processing Time Time to Promised Completion

A 6 hours 10 hours

B 12 16

C 9 8

D 14 14

E 8 7

FCFS Rule Sequence: A > B > C > D > E

Processing Promised Flow Job Time Completion Time Lateness

A 6 10 6 0

B 12 16 18 2

C 9 8 27 19

D 14 14 41 27

E 8 7 49 42

49 141 90

141

FCFS Rule Performance:

– Average flow time:

141/5 = 28.2 hours

– Average number of jobs in the system:

141/49 = 2.88 jobs

– Average job lateness:

90/5 = 18.0 hours

SPT Rule Sequence: A > E > C > B > D

Processing Promised Flow

Job Time Completion Time Lateness

A 6 10 6 0

E 8 7 14 7

C 9 8 23 15

B 12 16 35 19

D 14 14 49 35

49 127 76

142

SPT Rule Performance:


127/5 = 25.4 hours


127/49 = 2.59 jobs


76/5 = 15.2 hours

Critical Ratio Rule Sequence: E > C > D > B > A

Processing Promised Flow

Job Time Completion Time Lateness

E (.875) 8 7 8 1

C (.889) 9 8 17 9

D (1.00) 14 14 31 17

B (1.33) 12 16 43 27

A (1.67) 6 10 49 39

49 148 93

143

Critical Ratio Rule Performance:


148/5 = 29.6 hours


148/49 = 3.02 jobs


93/5 = 18.6 hours

Comparison of Rule Performance:

Average Average Average

Flow Number of Jobs Job

Rule Time in System Lateness

FCFS 28.2 2.88 18.0

SPT 25.4 2.59 15.2

CR 29.6 3.02 18.6

SPT rule was superior for all 3 performance

criteria.

144

Changeover costs - costs of changing a

processing step in a production system over

from one job to another:

– Changing machine settings

– Getting job instructions

– Changing material

– Changing tools

Usually, jobs should be processed in a

sequence that minimizes changeover costs

Controlling Changeover Costs

1. First, select the lowest changeover cost

among all changeovers (this establishes the

first two jobs in the sequence)

2. The next job to be selected will have the

lowest changeover cost among the

remaining jobs that follow the previously

selected job

Controlling Changeover Costs:

Job Sequencing Heuristic

145

Example 1: Minimizing Changeover Costs

Hardtimes Heat Treating Service has 5 jobs waiting

to be processed at work center #11. The job-to-job

changeover costs are listed below. What should the

job sequence be?

Jobs That Precede

A B C D E

A -- 65 80 50 62

B 95 -- 69 67 65

C 92 71 -- 67 75

D 85 105 65 -- 95

E 125 75 95 105 --

Jobs

That

Follow

A follows D ($50 is the least c.o. cost)

C follows A ($92 is the least following c.o. cost)

B follows C ($69 is the least following c.o. cost)

E follows B (E is the only remaining job)

Job sequence is D – A – C – B – E

Total changeover cost = $50 + 92 + 69 + 75

= $286

Job sequence with minimum changeover cost:

146

Example 2: Minimizing Changeover Costs

A printing company does custom printing jobs for

local firms and schools. The operations manager is

currently developing a weekly printing schedule

for the printing press. He has developed

changeover costs for six waiting jobs. All jobs

carry equal priority, so the deciding factor in

selecting a job sequence is the total changeover

cost for the six jobs.

What is the preferred job sequence?

Jobs with Changeover Costs

Jobs That Precede

A B C D E F

A -- 12 15 10 35 20

B 25 -- 20 20 25 20

C 27 15 -- 12 20 15

D 16 30 10 -- 25 30

E 35 20 25 30 -- 30

F 20 25 15 25 30 --

Jobs

That

Follow

147

There is a tie on the starting lowest changeover cost between (D-A) and (C-D). Therefore develop two sequences and choose one with least total cost.

Sequence 1: D – A – F – C – B – E

Total changeover cost = 10 + 20 + 15 + 20 + 20

= $85

Sequence 2: C – D – A – F – B – E

Total changeover cost = 10 + 10 + 20 + 20 + 20

= $80

In this case sequence C-D-A-F-B-E is the preferred sequence.

Job sequence with minimum changeover cost:

7.4 Minimizing Total Production Time

Sequencing n Jobs through Two Work

Centers

– When several jobs must be sequenced through

two work centers, we may want to select a

sequence that must hold for both work centers

– Johnson’s rule can be used to find the sequence

that minimizes the total production time

through both work centers

148

Johnson’s Rule

1. Select the shortest processing time in either work

center

2. If the shortest time is at the first work center, put the

job in the first unassigned slot in the schedule. If the

shortest time is at the second work center, put the

job in the last unassigned slot in the schedule.

3. Eliminate the job assigned in step 2.

4. Repeat steps 1-3, filling the schedule from the front

and back, until all jobs have been assigned a slot.

Example: Minimizing Total Production Time

It is early Saturday morning and The Finest

Detail has five automobiles waiting for detailing

service. Each vehicle goes through a thorough

exterior wash/wax process and then an interior

vacuum/shampoo/polish process.

The entire detailing crew must stay until the last

vehicle is completed. If the five vehicles are

sequenced so that the total processing time is

minimized, when can the crew go home. They

will start the first vehicle at 7:30 a.m.

149

Time Estimates for the jobs at each work station

Exterior Interior

Job Time (hrs.) Time (hrs.)

Cadillac 2.0 2.5

Bentley 2.1 2.4

Lexus 1.9 2.2

Porsche 1.8 1.6

Infiniti 1.5 1.4

Johnson’s Rule

Least Work Schedule

Time Job Center Slot

1.4 Infiniti Interior 5th

1.6 Porsche Interior 4th

1.9 Lexus Exterior 1st

2.0 Cadillac Exterior 2nd

2.1 Bentley Exterior 3rd

150

Gantt Chart of Job Sequencing

Exterior

Interior

0 1.9 3.9 6.0 7.8 9.3 12.0

0 1.9 4.1 6.6 9.0 10.6 12.0

L C B

L

P I

Idle C B P I

Idle

It will take from 7:30 a.m. until 7:30 p.m. (not

allowing for breaks) to complete the five vehicles.

Unit 8

Total Quality Management

8.1 Quality Definition

8.2 Nature of Quality

8.3 Quality Advocates

8.4 Total Quality Management

151

8.1 Quality Definition

“The quality of a product or service is a

customer’s perception of the degree to which the

product or service meets his or her expectations.”

Design quality

– How does the product or service appear in pre-

production or pre-delivery phases (i.e. on a

CAD terminal, on a blueprint, etc.)

Conformance quality

– Degree to which the design specifications are met by the production or delivery system

Quality Definitions (1/2)

152

Performance quality

– Degree to which the product or service meets or

exceeds customer expectations in the marketplace

(i.e. product or service performance characteristics)

Societal quality

– Degree to which the product or service meets or

exceeds societal expectations (i.e. product or

service performance characteristics important to the

general public)

Quality Definitions (2/2)

‘Fitness for Purpose’ – Juran

‘The totality of features and characteristics of a product

or service that bear on its ability to satisfy stated or

implied needs’ – BS 4778:1987 (ISO 8402, 1986)

‘Quality should be aimed at the needs of the customer,

present and future’ – Deming

The total composite product and service characteristics

of marketing, engineering, manufacture and

maintenance through which the product and service in

use will meet the expectation by the customer’ –

Feigenbaum

‘Conformance to requirements’ - Crosby

Quality: Meeting the Customer Requirements

153

“Meeting or exceeding customer

expectations”

Customers can be...

External customers

Internal customers

Customer-Driven Quality

Dimensions of Quality

Determinants of Quality

Costs of Quality

8.2 Nature of Quality

154

1. Performance – A product’s primary operating characteristics

2. Features – Special characteristics that appeal to customers

3. Reliability – The probability of a product’s surviving over a

specified period under stated conditions of use

4. Serviceability – The speed, courtesy, cost and convenience of

repairs and maintenance

5. Durability – The amount of use one gets from a product before

it physically deteriorates or until replacement is preferable

6. Aesthetics – How a product looks, feels, sounds, tastes, or

smells

7. Conformance – The degree to which physical and performance

characteristics of a product match pre-established standards

8. Safety – To both users and the environment

Dimensions of Product Quality

1. Reliability – The ability to provide what was promised,

dependably and accurately

2. Assurance – The knowledge and courtesy of employees

and their ability to convey trust and confidence

3. Tangibles – The physical facilities and equipment, and

appearance of personnel

4. Empathy – The degree of caring and individual attention

provided to customers

5. Responsiveness – The willingness to help customers and

provide prompt service

Dimensions of Service Quality

155

Quality of design – products/service designed based on customers’ expectations and desires

Quality capability of production processes – processes must be capable of producing the products designed for the customers

Quality of conformance – capable processes can produce inferior product if not operated properly

Quality of customer service – a superior product does not mean success; must have quality service also

Organization quality culture – superior product and service requires organization-wide focus on quality

Determinants of Quality

Cost of Quality (COQ) provides a basis for

identifying improvement opportunities and

success of quality improvement programs

A quality cost is considered to be any cost

that the company or organization would not

have incurred if the quality of the product or

service were perfect

Cost of Quality (COQ) is the cost of avoiding

poor quality, or incurred as a result of poor

quality

Costs of Quality

156

Cost of Quality Categories

Prevention Costs: investments made to keep

nonconforming products from occurring and reaching

the customer

Appraisal Costs: associated with efforts to ensure

conformance to requirements, generally through

measurement and analysis of data to detect non-

conformances

Internal Failure Costs: incurred as a result of

unsatisfactory quality found before product delivery to

the customer

External Failure Costs: occur after poor-quality

products reach the customer

Prevention costs: training, redesigns, inspection

procedures, supplier quality surveys, etc.

Appraisal costs: tests and inspections, process

measurement and control, etc.

Internal failure costs: scrap and rework, costs of

correcting errors before they reach the

customer, and downgrading costs.

External failure costs: warranty repairs,

replacements, complaints, legal expenses, lost

business and goodwill.

Costs of Quality Examples

157

Contrasting Traditional and Modern

Approaches to Quality

Passive (Inspect)

Find it and fix it

Emphasize on Acceptable Quality Level

It is a job of Quality Control department

Quantity is top priority

Quality is expensive

Active (Prevent)

Build Quality into the product

Focus on Process Control and Continuous Improvement

It is a company-wide concern

Quality is top priority

Higher Quality means lower cost

Traditional Modern

Edwards W. Deming

Joseph M. Juran

Philip B. Crosby

Armand V. Feigenbaum

Kaoru Ishikawa

8.3 Quality Advocates

158

Edwards W. Deming

Assisted Japan in improving productivity and

quality after World War II

In 1951 Japan established Deming Prize

US was slow in recognizing his contributions

Introduced Japanese companies to the Plan-

Do-Check-Act (PDCA) cycle

Developed 14 Points for managers

PDCA Cycle

2. DO

Try plan on a test basis

1. PLAN

Identify Improvement And develop

plan

3. CHECK

Evaluate plan to see if it works

4. ACT

Permanently implement

improvements

159

Deming’s 14 Points for Managers

1. Create constancy of purpose toward product quality to achieve organizational goals

2. Refuse to allow commonly accepted levels of poor quality

3. Stop depending on inspection to achieve quality

4. Use fewer suppliers, selected based on quality and dependability instead of price

5. Instill programs for continuous improvement of costs, quality, service, and productivity

Deming’s 14 Points for Managers (contd)

6. Train all employees on quality concepts

7. Focus supervision on helping people do a better

job

8. Eliminate fear, create trust, and encourage two-

way communications between workers and

management

9. Eliminate barriers between departments and

encourage joint problem-solving

10. Eliminate the use of numerical goals and slogans

to make workers work harder

160

Deming’s 14 Points for Managers (contd)

11. Use statistical methods for continuous

improvement of quality and productivity

instead of numerical quotas

12. Remove barriers to pride of workmanship

13. Encourage education and self-improvement

14. Clearly define management’s permanent

commitment to quality and productivity

Joseph M. Juran

Like Deming, discovered late by US

companies

Played early role in teaching Japan about

quality

Wrote Quality Control Handbook

161

Philip B. Crosby

Wrote Quality Is Free in 1979

Company should have the goal of zero defects

Cost of poor quality is greatly underestimated.

It should include all the costs of not doing the

job right the first time!

Traditional trade-off between costs of

improving quality and costs of poor quality is

erroneous

Armand V. Feigenbaum

Developed concept of total quality control

(TQC) (book published in 1983)

Responsibility for quality must rest with the

persons who do the work (quality at the

source)

Product quality is more important than

production rates (workers are given authority

to stop production whenever quality problems

occur)

162

Kaoru Ishikawa

Wrote Guide to Quality Control in 1972

Credited with the concept of quality circles

Suggested the use of fishbone diagrams

Effect

Man Machine

Material Method

Environment

TQM may be defined as managing the entire

organization so that it excels on all

dimensions of products and services that are

important to the customer.

TQM is not a collection of tools and

techniques. It is a culture.

TQM is an attitude of mind based on pride in

job, teamwork, and management

commitment extending to all employees at all

levels and in all departments.

8.4 Total Quality Management

163

Elements of TQM

Top management commitment and

involvement

Customer involvement

Design products for quality

Design production processes for quality

Control production processes for quality

Develop supplier partnerships

Customer service, distribution and installation

Building teams of empowered employees

Benchmarking and continuous improvement

Top Management

Commitment and Involvement

Support must be genuine or TQM will be seen

as just another passing fad

Fundamental changes must occur in the

culture of the organization

Such fundamental changes are not easy, but

are impossible without top management’s

commitment and involvement

164

Customer Involvement

Mechanisms to involve the customer

– Focus groups

– Market surveys

– Customer questionnaires

– Market research programs

Quality Function Deployment (QFD)

– Formal system for identifying customer wants

– Eliminate wasteful product features and activities

that do not contribute

Building Teams of Empowered

Employees

Employee training programs

– Employees at all levels are trained in quality

Works teams and empowerment

– Workers are given the authority to act

Quality at the source

– Workers are responsible for their own work

Quality circles

– Small groups of employees who analyze and solve

quality problems and implement improvement

programs

165

Quality Circles

Group of 6-12 employees from same work

area

Meet regularly to solve work-related

problems

4 hours/month

Facilitator trains and helps with meetings

Benchmarking and Continuous

Improvement

Benchmarking

– The practice of establishing internal standards of

performance by looking to how world-class

companies run their businesses

Continuous Improvement

– The company makes small incremental

improvements toward excellence on a continual

basis

166

Obstacles to Implementing TQM

Lack of management commitment

Inability to change organisational culture

Improper planning

Lack of continuous training and education

Incompatible organisational structure and isolated

individuals and departments

Paying inadequate attention to internal and

external customers

Inadequate use of empowerment and teamwork

Failure to continually improve

Unit 9

Capacity Requirement Planning

9.1 Definitions of Capacity

9.2 Measurements of Capacity

9.3 Capacity Planning Process

9.4 Decision Tree Analysis

167

9.1 Definitions of Capacity

Capacity is defined as the maximum output of a

system in a given period under ideal conditions

In general, production capacity is the

maximum production rate of an organization.

Capacity can be difficult to quantify due to …

Day-to-day uncertainties such as employee

absences, equipment breakdowns, and material

delivery delays

Products and services differ in production rates (so

product mix is a factor)

Different interpretations of maximum capacity

Many organizations operate their facilities at a

rate less than capacity

Expected capacity is the greatest level of

output that a plant can maintain …

within the framework of a realistic work

schedule

taking account of normal downtime

assuming sufficient availability of inputs to

operate the machinery and equipment in place

Definitions of Capacity…

168

Effective Capacity or Utilization is the capacity

a firm can expect to achieve given its product

mix, methods of scheduling, maintenance, and

standards of quality …

Utilization = Expected Capacity/Capacity

Definitions of Capacity…

Output Rate Capacity

For a facility having a single product or a

few homogeneous products, the unit of

measure is straightforward (e.g. barrels of

beer per month)

For a facility having a diverse mix of

products, an aggregate unit of capacity must

be established using a common unit of

output (sales dollars per week)

9.2 Measurements of Capacity

169

Input Rate Capacity

Commonly used for service operations

where output measures are particularly

difficult

Hospitals use available beds per month

Airlines use available seat-miles per month

Movie theatres use available seats per month

Measurements of Capacity…

Capacity Utilization Percentage (Efficiency)

Relates actual output to output capacity

Example: Actual automobiles produced in a

quarter divided by the quarterly automobile

production capacity

Relates actual input used to input capacity

Example: Actual accountant hours used in a

month divided by the monthly account-hours

available


170

Rated Capacity

Measure of the maximum usable capacity of

a particular facility

Rated capacity will always be less than or

equal to the capacity …..

Rated capacity = (capacity)(utilization)(efficiency)


Capacity Cushion

An additional amount of capacity added onto

the expected demand to allow for:

greater than expected demand

demand during peak demand seasons

lower production costs

product and volume flexibility

improved quality of products and services


171

Rated Capacity Example

The Bakery in Chudleigh Central Street has a

plant for processing breakfast rolls. The facility

has an efficiency of 90%, and the utilization is

80%. Three process lines are used to produce

the rolls. The lines operate 7 days a week and

three 8-hour shifts per day. Each line was

designed to process 120 standard (that is, plain)

rolls per hour. What is the rated capacity?

Bakery Rated Capacity

Bakery capacity = 3*120 rolls/hr

Weekly capacity = 3*120*8*7*3 = 60,480 rolls/week

Utilization = 0.8

Efficiency = 0.9

Rated Capacity = (capacity)(utilization)(efficiency)

= (60,480)(0.8)(0.9)

= 43,545.6

≈ 43,546 rolls/week

172

Estimate the capacity of the present

facilities

Forecast the long-range future capacity

needs

Identify and analyze sources of capacity to

meet these needs

Select from among the alternative sources

of capacity

9.3 Capacity Planning Process

Consider the life of the input (e.g. facility is 10-

30 yr)

Understand product life cycle as it impacts

capacity

Anticipate technological developments

Anticipate competitors’ actions

Forecast the firm’s demand

Resource availability

Accuracy of the long-range forecast

Capacity cushion

Changes in competitive environment

Forecasting Capacity Requirements

173

Subcontract with other companies

Acquire other companies, facilities, or

resources

Develop sites, construct buildings, buy

equipment

Expand, update, or modify existing facilities

Reactivate standby facilities

Expansion of Long-Term Capacity

Sell off existing resources, lay off

employees

Mothball facilities, transfer employees

Develop and phase in new

products/services

Reduction of Long-Term Capacity

174

All at Once – build the ultimate facility now

and grow into it

Incrementally – build incrementally as

capacity demand grows

Approaches to Expanding Long-Term

Capacity

Little risk of having to turn down business due

to inadequate capacity

Less interruption of production

One large construction project costs less than

several smaller projects

Due to inflation, construction costs will be

higher in the future

Most appropriate for mature products with

stable demand

Expanding Long-Term Capacity

All at Once

175

Less risky if forecast needs do not

materialize

Funds that could be used for other types of

investments will not be tied up in excess

capacity

More appropriate for new products

Expanding Long-Term Capacity

Incrementally

Structures complex multiphase decisions,

showing:

What decisions must be made

What sequence the decisions must occur

Interdependence of the decisions

Allows objective evaluation of alternatives

Incorporates uncertainty

Develops expected values

9.4 Decision Tree Analysis

176

Decision Tree Analysis Example

News Café is about to build a new restaurant.

An architect has developed three building

designs, each with a different seating capacity.

News Café estimates that the average number of

customers per hour will be 80, 100, or 120 with

respective probabilities of 0.4, 0.2, and 0.4. The

payoff table showing the profits for the three

designs is shown next.

Average Number of Customers Per Hour

c1 = 80 c2 = 100 c3 = 120

Design A $10,000 $15,000 $14,000

Design B $ 8,000 $18,000 $12,000

Design C $ 6,000 $16,000 $21,000

News Café Designs’ Payoff Table

177

Calculate the expected value for each decision.

The decision tree can assist in this calculation.

Here d1, d2, d3 represent the decision

alternatives of designs A, B, C, and c1, c2, c3

represent the different average customer

volumes (80, 100, and 120) that might occur.

Expected Value Approach

1

(.2)

(.4)

(.4)

(.4)

(.2)

(.4)

(.4)

(.2)

(.4)

d1

d2

d3

c1

c1

c1

c2

c3

c2

c2

c3

c3

Payoffs

10,000

15,000

14,000

8,000

18,000

12,000

6,000

16,000

21,000

2

3

4

News Café Decision Tree

178

Choose the design with largest EV -- Design C.

3

4

d1

d2

d3

EV = .4(10,000) + .2(15,000) + .4(14,000)

= $12,600

EV = .4(8,000) + .2(18,000) + .4(12,000)

= $11,600

EV = .4(6,000) + .2(16,000) + .4(21,000)

= $14,000

Design A

Design B

Design C

2

1

News Café Expected Value for Each Decision

Capacity Decisions Exercise 1

Nitrogen Chemicals is considering capacity expansion. Its

major alternatives are; to do nothing, build a small plant, build a

medium plant or build a large plant. The new facility would

produce a new type of fertiliser and currently the potential or

marketability for this product is unknown. If a large plant is

built and a favourable market exists, a profit of $100,000 could

be realized. An unfavourable market would yield a $90,000

loss. However, a medium plant would earn a $60,000 profit

with a favourable market. A $10,000 loss would result from an

unfavourable market. A small plant, on the other hand, would

return $40, 000 with favourable market conditions and lose only

$5,000 in an unfavourable market. Of course, there is always

the option of doing nothing.

179


If recent market research indicates that there is a 0.4

probability of a favourable market, which means that there

is also 0.6 probability of an unfavourable market. With

this information, the alternative that will result in the

highest expected monetary value (EMV) can be selected:

EMV (large plant) = (0.4)($100,000) + (0.6)(-$90,000)

= -$14,000

EMV (medium plant) = (0.4)($60, 000) + (0.6)(-$10,000)

= +$18,000

EMV (small plant) = (0.4)($40,000) + (0.6)(-$5,000)

= +$13,000

EMV (do nothing) = $0

CompuServ is considering what to do with his business over

the next five years. Sales growth over the past couple of

years has been good, but sales could grow substantially if a

major electronics firm is built in the Store’s area as

proposed. CompuServ’s owner sees three options:

1. Enlarge the current store; 2. Locate at a new site; 3. Do

nothing.

Options 1 and 2 would take little time and, therefore, the

store would not lose revenue. If nothing was done the first

year and strong growth occurred, then the decision to

expand would be reconsidered. Waiting longer than one year

would allow competition to move in and make expansion no

longer feasible.


180

CompuServ has gathered the following information:

1. Strong growth has a 55% probability

2. Strong growth with new site annual returns $195,000; Weak

growth with new site mean annual returns $115,000.

3. Strong growth with an expansion annual returns $190,000;

Weak growth with an expansion mean annual returns $100,000.

4. Strong growth with existing store and do nothing annual returns

$170,000 while weak growth $105,000 per year.

5. Expansion at current site cost $87,000.

6. Moving to new site cost $210,000.

7. If growth is strong and expanding in second year cost $87,000.

8. Operating costs for all options are equal.

What is the best option for CompuServ?


GBS 660 – Production and Operations

Management

2016 version of the module study notes compiled by

Levy Siaminwe , PhD

Adjunct Associate Professor – University of Lusaka

production and operations management · 2 production and operations management module contents unit...

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