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Introduction operations as a competitive weapon-123

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1-1 Introduction to Operations Management Introduction- Operations as a Competitive Weapon
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Page 1: Introduction operations as a competitive weapon-123

1-1 Introduction to Operations Management

Introduction- Operations as a Competitive

Weapon

Page 2: Introduction operations as a competitive weapon-123

1-2 Introduction to Operations Management

Operations-A Process ViewOperations-A Process View

Inputs

Land

Labor

Capital

Transformation/

Conversion

process

Outputs

Goods

Services

Control

Feedback

FeedbackFeedback

Page 3: Introduction operations as a competitive weapon-123

1-3 Introduction to Operations Management

Operations Management-Definition

• The operations function– Consists of all activities directly related to

producing goods or providing services

The management of systems or processes that create goods and/or provide services

Page 4: Introduction operations as a competitive weapon-123

1-4 Introduction to Operations Management

Food ProcessorFood Processor

Inputs Processing Outputs

Raw Vegetables Cleaning Canned vegetables Metal Sheets Making cans

Water CuttingEnergy CookingLabor PackingBuilding LabelingEquipment

Table 1.2

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1-5 Introduction to Operations Management

Hospital ProcessHospital Process

Inputs Processing Outputs

Doctors, nurses Examination Healthy patientsHospital Surgery

Medical Supplies MonitoringEquipment MedicationLaboratories Therapy

Table 1.2

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1-6 Introduction to Operations Management

Manufacturing vs ServiceManufacturing vs Service

Characteristic Manufacturing ServiceOutput

Customer contact

Uniformity of input

Labor content

Uniformity of output

Measurement of productivity

Opportunity to correct

Tangible

Low

High

Low

High

Easy

High

Intangible

High

Low

High

Low

Difficult

Lowquality problems

High

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1-7 Introduction to Operations Management

Goods and Services-Key Differences

1. Customer contact2. Uniformity of input3. Labor content of jobs4. Uniformity of output5. Measurement of productivity6. Production and delivery7. Quality assurance8. Amount of inventory

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1-8 Introduction to Operations Management

Business Operations OverlapBusiness Operations Overlap

Operations

FinanceMarketing

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1-9 Introduction to Operations Management

Adding Value-The Value ChainAdding Value-The Value Chain

The difference between the cost of inputs and the value or price of outputs.

Inputs Land Labor Capital

Transformation/Conversion

process

Outputs Goods Services

Control

Feedback

FeedbackFeedback

Value added

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1-10 Introduction to Operations Management

Operations-The Supply Chain View

Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.

Support Processes

Ext

ern

al s

up

pli

ers

Exte

rnal cu

stom

ers

Supplier relationship process

Internal processes

Customer relationship management

Figure 1.4

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1-11 Introduction to Operations Management

Business Operations OverlapBusiness Operations Overlap

Operations

CorporateMarketing

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1-12 Introduction to Operations Management

1-12

Flows in a Supply Chain

Customer

Information

Product

Funds

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1-13 Introduction to Operations Management

Global Environment and Challenges for Operations Managers

• Global Competition• Productivity Improvement-service sector

productivity gains much lower in comparison with the manufacturing sector

• Rapid Technological Change• Ethical, Workforce Diversity and

Environmental Issues

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1-14 Introduction to Operations Management

Operations Management Decisions

• Strategic • Planning• Tactical

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1-15 Introduction to Operations Management

Operations Management Tools and Techniques

• Forecasting • Lean Systems• Operations Research

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1-16 Introduction to Operations Management

Forecasting

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1-17 Introduction to Operations Management

FORECAST:• A statement about the future

• Used to help managers– Plan the system– Plan the use of the system

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1-18 Introduction to Operations Management

Forecast Uses• Plan the system

– Generally involves long-range plans related to:• Types of products and services to offer• Facility and equipment levels• Facility location

• Plan the use of the system– Generally involves short- and medium-range plans related to:

• Inventory management• Workforce levels• Purchasing• Budgeting

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1-19 Introduction to Operations Management

• Assumes causal systempast ==> future

• Forecasts rarely perfect because of randomness

• Forecasts more accurate forgroups vs. individuals

• Forecast accuracy decreases as time horizon increases

I see that you willget an A this quarter.

Common Features

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1-20 Introduction to Operations Management

Elements of a Good Forecast

Timely

AccurateReliable

Mea

ningfu

l

Written

Easy

to u

seCost

effe

ctiv

e

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1-21 Introduction to Operations Management

Steps in the Forecasting Process

Step 1 Determine purpose of forecast

Step 2 Establish a time horizon

Step 3 Select a forecasting technique

Step 4 Gather and analyze data

Step 5 Make the forecast

Step 6 Monitor the forecast

“The forecast”

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1-22 Introduction to Operations Management

Types of Forecasts

• Judgmental - uses subjective inputs (qualitative)

• Time series - uses historical data assuming the future will be like the past (quantitative)

• Associative models - uses explanatory variables to predict the future (quantitative)

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1-23 Introduction to Operations Management

Judgmental Forecasts(Qualitative)

•Consumer surveys

•Delphi method

•Executive opinions

– Opinions of managers and staff

•Sales force.

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1-24 Introduction to Operations Management

Time Series Forecasts

• Trend - long-term movement in data• Seasonality - short-term regular variations in

data• Cycle – wavelike variations of more than one

year’s duration• Irregular variations - caused by unusual

circumstances• Random variations (Stable)- caused by

chance

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1-25 Introduction to Operations Management

Forecast Variations

Trend

Irregularvariation

Seasonal variations

908988

Figure 3.1

Cycles

Randomvariation

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1-26 Introduction to Operations Management

Time Series Forecasts-Methods

• Naive• Averaging• Trend• Seasonality• Exponential smoothing

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1-27 Introduction to Operations Management

Naive Method

Uh, give me a minute.... We sold 250 wheels lastweek.... Now, next week we should sell....

The forecast for any period equals the previous period’s actual value.

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1-28 Introduction to Operations Management

Naïve Method

• Simple to use• Virtually no cost• Quick and easy to prepare• Data analysis is nonexistent• Easily understandable• Cannot provide high accuracy• Can be a standard for accuracy

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1-29 Introduction to Operations Management

Naïve Method

• Stable time series data• Seasonal variations

– Next value in a series will equal the previous value in a comparable period

• Data with trends– F(t) = A(t-1) + (A(t-1) – A(t-2))

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1-30 Introduction to Operations Management

Averaging Method

• Simple moving average

• Weighted moving average

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1-31 Introduction to Operations Management

Moving Averages

• Simple Moving average – A technique that averages a number of recent actual values, updated as new values become available.

• Weighted moving average – More recent values in a series are given more weight in computing the forecast.

MAn = n

Aii = 1n

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1-32 Introduction to Operations Management

Simple Moving Average

MAn = n

Aii = 1n

35

37

39

41

43

45

47

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

Actual

MA3

MA5

Page 33: Introduction operations as a competitive weapon-123

1-33 Introduction to Operations Management

Trend Method-Linear Trend Equation

• Ft = Forecast for period t• t = Specified number of time periods• a = Value of Ft at t = 0• b = Slope of the line

Ft = a + bt

0 1 2 3 4 5 t

Ft

Page 34: Introduction operations as a competitive weapon-123

1-34 Introduction to Operations Management

Calculating a and b

b = n (ty) - t y

n t2 - ( t)2

a = y - b t

n

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1-35 Introduction to Operations Management

Linear Trend Equation Example

t yW e e k t 2 S a l e s t y

1 1 1 5 0 1 5 02 4 1 5 7 3 1 43 9 1 6 2 4 8 64 1 6 1 6 6 6 6 45 2 5 1 7 7 8 8 5

t = 1 5 t 2 = 5 5 y = 8 1 2 t y = 2 4 9 9( t ) 2 = 2 2 5

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1-36 Introduction to Operations Management

Linear Trend Calculation

y = 143.5 + 6.3t

a = 812 - 6.3(15)

5 =

b = 5 (2499) - 15(812)

5(55) - 225 =

12495-12180

275 -225 = 6.3

143.5

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1-37 Introduction to Operations Management

Seasonality

• Multiplicative Model

Demand=Trend x Seasonality (Seasonal Index)

Seasonality is the percentage of average (or trend) amount

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1-38 Introduction to Operations Management

Exponential Smoothing

• Next forecast=α(Actual)+(1- α)(Previous forecast)

• α is the Smoothing Constant

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1-39 Introduction to Operations Management

Associative Forecasting

• Predictor variables and variables of interest

• Simple Linear Regression – linear variation between the two variables

• Correlation coefficient r gives an indication of the strength of relationship between the two

variables.

• http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient

• r2>0.8 good prediction;<0.25 poor prediction

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1-40 Introduction to Operations Management

Forecast Accuracy

• Error - difference between actual value and predicted value

• Mean Absolute Deviation (MAD)– Average absolute error

• Mean Squared Error (MSE)– Average of squared error

• Mean Absolute Percent Error (MAPE)

– Average absolute percent error

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1-41 Introduction to Operations Management

MAD, MSE, and MAPE

MAD = Actual forecast

n

MSE = Actual forecast)

-1

2

n

(

MAPE = Actual forecast

n

/ Actual*100)

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1-42 Introduction to Operations Management

Example 10

Period Actual Forecast (A-F) |A-F| (A-F)^2 (|A-F|/Actual)*1001 217 215 2 2 4 0.922 213 216 -3 3 9 1.413 216 215 1 1 1 0.464 210 214 -4 4 16 1.905 213 211 2 2 4 0.946 219 214 5 5 25 2.287 216 217 -1 1 1 0.468 212 216 -4 4 16 1.89

-2 22 76 10.26

MAD= 2.75MSE= 10.86

MAPE= 1.28

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1-43 Introduction to Operations Management

Controlling the Forecast

• Control chart• Tracking signal

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1-44 Introduction to Operations Management

Control chart• Control chart

– A visual tool for monitoring forecast errors– Used to detect non-randomness in errors

• Control limits: UCL=0+z√MSE;LCL=0-z√MSE (z typically=2 or 3)

• Forecasting errors are in control if

– All errors are within the control limits– No patterns, such as trends are present

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1-45 Introduction to Operations Management

Tracking Signal

Tracking signal = (Actual-forecast)

MAD

•Tracking signal

–Ratio of cumulative error to MAD

Bias – Persistent tendency for forecasts to beGreater or less than actual values.Value of zero would be ideal for Tracking signal.Limits of +/-4 or +/- 5are often used for a range of acceptable values of the tracking signal.

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1-46 Introduction to Operations Management

Sources of Forecast errors

• Model may be inadequate• Irregular variations• Incorrect use of forecasting technique

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1-47 Introduction to Operations Management

Choosing a Forecasting Technique

• No single technique works in every situation• Two most important factors

– Cost– Accuracy

• Other factors include the availability of:– Historical data– Computers– Time needed to gather and analyze the data– Forecast horizon

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1-48 Introduction to Operations Management

Operations Research

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1-49 Introduction to Operations Management

49

What is Operations Research?• Operations Research is the scientific

approach to execute decision making, which consists of:

– The art of mathematical modeling of complex situations

– The science of the development of solution techniques used to solve these models

– The ability to effectively communicate the results to the decision maker

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1-50 Introduction to Operations Management

50

Operations Research Models

Deterministic Models Stochastic Models

• Linear Programming • Discrete-Time Markov Chains

• Network Optimization • Continuous-Time Markov Chains

• Integer Programming • Queuing Theory (waiting lines)

• Nonlinear Programming • Decision Analysis

• Inventory Models Game Theory

Inventory models

Simulation

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1-51 Introduction to Operations Management

CHAPTER9

Management of Quality

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1-52 Introduction to Operations Management

Agenda• Insights on Quality Management

– Defining Quality– Dimensions and Determinants

– The Consequences of Poor Quality

– Responsibility for Quality

– The Costs of Quality

– Ethics and Quality Management

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1-53 Introduction to Operations Management

Agenda Contd.

• The Evolution of Quality

• Total Quality Management (TQM)

– TQM Tools

– Quality Function Deployment (QFD)

• Six-Sigma Quality Control

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1-54 Introduction to Operations Management

Defining Quality-Dimensions of Quality

– Performance - main characteristics of the product/service

– Aesthetics - appearance, feel, smell, taste

– Special Features - extra characteristics

– Conformance - how well product/service conforms to customer’s expectations

– Reliability - consistency of performance

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1-55 Introduction to Operations Management

Dimensions of Quality (Cont’d)

– Durability - useful life of the product/service

– Perceived Quality - indirect evaluation of quality (e.g. reputation)

– Serviceability - service after sale

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1-56 Introduction to Operations Management

Examples of Quality Dimensions

Dimension

1. Performance 2. Aesthetics 3. Special features

(Product) Automobile

Everything works, fit & finish Ride, handling, grade of materials used Interior design, soft touch Gauge/control placement Cellular phone, CD player

(Service) Auto Repair

All work done, at agreed price Friendliness, courtesy, Competency, quickness Clean work/waiting area Location, call when ready Computer diagnostics

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1-57 Introduction to Operations Management

Examples of Quality Dimensions (Cont’d)

Dimension

5. Reliability 6. Durability 7. Perceived quality 8. Serviceability

(Product) Automobile

Infrequency of breakdowns Useful life in miles, resistance to rust & corrosion Top-rated car Handling of complaints and/or requests for information

(Service) Auto Repair

Work done correctly, ready when promised Work holds up over time Award-winning service department Handling of complaints

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1-58 Introduction to Operations Management

Defining Quality-Determinants of Quality

Service

Ease ofuse

Conforms to design

Design

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1-59 Introduction to Operations Management

The Consequences of Poor Quality

• Loss of business• Liability• Productivity• Costs

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1-60 Introduction to Operations Management

• Top management• Design• Procurement• Production/operations• Quality assurance• Packaging and shipping• Marketing and sales• Customer service

Responsibility for Quality

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1-61 Introduction to Operations Management

Costs of Quality

• Failure Costs - costs incurred by defective parts/products or faulty services.

• Internal Failure Costs

– Costs incurred to fix problems that are detected before the product/service is delivered to the customer.

• External Failure Costs

– All costs incurred to fix problems that are detected after the product/service is delivered to the customer.

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1-62 Introduction to Operations Management

Costs of Quality (continued)

• Appraisal Costs– Costs of activities designed to ensure quality or

uncover defects• Prevention Costs

– All TQ training, TQ planning, customer assessment, process control, and quality improvement costs to prevent defects from occurring

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1-63 Introduction to Operations Management

• Substandard work– Defective products– Substandard service– Poor designs– Shoddy workmanship– Substandard parts and materials

Ethics and Quality

Having knowledge of this and failing to correctand report it in a timely manner is unethical.

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1-64 Introduction to Operations Management

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ow

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ow

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Page 65: Introduction operations as a competitive weapon-123

1-65 Introduction to Operations Management

Total Quality Management (TQM)A philosophy that involves everyone in an organization in a continual effort to improve quality and achieve customer satisfaction.

T Q M

Page 66: Introduction operations as a competitive weapon-123

1-66 Introduction to Operations Management

TQM ToolsPurpose of the Tool Quality Control tools Management tools

Problem solving Control charts

Histograms

Check sheets

Pareto diagrams

Scatter diagrams

Graphs

Cause and Effect diagram

Operational Planning Arrow diagram

Poka yoke

Strategic Planning Quality function deployment

Plan-Do-Check-Act (PDCA)

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1-67 Introduction to Operations Management

QFD

Identify customer wants Identify how the good/service will satisfy customer

wants Relate customer wants to product hows Identify relationships between the firm’s hows Develop importance ratings Evaluate competing products Compare performance to desirable technical attributes

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1-68 Introduction to Operations Management

QFD House of Quality

Relationshipmatrix

How to satisfycustomer wants

Interrelationships

Co

mp

etit

ive

asse

ssm

ent

Technicalevaluation

Target values

What the customer

wants

Customer Customer importance importance

ratingsratings

Weighted Weighted ratingrating

Page 69: Introduction operations as a competitive weapon-123

1-69 Introduction to Operations Management

House of Quality Example

Your team has been charged with Your team has been charged with designing a new camera for Great designing a new camera for Great Cameras, Inc.Cameras, Inc.

The first action is The first action is to construct a to construct a House of QualityHouse of Quality

Page 70: Introduction operations as a competitive weapon-123

1-70 Introduction to Operations Management

House of Quality Example

CustomerCustomerimportanceimportance

ratingrating(5 = highest)(5 = highest)

Lightweight 3Easy to use 4Reliable 5Easy to hold steady 2Color correction 1

What the What the customer customer

wantswants

What the Customer

Wants

RelationshipMatrix

TechnicalAttributes and

Evaluation

How to SatisfyCustomer Wants

Interrelationships

An

alys

is o

fC

om

pet

ito

rs

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1-71 Introduction to Operations Management

House of Quality ExampleWhat the Customer

Wants

RelationshipMatrix

TechnicalAttributes and

Evaluation

How to SatisfyCustomer Wants

Interrelationships

An

alys

is o

fC

om

pet

ito

rs

Lo

w e

lect

rici

ty r

equ

irem

ents

Alu

min

um

co

mp

on

ents

Au

to f

ocu

s

Au

to e

xpo

sure

Pai

nt

pal

let

Erg

on

om

ic d

esig

n

How to SatisfyCustomer Wants

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1-72 Introduction to Operations Management

Lightweight 3Easy to use 4Reliable 5Easy to hold steady 2Color corrections 1

House of Quality ExampleWhat the Customer

Wants

RelationshipMatrix

TechnicalAttributes and

Evaluation

How to SatisfyCustomer Wants

Interrelationships

An

alys

is o

fC

om

pet

ito

rs

High relationshipHigh relationship

Medium relationshipMedium relationship

Low relationshipLow relationship

Relationship matrixRelationship matrix

Page 73: Introduction operations as a competitive weapon-123

1-73 Introduction to Operations Management

House of Quality ExampleWhat the Customer

Wants

RelationshipMatrix

TechnicalAttributes and

Evaluation

How to SatisfyCustomer Wants

Interrelationships

An

alys

is o

fC

om

pet

ito

rs

Lo

w e

lect

rici

ty r

equ

irem

ents

Alu

min

um

co

mp

on

ents

Au

to f

ocu

s

Au

to e

xpo

sure

Pai

nt

pal

let

Erg

on

om

ic d

esig

n

Relationships Relationships between the between the things we can dothings we can do

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1-74 Introduction to Operations Management

House of Quality Example

Weighted Weighted ratingrating

What the Customer

Wants

RelationshipMatrix

TechnicalAttributes and

Evaluation

How to SatisfyCustomer Wants

Interrelationships

An

alys

is o

fC

om

pet

ito

rs

Lightweight 3Easy to use 4Reliable 5Easy to hold steady 2Color corrections 1

Our importance ratings 22 9 27 27 32 25

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1-75 Introduction to Operations Management

House of Quality Example

Co

mp

any

A

Co

mp

any

B

G PG PF GG PP P

Lightweight 3Easy to use 4Reliable 5Easy to hold steady 2Color corrections 1

Our importance ratings 22 5

How well do How well do competing products competing products meet customer wantsmeet customer wants

What the Customer

Wants

RelationshipMatrix

TechnicalAttributes and

Evaluation

How to SatisfyCustomer Wants

Interrelationships

An

alys

is o

fC

om

pet

ito

rs

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1-76 Introduction to Operations Management

House of Quality ExampleWhat the Customer

Wants

RelationshipMatrix

TechnicalAttributes and

Evaluation

How to SatisfyCustomer Wants

Interrelationships

An

alys

is o

fC

om

pet

ito

rsTarget values(Technical attributes)

Technical evaluation

Company A 0.7 60% yes 1 ok G

Company B 0.6 50% yes 2 ok F

Us 0.5 75% yes 2 ok G

0.5

A

75%

2’ t

o ∞

2 ci

rcu

its

Fai

lure

1 p

er 1

0,00

0

Pan

el r

anki

ng

Page 77: Introduction operations as a competitive weapon-123

1-77 Introduction to Operations Management

House of Quality Example

Completed Completed House of House of QualityQuality

Lightweight 3

Easy to use 4

Reliable 5

Easy to hold steady 2

Color correction 1

Our importance ratings

Lo

w e

lect

rici

ty r

equ

irem

ents

Alu

min

um

co

mp

on

ents

Au

to f

ocu

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Au

to e

xpo

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Pai

nt

pal

let

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ic d

esig

n

Co

mp

any

A

Co

mp

any

B

G P

G P

F G

G P

P P

Target values(Technical attributes)

Technical evaluation

Company A 0.7 60% yes 1 ok G

Company B 0.6 50% yes 2 ok F

Us 0.5 75% yes 2 ok G

0.5

A

75

%

2’

to ∞

2 c

irc

uit

s

Fa

ilu

re 1

pe

r 1

0,0

00

Pa

ne

l ra

nk

ing

22 9 27 27 32 25

Page 78: Introduction operations as a competitive weapon-123

1-78 Introduction to Operations Management

House of Quality Sequence

Des

ign

ch

arac

teri

stic

s

Specific components

House 2

Cu

sto

mer

re

qu

irem

ents

Design characteristics

House 1

Sp

ecif

ic

com

po

nen

ts

Production process

House 3 P

rod

uct

ion

p

roce

ss

Quality plan

House 4

Figure 5.4Figure 5.4

Deploying resources through the Deploying resources through the organization in response to organization in response to customer requirementscustomer requirements

Page 79: Introduction operations as a competitive weapon-123

1-79 Introduction to Operations Management

Six Sigma Quality Control

• Application of statistical tools• New approach to process control• Enables organizations to improve their

quality to near-zero defect levels


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GAO-12-983R; Department of Defense's Waiver of Competitive ... · Department of Defense’s Waiver of Competitive PrototypingRequirement for Enhanced Polar System Program The Weapon

GAO-12-983R; Department of Defense's Waiver of Competitive ... · Department of Defense’s Waiver of Competitive PrototypingRequirement for Enhanced Polar System Program The Weapon

Documents

A Weapon in Your Competitive Arsenal: The Data Warehouseok-air.org/documents/2005 Fall/F05_ROMEDataWarehouse.pdf · Snowflake Schema Operation Data Store (ODS) XML ... • Mission

A Weapon in Your Competitive Arsenal: The Data Warehouseok-air.org/documents/2005 Fall/F05_ROMEDataWarehouse.pdf · Snowflake Schema Operation Data Store (ODS) XML ... • Mission

Documents

Optimising forklift activities - · PDF fileOPTIMISING FORKLIFT ACTIVITIES IN WIDE-AISLE ... a competitive weapon. 75 % of warehouses retrieve ... manage resource operation by integrating

Optimising forklift activities - · PDF fileOPTIMISING FORKLIFT ACTIVITIES IN WIDE-AISLE ... a competitive weapon. 75 % of warehouses retrieve ... manage resource operation by integrating

Documents

Source of Knowledge Blooming Like a Lotus Knowledge is the competitive weapon of the 21 st century Intellectual Professional Cheerfulness Morality.

Source of Knowledge Blooming Like a Lotus Knowledge is the competitive weapon of the 21 st century Intellectual Professional Cheerfulness Morality.

Documents

TURN YOUR DIRTY DATA INTO A COMPETITIVE WEAPON · 2018-05-17 · 2 EPUSH Turn Your Dirty Data Into A Competitive Weapon ©2018 ePush!, LLC For better or worse, Big Data has covered

TURN YOUR DIRTY DATA INTO A COMPETITIVE WEAPON · 2018-05-17 · 2 EPUSH Turn Your Dirty Data Into A Competitive Weapon ©2018 ePush!, LLC For better or worse, Big Data has covered

Documents

Using IT as a Competitive Weapon

Using IT as a Competitive Weapon

Documents

Module 2: Total Quality Management Operations Management as a Competitive Weapon.

Module 2: Total Quality Management Operations Management as a Competitive Weapon.

Documents

Chapter 1-operations-as-a-competitive-weapon

Chapter 1-operations-as-a-competitive-weapon

Education