Introduction-Operations as a Competitive Weapon

1-1 Introduction to Operations Management

Introduction- Operations as a Competitive

Weapon


Operations-A Process ViewOperations-A Process View

Inputs

Land Labor

Capital

Transformation/

Conversion process

Outputs

Goods Services

Control

Feedback

FeedbackFeedback


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


Food ProcessorFood Processor

Inputs Processing Outputs

Raw Vegetables Cleaning Canned vegetables Metal Sheets Making cans

Water CuttingEnergy CookingLabor PackingBuilding LabelingEquipment

Table 1.2


Hospital ProcessHospital Process

Inputs Processing Outputs

Doctors, nurses Examination Healthy patientsHospital Surgery

Medical Supplies MonitoringEquipment MedicationLaboratories Therapy

Table 1.2


Manufacturing vs ServiceManufacturing vs ServiceCharacteristic 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


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


Business Operations OverlapBusiness Operations Overlap

Operations

FinanceMarketing


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


Operations-The Supply Chain View

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

Support Processes

Exte

rnal

sup

plie

rs

External customers

Supplier relationship process

Internal processes

Customer relationship management

Figure 1.4


Business Operations OverlapBusiness Operations Overlap

Operations

CorporateMarketing


1-12

Flows in a Supply Chain

Customer

Information

Product

Funds


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


Operations Management Decisions

• Strategic • Planning• Tactical


Operations Management Tools and Techniques

• Forecasting • Lean Systems• Operations Research


Lean Systems


Lean Systems-Characteristics• “Pull” method of work flow-a method in which customer demand activates production of

the service or item.• Quality at the Source-one approach is to use “poka-yoke” or a mistake-proofing method

aimed at designing fail-safe systems that minimize human error.• Small lot sizes/set up times.• Uniform workstation loads.-advance scheduling,differential pricing. • Standardized components and work methods-this increases repeatability..• Close supplier ties-frequent supplies,short lead time,high quality..• Flexible workforce-to help relieve bottlenecks as they arise without the need for inventory

buffers.• Line flows-OWMM and Group Technology methods.• Automation-ex. bank ATMs.• Five “S”-Sort,Straighten,Shine,Standardize,Sustain.• Preventive maintenance.


Continuous improvement (“kaizen”) using Lean Systems approach

• Excess capacity or inventory hides underlying problems with processes that produce a service or a product (synonymous to water surface hiding the rocks).

• Lean Systems provide the mechanism for management to reveal the problems by systematically lowering capacities or inventories till the problems are exposed.


Lean Systems-Mechanisms

• The “Kanban” System-A Japanese system used to control the flow of production through a factory.

• Value Stream Mapping-A qualitative tool for eliminating waste or “muda” that involves a current state drawing,a future state drawing and an implementation plan.

• JIT II-The supplier is brought into the plant to be an active member of the purchasing office of the customer by way of an “in-plant representative” of the supplier stationed full-time at the supplier’s expense..


The Seven types of Waste or “Muda”Waste Definition

1. Overproduction Manufacturing an item before it is needed.

2. Inappropriate Processing

Using expensive high precision equipment when simpler machines would suffice.

3. Waiting Wasteful time of people incurred when product is not being moved or processed.

4. Transportation Excessive movement and material handling of product between processes.

5. Motion Unnecessary effort related to the ergonomics of bending, stretching, reaching, lifting, and walking.

6. Inventory Excess inventory hides problems on the shop floor, consumes space, increases lead times, and inhibits communication.

7. Defects Quality defects result in rework and scrap, and add wasteful costs to the system in the form of lost capacity, rescheduling effort, increased inspection, and loss of customer good will.


Operations Research


22

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


23

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


Forecasting


FORECAST:• A statement about the future value of a variable of interest such

as demand.• Forecasts affect decisions and activities throughout an

organization– Accounting, finance– Human resources– Marketing– MIS– Operations– Product / service design


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


Forecast Variations

Trend

Irregularvariation

Seasonal variations

908988

Figure 3.1

Cycles

Randomvariation


Time Series Forecasts-Methods

• Naive• Averaging• Trend• Seasonality• Exponential smoothing


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.


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


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))


Averaging Method

• Simple moving average

• Weighted moving average


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


Simple Moving Average

MAn = n

Aii = 1n

35373941434547

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

Actual

MA3

MA5


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


Calculating a and b

b = n (ty) - t y

n t2 - ( t)2

a = y - b tn


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


Linear Trend Calculation

y = 143.5 + 6.3t

a = 812 - 6.3(15)5

=

b = 5 (2499) - 15(812)5(55) - 225

= 12495-12180275 -225

= 6.3

143.5


Seasonality

• Multiplicative Model

Demand=Trend x Seasonality (Seasonal Index)

Seasonality is the percentage of average (or trend) amount


Exponential Smoothing

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

• α is the Smoothing Constant


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

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

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


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


MAD, MSE, and MAPE

MAD = Actual forecast

n

MSE = Actual forecast)

-1

2

n

(

MAPE = Actual forecast

n

/ Actual*100)


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


Controlling the Forecast

• Control chart• Tracking signal


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


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.


Sources of Forecast errors

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


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

Date post:	08-Feb-2016
Category:	Documents
Upload:	rohanharsh
View:	19 times
Download:	2 times

Introduction-Operations as a Competitive Weapon

Documents