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1-1 Introduction to Operations Management
Introduction- Operations as a Competitive
Weapon
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
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
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
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
1-6 Introduction to Operations Management
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
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
1-8 Introduction to Operations Management
Business Operations OverlapBusiness Operations Overlap
Operations
FinanceMarketing
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
1-10 Introduction to Operations Management
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
1-11 Introduction to Operations Management
Business Operations OverlapBusiness Operations Overlap
Operations
CorporateMarketing
1-12 Introduction to Operations Management
1-12
Flows in a Supply Chain
Customer
Information
Product
Funds
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
1-14 Introduction to Operations Management
Operations Management Decisions
• Strategic • Planning• Tactical
1-15 Introduction to Operations Management
Operations Management Tools and Techniques
• Forecasting • Lean Systems• Operations Research
1-16 Introduction to Operations Management
Lean Systems
1-17 Introduction to Operations Management
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.
1-18 Introduction to Operations Management
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.
1-19 Introduction to Operations Management
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..
1-20 Introduction to Operations Management
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.
1-21 Introduction to Operations Management
Operations Research
1-22 Introduction to Operations Management
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
1-23 Introduction to Operations Management
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
1-24 Introduction to Operations Management
Forecasting
1-25 Introduction to Operations Management
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
1-26 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
1-27 Introduction to Operations Management
Forecast Variations
Trend
Irregularvariation
Seasonal variations
908988
Figure 3.1
Cycles
Randomvariation
1-28 Introduction to Operations Management
Time Series Forecasts-Methods
• Naive• Averaging• Trend• Seasonality• Exponential smoothing
1-29 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.
1-30 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
1-31 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))
1-32 Introduction to Operations Management
Averaging Method
• Simple moving average
• Weighted moving average
1-33 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
1-34 Introduction to Operations Management
Simple Moving Average
MAn = n
Aii = 1n
35373941434547
1 2 3 4 5 6 7 8 9 10 11 12
Actual
MA3
MA5
1-35 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
1-36 Introduction to Operations Management
Calculating a and b
b = n (ty) - t y
n t2 - ( t)2
a = y - b tn
1-37 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
1-38 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
= 12495-12180275 -225
= 6.3
143.5
1-39 Introduction to Operations Management
Seasonality
• Multiplicative Model
Demand=Trend x Seasonality (Seasonal Index)
Seasonality is the percentage of average (or trend) amount
1-40 Introduction to Operations Management
Exponential Smoothing
• Next forecast=α(Actual)+(1- α)(Previous forecast)
• α is the Smoothing Constant
1-41 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
1-42 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
1-43 Introduction to Operations Management
MAD, MSE, and MAPE
MAD = Actual forecast
n
MSE = Actual forecast)
-1
2
n
(
MAPE = Actual forecast
n
/ Actual*100)
1-44 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
1-45 Introduction to Operations Management
Controlling the Forecast
• Control chart• Tracking signal
1-46 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
1-47 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.
1-48 Introduction to Operations Management
Sources of Forecast errors
• Model may be inadequate• Irregular variations• Incorrect use of forecasting technique
1-49 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