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Operations Strategy

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Operations Strategy If different departments of a company work

toward different Goals, individual efforts are

wasted. Top Managers are responsible for setting

Overall Goals for everyone in the company.

The Corporate Strategy of a company stateshow will the company achieve its OverallGoals and objectives.

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Operations Strategy

Through strategic

planning,managersestablish thedirection forCompany.

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Operations Strategy

At the same time the corporate strategyis formulated, each functional areadevelops its own functional strategy.

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Operations Strategy Each function in a business has a

functional strategy.

A functional strategy details how afunctional area will contribute to theachievement of the firms corporate

goals and objectives.

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Operations Strategy The operations strategy is a statement of

how operations function will contribute to the

achievement of corporate goals. Operations function is responsible for

producing goods.

Therefore, it has a major role in carrying outmuch of the business strategy.

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Operations Strategy Operations function has an important

influence on the

COST

QUALITY

AVA

ILAB

IL

ITY

of the companys products.

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Operations Strategy

Therefore, Operations strengths and

weaknesses have a great impact onsuccess of companys overall strategy.

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Develop an Operations

Strategy -What products can be produced in

which facility and how much?

-Which products are going to beproduced internally, and which ones willbe purchased?

-How many facilities are needed?

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Develop an Operations

Strategy -Where will the facilities be located,

with how much capacity?

-What type of processes will be utilizedto produce products?

-How much flexibility is required fromeach process and each product?

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Develop an Operations

Strategy -What level of technology (automation,

etc.) will be used?

-Are the resources going to be ownedor bought?

-How will the products be distributed tothe end customers?

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Develop an Operations

Strategy -Which suppliers will provide materials,

and how much?

-What kind of human skills are needed?

-And so on.

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Develop an Operations

Strategy Operations decisions given regarding

these issues must be consistent withthe firms corporate strategy.

These decisions made by operationsmanagers are going to be viewed in

detail throughout this course.

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DEMANDMANAGEMENT The first step in operations

management is to know whatcustomers want, and how much do theywant.

Apart of a firms strategic planning

involves identifying current andpotential demands of its customers.

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DEMANDMANAGEMENTWhat should be produced?How much should be produced?

Where and When should be produced?

ARE the questions of DemandManagement.

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DEMANDMANAGEMENT Demand management is

1) To recognize the sources of demandfor a firms products,

2) To forecast demand,

3) To determine how the firm will satisfythat demand.

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DEMANDMANAGEMENT ACompanys Marketing function

provides market-related informationand demand forecast TO THEOPERATIONS FUNCTION.

AND THEN, Operations function makes

sure that the demanded products areprovided when they are needed.

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DEMANDMANAGEMENT Although this exchange of Demand

Information between Marketing andOperations seems straightforward,

It is often complicated by inconsistentOR inaccurate data flowing in both

directions.

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DEMANDMANAGEMENT This confusion results from:

- L

arge number of parts and finishedproducts,

- Multiple sources of demand, AND

- Differences in the timing of demandby Marketing and Operations.

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DEMANDMANAGEMENT In addition, there are usually additional

demands for end-products, resources, and

materials. For example, unplanned shortages OR after

sales service parts CAN BE reasons foradditional demand. (ex: car spare parts)

For these reasons, It is generally difficult topredict future demands.

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FORECASTINGDEMAND A forecast is an Inference of what is

likely to happen in future.

Forecast can be wrong.

Businesses may use Forecasts in severalsubjects.

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FORECASTINGDEMAND Some of the major forecasting areas are

(1) Economic Forecasting, (2)Technological Forecasting, and (3)Demand Forecasting.

Economic Forecast is a prediction of

what general business conditions will bein the future.

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FORECASTINGDEMAND Some examples of economic forecasting

are: Inflation rates, Gross NationalProduct, Personal Income, Taxrevenues, Level of employment, and soon.

Economic forecast is usually made byGovernmentAgencies, Banks, andEconometric Forecasting Services.

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FORECASTINGDEMAND Another application of

forecasting is

Technological Forecasting. Technological forecast

predicts the probabilityand significance of

possible futuredevelopments intechnology.

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FORECASTINGDEMAND What technology will the firms

competitors incorporate into their

products and processes?

Are there any technological advanceswith which the firm can create a

competitive advantage?

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FORECASTINGDEMAND For example, development of electric

cars in U.S. seems like a challenging

shift for car manufacturers. But what time and how will they be in

the market is a concern of technologicalforecasting.

Technological forecast can helpanswering these questions.

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FORECASTINGDEMAND The result ofEconomic Forecast and

Technological Forecast

Are combined with Previous DemandData to develop Demand Forecast.

Demand Forecast predicts the quantity

and timing of demand for a firmsproducts.

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Factors Affecting Demand

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Factors Affecting Demand Status of the General Economy is about

the business life that may go through

some phases of Inflation, Recession, orDepression.

These specific conditions affect all the

companies in a country in general.

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Factors Affecting Demand The concept of ProductLife Cycle, on

the other hand, addresses the pattern

of changes in Sales, ProductStandardization, and CompetitivePressures

exhibited by most of the products in themarketplace.

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Factors Affecting Demand The changes in sales of products

usually follow a similar shape for many

products:

This shape is called ProductLife CycleCurve.

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Factors Affecting Demand

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Factors Affecting Demand

There are several other factors that affect

demand and the company may haveControl:

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Forecast Horizon

ForecastHorizon

is the number offuture periodsthat theforecasting makespredictions.

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Forecast Horizon Based on the length of the horizon, there are

three types of forecasting:

1) Long Range Forecasting (which concernspredictions for over 5 years in future)

2) Intermediate Forecasting (which is usuallyfor predictions of up to 2 years), and

3) ShortRange Forecasting (which predictsbetween 1 day and 1 year horizons).

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Forecast Horizon

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Forecast Horizon An Aggregate Production Plan is a general

schedule that specifies the quantity of each

product family (or product group) that will beproduced in each period.

The Aggregate Plan is converted to DetailedJob Scheduling and Material Purchasing

Requirements in the short range planning.

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Forecast Horizon It is generally true that SHORT RANGE

forecast results are More Accurate than

long-range forecast results. In other words, A forecast for next

month should be more accurate than aforecast for the same month Next Year.

All firms want to have the mostaccurate forecast.

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Forecast Horizon

The more accuratethe forecast, themore efficient andeffective a firm canuse its resources tosatisfy demand.

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Forecasting Methods

A forecast can be developed througheither a Subjective approach, OR anObjective approach.

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Forecasting Methods Subjective approaches are qualitative in

nature AND they are usually based on the

opinions of people (that is why they aresubjective).

Objective approaches involve Quantitativemethods and Mathematical formulations.

(They cab also be referred as Statisticalforecasting)

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Subjective (Qualitative)

Forecasting Methods

There are four major qualitativeforecasting techniques:

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1) Executive Committee

Consensus Here, a forecast is developed by asking

a group of Knowledgeable Executives

To discuss their opinions Regarding thefuture values of the items Beingforecasted.

This method provides forecast in arelatively short time.

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1) Executive Committee

Consensus But, Presence of a Powerful member in

the group May prevent the committee

from achieving a consensus.

In addition, it requires the valuable timeof highly paid executives.

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1) Executive Committee

Consensus This method is mostly used for long and

medium range forecasting purposes.

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2) Delphi Method The Delphi Method also involves a

Group ofExperts who eventually

develop a consensus.

They usually make long rangeforecasting for future technologies OR

future sales of a new product.

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2) Delphi Method The difference here is that, In this

method, the panel members are located

in different places AND do not knoweach other.

This reduces the influence of powerful

executives.

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2) Delphi Method There is one coordinator who knows all

the participants, And all participants

only contact with the coordinator.

First, Each member completes aquestionnaire and returns it to the

coordinator.

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2) Delphi Method The results are summarized by the

coordinator and a new questionnaire is

developed based on these results.

This summary report is sent back to theparticipants.

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2) Delphi Method The participants review this reportAND

they either defend OR modify their

original views. The process is repeated until a

consensus is reached.

The quality of the consensus and finaldecision is largely dependent on thecoordinator.

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3) Sales Force Composite

Since Sales people in a company directly

deal with customers, They are a goodsource of information regardingcustomers future intentions to buy aproduct.

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3) Sales Force Composite They can help a firm obtain a forecast

quickly and inexpensively.

In this technique, each salesrepresentative is asked to estimatesales in his/her territory.

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3) Sales Force Composite These individual estimates are then

combined together by Upper Managers

to develop Regional Sales forecast. This method is more suitable for

forecasting sales volume of a newproduct.

But still it is subject to opinion basedterms.

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4) Customer Surveys

By using a customer survey, a Firm canbase its demand forecast on the

customers purchasing plans.

This information can be directlyobtained from the customers

themselves.

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4) Customer Surveys

This can be done through personal,telephone Or mail surveys.

Customer survey method is also anexcellent opportunity for understandingthe thinking behind customers when

they are purchasing and selecting aproduct.

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4) Customer Surveys

However, asking questions may annoysome customers.

And, this method requires considerabletime and large staff for surveys.

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Quantitative ForecastingM

ethods Quantitative forecasting methods

employ mathematical models and

historical data to predict demand.

The first step in developing aquantitative forecast model is to Collect

sufficient data on Past levels ofdemand.

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Quantitative ForecastingM

ethods For example, data obtained for at least

2 to 3 years of pastARE desirable.

In addition, the effects of unusual orirregular events That caused a changein demand Should be removed from the

data (such as natural disasters, orOlympics).

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Quantitative ForecastingM

ethods There are two major types of

quantitative forecast models:

1) Time Series Models, AND

2) Causal Models

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Quantitative ForecastingM

ethods The main difference between the two

models is that:

In time series modeling technique, Theonly independent variable is the time.

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Quantitative ForecastingM

ethods

In contrast, Causal Models may employsome factors other than Time, Whenpredicting forecast values.

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Time Series Modeling Time Series modeling involves plotting

demand data on a time scale.

A time series is a sequence ofchronologically arranged observationstaken at regular intervals for a

particular variable.

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Time Series Modeling Daily, weekly, monthly sales data are

examples for time series.

Time series are frequently analyzed toidentify any 1) Trends, or 2) SeasonalFactors, or 3) Cyclical Factors that

influence the demand data.

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Time Series Modeling Trend, is a gradual upward or

downward movement of data over time.

Trends reflect changes in populationlevels, technology, and living standards.

Seasonality, is variation that repeatsitself at fixed intervals.

It can be as long as a Year, OR as shortas a few hours.

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Time Series Modeling Seasonal variations can correspond to

the Seasons of the Year, Holidays, OR

other special periods. For example, They can be caused by

weather conditions (e.g., sales of air

conditions).

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Time Series Modeling Cyclical variation has a duration of at

least one year; The duration varies from

cycle to cycle. Therefore, Cyclical variation requires

many years of data to determine its

repetitiveness.

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Time Series Modeling For example, The ups and downs of

general business economy Represent a

form of cyclical variation. Finally, Random variations are

variations in demand that cannot be

explained by Trends, Seasonality, orCyclicality.

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Time Series ModelingAn unpredictable event such as a war, astrike, or an earthquake CAN cause

large Random variations.

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Types ofTime SeriesM

odeling There are two major types of time

series models:

1- Smoothing Models

2- Time Series Decomposition Models

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1-

Smoothing Models- Moving Average (Simple & Weighted)

- Single Exponential Smoothing

- Double Exponential Smoothing

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SmoothingM

odels When many short-term demand

forecasts are required, developing a

Complex Forecasting model for eachitem may be Too Expensive and time-consuming.

(for example, a large number of low-cost inventory items)

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SmoothingM

odels In such cases, Simple Smoothing

models such as Moving Averages and

Exponential Smoothing often providequick and inexpensive forecasting.

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Simple MovingA

verage A simple moving average calculates the

average demand for the last n periods.

The calculated average value is theforecast for the next time period.

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Simple MovingA

verage Since all values in the time series are

combined in an average, individual

Highs and Lows offset each other. And, This reduces the likelihood of

random variations.

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EXAMP

LE

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EXAMP

LE

Actual monthly demands of a productare given above.

ATwo-month (n=2) and Five-month(n=5) moving average were used to

predict monthly demand for thisproduct.

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EXAMP

LE

For example, for n=2, The forecastvalue for March in Year 5 is calculated

as:

FMar = (920 + 940) / 2 = 930

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EXAMP

LE

Similarly, for n=5, The forecast valuefor March in Year 5 is calculated as:

FMar = (920 + 940 + 1020 + 1030 +1040) / 5 = 990

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EXAMP

LE

At the end of March, we can forecastdemand for April (with the new actual

data).What value should be used for n?

In general, the higher the level of n, the

less responsive the average is to therecent changes in demand.

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EXAMP

LE

Therefore, it appears that: If thepattern of data is changing too much, it

is best to use a small value for n.

However, if n is too small, the average

could be too responsive to variations indemand that are random, but not true.

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EXAMP

LE

Then, If the pattern is relatively stableOR If there is substantial random

variation, we should increase the valueof n.