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LAUNCHING OF A PRODUCT: STARLINE WASHING
MACHINEDETERGENT-FREE
DOESNT NEED
WATER
WORKS ONBATTERY POWER
IRONS your
clothes too!!
A PROJECT ON:
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FEATURES
Strong Iron Programme (SIP)
Flexibility in Placement
Teflon Coating
Detergent-Free
Cost-Saving
Environment-Friendly Appliance
Doesn't Need Water
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INTRODUCTION
Consumers look for one or more of these features in a washing machine:
1. Power efficiency
2. Less detergent requirement
3. Less water requirement
4. Less space occupying
5. Attractive aesthetics
Consumers are also likely to go for a power stabilizer along with the
washing machine.
The issue that a consumer is faced with is that all these features do not
come together in a single washing machine.
Starline Washing Machine brings to you all these features packed in a
single unit along with all other regular features.
6. Clothes drying facility
7. Ironing of clothes
8. Quick wash
9. Auto timer
10.Alarm
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FEATURES
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THE STAGES OF DESIGN
Preliminary Design
Evaluation & Improve
Prototype & Final
Design
Market Testing
Ramp Up
Screening
Concept Generation
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DESIGN FOR MANUFACTURING
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PRODUCTION
RAW MATERIALS:
Sheet steel coated with zinc: for rust resistance
Stainless steel: for spin tub
Plastic: for outer wash tub Aluminum: for the transmission
Hoses
Controls timers & switches
Motors Cables, LCD Display
Battery
Heat Chamber & Suction Box
Other electronic components
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PRODUCTION
MANUFACTURING PROCESS:
Sequence of steps involved in the manufacturing process:
Fabrication (making parts)
Sub-assembling (putting parts together to make
components)
Assembling (putting the components together to form
the final product)
Testing
Packaging
Shipping
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LOCATION
Power supply
Transport
Water
Labour
Land
Raw materials
Housing welfare/education
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CHOOSING A SITECHOOSING A SITE
BRIDGEMANS DIMENSIONAL ANALYSIS:
It helps in computing the relative merits (or cost
ratios) for each of the cost items, giving each of theratios an appropriate weightage by means of the power
(index) to which it is raised and multiplying these
weighted ratios in order to come up with a
comprehensive figure on the relative merit of the twoalternative sites. Choosing a location becomes easier.
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CHOOSING A SITECHOOSING A SITE
ILLUSTRATION:
S. No Factors Site A Site B Weights
1 Building & EquipmentCosts (Rs.)
50,00,000 40,00,000 4
2 Taxes per year (Rs.) 50,000 50,000 2
3 Power Cost per Year 1,00,000 1,20,000 3
4 Community Attitude 5 4 1
5 Product Quality as a
function of worker skill
12 11 5
6 Flexibility to adopt to
changed situation
4 4 3
Figures in the above table are hypothetical; used only for illustration
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CHOOSING A SITECHOOSING A SITE
Preference Merit Ratio (R)
= Preference for location A / Preference for location B
Conditions:
If R1, choose location B
If R=1, choose location A or B
R= (5000000)4X (50000)2 X (100000)3X (5)1 X (12)5X (4)3(4000000)4X (50000)2 X (120000)3X (4)1 X (11)5X (4)3
= 2.72
Since R>1, Location B will be chosen
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PRODUCT LAYOUTPRODUCT LAYOUT
Assembly
TestingPackagingShipping
Sub AssemblyFabrication
Product layout is best suited for this kind of
manufacturing process as:
Output: Product
layouts
can
generate
a
largev
olume
ofproductsinashorttime.
Cost: Unitcostislowasaresultofthehigh volume.
Utilization: Thereisahighdegreeoflabourand
equipment
utilization
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THE ASSEMBLY LINE LAYOUT THE ASSEMBLY LINE LAYOUT
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FACTORY LAYOUTFACTORY LAYOUT
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F O R E C A S T I N GF O R E C A S T I N G
Demand forecasting & Production system
Information on most recent
demand
Demand forecast
for Operation
Planning thesystem,
Product design,
Process design,
Eqpt. Investment
Capacity planning
Scheduling,
Aggregate
planning,
Operations
scheduling.
Controlling thesystem,
Production
control, Inventorycontrol,
Cost control.
Output: Washing
Machines
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F O R E C A S T I N GF O R E C A S T I N G
Month
Forecasted
Demand
(F)
Actual
Demand
(A)
Forecast
Error
(A-F)
Absolute
Deviation
|A-F|
Sum of
Absolute
Deviation
Mean
Absolute
Deviation
Running
Sum of
Forecast
Error
Tracking
Signal
1 1000 950 -50 50 50 50 -50 -1
2 1000 1070 70 70 120 60 20 0.33
3 1000 1100 100 100 220 73.33 120 1.64
4 1000 960 -40 40 260 65 80 1.23
5 1000 1090 90 90 350 70 170 2.43
6 1000 1050 50 50 400 66.67 220 3.30
Figures in the above table are hypothetical; used only for illustration
ILLUSTRATION:
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STEPS FOR THE FORECASTSTEPS FOR THE FORECAST
1. Objective of the forecast:QuantityofWashing Machines
tobeproduced
2. Items to be forecasted:UnitsofWashingmachinethe
marketdemands
3. Determine the time-horizon of the forecast : short,
mediumorlong-term
4. Forecasting models: Nominalgrouptechnique,market
surveyetc
5. Gather data needed to make the forecast6. Validate the forecasting model
7. Make the forecast
8. Implement the results: Produceandsupplytheforecasted
no.ofunits
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C A P A C I T Y P L A N N I N GC A P A C I T Y P L A N N I N G
Capacity planning is the analysis of how many
washing machines we are capable of producing versus
what our expected demand will be.
CAPACITY PLANNING STRATEGIES:
Long-term
Short-term
Medium-term
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QU A L I T Y M A N A G E M E N T QU A L I T Y M A N A G E M E N T
Pr ct lity ill s r y c c tr ti g t
f ll i g i si s:
Perf r ce
Feat res
eliability
f r ance
Durability Serviceability
est etics
Perceived Quality
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PROMOTIONSPROMOTIONS
Attractive introductory offers
Washing machine sales rise with the onset of monsoon. So the
product will be launched a few weeks before the season while the
promotions in both print and electronic media will start with the
production of first machine to create curiosity. Emails will be sent out to the target segment notifying about the
washing machine and feedback will be requested.
Large banners, posters and hoardings at prime locations.
Decorative body stickers will be given out to customers to match
the products look with the interiors of their homes.
Customer response awards scheme will be launched. Customers
who send maximum number of suggestions for improvement of
the product in any given month will be rewarded.
Incentives and contracts with retailers and distributors
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C O M P A R I S O NC O M P A R I S O N
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C O M P A R I S O NC O M P A R I S O N
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A CK N O W L E DGE M E N T A CK N O W L E DGE M E N T
We wish to express our sincere thanks to:
Mr. Subrata Kar faculty, Globsyn Business School
for giving us the opportunity to work on this
project. This has given us the insight of how
Operations Management is applicable to the
launching of new products in real world.
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