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8/12/2019 GSEP13GLSCM045_SCM Operations Assignement
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Annual Demand D 5600
Cost C 45
Setup Cost S 1350
Holding Cost (@20%) H 9 per unit/year
Lead Time L 2 Weeks
Production Schedule 1 per monthStandard Deviation Sigma 15 for Weekly Demand
Working per year 50 Weeks
Service Level 95%
Normsinv of 95% Z 1.6449
Annual Demand 5600
No of Containers Required 10
Fixed Cost per container 800
Total Fixed Cost per year 8000
Per Chair Cost 47
Total Cost of Chair 263200
Total Final Cost 271200
Loss if Importing from
Korea v/s Current Setup -900
Loss if Importing from
Korea v/s 4 Setups/Year -7500
Loss if Importing fromKorea v/s 4 Setups/Year -7410
Answer 3: As per the c
we clearly see that eve
cheaper to make the c
KOREAN ALTERNATIVE
Given Data
Answer 1: In the curre
run is small due to whi
Lot size for each produ
reduce the no of produ
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Current Production Lot Q 466.67 EOQ
Total Production Cost D*C 252000 Production Run Required
Total Setup Cost S*12 16200
Total Holding Cost H*(Q/2) 2100
Final Cost to Company 270300
Total Production Qty/RunProduction Cost
Setup Cost
Sigma * sqrt(L) 21.21320344 Holding Cost
Weekly Demand 112 Total Cost
Weekly Demand in 2 Weeks 224 Total Saving
Saftey Stock 34.89261461
Reorder Point 259
Total Production Qty/Run
Production CostSetup Cost
Holding Cost
Total Cost
Total Saving
lucations done for importing the chairs from korea
n if the current lot size of production is used it is
airs inhouse rather than importing it from korea.
EOQ Modeli
Total Cost Considering 4 Pr
Total Cost Considering 5 Pr
Current Scenario
Reorder Point
t production model, the lot size of each production
h the setup cost is more. If we you use EOQ to find the
ction run depending on the annual demand, this will
ction runs hence decreasing the annual setup cost.
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Q* 1297
Q*/D 4.31765613
1400252000
5400
6300
263700
6600
1120
2520006750
5040
263790
6510
ng
duction Run/Year
duction Run/Year
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S.No Month Demand Sales staff forecast Error Abs. Error CFE
Cummulat
ive
Abs.Error
Point Wise
MAD
1 April 234 225 9 9 9 9 9.00
2 May 254 240 14 14 23 23 11.50
3 June 264 263 1 1 24 24 8.00
4 July 227 230 -3 3 21 27 6.75
5 August 242 239 3 3 24 30 6.00
6 September 233 227 6 6 30 36 6.00
7 October 254 239 15 15 45 51 7.29
8 November 266 260 6 6 51 57 7.13
9 December 351 320 31 31 82 88 9.78
10 January 212 234 -22 22 60 110 11.00
253.7 60 110
MAD 11
CFE 60
TS 5.454545
BIAS 6
MAPE 0.043358
alpha 0.4
S.No Month DemandForcasting Using
Alpha 0.4Error abs error CFE
Cummulat
ive
Abs.Error
Point Wise
MAD
1 April 234 245 -11 11 -11 11 11.00
2 May 254 241 13 13 2 24 12.00
3 June 264 246 18 18 20 42 14.00
4 July 227 253 -26 26 -6 68 17.00
5 August 242 243 -1 1 -7 69 13.80
6 September 233 243 -10 10 -17 79 13.17
7 October 254 239 15 15 -2 94 13.43
8 November 266 245 21 21 19 115 14.38
9 December 351 253 98 98 117 213 23.67
10 January 212 292 -80 80 37 293 29.30
255 17 251
MAD 35.85714
CFE 17
TS 0.474104
Exponential Smoothening
8/12/2019 GSEP13GLSCM045_SCM Operations Assignement
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Point
Wise
Tracking
Signal
1.00
S.No
2.00 1
3.00 2
3.11 3
4.00 4
5.00 5
6.18 6
7.16 7
8.39 8
5.45 9
10
PointWise
Tracking
Signal
-1.00
0.17
1.43
-0.35
-0.51
-1.29
-0.15
1.32
4.94
1.26
Ignoring the 1st 3 values as the fist value is based on assumed forcast of 245, the second
forcast, the third value will be 40% 0f 60% which is 36% based on initia
In this method of forcasting the issue lies in the continous increasing tracking
signal, this is because of the bias in the forcasting system.
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
0 2 4 6 8 10 12
Point Wise Tracking Signal Exisiting Method
-2.00
-1.00
0.00
1.00
2.00
3.00
4.00
5.006.00
0 1 2 3 4 5 6 7 8
Point Wise Tracking Signal For Exponential Smoothing
Method
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In the exponential smoothing method the TS is found to be in between the limits of +/-
1.5 but then the MAD is 35.85, which shows that the Abs error variation is still very high.
And this causes a lag between demand and forcast.
COMPARISION BETWEEN FORCASTING METHOD
Demand Sales staff forecast Forcasting Using
Alpha 0.4
Forcast after
Bias Removal
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Month demand sales staff forecastForcast afterBias Removal error abs. Error CFE
April 234 225 231 3 3 3
May 254 240 246 8 8 11
June 264 263 269 -5 5 6
July 227 230 236 -9 9 -3
August 242 239 245 -3 3 -6
September 233 227 233 0 0 -6
October 254 239 245 9 9 3
November 266 260 266 0 0 3
December 351 320 326 25 25 28
January 212 234 240 -28 28 0
253.7 0 90
MAD 9
CFE 0
TS 0
BIAS 0
MAPE 0.035475
alue will be 60% based on our assumed first
l assumed value of forcast
Alternative Method
In this Bias removal method we have taken out the bias
method. By this we have made a model which has a Tra
biasness.
-2.00
-1.00
0.00
1.00
2.00
3.00
4.00
5.00
0 2 4 6
Point Wise Tracking Signal Bias Re
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This comparision graph between the demand and the different forcasted values by usin
method, shows clearly that Bias removal method is the best method which can be used
forcasting because it follows the trend of the demand and there is no biasness in ther s
which is there in sales force model.
Exponetial smoothing shows a lage in forcasting te spike in demand.
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Cummulat
ive
Abs.Error
Point
Wise
MAD
Point
Wise
TrackingSignal
3 3.00 1.00
11 5.50 2.00
16 5.33 1.13
25 6.25 -0.48
28 5.60 -1.07
28 4.67 -1.29
37 5.29 0.57
37 4.63 0.65
62 6.89 4.06
90 9.00 0.00
which was acting in sales force forcasting
cking signal of Zero and MAD of 9 with no
8 10 12
oval Method
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g different
in
stem
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Demand (Weekly Avg) cars/outlet
No of Outlets
Total Demand
Std Deviation
Lead Time
Std Deviation of DemandTarget Service Level
Z
Saftey Stock per Outlet
Saftey Stock for 4 Outlet
Demand (Weekly Avg) (25 cars/outlet)
Std Deviation
Lead Time
Std Deviation of Demand
Target Service Level
Z
Saftey Stock 19
100
10
2
14.14213562
90%
1.281551566
When we have a decentralized m
more because std deviation for e
centralized model the std deviati
level the safety stock decreases i
The Assumption here are:
1. Target Service Level remains Sa
2. Demand between the outlets a
Proposed Centralized Model
Current Decentralized Model
25
4
100
5
2
7.07106781290%
1.281551566
9.061938024
37
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del the safety stock requirement is
ch outlet is taken. When we use a
n gets lowered. Hence for the same Service
Centralized model.
me.
re negatively correlated.
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Annual Demand D 25300 boxes
Cost C 112 per box
Setup Cost S 1560
Holding Cost (@30%) H 33.6
Lead Time L 3 Weeks
Standard Deviation Sigma 54 for Weekly Demand
Working per year 50 Weeks
Service Level 95%
Normsinv of 95% Z 1.644853627
EOQ Q* 1533
Production Run Required Q*/D 16.50358774
Sigma * sqrt(L) 93.53074361
Weekly Demand 506
Weekly Demand in 3 Weeks 1518
Saftey Stock 153.8443829
Reorder Point 1672
IPOH
SR
BO
OH 0
SR 1000
BO 500
IP 500
Present Week 1 Week 2 Week 3
Stock On Hand 0 1000 0 2672
Scheduled Receipts 1000 2672 2672 0
Backorders 500 1000 500 1000
Avg Weekly Demand - 500 500 500
Planned Orders 2672 0 0 1500
Given Data
Production Run Required as per EOQ Modeling
Reorder Point
IP=OH+SR-BO
As per the calculation my recomm
the maximum cost benefit, also It i
case the first order which has to b
sum of Re-order qty and EOQ qua
reorder again once they go below
Inventory Position
Inventory PositionOn-Hand Inventory
Open Order in Pipeline
Back-orderAs the Inventory Position is less th
the order to the factory.Assuming
are allowed and does not result in
2672 units (Reorder Quantity (167
(present 500 + next one week 500
3rd week the company should ord
depleted.
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Total Production Qty/Run 1582
Production Cost 2833600
Setup Cost 24960
Holding Cost 26577.6
Total Cost 2885138
Total Production Qty/Run 1489
Production Cost 2833600
Setup Cost 26520
Holding Cost 25015.2
Total Cost 2885135
Week 4 Week 5 Week 6 Week 7 Week 8 Week 9
1172 672 1672 1172 672 1672
1500 1500 0 1500 1500 0
0 0 0 0 0 0
500 500 500 500 500 500
0 0 1500 0 0 1500
Total Cost Considering 17 Production Run/Year
Total Cost Considering 16 Production Run/Year
ntdation would be to go for a 17 Production run/year to have
s seen that the EOQ is less than the Re-order point qty. In this
made by warehouse should be equal to or greater than the
tity, and then as per the demand or usage they should
re-order qty.
an the Re-order point the warehouse should place
that the Avg demand is 500 and that backorders
lost sales, the company should place an order for
) + Accumulated back order till next week
) + Week 2 & 3 demand (1000) - SR(1000). After
er EOQ quantity and if reqd. safety stock if it is