GSEP13GLSCM045_SCM Operations Assignement

8/12/2019 GSEP13GLSCM045_SCM Operations Assignement

1/15

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


2/15

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.


3/15

Q* 1297

Q*/D 4.31765613

1400252000

5400

6300

263700

6600

1120

2520006750

5040

263790

6510

ng

duction Run/Year

duction Run/Year


4/15

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


5/15


6/15

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


7/15

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


8/15

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


9/15

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.


10/15

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


11/15

g different

in

stem


12/15

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


13/15

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.


14/15

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.


15/15

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

GSEP13GLSCM045_SCM Operations Assignement

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