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SUPPLY CHAIN MANAGEMENT

Submitted By: Group 5Chetan G. Patil / (MS12V016)Piyush Raj / (MS12V017)

Sharad Katwa / (MS12V027)Sitikantha Das / (MS12V028)

Demand Forecasting & Aggregate Planning at GE

Submitted to :

Prof R. P. Sundarraj


2/23

GE BEL

Joint Venture - Nov 1997 GE 74%; BEL 26%

Site Total Area: 350 K Sq. Ft. Built Up Area: 183 K Sq Ft.

700 Employees - 500 GE, 200 Contract

100% Export Oriented Unit


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C-Arm X-ray Radiography Mammography Vascular CTMR

Generator / HV Tanks CT Detector Module

Diverse Application - Catering to wide range of Healthcare products

Products and Applications:

Tubes


4/23

DEMAND :

Problem Statement:

2008 2009 2010 2011

Q1'08 Q2'08 Q3'08 Q4'08 Q1'09 Q2'09 Q3'09 Q4'09 Q1'10 Q2'10 Q3'10 Q4'10 Q1'11 Q2'11 Q3'11 Q4'11

1372 1557 1639 1462 1151 1122 1344 1484 1445 1510 1494 1557 1304 1321 1275 1339

Deman

d

Demand for X Ray tube shows seasonal variations.

Study aims to propose suitable :

(i) Forecasting Model (ii) Aggregate Production Planning Model

1000

1100

1200

1300

1400

1500

1600

1700



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Selection : Holts & Winter Method

Qualitative

Time Series

- Static

- Adaptive

Casual Simulation

FORECASTING

METHOD

Forecasting Methods:


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6

A static method assumes that

the estimates of level, trend

and seasonality within the

systematic component do not

vary as new demand is

observed

In adaptive forecasting, the

estimates of level, trend and

seasonality are updated after each

demand observation.

Time Series Forecasting Methods:


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HOLTs Model:


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8

Regression

Analysis

1000

1100

1200

1300

1400

1500

1600

1700


Estimation of Level & Trend:


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HOLTs Model:


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Here MADt=-1.21. Thus the estimate of standard deviation of

forecast error using holt's model with alpha=.1 and beta=.2 is

-1.515968

9 P9=L8+T8 1664.79

10 P10=L8+2T8 1725.98

11 P11=L8+3T8 1787.18

12 P12=L8+4T8 1848.37

HOLTs Model Forecasted Values:


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WINTER Model :


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Period Demand Deseasonalized Demand SeasonalFactor

1 1151.00 1174 0.980409

2 1122.00 1236 0.907767

3 1344.00 1298 1.035439

4 1484.00 1360 1.091176

5 1445.00 1422 1.016174

6 1510.00 1484 1.01752

7 1494.00 1546 0.966365

8 1557.00 1608 0.968284

Estimation Of Seasonal Factor:


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PeriodT

DemandD1 Level L1 Trend T1

SeasonalFactor

S1Forecas

t F1 Error E1AbsoluteError A1

Mean SquaredError MSE1 MAD % Error MAPE TS1

1112.00 62.00

1.00 1151.00 1174.05 62.01 0.98 1150.52 -0.48 0.48 0.23 0.48 0.04 0.04 -1.00

2.00 1122.00 1237.12 62.22 0.90 1112.45 -9.55 9.55 45.69 5.01 0.85 0.45 -2.00

3.00 1344.00 1299.89 62.33 1.03 1338.32 -5.68 5.68 41.21 5.24 0.42 0.44 -3.00

4.00 1484.00 1362.15 62.32 1.09 1484.83 0.83 0.83 31.08 4.13 0.06 0.34 -3.60

5.00 1445.00 1429.46 63.32 0.98 1396.03 -48.97 48.97 504.48 13.10 3.39 0.95 -4.87

6.00 1510.00 1511.15 66.99 0.90 1344.54 -165.46 165.46 4983.32 38.49 10.96 2.62 -5.96

7.00 1494.00 1565.32 64.43 1.03 1626.11 132.11 132.11 6764.53 51.87 8.84 3.51 -1.87

8.00 1557.00 1609.62 60.40 1.09 1776.33 219.33 219.33 11932.41 72.80 14.09 4.83 1.68

WINTER Model:


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Thus using Winter Model the forecast for next 4

periods is given by the following

P9=(L8+T8)*S1 = 1636.62

P10=(L8+2T8)*S1 = 1557.38

P11=(L8+3T8)*S1 = 1844.55

P12=(L8+4T8)*S1 = 2017.84

Here MADt=72.8. Thus the estimate of standard

deviation of forecast error using winter model

with alpha=.1 and beta=.2 ,gama = .1,is 91

WINTER Model Forecasted Values:


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Forecasting method MAD % Error MAPE

Holt's method -1.213 3.325 4.53

Winter method 72.8 14.09 4.83

Deviation for winter is high

Holts method is the appropriate best model for forecasting.

Analysis : Error Estimates for Forecast


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Aggregate Planning:

Aimed to determine ideal levels of capacity , production ,

subcontracting , inventory , stock outs over a specifiedperiod of time horizon.

Objectives: Determine

ProductionRateSubcontracting

OvertimeWorkforce

Levels

MachineCapacityBacklog


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Demand Forecast Production Costs Labour HRS / unit

Inventory Holding Cost Backlog Cost

Inputs & Constraints:

INPUTS :

CONSTRAINTS :

Overtime Layoffs Capital available

Backlog From Supplier


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Assumptions

October starting Inventory 100 UnitsOctober starting workforce 50 Associates

Working days / month 20

Working HRS / day 8

Cost StructureMaterial cost 50000 per unit

Inventory Holding cost 10000 per unit/week

Cost of stockout/backlog 20000 per unit/week

Hiring & Training cost 5000 per worker

Layoff cost 5000 per worker

Labour HRS required 10 per unit

Regular Time cost 63 perhour

Overtime cost 100 per hour

Cost of subcontracting 1000000 per unit

GE BEL Data:


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Regular time labour cost 10080 x Wt (63/HR x 8 HRS / day x 20

days/month = 10080)

Over time labour cost 100 x Ot

Cost of hiring & lay off - 5000 x Ht + 5000 x Lt

Cost of Inventory & stock out - 10000 It+ 20000 St

Cost of Material & subcontracting - 50000 Pt+ 1000000 Ct

Objective Function :

Linear Programing Model:

10080 x Wt + 100 x Ot + 5000 x Ht + 5000 x Lt+

10000 It + 20000 St + 50000 Pt + 1000000 Ct

Total cost incurred during planning horizon :


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Workforce , hiring & layoff : Wt - Wt-1 Ht + Lt = 0

Capacity constraint : 16 Wt + 0.1 x Ot

Inventory balance constraint : It + Pt + Ct = Dt + St-1 + It + St

Overtime constraint : Ot


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Snapshot:


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Conclusion:

Linear Programing can be used as a flexible

tool, by Operations Manager to meetProduction targets , satisfying all constraints.


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