Forecast On intek Tapes

8/13/2019 Forecast On intek Tapes

1/23

FORECAST PROPOSAL FOR INTEK TAPES

Project work submitted to:

University at Buffalo, The State University at New York

(in partial fulfillment of the requirements for the credits in

IE505 Production Planning and Control)

By:

Group #12

Nilesh Ananthanarayanan (50092066)

Krishnaraj Muthukumar (50097868)

Santosh Kumar Nandakumar (50095660)

Nithin Punaroor Narayanan (50097880)

Arvind Korkadu Sucharitha Sridhar (50095581)

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2/23

OBJECTIVE

The objective of this project is to propose a accurate

forecast model for Intek Tapes Private Ltd.

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3/23

COMPANY DESCRIPTION

Intek Tapes Private Limited is a company that is

involved in the manufacture of a wide range of

pressure sensitive adhesive tapes.

These tapes are marketed under the renowned

Fixonbrand name.

Promoted in 1988; 18,000 sqft. Manufacturing plant.

Products that are designed to suit an array of

specifications in the Leather, Electrical, Electronics,

Printing and Packaging Industry.

Electrical, Shoe upper, Leather goods, Double sided,

Industrial, Masking and Foil tapes.

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4/23

PRODUCT DETAILS

Product Unit Cost (in INR)

F-620 34.81

F-620 YELLOW 73.43

F-389 1685.40

F-317 223.20

F-391 458.30

F-372 164.51F-KP1 1245

F-376 310.42

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5/23

PROBLEM FORMULATION

Currently the company doesnt use any forecast

model

The company calculates its demand by assuming a

growth of 20%

This method is not accurate and the error between

demand and forecast is huge.

This method doesnttake into account factors such

as trend , seasonality.

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6/23

ANALYSIS

We analyzed the data for trend and seasonality.

Depending on our findings the following methods

could be used.

Forecasting method Applicability

Moving average No trend or seasonality

Simple exponential

smoothing

No trend or seasonality

Holts model Trend but no seasonality

Winters model Trend and seasonality

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7/23

LINEAR REGRESSION

Linear regression is an approach to model the

relationship between a scalar dependent variable y

and one or more explanatory variables denoted X.

This method is used to calculate the initial Level

and Trend values.

The excel solver is used to calculate the regression

models

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8/23

Regression Statistics

Multiple R 0.988621

R Square 0.977372

Adjusted R Square 0.971715

Standard Error 16.93607

Observations 6

ANOVA

df SS MS F

Significan

ce F

Regression 1 49555.8 49555.8 172.7704 0.000193

Residual 4 1147.321 286.8304

Total 5 50703.13

Coefficients

StandardError t Stat P-value

Lower95%

Upper95%

Lower95.0%

Upper95.0%

Intercept 3507.321 23.31548 150.4288 1.17E-08 3442.587 3572.056 3442.587 3572.056

X Variable 1 53.21429 4.048494 13.14422 0.000193 41.97386 64.45471 41.97386 64.45471

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9/23

WINTERS METHOD

Exponential smoothing is a technique used to

smooth and forecast a time series without the

necessity of fitting a parametric model.

It is based on a recursive computing scheme , where

the forecasts are updated for each new observation.

Winters method , also referred to as double

exponential smoothing is an extension of

exponential smoothing designed for trended andseasonal time series.

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10/23

SMOOTHING CONSTANTS

is a smoothing constant for the level. The current

estimate of the level is a weighted average of all

the past observations , with recent observations

weighted higher then older observations. A highervalue of corresponds to a forecast that is more

responsive to recent observations.

is the smoothing constant for trend. Like a higher

value for corresponds to a forecast that is moreresponsive to recent observations, also

corresponds to forecast that is more responsive to

recent observations.

is a smoothing constant for the seasonal factor. 10


11/23

SIMPLE EXPONENTIAL

Ft+1= Ft+2 == Ft+n = Lt

Lt+1= aDt+1+ (1-a)Lt

L= Level F= forecast

= Smoothing constant

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12/23

HOLTS METHOD

Ft+1= Lt+ Tt

Ft+n= Lt+ nTt

Lt+1= aDt+1+ (1-a)(Lt+ Tt) Tt+1= b(Lt+1- Lt) + (1-b)Tt

F = forecast

L = level

T= trend = smoothing constant for level

= smoothing constant for trend

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13/23

WINTERS METHOD

Ft+1= (Lt+Tt)(St+1) and Ft+n= (Lt+ nTt)St+n

Lt+1= a(Dt+1/St+1) + (1-a)(Lt+Tt)

Tt+1= b(Lt+1- Lt) + (1-b)Tt St+p+1 = g(Dt+1/Lt+1) + (1-g)St+1

= smoothing constant for level

= smoothing constant for trend

= smoothing constant for seasonal factor L= level

T= Trend

S= Seasonal Factor13


14/23


15/23

Calculations using Forecasting methods

(using Holts method for F-620)

15

Period Demand Level Trend ForecastAbsolute

ErrorPE MAPE

3523.33

3

52.1212

1

1 3700 3575.45 52.12 3575.45 124.55 3.37 4.712 3450 3627.57 52.12 3627.57 177.57 5.15 4.75

3 3800 3679.69 52.12 3679.69 120.31 3.17 4.64

4 3650 3731.81 52.12 3731.81 81.81 2.24 4.48

5 3900 3783.93 52.12 3783.93 116.07 2.98 4.38

6 3500 3836.05 52.12 3836.05 336.05 9.6 4.69

7 4250 3888.17 52.12 3888.17 361.83 8.51 4.9

8 3850 3940.29 52.12 3940.29 90.29 2.35 4.77

9 3800 3992.41 52.12 3992.41 192.41 5.06 4.78

10 4200 4044.53 52.12 4044.53 155.47 3.7 4.73

4.683


16/23


(using Winters model for F-620)

16

Period DemandDeseasonalized

DemandForecast D/F ratio

Seasonal

factor

1 3700 3560.536 1.039 1.009

2 3450 3613.75 0.955 0.97

3 3800 3675 3666.964 1.036 1.066

4 3650 3706.25 3720.179 0.981 0.98

5 3900 3768.75 3773.393 1.034

6 3500 3850 3826.607 0.9157 4250 3862.5 3879.821 1.095

8 3850 3937.5 3933.036 0.979

9 3800 3986.25 0.953

10 4200 4039.464 1.04


17/23


(using Winters model for F-620)

17

Period Demand Level Trend Seasonility Forecast AbsoluteEr

ror

% Error MAPE

0 3507.321 53.21429

1 3700 3571.182 54.279 1.009 3592.581 107.419 2.903216 2.903216

2 3450 3618.585 53.591 0.97 3516.697 66.697 1.933246 2.418231

3 3800 3661.431 52.517 1.066 3914.54 114.54 3.014211 2.616891

4 3650 3715.002 52.622 0.98 3639.669 10.331 0.283041 2.033429

5 3900 3776.237 53.483 1.012 3812.835 87.165 2.235 2.073743

6 3500 3808.318 51.343 0.968 3707.169 207.169 5.919114 2.714638

7 4250 3873.507 52.728 1.063 4102.82 147.18 3.463059 2.821555

8 3850 3926.469 52.751 0.98 3847.71 2.29 0.059481 2.476296

9 3800 3956.051 50.434 1.014 4034.929 234.929 6.182342 2.888079

10 4200 4041.974 53.983 0.963 3858.245 341.755 8.137024 3.412973

2.635905


18/23

MAPE Calculations for different

productsPRODUCT SIMPLE

EXPONENTIAL

HOLTS WINTERS

F-620 4.837 4.373 2.6359

F-620 yellow 18.095 18.62 16.6769

F-389 29.117 30.539 27.70925

F-317 45.816 46.387 42.265

F-391 29.38 35.725 25.960

F-372 49.326 22.848 27.51834

F-KP1 82.047 80.727 46.71

F-376 57.976 17.811 15.511

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19/23


(Savings in cost using Winters model for F-620)

19

Period Companys model Our modelAbsolute Error

1 3700 3592.581 108

2 3450 3516.697 67

3 3800 3914.54 115

4 3650 3639.669 11

5 3900 3812.835 886 3500 3707.169 208

7 4250 4102.82 148

8 3850 3847.71 3

9 3800 4034.929 235

10 4200 3858.245 342

11 5100 4366.29 734

12 4620 3625.396 995

13 4560 3335.224 1225

14 5040 2833.283 2207

15 6120 2435.771 3685

16 5544 1917.743 3627

Total Error 13798

Unit Cost 34.81

Loss 480,308.38


20/23

20

Demand0

1000

2000

3000

4000

5000

6000

7000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

DemandForecast


21/23

RESULTS

From the MAPE calculations you can see that winters

model is the most accurate.

If this model is used to forecast demand the

company can save a lot of money

The company can also reduce over head costs such

as inventory and transportation costs by applying

this model.

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22/23


23/23

Happy Thanksgiving!!!

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Forecast On intek Tapes