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ANALYSIS OF TRENDS FOR COMPETITIVE POSITIONING OF HORIZONTAL MACHINING

CENTERS

BY

ANUP. H.A

1RV12MEM04

UNDER THE GUIDANCE OF

INTERNAL GUIDE EXTERNAL GUIDE

Dr. K.V.S. RAJESWARA RAO JAGADISH A.R

Associate Professor Sr. Manager-Technical Sales

Department of IEM Starrag India Private Limited

R.V. College of Engineering

MACHINING CENTERS• Depending on the orientation of the spindle with respect to the work table,

machining centers are classified as Horizontal Machining Centers (HMC’s) and Vertical Machining Centers (VMC’s)

• Advantages1. Greater productivity.

2. Longer tool life and better surface finish.

3. Greater rigidity, leading to lesser vibration during operation.

4. Availability of spindle coolant.

• Drawbacks1. Harder to use.

2. More expensive compared to VMC.

3. Fewer people have experience of using them.

BEST OPPORTUNITIES FOR HMC AND VMC

• HMC’s can be used for manufacturing facilities that have the best expertise available, need to be more competitive and can afford volume production.

• VMC’s are advisable if there are constraints in capital, skill, or experience to make optimal use of HMC. It is preferred for manufacturing facilities that have just started out, or have low volumes and need simplicity.

5%

10%

14%

28%

24%

11%

8%

Distribution of various classes of machine tool manufacturers in INDIA.

Class I Class II

Class III Class IV

Class V Class VI

Class VII

14%

5%

12%

2%

28%

29%

3%

7%

SCATTER OF MACHINE TOOL MANUFACTURERES IN INDIA.

Andhra Pradesh

Delhi

Gujarat

Haryana

Karnataka

Maharashtra

Punjab

Tamil Nadu

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 20120

5

10

15

20

25

30

35

6 6.58

9 9.510.5

1416

2830

2928

25.5

19

22.5

2523

2120

14

18

2224

25

2122

20.5 2019

1718

19.5 20

14 14.5 15

Production share of market leaders.

China Japan Germany

Year

% o

f wor

ld o

utpu

t

SIGNIFICANCE OF HORIZONTAL MACHINING CENTERS

• According to studies conducted by modern machine shop, it was found that some of the more profitable machine shops spent almost 10% of the gross revenue on equipment, with about 61% of the top shops investing in HMC’s.

• Another significant advantage is that a single HMC can be as productive as two or more VMC’s, with HMC’s having a spindle run time of 85%, as compared to 25% for a VMC.

• The major drawback, however, is the cost, with HMC’s costing on an average of $375,000, compared to $115,000 for a VMC; however, using a HMC, can lead to savings of $12,000, on an average, every month.

COMPANY PROFILE

• Starrag India Private Limited, a subsidiary of the Starrag group, headquartered in Switzerland, is a global technology leader in manufacturing high precision machine tools for milling, boring, turning and grinding operations.

• Customers are primarily active customers in Aerospace, Transport, Industrial, Energy, Medical and Watch and Jewellery sectors.

• Products are marketed under ten strategic brands, namely, Berthiez, Bumotec, Dӧrries, Droop+Rein, Heckert, Scharmann, SIP, Starrag, TTL and WMW.

Brand Equipment/Products

Starrag 5-Axis Horizontal Milling Centers.

Heckert 4-Axis Horizontal Milling Centers.

Dӧrries Vertical lathes.

Scharmann Horizontal Milling Centers, Boring and Milling machines.

SIP 3 to 5 axes ultra precision milling centers.

Droop+Rein Large machining centers in portal design.

Berthiez Turning and grinding machines.

Bumotec Milling machines and lathes for very small components of watches, jewellery and medical implants.

WMW 4-Axis Horizontal Milling Centers.

TTL Software solutions for milling.

PROBLEM DEFINITION AND OBJECTIVES

• Non-awareness of market consumption of specific products.

• Difficulties in launching right products.

• Market acceptance and affordability.

• Difficulties in predicting future trends for secured investment

OBJECTIVES

• Analyse the current trends in the machining center requirements and consumption patterns.

• Estimate the requirements of Horizontal Machining Center in India for the next five years, based on the current consumption patterns.

REVIEW OF LITERATUREPaper Title Summary

1.) Designing a decision support system for new product sales forecasting

This paper describes the various techniques that can be used to estimate the requirements/consumption of new products launched in the marketplace.

2.) Taylor series prediction of time series data

The paper describes the application of Taylor series method to estimate the number of sun spots detected on the surface of the sun, and compares the method to the time series analysis technique.

3.) A brief review of forecasting techniques

Review of the various forecasting techniques used, and also suggests techniques to develop estimates for new products, or develop estimates in case of non-availability of sufficient data.

TIME SERIES ANALYSIS

• Statistical approach applied for demand forecasting, with an aim to detect patterns in the data, and extend those trends as projections.

• Main objectives are data compression, explanatory, signal processing and prediction.

• Usage of time series models is twofold, namely provide an understanding of forces and structure that produces the observed data, and fit a model, which can be followed by forecasting, monitoring and feedback.

• Time series analysis finds applications in domains such as budgetary analysis, stock market analysis, yield projections, inventory studies, workload projections and census analysis.

EXPONENTIAL SMOOTHING

• One of the most widely used procedure for smoothing discrete time series data.

• Gained a lot of popularity in the recent past due to it’s simplicity, ease of adjusting the model’s responsiveness, computational efficiency, reasonable accuracy, etc.

• A simple and pragmatic approach to forecasting, wherein a forecast can be constructed from exponentially weighted average of past observations.

• Recommended when there is no pronounced historical trend, or cyclic variation in the data.

• Most commonly applied to analyse financial markets and economic data.

NEW PRODUCT FORECASTING SYSTEMS

• Product forecasting can be defined as the science of predicting the degree of success that a new product might enjoy.

• Characteristics of new product forecasts are:1. Strategically important for business.

2. Demand pattern for immediate future is highly uncertain.

3. Demand is unstable.

4. Little or no demand history to guide the forecast.

• Major difficulties faced by organizations regarding the development of forecasts for new products include unavailability of sales data, lack of knowledge regarding the forecasting technique to be applied and lack of a standard against which the suitability of the forecasting technique can be determined.

RESEARCH METHODOLOGY

• Data was primarily collected from a single source, namely the Indian Machine Tool Manufacturers Association (IMTMA)

• The secondary source of data includes the product brochures, i.e. the machining center specification catalogues.

• The data collected was first segregated for Horizontal Machining Centers for the years from 2000-01 to 2010-11. Data pertaining to the sales of HMC’s for 2011-12 to 2013-14 was collected separately.

• Only new HMC’s were considered for the period considered for this study.

• Based on the pallet size, HMC’s were segregated into five categories, namely 400mm pallet HMC, 500mm pallet HMC, 630mm pallet HMC, 800mm pallet HMC and 1000mm pallet HMC.

• The 1000mm pallet HMC’s include those HMC’s with a pallet size 1000mm, or greater than 1000mm, such as 1200mm, 1600mm, etc.

• Three techniques were used to estimate the requirements, namely time series analysis technique, exponential smoothing method and truncated Taylor series method.

• Effectiveness of each technique was determined using the Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE)

ANALYSIS OF TRENDS OF HORIZONTAL MACHINING CENTERS

• Three techniques were used to estimate the requirements, namely time series analysis technique, exponential smoothing method, and truncated Taylor series method.

• When estimating the requirements using the exponential smoothing method, three values for the smoothing constant α were considered, namely 0.3, 0.6 and 0.9.

Year Period Actual demand

Estimated demand

(Time series analysis)

Estimated demand

(Exponential smoothing

α=0.3)

Estimated demand


α=0.6)

Estimated demand


α=0.9)

Estimated demand

(truncated Taylor series

method)

2000-01 1 12001-02 2 162002-03 3 172003-04 4 25 28 8 10 15 262004-05 5 58 33 23 19 25 402005-06 6 73 61 38 42 55 712006-07 7 93 82 54 61 71 982007-08 8 127 102 76 80 91 1372008-09 9 211 130 117 108 123 2172009-10 10 26 184 89 170 202 2222010-11 11 59 144 80 84 44 952011-12 12 96 127 85 69 57 602012-13 13 112 127 93 85 92 852013-14 14 20 99 71 101 110 92

2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-140

50

100

150

200

250

1

16 1725

58

73

93

127

211

26

59

96

112

2028 33

61

82

102

130

184

144

127 127

98.5

Plot of actual demand vs. estimated demand (Time series ana-lysis technique)

Actual Demand Estimated Demand (Time series)

Year

No.

of H

MC

's im

porte

d

2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-140

50

100

150

200

250

116 17

25

5873

93

127

211

26

59

96

112

208

2338

54

76

117

8980 85

93

71

1019

42

61

80

108

170

8469

85

101

1525

55

71

91

123

202

4457

92

110

Plot of actual demand vs. estimated demand (exponential smoothing)

Actual Demand Estimated demand (α=0.3) Estimated demand (α=0.6) Estimated demand (α=0.9)

Year

No.

of H

MC

’s im

porte

d

2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-140

50

100

150

200

250

116 17

25

5873

93

127

211

26

59

96

112

202640

71

98

137

217 222

95

60

8592

Plot of actual demand vs. estimated demand (Taylor series technique)

Actual Demand Taylor Series Estimate

Year

No. o

f HM

C's i

mpp

orte

d

RESULTSTECHNIQUE ESTIMATE

FOR 2014-15ESTIMATE

FOR 2015-16ESTIMATE

FOR 2016-17ESTIMATE

FOR 2017-18ESTIMATE

FOR 2018-19MAD MAPE


109 115 120 126 131 48 10.62


(α=0.3)

93 71 78 76 74 40 20.55


(α=0.6)

101 53 77 77 76 52 20.48


(α=0.9)

110 29 87 73 77 50 15.15

TRUNCATED TAYLOR SERIES

METHOD

92 110 115 120 125 37 8.25

2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-190

50

100

150

200

250

1

16 1725

58

73

93

127

211

26

59

96

112

20

109115 120

126131

116 17

25

5873

93

127

211

26

59

96112

20

93

71 78 76 74

116 17

25

5873

93

127

211

26

59

96112

20

101

53

77 77 76

1

16 1725

58

73

93

127

211

26

59

96

112

20

110

29

87

73 77

116 17

25

5873

93

127

211

26

59

96112

20

92

110 115 120 125

Estimates for 400mm pallet HMC

Estimated demand (Time series) Estimated demand (Exponential smoothing α=0.3)Estimated demand (Exponential smoothing α=0.6) Estimated demand (Exponential smoothing α=0.9)Estimated demand (Taylor series)

Year

No.

of H

MC

's im

porte

d

500MM PALLET HMC RESULTSTECHNIQUE ESTIMATE

FOR 2014-15ESTIMATE FOR 2015-16

ESTIMATE FOR 2016-17



MAD MAPE


75 79 83 88 92 22 5.84


(α=0.3)

68 51 56 55 56 31 46.22


(α=0.6)

38 56 53 55 55 26 27.90


(α=0.9)

18 63 52 56 55 24 27.6


METHOD

106 90 102 114 126 21 7.49

2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-190

20

40

60

80

100

120

140

20

6 7 4

3240

7267

74

23

77 80 82

11

75 79 8388 92

20

6 7 4

3240

7267

74

23

77 80 82

11

68

5156 55 56

20

6 7 4

3240

7267

74

23

77 80 82

1118

63

52 56 55

20

6 7 4

32

40

7267

74

23

77 80 82

11

106

90

102

114

126


Estimated demand (Time series) Estimated demand (Exponential smoothing α=0.3)Estimated demand (Exponential smoothing α=0.6) Estimated demand (Exponential smoothing α=0.9)Estimated demand (Taylor series) Estimated demand (Taylor series)

Year

No.

of H

MC

's im

porte

d

630MM PALLET HMC RESULTSTECHNIQUE ESTIMATE

FOR 2014-15ESTIMATE

FOR 2015-16ESTIMATE

FOR 2016-17ESTIMATE

FOR 2017-18ESTIMATE

FOR 2018-19MAD MAPE


112 121 130 138 147 35 15.04


(α=0.3)

80 64 69 67 68 40 20.55


(α=0.6)

50 68 66 68 67 50 51.80


(α=0.9)

50 68 66 68 67 50 51.80


METHOD

123 136 140 143 147 38 11.68

2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-190

20

40

60

80

100

120

140

160

5 3 7 9

23

43

8089

74

22

82

149

68

26

112121

130138

147

5 3 7 9

23

43

8089

74

22

82

149

68

26

80

6469 67 68

5 3 7 9

23

43

8089

74

22

82

149

68

26

50

68 66 68 67

5 3 7 9

23

43

8089

74

22

82

149

68

26

123

136 140 143 147



Year

No.

of H

MC

's im

porte

d

800MM PALLET HMC RESULTS

TECHNIQUE ESTIMATE FOR 2014-15




ESTIMATE FOR 2018-

19

MAD MAPE


35 38 41 43 46 10 12.42


(α=0.3)

25 25 25 17 23 7 28.22


(α=0.6)

26 25 25 20 25 10 15.06


(α=0.9)

26 25 25 18 25 7 16.29


METHOD

48 43 47 51 55 19 16.31

2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-190

10

20

30

40

50

60

20 0

47

15

41

26

20 20

2629

26 25

3538

4143

46

20 0

47

15

41

26

20 20

2629

26 25 26 25 25

20

25

20 0

47

15

41

26

20 20

2629

26 25 26 25 25

18

25

20 0

47

15

41

26

20 20

2629

26 25

48

4347

5155



Year

No.

of H

MC

's im

porte

d

1000MM PALLET HMC RESULTS

TECHNIQUE ESTIMATE FOR 2014-15





MAD MAPE


21 22 23 25 26 8 12.97


(α=0.3)

15 13 13 9 12 9 30.18


(α=0.6)

14 10 12 11 12 8 15.68


(α=0.9)

10 7 14 13 13 8 12.54


METHOD

24 16 16 16 13 9 13.90

2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-190

5

10

15

20

25

30

20 0 0

3

10

20

14

26

7

26

23

87

2122

2325

26

20 0 0

3

10

20

14

26

7

26

23

87

1513 13

9

12

20 0 0

3

10

20

14

26

7

26

23

87

10

7

1413 13

20 0 0

3

10

20

14

26

7

26

23

87

24

16 16 16

13



Year

No.

of H

MC

's im

porte

d

CONCLUSION AND FUTURE SCOPE OF WORK

• Analysis of the current consumption patterns of the HMC’s has shown a strong preference towards the 400mm pallet and 630mm pallet HMC’s.

• A strong positive trend has been predicted to prevail in the machine tool industry in India for the next five years, i.e. from 2014-15 to 2018-19.

• The highest demand has been estimated for the 630mm pallet HMC’s, with an estimated demand of 130-150 HMC’s over the next five years.

• The 400mm pallet HMC has been estimated to be the largest growing segment, with an estimated average increase in consumption of 4.4%.

• FUTURE SCOPE OF WORK

• Impact studies to determine the effect of machine tool industry on the manufacturing growth of the country.

Thank You

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