1. The document analyzes trends in horizontal machining centers (HMCs) in India to estimate future demand. It uses time series analysis, exponential smoothing, and Taylor series methods to forecast HMC demand from 2014-2019. 2. Results show the Taylor series method had the lowest errors, estimating demand to be 92-125 units annually. Exponential smoothing accuracy depended on the smoothing constant value. 3. Demand for 500mm pallet HMCs was estimated to be 68-126 units yearly, with Taylor series again providing the most accurate forecasts.

1. 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

2. 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)
• Advantages
1. Greater productivity.
2. Longer tool life and better surface finish.
3. Greater rigidity, leading to lesser vibration during operation.
4. Availability of spindle coolant.
• Drawbacks
1. Harder to use.
2. More expensive compared to VMC.
3. Fewer people have experience of using them.

4. 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. 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

6. 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

7. 6
6.5
8
9
9.5
10.5
14
16
28
30
29
28
25.5
19
22.5
25
23
21
20
14
18
22
24
25
21
22
20.5
20
19
17
18
19.5
20
14
14.5
15
0
5
10
15
20
25
30
35
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
%ofworldoutput
Year
Production share of market leaders.
China Japan Germany

8. 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.

9. 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.

10. 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.

11. 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.

12. REVIEW OF LITERATURE
Paper 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.

13. 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.

14. 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.

15. 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.

16. 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.

17. • 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)

18. 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.

19. Year Period Actual
demand
Estimated
demand
(Time series
analysis)
Estimated
demand
(Exponential
smoothing
α=0.3)
Estimated
demand
(Exponential
smoothing
α=0.6)
Estimated
demand
(Exponential
smoothing
α=0.9)
Estimated
demand
(truncated
Taylor series
method)
2000-01 1 1
2001-02 2 16
2002-03 3 17
2003-04 4 25 28 8 10 15 26
2004-05 5 58 33 23 19 25 40
2005-06 6 73 61 38 42 55 71
2006-07 7 93 82 54 61 71 98
2007-08 8 127 102 76 80 91 137
2008-09 9 211 130 117 108 123 217
2009-10 10 26 184 89 170 202 222
2010-11 11 59 144 80 84 44 95
2011-12 12 96 127 85 69 57 60
2012-13 13 112 127 93 85 92 85

20. 1
16 17
25
58
73
93
127
211
26
59
96
112
20
28
33
61
82
102
130
184
144
127 127
99
0
50
100
150
200
250
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
No.ofHMC'simported
Year
Plot of actual demand vs. estimated demand (Time series analysis technique)
Actual Demand Estimated Demand (Time series)

21. 1
16 17
25
58
73
93
127
211
26
59
96
112
20
8
23
38
54
76
117
89
80
85
93
71
10
19
42
61
80
108
170
84
69
85
101
15
25
55
71
91
123
202
44
57
92
110
0
50
100
150
200
250
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
No.ofHMC’simported
Year
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)

22. 1
16 17
25
58
73
93
127
211
26
59
96
112
20
26
40
71
98
137
217
222
95
60
85
92
0
50
100
150
200
250
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
No.ofHMC'simpported
Year
Plot of actual demand vs. estimated demand (Taylor series technique)
Actual Demand Taylor Series Estimate

23. RESULTS
TECHNIQUE ESTIMATE
FOR 2014-15
ESTIMATE
FOR 2015-16
ESTIMATE
FOR 2016-17
ESTIMATE
FOR 2017-18
ESTIMATE
FOR 2018-19
MAD MAPE
TIME SERIES
ANALYSIS
109 115 120 126 131 48 10.62
EXPONENTIAL
SMOOTHING
(α=0.3)
93 71 78 76 74 40 20.55
EXPONENTIAL
SMOOTHING
(α=0.6)
101 53 77 77 76 52 20.48
EXPONENTIAL
SMOOTHING
(α=0.9)
110 29 87 73 77 50 15.15
TRUNCATED
TAYLOR
SERIES
92 110 115 120 125 37 8.25

24. 1
16 17
25
58
73
93
127
211
26
59
96
112
20
109
115
120
126
131
1
16 17
25
58
73
93
127
211
26
59
96
112
20
93
71
78 76 74
1
16 17
25
58
73
93
127
211
26
59
96
112
20
101
53
77 77 76
1
16 17
25
58
73
93
127
211
26
59
96
112
20
110
29
87
73
77
1
16 17
25
58
73
93
127
211
26
59
96
112
20
92
110
115
120
125
0
50
100
150
200
250
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 2018-19
No.ofHMC'simported
Year
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)

25. 500MM PALLET HMC RESULTS
TECHNIQUE ESTIMATE
FOR 2014-15
ESTIMATE
FOR 2015-16
ESTIMATE
FOR 2016-17
ESTIMATE
FOR 2017-18
ESTIMATE
FOR 2018-19
MAD MAPE
TIME SERIES
ANALYSIS
75 79 83 88 92 22 5.84
EXPONENTIAL
SMOOTHING
(α=0.3)
68 51 56 55 56 31 46.22
EXPONENTIAL
SMOOTHING
(α=0.6)
38 56 53 55 55 26 27.90
EXPONENTIAL
SMOOTHING
(α=0.9)
18 63 52 56 55 24 27.6
TRUNCATED
TAYLOR
SERIES
METHOD
106 90 102 114 126 21 7.49

26. 20
6 7
4
32
40
72
67
74
23
77
80 82
11
75
79
83
88
92
20
6 7
4
32
40
72
67
74
23
77
80 82
11
68
51
56 55 56
20
6 7
4
32
40
72
67
74
23
77
80 82
11
18
63
52
56 55
20
6 7
4
32
40
72
67
74
23
77
80 82
11
106
90
102
114
126
0
20
40
60
80
100
120
140
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 2018-19
No.ofHMC'simported
Year
Estimates for 500mm 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) Estimated demand (Taylor series)

27. 630MM PALLET HMC RESULTS
TECHNIQUE ESTIMATE
FOR 2014-15
ESTIMATE
FOR 2015-16
ESTIMATE
FOR 2016-17
ESTIMATE
FOR 2017-18
ESTIMATE
FOR 2018-19
MAD MAPE
TIME SERIES
ANALYSIS
112 121 130 138 147 35 15.04
EXPONENTIAL
SMOOTHING
(α=0.3)
80 64 69 67 68 40 20.55
EXPONENTIAL
SMOOTHING
(α=0.6)
50 68 66 68 67 50 51.80
EXPONENTIAL
SMOOTHING
(α=0.9)
50 68 66 68 67 50 51.80
TRUNCATED
TAYLOR SERIES
METHOD
123 136 140 143 147 38 11.68

28. 5 3
7 9
23
43
80
89
74
22
82
149
68
26
112
121
130
138
147
5 3
7 9
23
43
80
89
74
22
82
149
68
26
80
64
69 67 68
5 3
7 9
23
43
80
89
74
22
82
149
68
26
50
68 66 68 67
5 3
7 9
23
43
80
89
74
22
82
149
68
26
123
136
140
143
147
0
20
40
60
80
100
120
140
160
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 2018-19
No.ofHMC'simported
Year
Estimates for 630mm 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)

29. 800MM PALLET HMC RESULTS
TECHNIQUE ESTIMATE
FOR 2014-15
ESTIMATE
FOR 2015-16
ESTIMATE
FOR 2016-17
ESTIMATE
FOR 2017-18
ESTIMATE
FOR 2018-19
MAD MAPE
TIME SERIES
ANALYSIS
35 38 41 43 46 10 12.42
EXPONENTIAL
SMOOTHING
(α=0.3)
25 25 25 17 23 7 28.22
EXPONENTIAL
SMOOTHING
(α=0.6)
26 25 25 20 25 10 15.06
EXPONENTIAL
SMOOTHING
(α=0.9)
26 25 25 18 25 7 16.29
TRUNCATED
TAYLOR
SERIES
METHOD
48 43 47 51 55 19 16.31

30. 2
0 0
4
7
15
41
26
20 20
26
29
26
25
35
38
41
43
46
2
0 0
4
7
15
41
26
20 20
26
29
26
25
26
25 25
20
25
2
0 0
4
7
15
41
26
20 20
26
29
26
25
26
25 25
18
25
2
0 0
4
7
15
41
26
20 20
26
29
26
25
48
43
47
51
55
0
10
20
30
40
50
60
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 2018-19
No.ofHMC'simported
Year
Estimates for 800mm 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)

31. 1000MM PALLET HMC RESULTS
TECHNIQUE ESTIMATE
FOR 2014-15
ESTIMATE
FOR 2015-16
ESTIMATE
FOR 2016-17
ESTIMATE
FOR 2017-18
ESTIMATE
FOR 2018-19
MAD MAPE
TIME SERIES
ANALYSIS
21 22 23 25 26 8 12.97
EXPONENTIAL
SMOOTHING
(α=0.3)
15 13 13 9 12 9 30.18
EXPONENTIAL
SMOOTHING
(α=0.6)
14 10 12 11 12 8 15.68
EXPONENTIAL
SMOOTHING
(α=0.9)
10 7 14 13 13 8 12.54
TRUNCATED
TAYLOR SERIES
METHOD
24 16 16 16 13 9 13.90

32. 2
0 0 0
3
10
20
14
26
7
26
23
8
7
21
22
23
25
26
2
0 0 0
3
10
20
14
26
7
26
23
8
7
15
13 13
9
12
2
0 0 0
3
10
20
14
26
7
26
23
8
7
10
7
14
13 13
2
0 0 0
3
10
20
14
26
7
26
23
8
7
24
16 16 16
13
0
5
10
15
20
25
30
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 2018-19
No.ofHMC'simported
Year
Estimates for 1000mm 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)

33. 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.

34. Thank You