Page 29 - 2016年光通信竞争力报告(摘要版)

The 10th Ranking Event of Top 10 competitiveness enterprises in the optical communications field of China & Global Market (2016) 22 The competitiveness report on "the Top 10 competitiveness enterprises in the optical communications industry of China & Global market in 2016” (Abstract Edition)
Section2: Ranking of the Top 10 comprehensive competitiveness enterprises in the optical communications field of Global market during 2015-2016

1. Ranking of the Top 10 competitiveness enterprises in the optical fiber and cable field of Global market during 2015-2016

Ranking of "the Top 10 competitiveness enterprises in the optical fiber and cable field of Global market during 2015-2016"

Standard value weighted of the financial data(70% weight) Standard value weighted of the survey data(30% weight)

Return Return Sales The ratio of The average The average Total Management Comprehensive
on total on net revenues international growth rate of growth rate standard level of Corporation Total standard index of Comprehensive
Company Sales Net Net assets assets contribution revenue sales revenues of net profit Technology Customer Brand enterprise culture value weighted competitiveness score of
Ranking revenues assets profit for the last value innovation satisfaction awareness Source of financial data Country
weight per to total sales for the last three years weighted of of the survey
weight weight weight 8% employee revenues three years the financial competitiveness
18% 13% 13% data $
%

%
weight weight weight weight weight data weight weight weight weight weight
15% $ 34% 18% 12% 11% 25%
8% 5% 6% 14%

Corning 1 0.8794 0.7020 0.3082 0.0442 -0.0005 0.0288 0.0489 -0.1400 -0.1500 1.7210 0.8342 0.5014 0.6360 0.3147 0.6084 2.8947 2.0731 1000 Annual report of listed company USA

YOFC 2 0.5871 0.2431 0.1606 0.1066 0.0511 0.0695 0.0012 -0.0762 0.1500 1.2931 0.6352 0.5875 0.5325 0.4212 0.4935 2.6699 1.7061 983 Annual report of listed company China

HTGD 3 0.3261 0.3189 0.1612 0.0452 0.0550 0.0222 0.0005 0.1400 0.1500 1.2192 0.5261 0.4875 0.4287 0.4930 0.5645 2.4998 1.6034 970 Annual report of listed company China

Prysmian 4 0.5959 0.2704 0.0551 0.1102 0.0402 0.0870 0.0503 -0.1400 0.1500 1.2190 0.5614 0.4642 0.4667 0.4091 0.5973 2.4987 1.6029 969 Annual report of listed company Italy
0.0106 -0.1400 2.2173 1.3852
Furukawa 5 0.6331 0.2661 0.0853 0.0006 0.0158 0.0069 0.0098 0.0916 0.1500 1.0285 0.6722 0.3228 0.3359 0.3712 0.5152 2.1194 1.2289 958 Annual report of listed company Japan
Electric/OFS 6 0.0114 0.4999 0.1873 0.0202 0.0181 0.0025 -0.0031 0.1400 2.1201 1.2284
FiberHome 0.2474 0.2496 0.1081 0.0648 0.0158 0.0474 -0.1145 0.0065 0.8473 0.7304 0.3112 0.3484 0.3480 0.3814 2.0999 1.1242 951 Annual report of listed company China
Communications Taxationǃresearch ˂ survey

Futong 7 -0.0238 0.8462 0.4592 0.4190 0.3730 0.4153 0.4536 950 information˗self-reported figures China
and operators' tender results
Sumitomo Electric 8 0.1121 0.3760 0.1062 0.0530 0.0110 -0.0324 0.0447 0.1500 0.7061 0.7690 0.3044 0.2904 0.2630 0.4731
944 Annual report of listed company Japan

ZTT 9 0.0208 0.2454 0.1166 0.0934 0.0214 0.0512 0.0070 0.1400 0.1500 0.8459 0.2949 0.3760 0.3765 0.3270 0.3766 1.7510 1.1175 943 Annual report of listed company China

Fujikura 10 -0.1183 0.2973 0.0457 0.0100 0.0036 -0.0310 0.0429 -0.1400 0.1500 0.2602 0.6455 0.2862 0.2821 0.1561 0.4137 1.7836 0.7172 921 Annual report of listed company Japan

Note 1: For the financial data of sales revenues indicator, due to enterprises’ sales revenues derived from diversified products, the data we collect is based on the revenues of relevant products in each sub-sectors of optical communications division. Net profit data is collected from
the relevant products in the optical communications division of the enterprise; Net assets data is collected from the optical communications division of the enterprise.
Note 2: The calculation data of return on net assets and return on total assets are collected from the net profit, net assetsand total assets of the communication division of the selected enterprises. If the enterprise listed does not reveal relevant data in its annual report, they will be
calculated according to the contribution rate of the enterprise’s total profit rate, net assets and total assets.
Note 3: As for the four indicators of sales revenues contribution per employee, the ratio of international revenue to total sales revenues, the average growth rate of sales revenues for the last three years and the average growth rate of net profit for the last three years, we refer to
the released annual reports of the enterprises listed in the rankings, and make no more in subdivision.
Note 4: “Return on net assets” can be defined in different formula. To avoid the incomparable problem of net profit caused by the different corporate income tax rate in listed companies and unlisted companies, we define the molecular in the formula as net profit before tax instead
of net profits. The formula of calculating “return on net assets” is: Return on net assets=net profit before tax/ net assets; Return on total assets=Net profits before tax/ total assets.
Note 5: From the monitoring data, it is found that if the enterprise competitiveness comes mainly from the increase indicators (that is, the average growth rate of revenues for the last three years & the average growth rate of net profit for the last three years), the monitoring data of
the enterprise competitiveness is usually unstable. The main reason for the enterprise competitiveness instability is that the original sales revenues base in these enterprises was small and the increasing sales revenues of recent 2 years make the averag e growth rate of the past
3 years far higher than the industry average level. The extremely high standard value of a certain indicator in the company may cause the standard value of financial data competitiveness index over high on the whole. But in the second or third years, when the growth rate of sales
revenues drops to the normal average level and instead there is no higher growth in the other indicators, the monitoring index of the enterprise competitiveness will decline significantly. To avoid the impact of abnormal change in financial indicators on the objectivity of the enterprise
competitiveness evaluation, we find a practical way to improve it. That is, we set the upper and low limit of standard value in the increase indicators (the average growth rate of revenues for the last three years & the average growth rate of net profit for the last three years) within
[-1,1]. With the consistency of statistical test, the overdone impact on overall standard value of financial data by the abnormal data of growth index can be eliminated.