A Methodology for Searching and Allocating Enterprises in the Supply Chain by Using the Business Information Database and Trademark Rights Database

Abstract

As a number of support programmes for competence improvement are provided to small and medium enterprises (SMEs) in Korea, it is recommended that these enterprises collaborate with each other. However, without information about other enterprises in the industry that they belong to or are planning to enter, many SMEs f ind it diff icult to look for partners that they can collaborate with.

This article focuses on extracting a list of enterprises in a supply chain of a specif ic industry through a business information database and a Korean trademark rights database. Also, it focuses on suggesting a methodology that would show a list of actual deals made between enterprises found through a business deal database.

Through these, it becomes possible for SMEs to have knowledge about other enterprises and their previous deals in a supply chain or a system of a specif ic industry. SMEs will thus be able to build a suitable business plan based on such information.

Moreover, this article will be helpful for policymakers as it will help them understand the system and characteristics of the industry and come up with a suitable programme for the industry that can support SMEs.

Keywords

supply chain business information trademark business deal

Introduction

In 2014, the total number of Korean enterprises reached 3,545,473 with 99.9 per cent being small and medium enterprises (SMEs).¹ Out of the 15,962,745 employees in Korea, 87.9 per cent work for SMEs (Korea Federation of Small and Medium Business, 2016).

However, the income of SMEs is only 35.1 per cent of the total income. Also, when one takes a look at the business prof it rate or debt rate, it is obvious that these enterprises have a weak f inancial structure.

As seen from the aforementioned, SMEs comprise a large part of the Korean economy in terms of quantity; however, the SMEs’ labour productivity is just 32.5 per cent relative to large enterprises (see Table 1). The weak f inancial structure and low labour productivity cause the little competence of SMEs. Indeed, about one-third of SMEs do not earn enough to even cover their interest payments, while small f irms have had negative operating prof its since 2006. In addition, the technological capacity of workers in SMEs is weaker than in manufacturing, which is dominated by large f irms (OECD, 2016).

Table 1

Comparison between Small and Medium Enterprises and Large Enterprises (2014)

	Large Enterprises	Small and Medium Enterprises
No. of firms^(a)(Ratio, per cent)	3,123(0.1)	3,542,350(99.9)
No. of employees^(a)(Ratio, per cent)	1,935,109(12.1)	14,027,636(87.9)
Sales^(b)(Trillion USD)	2.00	1.09
Business profit rate^(b)(Per cent)	4.42	3.12
Debt rate^(b)(Per cent)	127.01	161.45
Value added^(a)(Per cent of GDP)	17.4	14.4
Labour productivity per worker^(a)(USD)	290,428	94,480

Source: (a) Korea Federation of Small and Medium Business (2016).

(b) Jeon (2015).

To support the development of SMEs and reinforce their competence, the Korean government established the Small and Medium Business Administration (SMBA)² in 1996 and established the ‘Act on the Promotion of Technology Innovation of Small and Medium Enterprises’ to provide a legal basis and consistently provide them with support for the promotion of technology innovation of SMEs³. The government budget on SMEs was KRW1.014,8 trillion (USD877 million) in 2007. F ive years later, in 2012, this amount has more than doubled as it reached KRW2.095,6 trillion (USD1,811 million). It is expected that the government budget will continue to grow and take up a large part of the research and development costs. However, based on recent analysis, it is suspected that the government R&D budget for supporting SMEs needs to improve its eff iciency⁴ (National Assembly Budget Off ice, 2014).

Noh (2014) insisted that it is required to have an objective investigation of whether the government R&D investment has actually succeeded in supporting SMEs and eff iciently allocating the resources. It is because various divisions carry out the government support R&D programmes on a project basis and not all of those divisions are accessible to the integrated management database.

An alternative to improving the eff iciency of these government support R&D programmes is having a networked collaboration among SMEs. Networked collaboration refers to a horizontal relationship where a number of SMEs voluntarily participate in making deals and sharing technology, manpower and resources instead of depending on a contractor–subcontractor relationship (Kim, 2011).

However, this networked collaboration relatively lacks in capital, manpower, technology and resources. Thus, these SMEs can work together on the development of new products and new markets, thus allowing them to overcome their limitation—lack of resources (Kim, 2007). Besides, it is also possible to improve the budget eff iciency and reduce the duplicated investment by sharing the results of their collaborative research and development (Korea Institute for Industrial Economics and Trade, 2004).

Based on such advantages, the Korean government has been offering support programmes to encourage collaboration among SMEs since 2003. There are two types of support programmes: ‘collaboration between vendors’ through which SMEs deliver products to large enterprises and ‘collaboration for the development of new products’ where small enterprises from different industries collaborate with each other.⁵ For more information on the collaboration types and characteristics, refer to Table 2.

Table 2

Collaboration Types among Small and Medium Enterprises

	Collaboration between Vendors	Collaboration for the Development of New Products
Type of participant	(a) First, second and third vendors of large enterprises	(a) Small and medium enterprises from different industry
Collaboration type	(a) Collaboration on production(b) Collaboration on quality management(c) Collaboration on the development of technology(d) Collaboration on negotiating deals with large enterprises	(b) Two to five enterprises collaborate with each other(c) A one-time collaboration for the development of a specific product or technology(d) Complementary collaboration throughout the process of R&D, production and sales
Expected outcome	(a) Win-win situation between large enterprises and vendors(b) Improvement of product competence	(a) Development of new products and technology(b) Development of new markets and business transition

Source: Kim (2007).

The most important factors that make such collaboration among SMEs possible are the following: establishment of a collaborative organisation (29.8 per cent), exchanging and sharing of information (28.3 per cent) and collaboration on the development of new technology (21.9 per cent) (see Table 3; Kim, 2007).⁶

Table 3

The Important Factors for the Success of Collaboration among Small and Medium Enterprises

Section	Answer (Per cent)
Collaboration on the development of technology	21.9
Establishment of a collaborative organisation	29.8
Group purchase of relevant components	2.9
Encouragement from relevant large enterprises	4.8
Exchanging and sharing of information	28.3
Collaboration on negotiating deals with large enterprises	10.2
Government financial support for collaboration	0.3
No answer	1.9
Total	100

Source: Kim (2007).

‘Exchanging and sharing information’ or ‘collaboration on the development of technology’ can be seen as an actual form of collaboration whose precondition will be the ‘establishment of a collaborative organisation’. One of the great challenges for SMEs applying for a government support programme for collaboration is the ‘diff iculty with building a proper collaborative body’ (Jang, 2010).

When SMBA provides f inancial support to a collaborative R&D project in which more than two SMEs participate for ‘technology development through collaborations among SMEs’, it is common that most enterprises f ind their partner through ‘individual contacts’⁷ (see Table 4).

Table 4

Ways to Find Partners for Collaboration

				(Unit: Per cent)
Ways to Find Partners	2006	2007	2008	Average
Individual contacts	72.7	74.4	55.6	67.6
Relevant associations	3.0	2.3	0.0	1.8
Conference	6.1	7.0	11.1	8.1
University	3.0	4.7	0.0	2.6
Government support programme for collaboration	17.2	9.3	33.3	19.2
Other	0.0	2.3	0.0	0.7

Source: Shin (2012).

In the case of ‘individual contacts’, it is diff icult for SMEs that have lesser human resources and information than large enterprises to f ind candidates for future partnership. Kim (2011) suggested building online and off-line infrastructures that can support collaboration among SMEs.⁸

In line with that, this article attempts to show a methodology for building an online infrastructure that can help SMEs f ind candidates for future partnerships in the industry.

More specif ically, this article focuses on a methodology about searching and allocating enterprises that produce or distribute items in a supply chain⁹ of a specif ic industry through a business information database, a business deal database and a Korean trademark rights database.¹⁰

Materials

In order to develop a methodology for searching and extracting company names included in the supply chain, the business information database and business deal database of Korea Enterprise Data (KED) and the trademark rights database of the Korean Intellectual Property Off ice (KIPO) have been used.

KED is a company founded in 2005 by the Korean government and is a specialised credit-rating agency for Korean enterprises. Its stockholders include national banks such as the Korea Technology F inance Corporation (KIBO), the Korea Development Bank, the Industrial Bank of Korea and the Korea Federation of Banks, as well as major Korean commercial banks. These stockholders offer documents submitted—ignoring loans or surety bonds for businesses—to KED, who would then build a database from the collected information. KED provides business information such as enterprise name, address, major products and its Korean Standard Industrial Classif ication (KSIC).¹¹ In the business deal database, there is information on the business deals made and collected by KED from 2000 to 2015.¹²

The trademark rights database of KIPO includes information about the patent applicant, application number, designated goods or services and the Korean Trademark Classif ication Code (KTCC)¹³ (see Table 5).

Table 5

Used Database

Name of DB	Major Data Contents	Number of Data	Source
Business information DB	Company name, CEO name, office address, products, financial statements, Korean Standard Industrial Classification (KSIC), etc.	3,000,000	Korean Enterprise Data
Business deals DB	Sales company name, products for sale, transaction amount, purchase company name, date of purchase	16,000,000	Korean Enterprise Data
Trademark rights DB	Applicant (company or individual), designated goods, Korean Trademark Classification Code (KTCC), etc.	950,000	Korean Intellectual Property Office

Source: Authors’ own.

Methods for Mapping Out the Supply Chain

As a preliminary stage of this research, it is required to def ine the structure and items of the supply chain, ‘known companies’ and ‘keywords’ through the review of literature and consultations of experts.

Figure 1 is a simplif ied illustration of hierarchical structure of automotive industry. Through the consultation of experts in the automobile industry, various items (such as parts, modules, f inal products) constituting the automobile are extracted and their hierarchical structure is formed.

Then, the ‘known companies’ and ‘keywords’ that refer to each item are def ined by experts. The ‘known companies’ means a list of manufacturing companies that experts have chosen to be producers of each item (see Figure 1).

Figure 1

Source: Authors’ own.

Step 1. Extracting Conf irmed Companies

The ‘ref ined keywords’ in this research is used to avoid errors that can happen when one searches for companies only through keywords and to improve the accuracy of extracting the list of enterprises that produce the items that one is looking for. In case of ‘muffler’ from the example in Figure 1, the keyword ‘muffler’ has two meanings: ‘a device attached to the engine of a vehicle to make it quieter’ and ‘a piece of cloth worn around your neck to keep it warm’. Because a muffler has two meanings, if one tries to extract companies by using ‘muffler’ as a keyword, companies in the clothing industry will also be extracted. To avoid such error, ref ined keywords may be used—a combination of the item name and the f inal product name made with the item such as ‘car or truck or vehicle and muffler or silencer’.

The enterprises extracted from the search results by the ‘ref ined keywords’ in the ‘product’ f ield of ‘Business Information DB’ is def ined as ‘ref ined companies’.

Both ‘known companies’ and ‘ref ined companies’ are turned out as the actual manufactures of target item (mufflers for automobile) and redef ined as ‘conf irmed companies’ (see (a) of Figure 2).

Figure 2

Source: Authors’ own.

Step 2. Extracting Companies by Common KSICs and KTCCs

As the ‘Business Information DB’ is based on the submitted data from each company, there is a higher possibility of getting the inexact name of the product written in the f ield of ‘products’. Although it is a company that produces a muffler for automobiles, if it is described in an ambiguous or broad expression manner, such as ‘various parts for automobile’, or ‘industrial silencer’ is written in ‘products’ f ield of ‘Business Information DB’, the company cannot be extracted by only Step 1.¹⁴

Thus, the common KSICs and KTCCs extracted from the ‘conf irmed companies’ are proposed to extract another companies which are not belong to ‘conf irmed companies’ from Step 1.

The common KSICs or KTCCs are the set of parameters those ‘conf irmed companies’ have in common. And they can be extracted from ‘Business Information DB’ or ‘Trademark rights DB’, respectively.

It is proposed to extract companies by using the keywords, the common KSICs and the common KTCCs as search variables in the integrated DB of the ‘Business Information DB’ and the ‘Trademark rights DB’. First, extract the companies those are matched the keyword in the f ields of ‘products’ from ‘Business Information DB’ and ‘designated goods’ from ‘Trademark rights DB’. Second, extract the companies those are matched the common KSICs in the f ields of ‘KSIC’ from ‘Business Information DB’. Third, extract the companies those are matched the common KTCCs in the f ields of ‘KTCC’ from ‘Trademark rights DB’.

This method is based on the idea that the companies extracted through these three steps are almost certain that the f iltered companies are manufacturers of the target item, such as ‘muffler for automobiles’ (see (b) of Figure 2).

The ‘conf irmed companies’ and the ‘search out companies by keywords, KSICs and KTCCs’ can be listed as ‘name of manufacturer for each item’ shown at Figure 3 (in case of ‘muffler’, 11 manufacturers are listed through the Step1 and Step 2).

By repeating Step1 and Step 2 for each item shown at Figure 1, it is possible to extract companies in the automotive industry by items as shown at Figure 3.

In addition, by comparing the name of the extracted companies with the ‘sales company name’ and ‘purchase company name’ in the ‘Business Deals DB’, it is possible to map out transactions among companies (see Figure 3).

Discussion

The supply chain and allocating companies for each items of automotive industry in Korea by the mentioned methodology is established and is scheduled to verify their reliability. After verif ication, the ‘supply chain visualisation system’ based on the methodology is scheduled to be established as shown at Figure 3.

The system will provide a certain company with information on its location in the supply chain and the other participants such as competitors, upstream companies or downstream companies in the supply chain it included. If the system can provide information on the technology, f inance, business relations of other participant in supply chain, all these information will be important factors for the certain company to f ind candidates for a future collaboration and decide who would be the right partner for them.

For example, assume ‘Company A’ which receives a part (P1) from a company ‘S1’, manufactures a module ‘M1’ using P1 and sell P1 to company C1. From the perspective of Company A, S1 is the current supplier and C1 is the current customer. Company A can f ind out that Company B sells module M1 to C2 as well as C1, and receives P1 from another company S2 through ‘supply chain visualisation system’ (see Figure 4).

Figure 3

Source: Authors’ own.

From the perspective of ‘Company A’, Company B would be a competitor as it provides its products to Company A’s customers. On the other hand, C2 would be Company A’s ‘potential customer’ as B provides the same products as A to C2. Meanwhile, S2, the supplier of B, would be a ‘potential supplier’ of A as well. Company A will be able to identify the competitive relation in the industry, and establish collaboration or competition strategies with its participants.

Based on the methodology discussed in this article, the supply chain can be utilised in various ways, depending on the roles of the employees in the enterprise.

For R&D Staffs

R&D staffs in a company, such as CA, can use their understanding of the supply chain to analyse the products of their competitors. And they can also get good ideas on how to expand their business by reviewing the products of upstream and downstream companies and competitors.

Figure 4

Source: Authors’ own.

For Sales Staffs

Sales staffs in a company, such as CA, can check out the customers of their competitors. Moreover, they can use the information about the downstream companies as a stepping-stone for discovering potential customers. Also, by reviewing the products of the downstream companies, they can get ideas for new business opportunities. Besides, by analysing the product list of the competitors, it is possible to understand the activities of their competitors.

For Purchase Staffs

Purchase staffs in a company, such as CA, can also discover potential suppliers by checking where their competitors buy goods. Based on the information about the product of the upstream companies, it is possible to work on the improvement of their products.

In addition, through f inancial information about upstream and downstream companies and competitors, they can provide analysis indicators that allow them to understand the technological and entrepreneurial competence of the candidates for future partnership (see Table 6).

Table 6

Example of Analysis Indicators based on Business Information DB

Analysis Indicators	Formula
Technological competence	(Research and development cost of an enterprise/amount of sales/Average research and development cost per item of the filtered companies/amount of sales)
Rate of technological growth	(Rate of increase of [research and development cost of an enterprise/amount of sales] for the last three years/Rate of increase of [average research and development cost per item of the filtered companies/amount of sales] for the last three years)
Growth	Rate of increase of the amount of sales for last three years/average amount of sales of the filtered companies for the last three years
Profitability	Business profit rate/average business profit rate of the filtered companies per item
Safety	Debt rate/average debt rate of the filtered companies per item
Activity	Working capital turnover ratio/average working capital turnover ratio of the filtered companies per item

Source: Authors’ own.

Conclusion

While SMEs in Korea make up 99.9 per cent of the total number of the enterprises, their income is only 35.1 per cent of the total income. Moreover, they are often seen as having relatively less competence than large enterprises. In order to support the growth of SMEs and the reinforcement of their competence, the Korean government has been increasing the budget for SMEs. However, questions on whether various government support programmes are eff icient enough continue to arise.

An alternative to improving the eff iciency of these government support programmes is that the government provides exclusive f inancial support when SMEs voluntarily gather and begin to collaborate.

However, it is not easy for SMEs to f ind partners who can collaborate on the development of new technology as they relatively lack in human resources or information compared to large enterprises.

Therefore, this article attempts to suggest a method searching and allocating companies that produce certain items and constitute the supply chain of a specif ic industry.

If the companies in the supply chain incorporate this methodology into their business, it is possible to f ind candidates for potential partnership based on the information of the upstream and downstream companies and of their competitors in the industry. Besides, through analysis indicators based on the business information, they can evaluate their candidates for potential partnership.

In addition, policymakers for SMEs can get information about the distribution of enterprises that produce or distribute parts, modules and products, as well as those that constitute the supply chain of a specif ic industry. In addition, information about their business deals can also be taken. With an appropriate understanding of the industry, these policymakers will consequently be able to plan better support programmes for SMEs.

The Korea Institute of Science and Technology Information (KISTI) is currently planning to build a system, tentatively named ‘system for providing information about the supply chain’, based on this methodology. In addition, KISTI is also planning to carry out research for developing a user-oriented system that can intuitively monitor the supply chain and the trend of the items as well as automatically offer a data analysis report.

Footnotes

Note

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