Decision Support System for HS Classification of Commodities Awdhesh Kumar Singh* Rajendra Sahu** ABV-Indian Institute of Information Technology and Management, Gwalior, India *Email:
[email protected] **Email:
[email protected]
Abstract In the International trade of the commodities, the rate of customs duties and other local duties are determined on the basis of World Customs Organization (WCO) approved HS classification that is followed in most of the countries. Since the classification is based on a number of rules that are subjected to different interpretation, disputes may arise between the customs department and the importer whenever there are different rates of duties in the possible headings. In this paper, we have developed a DSS, which helps the customs officers to select only such cases for re-classification where the implication of revenue is involved. The DSS also helps the customs authorities to decide the classification quickly and uniformly without any bias or prejudice. Keywords Decision Support System, Fuzzy Logic, Expert Systems, Customs Assessment, HS Classification
1. INTRODUCTION The process of globalisation followed in last decade has increased the volume of international trade substantially. World Trade Organization (WTO) has played important role in lowering of tariff and removal of non-tariff barriers to facilitate flow of goods and services across national boundaries. The increased import and export of goods is posing new challenges before the customs. The principal job of the customs department is to collect customs duty, which includes basic customs duty (levied only on the imported goods) and local duties like excise, sales tax, Goods and services Tax (GST), levied on the domestic goods. Most of the goods are charged with advalorem duty i.e. the duty is proportional to value assessed by the customs. The rate of customs duty on the imported goods and the amount of incentive given on the export good is usually calculated by help of the customs tariff of the country, which are based on Harmonized Systems. The Harmonized Commodity Description and Coding System, generally referred to as "Harmonized System" or simply "HS", is a multipurpose international product nomenclature developed by the World Customs Organization. It comprises about 5,000 commodity groups, each identified by a six digit code, arranged in a legal and logical structure and is supported by well-defined rules to achieve uniform classification. The HS Tariff Nomenclature is being used as a basis for collecting Customs duties and international trade statistics by almost all countries. There are more than 110 contracting parties to the HS convention and over 190 user countries accounting for almost 100% of world trade. Thus HS has truly become the universal language of international trade (WCO 2004a). Different countries also use the HS classification for many purposes such as collection of Excise duty, Sales tax and compilation of various statistics. The entries in the customs tariff usually do not contain the name of the item but only the broader description of the goods. Since the duty rate depends on the appropriate classification of the goods, disputes arises as the importer seek classification in the heading where the rate of duty is lower and the customs want otherwise. Since, the appropriate classification of goods is the responsibility of the customs department so any wrong classification may cause revenue loss to the customs. However, since there are millions of types of product and each product may contain hundreds or even thousands of parts, the database of the HS classification of all items is virtually impossible. The biggest commodity databank is probably maintained by WCO (2004a) that lists some 200,000 commodities traded internationally and specifies their six-digit number. The data bank may still cover only a fraction of items being traded internationally. The determination of HS classification of each commodity is quite time consuming because each import or expert document may consist of hundreds of items. Quick customs clearance is one of the most important measure for trade facilitation and most of the governments promise customs clearance to the importer within a 745
Decision Support System for HS Classification of Commodities stipulated time. For example the Customs department in India (CBEC, 2004a), in their citizen’s charter has committed to assess (approve) the import document within 24 hours and expert documents within 8 hours of its filing in EDI system. Therefore the customs officers must finalize the assessment of documents quickly without compromising the interest of the revenue. Singh et al. (2003) developed DSS for detecting valuation frauds in Customs assessment on the basis of an expert system using fuzzy logic, which was able to detect all the valuation frauds by mere scrutiny of 5% of the import document (called Bills of Entry by Indian Customs). The DSS enabled the officers to focus only on the most sensitive cases of import and allowing automatic clearance of the import consignment where the sensitivity was low. The DSS thus could provide enhanced productivity to the customs while preventing the duty evasion more effectively. DSS developed in this paper is based on the expertise of the customs officers who have considerable experience in the customs assessment. One important Air Cargo of India was visited and the expertise of customs officers was collected by conducting interviews by help of questionnaires. It was observed that the customs officers uses a number of heuristics or thumb rules while deciding classification issues. The expertise is lost with the transfer or retirement of the officer. The new officer needs considerable time to develop these expertise and the chances of committing mistakes is quite high in the learning period. In this paper we have highlighted the difficulties in deciding classification by help of suitable examples. The paper describes the development of a Classification Decision Support System (CDSS) using the concept of Expert System, DBMS and Fuzzy Logic to achieve following objectives a)
Automatic approval of the classification of import and export documents where the classification is well settled
b) Detect the classifications which are doubtful and may cause revenue loss for proper classification investigations c)
Assist the officers in investigation of the classification quickly using heuristics of the expert customs officers
2. THE UNCERTAINITIES IN HS CLASSIFICATION HS Classification (or “Classification”) developed by WCO (2004) is quite complex task due to large number of commodities/items produced or manufactures in the world. There are 21 sections in the HS tariff; each covers a particular group like live animals, Machinery and Mechanical appliances etc. The Sections are further divided into Chapters and there are 99 chapters in the tariff, which covers more specific category like electrical equipments, optical equipments. The chapters are further divided into headings, which are again divided into sub-headings. There are approximately 5000 subheadings in the tariff, which are expressed, in 6-digit code. The first two digits represent chapter number, the next two represent heading number and the last two represents subheading numbers. Countries are allowed to further classify these six digits code specified by WCO. For example Indian Customs adds two more digits and follow eight-digit code for classification (CBEC, 2004), while Australian Customs (2004) adds four more digits and follows a ten-digit classification. However, the fist six digits in all the cases are same in all the countries. The tariff contains the general description of the item and very few items are specified by its commercial name that makes the classification quite uncertain. For example whether to classify a rubber oil seal used in a fan as rubber part or part of the fan or whether to classify a steel screw used in a computer as article of steel or part of computer. There are also items, which may have multiple applications. The HS classification often gives the broad description of the good, for example sub-heading 8413.91 corresponds to “parts of pumps”. A pump may have thousands of components all of which may not be covered under 8413.91 (for example screw, gears, oil seals that are specified in different HS in the tariff). The decision of classification is made on the basis of thousands of chapter notes and sections notes given in the HS. WCO also publishes HSN Explanatory Notes in four volumes (consists of thousands of pages) to further clarify the classification. They Rules of Interpretation are prescribed in the HS so as to ensure that uniquely classification of each item.
3. THE NEED FOR CLASSIFICATION INVESTIGATIONS If all the items are charged with the same rate of duty, then there is hardly any need for classification for revenue point of view, as classification would not make any impact on the customs revenue. However, in most of the countries there are many slabs of duties and the correct classification is desirable for maximizing the collection of revenue.
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Decision Support in an Uncertain and Complex World: The IFIP TC8/WG8.3 International Conference 2004 The determination of correct classification, is however, very long process involving time and cost. It needs correct understanding of the functions of the commodity, its material compositions and interpretation of customs HS tariff, and study of past case laws of courts, tribunals or WCO of similar items. In reality it takes days and sometimes months to decide a classification. Clearly, if the customs department desires that each item is be classified correctly, they must investigate the classification of each imported or exported item. In reality, in a country like India, tens of thousands of import documents are filed every day and each document may contain hundreds of items. Therefore every day thousands of classifications has to be decided which can be made only by appointment of large number of customs officers which increases the cost to the customs besides causing delay in approval of the import consignment. Figure 1 illustrates the implication of increasing classification investigation over the net revenue i.e. the difference between the total revenue collected and the cost of collection of revenue. Revenue Net Revenue Collection Cost
0%
Extent of Classification Investigations
100%
Figure 1: Implication of increasing classification investigation over Net Revenue As shown in the Figure 1, if the customs conducts no investigations at all, importers would seek classification in the lowest rate and revenue collection would be minimum. The investigations will increase the revenue by correct classification. However, the net revenue to the customs would decrease gradually due to the increased cost of classification investigation. Therefore, we have to take into consideration the cost of investigation and the benefit of identifying wrong classification (Bolten, 2002) while deciding the extent of investigations to be conducted.
4. DSS FOR HS CLASSIFICATION Some researchers like Gory and Scott Morton (1971) defines a DSS as a computer system that dealt with a problem, which constitute at least some degree of unstructured or semi-structured component. The classification of common items may be listed in the database of customs or WCO (2004a) that can be automatically approved by the systems. For other items the DSS is required to be developed to assist the customs officers. The problem of classification may be divided into three categories (Courtney, 2001) as shown in Table 1. CLASSIFICATION TYPE Structured Semi-structured
Unstructured
EXAMPLES Items specified in the tariff by name like mobiles phones, ICs, Cars Classification not established for the exact item but classification of items having similar functions is available. New items, innovations, parts and accessories
SUGGESTED SOLUTION Search from the database of established classification Refer to the classification established for similar items, refer to the WCO rulings, court judgments, Government Circulars, Chapter Section Notes Refer to the Chapter Notes, Section Notes, Rules of Interpretation, Explanatory Notes,
Table 1: Types of HS classification Problems
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Decision Support System for HS Classification of Commodities
5. DEVELOPMENT OF CLASSIFICAITON DSS The Classification DSS has been designed to assist the Authorities to take quick and correct decisions on classification matter. The block diagram of the Classification DSS (CDSS) is shown in Figure 2. The details of the components of CDSS are provided in the subsequent paragraphs. Importer
Customs EDI System
Verified
Classification Database
YES Add HS Database Recommendation Engine
Need Investigation
Classification Guide
NO
NO
New Classification
YES
FES CLASSIFICATION DSS
APPROVED CLASSIFICATION
Figure 2: Block Diagram of Classification DSS 5.1 The Expert/Production Rules for HS Classification DSS is based on the expertise of the customs officers who have considerable experience in the HS classification of commodities. We have defined a term “classification sensitivity (λ)” as the estimation of the probability for loss of customs duty/revenue due to misclassification of the goods in the import documents. The value of λ is measures in the scale of 0 to 1.0 where λ =0 implies absolutely no chance of loss of revenue (due to misclassification) and a score of 1.0 indicates the highest chance of loss of revenue (due to misclassification Sample of some expert rules obtained from the customs officers are given in Table 2. RULE No 1 2 3 4 5 6 7
8
RULE IF the Rate of Duty is High THEN sensitivity is Low IF the value of the item is very high THEN sensitivity is high IF imports made by a specified class of importer (like government) charged with fixed rate THEN sensitivity is lowest IF import item is routine or common THEN sensitivity is low IF the item is specified by any of the alternative name in tariff THEN sensitivity is lowest IF the items were disputed earlier THEN sensitivity is highest IF imported items are accessories of items attracting low rate of duty THEN sensitivity is highest If the Item is new product THEN sensitivity is highest
REASONS Since the importer is already paying the duty at highest rate, he can’t be benefited by any misclassification A small change in the rate of duty can have large revenue implication All items irrespective of classification attract same rate so no chance of any revenue loss The classification practices are well established for routine items No ambiguity may arise as items are covered by name in the HS Tariff Disputes arises mostly in ambiguous cases Same apparatus can be used as attachment or Accessories of many equipments and the importer would like to claim it an accessory to the equipment which attract lowest rate of duty No previous classification would be available in literature
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Decision Support in an Uncertain and Complex World: The IFIP TC8/WG8.3 International Conference 2004 RULE No 9
10 11
RULE
REASONS
If the item is a products having Composite functions THEN sensitivity is high IF the import is project imports or baggage imports THEN sensitivity is lowest If Items are specified in the Section or Chapter notes THEN sensitivity is high
Since the product have functions of more than one equipment, the principal or dominant functions to be ascertained to properly classify the goods Since all items under project/baggage imports are classified under same tariff heading the chapter or section notes are incorporated for the purpose of removing ambiguity
Table 2: Expert Rules for Classification Sensitivity of Commodities 5.2 Fuzzy Expert System Newel (1972) defined Expert System (ES) is a branch of Artificial Intelligence (AI) that makes extensive use of specialized knowledge to solve problems at the level of a human expert. An expert is a person who has expertise in a specialised area by virtue of his experience in solving the real life problems by repeated application of the knowledge pertinent to the field. An expert system is designed so that it uses the consolidated human expertise of many experts either to take decisions or to assist the human experts in taking the right decisions quickly. The user enters the relevant facts in the ES and the expert system provides the expert opinion by using the knowledge base through inference engines. The block diagram of a typical Expert System is shown in Figure 3. FACTS
KNOWLEGE BASE
USER INFERENCE ENGINE
EXPERT OPINION Figure 3: Block Diagram of an Expert System
Fuzzy Expert System (FES) may be defined as an ES, which uses fuzzy logic. FES are also referred in literatures (Munakata 1998), (Mendel 2001) by different names such as Fuzzy Logic System (FLS), Fuzzy Rule Based System, Fuzzy Inference System (FIS), Fuzzy Controllers, Fuzzy Systems etc. The block diagram of a FES is shown in Figure 4.
Rules
Crisp Input (x)
Fuzzifier
FES
Defuzzifier
Crisp Output (y)
Inference Fuzzy Input
Fuzzy y = f (x)
Figure 4. Block Diagram of a Fuzzy Expert System (FES) The components of a FES and their functioning are discussed in detail by Munakata (1998). In brief the Fuzzifier maps crisp inputs (x) into corresponding fuzzy memberships. It is needed to activate rules that are in
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Decision Support System for HS Classification of Commodities terms of linguistic variables. Inference Engines maps fuzzy input sets into fuzzy output sets using the “production rules” (or simply “rules”) as shown in Table 2. It determines the degree to which the antecedents are satisfied for each rule. The outputs of all the rules from the Inference Engine are aggregated. During aggregation output fuzzy sets of all the rules are combined to into a single fuzzy set. During Defuzzification process the output fuzzy sets are mapped into a single crisp number (y), which is the output of the FES. This model is also known as Mamdani (1977) model. The details can be found in the classic tutorial paper by Mendel (1995). 5.3 Fuzzy Membership Functions FES is the most important constituent of CDSS which uses the expert rules for deciding the desirability for classification investigations. The Table 2 consists of a total of 11 such sample rules. Each rule provides a classification sensitivity value (λi) where “i" is the Rule Number. The expert rules described in Table 2 consists of both crisp and fuzzy variables. For example the “high rate of duty” or “value of the item” in rules 1 and rule 2 respectively are fuzzy variables while variables like “project imports” and “routine items” are “crisp” variables. The membership functions assigned to classification sensitivity (λ) for linguistic variables lowest, low, medium, high and highest are assigned the value of 0.0, 0.25, 0.5, 0.75 and 1.00 respectively. The calculation of λ for fuzzy variable is made by using Mamdani model. 5.3.1 Illustration of Calculation of Classification Sensitivity using Expert Rules Here we describe the use of fuzzy logic for calculating the classification sensitivity as using Rule 1 (Table 2). IF the Rate of Duty is High THEN classification sensitivity is Low …….(1) The implied Rule (1A) from Rule 1 can be expressed as below IF the Rate of Duty is Low THEN classification sensitivity is High……..(IA) The membership function for rate of duty and the classification sensitivity is shown in Figure 5 & Figure 6 respectively. We have used trapezoidal membership function having two linguistic variables for simplicity of design. Low
1.0
High
Membership Function (µ)
0 0
50
Rate of Duty
Figure 5: Membership Function of Input (Rate of Duty) Low
1.0
High
Membership Function (µ)
0 0
Classification Sensitivity (λ1)
1.0
Figure 6: Membership Function of Classification Sensitivity
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Decision Support in an Uncertain and Complex World: The IFIP TC8/WG8.3 International Conference 2004 5.4 Calculation of Aggregate Classification Sensitivity The CDSS may use a number of expert rules for calculating the aggregate classification sensitivity of an item. The Table 2 consists of a total of 11 such expert rules. Each rule provides a classification sensitivity value (λi) where “i" is the Rule Number. For example consider an example of an import consignment that consists of several items as specified in Column 2 of the Table 3. Here the classification sensitivity is calculated by using FES. ITEM NO
ITEM NAME
1
Mobile Phone
2
LPG Gas Conversion Kit Smart Card Printer Compact Disc
3 4
RATE OF DUTY 25% 50% 30% 0%
DESCRIPTION
RULES FIRED
CLASSIFICATIO N SENSITIVITIES
Common Item
1, 4
λ1= 0.5, λ4 = 0.25
AGGREGATE CLASSIFICATION SENSITIVITY (λ) 0.125
Specified in Chapter Note Composite Item Specified in Tariff by Name, Routine
1, 11
λ1= 0.3, λ11 = 0.75
0.225
1, 9 1,4,5
λ1= 0.438, λ9 = 0.75 λ1= 0.7, λ4 = 0.25, λ5= 0.00
0.3285 0.00
Table 3: Sample Calculation of Aggregate Sensitivity of few Items The calculation of aggregate sensitivity of classification is shown in Table 3. For example, mobile phone is a common or routine item and the rate of duty is 25%. Accordingly rule 1 and rule 4 of Table 2 are fired giving classification sensitivities as 0.5 (by FES) and 0.25 (low sensitivity) respectively. The aggregate sensitivity of classification is calculated by multiplication of all classification sensitivities, which is calculated as 0.125. The sensitivities of other items are also calculated by the classification FES in the same manner. 5.5 Recommendation Engine Warren et al. (2000) used fuzzy logic to develop DSS in clinical practice system which provided the recommendation in the natural language which were quite similar to the recommendations provided by human expert viz. doctor. For Customs department, the decision to start investigation for classification is very important. The information available from the FES only gives an estimation of misclassification. In real life, an expert customs officer may recommend the need for classification investigation by using the phrases like “suggested”, “recommended”, “Highly Recommended”. Therefore, the DSS should also provide similar recommendations so that the customs officers can take the decision of re-classification. The recommendations of DSS is provided by help of “recommendation engine” which is based on the output of the FES (λ) as shown in Table 4. Range of Aggregate Classification Sensitivity λ
