Designing Dairy Farming Credit Risk Evaluation Expert System Prototype 1
Dr. D.K.Sreekantha, 2Dr. R.H. Fattepur, and 3Sameer R. Fattepur
Abstract: Dairy industry is one of the key industries in India which is a leading producer and also the consumer of milk, milkbased products in the world. The projected demand for milk by 202122 is estimated at 180 million tones, that is double of the current milk production. Milk consumption is growing at rate of 5% while the production is growing at 3.3%, thus creating a gap between demand and supply. This gap situation calls for investments and innovations in dairy farming industry to cater to the future demands. On the supply side, relatively favorable weather conditions, higher export prices, improved efficiency gains and continued focus on innovation and technology has resulted in improved supplies around the globe. Transformation of the traditional structure of dairy farming into a modern technological set-up has brought the entire dairy market on an advanced platform, high-tech equipment, technology and heavy investment. Many investors are venturing into dairy industry. These investors are approaching the banks and financial intuitions for credit to setup the state of art dairy farming facilities or upgrading their existing ones. Keywords: Expert System, Fuzzy Logic, Cattle Selection, Knowledge base 1. Introduction NDIA is the world's largest milk producer, accounting for more than 16% of world's total milk production and also the world's largest consumer of dairy products. After agriculture industry, the dairy farming is the second most important field of serving food. Dairy farming is an important secondary source of income for Indian farmers. Technological innovations and improvements in the production process and efficiency gains have enabled dairy producers worldwide to cater to the increasing needs of customers in different forms like condensed milk, homogenized milk, buttermilk, cheese, casein, yogurt, gelato, and ice creams, while maintaining the quality standards at the same time. Union Govt. has started a National Dairy Plan – Phase 1 scheme for a period of 2011-12 to 2016 -17 in order to meet the rapid growing demand and to increase the milk production. This scheme will be implemented with a total investment of about 2242 crore. Many venture capitalist and corporates are venturing in to dairy industry. The clients who would like to establish a dairy industry will approach the banks and financial institutions for credit. Dairy financing consists of making a careful and critical analysis and measurement of all risk parameters to perform credit risk evaluation. The crediting rating executives of the banks needs to assess the credit worthiness of these clients based on the information provided in the loan application forms and the supporting documents. The credit
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Professor, Dept. of CSE, NMAM Institute of Technology, Nitte-574110
[email protected]. 2,3 Basaveshwar Science College, Bagalkot
Risk evaluation is very critical process that affects the profitability of the bank. Banks are in need of an efficient and integrated credit risk management tools to minimize the risk and maximize the profits. 1.1 National status of dairy industry India milk production has grown from 53.9 million tons in the year 1990- 1991 to 137.7 million tons in 2013-14. (Source http://www.dairyknowledge.in/content/01milk-productionand-capita-availability-india). This growth represents sustainable development in the availability of raw milk and milk products for the fast growing population of India. The value-added dairy products market in India is expected to treble to Rs.12,000 crore by 2014. The National Dairy PlanThe Intensive Dairy Development Programme has the outlay of 481.50 crore in Financial Budget of 2015-16) (17) (Source: http://indiabudget.nic.in/ub2015-16/eb/po.pdf). The dairy entrepreneurship program has been framed for strengthening the infrastructure for producing high quality and hygiene milk. Dairy Entrepreneurship Development Scheme is one of the Indian Government’s important programme to meet the growing demand for milk. The National Project for Cattle and Buffalo Breeding is under implementation since 2000. Gujarat Cooperative Milk Marketing Federation and some private dairies are in the ‘AA’ and ‘A’ rating category on account of their superior procurement and marketing channels and high share of VADPs in product portfolio. Karnataka Milk Federation (KMF) is the largest Cooperative Dairy Federation in South India, owned and managed by milk producers of Karnataka State. KMF has over 2.29 million milk producers in over 12758 Dairy Cooperative Societies at village level, functioning under 13 District Cooperative Milk Unions in Karnataka State. 1.2 International status of dairy industry Total world milk production is estimated to grow from 692 million tons in 2010 to 827 million tons in 2020, a 19% increase (Source: Global Dairy Outlook Report 2012 released in February 2013). Global dairy products sales are forecast to reach $494 billion in 2015 of which the U.S. will account for 25% approximately. United States is one of the largest single global dairy markets and also a major dairy exporter. In 2012, 16.7% of U.S. milk production was exported, which was just 10% in 2010. Two-thirds of milk produced in the U.S. is for domestic use as fluid milk or processed into other dairy products (Source: Dairy Products: A Global Outlook, Global Industry Analysts, Inc., January, 2012).
Comment [W1]: Heading sections, please see the reviews.
1.3 Future prospects for dairy industry (Source: Kuldeep Sharma, Suruchi Consultants Dairy Industry Vision 2030 -Feb.2014) India is global leader having about 300 million number of cows and buffalos in Milk production producing 135 million tons of milk in a year The estimated production will be 216 Million Metric ton by 2030 with a per capita milk consumption of 390 ml at an estimated population of 1.53 billion. By the year 2030 the packaged milk consumption will grow from $7.76 billion to $32.9 billion annually The experts have predicted that more than 70% milk sold by the year 2030 would be branded, which is against the 31% at present. Its is anticipated that India’s per capita GDP is going to rise by 320% in the next 20 years, with an increase in food consumption by 4% per annum. Milk Consumers in India are estimated to spend more on the high value foods. Consumption patterns are going to shift from plant based protein to animal-based protein which will be a great shift and drastic change. 1.4 Problem Description In developing countries like India, dairy farming is considered as allied activity of agriculture. Farmers are doing small scale dairy farming in conventional way and do not possess professional dairy farming knowledge, technology, skills, capital, turnover and profits. To encash the future potential prospects discussed in section 1.3 dairy farming needs to be taken up as core business activity rather than allied activity. The financial budget, professional strategies, high tech operational environment, global marketing outlook are essential ingredients for any successful dairy industry. Dairy industry requires capital funding from bankers and financial institutions to establish and operate high tech dairy environment meeting global dairy industry standards. The client who would like to establish and prosper in dairy business will approach the bankers for funds. The credit rating executives in banks assess the credit worthiness of the client. An incorrect decision might risk banks profitability or opportunity loss. Bankers are in need of an integrated risk management system to maximize the profits and minimize the losses. So researchers aimed at developing expert system prototype to help lenders in assessing the credit risk associated with dairy industry clients. 2. Survey of Literature The researchers have studied many dairy industries in their regions. Authors have conducted an exhaustive survey of literature to understand the critical aspects in dairy farming business and also interacted with bankers to understand the financing criteria and parameters in dairy credit decision making. The study of the literature has been illustrated by some selected articles discussed below.
Terry R. Smith [1] has stated that expert systems will provide the users additional education and helps to face many challenges to become better managers. Expert systems helps the decision maker by providing ready access to the latest technical information to dynamically monitor and evaluate the performance of all aspects of the management system. The data intensive nature of dairy herd management analysis offers numerous opportunities to apply expert system concept for monitoring and controlling of herd. The ability to query the expert system rule structure during a consultation provides the user, the opportunity to view the flow of the rules used during the session that promotes user's expertise and provided an instructional experience. The expert system development process is necessarily an iterative and demands a highly flexible programming environment. An overview of factors to consider when evaluating the potential for using an expert system for a particular application and factors to consider when selecting an expert systems programming environment are discussed in this paper. Hosein Alizadeh, Alireza Hasani-Bafarani, Hamid Parvin, Behrouz Minaei and Mohammad R. Kangavari [2] have discussed the need for developing expert system for assessment of quality dairy cattle using the fuzzy logic. Authors are of the view that judging the cattle cow not only helps in increasing the profits, but also important in deciding the quality of the next generation of the cattle required. This paper discovers that there is a good relationship between the cow physics and the milk production. Authors have applied standard score cards in evaluating the parameters by acquiring human expert knowledge. This paper discussed the development of knowledge base and rule base framework for evaluating the quality of the cattle. Zhang Ping and Tian Debin [3] have carried out research on computing the growth prospects of dairy industry dynamics by applying fuzzy comprehensive evaluation Authors have used scientific principles, business management, production capacity etc for designing the evaluation index. The authors are of the opinion that technological innovation drives the dairy industry and the entrepreneurs play a vital role for this growth. Roii Spoliansky Yael Edan and Yisrael Parmet at. el. [4] have worked on developing the automatic body condition scoring improvements using fuzzy logic to improve classification of existing computer vision algorithms. Researchers have tested the system with collected 151 dairy cow samples. The results are compared with BCS references given by an expert, using the average and median error of the model's evaluation and the number of correctly classified cows. The model was developed using statistical and visual analysis of the data to derive the number and type of variables, membership functions, rules and optimization methods. The Matlab fuzzy tool box is used for the research and data analytics.
Allyn L. Lamb [6] has discussed the principles of portfolio management for the agricultural lending business line. Authors have discovered that more than 21% of new loans are approved to the young, small, minority or veteran farmers. All new loans are sanctioned to only the clients who have satisfied the standards or those have produced guarantee certificate from government. Researchers have noticed that not more than 3% of risk funds to be granted to any single borrower and not more than 60% of risk funds should not be granted to any single industry. Loans should not be sanctioned to certain industries with banned or restricted products. Authors recommend the use of different risk analysis tools to provide different perspectives and usage of strategic credit risk management on transaction individual risk ratings. A.B.Patil and R.V Kulkarni [7] have reviewed expert systems in animal health care. The authors have conducted a survey on the application of information technology in general and expert systems in particular to animal healthcare. The authors attempted to encode the domain knowledge of animal healthcare experts in to a knowledgebase to help livestock holders. The sharing of knowledge from veterinary consultants and scientists with livestock holders rarely happens for the improvement of animal health care. Authors surveyed existing literature to evaluate animal health and aim to build expert system prototype by applying best management practices and develop rule database for several of animal health care issues in lactating animals. T.HimaBindu and Dr. S.E.V.Subrahmanyam [8] have carried out the swot analysis of dairy industry with reference to India. India is primarily an agriculture based economy with a large population staying in the villages. Agriculture and live stock are the main sources of income. This paper has conducted the SWOT analysis of dairy industry. Authors have identified few factors influencing farm profitability they are stated 1. Effectively matching the animal feed demand and availability with current milk price, 2. The growth and utilization of pasture and forage crops, 3. Local climatic and seasonal conditions, 4. Availability of Irrigation water and Irrigation efficiency, 5. Appropriate farm infrastructure and labour efficiency, 5. Herd health and reproductive efficiency 6. Animal generic merit 7. Soil type and fertility. The dairy farming animal husbandry report [9] discusses the parameters that credit risk executives consider while assessing the credit proposal. They are 1. Location services and amenities including the milk availability in the district 2. Effective farm area assessment using farm maps, 3. Checking the status of forestry and protected areas, 4. Establishment of cow walking distance from dairy, 4. Infrastructure assessment of farm and its suitability for intended farming system, 5. Assessment of water sources at the farm location, 5. Climate assessment and rain fall 6. Soil quantity and quality assessment. 7. Assessment of pastor combinations and weed populations, 8. Legal implications of the proposal, 9. Limitations to land use, liens and encumbrances if any
building consents, restrictions, existing leases, existing contracts, purchases and suppliers 8. Environmental compliance, existing environmental plans/risk assessment, Tree clearing permits and forestry plans, Irrigation water permits/licenses, 9.Workplace health and safety records, 10. Outstanding legal proceedings. This report also discusses many facts for establishment of dairy farming. Dr. P. Renganathan [10] has discussed the risk factors that are involved in the dairy farming and identifies that dairy business risk arises from parameters such as yields of the crops, sudden changes in the markets and prices reproduction rates, climatic changeability, inflation and interest rates fluctuations, government policies unexpected changes, laws, rules and the regulations. Author summarizes that these different sources of business risk can be reduced to price and production. The final risk is high when the debt to equity ratio is high. The business and financial risks are used in assessing the dairy farming risk of new proposal. The information available is basis for good decision making in a given context. A decision is classified as good or bad depends on the subsequent consequences. Abhijit Ghosh, Shilpa Sindhu, Anupama Panghal and Sanjay Bhayana [11] designed the parameters for risk assessment in dairy industry. Authors have applied Interpretive Structural Modeling (ISM) tool for identifying the risk parameters. This tool has helped in discovering the key risk parameters that are driving and dependent for the dairy farming business success. Yue Jiaoa, Yu-Ru Syaub and E. Stanley Leec, [12] have designed credit rating model based on fuzzy adaptive network. This credit rating model is targeted to small and medium sized industries. The data of small and medium sized industries is fuzzified i.e represented in terms of fuzzy values. Inference engine is designed using inference rules. Inference engine used in building Fuzzy adaptive network. This network is trained. Fuzzy adaptive technique. The neural networks has the unique advantage of learning through examples. F.Hoffmann, B Baesens, C. Mues, T. Van Gestel and J. Vanthienen [13] have studied data mining problem by applying fuzzy classification rules. This paper has identified evolutionary fuzzy rule learning strategies. The first one evolution strategy that evolves a set of approximate fuzzy rules. Each fuzzy rule has defined set of membership functions and an algorithm bases genetic engineering. All fuzzy rules have a common, linguistically interpretable membership functions in disjunctive normal form. The comparative study of evolutionary fuzzy rule learners, Nefclass, a neurofuzzy classifier is carriedout. The publicly available data sets namely real life Benelux financial credit scoring data sets are used for this study. The results conclude that genetic fuzzy classifiers out perform other classifiers in terms of classification accuracy. Across the different data sets the approximate and descriptive fuzzy rules produce the same classification accuracy.
Zhang Ping and Tian Debin [14] have designed evaluation index system for assessing the growth ability of dairy enterprise by applying fuzzy comprehensive evaluation model to measure the real-time venture dynamics. This paper used many principles such as suitable to the dairy enterprises such as scientific principles, systematic principles, business management, production capacity for designing the evaluation index. This paper concludes that innovation in technology and entrepreneurs are most vital driving the dairy industry today. Hosein Alizadeh, Alireza Hasani Bafarani, Hamid Parvin, Behrouz Minaei and Mohammad R. Kangavari [15] have studied the physical characteristics of dairy cattle by applying fuzzy logic technique. This assessment is directly linked to milk production and hence the profitability of the dairy industry. This study also plays an important role in determining the quality of cattle required in future. This paper discovers a good relationship between the cow physics and the milk production. The authors developed a standard score cards in evaluating the parameters by acquiring human expert knowledge built a knowledge base. George macharia [16] has conducted a study of various stake holders of dairy industry. The stack holders considered in the study are inputs suppliers, vet services providers, farmers, producers, raw milk buyers, farm-gate off-takers, transporters, raw milk bulkers, dairy processors, distributors of end products. This paper identifies various financing issues to be addressed while lending to each of the stack holders. The risk associated with these stack holders are identified as part of the study. A. Shields (2011) [17] has worked on risk management for dairy farmers. Dairy farmers face challenge in balancing income pricing of their products. In recent years the rate at which milk prices are growing is less than the rate at which raw materials are growing. The fluctuations in the prices call for risk in dairy business management. Cattle feed represents about 75% of the operating cost of any dairy. The risk mitigation strategies are adopted by farmers by becoming members of milk cooperative society. The Govt. has also provides subsidies and tax benefits to farmers. Insurance of dairy business can be one of the risk mitigation strategies. All the stake holders of dairy industry generally support govt. policies promoting risk management strategies. 3. Research Methodology Researchers have conducted an extensive study of credit risk literature, credit policy documents and interacted with credit risk rating executives of the banks in their region. The real life decisions have to be made on the basis of incomplete or uncertain information. Hence, the expert must rely on past experiences, heuristics (rules of thumb) and his/her knowledge in the specific subject. An expert system is a computer software that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning about knowledge, represented
primarily as if–then rules rather than through conventional procedural code. This paper aims at developing expert system for assessing credit worthiness of the dairy client. The efficiency in measuring the credit risk accurately has direct effect on the financial performance and profitability of the bank. 3.1 Credit Rating Framework Design Researchers have designed a Credit Rating Framework (CRF) to measure the objective and subjective risk parameters of credit decision environment. The credit rating framework organizes all the risk parameters of the client data in to five levels depending on their nature and criticality. These risk parameters are represented as the linguistic variables of fuzzy logic. Every risk parameter is assigned a weight based on its significance in credit decision-making. Each client’s actual credit weight is computed on the basis of client’s credit worthiness. This framework forms the basis for knowledgebase design. This credit risk evaluation resembles the credit risk executive’s thought process. The Credit rating frame work is described in the set of tables below.
Table: 1- Major Risk Parameters Code 1 2 3 4 5
Risk Parameter
Linguistic Value
Dairy Technical Feasibility Management Commitment Commercial Viability Financial Analysis Economic Analysis Total
Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor
Max Weight 150 100 100 75 75 500
Table: 2 - Technical Feasibility (150) Sub Code 1.1 1.2 1.3 1.4 1.5
Risk Parameter
Linguistic Value
Location Analysis Infrastructure Facilities Utilities Management Power Resources Transport Facilities
Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor
Max Weight 20 30 25 15 25
1.6
Human Resources
Excellent/Good/Ok/Poor
15
1.7
Technology Adopted
Excellent/Good/Ok/Poor Total
20 150
Table 3- 1.7 Selection of Technology (20) Sub Code 1.7.1 1.7.2 1.7.3 1.7.4 1.7.5 1.7.6 1.7.7 1.7.8 1.7.9 1.7.10
Risk Parameter
Linguistic Value
Calf Cutter Milking Machine Generators Water Pump Chiller Plant Ventilation Fans Water Pressure Pump Pest Controllers Foggers Milk Quality & Health Testing Equipment
Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor Total
Max Weight 1 3 3 3 1 1 2 2 2 2 20
Table 4 : Risk Parameters of Management Commitment 2.1.2 Enthusiasm Sub Code 2.1.2.1 2.1.2.2 2.1.2.3 2.1.2.4 2.1.2.5 2.1.2.6 2.1.2.7 2.1.2.8 2.1.2.9
Risk Parameter Friends Circle Business Partner Family Background Guarantor for Loan Financial Status Commitment for repayment Collateral Securities Support of Business leaders Bank Relationship
Table: 8- Typical Dairy Client test case Code
Max Weight Excellent/Good/Ok/Poor 0.5 Excellent/Good/Ok/Poor 0.5 Excellent/Good/Ok/Poor 0.5 Excellent/Good/Ok/Poor 0.5 Extra/Sufficient/Insufficient 1.0 Excellent/Good/Ok/Poor 0.5
3.8 3.9 3.10
Risk Parameter Profit Margin Product Viability Product Profile Market Share Profile of the Customer Customer Satisfaction Letter of Recommendation Competitive Strategy Sales Mode Product Cost
Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor Total
0.5 0.5 0.5 8.0
Linguistic Value
Max Weight Excellent/Good/Ok/Poor 10 Excellent/Good/Ok/Poor 10 Excellent/Good/Ok/Poor 10 Extra/Sufficient/Insufficient 15 Excellent/Good/Ok/Poor 10 Excellent/Good/Ok/Poor 10 Excellent/Good/Ok/Poor 5 Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor Total
10 10 10 100
Table 6: Risk Parameter of Dairy Farm Financial Anyalsis(75) Sub Code 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11 4.12
Risk Parameter
Linguistic Value
Earnings and Growth trend Finance Ratios Loan Profile Loan Amount Details Project Cost Liquidity Recurring Cost Tax Liability Capital Resources Repayment of Interest Business Ratio Current Assets
Excellent/Good/ok/Poor Extra/Sufficient/Insufficient Excellent/Good/ok/Poor Excellent/Good/ok/Poor Excellent/Good/ok/Poor Excellent/Good/ok/Poor Excellent/Good/ok/Poor Yes/No Excellent/Good/ok/Poor Excellent/Good/ok/Poor Excellent/Good/ok/Poor Excellent/Good/ok/Poor Total
Max Weight 2 15 5 3 15 5 5 5 5 5 10 5 80
Table 7: Risk Parameter of Dairy Farm Economic Anyalsics 5. Economic Analysis (50) Sub Code 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10
Risk Parameter
Linguistic Value
Monthly Gross Margin Here’d Size Cost of Concentrated Feed Cost of Green Feed Labour Cost Milk Cost/Ltr Revenue from Sale Manure Education Qualification Experience
Excellent/Good/Ok/Poor Big/Medium/Small High/Medium/Low High/Medium/Low High/Medium/Low High/Medium/Low Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor Excellent/Good/Ok/Poor Long/Medium/Short/Very short Total
Distance to market
Linguistic Value
Dairy Technical Feasibility Management Efficiency Commercial Viability
Excellent/Very Good/ Good/OK/Poor Excellent/Very Good/ Good/OK/Poor Excellent/Very Good / Good/OK/Poor
Financial Analysis
Excellent/Very Good/ Good/OK/Poor
Economic Analysis Total weight
Excellent/Very Good/ Good/OK/Poor
Linguistic Value
Table 5: Risk Parameters of Commercial Viability (100) Sub Code 3.1 3.2 3.3 3.4 3.5 3.6 3.7
Risk Parameter
Max Weight 3 5 3 2 5 5 10 5 10 2 50
1 2 3 4 5
Max Weight
Actual Weight
200
180
100
85
75
65
75
70
50
45
500
420
Table 9: Credit Rating Standards Sl. No 1 2 3 4
Actual Weight
Client Credit Rating
Risk Level
=40=60=80
