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Infrastructure development is the new buzzword for India Inc.Policy makers are putting emphasis on development of roads, ports, airports, and urban infrastructure to facilitate growth. The government is opening up private investment in the infrastructure through Special Purpose Vehicles (SPV). With the changing regulations, however, infrastructure finance so far has been untouched by the commercial banks. but this is the new avenue to gear up their fund based activities. With increased exposure in infrastructure, banks need to be cautious about the credit risks inherent in the projects with long gestation periods. It was found that infrastructure development has a high correlation with the macroeconomic factors like GDP growth rate of the country. Such macroeconomic trends actually influence income generation and timely recovery of the credit extended. So for greater risk sensitivity a model pricing mechanism has been developed to address the macroeconomic changes in the economy for better risk management.

It is an obvious fact that risk is inherent in every action. In extending credit to other parties one of the main risks of the Bank is Credit Risk. The possibility of losses associated with diminution in the credit quality of borrowers/counter parties is called credit risk. In a simpler way, credit risk may be defined as the potential threat that a borrower will fail to meet its obligations in accordance with the agreed terms. But for that reason banks cannot stop extending loans to borrowers. For business growth banks are required to find out new avenues to deploy their funds and generate income. Infrastructure sector is one where funding was done previously by government funds and with the assistance of Development Financial Institutions (DFIs) like IDBI & IFCI, who dedicated themselves to finance infrastructure development projects but incurred huge losses due to burgeoning NPAs.. But with changing nature of the FIs in India and all over the world banks are increasingly participating in infrastructure financing now. Here lies the importance of analyzing the performance after properly identifying and measuring the risks associated in an exposure for continuous improvement of the bottom line of Banks.

For an exposure in a sector like infrastructure it is imperative to consider the risk factors in determining the price of the loan. As the asset-liability mismatch problem is more pronounced in case of infrastructure financing proper assessment of risk is very much needed Banks should also have some reward for taking additional risk in providing long term finance. This is only possible if Banks can assess the risk inherent in this sector properly. New Basel II norms indicate that the banks assess and measure these risks from different angles. But the fact remains that apart from some big banks, most of the others are not technically sound enough to adopt those norms. Experts opine that it will still require three to five years to implement the advanced system in India. But banks need to adopt some measures for preparing the launch pad wherefrom the advanced system can take off. For that purpose a suggestive model for risk pricing has been discussed here keeping in mind the constraints to adopt the advanced system. And once the banking system is matured enough it can easily take off from the ground work it had already done.

Risk Based Pricing
Loan pricing is critically important function in a bank’s operation. Loan pricing decision directly affects the safety and soundness of the banks through their effect on earnings, credit risk evaluation, and ultimately capital adequacy. The price charged for an individual loan should not only cover cost of funds, overhead and administrative costs, and profit margin but also it should cover the probable risk factors that the bank is expecting in that particular exposure. Traditional loan pricing only consider the first factors but does not adequately address the issue of risk. Again Basel II norms have suggested certain proactive measures for loan pricing mechanism that will adequately address the risk associated in an exposure. It has suggested calculation of Credit Risk by quantifying Probability of Default, Loss Given Default, and Exposure at Default. Efforts have been taken here to achieve a risk based pricing framework in line with such recommendation for efficient credit risk management.

Finding probability of default
It is a quantitative input that aims to measure the likelihood of a borrower defaulting over a specific time horizon. It should be computed with a database of minimum period of five years. The word default is very much inherent in every loan exposure. To quantify the default probability over the years based on the bank’s experience a simple approach has been followed here.

We know probability of an event is calculated as dividing the favorable event by the total number of events. In the same way past default probability of the bank can be calculated using this formula:

PD = Actual exposure value that defaulted / Total outstanding balances in that period

This approach has been taken to avoid certain complexities in applying the more advanced model like Transition Matrix. And this needs last 5 to 7 years of default data of individual corporate and their transition data in the matrix. These require a strong information system and scientific credit monitoring system which, were not in place in many of the Indian banks. So a simpler approach has been taken as a stand by measure.

Keeping these limitations in mind, it is very obvious that the bank is not in a position to adopt the transition matrix system for calculation of default probability. But it can easily calculate previous years PD in this simple method. It was already mentioned that performance of the SPVs and steady income generation from the projects are very much linked with the overall economic growth of the country. And GDP growth rate is a good indicator of the overall economic trend. Though the GDP growth rate has been calculated annually but it does not necessarily mean that these indicators are static or compartmentalized. That means if the GDP growth rate of 2003-04 is 8% after March 2004 there will be no effect on the economy for this 8% growth rate. It is not the case that from that very day economy will start moving at a different rate altogether. So the macroeconomic factors are always overlapping each other over the years. And here lies the key assumption of the model for calculating the PD that is the default probability for a year can be a linear function of the previous GDP growth rate in the economy acting as an independent variable. With a regression analysis of at least past five to seven years’ data the equation can be formed as below:

Next Year PD = A - B x (Previous Year GDP Growth Rate) Where A = Intercept /Constant. B = Coefficient of Regression.
With the negative sign it denotes the inverse relationship between PD and Previous Year GDP. A model calculation has been shown below:
Calculation of PD for Bank A
YEAR PD (%) PY GDP (%)
1997-98 5 7.8
1998-99 12 4.8
1999-00 11 6.5
2000-01 8 6.1
2001-02 18 4.4
2002-03 10 5.8
2003-04 13 4
2004-05 6 8.2

SUMMARY OUTPUT: PD/PY GDP Regression Statistics
Multiple R 0.863327189
R Square 0.745333835
Adjusted R Square 0.702889474
Standard Error 2.274402444
Observations 8 ANOVA df SS MS F Significance F
Regression 1 90.83756 90.83756 17.56026 0.005746089
Residual 6 31.03744 5.172906
Total 7 121.875

Coefficients Standard Error t Stat P-value
Intercept 24.39532396 3.441019 7.089564 0.000395
PY GDP -2.356356968 0.56231 -4.1905 0.005746

Next Year PD = 24.4 - 2.36 x (Previous Year GDP Growth Rate)

It has been found from the statistical analysis that R2 value and adjusted R2 valued are quite high and overall significance level 0.0058 which is significantly lower than the permissible level of 0.05. That means the analysis is showing high acceptability of this model with more than 95% confidence level.

According to this model if GDP growth rate of 2004-05 is 7% then PD for the FY06 will be
FY06 PD = 24.4 – 2.36 x 7 = 7.88% Calculation of LGD 1
This input measures the proportion of the exposure that will be lost if a default occurs in an exposure. In case of calculation there are certain other things that need to be taken into consideration. Over the past seven to ten years the loss arising due to default of a loan is to be calculated
i.e. LGD = Exposure x (1 – Recovery Rate %), and to be represented in percentage value.

But the new Basel II accord is suggesting to consider the collateral factors while calculating the LGD. So the ultimate representation will be like this:
LGD* = LGD x (E*/E) Where, LGD* = Effective LGD after consideration of collateral E* = Exposure after adjustment of collateral E = Actual exposure.

Calculation of Effective LGD 2

According to Basel II norms for a collateralised transaction, the exposure amount after risk mitigation is calculated as follows:

E* = max {0, [E x (1 + He) - C x (1 - Hc - Hfx)]} where: E*= the exposure value after risk mitigation
E = current value of the exposure for which the collateral qualifies as a risk mitigant
He= haircut appropriate to the exposure
C= the current value of the collateral received
Hc= haircut appropriate to the collateral
Hfx= haircut appropriate for currency mismatch between the collateral and exposure

The exposure amount after risk mitigation (i.e., E*) will be multiplied by the risk weight of the counterparty to obtain the risk-weighted asset amount for the collateralised transaction.

Haircut represents reduction in the value of collateral and exposure by a specific percentage based on the nature and quality of the collateral and exposure respectively.
In principle, banks have two ways of calculating the haircuts: (i) standard supervisory haircuts, using parameters set by the Committee, and (ii) own-estimate haircuts, using banks’ own internal estimates of market price volatility. Banks in India will be allowed to use only the standard supervisory haircuts for both the exposure as well as the collateral.

1 Paragraph 291 of International Convergence of Capital Measurement and Capital Standards, BCBS, June 2004
2 Paragraphs 7.3.5 – 6 of Draft Guidelines for Implementation of the New Capital Adequacy Framework, RBI, February 2005 The Standard Supervisory Haircuts (assuming daily mark-to-market, daily re-margining and a 10 business day holding period) expressed as percentages are as under:
Issue rating for debt securities Residual Maturity Sovereigns Other issues

AAA to AA-/!-1 > 1 year 0.5 1 > 1 year, < 5 years 2 4 > 5 years 4 8
A + to BBB-/
A-2/A-3/P-3 and
Unrated bank securities < 1 year 1 2 > 1 year, < 5 years 3 6 > 5 years 6 12
BB + to BB- All 15
Main index equities (including convertible bonds) and Gold 15
Other equities (including convertible bonds) listed on a recognized exchange 25
UCITs/Mutual funds Highest haircut applicable to any security in which the fund can invest
Cash in the same currency 0

The standard supervisory haircuts applicable to exposure/ securities issued by the Central or State Governments, Indira Vikas Patras, Kisan Vikas Patras, National Savings Certificates will be the same as applicable to AAA rated debt securities.

Sovereign will include Reserve Bank of India, MDBs, ECGC CGTSI etc. which are eligible for zero per cent risk weight.

The standard supervisory haircut for currency risk where exposure and collateral are denominated in different currencies is 8% (also based on a 10-business day holding period and daily mark-to-market).

Where the collateral is a basket of assets, the haircut on the basket will be, , where is the weight of the asset (as measured by units of currency) in the basket and the haircut applicable to that asset.

For banks using the standard supervisory haircuts, the 10- business day haircuts provided above will be the basis and this haircut will be scaled up or down depending on the type of transaction and the frequency of re-margining or revaluation using the formula below:

H = haircut
H10 = 10-business day standard supervisory haircut for instrument
NR = actual number of business days between remargining for capital market transactions or revaluation for secured transactions.
TM = minimum holding period for the type of transaction

A sample calculation has been shown here for the measurement of Collateralized Exposure.

E* = Max {0, [E x (1 + He) – C x (1- Hc-HFX) ] }

E* = Exposure value after risk mitigation
E = Current value of the exposure
He = Haircut appropriate to the exposure
C = Current value of the collateral received
HC = Haircut appropriate to the collateral
HFX = Haircut appropriate for currency mismatch between the collateral and exposure

A bank has an exposure towards a term loan facility of Rs. 100. The tenor of the loan is 1 year. The bank has received debt security as collateral which is rated A+. There is no maturity mismatch between the exposure and the collateral. The collateral received by the bank qualifies for recognition under the credit risk mitigation. The exposure value after mitigation would be as under:

Current value of the exposure (E) = Rs. 100,
Haircut app. to the exposure (He) = 0
Current Value of the collateral (C) = Rs. 100
Haircut appropriate to the collateral
[≤ 1 year – Standard haircut] (HC) = 1% (i.e.0.01)
Haircut app. for currency mismatch between collateral and exposure (HFX) = 8% (i.e. 0.08)

E* = Max { 0, [100 x (1 + 0) – 100 x (1- 0.01- 0.08) ] } = Max { 0, [100 – 100 x (0.91)]} = Max { 0, [100 – 91]} = Max { 0, 9 } = 9
The exposure value after risk mitigation will be Rs.9.

On the basis of above calculation the Effective LGD will be ascertained. If it is assumed that after normal recovery LGD is 20%, then Effective LGD will be as follows:

LGD* = LGD x (E*/E) = 20% x (9/100) = 1.8%
The Effective LGD after collateral adjustment will be 1.8%.

Exposure at Default (EAD)
This is worked out at the time of default in an exposure. It measures the quantitative aspect of any facility (loan, OD etc.) that is likely to be drawn and to which a bank is already exposed to. This is calculated on absolute value basis. The database should be a minimum period of five years. For a loan/OD, the amount already drawn and the amount not yet drawn but available under the limit are to be worked out. It may generally be assumed that the full amount was drawn at the time of default.

Expected Loss (EL)
This concept is simply the product of the above three metrics multiplied by the exposure and is a measure of the probability of credit loss over a given time horizon. For a loan portfolio, over an extended period of time, expected loss should be comparable to the net charge-off rate. Mathematical representation can be like this:
Expected Loss = % of PD x % of LGD x amount of EAD
Unexpected loss (UL)
Losses incurred under a high-stress scenario; typically identified as a point in the tail of a credit loss distribution (e.g., at the 99th percentile). This is not coming under normal cases but an extraordinary case which is not regular and cannot be measured with certainty. Today banks are more concerned about the measurement of this loss and to find out the economic capital to address this loss as regulatory capital has been taking care of the expected loss amount.

Economic Capital (EC)
It is the fund as a ‘cushion’ available in the business to absorb unexpected losses (i.e. more than average or mean losses) in credit portfolio up to a desired confidence level. In allocating economic capital, the choice of confidence level and risk horizon depends upon the risk appetite of the organization are very important.

Calculation of EL & EC 3

With the given values from the above calculations the next job is to work out the EL and EC. The formula for EL has already been discussed. Here one important thing is needed to be kept in mind that banks must calculate Exposure at Default (EAD) without taking into account the presence of collateral, unless otherwise specified for the calculation of EL and EC.

Expected Loss = % of PD x % of LGD x amount of EAD

After EL, the next step is to calculation of EC, which can be done by using the standard formula.

EC = EAD x (PD x 2LGD + LGD x 2PD) 1/2 Where, EAD = the amount of exposure PD & LGD = calculated as percentage value 2LGD & 2PD = variance calculated over a specified period

The concept of Risk Adjusted Return on Capital (RAROC) is rooted in the concept that the traditional way of evaluating return from a loan using interest and fee income does not provide a true picture of the credit risk efficiently of an organization. This is so because it does not take into account the various kinds of risks involved and unless the risk element of each loan asset is related to earnings, effective pricing cannot be said to exist in the operation of the bank.

RAROC is a process concerned with adjusting net return from a risk position with the estimated amount of unexpected loss arising from it and discounting it by economic capital so as to activate an appropriate risk/reward environment across the organization. According to RBI, RAROC is a system that allocates a capita charge to a transaction or a line of business at an amount equal to the maximum expected loss over one year on an after-tax basis.

3 ‘Economic Capital Defined’ of Credit Risk Management by S.K. Bagchi, Jaico Publishing House, 2004
4 ‘Risk Adjusted Performance’ of Risk Management in Banking by Joel Bessis, John Wiley & Sons Ltd., 2003

Calculation of RAROC
Theoretical concept of RAROC has been discussed earlier. Now the effort has been taken to quantify it. The RAROC ratio adjusts the earnings with EL and uses risk contribution as economic capital. The revenues are the All-in-Spread (AIS), which averages all interest revenues and fees into an annualized average. The formula is:

Further break up of the above expression leads to:

RAROC = [r x E – i x D – oc x E – EL x E]/K
Since A = D + K
Above expression can be simplified as below:
RAROC = i + (E/K) x (r – i – EL – oc) where, i = Cost of Debt D = Allocated Debt E = Exposure K = Capital allocation r = Rate of Return from the exposure EL = Expected Loss oc = Operating cost

This represents that the RAROC is the excess over the cost of debt of the AIS minus the cost of expected loss and operating costs and multiplied by the ratio of assets to capital. The equation can truly measure the return from an exposure after considering the risk associated within the exposure. For infrastructure finance this measure will be very helpful for the banks to ascertain the actual return while considering the high risk involved in financing long term projects. Here from this equation it can be easily said that with a higher default probability of an exposure the EL will be higher and that will ultimately affect the return. And this analysis leads to a new idea for developing a pricing mechanism that will actually represent the amount of risk inherent in the exposure, which will ultimately fulfill the objective.

Now to achieve risk based pricing mechanism using the concept of RAROC, a bottom up approach is to be considered. In this approach first, a Target Return or RAROC is to be determined. Now what is this target return? This is called Hurdle rate (k%) 5, which means the minimum acceptable rate of return by the shareholder from the business activity. This can be derived from the price of risk in the capital market from well known equity return models. It is the minimum return required by the shareholders given the risk of the stock as measured by the beta of the bank’s stocks, following the CAPM framework. A standard Hurdle Rate has been taken as in the range of 20% to 25%. Now a backward approach will be taken to calculate the price of the loan.

From the RAROC measure previously described the formula is:
RAROC = i + (E/K) x (r – i – EL – oc)

Now with the concept of Hurdle rate in the above expression RAROC will be equal to or greater than the minimum Hurdle rate (k%) and with given values of other variables the unknown variable ‘r’ can be calculated as follows:

5. ‘Risk Adjusted Performance’ of Risk Management in Banking by Joel Bessis, John Wiley & Sons Ltd., 2003

RAROC = i + (E/K) x (r – i – EL – oc)  k
i.e. i + (E/K) x (r – i – EL – oc)  k (E/K) x (r – i – EL – oc)  (k – i) (r – i – EL – oc)  (k – i) x K/E

The relation implies the minimum value of ‘r’ will be: r  [(k – i) x K/E] +[i + oc +EL]…………………………Eq. 1

A sample calculation of Risk-based pricing has been shown in below. This example assumes two exposures of same value but with a different risk category. A comparative analysis has been done to understand the difference between a high and low risk exposure and the benefit of Risk-based Pricing mechanism.

For the simplicity of the calculation certain rates have been assumed which is shown in the table below;
Particulars Case A Case B
Exposure (Rs.) 100 100
Risk Low High
Expected Loss 1% 3%
Economic Capital 5% 10%
Minimum RAROC 20% 20%
Operating Cost (oc%) 2% 2%
Cost of Debt (i%) 6% 6%

Now after putting the values in Eq.1, we get the minimum ‘r’ value:
For Case A r  6 + 2 + 1 + 5/100(20 – 6) r  9.70

For Case B r  6 + 2 + 3 + 10/100(20 – 6) r  12.40
From the above analysis it is very much clear that with a low risk exposure bank can afford of charging a lower rate but in case of high risk exposure bank needs to charge a higher rate to maintain a specific return and create value to its shareholders.

The biggest advantage of the model is its flexibility. Bankers can consider any independent variables that can affect the PD for the current year. But in case of more than one variable it will be multivariate regression analysis. This is not a “one size fits all” process. The model itself (calculation of PD) depends upon the trend of past records of individual bank. In this way a particular bank can analyze various other factors and make suitable model as per their needs.
The ultimate result will provide the banker a definite quantitative measure to what extent she can lower the loan price in case of strong negotiation from the counter party. It will also help the bank in deciding the rate in case of a consortium arrangement.

Macro economic factors need to be tested vigorously over a long period of time to gain accuracy of the system. But sometimes unavailability of internal information over the periods creates the biggest hurdle to test and run the model and make necessary corrective measures to make it full proof. Also this model is not going into the individual borrower level so it is not possible to distinguish two loan applications of one particular bank from this view point.

With the opening up of economy and globalization Indian banking sector is experiencing a continuous reform process. On the other hand Basel Committee on Banking Supervision has come up with some advanced measure in banking operation that will make the banks internationally sound. Indian banks also need to follow this new trend. But for the implementation of new Basel II norms in banking operation banks need to be very efficient in data management. It should have an efficient Management Information System (MIS) and internal credit monitoring system in place to develop complex model like Transitional Matrix, KMV model, CPV model or others. For Indian banking system it will need some time to make them oriented with the system. Now banks are increasing its capital base from different sources to invest more in the advanced technology measures and system implementation.

Once these facilities will be in place bank can afford handling those complex measures in risk management system. But the problem is some banks in India are in a position to adopt the system much earlier. And with the presence of a cut-throat competition in this sector early implementation of the advanced system will definitely give an edge to those organizations. And this will be an opportunity loss for the other players which will ultimately going to affect their market share. So this time gap is very crucial for the bank in terms of learning and experimenting with the new system. For this purpose the bank can adopt some simplistic approach as shown in the Risk based Pricing Mechanism for a parallel run and compare the differences with the traditional measures. This will provide the bank greater risk sensitivity and a definite quantitative analysis of loan pricing. It will also help understanding the complexity of the new norms from a different angle with relation to macro economic changes in the country, the efficiency level and systems required for effective implementation of advanced risk management environment to increase its operational efficiency and profitability.


Bagchi S.K. (2004) Credit Risk Management, Jaico Publishing House, 151-164.

Basel Committee on Banking Supervision (June 2004) International Convergence of Capital Measurement and Capital Standards – A Revised Framework, BCBS, Basel, Switzerland.

Davis P.O. (April 2004) Credit Risk Measurement: Avoiding Unintended Results, The RMA Journal.

Khandelwal A.K. (January 2004) Emerging Challenges in Credit Risk Management, Professional Banker, ICFAI Press.

Pahuja Y.P. (January 2004) New Approaches to Credit Risk Management, Professional Banker, ICFAI Press.

Reserve Bank of India (February 2005) Draft Guidelines for Implementation of the New Capital Adequacy Framework, RBI, Mumbai, India

Sabnavis M. (January 2004) Basel II and Credit Risk, Professional Banker, ICFAI Press.…...

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...Material management department is typically responsible for directing the supply chain. Material management is an umbrella department that has many other functions (e.g., central store, laundry and linen operations, and sterile processing). The core purpose of material management is to direct and control the movements of goods in an efficient manner through a hospital system (Langabeer, 2008). This material management proposal document will elaborate on the role materials management plays within a hospital and the role of operations managers in this process. This document will further identify possible constraints a hospital may experience in its supply chain, the potential effects and justification on implementing a new collaborative planning process, and provide suggestions on how to manage a hospital supplied during a disaster. Role of Materials and Operations Management The role of materials and operations management plays within a hospital system is vital to the success of any health care organization. According to Langabeer (2008) material management controls significant resources and have total expenditures, or spending at 50% of a hospital budget. Materials management not only directs and controls the supply chain of a health care organization it is responsible for managing the flow of goods throughout the hospital and carry out supply and resource logistics. Materials management has numerous meanings and some hospitals view material management as an umbrella......

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...informational meeting SumMer Study Abroad Program February 6th at 2 pm STellar Building, Room 215 We’ll have snacks, so bring your appetite! Management is a necessary evil of any business. It provides the leadership and administration that will ensure that the organization runs the way that it should. According to the online business dictionary (2011), management is defined as the organization and coordination of the activities of an enterprise in accordance with certain policies and in achievement of defined objectives. It is the process of using organizational resources to achieve organizational objectives through planning, organizing and staffing, leading, and controlling. Management is an essential factor of production along with machines, materials, and money. As a discipline, management consists of the interlocking functions of formulating corporate policy and organizing, planning, controlling, and directing an organization's resources to achieve the policy's objectives. Management can also imply the directors and managers who have the power and responsibility to make decisions to manage an enterprise.Management is a necessary evil of any business. It provides the leadership and administration that will ensure that the organization runs the way that it should. According to the online business dictionary (2011), management is defined as the organization and coordination of the activities of an enterprise in accordance with certain policies and in achievement of......

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...Lahore School of Economics Course: Management: Policy and Practice (MGT 503) Semester: Winter Semester 2012 Part- I Class: MBA for Professionals I Sec B Course Instructor: Usamah Iyyaz Billah (email: ) Class Day & Timings: Sunday: 12:45 PM - 02:45 PM + 3.00PM - 5.00PM SYLLABUS 1. Introduction to Case Study Method: The case study method is radically different from traditional teaching methods. Students will learn the basic rules of an Oral Case Analysis Presentations as well as how to prepare and present Written Analysis for comprehensive management cases. 2. Defining the Managers Terrain: What is a management function, roles and kills? Universality of Management. A brief History of Management. Organizational Culture and Environment. Classifying managers and nominal employees. How the work done by managers is important for the organizations and how difficult it is to be a good manager who carries Social Responsibility and Good Ethics. 3. Planning: Managers as Decision makes, Decision making Process. Classifying decisions and decision making conditions. Techniques for effective decision making. Foundation of Planning. Setting goals/objectives and developing plans. Strategic management and planning. 4. Organizing: Organizational structure and design. Functional, geographic, product, process, customer departmentalization. Chain of command and span of control. Decisions......

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...Scientific management is a theory of systematic study with great influence that attempts to apply science to the process of management with the main objective to improve economic efficiency. The theory was first introduced by Frederick Winslow Taylor in 1912 and has been altered and developed for several times. (Freeman, 1996, P35) Is scientific management still relevant to a predominantly service economy is debatable. The aim of this essay is to discuss the supporting viewpoints. Several reasons are provided to support the relevance of scientific management and the current service economy which are listed as followings: firstly, applying science for each element of work to create best way to perform the task; secondly, division of labor, specialization and total quality management system are used to increase efficiency of the production; finally, piece-rate as one of the reward systems of scientific management is widely used till nowadays. At first, Taylor stated the measurement of scientific management is to develop a science for each element of a work which includes discovering one best way to perform the task, planning each task in advance with instructions describing in details and determining the correct time to complete each task. (Aguiar, 2001, P257) For instance, Aguiar identifies how scientific management is applying to the measurement of cleaning work in the new building workplace, which is one example of service economy. Science is used to calculate the size......

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