Trávníčková credit management

advertisement
MENDEL UNIVERSITY IN BRNO
Faculty of Business and Economics
Department of Business Economics
Project of Credit Management System in the Daughter Company
of Robert Bosch GmbH
DIPLOMA THESIS
Supervised by:
Ing. Michaela Beranová, Ph.D.
Brno, 2010
Elaborated by:
Bc. Marcela Trávníčková
Declaration
I declare that I elaborated my diploma thesis “Project of Credit Management System in the
Daughter Company of Robert Bosch GmbH” individually and mentioned all used literature
and competent sources in the list of references.
Brno 14. December 2010
Bc. Marcela Trávníčková
Acknowledgement
Firstly, I would like to thank to my university supervisor of my diploma thesis, Ing. Michaela
Beranová, Ph.D., for her help and advices, that certainly formed my work.
Secondly, I would like to thank to my supervisor in the company where I wrote my diploma
thesis, Dr. Arne Flemming for his support and advices, gaining thus a real insight into
business processes.
Furthermore, I would like to thank to Mrs Dana Rybníčková for her kind assistance
concerning the credit management processes in the chosen company.
I would also like to thank to Ing. Václav Adamec, Ph.D. and Mgr. Martin Řezáč, Ph.D. for
their consultancy concerning the statistical hypothesis testing and the scoring models.
Finally, I cannot forget to thank to my parents, grandparents, my sister and my boyfriend, who
always stand by me side, helping and supporting me. Their support has shaped my personality
and therefore also this work.
Abstract
Trávníčková, M. Project of Credit Management System in the Daughter Company of Robert
Bosch GmbH. Diploma Thesis. Brno, 2010.
This work proposes a model of efficient credit management system for the daughter company
of Robert Bosch GmbH. The aim is to determine influences of chosen processes of credit
management on efficiency of entire credit management system. This is done by hypothesis
testing, while taking advantage of ordinary least squares method. The further concern is
assigning individual customers their real strategic importance within company’s customer
portfolio, in order to facilitate optimal allocation of financial resources. For that, two different
methods are used, namely customer portfolio analysis and scoring method by means of
logistic regression.
Key words: credit management, trade credit, hypothesis testing, credit scoring,
logistic regression.
Abstrakt
Trávníčková, M. Projekt systému řízení pohledávek v dceřiné společnosti Robert Bosch
GmbH. Diplomová práce. Brno, 2010.
Tato práce se zabývá návrhem efektivního systému řízení pohledávek v dceřiné společnosti
Robert Bosch GmbH. Snahou je ohodnotit vlivy vybraných procesů řízení pohledávek na
výkonnost celého systému řízení pohledávek. Hodnocení je provedeno formou testování
hypotéz pomocí metody nejmenších čtverců. Tato práce se dále zaměřuje na rozdělení
zákazníků dle jejich strategické důležitosti v rámci zákaznického portfolia společnosti s cílem
optimalizovat rozmístění finančních zdrojů. K tomuto rozdělení jsou použity dvě rozdílné
metody, a to analýza portfolia zákazníků a skóringová metoda prostřednictvím logistické
regrese.
Klíčová slova: řízení pohledávek, obchodní úvěr, testování hypotéz, kreditní skóring,
logistická regrese.
Contents
1
INTRODUCTION............................................................................................................ 7
2
THE GOAL AND METHODOLOGY........................................................................... 8
3
THEORETICAL PART .................................................................................................. 9
3.1
THE CREDIT MANAGEMENT FUNCTION......................................................................... 9
3.1.1 Credit past and future .......................................................................................... 10
3.1.2 Credit policy......................................................................................................... 12
3.1.3 Credit organization .............................................................................................. 13
3.2
ACCOUNT OPENING PROCEDURE ................................................................................ 14
3.2.1 Risk assessment in trade credit ............................................................................ 15
3.2.2 Credit terms and conditions of sale...................................................................... 20
3.2.3 Precautionary securing of accounts receivable ................................................... 22
3.3
CASH COLLECTION .................................................................................................... 24
3.3.1 Sales ledger administration.................................................................................. 25
3.3.2 Planning, measuring and reporting debtors ........................................................ 26
3.3.3 Dunning process................................................................................................... 29
3.3.4 Suspension of supplies.......................................................................................... 31
3.3.5 Credit scoring and insolvency prediction ............................................................ 32
3.3.6 Customer portfolio analysis ................................................................................. 37
3.4
MEANS OF DEBT RECOVERY ...................................................................................... 38
3.4.1 Collection agencies .............................................................................................. 39
3.4.2 Voluntary settlements of debts.............................................................................. 40
3.4.3 Legal proceedings ................................................................................................ 42
4
PRACTICAL PART ...................................................................................................... 45
4.1
CREDIT MANAGEMENT SYSTEM IN THE COMPANY BOSCH ......................................... 45
4.1.1 Credit management organization and processes ................................................. 46
4.1.2 Account opening procedure ................................................................................. 48
4.1.3 Cash collection ..................................................................................................... 51
4.1.4 Means of debt recovery ........................................................................................ 54
4.2
IMPACT OF SELECTED FACTORS ON ACCOUNTS RECEIVABLE COLLECTED .................. 57
4.2.1 Selected factors .................................................................................................... 57
4.2.2 Hypothesis testing ................................................................................................ 59
4.3
DIVISION OF THE CUSTOMERS ACCORDING TO THE PORTFOLIO ANALYSIS ................. 65
4.3.1 Investigation of existing portfolio models ............................................................ 65
4.3.2 Evaluation factors for the model, their scoring and weighting............................ 68
4.3.3 Creation of the customer portfolio matrix............................................................ 70
4.4
DIVISION OF THE CUSTOMERS ACCORDING TO THE SCORING FUNCTION..................... 73
4.4.1 Creation of a logit model for binary data ............................................................ 74
4.4.2 Scoring of the customers using logistic regression.............................................. 78
5
DISCUSSION AND CONCLUSION............................................................................ 81
6
REFERENCES ............................................................................................................... 85
7
LIST OF FIGURES AND TABLES ............................................................................. 88
8
APPENDICES ................................................................................................................ 89
1 INTRODUCTION
I decided to choose “Project of Credit Management System in the Daughter Company of
Robert Bosch GmbH” as my diploma thesis topic, due to my interest concerning credit
management from a theoretical point of view as well as a practical implementation in a real
company. It is also due to my one-year internship at headquarters of Robert Bosch GmbH in
Germany, why I decided to apply for my diploma thesis topic in their Czech subsidiary, to get
an insight into a real business environment.
Credit management has been gaining in its importance throughout the whole period of its
existence, becoming a today’s hot topic among companies which are willing to succeed on the
market. The time has proved that deliberate overlooking of credit control and thinking of it as
of something that one could manage by doing it once a week, is old-fashioned and
reactionary. Due to the fact that credit management is increasingly being seen as a customer
service as well as a core internal process of the company, it must be understood that it is an
important profession and it certainly matters that a credit manager does a professional job all
the times.
Thinking that a company could be totally insulated from bad debt losses, is foolishness. The
fact is that bad debt losses can occur even in the best regulated environment. There are many
more companies in difficulty than really considered and these are the ones causing the serious
overdue accounts. They are like an iceberg, where actual insolvent companies are on its
visible top, but the part causing much more serious damage is the mass hidden below the
surface.
Already today, not only huge concerns but also middle-sized companies having a strong
determination for prospecting growth are aware that they need qualified credit management
professionals. And this need is increasing. The contribution of credit management will
continue to grow and credit managers will be forced to develop and improve their skills
continuously. A future prognosis speaks about new specialized professions of credit
management to arise. Hence, it might be necessary to establish further subdivisions of
technical areas being specialized for trade, consumer service or export.
7
2 THE GOAL AND METHODOLOGY
The main goal of this diploma thesis is to propose a model of efficient credit management
system for the daughter company of Robert Bosch GmbH. The first partial goal is to
determine influences of chosen processes of credit management on the efficiency of entire
credit management system. With respect to the main goal of the diploma thesis, a proper
allocation of individual customers within the entire customer portfolio of the company
represents a second partial goal of the diploma thesis. Determination of customer’s strategic
importance to the company is a step to be made when optimizing allocation of company’s
financial resources and therefore whole credit management system.
The methodology of the diploma thesis consists of division in two main parts, a theoretical
part and a practical part. The focus of the theoretical part is a review of available literature
resources covering main areas of credit management in general. The areas to be covered are
the function of credit management, its historical evolution, presence and future potential.
Further on essentials of operative credit management as account opening procedure, cash
collection and means of debt recovery are to be explained. The practical part will be oriented
on actual credit management system in the daughter company of Robert Bosch GmbH, on
testing how selected factors influence accounts receivable collected and last but not least on a
division of customers once according to portfolio analysis and secondly according to scoring
function. All partial results from testing as well as source data for various graphs will be
available in appendices.
There are various methods to be used in this diploma thesis. The ordinary least squares
method is going to be used for hypothesis testing concerning influence of selected factors on
accounts receivable collected. Here series of tests will appear to examine features of the
model. Such tests will include the Ramsey’s RESET test for correct model’s specification,
followed by Lagrange multiplier test of non-linearity of the model and finally White’s test
examining the constant variance of error terms. Portfolio analysis will be a second method in
use specifying customers’ allocation among the others in company’s portfolio. The last
method to be implemented is scoring method in order to diversify the customers by their
scoring. It will take advantage of logistic regression using data coming from a logit model for
binary data.
8
3 THEORETICAL PART
The scope of the theoretical part is to review basics and individual processes of credit
management in general. The insight into the theoretical background will create a substantial
knowledge of the problematic which is to be used later on.
The first focus will be dedicated to introduction of credit management function. Here history
of a credit and its management will be reviewed, as well as essentials of credit policy and
credit organization.
A milestone of credit management will be represented by account opening procedure. In this
part, risk assessment in trade credit will be mentioned as a preliminary assumption for
determination of credit terms and conditions of sale. Another important point will be how to
secure accounts receivable as precaution.
A special attention will be paid to cash collection as a core process of credit management.
Here routine processes as sales ledger administration, debtors planning and reporting as well
as dunning or suspension of supplies will be examined. An equally important point to mention
will be credit scoring, insolvency prediction and customer portfolio analysis.
The end process of credit management will be described as well and hence by available means
of debt recovery. Special focus is to be given to collection agencies, voluntary settlements of
debts and to legal proceedings as a final stage.
3.1
The credit management function
If one thought of a credit as a modern invention like an iPhone or a Global Positioning
System he would find out later or sooner that as well as these both gadgets are just more
sophisticated developments of previous methods of display and communication, also the
credit is nothing new. Credit has been part of human existence for a very long time and the
levels of its sophistication and progress in utilizing and controlling continue to improve.
Credit is the oil of commerce, and it has been an accepted feature since the early part of the
twentieth century, that businesses allow customers time to pay. (Clarke, 2001)
The secret of efficient credit management is a proper credit policy. Every company’s
management should have the philosophy of credit granting in order to facilitate sales,
collecting sales revenue efficiently, servicing customer complaints rapidly, while using the
best people and technology to achieve this. It is vital that company’s management supports a
creation of policy and procedures documentation fully, which should be issued to all affected
functions consequently; in particular to sales, production, quality control and customer
service.
9
The person in an overall control of a credit function is a credit manager, who is also
responsible for the management of the vital debtors’ asset. This task requires a full
management responsibility. In smaller organizations it can be a financial director who is
responsible for credit management and has credit controllers to undertake the ledger work.
3.1.1 Credit past and future
The history of credit dates back to ancient civilizations of Egypt, Assyria and Babylon over
3,000 years ago. But it is the period of middle Ages and European area where the substantial
growth in credit trading started.
Great trading fairs were held in Europe in the twelfth century, with merchants travelling from
one place to another, buying and selling on an ongoing basis. It become common for a trader
in one place to buy out of the proceeds of sale in another place and it was the same time when
a trade agent came into being in Italy. The agent was supposed to handle all buying, selling
and settlement details on behalf of the travelling agents. (Edwards, 2004)
The idea of discount for bulk purchase and cash paid in advance is not new. It was England
where medieval monasteries dependent on their income from wool sale offered attractive
discounts for large purchases by Italian and Flemish merchants for delivery in the next season.
According to Edwards (2004), the bill of exchange, much as known today, was a product of a
fourteenth-century Italy on the basis that gold and silver were available at all times to cover
acceptance values.
The Industrial Revolution, born in the United Kingdom at the end of the eighteen century
made unprecedented demands on the credit culture. Many various products were made and
sold to new markets and customers all over the world, while risk and unknown exposure were
increasing. At that time new credit and financing methods were introduced. Trade expansion
was assisted by bank loans offered to local businesses, whereas trade credit became a
significant financing source for businesses delivering diverse products on diverse markets.
Methods of payment progressed, keeping pace with development. Cheques were considered
rarity until approximately 90´s of nineteenth-century, debts were usually settled about onethird by cash and two-third by bill of exchange.
The expansion of trade, the proliferation of customers in remote places in a variety of shapes
and sizes, brought an appreciation, that giving credit was an aspect of the trading activity
which required the same degree of management and discipline which applied to other aspects
of day-to-day business operations.
10
Today, in normal trading, not to give a credit would mean sales restriction, volumes reduction
and unit costs increase. In the years which have passed, it could easily be argued that nothing
has really changed or that everything has changed. The essence of a credit and trust, which is
underlying to that, has not changed. It is true that allowing customers, time to pay for goods
or services will always carry the risk that payment will be late or not made at all. The amount
of a credit and its credit period will depend on risk assessment and will be influenced by all
those elements of human nature that have existed ever.
However, one could oppose that the credit scene has certainly changed. Yes it is true. A huge
turnover in the situation has been definitely caused by mass usage of computers, electronic
communication, Internet and data transfer. Globalization and the European Union bringing its
currency in the play, definitely have their share on the nowadays situation as well.
It is no longer acceptable for the credit function to be seen in isolation, either as a debt
collection operation or another barrier. (Clarke, 2001)
Overlooking credit control and thinking about it as of something that one comes to do only
once upon a week is definitely viewed as unacceptable. Therefore cannot be also a credit
manager understood as a person in charge of reporting profits and company health.
According to Capell (1981), is credit management increasingly being seen as customer
service and marketing function as well and therefore sits just as easily with sales as it does
with finance. Credit management is an important profession as the others are and it certainly
matters that the credit manager does a professional job at all times.
Credit is all about trust. The word itself derives from Latin word “credere” meaning trust.
There can be no doubt as to the serious nature of credit management that the whole business
of granting credit domestically and internationally, has its roots in trust. The credit manager’s
job is incorporating that trust in granting the required facilities, and to maintain that trust
through the cycle of trading. (Bass, 1991)
The message is clear and thus that there is an increasing need for growing firms to employ
qualified credit management professionals. The contribution of credit management will
continue to grow, and credit managers have to develop and improve their skills continually. It
could happen one day that also other specialized professions arise under credit management
itself. It might be needed to establish further subdivisions into technical areas of specialization
for trade, consumer service and export.
11
3.1.2 Credit policy
If one accepts the fact that it is simply not good enough to sell a product, issue an invoice, sit
and wait for the payment to come, then he must also face the reality that it is equally unwise
to conduct any operation without really understanding why and for what it is done.
The goal of every credit manager is to achieve “the highest level of profitable sales, over the
shortest period of time, with the minimum of bad debts.” (Besanko, 2004)
Every company does in fact have a credit policy, even if they do not realize it. It may not be
written anywhere and may simply be passed through the organization by word of mouth,
understood by all the staff as an accepted working practice. It is clear, therefore, that in the
environment where competitive pressures exist, a clear and understood credit policy is of the
highest importance.
According to Emery (2004), should be no credit policy drawn up in isolation. Many factors
contribute to the policy’s actual nature and contents. Rules are always much easier understood
and more likely to be followed if all participants are involved in their formulation.
A policy drawn up by credit and sales staff and then agreed by the board has a far greater
chance of successful implementation than one worked out and imposed by a single executive
isolated from the outside world. The credit policy should always take into account actual
business conditions, both in respect of the company’s own market place, and in the general
economic climate. Drawing the credit policy up involves input from finance, sales, marketing
and general guidance from the board. If starting from scratch, or if reviewing an existing
credit policy with the intention of updating it, those involved should set out the criteria by
which they will operate. (Rödl, 1983)
According to Bass (1991) the policy itself does not have to be too complex or too difficult to
understand due to hard-to-follow equations. All that is required is a clear statement of aims
and intentions. Among these definitely count the company’s business and aims, followed by
types of customers and business sectors. Conditions of sale as issued to customers together
with payment terms required by credit management are an equal part as well. A thing not to
be forgotten is specifying days sales outstanding (DSO) objective and system of vetting
customers. Last but not least, collection methods and timetable are to be included as well.
Finally, it is vital to specify staff responsible for policy implementation and responsibility of
other departments to help achieving firm’s credit objectives.
The overall benefits of having a credit policy can be summed up as following. Firstly, it
represents the company’s intentions for the granting of a credit. Secondly it removes any
12
uncertainties about the authority levels and responsibilities for the setting of credit amounts,
payment terms, risk categories and for accepting orders. (Capell, 1981)
It is true that credit policy provides an operating guide for credit staff and helps to eliminate
“special” credit deals by unauthorized personnel. Having a credit policy simply demonstrates
a positive business attitude towards customers and simplifies the work of auditors and other
visitors. It is a kind of recognition by the highest level in the company that credit management
and its contribution to sales and profits plays an important role, and thus it needs to be
supported fully.
3.1.3 Credit organization
For many years, credit control or credit management was understood in many companies as a
simple debt collection. But in recent years the role of credit management has grown
significantly more in its importance. Although collection of funds remains one of the most
important parts of the credit function, it is only a part.
According to Rödl (1983), there are following main areas of operation covered by a credit
department:
−
Assessment of credit risk which tries to find ways of accepting and controlling all
businesses, including high risk opportunities.
−
Establishment of credit terms and limits which takes into account the risk involved and
liaises closely with sales.
−
Monitoring and control of debt which ensures that agreed terms are adhered to, all high
risk customer are kept under control, and action is taken promptly to resolve any
queries or disputes.
−
Maintenance of the sales ledger which ensures that the customer master file is up-todate and accurate, and that payments and other adjustments have been applied promptly
and accurately.
−
Collection of payment in a manner which creates optimum cash inflow ensuring thus
continuity of business.
Below the level of the board of directors, somebody has to be responsible for running the
credit function on a day-to-day basis. A credit manager is often seen as person who is cynic,
who would never believe anything he is told until it can be verified. His job is one of the few
jobs in a company where the responsibility exceeds his authority.
13
It is a fact that a credit manager has the responsibility for an asset (debtors) worth, possibly
many millions, but does not have the actual authority to do what is expected. The difference
between responsibility and authority is therefore a gap which every credit manager needs to
fill by his talent for persuading other managers to do the right things all the time. (Clarke,
2001)
According to Edwards (2004), reporting channels for credit management (see Fig. 1) have
been the subject of a hot debate amongst credit managers for many years. As a function which
handles money, it has been held for a very long time that credit management sits more
comfortably within finance or accounting. The people in the opposition argue that promoting
profitable sales places it clearly in the sales area, and that reporting to the sales director is
more natural.
Edwards (2004) further denotes, that there are people who think, that it is very important to
keep reporting channels separated from both sales and finance, and thus go directly to the
managing director or chief executive. It is also worth mentioning that a view exists, saying
that asset control is a treasury function.
Because of different company cultures, it is not possible to be definite about the best
“location” for credit management, though without any doubts it is more than clear that the
credit manager is a bridge between finance and sales.
Accounting
Treasury/Finance
Sales
Credit manager
Risk analysis
Sales ledger
Collections
Fig. 1 The reporting channels for credit management
(Source: Edwards (2004))
3.2
Account opening procedure
Successful companies know the value of cash inflow but, they also know that monitoring
customer’s ability to pay must not hinder sales growth.
14
Good risk assessment methods not only increase profits by avoiding the costs of waiting and
bad debts, but also increase sales opportunities by directing competitive selling efforts away
from a failing customer to those with good prospects for growth. (Bass 1991)
Risk assessment in trade credit should not be viewed as anything other than structured and
logical. To allow business relationship to grow, a well-defined sequence of events is to be
established from the beginning. Then it can be followed by both credit and sales staff in order
to define, from the earliest point, the manner in which the customer relationship will be
conducted.
According to Capell (1981) a simple and reliable sequence is:
−
Credit Application Form which represents the customer’s request to borrow money.
−
Check on creditworthiness which is thorough or brief, according to order value or
projected volumes.
−
Credit rating (limit) and/or risk category which are based on the application form and
the credit report information, coming out from the decision as to how much one can
allow, and what the perceived risk is likely to be.
−
Credit term which can be standard or special, according to the buyer’s status.
−
Allocation of an account number which encompasses that no delivery takes place until
this is done by signalling that a credit may be allowed.
−
Welcome letter to a customer which means an important first contact with the person
responsible for payment.
−
Special ledger section for three months which allows close monitoring of a new
account, making extra contact in the initial stages to help the customer avoid bad
payment habits.
A credit manager should always think ahead and see scenarios that might happen due to the
market or business risk. Therefore securing of the accounts receivable in a chosen way and
scale appropriate to the market or customer specifics is outright, since it can overcome serious
difficulties in the future.
3.2.1 Risk assessment in trade credit
One can make a very nice picture of company’s health after going through its financial
statements and carrying out so called internal credit analysis. This also gives a credit manager
an excellent opportunity to calculate credit levels by the use of ratios.
15
In fact, a balance sheet is a historical snapshot of a company at a moment in time which is
now well past. However, the vast majority of accounts are straightforward, and analysts can
develop experience in studying their customers’ accounts, spotting inconsistencies or
identifying misleading parts. (Kolb, 1992)
According to Emery (2004), can be the statutory set of documents submitted in a wide variety
of styles and quality. Nevertheless, the content should still consist of following key
documents. These should definitely include cover page with company’s name and date of
balance sheet, list of directors, registered office, auditors and bankers. Furthermore report of
directors to shareholders should be enclosed as well as auditor’s report to shareholders. Profit
and loss account for the year up to the balance sheet date should not be missing as well as the
balance sheet corresponding the date shown. Last but not least, also source and application of
funds statement or “funds flow” statement is to be included together with the last item, notes
to the accounts.
It is definitely worth deciding a “pain” level, an amount which would really hurt if it were
lost, and then regularly review the financial status of all debtors above this level, using an
analysis of key balance sheet items. Even when analysis is not made in depth, most credit
mangers should at least check the basic solvency and liquidity of customers with significant
exposures. One can then save much time and expense by not giving so deep analysis to small
value accounts.
Since ratios alone can be misleading, it is always better to compare any one ratio with the
same ratio for the previous year, or better, two years.
Edwards (2004) supports the idea that three successive years of financial ratios are a reliable
indicator of a company’s progress. He adds that, it is worth devising a standard worksheet
(see Tab. 1) to record a customer’s ratios and trends. The worksheet can be then available at a
glance instead of having to remember the basis for previous credit decisions.
16
Tab. 1 Financial ratios for credit assessment worksheet
Liquidity ratios
Cash ratio
Definition
current assets
current liabilities
receivables
current liabilities
financial assets
current liabilities
Net working capital
current assets - current liabilities
Financial leverage ratios
Definition
total liabilities
total assets
total debt
total equity
Current ratio
Quick ratio
Debt Ratio
Debt to Equity Ratio
Profitability ratios
Definition
sales - cost of goods sold
sales
net profit
total assets
net profit
shareholder equity
Gross Profit Margin
Return on assets
Return on equity
Activity ratios
Definition
accounts receivable * 365 days
sales
accounts payable * 365 days
purchases
inventory * 365 days
sales
sales
total assets
Days Sales Outstanding
Average Payment Period
Inventory Turnover
Total Asset Turnover
(Source: Jakubík (2007))
There are many computer-assisted methods available for risk assessment, which include selfdesigned and appropriate spreadsheet programs, so called PC-based scoring systems1. It uses
balance sheet data loaded by the user. Here one can specify the data to be loaded, with some
space for preferred weighting ratios. With a self-designed system, the credit manager can
produce ratios in-house, which may serve for setting of credit ratings and risk codes.
It is worth bearing in mind that credit is always a risk, but should never become a gamble.
Risk is determined by assessing its likelihood, therefore it is necessary to detect customer’s
1
Assessing a firm’s ability to pay its debts is a complex judgment, because many factors affect creditworthiness.
Credit scoring combines several financial variables into a single score, or index, that measures
creditworthiness. The score is often a linear combination of several specific variables. (Clarke, 2001)
17
possible behaviour in the future, which may bring slow payment or non-payment at all. This
should be done by gathering as much information as it is both possible and feasible to obtain
by using so called external credit analysis.
According to Kolb (1992), there are following available sources of information:
−
Industry credit circles often form part of trade associations and can be extremely
useful. The benefits depend on input, and it should be viewed as an opportunity to
exchange accurate customer information and keep up to date within industry practice. It
is in fact no more than a kind of a personal trade reference, where provided discussion
is restricted to past facts with no intention or collaboration to restrict future trade.
−
Press reports contain useful interim company results of publicly quoted companies,
and reports of resignations and appointments of key people.
−
Credit agency reports2 remain the most comprehensive form of data where either the
completely stated credit ratings are accepted, or are further processed by the credit
manager to calculate his own ratings. These reports can vary in form and content,
ranging from a brief summary of main details to a complex financial analysis of the
customer and industry, with a recommended credit limit.
−
Bank references have been around for a long time. In the past, requests for bank
references were usual for a customer credit so to say for individual people, rather than
companies, and banks were always more than outspoken.
−
Trade references were once as common as bank references, and again thought by
many to be inexpensive and quick. Like bank references, however, they have long been
considered to be of limited use, and not recommended if supplied by the customer
himself.
The analysis for credit-granting decisions, which has been already described above, see also
Fig. 2, is the same as for other financial decisions. Many companies talk in this sense of credit
limits or credit lines.
Credit managers usually have individual ideas about what is the best way of credit limits
calculation. It has to be said that there is no standardized accepted method. There must be
2
The credit quality of a commercial paper is rated by agencies such as Moody’s, Standard & Poor’s and Fitch.
The agencies apply similar rating criteria. Moody’s has two basic commercial paper rating categories: “Prime”
and “Not prime.” The Prime category is subdivided into P-1 (highest quality), P-2, and P-3. Standard & Poor’s
has four basic commercial paper rating categories: A, B, C, and D. The A category corresponds to Moody’s
Prime category and is subdivided into A-1+ (highest), A-1, A-2 and A-3. (Emery, 2004)
18
however a bottom line existing, as an answer to the question, how much is one happy to be
owed by his customer. (Vožňáková, 2004)
Should credit
be granted?
Past payment
history
Stage 1
Company files
No
information
Good history
Detailed
credit
application
Accept
Stage 2
Internal
credit analysis
Stage 3
External
credit analysis
Bad history
Reject
Low risk
Medium risk
High risk
Accept
Credit agency
investigation
Reject
Accept
Reject
Fig. 2 Sequential credit analysis
(Source: Own elaboration based on Kolb (1992))
According to Kolb (1992), there are two approaches to set an own credit limit:
−
A credit limit to support sales levels with the meaning that if references are good
enough, the credit limit equals twice the monthly sales figure for that customer.
−
A maximum amount one is prepared to be owed regardless to current sales levels,
having a popular calculation, the lower of 10 % net worth or 20 % working capital.
19
3.2.2 Credit terms and conditions of sale
As already said in previous chapters, there are both benefits and costs for the seller in granting
a credit. In reality, trade needs credit to stimulate sales growth, and credit enables sales to be
made which would not otherwise be possible. Offering time to pay adds value to the
relationship between seller and buyer. It promotes customer loyalty and encourages repeating
the business. Contract negotiations involve many factors.
Credit terms are the core of the contract, since they encompass the planned profit, the sellers
need for cash funds and the competitive situation. As every contract terms, they must be free
from duress or onerous conditions. A monopoly situation which enables a seller to impose
onerous credit terms is always vulnerable to legal pressure. (Vožňáková, 2004)
Rödl (1983) outlines number of factors which can influence the choice of credit terms.
Among such factors belong the seller’s strength in the market, the credit terms which the
seller gets from its own suppliers and the availability of the capital needed to finance sales.
They are followed by the volumes of sales and the range of customers, the profit margin and
any special payment arrangements. An equal part are also competitive pressures, character of
the market, seasonal and incentive factors and as the last one, the existence of any form of
protection against potential exposure.
Apart from the great mass of transactions in retailing on a cash or credit card basis, most sales
between companies, referred to as a trade credit, are on open account terms. Open account
represents the simplest basis of supply by the seller, but must always state a period of credit.
An invoice is sent for each transaction, and the seller waits until the due date for payment.
(Bass, 1991)
Clarke (2001) mentions that some transactions can be less open, with various degrees of
security being sought, and that it is quite usual in export to sell on the basis of sight drafts,
promissory notes or letters of credit. The normal range of terms associated with open account
is related to delivery or time.
According to Clarke (2001), payment terms related to delivery are:
−
CWO, cash with order,
−
CIA, cash in advance,
−
CBS, cash before shipment,
−
COD, cash on delivery,
−
Net, payment due on delivery,
20
−
CND, cash next delivery,
−
PF, pro forma that is cash before shipment.
According to Clarke (2001), payment terms related to time are:
−
net 7, payment 7 days after delivery,
−
net 10, payment 10 days after delivery,
−
weekly credit, payment of all supplies Monday to Sunday or as otherwise defined by a
specified day in the next week,
−
half-monthly credit, payment of all supplies from the 1st to the 15th of the month by a
specified date in the second half of that month, payment of the 16th to month end by a
second date in the first half of the next month,
−
10th and 25th, payment by the 10th of the month covering supplies from the 16th to
month end and 25th of the month covering supplies from the first half of the month,
−
(net) monthly account, payment of all invoices dated in one month by the end of the
following,
−
net 7 prox, payment by the 7th of the following month,
−
two-monthly credit, monthly account but with one extra calendar month,
−
30 (or 60 or 90) days, payment due by the 30th (or 60th or 90th) day calculated from the
date of invoice.
According to Clarke (2001), among other payment terms relate:
−
journey terms, payment made to the representative or van sales-person,
−
contra terms, payment effected by offsetting the value of supplies against purchases
from the same firm.
Cash discounts are vital part of the credit terms as well, since they offer a benefit to both
seller and buyer. An example would be “2/10, net 30” which means that 2% discount may be
deducted by a buyer for settling within 10 days, or alternatively, the full amount is due at 30
days. (Capell, 1981)
Where a seller can afford them, early payment discounts should be announced at the same
time as a price increase, this being a way for the buyer to offset increased cost. In any other
circumstances, the seller should consider spending the extra expense on improving collection
procedures and activities, which would give a much better return. Most customers, who pay
21
late and might be interested in a discount for paying earlier, are taking up to two months extra
credit. For simple comparison, if a seller borrows at 12 % per annum, then giving customers
2 % discount costs as much as waiting 60 days for late payment.
Edwards (2004), names following possible ways, how customers can make payment for
supplies:
−
Stage (or progress) payments are used for contracts with considerable capital outlay
and an extended period before delivery or completion such as large projects of building
bridges or dams.
−
Retentions are usually associated with capital intensive industry, where a percentage of
the purchase price is held back for a period, making thus the supplier accept a
continuing responsibility for the building or machinery supplied.
−
Consignment account is a variation of “sale or return” and is more common in export
where lines of supply are very long and the need for delivery certainty is critical,
therefore title is passed to the end customer only when payment is made.
3.2.3 Precautionary securing of accounts receivable
Companies doing business across international borders face many of the same risks that
would normally be evident in strictly domestic transactions. Among such risks belong
political risks, war, natural disasters and other uncontrollable events. Credit securing should
be therefore considered by every credit manager as a number one strategy how to minimize
possible future problems if customers fail to pay.
Over the last century, credit insurance has developed to protect suppliers of goods and
services against unexpected bad debt losses, and through the provision of credit limit
underwriting it manages the exposure to the risk of bad debts from its trade debtors. Its
essence is to indemnify the insured against the financial consequences of a defined loss. In the
case of credit insurance, that loss is the non-payment of a valid trade debt usually for reasons
of insolvency. (Vožňáková, 2004)
Today, there are three biggest credit insurers in the Czech Republic, EGAP, ČESCOB and
Gerling. ČESCOB belongs to the international holding Euler Group and as a private
corporation provides mainly credit insurance of domestic credit. Whereas EGAP is a stateowned corporation focused mainly on credit insurance in international trade. Among the
credit insurers present on the international market belong AIG, Coface, Euler TI and Gerling
NCM.
22
Guarantee is another commonly recognized mean of securing, adjusted for the field of
business relations in the Commercial Code § 303 to 358. A contract of guarantee involves
three persons, a person who gives the guarantee the “surety”, the person in respect of whose
default the guarantee is given the “principal debtor”, and the person to whom the guarantee is
given the “creditor.” A contract of guarantee must be in written form and it represents a
conditional promise by the surety that if the principal debtor defaults he shall be liable to the
creditor. A bank guarantee is a specific type of the guarantee adjusted by § 313 to 322 in the
Commercial Code and is defined as a guarantee from a lending institution ensuring that the
liabilities of a debtor will be met. In other words, if the debtor fails to settle a debt, the bank
will cover it. (Drbohlav, 2006)
The two following ways of securing are based on the commitment to pay the outstanding debt
after the shipping documents are handed over or vice versa. It surely depends on individual
reasons, local practise or past experience of both parties why they choose the given sequence
of payment and documents handover.
Vožňáková (2004) describes a letter of credit (L/C) as a binding document that a buyer can
request from his bank in order to guarantee that the payment for goods will be transferred to
the seller. It is used above all in international trade. Basically, it gives the seller reassurance
that he will receive the payment for the goods. In order for the payment to occur, the seller
has to present the bank all necessary shipping documents confirming the shipment of goods
within a given time frame.
According to Vožňáková (2004), common types of L/C are:
−
An irrevocable L/C which can neither be amended nor cancelled without the agreement
of all parties and where the importer’s bank makes a commitment to the supplier
provided all the terms and conditions of the credit are fulfilled.
−
Unconfirmed L/C which is forwarded directly from the advising bank to the exporter
without adding its own commitment to make payment or accept responsibility for
payment at a future date, but confirming its authenticity.
−
A confirmed L/C which is the one in where the advising bank, on the instructions of the
issuing bank, has added a confirmation that payment will be made as long as compliant
documents are presented, stating that the commitment holds even if the issuing bank or
the buyer fails to make payment.
23
Bařinová (2007) refers to Documentary Collection as to a securing process, in which the seller
instructs his bank to forward documents related to the export of goods to the buyer’s bank
upon the payment. Again, it is used in international trade mainly.
Bařinová (2007) speaks about two types of documentary collection:
−
Documents against Payment (D/P), where the documents are only released to the
buyer after payment has been made.
−
Documents against Acceptance (D/A), where buyer accepts a bill of exchange issued
by the seller and payable at a certain date in the future in exchange for the documents.
The following means of securing the accounts receivable are suitable when the seller knows
in advance that due to some foreign country specifics or other reasons the role of a third party
is inevitable. A third party would then buy the outstandings at a full or discounted price in
order to deal with them on their own risk later.
Factoring as a way of securing advantageous above all for export, it is suitable when a seller
is not be familiar with environment of a foreign country. It consists in selling of a company’s
short-term accounts receivable before they are due, at a discount, to a factor who then
assumes the credit risk of the debtors account. The account receivable is usually non-secured
(no L/C or bank guarantee). (Drbohlav, 2006)
Forfaiting as a second possible way which means that a bank or a finance company purchase
freely-negotiable instruments such as unconditionally-guaranteed letters of credit and “to
order” bills of exchange at a discount from an exporter. Unlike factoring, forfeiting is
available for 100 % of the payment amount, however only for relatively larger sums and for
longer maturity dates, usually one to five years. Nevertheless, such periods as 180 days or ten
years may be considered as well. (Bařinová, 2007)
3.3
Cash collection
Whatever the fond memories, today the market place is a jungle, and nowadays payment
habits are certainly not supporting to invoice and let’s see what happens. The changing role of
banks in the last quarter of a century is said by many to have contributed to a late payment
culture. Now, when a customer runs out of bank funds, instead of borrowing more to pay his
bills on time, he prefers to take unauthorized credit from his suppliers.
The reasons for the deterioration in payment habits are complex and there are nowadays only
four kinds of customer. The first one pays when they should, the second one pays when
reminded, the third pays only when threatened and the last one goes bust before they pay. In a
company’s customer list, the second type is the predominant one. Most customers pay when
24
reminded, and if they are not reminded, they will pay when it best suits them, not the seller.
(Clarke, 2001)
Collection begins way back and it is a fundamental reminder to companies that they should
not just sell, and then expect accounts staff to collect whatever comes up on the ledger. The
collectability of sales starts with the selection of customers and identifying their ability to pay.
It continues with the way credit terms are communicated to the customer, with the promptness
of invoices and statements, and with the time created for personal contact with key people.
The debtors must be measured and reported on a regular basis, so that a substantial attention is
dedicated to the right customers at right time. It is a simple truth that the sooner one asks, and
the better one asks, the sooner he will get paid. Therefore a good method of accounts
collection, known as dunning is expected.
It has no sense to supply a customer who is obviously unable to pay. The supplies of that
customer should be suspended then. However, one should examine all causes of non-payment
properly, before taking a wrong decision. Sometimes visible clues might show that there is
something going wrong with the customer. To expect unexpected, it is essential to bear in
mind that predicting insolvency is a part of credit management as well.
3.3.1 Sales ledger administration
According to Clarke (2001) is a sales ledger a complete record of all sales transactions made
by the company, incorporating as it does sales and invoices sent to customers, receipts, credit
note for goods returned, allowance or goodwill gesture, write offs, write ons and last but not
least adjustments and transfers.
All these entries must be traced so that the final summary is an accurate record of all sales and
the total figure of gross debtors can be posted to the balance sheet.
At any given moment, the difference between what has been invoiced to customers and what
has been paid by them is the balance owing. That owed balance is the amount at risk of not
being paid, and is the centre of attention for credit managers and their teams. (Rödl, 1983)
Bass (1991) states, that the sales ledger represents the source of evidence, the pursuit of cash,
the analysis of outstanding and the identity of income sources. Apart from the paramount need
for a company’s friends, to know its daily, weekly or monthly sales, the analysis of the debts
can be used for setting collection targets, forecasting cash receipts and identification of
priority follow-up activities.
An automated sales ledger comes top of the list of all trade credit departments as the principal
system requirement. At the very least, it provides the means to hold and update customer
25
information, past all billing transactions (invoices, credit notes and debit notes) to the ledger,
apply incoming cash and generate the appropriate general ledger accounting entries.
According to Edwards (2004), there are six main ingredients a sales ledger should include,
namely billing, collection aids, cash allocation, customer reconciliation, general ledger
postings and last but not least, customer information.
Item status indicators to distinguish between normal invoices and those in query or dispute are
a key requirement. Queries have to be resolved before collection can proceed, and the system
needs to be able to show at a glance those items, if any, which need resolution. Another useful
feature could be the ability to re-sequence, the items on the customer account. They may be
held in chronological or due date sequence most of the time, but it may be perhaps needed to
reorder them onto purchase order number for reconciling with the customer over the phone.
Re-ageing, or changing due dates, may also be required, in the event of special arrangements.
It is also quite useful to allow a system to remove accounts automatically if there has been no
activity for a specified period of time. Many companies set a criterion of 24 months.
3.3.2 Planning, measuring and reporting debtors
The cash planning is a crucial part of overall credit management system since debtors are
usually the largest company asset, which should be constantly under detailed control.
(Capell, 1981)
The diagram below (see Fig. 3) illustrates factors affecting company’s level of investment in
the debtor’s asset. It also shows an understanding of the components that contribute to the
make-up of that asset, and where and when intervention is required. Debtors are not different
when viewed as an asset. The company can only support such level of debtors which it can
afford. Exceeding that level, planned or unplanned, can lead to severe financial problems.
26
Credit decisions
Non-credit decisions
Credit ratings
Credit terms
Risk control
Collection resources
Cash discounts
Use of third parties
Price
Quality
Promotion
Advertising
Sales resources
Stock levels
Average time accounts
remain unpaid
Sales volume
Business conditions
and economic
climate
Level of accounts receivable
Fig. 3 Factors affecting level of investment in debtors
(Source: Edwards (2004))
The size and quality of the debtors’ ledger should be regularly updated by the credit manager,
the finance director and the main board of directors.
According to Emery (2004), the way in which the size and quality of the debtor’s asset is
reviewed should involve the following measurements:
−
Aged debt analysis which would list all accounts in alpha, numeric or descending value
order, with column for current months and over 3 months overdue, plus other details.
−
Cash target analysis which would list debts comprising for instance 80 % of the
month’s cash requirement, calculated, and showing actions taken, payments arranged
and payments received.
−
Cash forecast sheet which would show total amount of cash expected, allocated by
type of account, either as single totals or divided into daily or weekly totals for the
month ahead.
−
Monthly debtor’s report which would depict total, current, overdue and disputed
debtors, all in sections as required, with aged subtotals, and columns for last month and
budget or forecast.
27
To ensure that the quality of the sales ledger is maintained, it is imperative to ensure the
prompt allocation and accurate reconciliation of incoming cash. Cash should be allocated to
customer accounts to clear unpaid invoices immediately as it comes in.
According to Kolb (1992), the sales ledger, and its derivative the aged debt analysis, are the
collector’s most useful tool. From the input created during the course of an accounting period,
usually a month, a statement of account is produced showing the individual balance on each
account.
Emery (2004), further denotes, that it is a good practice to be able to restate the month-end
debtors total, or fund of cash, onto an index which relates the debts to all the sales made. This
will show how much time is being taken by customers, and will include current, overdue and
awaiting credit notes as well as the total of the sales ledger.
According to Clarke (2001), days sales outstanding (DSO), represents an index revealing the
credit being taken by all customers, on average, and can be compared to the intended credit
period allowed by the seller. From that point, it allows comparisons of collection performance
month on month. The DSO is a perfect tool for calculating specifically just how much cash
needs to be collected during the current accounting period in order to be able to finish with a
planned level of debtors.
The usual method of calculating DSO is known as “add back” or “count back” taking total
debtors at month end, and deducting total monthly sales going back in time until the debtors
figure is used up. (Kolb, 1992)
That activity should also include targeting positive quality improvements, such as clearing old
overdues, resolving disputes and special exercises on certain accounts in a form of a plan.
It is extremely motivational to use charts to reporting monthly cash targets and daily progress
towards achieving them. These should be displayed prominently in the cash collection office,
often on a wipe board with daily intake added, totals adjusted and amounts still needed
highlighted. (Bass, 1991)
Bass (1991) also says that it is also useful to produce special listings for each collector, not
just showing monetary values, but specific customers in descending value order, to illustrate
the larger accounts to be collected that month.
According to Edwards (2004), there is a cut-off value, below which smaller accounts can be
grouped into a single line, with comments columns for payments arranged and such actually
received. As the month progresses, collectors can focus on the gaps, for example, at midmonth where no promises have yet been received, and at nearly month end on promised
payments which have not yet arrived.
28
3.3.3 Dunning process
The debate about traditional methods of collecting accounts also referred to as dunning
process has been always a hot subject for discussions among credit managers. Before looking
at actual methods like letters, phone calls or personal visits, it is worth reminding that
knowing who the customers are is as vital as cultivation of the accounts with huge potential
impact on collection performance and actual cash totals.
Kolb (1992) says that the primary objective of cultivating key accounts is to obtain
consistently reliable payments as a most favoured supplier. In most businesses where the
80/20 principle applies, meaning that about 20 % of customers account for 80 % of sales and
cash, cash targets can only be achieved if the really large accounts pay reliably each month on
time.
Customer contact at all levels is vitally important, and visits by the customer to sales or
technical staff in one’s company should be taken as an opportunity for the credit manager and
key credit people to meet him.
According to Rödl (1983), can friendly meetings pay dividends in the long run, and the
objects of the exercise are to have easy future access by telephone to the right person and to
be in the top priority group of the customer’s payable pile.
Comparison of the efficiency of phone calls versus letters have shown conclusively that phone
calls win, stating also that it does not mean that there is no place in collection process for
reminder letters. (Edwards, 2004)
The phone has the advantage over letter of making contact with the person actually wanted.
Speaking to a person directly means that he or she has to answer the questions; the letter can
be thrown into a dustbin. The main disadvantage is the time constraint, meaning that the fewer
customers can be contacted by this method, and call costs. Therefore the right balance has to
be struck to make it cost-effective. The secret is to get to know the customer, discover the best
time to contact the right person, and to note it on the customer file.
Clarke (2001) says that the key elements of a telephone collection are:
−
preparation, avoiding the need to call back,
−
control of the conversation,
−
closing the call when a reliable promise has been obtained.
On the contrary, a good letter program can often be the only cost-effective method of
collection. Lack of time precludes phoning every account, so letters need to be sent, and in
29
addition, many accounts are so small that they do not carry enough profit to justify telephone
calls. The automation of reminder letters guarantees that every account of the ledger that
needs reminding has in fact been contacted. Furthermore the reminder letters are in any event
the seller’s evidence of the request made.
Edwards (2004) speaks about letters as an integral collection feature, which is worth making
as effective as possible. Key point for collection letters is addressing them to a named
individual by signing them personally. The sender’s job title should be shown as one with
authority and a letter text should use simple words with a direct meaning, making thus the
collection letter maximally one page long. It is also very important to give prominence to the
amount being claimed by placing it at the top of the letter, showing also how the debt is made
up. Inclusion of the debtor’s buyer due to his potential influence in ordering payment is
definitely worth considering. Last but not least, the salesperson needs to be kept informed
about the collection letter stage.
Unless one has some specific information, it would be wrong to assume the worst at the
outset. The first letter should therefore be polite and simple. The customer should be asked to
pay the overdue account or tell why he cannot. The purpose of the letter is to invoke a
response, ideally a payment, but otherwise a query or a dispute.
According to Clarke (2001) should the letter never be revealed as merely part of a longer
routine, surely there are no longer any companies who number reminder letters to customers.
Reminder letters should look as if they have been individually produced for that customer.
The overdue debt continues to cost dearly as long as it remains unpaid. It is entitled to be
unhappy with the customer who has not only withheld payment unlawfully but has now also
failed to respond to a polite reminder. So the final reminder letter must be firmly worded. It
should refer to the lack of response to previous reminders, ask for the overdue debt and end
with the threat of some special action, such as a debt collection by a third-party agency,
referral to solicitors for possible court action and a hold on the vital further supplies.
Bass (1991), correctly denotes, that one should never threaten by anything that he does not
intend, or is not prepared, to undertake. The debtor will soon discover it and the creditor’s
position will be weaker than before.
If the partial payment is received, one should not just continue the letter sequence, but
acknowledge the part payment, and insist on the remainder by return. The only justifiable
reason for interrupting the letter series is where the customer does respond with a query or
complaint, either by letter or by telephone. The response has to be dealt with. There can be
30
little more damaging to customer relations than reminder letters sent without any
acknowledgement that a disputed amount is being dealt with.
Clarke (2001) agrees that collection is all about timing and proposes a following letter
program:
−
sending the invoice the same day as the goods,
−
sending a statement immediately following month end,
−
sending a simple reminder seven days after due date,
−
sending a final reminder no more than fourteen days later,
−
completing all routine reminders within four weeks from due date.
Timing should be varied occasionally so that customer does not become over-familiar with
one’s routines by taking advantage subsequently.
There is nothing to be gained, and certainly no profit to be made, in continuing to supply
customers who obviously cannot pay for past supplies. It is turn of a special action to take
place, if the customer does not communicate. Then it is no point in carrying on a dunning
process anymore.
3.3.4 Suspension of supplies
Continuity of supply is of prime importance to many buyers, especially if they depend on a
supplier’s particular product or it is the price and service which satisfactorily suits their
requirements. Under these circumstances, a suspension of supplies to slow payers is a prime
collection tool.
A well-defined credit policy needs to deal with how much tolerance to show to slow payers
before supplies are discontinued. If it is the intention to hold orders, the collection cycle must
include notifying the customer’s buyer. A fax or email to that person before the stop is
implemented can produce the required payment, allowing the order to go forward.
According to Bass (1991), can be stopping procedures tailored to suit almost any business
environment and computer system. For example, if risk categories are in use, overdues of a
customer, who is designated to a category “A” with meaning no risk, would not cause a stop.
At the other end of the scale, the high risk category accounts designated as “C” would be
31
subject to an automatic stop when an overdue situation arose. The stop scenario3 applies to
credit limits as well as overdues.
It should be remembered that stopping due to exceeding the credit limit is tied very closely
with having the right credit limit in the first place, and ensuring that credit limits are
constantly and regularly reviewed.
A sensible approach to stopping supplies is to produce a pre-stop list. The system provides the
accounts which are overdue in a listing for both credit and sales to preview and edit. Accounts
can be deleted, added, values amended, and completion of that exercise prompts the issue of
the live stop list. (Edwards, 2004)
The disadvantage is that this can be labour-intensive, nevertheless the advantage lies in a
cooperation and collaboration between credit and sales, early involvement of sales in the
collection process and a reduction in the likelihood of inappropriate stopped accounts. The
stop list is updated daily to take account of cash received.
Bass (1991) denotes that notices of meetings of creditors or appointments of administrative
receivers are usually predictable, but they can come out unexpected. In such instances, the
account should be immediately frozen. Equally, a dishonoured cheque should set off alarm,
with the account stopped until the matter is resolved.
The subjects of stopping supplies are emotive, being the cause of more friction between sales
and credit than perhaps any other credit activity. It is vital to have some real-time method of
flagging queries so that customers are not placed on stop for unpaid items which are subject to
a genuine unresolved query.
3.3.5 Credit scoring and insolvency prediction
No company is totally insulated from bad debt losses, because they will occur in the best
regulated environment. There are many more companies in difficulty and these are the ones
that cause the serious overdue accounts. It is like an iceberg risk where actual insolvent
companies are the top which is visible and the more dangerous part causing much more
damage to sellers is the mass below the surface.
Being first introduced as a tool for underwriting retail credit, such as residential mortgages,
credit cards, instalments loans, and small business credits; credit scoring is nowadays being
used to administer and follow-up default risk across the entire credit portfolio of a financial
3
Vetting of incoming orders against credit limits and past due accounts is essential. Where the product being
sole has a lengthy lead time, order vetting can be carried out on receipt of the order, and again when ready for
dispatch. (Clarke, 2001)
32
institution covering firms, sovereigns, local authorities, project finance and financial
institutions. (Anderson, 2007)
Gestel (2009) further explains that scoring systems rank observations from low to high scores.
Low scores typically indicate more risky counterparts, while a good score typically indicates
good credit quality.
According to Gestel (2009), following score types exist:
−
Application score which evaluates a risk of a borrower at a moment of credit granting.
−
Fraud score which estimates probability of fraudulent information from credit
applicants.
−
Performance score which evaluates the risk of an existing customer during its
performance stage.
−
Behavioural score which predicts probability of the customer’s default (PD).
−
Early warning score which aims to detect potential crises with the counterparts.
−
Retention score which can detect customers likely to reduce their activities with the
counterpart significantly.
−
Collections scoring which updates risk parameters when a credit becomes delinquent.
Application
Performance
Application
score
Performance
score
Fraud score
Behavioral
score
Collection
Collection
score
Retention
score
Early
warning score
Fig. 4 Overview of scoring systems suitable for different stages of a customer credit
(Source: Gestel (2009))
33
Important choice in a rating system development is a choice of an overall architecture and of a
modelling technique which depends however, upon the availability of adequate model
formulations, data and implementation constraints (see Fig. 5).
In structural and reduced-form models the risk parameters are determined with a model
derived from financial theory. Empirical models estimate and explain the risk parameters by
learning from past observations. Expert models and expert analysis reflect human expert
knowledge. (Anderson, 2007)
Rating models
overview
Financial
models
Empirical data
based models
Expert models
Financial structure
Merton model
KMW model
Statistics
Linerar regression
Logistic regression
Additive models
Expert models
Expert rules
Expert scorecard
Cash flow
Gambler´s ruin
Cash-flow simulations
Artificial intelligence
Neural networks
Support vector machines
Kernel-based learning
Expert assessments
Expert PD ratings
Expert collateral
valuation
Market implied
Reduced-form models
Bond, derivative and equity
prices
Machine learning
Neares neighbors
Decision trees
Graphical models
Fig. 5 Overview of modelling techniques
(Source: Gestel (2009))
Wide variety of modelling techniques exist, nevertheless it is useful to know that for most
applications, the industry standard uses only a limited number of them.
Altman Z-Score has remained the foundation of corporate scoring principles since the time of
its creation in 1968. Based on the statistical analysis, it incorporates the most pertinent
variables present in insolvency. (Gestel, 2009)
Colquitt (2007) describes the major components of the Z-score formula, showing different
weighting according to the fact if the company is a private (see eqn. 3.2.) or a publicly quoted
one (see eqn. 3.1.).
34
Ζ = 1.2 X 1 + 1.4 X 2 + 3.3 X 3 + 0.6 X 4 + 1.0 X 5
(3.1)
Ζ = 6.56 X 1 + 3.26 X 2 + 6.72 X 3 + 1.05 X 4
(3.2)
Colquitt (2007) further explains the calculation of the individual components:
X1 =
current assets − current liabilities
total assets
(3.3)
X2 =
retained earnings
total assets
(3.4)
X3 =
earnings before taxes + interest
total assets
(3.5)
X4 =
market value of equity
total assets
(3.6)
X5 =
net sales
total assets
(3.7)
Anderson (2007) speaks about the resultant score as of the indicator of likely failure or
continued success, adding that interpretations vary between analysts due to influencing factors
in different industries.
According to Anderson (2007), the following can be deduced from final scores:
−
3.0 and more, the most likely to survive,
−
2.7 to 3.0, should survive but bordering on a grey area, certainly below the line for
more definite chances of survival,
−
1.8 to 2.7, risk of insolvency within two years, serious action for survival needed,
−
below 1.8, most likely to founder, rarely expected to recover in time.
The Z-score model has been many times extended and refined in various ways and is still
popular nowadays. Despite this, for default4 prediction, the benchmark industry standard
today is logistic regression. In this case it is presumed that explanatory variables multiplied
by corresponding coefficients have a linear relationship to a natural logarithm of default
occurrence frequency referred to as logit. (Vojtek, 2006)
4
Default is generally defined as a breach of debtor’s obligation coming from the credit agreement, for example a
debt which is 90 days overdue. (Knot, 2005)
35
ln
N
s
= b0 + ∑ bi xi
1− s
i =1
(3.8)
Where following variables are present:
−
s, as company’s default probability in a one year prediction horizon,
−
xi, as financial ratios of a company,
−
bi, as coefficients of corresponding ratios of a scoring function.
From this equation the relation for default probability can be derived. This relation can be
described by a logistic curve. (Jakubík, 2007)
1
s=
1+ e
− b0 −
(3.9)
N
∑
bi xi
i =1
Jakubík (2007) mentions that due to the number of financial ratios (see Tab. 1) available as
explanatory variables, stepwise regression5 is used for their selection. The coefficients of the
function can be estimated via a maximum-likelihood method.
The output of the logistic regression model can be translated to an existing rating scale that
defines the limits of the internal ratings and the risk levels. An alternative approach is to apply
a segmentation criterion to group the scores into homogeneous groups with the same
empirical default behaviour and different default rates across ratings. (Vojtek, 2006)
Due to the significant development of credit risk techniques in recent decades, new methods
estimating potential bankruptcy of borrowing entities and parameters specifying possible
losses have been developed. These parameters include Loss Given Default (LGD), expressing
the percentage of an exposure which will not be recovered after counterparty defaults. For the
past 20 years, the estimation of the probability of default (PD) was receiving considerable
attention, the turnover came with the introduction of the New Basel Accord6 identifying the
LGD as one of the key risk parameters. Since then, LGD has gained greater acceptance.
(Seidler, 2009)
It is sometimes difficult for a creditor to know what is going on, unless either the customer
tells him, or someone else does. That someone else could be another creditor, or an up-to-date
credit agency report. Many creditors are put in an impossible situation by customers, who will
5
This metod consists in testing of different combinations of variables which maximaze the model quality.
(Jakubík, 2007)
6
The Basel Capital Accord was concluded by the Basel Committee on Banking Supervision in 1988 and created
a level playing field of homogenous capital rules for internationally active banks. In 2006, the Basel II Capital
Accord placed the importance upon rules for credit risk as well as greater reliance on the bank´s internal
experise, historical databases, risk methodologies, models and risk-parameter estimates. (Chaloupka, 2009)
36
not respond to requests for payment or requests for information and the creditor is left with no
alternative but to fear the worst and act accordingly. Therefore the creditor should really be
trying to read the signs and be proactive in advance of the really bad news.
According to Capell (1981), the signs are always there for those who bother to look. Among
most obvious ones belong payments getting slower with increasingly poor excuses, worsening
atmosphere an morale in customer’s premises and sudden closing down of premises by
rationalizing activities into fewer locations. Another signs may be changing banks or frequent
changes of suppliers, adverse press comments about profit dives and reorganizations or court
judgments recorded recently. Inputs from credit agency watch services are worth to be
observed as well together with announced payment moratoria, refinancing discussions with
the bank and severe downward trend in key ratios. More than an outspoken sign is then
appointment of administrative receiver, meeting of creditors and last but not least petition for
winding-up of a company or bankruptcy of an individual.
Companies can avoid failure themselves by taking all the action that is necessary to ensure a
profitable and healthy enterprise. Suppliers, on the other hand, can avoid losses by taking all
the risk assessment precautions in the first place, closely monitoring the account and taking
prompt action when required. However, it is the nature of free enterprise that some will
succeed and other will fail, sometimes with the best will in the world. No losses can be totally
eliminated, though they can be substantially reduced.
3.3.6 Customer portfolio analysis
Over the past decade customer relationship management (CRM) has emerged as an important
domain of both marketing research and practice in order to optimize the resources allocation.
Supported by the developments of information and communication technology and the
network economy, CRM has even come to replace the concept of relationship marketing.
In business markets, customer relationship management has long been recognized as a key to
competitive success particularly in increasingly networked global and dynamic business
environments. Four interrelated levels of customer relationship management have been
delineated. These are networks, nets, portfolios and individual relationships. (Halinen, 2007)
Over the past 30 years most of the concrete, customer management tools proposals are made
at the customer portfolio level. Along with the recent CRM boom, customer portfolio
management has become of interest again.
37
Broadly defined, the customer portfolio analysis is an activity by which a company analyzes a
current and future value of its customer for developing a balanced customer structure through
effective resource allocation to different customer.7 (Zolkiewski, 2002)
A central goal of customer portfolio analysis is a balance of a customer portfolio. While
looking back at the evolution of various portfolio models, it is interesting to learn that initial
portfolio models in finance and marketing were based mainly on mathematical optimizing
being strongly focused on forming of an optimal portfolio of stocks or products.
In later marketing portfolio models the aim of mathematical optimizing moved into the
seeking of portfolio balance. For instance in relationship portfolio models, the balance aspect
is rarely present and the aim is mostly a balanced combination of relationships in the customer
base that serves the focal firms’ long-term profitability and effectiveness goals.
Today, all portfolio models involve effective resource allocation, which can be seen as a key
factor for developing a balanced customer structure. Nowadays research on portfolio analysis
and management concentrates on two major issues which are proposing formal, mainly
matrix-form portfolio models for customer management tasks and testing theoretically driven
models with data from case companies at one point in time. (Terho, 2008)
At the same time, the very few empirical studies show that strictly formal, matrix-form of
customer portfolio analysis is relatively rare in business, even though companies tend to
analyze and manage their customer bases systematically. A number of researchers also
criticize customer portfolio models for their offer of an excessively simplified view of reality
and overlooking in particular the network effects, as an interconnectedness of business
factors.
3.4
Means of debt recovery
It is almost inevitable in any trading organization that a proportion of debts will remain
unpaid in spite of the best efforts of the collection staff. This does not need to be taken as a
criticism of either staff or procedures in those organizations. Equally, it is not because of
ineffective phone calls or reminder letters, lack of training or management support. It is
simply to recognize that some debtors will not pay until they really have to, despite
contractual terms.
7
The concept of customer portfolio analysis is close to the concept of segmentation but still differs significantly.
Portfolio analysis focuses on analyzing the value of existing customers from the focal company’s point of view
whereas segmentation generally focuses on dividing the market into distinct subsets of homogeneous
customers on the basis of customer needs or buying behavior, or more generally on customers’ expected
response to marketing mix stimuli. (Halinen, 2007)
38
Also at this stage of the collection process, the supplier has to weigh up the expense of his
staff resources, time and effort in pursuing the delinquent debtor. The use of a collection
agency as a third-party assistance may prove to be efficient on one hand but costly on the
other.
Voluntary settlement of debts should be viewed as an optimal way of settling the debts out of
court. Unfortunately, it may happen that both parties cannot find a consensus or are simply
not willing to undertake it.
Then if no other reasonable manner for solution can be found, it is by no means inevitable that
the next move requires the use of legal action. Nevertheless, even if litigation may ultimately
be the only remaining course of action, it should always be regarded as the absolutely last
option.
3.4.1 Collection agencies
Each company should decide the most convenient way of debt recovery, whether with a help
of a specialized collection agency or on their own. It is difficult to see total costs if one works
on his own, since they are not visible in accounting directly and are on the contrary covered in
wages, travelling, postage expenses and legal assistance.
Independent collection agencies are without doubt the most accessible and economically
effective third-party debt collection assistance available. Use of an agency to collect a debt is
an indication to the defaulting customer that the situation has gone too far and tells him that
the supplier intends to pursue for recovery. (Drbohlav, 2006)
The agency should offer debt collection by a rapid and short-lasting series of letters, phone
calls, faxes, emails, personal visits, or a planned combination of these. The way in which
payments from debtors are passed on to the client, whether directly or via the agency should
be flexible. There should be a minimum of delay in the client receiving debtors’ payments and
clients should be on the contrary kept up-to-date with the status of accounts passed for
collection.
Professional collection agency provides their client with complete credit management. This
means that besides cash collection also other ordinary activities are provided. Among these
activities belong preparing applications for court, ensuring background information for
execution, dealing with jurists. They can also overtake debts which have been partially
fulfilled.
Most agencies work on the basis “no success – no fee” and only charge a negotiated and
agreed rate of commission on actual recoveries. In other words, it is possible that an
39
unsuccessful collection attempt costs nothing up to the litigation stage, yet for no fee at all,
the seller has discovered that the debtor has gone away or has no assets worth pursuing in
court, thus saving substantial court costs and further delays. (Bařinová, 2007)
As commission rates are usually negotiable, client should always be aware that they will get
what they pay for and the lowest commission rate does not necessarily mean the best service.
Indeed, the highly professional agencies invest heavily in skilled staff and the latest
technological developments, so that cut-throat commission rates are as damaging to the
industry as are disreputable operators, which is ultimately to the detriment of services
available to clients.
3.4.2 Voluntary settlements of debts
The introduction of a third party into the supplier-customer relationship could well lead to
difficulties in obtaining credit facilities elsewhere, a fact of which many experienced
defaulters are well aware. It is therefore clear that third-party intervention is usually enough to
secure payment and litigation becomes unnecessary.
In the first place, a creditor should think on a securing of a debt. It is for the reason that if any
of the shipping documents went lost, it would be the only evidence of debt existence for
judicial proceedings.
Recognition of a commitment is an easiest way how to do it. The essence is that a debtor
recognizes his debt to a creditor in a written form. From the legal point of view a commitment
is seen as recognized also in a form of a written order for a bank to undertake a payment on a
behalf of a debtor or a partial fulfilment of given commitment. According to § 407 of the
Commercial Code, a new prescription period starts to run in a length of four years, upon the
debt recognition. The Commercial Code places no duty on a debtor to recognize his
commitment; hence it is no law infringement if a debtor denies doing so. (Kráčalíková, 2004)
According to Drbohlav (2006), the public notary’s deed is suitable for larger amounts
outstanding where undeniable recognition by debtor is vital. It assures legal recognition of
debt and in a case of non-payment by a debtor it is to be effected according to the provision
§ 274 letter e) of Civil Juridical Order where an execution creditor is entitled to commence
execution against the debtor upon the request of a creditor without further prosecution.
The overdue debt itself can be settled in a form of compensation when both parties agree to
confront the debts they have to each other by annulling them, with no further demand for their
payment. Some companies choose another option and hence, selling the overdue debt
discounted to third party who takes all risks over.
40
Debt inclusion represents the above mentioned mutual compensation of debts and is
constituted by an agreement. It is governed in business relations by § 358 and 364 of the
Commercial Code and can be either unilateral or multilateral. In the case of multilateral
inclusions is to be kept in mind that a so called circle must be closed. It is good to mention
that it is possible to include an existing debt only. (Pilátová, 2009)
Kráčalíková (2004) states that in the case of debt assignment all garniture and rights applying
to it are assigned as well. In case of debtor’s bankruptcy petition, the creditor has to announce
this to the debtor immediately, having an advantage of getting the debt recovered faster than
by waiting till the process ends.
If there is a genuine problem with the goods or services provided then every endeavour should
be made to reach an amicable solution with the debtor. This may avoid a costly defended
action. It must be remembered that the vast majority of cases can be settled prior to the trial.
There is no point in settling an action after incurring substantial legal costs when it could have
been settled at the outset, for instance in a form of an alternative dispute resolution (ADR).
Arbitration, as a first form of ADR, is a legal technique for the resolution of disputes out of
courts. They are useful if there is a single technical dispute between the parties. The parties
refer their dispute to one or more persons the “arbiters” whose decision the “award” is
deemed as bound with a same strength as a judgment. Arbitration has following advantages
while compared to litigation. It is one instance only and request far minor formalities,
therefore it can act fast. With respect to the provisions of the New York Convention in 1958,
arbitration awards are generally easier to enforce in other nations than court judgments.
(Arbcourt.cz, 2007)
The Arbitration Court of Czech Republic was founded in 1949 and pursuant to the Act. No.
223 of 1994, Coll. of Laws, it is attached to the Economic Chamber of the Czech Republic
and Agricultural Chamber of the Czech Republic. (Arbcourt.cz, 2007)
Procházková (1996) mentions mediation, as a second type of ADR, which represents a nonbinding process where the role of the “mediator” is not to decide the issue but to try to
facilitate an agreement between the parties. In some cases, mediators may express a view on
what might be a fair or reasonable settlement, generally where all the parties agree that the
mediator may do so. It is useful in all types of dispute, especially where the costs of litigation
will be significant. An example of an internationally recognized form of mediation is a
mediation lead by the Commercial Chamber in Zurich.
41
3.4.3 Legal proceedings
It is always good to consider that no matter how much is owed to one’s company it is
pointless issuing proceedings against a debtor who does not have the means to pay with. As it
is often said, there is no point throwing good money after bad.
If a dispute of a technical nature is raised one should consider whether a report of a jointly
instructed expert would assist. The court is likely to require such a report after the
proceedings have been issued. Obtaining a pre-action report may prevent litigation and will
certainly give and indication of the likely outcome of the case. (Rödl, 1983)
It is important to avoid the temptation of sending all outstanding debts to one’s legal
department or a solicitor to issue proceedings without making all necessary checks. The
reason is that a court can easily form the view that proceedings would not have been
necessary had one acted in a more reasonable manner. As a consequence, the creditor may not
obtain all the costs from the losing party despite winning in the lawsuit.
When there is no other option how to dissolve the dispute in a form of an alternative
resolution other than litigation, one should commence with a judicial enforcement. This can
be done by an appeal for a petition which should include besides trade name and registered
office of a debtor, amount outstanding, photocopies of all agreements, shipping documents,
invoices, dunning letters and information about partial debt recovery. The legal proceeding
starts upon a delivery of the petition to the court. (Procházková, 1996)
According to Kráčalíková (2004), the court can end the proceeding with one of following
verdicts:
−
judgment, the court meets a decision in a given matter, appeal to the law is possible,
−
resolution, the court decides in all cases, appeal is sometimes interdicted,
−
payment order.
Filing a proposal for a payment order issue can be done by a creditor himself, assuring thus a
possible solution if a debtor does not react to his collection letters. This proposal should
include the amount outstanding, interest on delayed payment and legal proceedings
expenditures. After the issue of the payment order by the court, the debtor has 14 days to
express his protest. If he does not do so, the payment order takes over the legal effect. As soon
as the payment order takes effect of a final judgment, the claimant can ask a given court to
commence execution proceedings. The advantage of a payment order is the fact, that it
ensures the debt imprescriptibility in a length of ten years from the taking the legal effect
over. (Drbohlav 2006)
42
Unfortunately, even a judgment may not force the defendant to make a payment. On some
occasions, one may need to take steps to enforce the judgment in order to recover the monies
due. Each procedure usually involves completing a request to the court and paying the
appropriate fee.
Procházková (1996) explains execution as an instruction to a bailiff to visit the defendant’s
address to either obtain payment or seize his goods to the value of the outstanding debt. This
is the appropriate method to enforce the judgment if the debtor has assets that are easy to
remove and sell. If the defendant fails to pay, goods taken by the bailiff are sold at public
auction and once all expenses are discharged and creditors’ claims satisfied, the remaining
can be paid back to the debtor.
Due to the busyness of the courts which caused lengthy and ineffective execution proceedings
in the past, the Act No. 120/2001 established so called executor offices. An executor as a
private entity appointed by the Minister of Justice, is empowered to dispose the rights which
otherwise only courts have; however in a limited scale. The petition for execution can be thus
addressed to a chosen executor directly or to an appropriate court. (Drbohlav, 2006)
An executor can in contradiction to a bailiff decide how his decision is going to be enforced
and can seize the recorded belongings directly in such way. Among such enforcements belong
for instance payroll deductions or credit order. An executor can either order a bank to settle a
debt from a debtor’s account or a third party who owes money to the debtor to pay it directly
to the claimant.
The management of a credit has its specifics if a debtor goes insolvent. Insolvency Act No.
182/2006, further as “Insolvency Act” only, coming into effect on 1. July 2007, has brought
new solutions for dealing with bankruptcy. (Bařinová, 2007)
Bařinová (2007) speaks about three basic ways how to deal with bankruptcy:
−
discharge from debts by realization of debtor’s assets or a timetable for repayment,
−
reorganization by means of a reorganization plan,
−
bankruptcy order and its alternatives.
It came out from a past experience that a direct liquidation was lengthy, inefficient, drastic
and above all not suitable for all cases. Since then the focus has changed and it is today’s
target to enable recovery of an insolvent debtor under the condition that he will aim to satisfy
his creditors at least partially. It is also a kind of motivation for a debtor, who knows that he
has been given a last chance to survive.
43
Discharge from debts is a newcomer in dealings with bankruptcy. It relates to debtors who are
not entrepreneurs. The essential idea is to discharge claims of creditors at 30 % of the total
value at least. The lower amount is possible upon the approval of the creditors. Discharge
from debts can be carried out in two ways. The first one is realization of debtor’s assets,
which is similar to bankruptcy order with a difference that assets regained during insolvency
proceedings after discharge from debts are exempted from that. The second option is a
timetable for repayment, which is usually set to five years where debt payment is taken from
the debtor’s income. The essence is to motivate the debtor to pay his debts even if in a limited
scale. (Business.center.cz, 2006)
Business.center.cz (2006) further speaks about reorganization, as of a second possible way of
dealing with bankruptcy. The target is to satisfy creditors’ claims by keeping the debtor’s
company running according to a plan approved by a court and under continuous control of the
creditors. Reorganization has its specifics and thus that the debtor must not be a legal person
in a liquidation, security trader or a person operating on a commodity exchange. Furthermore,
the debtor must prove a turnover of 100,000,000 CZK at minimum in the last accounting
period before going insolvent or having minimally 100 employees. During the reorganization,
the debtor usually has a disposition right, where only cardinal legal operations need to be
approved by the committee of creditors. His actions are under the control of a trustee.
Reorganization can be in principle terminated only by a fulfilment of the reorganization plan
or by a filing a petition for bankruptcy.
Bankruptcy order corresponds in a certain scale to an original Bankruptcy and Compensation
Act No. 328/1991, being amended by the Insolvency Act. Based on the bankruptcy order, the
substance is a satisfaction of all known creditors’ claims from realization of debtor’s assets
where unsatisfied claims cease subsequently to exist. There are two alternative ways of a
bankruptcy order. The first one is a small bankruptcy, which offers simplified processes in
insolvency proceeding by court and creditors towards the debtor. The debtor is on the other
hand conditioned to be a physical entity with a total turnover of 2,000,000 CZK at maximum
in the last accounting period before bankruptcy order. Moratorium is a second alternative with
the meaning that a protection period was given by court to the debtor as a last chance to pay
up his debts before bankruptcy order takes its turn. In this period, the creditors are entitled to
declare their claims, whereas these acts can take effect first upon the moratorium expiration.
Creditors are in capacity to propose its cancellation according to the total of their amounts
outstanding, or moratorium cease to exist if it is proved that the debtor declared false
information. (Bařinová, 2007)
44
4 PRACTICAL PART
The scope of the practical part is to review and analyze processes of credit management
system of the daughter company of Robert Bosch GmbH, further in the text as “Bosch” only.
Evaluation of a current system should bring proposals for its further improvement and
development.
The focus is on the first place dedicated to the explanation of current credit management
system which is being run in the company. The corporate culture is rooted deeply within the
whole Bosch Group8, having thus a substantial impact on the group members. Therefore this
needs to be explained as background information. After that, the second step can be taken.
This second step is impact evaluation of the selected factors on the accounts receivable
collected. Here all the relevant factors connected with cash monitoring, cash collection and
debt recovery are confronted against the result, accounts receivable collected. The target is to
reveal reciprocal relations and impact of individual factors.
The third focus is dedicated to the evaluation of customer’s importance from two different
viewpoints, and hence by customer portfolio matrix on the one side and the scoring function
represented by logistic regression on the other one. The essence in here is that the correct
customer’s allocation can help to improve credit management system in the company.
4.1
Credit management system in the company Bosch
The first part of credit management system in the company Bosch to be explained, is its
organization and processes. Here a functional organization together with the process flow is
depicted in a clear way. Knowing the people who are standing behind the whole processes is
as important as the detailed understanding of individual sequences of the process flow.
Account opening procedure represents a starting phase of whole credit management in the
company. Everything starts with creation of a new customer. Whereupon payment terms are
agreed and credit limit set in accordance to a risk category the customer is assigned to.
Potential country risks are covered by insurance.
A core phase of credit management is cash collection. The assumption for successful cash
collection is on the first place accounts receivable monitoring and regular adjustments of
credit limits. An equal part of cash collection is also dunning process and suspension of orders
or supplies, by which the customers are put into pressure, being warned due to their inactivity.
For cases with hopeless chance of debt repayment due to an insolvent party, so called write8
Bosch Group is a common expression used when speaking about all the subsidiaries and affiliates of Robert
Bosch GmbH worldwide.
45
offs are used. It is also worth mentioning, that annual internal controls are carried out to
eliminate possible errors caused by a human factor.
The end phase of credit management is in inevitable cases, means of debt recovery. The
enforcement via a lawyer as well as a debt collection agency is described, while their pros and
cons are considered.
4.1.1 Credit management organization and processes
Due to the fact that Robert Bosch GmbH is a huge concern, operating through its subsidiaries
on international markets of more than 60 countries, the importance of processes
standardization has been encompassed in the corporate identity. A system of central directives
exists, which are either directly applicable for the daughter companies or adjusted into a local
version which fits the regional specifics of the country where the subsidiary operates.
The credit policy is ruled by a set of central and local directives, among which belong those
regulating approval of new customers in accordance with check on creditworthiness and
country risk and those setting credit limits, payment terms and conditions. There are another
directives ruling continuous monitoring of credit receivables, dunning, suspension of supplies
with their subsequent release, and last but not least means of debt recovery.
Individual tasks of credit management (see Fig. 6) are divided among sales representatives,
Sales administration department and Financial department, all under the control and
supervision of the financial director, who is in fact the credit manager.
Financial director
Sales
representatives
Sales administration
department
Financial
department
Fig. 6 Credit management organization of Bosch
(Source: Own elaboration based on central directives of Bosch)
46
Customer
purchase order
Creation of a new customer
in the database, conditioned
by checking of his
creditworthiness and
presence on antiterror-list
no
The customer
exists in the
database
yes
Customer is
creditworthy,
antiterror-list
negative
yes
no
The customer´s status,
payment moral and
solvency checked in SAP
Customer´s creation declined
New customer created
End
Bad payment
moral, invoice
payment more
than 90 days
after due date
yes
Advance
payment at the
amount of 100 %
of the invoiced
price demanded
Credit limit, payment terms
and conditions set
no
Payment
is made
yes
no
Delivery of goods
The purchase
order is blocked
End
End
Goods delivery together
with the invoice bill
Accounts receivable
monitoring
Payment
is made
until the
due date
yes
End
no
Reminder
letter 1
Reminder
letter 2
> 200,000
CZK
Reminder
letter 3
Sending monthly overview of overdue
receivables to sales representatives
monitoring
Clarification of
open issues
no
Payment
calendar
Payment
promise
Informing
Financial
department
Collection
agency
yes
Expacting the
payment
Payment
no
Acounts receivable paid
End
Fig. 7 Process flow chart of credit management in Bosch
(Source: Own elaboration based on central directives of Bosch)
47
Company
lawyer
yes
The process flow (see Fig. 7) starts with the customer purchase order delivered by the sales
representative. In the case of an absolutely new customer, the process of checking of his legal
existence and creditworthiness follows. This is done by the Sales administration department
that creates new customers in the database accordingly. Credit limits are set by the Financial
department whereas payment terms and conditions are usually agreed by a sales
representative himself. It is common, that customers with bad payment moral get their goods
delivered upon special payment terms, approved either by the Financial department, or by the
financial director with the respect to the order value. Accounts monitoring and dunning is
carried out by the Financial department, that reports debtor’s balance to the financial director
at the month end.
4.1.2 Account opening procedure
The process of the entry and the distribution of the customer master data at the chosen
company is called customer master data management.
The new entry of a customer is usually requested by a sales representative being sent per
email to a local Sales administration department. A sales representative is responsible for
checking the status and payment moral of a potential customer prior to his creation request.
As soon as all the submitted documents of the customer are examined by the Sales
administration department, his legal existence is checked in the Commercial Register on
“ARES”9 as well as his absence in the Insolvency Register on “justice.cz.”10 The potential
customer is then confronted with the antiterror-list, which is done at the headquarters in
Germany. The creation of the customer is then performed on the global data client, where
only the basic customer master data including his customer number are stored for the use of
other international subsidiaries. The sales and accounting data are then maintained in the local
system by the responsible office for each sales area.
The personnel of the Sales administration department are entitled to establish only customers
from the Czech and Slovak Republic. Requests for foreign customers’ creation are forwarded
to the headquarters in Germany who takes the task over.
The applicable payment terms (see Tab. 2) are set according to the essence of the transaction.
Basically, they are viewed either as the ones made with foreign customers or the others within
the Bosch Group, so called intercompany transactions.
9
The application of the Ministry of Finance providing access to registers of economic subjects available on
http://portal.justice.cz/justice2/uvod/uvod.aspx.
10
The application of the Ministry of Justice providing acces to the Insolvency Register available on
http://wwwinfo.mfcr.cz/ares/ares_es.html.cz.
48
Tab. 2 Payment terms applicable in the company Bosch
Foreign customers
Europe
14 days 2%, 30 days net
Risk group 1
30 days 2%, 60 days net
Risk groups 2 - 5
Payment terms with safeguard against the economic and political risk
(e.g. Confirmed letters of credit, Hermes security)
other countries
Intercompany transactions
15. following month net
(Source: Central directive of Bosch)
For intercompany transactions, the delivery conditions are usually Free Carrier (FCA)11
supplying plant, if no fiscal, legal or other business-political requirements oppose. Otherwise
delivery terms, Delivered Duty Unpaid (DDU)12, are suitable. The delivery conditions for a
foreign customer are usually agreed between a sales representative and a customer himself.
Creation and adjustment of credit limits is governed according to the risk categories
customers are assigned to. A new customer is automatically given a risk category 2.
The specifics of each category are:
−
0, no credit check,
o Bosch Group;
−
1, low risk category,
o very good payment moral,
o payments maximally 5 days overdue;
−
2, medium risk category,
o payment moral is between good and worse,
o payments maximally 90 days overdue;
−
3, high risk category,
o very bad payment moral,
o payments more than 90 days overdue,
o customers handed over to a collection agency,
11
The seller has an obligation to deliver goods to a named place for transfer to a carrier. Afterwards, costs for
transportation and risk of loss pass on the buyer.
12
The seller is responsible for making a safe delivery of goods to a named destination, paying all transportation
expenses but not the duty.
49
o advance payment at the amount of the 100 % of the invoiced price demanded
(carriage costs or cash on delivery costs included);
−
4, highest risk category,
o customers currently or in past in insolvency proceedings,
o customers handed to the company lawyer for judicial enforcement,
o these customers are blocked, without possibility of delivery.
A credit limit of a new customer is 1 CZK, which needs to be adjusted by personnel of the
Financial department subsequently (see eqn. 4.1). The setting of the credit limit is based on
the commercial information about the creditworthiness of the customer and his expected
turnover. In the case of insufficiency of the commercial data, alternative informational
sources can be used. Among those belong, bank information, references, information of the
Chamber of Commerce or factoring company providing the export credit. The credit limit
must be always approved by the financial director.
Credit limit =
annual sales
* net payment target in months + 1 month ' s sales
12
(4.1)
In relevance to the country risks (political risks) that may arise to an exporter/lender as a
result of statutory or official measures, foreign revolution, uprising or restrictions in intergovernmental payment transactions, so called country risk categories (CRC) are established.
The country risk categories (CRC) are drawn as follows:
−
CRC I, for countries representing no identifiable risks,
−
CRC II, for countries representing a minimal political risk, in particular countries with
a good payment record over a number of years and minimal risk of payment problems,
−
CRC III, for emerging countries representing an average country risk with no specific
indications of imminent payment problems,
−
CRC IV, for countries representing an increased risk, in particular countries with their
debts rescheduled and considerable actual or potential payment difficulties,
−
CRC V, for countries representing a significantly increased and no longer acceptable
risk, with need for medium and long-term payment conditions, not servicing their
foreign debts anymore.
50
Country risks in the CRC II – V are, where possible, covered by appropriate instruments, e.g.
HERMES (ECA cover)13 or confirmed letter of credit. On the other hand, it is also common
that Bosch offers to their customers a bank guarantee for backlogs and defects in the sold
products14 in exchange for their promise to pay the outstanding accounts receivable to full
extent. The aim is to prevent customer from keeping part of his debt unpaid as his guarantee
for possible backlogs and defects.
4.1.3 Cash collection
For a functioning cash collection system, a proper accounts receivable monitoring must be
done. In Bosch, this is carried out mainly by a personnel of the Financial department who
dedicates to it substantial part of his total capacity. Other persons involved are individual sales
representatives who account for their customers. All of the above mentioned is done under the
overall supervision of the financial director who is responsible for the cash collection on the
first place.
The Financial department keeps an eye on accounts receivable and their daily trend
development. This is done by a comparison of actual open items, carried out usually on a
daily basis or every second day at latest. The frequency of this control is highly dependent on
the actual capacity of the personnel in charge, since the activity of the data download from the
SAP application and their further processing in Excel is time consuming. His daily task is also
an overall sales ledger administration in SAP and issue of credit notes to individual customers
when demanded. The follow-up process to the sales ledger administration is the immediate
reaction if a state of the open items does not change or substantially worsens, becoming thus
critical. The proceeding is an enforcement of a complete and timely payment of all
receivables together with a fulfilment of all agreed settlements. In case of disputable
receivables, a decision about their solution from the accounting and tax point of view is taken
by him as well. Last but not least, the personnel of the Financial department hands the bad
debts over to a collection agency or a company lawyer if there is no other chance for their
recovery.
In accounts receivable monitoring, are sales representatives the ones who are responsible for
a direct clarification of disputed causes with a customer, being obliged to inform the Financial
department subsequently. This is viewed as a common practise, since the sales representatives
know their customer the best and usually have good relations. Another fact is, that due to
13
As an Export Credit Agency (ECA), Euler Hermes Group is the world’s leading credit insurer with a 36%
share of the market, operating in more than 50 countries. Hermes cover can be granted when the export
transactions are worth of supporting and the risks appear tolerable.
14
The backlogs and defects are usually covered at the amount of 5 – 10 % from the project’s value.
51
certain lack of communication and misunderstanding, the sales representatives do not
cooperate with the Financial department as smoothly as expected. For that a common past
situation was, that many overdue accounts receivable remained without further processing.
The last and also the highest level of receivables monitoring is done on a management level,
and thus by the financial director. To keep an up-to-date overview of debtors, a co called
receivables monitoring analysis is carried out on a monthly basis. Here all the debtors are
listed with detailed information about a debt amount and its nature, as well as the information
about its further processing. The debts are generally expected to be processed by form of
dunning, agreements about special payment terms as a payment calendar or in inevitable cases
by enforcement via a lawyer or a cash collection agency. The graphical outcome of this
analysis shows the changing proportion among the various debts processing, where the main
intention is to minimize the amount of the unprocessed debt. Furthermore, the second target is
to minimize the costs of debts processing, above all those imposed on the most costly
procedures, which are the external services of the lawyer and the cash collection agency. The
outcome of the analysis is distributed subsequently to the sales representatives for information
and as an impulse for their further activity.
Another part of a cash collection system, is credit limits adjustment of existing customers.
This takes place usually at the beginning of each month and is carried out by the personnel of
the Financial department, who submits his proposals to the financial director for an approval.
The maximal growth of the credit limit per month is 20 %, whereas its maximal monthly
decline is 35 %. Credit limits are adjusted for the risk category 3 (see eqn. 4.2), for the risk
category 2 (see eqn. 4.3), and for the risk category 1 (see eqn. 4.4) according to the following
rules.
New limit = current limit * (amount of purchase orders + amount of receivables ) *1.0 (4.2)
New limit = current limit * (amount of purchase orders + amount of receivables ) *1.1 (4.3)
New limit = current limit * (amount of purchase orders + amount of receivables ) *1.2 (4.4)
The dunning process is a further component of a cash collection in Bosch. The dunning
notices are issued to third parties and affiliates15 in respect of overdue receivables regularly at
least every month by the personnel of the Financial department. The total amount of reminder
letters is 3, with the essence that the last reminder letter is not send to a customer. It serves
only for internal information that the respective customer have been handed over to the
collection agency or the company lawyer. Disputed items may be excluded from the dunning
procedure for a period of up to 6 months. For related companies the dunning procedure can be
15
These are joint ventures as for instance Bosch and Siemens Hausgeräte GmbH.
52
limited to a quarterly notice. If the dunning procedure turns out to be ineffective, the
responsible personnel of the Financial department decides about further proceedings among
which belong advance payment, suspension of supplies, a payment calendar or litigation.
The last process to be mentioned as a part of a cash collection system in Bosch is a so called
suspension of orders and supplies. If a customer has an overdue debt or exceeds the credit
limit, his purchase orders and supplies are automatically blocked in SAP.
Following rules for suspension of purchase orders and supplies are valid for the risk
categories 1 to 4.
−
Customers with the risk category 4 cannot make any orders or supplies.
−
Offers16 are blocked only for the risk category 4, other categories remain unblocked,
being warned only.
−
Purchase order17 is blocked if the credit limit is exceeded and outstanding receivables
are more than 45 days overdue.
−
Direct delivery18 is blocked if the credit limit is exceeded and outstanding receivables
are more than 30 days overdue.
−
Supplies19 are blocked if the outstanding receivables are more than 30 days overdue.
The rules for release of blocked purchase orders and supplies are:
−
A customer exceeded the credit limit, has no overdue debt and belongs to the risk
category 1 or 2. The unblocking takes place at least twice a day and is carried out by a
responsible personnel of the Financial department.
−
A customer has overdue debt or belongs to the risk category 3. The unblocking is
carried out by the financial director upon a request for a release via SAP.
−
Customers of the risk category 4 remain blocked.
If there is no realistic assumption that the outstanding amount will be paid by the customer,
then the overdue receivables can be written-off. The write-offs are done by the responsible
personnel of the Financial department, who is in charge of credit management. The list of
receivables to write-off must be signed according to the signature specimen by the head of the
16
Based on an enquiry, a purchase offer represents a business proposal created by a sales representative.
Based on a purchase offer, a purchase order is delegated via a sales representative and thus represents a clear
instruction to order goods from Germany on stock of a Czech subsidiary.
18
The ordered goods manufactured in Germanny are sent directly to a Czech customer without stocking them in
the Czech Republic. The invoice is then sent by a Czech subsidiary whose customers are served.
19
In the case of blocked supplies, the ordered goods manufactured in Germany are already on the Czech stock.
17
53
Financial department and the general manager. It is necessary to keep the four-eye principle.
Decision about the tax recognition of the written-off items is directed by actual tax and
accounting standards coming from Income Tax Act and Capital Reserves Act.
Accounts that have shown no transactions and that include no open items for years, are
assigned the value of 1 CZK as credit limit and thus have their supplies blocked. The same
procedure relates to customers who became insolvent.
It is also worth mentioning that, an important component of credit management and risk
management are so called internal controls. They reduce risk resulting from work errors or
manipulations, leading to financial loss or incorrect reporting. The internal controls are
always conducted by a personnel who is impartial to controlled segments. For customer
accounts, the internal controls are carried out by a head of a Marketing department on a
quarterly basis. The process includes checks whether writing-off of account discrepancies and
bad debts as well as document changes to invoices in the IP- system20 were correct and
approved. It is further reviewed whether credit limits were calculated properly not exceeding
without approval and whether regular monitoring of credit limits was performed. Another
check is whether deliveries of goods and services rendered were completely invoiced and
creations and/or changes to sales condition21 in the master data were correct and approved.
Last but not least, it is also checked if prices, special prices, discounts as well as delivery and
payment terms invoiced, corresponded to valid price lists and agreements.
4.1.4 Means of debt recovery
For accounts receivable, where the dunning procedures turned out to be ineffective, further
steps in the enforcement of their payment are taken. The decision whether to use services of a
collection agency or a company lawyer is made according to the value of the outstanding
debt.
Due to the cost of the lawyer’s employment, his services are used only for accounts receivable
at the value greater than 200,000 CZK. Hence, the services of a collection agency are
preferred for all other cases.
Receivables from debtors that are under bankruptcy administration or are in receivership, or
that have delivered an affirmation in lieu of oath in the course of winding up proceedings, are
usually written off as unenforceable after bankruptcy proceedings are finished. The final
write-off must be approved by the head of the Financial department and the general manager.
20
21
Dunning block, value date in the SAP.
Prices, special prices, delivery and payment terms.
54
The chosen collection agency is B4B Inkasso s.r.o., with its principal residence in Havířov
and commercial representation in Brno. The agency works on the basis “no success no fee”
and accounts a commission rate only for successfully solved cases. The rate is calculated
according to the age of the account receivable, where for debts younger than 180 days
approximately 6 % is taken and the debt which overranges this border costs 10 %. The
percentage value always relates to the total value of the account receivable.
The development of accounts receivable which were handed over to the cash collection
agency B4B, being observed during the period from March 2010 to October 2010 shows a
quite steady tendency (see Fig. 8). For a better imagination, the amount of accounts receivable
taken over by B4B is shown as a percental part of the total turnover made in individual
months.
Accounts receivable handed to B4B
(in % of the total turnover)
0,0025
0,0020
0,0015
0,0010
0,0005
0,0000
03.2010 04.2010 05.2010 06.2010 07.2010 08.2010 09.2010 10.2010
Months
Fig. 8 Accounts receivable handed over to B4B
(Source: Own elaboration based on internal data of Bosch)
The accounts receivable which are to be enforced by a litigation process are usually handed
over to a company lawyer, who as a contract worker of Bosch works on individual cases
independently.
The development of accounts receivable which were handed over to the lawyer, being
observed during the period from March 2010 to October 2010 (see Fig. 9), shows a quite
steady tendency; which could have been seen in the previous graph as well. For a better
imagination, the amount of accounts receivable taken over by the lawyer is shown again as a
55
percental part of the total turnover achieved in individual months. It can be seen here that the
amount of accounts receivable is little bigger than that of B4B. This is caused naturally by the
fact that the accounts receivable taken over by the lawyer are always those of the highest
value, representing minimally 200,000 CZK.
Accounts receivable handed to the lawyer
(in % of the total turnover)
0,07
0,06
0,05
0,04
0,03
0,02
0,01
0,00
03.2010 04.2010 05.2010 06.2010 07.2010 08.2010 09.2010 10.2010
Months
Fig. 9 Accounts receivable handed over to the company lawyer
(Source: Own elaboration based on internal data of Bosch)
After taking a look at both graphs shown above, clearly saying that the accounts receivable at
B4B and the lawyer are never greater than 0.1 % of the total turnover; it could be easily said
that the results are quite satisfactory and acceptable. This would be a mistake, since it must
not be forgotten that the values shown are relative and even a negligible percental value
means huge amount of money in reality and therefore also substantial costs for Bosch.
Therefore it is vital to bear in mind that the primary target is to prevent such situations where
the accounts receivables become overdue and are later under ineffective dunning procedure.
In such cases, an important prerequisite is for instance that sales engineers themselves put the
debtors under pressure and demand the fulfilment of the agreed conditions. Then the amount
of overdue accounts receivable without further proceeding, being just dunned, would certainly
diminish.
If the precautionary measures turn out to be ineffective, then the second target to be focused
on is the minimization of the accounts receivable directed to the lawyer. It is always true that
the costs are substantial and the services of a cash collection agency are often more
56
advantageous from the financial viewpoint. Nevertheless, it is also clear that in some cases the
services of the lawyer are the only possible solution.
4.2
Impact of selected factors on accounts receivable collected
Knowing the impact value of individual credit management processes on the collected
accounts receivable is indisputably vital for planning and decision making. Therefore the most
influential processes will be selected and after their division into individual factors, they will
be tested under various hypotheses. The selected processes are credit terms determination,
form of dunning, means of clearing via debt collection agency, approval process upon credit
check of blocked orders and solvency check process.
To enable the hypothesis testing with the data available, it is necessary to divide them further
into following factors.
−
risk category,
−
dunning procedure (3 stages),
−
capacity of sales engineer,
−
payment calendar,
−
lawyer,
−
collection agency,
−
turnover,
−
profit margin,
−
total costs,
−
payment moral.
4.2.1 Selected factors
Before starting the testing of several hypotheses, it is necessary to describe all selected factors
in detail to understand their background and their estimated impact on accounts receivable
collected through the above mentioned processes.
Turnover as a factor is one of the criterions playing an important role in approval process
upon check of blocked orders. The more important the customer is, the higher the probability
that he will be given free in unblocking his orders and that he will pay his invoice as agreed.
Therefore the assumption is that the collected accounts receivable grow with the higher
turnover.
57
Profit margin is another factor, which in the same way as turnover influences the instant
decision of unblocking the orders. The assumption for account receivable collected is thus
also positive.
Total costs can be taken as a factor as well, since it can be said, that for instance huge projects
being expensive above all due to their development and application costs, represent a certain
danger of slower collection of accounts receivable.
Risk category is a factor according to which credit terms are determined. Five risk categories
exist to distinguish between the customers with very good payment moral and low nonpayment risk and those who are very risky due to their payment habits or cultural background.
The assumption is therefore, that the higher the risk category, the higher the probability of
non-payment and the lower the collected accounts receivable.
Payment moral as a factor is used for a process of a customer’s solvency check. It is shown in
a form of so called average arrears, which means how many days in average the customer
paid his invoices after the due date. The assumption is that the higher the number for payment
moral in form of average arrears, the higher the riskiness of non-payment by the customer and
therefore the lower collected accounts receivable.
Capacity of sales engineer is another factor having much in common with the dunning
procedure. Each sales engineer has his customers he takes care of, sells the products to and in
his way puts the non-paying customers under pressure. The assumption is that a bigger
amount of the total time dedicated to a customer brings more immediate payment due to
interpersonal contact and relations, and therefore also more collected accounts receivable.
Dunning procedure as a factor is in its simplicity done mainly in form of letters. It is divided
into three stages. Dunning 0 means that the customer has accounts payable which are not
overdue yet. Dunning 1, means that the customer has his debts more than 40 days overdue and
dunning 2 signifies more than 70 days overdue. The assumption is, the higher the stage of the
dunning letter, the higher the probability of non-payment by the customer and thus the lower
the collected accounts receivable.
Payment calendar as a factor is one of the options a collection agency can use while
persuading the customer to make his payment. It is assumed that a customer, who agrees on it,
wants to prevent litigation and makes his payments as scheduled. Therefore the probability of
higher accounts receivable collected grows with the number of concluded payment calendars.
Lawyer as a factor is another option a collection agency or Bosch directly can use. In this
case, it is assumed that with the growing costs of employing a lawyer and time-consuming
legal proceedings, also the amount of collected accounts receivable in the final effect declines.
58
The collection agency (B4B) itself as a factor, may process the overdue accounts in their form
of dunning, telephone calls or letters, before they conclude a payment calendar or hand the
case over to a lawyer. The fees are invoiced to Bosch only for successfully resolved cases.
Therefore the assumption is that the higher amount of resolved cases, the higher the fees and
the lower the accounts receivable collected.
4.2.2 Hypothesis testing
The dataset itself was built of 237 observations, representing selected customers of Bosch. It
consisted of one dependent variable, the collected accounts receivable, and twelve
independent variables having a substantial impact on it.
The dataset variables were represented by:
−
Y, as accounts receivable collected (in CZK) in the year 201022,
−
X1, as turnover (in CZK) in the year 2010,
−
X2, as profit margin (in CZK) in the year 2010,
−
X3, as total costs (in CZK) in the year 2010,
−
X4, as risk category (0 – 4),
−
X5, as payment moral (in days),
−
X6, as capacity demand (in %),
−
X7, as dunning 0 (in CZK),
−
X8, as dunning 1 (in CZK),
−
X9, as dunning 2 (in CZK),
−
X10, as payment calendar (in CZK),
−
X11, as lawyer (in CZK),
−
X12, as B4B (in CZK).
The estimated signs for individual factors were based on the descriptions above and were as
follows:
−
−
−
+
−
−
−
+
−
−
+ +

Yt = f  X 1 + X 2 + X 3 + X 4 + X 5 + X 6 + X 7 + X 8 + X 9 + X 10 + X 11 + X 12 


22
The data set was constructed from available data for periods January till September 2010.
59
(4.5)
The proposed functional form of the model was linear, which was due to the expected
properties of individual factors. The theoretical equation (see eqn. 4.6) and the estimated
equation (see eqn. 4.7) were defined as follow.
Yi = β0 + β1 X 1 + β2 X 2 + β3 X 3 + β4 X 4 + β5 X 5 + β6 X 6 + β7 X 7 + β8 X 8 + β9 X 9 + β10 X 10 + β11 X 11 + β12 X 12 + ε
(4.6)
∧
∧
∧
∧
∧
∧
∧
∧
∧
∧
∧
∧
∧
Yi = β 0 + β 1 X 1 +β 2 X 2 +β 3 X 3 +β 4 X 4 + β 5 X 5 +β 6 X 6 +β 7 X 7 +β 8 X 8 +β 9 X 9 +β 10 X 10 +β 11 X 11 + β 12 X 12
(4.7)
For hypothesis testing and data modelling, a statistical program Gretl23 was used. The
proposed linear functional form of the model turned out to be incorrect since the model itself
comprised bad features, as it was shown in individual tests. Such bad features were for
example heteroskedasticity of the model and inadequate model specification, as revealed by
the RESET test.
After series of models and their testing, an ideal functional form came out, showing requested
features. It was proved that the natural logarithm, thus the logarithmic functional form of
some of the independent variables made the data set more compatible. Two independent
variables were left in their original form due to negative values their included. These variables
were profit margin and payment moral. Another decision taken was to exclude from the
model those independent variables which showed very low data variability. This was done to
avoid data singularity, which had an inadequate impact on the model results in the previous
testing. The excluded variables were namely dunning 0, dunning 1, dunning 2, payment
calendar, lawyer and B4B.
The new dataset variables were represented by:
23
−
Y, as accounts receivable collected (in CZK) in the year 2010,
−
X1, as turnover (in CZK) in the year 2010,
−
X2, as profit margin (in CZK) in the year 2010,
−
X3, as total costs (in CZK) in the year 2010,
−
X4, as risk category (0 – 4),
−
X5, as payment moral (in days),
−
X6, as capacity demand (in %).
A freeware statistical program available on http://gretl.sourceforge.net/win32/.
60
The estimated signs for individual factors remained same as it was depicted at the first model
with the linear functional form and were as follows:
−
−
−
+
+ +

Yt = f  X 1 + X 2 + X 3 + X 4 + X 5 + X 6 


(4.8)
The new model which was tested is shown below in a form of the theoretical equation (see
eqn. 4.9) and the estimated equation (see eqn. 4.10).
ln(Yi ) = β 0 + β1 ln( X 1 ) + β 2 X 2 + β 3 ln( X 3 ) + β 4 ln( X 4 ) + β 5 X 5 + β 6 ln( X 6 ) + ε
∧
∧
∧
∧
∧
∧
(4.9)
∧
ln(Yi ) = β 0 + β 1 ln( X 1 ) + β 2 X 2 + β 3 ln( X 3 )+ β 4 ln( X 4 )+ β 5 X 5 + β 6 ln( X 6 )
(4.10)
The process flow for hypothesis testing was following:
−
OLS24 method for model estimation,
−
analysis of variance,
−
confidence intervals for regression parameters,
−
RESET test of correct specification,
−
linearity test of the model,
−
collinearity test,
−
test of normality,
−
test of heteroskedasticity.
The ordinary least squares method
The ordinary least squares method was used to construct a model to be tested, obtaining thus
numerical values for the coefficients25 of the previously constructed regression equation. The
variables were for the purpose of testing given nicknames coming out from their original
designations. The detailed calculation and testing process can be found in the Appendix 1
“Model by ordinary least squares method.”
In respect to the p-values, showing the significance of the independent variables in the model;
profit margin, total costs and capacity demand were excluded. The final model was defined
with its main features as follows (see Tab. 3).
24
25
Ordinary least squares method.
In the equation designed as β0 - β12.
61
Tab. 3 The final model by the ordinary least method
coefficient
std. error
t-ratio
p-value
----------------------------------------------------------------------------------------------------------const
0.137697
0.0808093
1.704
0.0897
l_Turn26
0.988920
0.00589940
167.6
1.39e-241 ***
l_RisCat27
-0.0841286
0.0315174
-2.669
0.0081
***
PayMor28
-0.00128078
0.000465112
-2.754
0.0064
***
R-squared
0.992116
*
Adjusted R-squared 0.992013
(Source: Own calculation in Gretl based on internal data of Bosch)
The first statement coming out of the model was, that the estimated signs of the individual
factors were correct, with turnover having positive effect on accounts receivable collected
while on the contrary risk category and payment moral having negative effect on it. The
second statement was that 99.21 % of estimated values were explained by the regression
equation, which was a very good result. This was proved by R2 explaining the quality of the
model in this way.
Analysis of variance
As a second step an analysis of variance was carried out to test the hypothesis of nonsignificance of the model. For this so called Anova table was used, from which the F-statistics
was calculated. Since the F calculated was 9605.91, and therefore greater than F-critical29
which was 2.64, the hypothesis was rejected with the conclusion that the overall model was
significant at 5 % of the significance level30.
Confidence intervals for the regression parameters
As a next step confidence intervals for the regression parameters were calculated, to test the
hypothesis of interval including zero coefficients. Since both intervals calculated did not
include zero, the hypothesis was rejected, concluding that the coefficients were significantly
different from zero.
26
Natural logarithm of turnover.
Natural logarithm of risk category.
28
Payment moral.
29
It is a quantile of distribution of test statistic separating acceptance and rejection region when performing
statistical test of a hypothesis.
30
Denoted as α and commonly being chosen as 5 %, it represents the probability of rejecting a true hypotesis by
mistake.
27
62
The Ramsey’s RESET test
The RESET test followed, testing the hypothesis of adequate model specification. It was
tested that there were no omitted variables in the model and that it did not have an incorrect
functional form. Since the p-value was 0.976 and therefore greater than 0.05 (α = 5 %) the
hypothesis was confirmed with the conclusion that the model was adequately specified.
Linearity test (Lagrange multiplier test of non-linearity)
Linearity test as a next step tested a hypothesis that the model was linear of squares and that a
linear functional form was incorrect. Since p-value was 0.62141, thus greater than the 5%
level of significance, the hypothesis was accepted, concluding that the model was linear of
squares.
As a second option, a similar hypothesis was tested whether the model was linear of logs and
a linear functional form was therefore incorrect. Since p-value was 0.30112, thus greater than
the 5% level of significance, the hypothesis was accepted, concluding that the model was
linear of logs.
The collinearity test
The collinearity test examined the probability of linear dependency between two
explanatory31 variables, taking into account also multicollinearity case where there would be
linear dependency between several explanatory variables. Since all the calculated variance
inflation factors were smaller than 10, it was concluded that there was no multicollinearity
present in the model.
The test of normality
In the test of normality, it was examined whether the error terms, known also as residuals,
were normally distributed as said in the hypothesis. Since the p-value was calculated as
0.00000, and was for that less than 0.05, the hypothesis was rejected with the conclusion that
the residuals were not normally distributed (see Fig. 10.).
31
An independent variable which explains and influences the dependent variable.
63
Fig. 10 Test statistic for normality
(Source: Own calculation in Gretl based on internal data of Bosch)
Test of heteroskedasticity (White’s test)
Test of heteroskedasticity examined the hypothesis of error terms having constant variance,
thus being homoskedastic. Since the p-value was calculated as 0.587312, it was therefore
more than 0.05 and the hypothesis could be accepted with the conclusion that the model was
homoskedastic.
Final overview of results
After undertaking all necessary hypotheses testing, the final overview of results can be
presented. The model equation looks as follows.
ln(Y )i = 0.1377 + 0.9889 * ln( X 1 ) − 0.0841*ln( X 2 ) − 0.0013 X 3
Where following variables are present:
−
Y, as accounts receivable collected (in CZK) in the year 2010,
−
X1, as turnover (in CZK) in the year 2010,
−
X2, as risk category (0 – 4),
−
X3, as payment moral (in days).
64
(4.11)
Given that the growth in all the factors determining the growth of collected accounts
receivable is zero, total collected accounts receivable will grow by 0.138 %32.
Given that all the factors determining growth of collected accounts receivable are constant, an
increase in turnover will result in an increase of total collected accounts receivable by
0.989 %.
Given that all the factors determining growth in collected accounts receivable are constant, an
increase in risk category and payment moral, represented by average arrears, will result in a
decrease of total collected accounts receivable by 0.084 % and 0.001 % respectively.
4.3
Division of the customers according to the portfolio analysis
The hypothesis testing and the final model equation have given the evidence about the impact
of credit risk category and payment moral as independent variables on the collected accounts
receivable as dependent variable. It is thus undeniable that the higher the risk category and the
worse payment moral, the lower the collected accounts receivable are.
Therefore one of proposals for the optimization of credit management system in the company
Bosch is to emphasize the importance of correct customer’s allocation to a corresponding risk
category as well as assignment of proper payment terms coming from the payment moral.
This will be based on outcome of a customer portfolio analysis, diversifying thus the
customers and indicating how they should be further handled.
An outcome of the analysis itself will be a matrix with a clear allocation of the key customers
so that the most convenient portfolio offering good business, financial solvency and trouble
freeliness could be selected. Then a common understanding of customers’ importance and
attitude how to deal with them will be raised. Sales engineers will be able to focus on key
customers and increase the cross selling potential33, whereas all the dunning efforts of the
Financial department personnel and costs on the collection agency and the lawyer will be
lowered.
4.3.1 Investigation of existing portfolio models
Investigation of existing portfolio models was the first step to be done. Here the essence of the
models, their advantages and disadvantages were examined so that the most optimal one
could be identified and adjusted for the customer portfolio analysis. A detailed graphical
interpretation of all selected models can be found in the Appendix 2 “Overview of existing
portfolio models.”
32
33
Due to natural logarithm in the equation, the coefficients represent percental values already.
Potential of selling new products and/or services to a customer.
65
Following portfolio models were investigated:
−
Boston Consulting Group matrix34 (1968),
−
Two-step customer portfolio analysis from Fiocca (1982),
−
Customer classification matrix from Shapiro (1987),
−
Three-dimensional classification matrix from Turnbull and Zolkiewski (1997).
Despite the length of its existence, the BSG matrix created by Boston Consulting Group
(1968) laid a base upon which other models were created. This model itself evaluates products
of an organization according to their market share and growth prospects. On that basis it can
reveal insight about their financial needs or their ability to generate cash.
The evaluation factors used are:
−
market share,
−
market growth.
The drawback of this model is that it is in fact relatively simplistic due to the historical
context of early seventies. Today it is known that high market share is not the only success
factor as well as market growth is not the only indicator for market attractiveness.
Belonging among significant portfolio models, the two-step customer portfolio analysis from
Fiocca (1982) is special for its creation process in two steps. The first one is an analysis of
customers according to their strategic importance and difficulty in managing their accounts.
Key accounts are then selected and further analyzed from the viewpoint of business
attractiveness and strength of the supplier-customer relationship.
The evaluation factors used are:
−
value/volume of purchases, customer potential and prestige, his market leadership,
−
competition level for a customer, his buying behaviour, product characteristics
required,
−
factors concerning customer’s market and his position there,
−
length of relationship, customer importance, friendship, product development
cooperation.
Also this model has its drawbacks, namely the fact that it does not consider customer’s
profitability and rejection of non-key customers in the first step is disputable.
34
Also comonnly known as BSG matrix.
66
Another significant portfolio model is customer classification matrix from Shapiro (1987).
Customers are viewed as profit centres, where costs to suppliers, their management and
behaviour are used to investigate the profit dispersion of their portfolio.
The evaluation factors used are:
−
presale, production, distribution and post sale service cost,
−
net price charged.
A great advantage of this model is a refined mechanism for customer’s profitability
calculation which is covered in the matrix. However the drawback remains in the point of preand post sale costs, whose determination can be really difficult.
The last portfolio model to be mentioned is a three-dimensional classification matrix from
Turnbull and Zolkiewski (1997). This three-dimensional analysis is based upon cost to serve,
net price and relationship value. It provides thus a mechanism of hard data represented by
customer’s profitability in combination with more judgmental data represented by relationship
value. The evaluation factors used are the combination of Fiocca (1982) and Shapiro (1987)
model.
The idea of three dimensions could be viewed as a breakthrough in portfolio models, which
used to be till that moment two-dimensional only. Nevertheless, this model is relatively new
and a strong evidence for correctness of axes choice is still missing.
After the investigation of the most significant portfolio models which were currently
available, a model for customer portfolio analysis (see Fig. 11) was chosen. The intention was
to combine the most useful findings of old models and create thus a brand new one.
The decision was taken in favour of a two-dimensional matrix due to the display simplicity. A
simple reason for that was the fact that it was not possible to create three-dimensional objects
in Excel, and another program having this capacity was not available.
Coming out from the designations of the dimensions chosen in the investigated models above
and the needs of the company Bosch, it was decided to compare customer’s status of today
with his status of tomorrow. The model could thus visualize two dimensions designated as
“customer today” and “customer tomorrow”.
67
Target customers
Customer
tomorrow
Customer
today
Fig. 11 A proposed model for customer portfolio analysis
(Source: Own interpretation based on Sieck (2002))
4.3.2 Evaluation factors for the model, their scoring and weighting
As seen in the investigated models of Fiocca (1982), and Turnbull with Zolkiewski (1997),
the idea of combining quantitative and qualitative data, was considered as a perfect
opportunity how to bring two different viewpoints into the analysis hence making it more
objective.
It was decided to take the quantitative data mainly from the information systems of the
company Bosch, namely from SAP and Business warehouse applications. The qualitative
data, on the other hand, was acquired from a questionnaire which was distributed to the sales
engineers. The questionnaire in its complexity can be found in the Appendix 3 “Questionnaire
for portfolio analysis.”
The choice of the evaluation factors for the model was based on previous models
investigation and on the principle of preferred data for inclusion by the management of the
company Bosch. It was simply important to include all the data which was seen as the most
influential for the analysis.
The dimension customer today investigated current state and prosperity of the customer,
including following evaluation factors:
−
annual turnover with Bosch,
−
annual profit margin,
68
−
risk category of the customer,
−
payment moral of the customer,
−
actual customer’s competitiveness on the Czech market.
The dimension customer tomorrow looked into near future from the viewpoint of the three
next years. It included following evaluation factors:
−
growth potential of the customer,
−
potential customer’s competitiveness on the Czech market,
−
future economic situation of the customer,
−
growth of the customer’s turnover with Bosch,
−
cross selling potential of the customer.
To prevent any misunderstanding concerning the nature of the selected factors, it is important
to specify details and the time period of the chosen data.
Data about turnover and profit margin made with the customer were taken as total values for
the whole year 2009, since this was the last fully completed period from the long-term point
of view. The value of the risk category was taken as an actual one at the moment of the
analysis. Also the payment moral, represented by average days the debt was overdue, was
taken as an actual value at the moment of the analysis. All these factors came from internal
information systems of the company Bosch.
The other factors were taken from the questionnaire. Customer’s growth potential and growth
of his turnover with Bosch were designed as quantitative data whereas customer’s actual and
potential competitiveness on the Czech market, his future economic situation and his cross
selling potential, meant as selling additional services and/or products to him, were all
designed as qualitative data.
As soon as the data was consolidated, the evaluation of the results could start. To ensure the
compatibility of the data for the analysis, it was vital to introduce some kind of a scoring
method.
The idea of factors scoring came out from the fact, that the consolidated data was diverse in
their nature and it was naturally expected that all the data had to be transformed to data of an
equal level. For that all factors of individual customers were assigned a score which
corresponded to the importance of the achieved value. A scoring table, according to which
scores were distributed, was designed as a scale of 8 values. The essence was that the higher
69
the score, the higher the importance of the achieved value. Therefore a customer with the
score 1 was the worst one, whereas on the contrary a customer with the score 8, was the best
one.
A second step could be taken, after the factors scoring was finished. This step was so called
weighting of the scores. It was necessary to undertake it due to the diverse nature of the
selected evaluation factors. There were two good points to think about. The first one was the
fact that all the evaluation factors were chosen according to the priorities and expectations of
the company Bosch. The second fact was that the consolidated data was both of quantitative
and qualitative nature. Hence a clear conclusion was that the scores needed to be diversified
according to the relative importance of the evaluation factors. A weight was then a coefficient
which multiplied the score, indicating thus a relative importance of the factor in comparison
to the others. The weights represented in fact values in the scale from 0 to 1 and their essence
was again, the higher the weight the higher the importance of the evaluation factor.
To enable a clear interpretation of the results, an overview of scoring and weighting tables is
provided in the Appendix 4 “Scoring and weighting tables for portfolio analysis.” The tables
were created in correspondence to the identified dimensions of the portfolio analysis,
“customer today” and “customer tomorrow.”
4.3.3 Creation of the customer portfolio matrix
As soon as all the evaluation factors were scored and weighted subsequently, the calculation
of final scores for both dimensions could be started. The final scores corresponded then to X
and Y axes, directing thus the position of the customer in the portfolio matrix.
The calculation of the score customer today representing the X axe and the score customer
tomorrow which stands for the Y axe can be viewed bellow (see eqn. 4.12 and eqn. 4.13).
Scorecustomer today = β1 X 1 + β2 X 2 + β3 X 3 + β4 X 4 + β5 X 5
Where following variables were present:
−
β1, as weight for annual turnover with Bosch,
−
X1, as score for annual turnover with Bosch,
−
β2, as weight for annual profit margin,
−
X2, as score for annual profit margin,
−
β3, as weight for risk category of the customer,
−
X3, as score for risk category of the customer,
70
(4.12)
−
β4, as weight for payment moral of the customer,
−
X4, as score for payment moral of the customer,
−
β5, as weight for actual customer’s competitiveness on the Czech market,
−
X5, as score for actual customer’s competitiveness on the Czech market.
Scorecustomer tomorrow = β1 X 1 + β2 X 2 +β3 X 3 + β4 X 4 + β5 X 5
(4.13)
Where following variables were present:
−
β1, as weight for growth potential of the customer,
−
X1, as score for growth potential of the customer,
−
β2, as weight for potential customer’s competitiveness on the Czech market,
−
X2, as score for potential customer’s competitiveness on the Czech market,
−
β3, as weight for future economic situation of the customer,
−
X3, as score for future economic situation of the customer,
−
β4, as weight for growth of the customer’s turnover with Bosch,
−
X4, as score for growth of the customer’s turnover with Bosch,
−
β5, as weight for cross selling potential of the customer,
−
X5, as score for cross selling potential of the customer.
The customer portfolio analysis performed on the customers of the company Bosch, brought
the final outcome as it can bee seen in the Fig. 12. The final customer portfolio matrix was
divided into nine cells in order to identify key customers of Bosch easily. The division
principle was, the higher the score of dimensions, the customer today and customer tomorrow,
the greater the customer’s importance for Bosch. The relevance of the customer, in the means
of his turnover with Bosch, was depicted by the size of each bubble.
The reason why the blue marked area was not started at the initial point of 0 on both axes but
at point 2 instead, was the position of all the relevant customers who were situated right there.
Those customers left behind, were kept outside intentionally as “dead” ones, since there was
no potential for business with them in the near future, as it had been learned during the data
consolidation.
The interpretation of the cells depicting the importance of individual customers to Bosch
follows. The cells II, III and VI represent the sought key customers, which should be focused
71
on as they bring the biggest benefit to Bosch. Here so called grow and build strategy should
be applied. It is clear that these customers should be handled differently than the others, by
offering to them preferential conditions and extraordinary services as higher credit limits and
extended payment terms. The cells I, V, IX represent those customers, who are important to
Bosch but definitely cannot be compared with the key customers. Here so called hold and
maintain strategy should be applied. These customers should be offered common conditions
without any extraordinary services as higher credit limits or extended payment terms. The
cells IV, VII and VIII represent the least important customers of Bosch. Here so called harvest
or exit strategy should be applied. It should be remembered that paying any extra attention or
efforts to these customers would not bring anything but a loss for the company and must be
therefore avoided.
9,0
8,0
I.
II.
III.
IV.
V.
VI.
VII.
VIII
IX.
Customer tomorrow
7,0
6,0
5,0
4,0
3,0
2,0
1,0
0,0
0,0
0,5
1,0
1,5
2,0
2,5
3,0
3,5
4,0
4,5
5,0
5,5
6,0
-1,0
Customer today
Fig. 12 Customer portfolio analysis for the company Bosch
(Source: Own calculation based on internal data of Bosch);
From the graph, it can be clearly seen that the customer’s importance is influenced by many
factors and hence taking a single factor of customer’s behaviour as an evidence of his
importance would be a mistake.
It is known from the past experience that the customers were classified mainly according to
their turnover with Bosch. Nevertheless this analysis proves that a customer with an
interesting turnover having qualities as good payment moral or low risk category is important
to the company and is to be taken seriously.
72
A second outcome of the analysis (see Fig. 13) is showing a ratio between the customer’s
benefit to Bosch versus his demand. The customer’s benefit is shown as a multiplication of
the scores “customer today” and “customer tomorrow” whereas the customer’s demand is
presented as a percental fragment of each sales representative’s total capacity devoted to his
customer. It would be ideal if the time and efforts dedicated to the customer corresponded to
the real importance he represents for Bosch.
However, the graph clearly shows that the reverse is often true. Smaller customers, with the
meaning of their importance, are often very demanding and time consuming, sometimes even
inadequately. It is also true that each sales representative would see his customers as the most
important ones, even if they were not as significant according to the overall company’s point
of view.
Customer's demand
(total capacity of sales engineers)
0,6
0,5
0,4
0,3
0,2
0,1
0,0
0
5
10
15
20
25
30
35
40
Customer's benefit (total score)
Fig. 13 Customer’s benefit versus demand
(Source: Own calculation based on internal data of Bosch)
There is a clear message saying that a focus on the key customers is vital, since they are those
who represent the greatest benefit to the company. Extra services, so called comfort and time
should be devoted exactly to them. On the contrary, the others should be devoted only as
much time as necessarily needed.
4.4
Division of the customers according to the scoring function
Continuing in the idea that a correct customer’s allocation to a corresponding risk category as
well as assigning him proper payment terms are very important for overall credit management
73
system, it was decided to introduce scoring function and dividing hence the customers of the
company Bosch according to their status.
In comparison to the customer portfolio analysis, this scoring method starts with the same
complex set of indicators, with the difference that occurrence of only two features is expected
as a result. These features are occurrence of a success or a failure. It is in fact a presence or
absence of an attribute of interest, which is in this case a good customer. Otherwise a so called
bad customer is present.
To be able to use the scoring function, a logit model for binary data will be created at first.
Here all the indicators having the impact on the status of a good customer will be tested in a
form of a model, and coefficients for those selected ones will be calculated.
The scoring method itself, the logistic regression, consists in score calculation which is to be
based on the outcomes of the logit model. The final result of the analysis will be a graph with
a clear allocation of good and bad customers according to the acquired scores.
Those good customers having the highest scores will represent then the most convenient
portfolio offering good business, financial solvency and trouble freeliness. It is expected that
such customers’ allocation will definitely improve credit management system of the company,
due to precise allocation of services and efforts.
4.4.1 Creation of a logit model for binary data
The dataset itself was built of 237 observations, representing selected customers of Bosch. It
consisted of one dependent variable, the type of a customer35, and six independent variables
having a substantial impact on it. The dependent variable was denoted as so called dummy
variable, with the meaning that it equalled 1 in case of a good customer and 0 in case of a bad
one.
The dummy variable was assigned to all customers according to the following criterion:
−
dummy equalled 1,
o customer with minimal monthly turnover of 100,000 CZK,
o and payment moral up to 90 days after due date;
−
dummy equalled 0,
o customer with monthly turnover smaller than 100,000 CZK,
35
The type of the customer was represented by only two possible values, success and failure. It was in fact a
presence or absence of an attribute of interest, which was in this case a good customer.
74
o and/or payment moral worse then 90 days after due date.
The dataset variables were represented by:
−
Y, as probability of occurrence of a good or a bad customer,
−
X1, as profit margin (in CZK) in the year 2010,
−
X2, as total costs (in CZK) in the year 2010,
−
X3, as risk category (0 – 4),
−
X4, as turnover (in CZK) in the year 2010,
−
X5, as accounts receivable collected,
−
X6, as payment moral (in days).
The estimated signs for individual factors were based on their nature and expected impact
they had on the probability of the occurrence of a good or a bad customer. The signs were as
follows:
−
+
+
−
+ −

Yt = f  X 1 + X 2 + X 3 + X 4 + X 5 + X 6 


(4.14)
For hypothesis testing and data modelling, a statistical program Gretl was used again. Series
of testing showed that it was necessary to convert some of the independent variables into
more compatible data within the whole data set. Therefore the empirical cumulative
distribution function (ECDF) was introduced, showing thus relative frequency of occurrence
of individual values among the others of their kind. Such conversion undertook only two
variables, namely profit margin and turnover.
The proposed functional form of the model was linear, which was due to the expected
properties of individual factors. The theoretical equation (see eqn. 4.15) and the estimated
equation (see eqn. 4.16) were defined as follow.
Yi = β 0 + β1 ECDF ( X 1 ) + β 2 X 2 + β 3 X 3 + β 4 ECDF ( X 4 ) + β 5 X 5 + β 6 X 6 + ε
∧
∧
∧
∧
∧
∧
∧
Yi = β 0 + β 1 ECDF ( X 1 ) + β 2 X 2 + β 3 X 3 + β 4 ECDF ( X 4 ) + β 5 X 5 + β 6 X 6
The process flow for hypothesis testing was following:
−
creation of a logit model for binary data,
−
confidence intervals for regression parameters,
75
(4.15)
(4.16)
−
collinearity test,
−
test of normality.
The logit model for binary data
The logit model for binary data was constructed, obtaining thus numerical values for the
coefficients36 of the previously constructed regression equation. The variables were for the
purpose of testing given nicknames coming out from their original designations. The detailed
calculation and testing process can be found in the Appendix 5 “Logit model for binary data.”
In respect to the p-values, showing the significance of the independent variables in the model;
total costs, risk category and accounts receivable collected were excluded. The final model
was defined with its main features as follows.
Tab. 4 The final logit model for binary data
coefficient
std. error
t-ratio
p-value
------------------------------------------------------------------------------------------------------const
-7.27547
2.55e-05 ***
1.72799
-4.210
ECDF_ProfMarg37 9.73058
3.18142
3.059
0.0022 ***
ECDF_Turn38
24.3314
5.17502
4.702
2.58e-06 ***
PayMor39
-0.0868706
0.0199073
-4.364
1.28e-05 ***
McFadden R-squared 0.749414
Adjusted R-squared 0.712233
(Source: Own calculation in Gretl based on internal data of Bosch)
The first statement coming out of the model was, that the estimated signs of the individual
factors were correct, with profit margin and turnover having positive effect on accounts
receivable collected while on the contrary payment moral having negative effect on it. The
second statement was that 74.94 % of estimated values were explained by the regression
equation, which was a very good result. This was proved by R2 explaining the quality of the
model in this way.
In the equation designed as β0 - β6.
Empirical cumulative distribution function of profit margin.
38
Empirical cumulative distribution function of turnover.
39
Payment moral.
36
37
76
Confidence intervals for the regression parameters
As a next step confidence intervals for the regression parameters were calculated, to test the
hypothesis of interval including zero coefficients. Since both intervals calculated did not
include zero, the hypothesis was rejected, concluding that the coefficients were significantly
different from zero.
The collinearity test
The collinearity test examined the probability of linear dependency between two explanatory
variables, taking into account also multicollinearity case where there would be linear
dependency between several explanatory variables. Since all the calculated variance inflation
factors were smaller than 10, it was concluded that there was no multicollinearity present in
the model.
The test of normality
In the test of normality, it was examined whether the error terms, known also as residuals,
were normally distributed as said in the hypothesis. Since the p-value was calculated as
0.00000, and was for that less than 0.05, the hypothesis was rejected with the conclusion that
the residuals were not normally distributed (see Fig. 14.).
Fig. 14 Test statistic for normality
(Source: Own calculation based on internal data of Bosch)
77
Final overview of results
After undertaking all necessary hypotheses testing, the final overview of results can be
presented. The model equation looks as follows.
Y i = − 7 .2 7 5 5 + 9 .7 3 0 6 * E C D F ( X 1 ) + 2 4 .3 3 1 4 * E C D F ( X 2 ) − 0 .0 8 6 9 X
3
(4.17)
Where following variables are present:
−
Y, as probability of occurrence of a good or a bad customer,
−
X1, as profit margin (in CZK) in the year 2010,
−
X2, as turnover (in CZK) in the year 2010,
−
X3, as payment moral (in days).
Given that the growth in all the factors determining the amount of good customers among bad
ones is zero; this amount will decline by 0.073 %.
Given that all the factors determining the amount of good customers among bad ones are
constant, an increase in profit margin and turnover will result in an increase of this amount by
0.097 % and 0.243 % respectively.
Given that all the factors determining the amount of good customers among bad ones are
constant, an increase in payment moral, represented by average arrears, will result in decrease
of this amount by 0.001 %.
4.4.2 Scoring of the customers using logistic regression
As soon as the series of hypothesis testing were carried out and the logit model for binary data
was accepted, the calculation of the scores for individual customers could start. This was done
by so called logistic regression (see eqn. 4.18). The coefficients used came from the logit
model, which was constructed and tested in the previous chapter.
The goal of this scoring technique was to calculate a score, which would correspond to the
risk each customer represented to Bosch. The score itself could be any value between 0 and 1
inclusive. The essence was that, the higher the score of a customer, the lower the risk he
represented.
S co re =
1
1+ e
− ( − 7 .2 7 5 5 + 9 .7 3 0 6 x 1 + 2 4 .3 3 1 4 x 2 − 0 .0 8 6 9 x 3 )
78
(4.18)
Where following variables were present:
−
X1, as profit margin (in CZK) in the year 2010,
−
X2, as turnover (in CZK) in the year 2010,
−
X3, as payment moral (in days).
A division of the companies from the selected data sample corresponding to their achieved
score; was depicted in the Fig. 15. The grey columns as a percental representation of the good
customers whereas the red columns as a percental representation of the bad customers; both
for each interval of the score. An ideal situation would be, if the score 1 was assigned to all
the good customers and the score 0 to all the bad ones.
However, the reality was different. It was quite complicated and demanding to examine all the
characteristics of individual companies. For that reason the data processed had to be
considered as incomplete and information used as imperfect. It was clear, that the function
could not separate the companies according to their quality with 100 % precision. There
would be always certain amount of the bad customers which were classified as good ones and
vice versa. Thus the remaining intention was to eliminate such cases.
0,9
Amount of customers (in %)
0,8
0,7
0,6
0,5
Good customers
0,4
Bad customers
0,3
0,2
0,1
0,0
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
Achieved score (in %)
Fig. 15 Histogram of the estimated scoring function
(Source: Own calculation based on internal data of Bosch)
79
The interpretation of the scores depicting the importance of individual customers to Bosch
follows. The essence of the assigned scores is, the higher the score the better the customer,
hence also the higher his importance to Bosch. Therefore grow and build strategy should be
applied to the customers with the highest scores, offering to them preferential conditions and
extraordinary services as higher credit limits and extended payment terms. Whereas hold and
maintain strategy should be applied to the customer with average scores, by offering them
common conditions without any extraordinary services. The customers with very low scores
should be on the contrary handled cautiously, without any extra attention or efforts due to the
potential loss they represent.
A cumulative distribution of the scores allocated to the good and the bad customers was
depicted in the Fig. 16. An ideal situation with 100 % guaranteed separation would show the
Lorenz curve shaped as a right-angle. The source data for both graphs as well as for Gini
coefficient calculation can be found in the Appendix 6 “Scoring function.”
1,0
0,9
Good customers (in %)
0,8
0,7
0,6
Lorenz curve
0,5
Equality curve
0,4
0,3
0,2
0,1
0,0
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
Bad customers (in %)
Fig. 16 Lorenz curve of the estimated scoring function
(Source: Own calculation based on internal data of Bosch)
According to Jakubík (2007), is the generally accepted value of the Gini coefficient for this
type of the model above the 60% border in dependence to the used data and the scoring target.
Therefore it can be said that the estimated model with the calculated Gini coefficient of
65.59 % fulfils the criterion. The separation of the companies from the selected data sample is
thus adequate.
80
5 DISCUSSION AND CONCLUSION
The main goal of this diploma thesis was to propose a model of efficient credit management
system of the daughter company of Robert Bosch GmbH. The first fact to mention is that the
organization together with the process flow of actual credit management system is wellthough out, however not used to its full potential yet.
The first drawback is the actual way of risk categories specification. There are actually five
different risk categories having the ridge between a good and bad customer between category
2 and 3. Frankly said, it is quite unfair to label a customer who pays 6 days overdue and those
paying up to 90 days overdue with the same category 3. An ideal solution would be a creation
of a new risk category which would overlap the gap between risk category 1 and 2, making
thus the passage to the category 3 more smooth. The new risk category would then include
payments up to 45 days overdue.
The second drawback of current system is that credit limits need to be calculated manually
and inserted in the system for their adjustments. This is very time consuming and due to the
lack of personal capacity cannot be done regularly. With respect to this fact, the credit limits
are outdated not corresponding to the actual status of the customer. Therefore the actual rate
of maximal monthly growth or decline of a credit limit is insufficient and customer’s orders or
supplies get automatically blocked in the system. This would not happen if the credit limits
were adjusted regularly. The proposal here is to either have the information system fully
automated, which is naturally costly, or to employ an analysis which would reveal together
critical and excellent payment tendency of the customers enabling hence immediate reaction
for credit limits adjustment.
The third drawback observed in actual credit management system is the dunning process
which is currently done mainly by the personnel of the Financial department. The fact that in
the past many overdue accounts were left without further processing, mainly from the side of
the sales representatives, has shown that there is an immediate need for a change. Sales
representatives need to be more motivated in order to carry out the dunning process partially
by themselves. They know the customer the best and know how to put him under pressure.
The proposal is to implement financial premiums as well as sanctions, for instance loss of
some privileges, according to the sales representative’s activity to make his customer pay.
The first partial goal was to determine the influences of chosen processes of credit
management on the efficiency of entire credit management system. In detail, the task was to
examine the relation between credit terms determination, forms of dunning, mean of clearing
via debt collection agency, way of approval process upon credit check of blocked orders and
81
consignments, process of solvency check as independent variables and accounts receivable
collected as a dependent variable. For that hypothesis testing of the estimated model including
selected processes, further divided into several factors, was carried out in respect to accounts
receivable collected, in representation of whole credit management system. For hypothesis
testing and data modelling, a statistical program Gretl was used.
After series of testing the initially proposed model with the factors mentioned above, turned to
be incorrectly specified due to the fact that the model itself comprised inacceptable features
such as heteroskedasticity and inadequate specification, as revealed by the White’s and
Ramsey’s RESET test. Therefore some of the factors had to be excluded due to very low data
variability and data singularity while another were excluded due to their inconclusiveness
manifested by their p-values. The final outcome of the testing has brought following
statements. Firstly, if the growth in all the factors determining the growth of collected
accounts receivable is zero, total collected accounts receivable will grow by 0.138 %.
Secondly, if all the factors determining growth of collected accounts receivable are constant,
an increase in turnover will result in an increase of total collected accounts receivable by
0.989 %. Finally, if all the factors determining growth in collected accounts receivable are
constant, an increase in risk category and payment moral, represented by average arrears, will
result in a decrease of total collected accounts receivable by 0.084 % and 0.001 %
respectively. It can be thus concluded that all the factors of the model have impact on
accounts receivable as expected. Turnover with the greatest impact among others is positively
influential on accounts receivable collected whereas risk category with payment moral have a
negative impact.
The hypothesis testing has given the evidence that factors like turnover, risk category and
payment moral are conclusive in their influence on accounts receivable collected, hence also
the processes they are part of are evidently essential in credit management system.
A second partial goal of this diploma thesis was a proper allocation of individual customers
within the entire customer portfolio due to the assumption that determination of customer’s
strategic importance to Bosch is important for resources allocation and therefore also for
whole credit management system.
The outcome of the customer portfolio analysis has shown the representation of the key
customer of Bosch placed in the upper right corner of the matrix. The essence for this was, the
higher the scores for dimensions, the customer today and customer tomorrow, the greater the
customer’s importance for Bosch. It has been therefore concluded that grow and build
strategy should be applied to the key customers, offering them preferential conditions and
extraordinary services as higher credit limits and extended payment terms. On the other hand
82
hold and maintain strategy should be applied for average important customers, where
common conditions without any extraordinary services should be offered. Last but not least,
harvest or exit strategy should be applied for the least important customers of Bosch, where
paying any extra attention or efforts would not bring anything but a loss for the company and
must be therefore avoided. A second important message was that the customer’s importance
derives from many factors and therefore taking a single factor of customer’s behaviour, for
instance a turnover, as a sole evidence of his importance can be a serious a mistake. It should
be therefore remembered that a customer with an interesting turnover having qualities as good
payment moral and low risk category is important and should be taken seriously.
An analysis of the relation between time dedicated by sales representatives to their customers
versus the benefit the customer brings to Bosch, has shown that smaller customers are often
very demanding and time consuming, sometimes even inadequately. The conclusion is
therefore clear, the key customers must be focused on; since they are those who represent the
greatest benefit to the company and exactly to them special services and time should be
devoted as extra comfort. Hence to the others, only as much time as necessarily needed should
be dedicated.
The scoring method in comparison to the customer portfolio analysis, took advantage of
focusing exclusively on two resulting features, occurrence of a good customer or his absence.
The assumption for the scoring method was a creation of a logit model. The logit model for
binary data started with the same set of factors as used in the portfolio matrix. After series of
testing, some of them had to be excluded due to inconclusiveness of the data manifested by
their p-values. These are the statements of the final model. Firstly, if the growth in all the
factors determining the amount of good customers among bad ones is zero, this amount will
decline by 0.073 %. Secondly, if all the factors determining the amount of good customers
among bad ones are constant, a percentage increase in profit margin and turnover will result
in an increase of this amount by 0.097 % and 0.243 % respectively. Finally, if all the factors
determining the amount of good customers among bad ones are constant, an increase in
payment moral, represented by average arrears, will result in a decrease of this amount by
0.001 %.
The hypothesis testing has given the evidence that factors like profit margin, turnover and
payment moral are conclusive in their impact on occurrence of a good customer; hence also
the processes they are part of are evidently essential in credit management system.
The final graph has showed a clear allocation of good and bad customers according to their
acquired scores in the essence, the higher the score the better the customer and the higher his
importance to Bosch. Therefore grow and build strategy should be applied to the customers
83
with the highest scores, offering to them preferential conditions and extraordinary services as
higher credit limits and extended payment terms. Whereas hold and maintain strategy should
be applied to the customer with average scores, by offering them common conditions without
any extraordinary services. The customers with very low scores should be on the contrary
handled cautiously, without any extra attention or efforts due to the potential loss they
represent. The sufficiently adequate separation of good and bad customers was proved by the
Lorenz curve having almost the right-angle shape and by the calculated Gini coefficient of
65.59 %.
To conclude, it is important to state that the customer’s scoring is simply a very interesting
tool for customers’ diversification in order to optimize credit management system of the
company. It is therefore highly recommended to implement it into company’s internal
processes if Bosch wants to score their customers from this viewpoint and with this target, or
for some other reasons.
84
6 REFERENCES
Anderson, R. The Credit Scoring ToolkitToolkit: Theory and Practice for Retail Credit Risk
Management and Decision Automation. 2007. [online]. [cit.2010-08-16]. Available on:
<http://books.google.com/books?id=7LlGfPvOJLoC&printsec=frontcover&dq=ANDERSON
+The+Credit+Scoring+Toolkit+Toolkit&hl=cs&ei=gRVpTJqxKaeW4ga9r5WZBA&sa=X&o
i=book_result&ct=result&resnum=1&ved=0CCMQ6AEwAA#v=onepage&q&f=false >.
Arbcourt.cz. The rules of arbitration court. 2007. [online]. [cit.2010-08-09]. Available on:
<http://www.arbcourt.cz/en_index.php?url=rady/en_rad_mez_od_20070201_uz.htm>.
Bařinová, D., Vožnáková, I. Pohledávky – právně – daňově – účetně. 3rd edition. Praha:
GRADA Publishing a.s., 2007. 136 pages. ISBN 978-80-247-1816-3.
Bass, R. M. V. Credit management: how to manage credit effectively and make a real
contribution to profits. 3rd edition. Cheltenham: Stanley Thornes Ltd, 1991. 295 pages. ISBN
0-7487-1374-3.
Besanko, D. Economics of strategy. 4th edition. Hoboken, NJ: J. Wiley & Sons, 2007.
606 pages. ISBN 978-0-471-67945-5.
Business.center.cz. Zákon č. 182/2006 Sb. o úpadku a způsobech jeho řešení. 2006. [online].
[cit.2010-08-12]. Available on:
<http://business.center.cz/business/pravo/zakony/insolvencni/>.
Capell, J., Swine, A. Internationales Kredit-Management. 1st edition. Frankfurt am Main:
Metzner, 1981. 200 pages. ISBN 3-7875-4203-5.
Clarke, B. W. Handbook of International Credit Management. 3rd edition. Burlington:
Gower Publishing Limited, 2001. 389 pages. ISBN 0-566-083760.
Colquitt, J. Credit risk management : how to avoid lending disasters and maximize earnings.
3rd edition. New York: McGraw-Hill, 2007. 373 pages. ISBN 978-0-07-144660-0.
Drbohlav, J., Pohl, T. Pohledávky z právního, účetního a daňového pohledu. 1st edition.
Praha: ASPI a.s., 2006. 220 pages. ISBN 80-7357-162-5.
Edwards, B. Credit management handbook. 5th edition. Aldershot: Gower Publishing
Limited, 2004. 561 pages. ISBN 0-566-08585-2.
Emery, D. R., Finnerty, J. D., Stowe J. D. Corporate Financial Management. 3rd edition.
New Jersey: Pearson Education Inc., 2004. 900 pages. ISBN 0-13-083236-X.
85
Gestel, T. V., Baesens, B. Credit risk management: basic concepts: financial risk
components, rating analysis, models, economic and regulatory capital. 1st edition. Oxford:
Oxford University Press, 2009. 535 pages. ISBN 978-0-19-954511-7.
Gretl. [online]. [cit.2010-10-20]. Gnu Regression, Econometrics and Time/series Library.
Available on: <http://gretl.sourceforge.net/win32/>.
Halinen, A., Terho, H. Customer portfolio analysis practices in different exchange contexts.
[online]. [cit.2010-10-20]. Available on:
<http://www.sciencedirect.com/science?_ob=ArticleListURL&_method=list&_ArticleListID
=1506328698&_sort=r&_st=13&view=c&_acct=C000048749&_version=1&_urlVersion=0&
_userid=939830&md5=b6684c77781634dcca0481543fedae81&searchtype=a>.
Chaloupka, R., Kopescni, J. Modeling Bank Loan LGD of Croporate and SME Segments:
A Case Study. 2009. [online]. [cit.2010-08-16]. Available on:
<http://journal.fsv.cuni.cz/storage/1165_360-382---chal.-kop.pdf>.
Jakubík, P., Teplý, P. Skóring jako indikátor finanční stability. 2007. [online]. [cit.2010-0815]. Available on:
<http://www.cnb.cz/miranda2/export/sites/www.cnb.cz/cs/financni_stabilita/zpravy_fs/fs_200
7/FS_2007_clanek_2.pdf>.
Knot, O., Vychodil, O. What drives the optimal bankruptcy law design? 2005. [online].
[cit.2010-08-11]. Available on: <http://journal.fsv.cuni.cz/storage/1011_s_110_123.pdf>.
Kolb, R. W., Rodriguez R. J. Financial Management. 1st edition. Toronto: D.C. Health and
Company, 1992. 709 pages. ISBN 0-669-27158-6.
Kráčalíková, G. Zajištění a vymáhání pohledávek v rámci konkurzního řízení kapitálových
společností v právních, daňových a účetních souvislostech. 1st edition. Praha: VOX, 2004.
120 pages. ISBN 80-86324-37-0.
Pilátová, J., Richter, J. Pohledávky a jejich řešení v podnikové praxi: praktická řešení a
vzory: daňová a účetní problematika: vybraná související ustanovení. 1st edition. Olomouc:
ANAG, 2009. 111 pages. ISBN 978-80-7263-534-4.
Portal ARES. [online]. [cit.2010-08-20]. Available on:
<http://wwwinfo.mfcr.cz/ares/ares_es.html.cz>.
Portal Justice.cz. [online]. [cit.2010-08-20]. Available on:
<http://portal.justice.cz/justice2/uvod/uvod.aspx>.
86
Portal
Value
Based
Management.
BSG
Matrix.
2007.
[online].
[cit.2010-09-
05]. Available on: <http://www.valuebasedmanagement.net/methods_bcgmatrix.html>.
Procházková, D., Hanuš, V. Nedobytné pohledávky: z účetního, daňového a právního
pohledu. 1st edition. Ostrava: MIRAGO, 1996. 69 pages. ISBN 80-85922-30-4.
Rödl, H., Winkels, A. Kreditmanagement in der Unternehmenspraxis. 1st edition. Stuttgart:
Schäffer, 1983. 194 pages. ISBN 3-8202-0235-8.
Seidler, J., Jakubík, P. The Merton Approach to Estimating Loss Given Default: Application
to
the
Czech
Republic.
2009.
[online].
[cit.2010-08-16].
Available
on:
<http://www.cnb.cz/miranda2/export/sites/www.cnb.cz/en/research/research_publications/cnb
_wp/download/cnbwp_2009_13.pdf>.
Sieck, H. Kundenportfoliomanagement Checkliste. 2006. [online]. [cit.2010-09-10].
Available on:
<http://www.sieck-consulting.de/tl_files/Sieck_Daten/News/SC_NEWS_August_2006_
Kundenportfoliomanagement.pdf>.
Terho, H. Customer portfolio management, the construct and performance. [online].
2008. [cit.2010-10-20]. Available on: <http://info.tse.fi/julkaisut/vk/Ae4_2008.pdf>.
Vojtek, M., Kočenda, E. Credit scoring methods. 2006. [online]. [cit.2010-08-16]. Available
on: <http://journal.fsv.cuni.cz/storage/1050_s_152_167.pdf>.
Vožňáková, I. Efektivní řízení pohledávek. 1st edition. Praha: GRADA Publishing a.s., 2004.
122 pages. ISBN 80-247-0770-5.
Zolkiewski, J. Relationship portfolios – Past, Present and Future. 2002. [online]. [cit.201010-15]. Available on: <http://www.impgroup.org/uploads/papers/136.pdf>.
87
7 LIST OF FIGURES AND TABLES
List of figures
Fig. 1 The reporting channels for credit management........................................................... 14
Fig. 2 Sequential credit analysis ............................................................................................ 19
Fig. 3 Factors affecting level of investment in debtors .......................................................... 27
Fig. 4 Overview of scoring systems suitable for different stages of a customer credit .......... 33
Fig. 5 Overview of modelling techniques ............................................................................... 34
Fig. 6 Credit management organization of Bosch ................................................................. 46
Fig. 7 Process flow chart of credit management in Bosch..................................................... 47
Fig. 8 Accounts receivable handed over to B4B .................................................................... 55
Fig. 9 Accounts receivable handed over to the company lawyer ........................................... 56
Fig. 10 Test statistic for normality ........................................................................................... 64
Fig. 11 A proposed model for customer portfolio analysis ...................................................... 68
Fig. 12 Customer portfolio analysis for the company Bosch ................................................... 72
Fig. 13 Customer’s benefit versus demand .............................................................................. 73
Fig. 14 Test statistic for normality ........................................................................................... 77
Fig. 15 Histogram of the estimated scoring function ............................................................... 79
Fig. 16 Lorenz curve of the estimated scoring function ........................................................... 80
Fig. 17 Boston Consulting Group Model ................................................................................. 93
Fig. 18 Two-step customer portfolio analysis from Fioca ....................................................... 93
Fig. 19 Customer classification matrix from Shapiro .............................................................. 94
Fig. 20 Three-dimensional classification matrix from Trunbull and Zolkiewski ..................... 94
Fig. 21 Sample questionnaire for sales representatives........................................................... 95
List of tables
Tab. 1 Financial ratios for credit assessment worksheet ....................................................... 17
Tab. 2 Payment terms applicable in the company Bosch ....................................................... 49
Tab. 3 The final model by the ordinary least method............................................................. 62
Tab. 4 The final logit model for binary data .......................................................................... 76
Tab. 5 Annual turnover with Bosch (in CZK) ........................................................................ 96
Tab. 6 Annual profit margin (in CZK) ................................................................................... 96
Tab. 7 Risk category of the customer ..................................................................................... 96
Tab. 8 Payment moral of the customer .................................................................................. 97
Tab. 9 Actual customer’s competitiveness on the Czech market ............................................ 97
Tab. 10 Weighting of scores for the dimension customer today .............................................. 97
Tab. 11 Growth potential of the customer................................................................................ 98
Tab. 12 Potential customer’s competitiveness on the Czech market ....................................... 98
Tab. 13 Future economic situation of the customer ................................................................. 98
Tab. 14 Growth of the customer’s turnover with Bosch .......................................................... 98
Tab. 15 Cross selling potential of the customer ....................................................................... 99
Tab. 16 Weighting of scores for the dimension customer tomorrow ....................................... 99
88
8 APPENDICES
Appendix 1: Model by ordinary least squares method
Model 1: OLS, using observations 1-237
Dependent variable: l_AccRecCol40
coefficient
std. error
t-ratio
p-value
----------------------------------------------------------------------------------------------------------const
-0.000196416
0.144846
-0.001356
0.9989
l_Turn41
1.06224
0.0621059
17.10
2.80e-042 ***
l_TotCosts42 -0.0616093
0.0591959
-1.041
0.2991
l_RisCat 43
-0.0531648
0.0286840
-1.853
0.0652 *
l_CapDem44 0.00979924
0.0252890
0.3875
0.6988
ProfMarg45
-2.54453e-08
2.19146e-08
-1.161
0.2469
PayMor46
-0.000665850
0.000439381
-1.515
0.1311
Dun047
-9.58350e-08
1.84955e-08
-5.182
4.97e-07 ***
Dun1
-4.26558e-07
1.93431e-07
-2.205
0.0285 **
Dun2
-1.61923e-06
9.78980e-06
-0.1654
0.8688
PayCal48
5.72655e-07
7.11400e-06
0.08050
0.9359
Law49
-4.40062e-06
8.20539e-07
-5.363
2.07e-07 ***
B4B50
-1.97993e-07
5.54098e-07
-0.3573
0.7212
Mean dependent var 13.00409
S.D. dependent var
1.743959
Sum squared resid
4.152202
S.E. of regression
0.137381
R-squared
0.994115
Adjusted R-squared 0.993794
40
Natural logarithm of accounts receivable collected.
Natural logarithm of turnover.
42
Natural logarithm of total costs.
43
Natural logarithm of risk category.
44
Natural logarithm of capacity demand.
45
Profit margin.
46
Payment moral.
47
Dunning.
48
Payment calendar.
49
Lawyer.
50
Cash collection agency B4B.
41
89
F(12, 220)
3097.136
P-value(F)
7.0e-238
Log-likelihood
138.5794
Akaike criterion
-251.1588
Schwarz criterion
-206.2953
Hannan-Quinn
-233.0678
Test statistic: F(7, 220) = 0.759591
with p-value = P(F(7, 220) > 0.759591) = 0.62179
Model 2: OLS, using observations 1-237
Dependent variable: l_AccRecCol
coefficient
std. error
t-ratio
p-value
----------------------------------------------------------------------------------------------------------const
-0.0537345
0.162293
-0.3311
0.7409
l_Turn
1.05891
0.0688012
15.39
4.84e-037 ***
l_TotCosts
-0.0632255
0.0654748
-0.9656
0.3353
l_RisCat
-0.0915881
0.0316385
-2.895
0.0042 ***
l_CapDem
-0.0364646
0.0267406
-1.364
0.1740
ProfMarg
-4.81939e-08
2.39822e-08
-2.010
0.0457 **
PayMor
-0.00111228
0.000470515
-2.364
0.0189 **
Mean dependent var 13.00409
S.D. dependent var
1.743959
Sum squared resid
5.444515
S.E. of regression
0.155212
R-squared
0.992284
Adjusted R-squared 0.992079
F(6, 226)
4843.892
P-value(F)
1.2e-235
Log-likelihood
107.0114
Akaike criterion
-200.0228
Schwarz criterion
-175.8655
Hannan-Quinn
-190.2815
Test statistic: F(3, 226) = 1.63762
with p-value = P(F(3, 226) > 1.63762) = 0.181494
90
Model 3: OLS, using observations 1-237
Dependent variable: l_AccRecCol
coefficient
std. error
t-ratio
p-value
----------------------------------------------------------------------------------------------------------const
0.137697
0.0808093
1.704
0.0897 *
l_Turn
0.988920
0.00589940
167.6
1.39e-241 ***
l_RisCat
-0.0841286
0.0315174
-2.669
0.0081 ***
PayMor
-0.00128078
0.000465112
-2.754
0.0064 ***
Mean dependent var 13.00409
S.D. dependent var
1.743959
Sum squared resid
5.562870
S.E. of regression
0.155859
R-squared
0.992116
Adjusted R-squared 0.992013
F(3, 229)
9605.907
P-value(F)
1.8e-240
Log-likelihood
104.5060
Akaike criterion
-201.0120
Schwarz criterion
-187.2078
Hannan-Quinn
-195.4455
Log-likelihood for AccRecCol = -2925.45
RESET test for specification
Null hypothesis: specification is adequate
Test statistic: F(2, 227) = 0.0238649
with p-value = P(F(2, 227) > 0.0238649) = 0.97642
White's test for heteroskedasticity
Null hypothesis: heteroskedasticity not present
Test statistic: LM = 7.47964
with p-value = P(Chi-Square(9) > 7.47964) = 0.587312
Test for normality of residual
Null hypothesis: error is normally distributed
Test statistic: Chi-square(2) = 806.339
with p-value = 8.05007e-176
91
Non-linearity test (squares)
Null hypothesis: relationship is linear
Test statistic: LM = 1.77034
with p-value = P(Chi-Square(3) > 1.77034) = 0.62141
Non-linearity test (logs)
Null hypothesis: relationship is linear
Test statistic: LM = 3.65572
with p-value = P(Chi-Square(3) > 3.65572) = 0.30112
Analysis of variance:
Sum of squares
df
Mean square
Regression
700.04
3
233.347
Residual
5.56287
229
0.024292
Total
705.603
232
3.04139
R^2 = 700.04 / 705.603 = 0.992116
F(3, 229) = 233.347 / 0.024292 = 9605.91 [p-value 1.81e-240]
F0.95;3;229=2.64.
Confidence interval for the regression parameters
t(229, 0.025) = 1.970
VARIABLE
COEFFICIENT
95% CONFIDENCE INTERVAL
const
0.137697
-0.0215280
0.296921
l_Turn
0.988920
0.977296
1.00054
l_RisCat
-0.0841286
-0.146230
-0.0220275
PayMor
-0.00128078
-0.00219723
-0.000364339
Collinearity test
l_Turn
1.021
l_RisCat
1.245
PayMor
1.235
92
Appendix 2: Overview of existing portfolio models
Fig. 17 Boston Consulting Group Model
(Source: Portal Value Based Management (2007))
Fig. 18 Two-step customer portfolio analysis from Fioca
(Source: Zolkiewski (2002))
93
Fig. 19 Customer classification matrix from Shapiro
(Source: Zolkiewski (2002))
Fig. 20 Three-dimensional classification matrix from Trunbull and Zolkiewski
(Source : Zolkiewski (2002))
94
Appendix 3: Questionnaire for portfolio analysis
Bosch Rexroth
Questionnaire for Customer Portfolio Analysis
Sample Questionnaire
Pavel Novak
Sales Engineer:
Customer:
Data evaluation:
15.11.2010
High Technology a.s.
Dear Mr Novak,
we are kindly asking you to fill out this questionnaire which is a part of the portfolio analysis of our customers.
For faster processing and data overwiew, we designed the questionnaire in two parts. The first part is this sample
questionnaire, which contains all questions. The second part is a table enabling the choice of your answers.
There you can also find the data about the customer' s turnover, profit margin, risk category and payment moral.
The goal of this questionnaire is to ensure an objective analysis based on the data comming from your expert
knowledge. Therefore we kindly ask you to send us back the filled out answer table per email within 5 working
days upon the receival of this questionnaire.
1. Customer´s growth potential forecasted for 3 next years is ...
more than -75 %
between 1 and 10 %
between -75 and -30 %
between 11 and 30 %
between -29 and -10 %
between 31 and 75 %
between -9 and 0 %
more than 75 %
2. Actual competitiveness of the customer on the Czech market is …
very bad
very good
bad
average
good
3. In the viewpoint of 3 next years, the competitiveness of the customer on the Czech market
will be ...
worse
same
better
4. Future economic situation of the customer will be … in comparison to the past.
worse
same
better
5. Potential turnover growth with the customer is expected to be … in 3 next years.
more than -75 %
between 1 and 10 %
between -75 and -30 %
between 11 and 30 %
between -29 and -10 %
between 31 and 75 %
between -9 and 0 %
more than 75 %
6. Sale of additional and/or new products to the customer is highly probable …
no
yes
7. Concerning one´s total capacity, servicing the customer takes …
less than 5 %
between 31 and 40 %
between 5 and 10 %
between 41 and 50 %
between 11 and 20 %
Fig. 21 Sample questionnaire for sales representatives
(Source : Own elaboration)
95
between 21 and 30 %
Appendix 4: Scoring and weighting tables for portfolio analysis
Tab. 5 Annual turnover with Bosch (in CZK)
Criteria
less than 5,000,000
between 5,000,000 and 10,000,000
between 11,000,000 and 20,000,000
between 21,000,000 and 30,000,000
between 31,000,000 and 40,000,000
between 41,000,000 and 50,000,000
between 51,000,000 and 60,000,000
more than 60,000,000
Score
1
2
3
4
5
6
7
8
(Source: Own elaboration based on internal data of Bosch)
Tab. 6 Annual profit margin (in CZK)
Criteria
more than -10,000,000
between -10,000,000 and -7,000,000
between -6,000,000 and -4,000,000
between -3,000,000 and 0
between 1,000,000 and 4,000,000
between 5,000,000 and 7,000,000
between 8,000,000 and 10,000,000
more than 10,000,000
Score
1
2
3
4
5
6
7
8
(Source: Own elaboration based on internal data of Bosch)
Tab. 7 Risk category of the customer
Criteria
003
002
001
Score
1
5
8
(Source: Own elaboration based on internal data of Bosch)
96
Tab. 8 Payment moral of the customer
Criteria
more than 200
between 200 and 151
between 150 and 101
between 100 and 51
between 50 and 26
between 25 and 1
between 0 and -25
more than -25
Score
1
2
3
4
5
6
7
8
(Source: Own elaboration based on internal data of Bosch)
Tab. 9 Actual customer’s competitiveness on the Czech market
Criteria
very bad
bad
average
good
very good
Score
1
2
4
6
8
(Source: Own elaboration based on internal data of Bosch)
Tab. 10 Weighting of scores for the dimension customer today
Factors
annual turnover with Bosch
annual profit margin
risk category of the customer
payment moral of the customer
customer' s competitiveness on the Czech market
total
Weighting (in %)
0,25
0,23
0,17
0,15
0,2
1
(Source: Own elaboration based on internal data of Bosch)
97
Tab. 11 Growth potential of the customer
Criteria
more than -75 %
between -75 and -30 %
between -29 and -10 %
between -9 and 0 %
between 1 and 10 %
between 11 and 30 %
between 31 and 75 %
more than 75 %
Score
1
2
3
4
5
6
7
8
(Source: Own elaboration based on internal data of Bosch)
Tab. 12 Potential customer’s competitiveness on the Czech market
Criteria
worse
same
better
Score
1
5
8
(Source: Own elaboration based on internal data of Bosch)
Tab. 13 Future economic situation of the customer
Criteria
worse
same
better
Score
1
5
8
(Source: Own elaboration based on internal data of Bosch)
Tab. 14 Growth of the customer’s turnover with Bosch
Criteria
more than -75 %
between - 75 % and -30 %
between -29 and -10 %
between -9 and 0 %
between 1 and 10 %
between 11 and 30 %
between 31 and 75 %
more than 75 %
Score
1
2
3
4
5
6
7
8
(Source: Own elaboration based on internal data of Bosch)
98
Tab. 15 Cross selling potential of the customer
Criteria
no
yes
Score
1
8
(Source: Own elaboration based on internal data of Bosch)
Tab. 16 Weighting of scores for the dimension customer tomorrow
Factors
growth potential of the customer
potential customer competitiveness on the Czech market
future economic situation of the customer
growth of the customer' s turnover with Bosch
cross selling potential of the customer
total
Weighting (in %)
0,2
0,23
0,15
0,25
0,17
1
(Source: Own elaboration based on internal data of Bosch)
99
Appendix 5: Logit model for binary data
Model 1: Logit, using observations 1-237
Dependent variable: Dummy
coefficient
std. error
t-ratio
p-value
---------------------------------------------------------------------------------------------------------------const
-8.34212
2.11480
-3.945
7.99e-05 ***
ECDF_ProfMarg
9.32558
3.24489
2.874
0.0041 ***
TotCosts
-1.70796e-06
2.31912e-06
-0.7365
0.4614
RisCat
0.682474
0.680833
1.002
0.3161
ECDF_Turn
24.5335
5.26819
4.657
3.21e-06 ***
AccRecCol
1.57696e-06
2.54630e-06
0.6193
0.5357
PayMor
-0.0885067
0.0200311
-4.418
9.94e-06 ***
Mean dependent var 0.831224
S.D. dependent var 0.000241
McFadden R-squared 0.756623
Adjusted R-squared 0.691558
Log-likelihood
-26.18342
Akaike criterion
66.36683
Schwarz criterion
90.64325
Hannan-Quinn
76.15177
Model 2: Logit, using observations 1-237
Dependent variable: Dummy
coefficient
std. error
t-ratio
p-value
------------------------------------------------------------------------------------------------------const
-7.27547
1.72799
-4.210
2.55e-05 ***
ECDF_ProfMarg
9.73058
3.18142
3.059
0.0022 ***
ECDF_Turn
24.3314
5.17502
4.702
2.58e-06 ***
PayMor
-0.0868706
0.0199073
-4.364
1.28e-05 ***
Mean dependent var 0.831224
S.D. dependent var
McFadden R-squared 0.749414
Adjusted R-squared 0.712233
Log-likelihood
Akaike criterion
-26.95908
100
0.000149
61.91816
Schwarz criterion
75.79040
Hannan-Quinn
67.50955
Confidence interval for the regression parameters
z(0.025) = 1.9600
VARIABLE
COEFFICIENT
95% CONFIDENCE INTERVAL
const
-7.27547
-10.6623
-3.88866
ECDF_ProfMarg
9.73058
3.49511
15.9661
ECDF_Turn
24.3314
14.1886
34.4743
PayMor
-0.0868706
-0.125888
-0.0478531
Collinearity test
ECDF_ProfMarg
1.005
ECDF_Turn
1.007
PayMor
1.002
Test of normality of residuals
Frequency distribution for uhat14, obs 1-237
number of bins = 15, mean = 8.79698e-013, sd = 0.194959
interval
< -0.77333
midpt
frequency rel.
cum.
-0.83666
3
1.27%
1.27%
-0.77333 - -0.64667
-0.71000
1
0.42%
1.69%
-0.64667 - -0.52001
-0.58334
0
0.00%
1.69%
-0.52001 - -0.39335
-0.45668
5
2.11%
3.80%
-0.39335 - -0.26669
-0.33002
4
1.69%
5.49%
-0.26669 - -0.14003
-0.20336
3
1.27%
6.75%
-0.14003 - -0.013364
-0.076695
18
7.59%
14.35% **
-0.013364 - 0.11330
0.049966
185 78.06% 92.41% **************************
0.11330 - 0.23996
0.17663
7
2.95%
95.36% *
0.23996 - 0.36662
0.30329
1
0.42%
95.78%
0.36662 - 0.49328
0.42995
3
1.27%
97.05%
101
0.49328 - 0.61994
0.55661
2
0.84%
97.89%
0.61994 - 0.74660
0.68327
3
1.27%
99.16%
0.74660 - 0.87326
0.80993
1
0.42%
99.58%
> = 0.87326
0.93660
1
0.42%
100.00%
Test for null hypothesis of normal distribution:
Chi-square(2) = 266.272 with p-value 0.00000
102
Appendix 6: Scoring function
Tab. 17 Source data for histogram of estimated scoring function
Score
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
Total
X_bad customers X (%)
Y_good customers Y (%)
11
0,2750
0
0,0000
11
0,2750
0
0,0000
4
0,1000
0
0,0000
2
0,0500
0
0,0000
3
0,0750
0
0,0000
2
0,0500
2
0,0102
1
0,0250
1
0,0051
1
0,0250
2
0,0102
1
0,0250
3
0,0152
0
0,0000
12
0,0609
4
0,1000
177
0,8985
40
1
197
1
(Source: Own calculation)
Tab. 18 Source data for Lorenz curve of the estimated scoring function
Score
X (%)
x
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
Total
x
0,2750
0,2750
0,1000
0,0500
0,0750
0,0500
0,0250
0,0250
0,0250
0,0000
0,1000
1
cum. X (%) Y (%)
cum. Y (%)
0
x
0
0,2750
0,0000
0,0000
0,5500
0,0000
0,0000
0,6500
0,0000
0,0000
0,7000
0,0000
0,0000
0,7750
0,0000
0,0000
0,8250
0,0102
0,0102
0,8500
0,0051
0,0152
0,8750
0,0102
0,0254
0,9000
0,0152
0,0406
0,9000
0,0609
0,1015
1,0000
0,8985
1,0000
x
1
x
(Source: Own calculation)
103
Tab. 19 Source data for calculation of Gini coefficient
Score
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
Total
Observations X
Y
cum. X
cum. Y
A
B
A*B
11
0,0909
0,0464
0,0909
0,0464
0,0464
0,0909
0,0042
11
0,0909
0,0464
0,1818
0,0928
0,1392
0,0909
0,0127
4
0,0909
0,0169
0,2727
0,1097
0,2025
0,0909
0,0184
2
0,0909
0,0084
0,3636
0,1181
0,2278
0,0909
0,0207
3
0,0909
0,0127
0,4545
0,1308
0,2489
0,0909
0,0226
4
0,0909
0,0169
0,5455
0,1477
0,2785
0,0909
0,0253
2
0,0909
0,0084
0,6364
0,1561
0,3038
0,0909
0,0276
3
0,0909
0,0127
0,7273
0,1688
0,3249
0,0909
0,0295
4
0,0909
0,0169
0,8182
0,1857
0,3544
0,0909
0,0322
12
0,0909
0,0506
0,9091
0,2363
0,4219
0,0909
0,0384
181
0,0909
0,7637
1,0000
1,0000
1,2363
0,0909
0,1124
237
1
1
x
x
x
x
0,3441
(Source: Own calculation)
Gini coefficient = 1 – 0.3441 = 0.6559 = 65.59 %
104
Download
Related flashcards
Create Flashcards