| The financial aspect is essential to any
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| | containing info on invoices, their birth
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| kind of business. How a company receives
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| | dates, adjournment periods for each of
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| funding and incomes determines its
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| | the debtors, actual dates of debtors'
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| overall welfare. For any B2B company, one
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| | payments that have occurred in the past,
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| of the major concerns is the control over
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| | we can view the statistics "Past payment
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| the payments of the non-bank debtors,
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| | delays". The density function of this
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| i.e. the payments resulting from sales of
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| | statistics can be viewed as a subnormal
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| goods and/or services. Indeed, this
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| | fuzzy set. This set, labeled "A", will be
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| inflow enables the company to assess its
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| | the first of the three fuzzy sets to be
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| efficiency, playing the role of the
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| | components of the resulting fuzzy set
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| factor underlying the company's profits.
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| | "payment date forecast". The density
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| Having produced some goods or services,
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| | function can give us a general idea about
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| the company sells the ware, receiving
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| | the "payment discipline" of a specific
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| money for the ware -- which becomes
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| | debtor in the past. The density function,
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| income. The company crucially needs this
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| | in a general case, will be containing
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| income in order to be able to buy some
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| | several "waves" because it's usually not
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| raw materials and equipment needed to
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| | a trend-containing characteristic as to
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| produce new portions of goods. Thus, it
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| | how many days a debtor will be evading
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| is essential that the company receives
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| | from paying the debt.Firstly, in most
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| income regularly. What is regularity, in
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| | cases the amount of days of a payment
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| this case? It's in fact receiving the
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| | delay is a random variable. It can be
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| money on a predetermined schedule. The
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| | fluctuating within a couple days' limits.
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| one that has been formed with a necessity
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| | Secondly, statistical forecasts of delays
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| in mind to meet the company's needs in
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| | may be differing significantly for
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| financing its expenditures. However, we
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| | different periods of time. This is
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| are living in a REAL world, which means
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| | because B2B relationships are not static,
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| that, inevitably, there are delays in
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| | they are developing all the time.
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| debtors' payments. This, in turn, can
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| | Sometimes, the selling company comes to
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| lead to a complete breakdown of the
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| | "shaking hands" with the buying company
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| financial plan. The latter may cause a
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| | for the latter to pay a couple days
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| non-reversible failure of the company.
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| | earlier, whereas sometimes the buying
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| Effective planning of these delays is the
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| | company may be facing temporary financial
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| key to successful financial
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| | problems (e.g., resulting from a huge
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| management.Given the stated facts, we
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| | credit to be returned to a bank by the
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| arrive at the importance of a system that
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| | buying company), so that the buying
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| would be able to forecast potential
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| | company warns the selling company that
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| delays in debtors' payments. Errors
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| | there may be slight delays of payments.
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| (deviations of the actual payment dates
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| | This is reflected in another component of
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| from forecasted dates) should be minimal
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| | the resulting fuzzy forecast, -- fuzzy
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| in order for such a system to be
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| | set "C". It is in fact a linguistic
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| considered effective. Now this is a tough
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| | variable "Payment delay most likely"
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| point. Existing works show that ordinary
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| | fuzzy set. The linguistic variable may
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| statistical models cannot bear really
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| | take one of the following values:
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| effective results that would be stable in
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| | "Neutral" (which means that there are no
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| time. From our viewpoint, the best way to
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| | specific anticipations of the payments
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| solve this issue is to use the so-called
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| | delay value for the specific debtor), "A
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| "fuzzy approach", which is based on the
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| | slight delay is possible", "A slight
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| fuzzy set theory, originally suggested by
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| | delay is most likely", "A large delay is
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| L. Zadeh.The basics of the fuzzy sets are
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| | most likely", "An on-time payment is most
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| explained in a huge amount of articles
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| | likely", "Payment in advance is most
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| and books -- use web search engines to
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| | likely". Each of these term-values has
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| find out what fuzzy logic is and how it
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| | its own membership function. A
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| all works, if there's such a need. Here,
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| | corresponding membership function is used
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| we only suggest a ready-to-use principle
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| | each time when building a forecast for a
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| of forecasting debtors' payments, basing
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| | specific debtor. The membership functions
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| on the fuzzy approach. The principle
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| | for the term-values of the linguistic
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| suggested in this article has been
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| | variable "Payment delay most likely" are
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| realized in the form of a computer
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| | given below:"An on-time payment is most
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| program. The program has been tested on
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| | likely": y=SQRT(1-ABS(x)/2), x belongs to
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| real data of a real company. The
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| | [-2;2]"A slight delay is most likely":
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| mean-square deviation thus calculated
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| | y=SQRT(1-ABS(x-4)/3), x belongs to
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| estimated 3, which suggests the idea that
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| | [1;7]"A slight delay is possible":
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| the principle presented herein is rather
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| | y=(1-ABS(x-4)/3)**2, x belongs to [1;7]"A
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| effective, but can be subject to further
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| | large delay is most likely":
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| improvement.Given a relational database
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| | y=SQRT(0.25-(12-x)/24)+0.5, x belongs
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| (which may be in fact realized in any
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| | [6;12]y=(0.71-(6-x)/4.23)**2, x belongs
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| way, including but not limited to, MS
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| | to [3;6)y=0, x12"Neutral": y=0.
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| Access, MS Excel DB-like data sets etc.)
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|
