developing a seasonal cash demand simulation for

35

Gadjah Mada International Journal of Business - January-April, Vol. 14, No. 1, 2012

Developing a Seasonal Cash Demand Simulation forAgricultural Cooperatives (Village Unit Cooperative)

in Indonesia

Gadjah Mada International Journal of BusinessVol. 14, No. 1 (January - April 2012): 35 - 59

Corresponding authors. E-mail: [email protected]

ISSN: 1141-1128http://www.gamaijb.mmugm.ac.id/

Abstract: Irrespective of the success of the Indonesian Government to achieve self-sufficiency of rice in1985, and the rice price stabilization, paddy growers still suffer from a very low price for their produce.Koperasi Unit Desa (KUDs) –in Indonesian, or Village Unit Cooperative (VUC) have been entrustedwith the marketing of rice paddies although their performance has always been less than satisfactory. Oneproblem experienced by the KUDs is not having sufficient cash to purchase and process paddies intorice. The purpose of this study is to develop and validate a simulation model to represent cash flowsduring one paddy plantation season for a KUD (VUC). The simulation model is a decision support toolthat enables a KUD’s (VUC) management to determine the maximum quantity of paddies to be pur-chased; the minimum borrowing and additional borrowing; and the earnings before taxes. An investiga-tor-administrated questionnaire was used to collect historical data of the twenty randomly selected KUDs(VUC) on Lombok Island to validate the sub-models of the simulation model. The Kolmogorov-Smirnov Goodness of Fit (two-sample), linear regression, correlation and t- tests were used to validate thesimulation model. The validation results have shown that the sub-models of the simulation model arevalid, and these may contribute to the valid results of the simulation model. This study has found that thelimitations of the paddy storage building, drying floor, and milling machine owned by the KUDs (VUC)may restrict the KUDs (VUC) from buying the entire paddies harvested. Therefore, further research isneeded to find out the minimum capacities of those facilities enabling the KUDs (VUC) to work effi-ciently.

Abstrak: Dibalik keberhasilan yang telah dicapai Pemerintah Indonesia dalam swasembada pangan/beras pada tahun 1985, dan stabilisasi harga beras, petani padi masih menderita akibat sangat murahnyaharga hasil produksinya. Koperasi Unit Desa (KUD) yang telah dipercaya berfungsi pemasaran gabah/beras padahal kinerjanya selalu menunjukan sangat buruk. Satu kendala yang dialami oleh KUD adalahtidak memiliki cukup dan untuk membeli gabah dan memprosesnya menjadi beras. Tujuan penelitian iniadalah mengembangkan dan mem-validasi simulasi aliran kas dalam satu musim tanam padi untuk KUD.Simulasi ini merupakan suatu alat bantu pengambilan keputusan yang memungkinkan pengelola KUDuntuk menentukan: jumlah maksimal gabah yang bisa dibeli; pinjaman minimum dan pinjaman tambahan;dan laba sebelum pajak. Daftar pertanyaan dimanfaatkan untuk mengumpulkan data dari dua puluhKUD di pulau Lombok yang dipilih secara acak untuk mem-validasi sub sistem simulasi. Kolmogorov-

Budi SantosoProgram Magister Manajemen, Universitas Mataram, Indonesia

Santoso

36

Keywords: agricultural cooperatives (Village Unit Cooperative); seasonal demands; cash flows;simulation; validation

Smirnov Goodness of Fit, regresi linear, uji korelasi dan t-test dipergunakan untuk mem-validasi simulasi.Hasil validasi menunjukan bahwa sub-sistem simulasi valid, dan ini bisa berkontribusi terhadap validitashasil simulasi secara keseluruhan. Dari hasil kajian ini, didapatkan pula bahwa keterbatasan kapasitas gudang,lantai jemur dan mesin giling yang dimiliki oleh KUD yang membatasinya untuk mampu membeli seluruhhasil panen petani padi di wilayahnya. Untuk itu, penelitian lanjutan diharapkan untuk dapat mengidentifikasikapasitas yang sekurang-kurangnya dimiliki oleh KUD untuk bisa bekerja secara efisien.

37


Introduction

Indonesia is commonly described as aDeveloping Country and about 58 percent ofits population lives in rural areas (World Bank1998a). In 1997, (the year for which the mostrecent figures are available) the total popula-tion of Indonesia (the fourth-populous coun-try in the world) is approximately 200.1 mil-lion people (World Bank 1998a). This meansthat approximately 116.06 million people livein rural areas. Generally, these areas are char-acterized by a traditional agricultural-basedlife style.

From an economic point of view, com-pared to other sectors, the agricultural sectorhas made the smallest total contribution toIndonesia’s Gross Domestic Product (GDP).At the end of the sixth five-year developmentera (1996/1997), agriculture accounted forapproximately 16.1 percent of IndonesianGDP, other sectors such as industrial sectorscontributed approximately 43.9 percent, andservice sectors approximately 40.1 percent(World Bank 1998a). Regardless of its smallcontribution to the Indonesian GDP, the ag-ricultural sector has been the largest employerof people who live mainly in rural areas. Ac-cording to the World Bank (1998b), Indone-sian people in employment number 91 mil-lion, of whom about 50.96 million (56%)worked in the agricultural sector (see also Ferry1988). The service sector, which is the sec-ond largest area of employment, employedabout 28.21 million (31%) people (WorldBank 1998b). The industrial sector employedapproximately 11.83 million (13%) people(World Bank 1998b). Thus, the agriculturalsector has a very important role in Indonesia’seconomy (Dillon 1992). Moreover, the most

recent reports have revealed that the agricul-tural sector isa dominant alternative employ-ment for people who have lost their jobs be-cause of the economic crisis in Indonesiasince late 1997 (IMF 1998; Pambudy 1998c;YAS 1998).

In 1996, approximately 51,101,506 tonsof paddies were produced nationally (BiroPusat Statistik 1999)1. This produce isequivalent to about 28,616,843.36 tons ofrice (assuming that the conversion rate is56%). Meanwhile, the national rice consump-tion per year is about 29,400,000 tons of rice(equivalent to 52,500,000 tons of paddies).This number is based on an average rice in-take per person per year of 147 kilograms.Again, from an economic viewpoint, therewas an excess demand of paddies/rice. Theo-retically, the excess demand should increasethe price of paddies/rice. In fact, every timea paddy harvest season comes, paddy grow-ers have to sell their produce at a lower pricethan the floor price set annually by the Indo-nesian Government (Suyanto 1996; Harijono1998; Suganda 1998; SAH. et al. 1999).

Agricultural cooperatives (Indonesian:Koperasi Unit Desa or KUD) were introducedand established in 1973, and they have beenentrusted with the marketing of paddies. AKUD may cover an area as small as one vil-lage. Based on the most recent data availablereleased by the Indonesian Board of Statis-tics (1993), about 8,679 KUDs operate inIndonesian villages. The main duties ofKUDs are to ensure their members (farmers)get agricultural inputs with a relatively stableprice, and sell produce with a price as low asthe floor price. The similar function of anagricultural cooperative was also found in the

1 Cited from Biro Pusat Statistik [Central Bureau of Statistics] homepage: http://www.bps.go.id/statbysector/agri/pangan/table1.shtml.

Santoso

38

U.S.A. (Oehler 1996; Knoeber 1997;Demetrakakes 1998), in India (Kumar 1990;Sidhu and Sidhu 1990), in Malaysia (Ramli1988), in Thailand (Boonma 1988), in Korea(Kim 1988; Lee 1988), in Taiwan (Chen1988), in Japan (Fukui and Hirose 1993),Nicaragua (Kroeke 1996), and in Australia(Langdon 1991; Langdon 1994). However,up to now, KUDs have been blamed for let-ting paddy growers suffer from a very lowprice for their produce. Paddy growers oftenhave had to sell their produce at a price lowerthan the floor price. Reports of the Indone-sian media and findings of recent research-ers have suggested that several factors maycontribute to the unsatisfactory performanceof KUDs. First, although the IndonesianGovernment provides a low interest rate forloans, KUDs are still unable to purchase con-tinuously their members’ produce because ofan apparent cash shortage (P3P 1997;Santoso 1993). This implies that wheneverKUDs did not purchase their members’ pro-duce the members (paddy growers) had to selltheir produce to private paddy wholesalersat a price lower than the price paid by KUDs.Second, a lack of transport facilities may alsocontribute to the inability of KUDs to pur-chase paddies directly from paddy growers,and on the other hand, farmers do not haveenough funds to transport their produce toKUD locations (Santoso 1993; P3P 1997).

The KUD is an intermediary organiza-tion of the Indonesian Government to helpand improve the welfare of farmers (paddygrowers), and to stabilize the price of rice(Timmer 1996). The rationale of setting afloor price annually for farmers’ produce isto help farmers get an income to support theminimum needs of their families. Timmer(1996, 1997), and Jones (1995) have statedthat the stable food price (rice) may contrib-

ute to the economic growth for developingcountries.

However, up to now, there have beenno serious attempts taken by the IndonesianGovernment or any other institution to solvethe low paddy price that was routinely expe-rienced by the paddy growers. Previous stud-ies have only covered the prescriptive aspectsof phenomena surrounding this problem.Pambudy (1998b), Ida (1996), andKrisnamurthi (1996) have argued that cor-ruption, bureaucracy and a lack of manage-rial skills contribute to the unsatisfactory per-formance of KUDs. However, these studieshave not given constructive and practicalapproaches to solve the problem.

Objectives

This study attempts to develop a simu-lation model to represent cash flows during apaddy plantation season for a KUD. The simu-lation model takes into account capacities ofresources (paddies/rice storage building,paddy drying floor and paddy milling ma-chine) owned by a KUD, and transport fa-cilities (truck and other traditional means)that are available in the KUD location. Also,the simulation model can be used, as a deci-sion support tool by management of a KUDto make a decision regarding the amount ofcash required to provide agricultural inputsand to buy paddies, and to determine a quan-tity of paddies, which can be purchased onthe basis of various scenarios. Therefore, themain purposes of this study are to develop asimulation model representing cash flows fora KUD during a paddy plantation season bytaking into consideration the transport costfor transporting paddies from paddy fields toa KUD’s location, to validate the simulationmodel, and to analyze the outputs of the simu-lation model.

39


Literature Review

Before commencing the discussion ofcash management models, it is necessary tocite three classical motives for holding cash.(Keynes, cited in Homonoff and Mullins1975: 3)

“…. the transaction motive relates to “hold-ing cash to bridge the interval between thereceipt of income and its disbursement [or]between the time incurring business costsand that of the receipt of the sale-proceeds.”The precautionary motive concerns hold-ing cash to provide “for contingencies re-quiring sudden expenditure and for unfore-seen opportunities of advantage purchases.”The speculative motive involves holdingcash in order to secure “profit from know-ing better than the market what the futurewill bring forth”; in other words, this mo-tive is a function of expectations. Keynesbelieved the transactions motive and the pre-cautionary motive to be essentially functionsof the level of income (i.e., the money valueof the transactions) and the speculativemotive to be a function of the interest rate.”

As far as this study is concerned, de-mand for cash is to fund the operating pro-cesses of a KUD during the paddy planta-tion season. This involves the first two mo-tives, the transaction motive and the precau-tionary motive. The last motive, speculativemotive, is not relevant to the nature of thecash flow model in this study. Holding cashin this study is not a function of the interestrate, because the interest rate of credit pro-vided by the Indonesian Government forKUD and the interest rate of credit lent byKUD to its members (for agricultural creditprogram/KUT) is the same.

Cash management models discussed inthe prior literature focus mainly on optimiz-ing the cash balance, speeding or delayingspecific cash flow components, and short run

investment strategies. Among these works, isthat of Baumol (1952) who applied an in-ventory modeling approach. He employed adeterministic model that assumes cash dis-bursements are spread over time, and cashreceipts occur periodically at discrete pointsin time. His suggested model concentrates onthe quantity and spending of funds transferredbetween transaction and investment ac-counts. In his model, Baumol assumed thatto ensure a positive cash balance, a firm con-stantly and continuously reserves its cashbalance by converting its asset to cash. Un-like Keynes, Baumol perceived that the trans-action cash demand was a function of bothincome and interest rate (the combination ofKeynes’s three motives).

Another researcher, Tobin (1956) ar-gued that the only motive for cash demandwould depend inversely on the interest rate.Similar to Baumol, Tobin also employed adeterministic model, but he permitted thenumber of transactions into cash to take onlypositive integral values. He also assumed that(supporting Baumol’s assumption as well)cash withdrawals should be evenly spreadover time and in equal amounts.

In contrast to Baumol’s deterministicmodel, Miller and Orr (1966) suggested aninventory modeling approach that assumescash balances could be completely describedby a stochastic generating process followinga Gaussian function. Miller and Orr deriveda control limit range (i.e., upper and lowerbonds) for cash balances on the basis of in-terest rates, transaction costs, and an assumedstable cash flow probability distribution. Fortheir modeling purpose, Miller and Orr as-sumed that there were only two assets of in-terest: cash and interest-bearing securities.The model incorporates the variance of dailycash balances into an expression for a firm’s

Santoso

40

optimal cash balance level. They found thatthe transaction demand for cash is sensitiveto income, interest rate and synchronization.

Neave (1970) examined the stochasticcash balance problem with fixed and propor-tional costs incurred whenever the inventoryincreases or decreases. The study focused onthe positive transfer costs of the fixed com-ponent of stochastic cash balance. Neaveemployed convex upper and lower bounds onthe cost functions partially to describe opti-mal policy. He argued that the convex bound-ing technique might provide an approach tostudy the complex optimal policy of inven-tory problems.

A more advance effort is that ofHomonoff and Mullins (1975) who examinedthe assumption used in the model suggestedby Miller and Orr in generating daily cashflow data. On the basis of extending theMiller-Orr model, they constructed andtested variations of an inventory control limitapproach, though they saw the presence ofsystematic effects and sequential patterns,which violate the assumption underlying theMiller-Orr model. Although their models per-formed slightly better than those of previousmodels, they did not explore sophisticatedapproaches, which take into considerationdynamic properties of data.

Similar to Miller and Orr (1966),Constantinides (1976) suggested a continu-ous time model of cash management assum-ing stochastic demand, but allowing for posi-tive and negative cash balances. He assumedfixed and proportional transaction costs. Un-like Neave, Constantinides assumed the op-timal policy form was very simple.

Seeing cash management models as aninventory problem, Mensching et al. (1978)suggested the simple cash balance problemto determine the timing, direction and mag-

nitude of transfers between a firm’s earningasset and cash balance. They assumed thatthere would be a discrete-time, multi-period,and deterministic economic lot-size modelallowing negative demands and disposal ofgoods. The heuristic procedures were used ina protective planning-horizon to determinethe future demand of cash. The model con-sidered the dynamic properties of data, butit did not take into account the stochasticdemand of cash.

A more advance effort than that ofMensching et al. (1978), Chand and Morton(1982) developed a perfect planning horizonprocedure for the simple deterministic cashbalance problem assuming the requirementsfor cash (positive or negative) are known forseveral periods ahead. They stated that themodel was guaranteed to obtain optimal ini-tial decisions for the infinite horizon of cashbalance problems by using the minimum pos-sible number of periods of forecast data.They employed an efficient forward dynamicprogramming algorithm. Although their ap-proach employed dynamic programming, theyneglected the stochastic properties of dailycash flows.

A more recent study by Stone and Miller(1987) used multiplicative models of cashflow patterns for daily cash forecasting. Theyalso underlined four common reasons con-tributing to the failure of most efforts at dailycash forecasting. First, the definition of ma-jor flows is not periodic and not generallyestimable from past data. Second, the break-down of non-major flows into componentsis sometimes neglected. Third, the require-ment of information systems support to trackhundreds of non-major cash flow streams aswell as hundreds of individual non-majorcash flow streams is ignored. Fourth, there isa failure to identify the pattern of daily cashflows. Stone and Miller employed log-linear

41


models for treating day-of-week and day-of-month interaction of cash flow streams. Nei-ther dynamic nor stochastic properties ofdaily cash flow data were included in theirmultiplicative models.

Another study, which considered cashbalance problems, is Bergh and Hallerbach(1991) who suggested a simple model to solveone period stochastic cash problem assum-ing a fixed cash outlay at the end of the pe-riod. They also assumed the decision makerfaces a single deterministic liability that is dueat a certain time. A constant overdraft facil-ity and the presence of an initial potentialfund needed to be fulfilled in the model. Irre-spective of the involvement of stochastic anddynamic properties of cash flow data, themodel failed to fulfil the nature of stochas-tic cash outlay and the overdraft facility, whichis stochastic in nature.

There have been a few attempts to studythe efforts of improving cash management.Thompson (1986), and Madura (1987) re-vealed that simulation models could be usedby decision makers to improve cash manage-ment. Moreover, Thompson (1986) statedthat a computer simulation could provide anoverview of the complex relationships withinthe business. A similar approach (a dynamic-stochastic simulation model) was also sug-gested by Golub et al. (1995) to managemoney for mortgage security.

Most prior researchers have attemptedto study cash management models as a par-tial approach. One stream of researchers con-sidered dynamic programming of cash man-agement models, but neglected the stochas-tic properties of cash flow data. The otherstream emphasized the stochastic propertiesof cash flow data on its cash managementmodel, but overlooked the dynamic aspectof the model. Most prior researchers agreed

that a simulation model might resolve thedifficulties and complexities of the integratedapproach of cash management models incor-porating dynamic programming and stochas-tic properties of cash flow data.

There have been only a few studieswhich research cash management of agricul-tural cooperatives. Rasmussen (1983) under-lined the difficulties of determining theproper level of cash to have on hand to meetliquidity and efficiency. Basset (1992) statedthat the substantial margin of error has to betolerantof forecasting cash to meet seasonalservice demand.

Levy et al. (1989) examined the impli-cations of financial cooperation in Israel’ssemi-cooperative villages. Their research fo-cused on the problems associated with po-tential free ride when farmers’ borrowings areuncoordinated. There are three main difficul-ties identified in their study. First, the usualpecuniary externality arose when cooperativemembers acted as price takers regarding thecost of credit, though the cooperative facedan upward sloping credit supply function.Second, the added uncertainty for the indi-vidual farmer stemmed from his dependenceon the uncoordinated borrowing and invest-ment behavior of other cooperative members.Third, the source of difficulties arose whencooperative members were heterogeneous intheir production efficiency and attitudes todebt bearing. Their dynamic simulation resultssuggested debt-prone members of the coop-erative would increase their debt position atthe expense of debt-averse members. This find-ing undermined the stability of the co-opera-tive. Based on theoretical considerations andempirical observations, each of these factorswas worse during periods of general finan-cial difficulty of farmers. However, the dy-namic simulation used in the study did not

Santoso

42

taken into account stochastic properties ofcash flow data.

Johnson and Crawford (1993) examinedcash management in rural America. Theirstudy described Basin Electric PowerCooperative’s innovations in cash manage-ment. They underlined the main problemfaced by the cooperative. At the same timethat the cooperative needed to borrow fundsto meet its seasonal cash requirements, itsmembers invested their seasonal access li-quidity in the same institution at a lower in-terest rate than that paid by the cooperative.The study described how the cooperativesolved its problem by opening a new busi-ness section to accommodate its members’savings. However, the study did not includeany dynamic and stochastic properties of cashflow data.

Zaman (1992) suggested a workingcapital management model for public enter-prise in Bangladesh by strengthening the cur-rent ratio. He also identified that the work-ing capital models suggested by Westernscholars and the Indian Reserve Bank werenot practically applicable under the environ-ment constraints of Bangladesh. The resultsof that study indicated that the suggestedprescriptive model improved the workingcapital position by gradually reducing thedependence on bank borrowings. However,the study did not consider stochastic and dy-namic properties of cash flow processes.

Previous research has mainly focused onthe role of the bank as an intermediary in theimprovement of financing of rural smallfarms. This study attempts to fill the gap thatis left by previous researchers. Hence thisstudy will focus on the KUD as a financialintermediary to help peasant farmers in In-donesia have better access to credit. Mostprevious studies have given more attentionto macroeconomic levels of rice price stabi-

lization. In addition, the KUD that directlydeals with farmers or producers was not afocus of the past studies. This study perceivesthe KUD as an important bridge betweenBULOG and farmers who need specific at-tention to obtain better income through gain-ing a reasonable price for their produce. Theresearch efforts and methodologies reviewedin this chapter provide the foundations forthe model developed in this thesis. The mod-eling approach provides a means for integrat-ing the dynamic and stochastic properties ofcash flow processes. This supplies the link-age between Baumol’s deterministic assump-tions and the purely stochastic propositionof Miller and Orr. The determinants investi-gated in this study include those identified inthe agricultural cooperative cash managementliterature. The exponential family function(i.e., LogNormal) is mainly adopted to gener-ate cash inflows and outflows associatedwithoperating processes within a KUD during thepaddy plantation season. Finally a simulationmodel is developed to represent integratedelements of the cash flow of a KUD duringa paddy plantation season by incorporating atransport cost, and by considering resourcesand capacities owned by a KUD.

Methods or Approaches

This part will discuss short descriptionsof the simulation model, how to build thesimulation, how data are to be collected torun and validate the simulation model, andhow to validate the simulation model.

Why Use Simulation as theResearch Method

It has been stated that a computer simu-lation should be regarded as the last resort tobe used to solve problems if all else fails (Pidd

43


1993). This implies that building a computersimulation or program is surprisingly timeconsuming, and involves long computer pro-gramming of some complexity. However,since the purpose of this study is to developa new cash flow model, which includes com-ponent costs (not existing in the current cashflow process), and complex processes, com-puter simulation may be the only way to copewith these phenomena. In general, there aretwo main reasons why a computer simulationshould be used as a research method. Thesereasons are to overcome problems associatedwith real experimentation, and as an alterna-tive to a mathematical model.

Overcoming Problems of RealExperimentation

Pidd (1993) has suggested four itemsthat have to be considered to argue that acomputer simulation can overcome problemsassociated with real experimentation. Thefirst item is cost - building a computer simu-lation may be time consuming and expensive,however, real experiments might be moreexpensive if something goes wrong. The timetaken to build a computer simulation is sec-ondary. Although it would be time consum-ing and involvecomplex computer program-ming, once the computer simulation has beenbuilt, it can be used to simulate weeks ormonths or years in just a matter of secondsor minutes in computer time. The third itemis replication. Under certain conditions, itwould be impossible to repeat strategies ortactics or any other events using real experi-ments, due to high costs. However, a com-puter simulation can be replicated to achievethe expected results of a certain strategy ortactic. Finally, safety is the essential issue,which has to be considered, when real experi-mentation deals with extreme conditions. A

computer simulation can cope with this re-quirement no matter how extreme the condi-tions are.

An Alternative to MathematicalModeling

The suggested cash flow model is sys-tem dynamic, involving sequential processesand transient effects resulting from tempo-rary impacts of rainfall, some capacities orconstraints, and machine breakdowns. Math-ematical models would not be able to copewith this process, and as a result, a computersimulation is the only way to tackle this pro-cess (Pidd 1993). In addition, mathematicalmodels such as queuing theory may only per-mit a certain type of sample distribution, buton the other hand, a computer simulation maycope with any types of sample distributions(Pidd 1993).

What is Borland Delphi?

Borland Delphi for Microsoft Windowsis a general purpose and relatively new na-tive code compiler programming language thatwas first introduced in the first quarter 1995by Borland International Corp. BorlandDelphi enables application developers tobuild applications visually by dragging anddropping built-in and/or customized compo-nents onto a form, and linking/hooking themwith minimum codes to respond to someevent handlers. Borland Delphi is based onObject-Oriented Programming (OOP) archi-tecture, Object Pascal. The Object Pascal isalso called a hybrid language (Wozniewicz etal. 1995). This means that not only does itsupport an OOP approach to programming,but it also enables programmers to write non-object oriented programs in a traditional,structured fashion. According to Calvert(1995), besides its capability to write/build

Santoso

44

one single executable program (without anyadditional files, except when Borland Data-base Engine is used) or library (dynamic linklibrary) that runs on Microsoft Windows,Borland Delphi is very easy to learn, and asresult, the learning curve is relatively short,usually less than one year. Thus, BorlandDelphi can be defined as a visual, object-ori-ented, and components-based development environ-ment (Pacheco and Teixeira 1995: 4).

Up to 1997, Borland International Corp.had released three versions of its Delphi fam-ily, namely: version 1.0, version 2.0, and ver-sion 3.0. Borland Delphi version 1.0 (herein-after simply called: Borland Delphi) is hostedon Microsoft Windows 3.1 (hereinaftercalled: Windows 3.1) operating system, and

is targeted to write 16-bit applications forWindows 3.1 or later. Of course, this doesmean that 16-bit applications built by BorlandDelphi can run on Microsoft Windows 95(hereinafter called: Windows 95) or later andMicrosoft Windows NT 3.51 (Service Pack5, hereinafter called: Windows NT 3.51) orlater. Meanwhile, Borland Delphi version 2.0,Borland Delphi version 3.0, and beyond runon Windows 95 or later and Windows NT3.51 or later, and are used to create 32-bitapplications for Windows 95 or later, andWindows NT 3.51 or later (see GreenBerg1996). In this thesis, Borland Delphi solelyrefers to version 1.0 because the simulationis written in Borland Delphi, and is intendedat minimum to be able to run on Windows3.1 or later.

Figure 1. Activities During Paddy Plantation Season and Cash Inflows and OutflowsAssociated with Each Activity - from a KUD’s Point of View

45


The Simulation Models

Mathematical model representations areused to visualize relationships of parametersand variables of real systems of cash flowsof a KUD.

Cash Flow Models

This section will be divided into fourmain subsections. The first subsection willpresent and describe the model of cash in-flows during the paddy season. The next sub-section will introduce and outline the modelof cash outflows of a KUD during the paddyseason. Third, the model of cash inflows dur-ing the paddy harvest season will be pre-sented. Finally, the discussion will be con-cluded by the introduction and descriptionof the model used to calculate cash outflowsduring the paddy harvest season. The expla-nations can be seen clearly from Figure 1above.

Cash inflows during the paddy season

During the paddy season, a KUD’s cashinflows are mainly associated with the rou-tine activities. The daily cash inflows may begenerated from cash sales and repayment ofcredit sales of the mini supermarket ownedby a KUD. Besides this source of cash, dur-ing this season a KUD may also receive loansfrom the People’s Bank of Indonesia to pro-vide paddy seeds, fertilizers, and pesticides.In addition, the bank may also provide loans(rice loans) to a KUD to purchase members’or farmers’ paddies when the paddy harvestseason is due. The fertilizer distributor maypay fees to a KUD resulting from fertilizersold. Thus, the cumulative cash inflows on acertain day (day t) during paddy season canbe represented in the following equation.

where,

t = 1, 2,........................, n

n = Poisson (PSm)

The cumulative cash inflows of a KUDon day t (PF

tps) during the paddy season (n) is

the summation of available cash on hand atthe beginning of that day (C

j), daily or rou-

tine cash received from cash sales and repay-ments of credit sales of mini market com-modities (CI

j), agricultural input loans (fer-

tilizer, pesticide and paddy seed loans) (FLj),

rice loan (RLj), and fertilizer fees (FF

j). The

length of the paddy season (n) or paddy ageis a random number following a Poisson dis-tribution [Poisson (PS

m)] given the mean

(PSm) of a number of days of previous paddy

seasons.

Cash outflows during the paddy season

As a formal organization, a KUD em-ploys several people to handle daily opera-tions. The people who work in a KUD in-clude voluntary workers and paid workers. Amanager, a secretary, a bookkeeper, someoffice workers, and an office guard are paidmonthly. There are some other costs that areincurred monthly, associated with a KUD’sroutine operations such as general costs (elec-tricity, telephone, water bills) and othermonthly costs. Daily cash disbursement mayalso be incurred in a daily KUD’s operation.There are also some cash disbursements topurchase paddy seeds, fertilizers, and pesti-cides. The cumulative cash outflows on day tduring the paddy season can be calculated bythe following equation.

The cumulative cash outflows on day t,noted by PU

tps, is a total of daily cash dis-

PF = (Cj +

CI

j +

FL

j +

RL

j +

FF

j)

ps

t

ps

t

PU = (COj +

FB

j +

S

j +

GC

j +

OC

j)

ps

t

t

j=1

Santoso

46

bursement (COj), fertilizers, paddy seeds, pes-

ticides and transport costs (FBj), salaries paid

monthly (Sj), general costs (GC

j), and other

costs (OCj).

Cash inflows during the paddy harvestseason

Figure 1 shows that as well as cash re-ceived from daily operations (from the minisupermarket and other non-agricultural ac-tivities), from the first day of the paddy har-vest season, a KUD will have additional cashinflows. These cash inflows may come frommembers’ fertilizer credit payments, rice soldto DOLOG, rice bran sold to customers suchas poultry, cattle and pig farmers, and ricefees paid by private paddy wholesalers whohave used a KUD’s stamp duty to sell theirrice to DOLOG. The cumulative cash inflowson day t during paddy harvest season can beseen in the following mathematical equation.

where

t = n + 1, N + 2, ........., n + k

k: the terminating day of the paddy planta-tion season is determined when all pur-chased paddies are dried, milled, and soldby a KUD

A KUD’s cumulative cash inflow on dayt during paddy harvest season (PF

tph) is a sum

of the net cash flows carried forward fromthe paddy season (C

j), rice loans (RL

j), cash

inflows from routine operations (CIj), cash

received from rice sold to DOLOG (RSj), cash

received from rice bran sold (BSj), cash re-

ceived from fertilizer credit repayments (FPj),

and rice fees paid by the private paddy whole-salers (RF

j).

Cash outflows during the paddy harvestseason

As well as routine daily and monthlycash disbursements incurred during the paddyseason, there are also several cash outflowsduring the paddy harvest season. These ad-ditional costs are cash disbursements for buy-ing and trucking paddies, drying paddies, andmilling paddies. Cumulative cash outflows onday t during the paddy harvest season can bemodeled as follows:

The cumulative cash outflows on day tduring the paddy harvest season (PU

tps) is a

sum of daily cash disbursed to fund routineactivities (CO

j), salaries (S

j), general costs

(GCj), other costs (OC

j) monthly paid, paddy

transport costs (PTCCj), cash disbursed to

buy paddies (PBCj), paddy drying costs

(PDCj), paddy milling costs (PMC

j), cash dis-

bursed to service or repair the break downof paddy milling machines (MSR

j), rice trans-

port costs (RTCCj), and interest payments

(IPj).

How to Build the Simulation

This section will explain the relation-ship of parameters and variables in the mod-els previously discussed. The relationships ofparameters and variables are translated intoprogramming algorithms, and coded inBorland Delphi (as shown in Figure 2). Sincethis simulation model, enriched by a graphi-cal user interface (GUI), will be built by us-ing the general purpose programming language(Borland Delphi), several frequently used pro-gramming terms will be used during subse-quent discussions. Delphi is an Integrated

PF = (Cj +

RL

j +

CI

j +

RS

j +

BS

j +

FP

j +

RF

j)

ph

t

t

j=n+1

PUt

ph=

( )t

j=n+1

COj +

S

j +

GC

j +

OC

j +

PTCC

j +

PBC

j +

PDCj +

PMC

j +

MSR

j +

RTCC

j +

IP

j

47


Development Environment (IDE) that en-ables a programmer to write (code), executeand debug applications without leaving theDelphi environment. Delphi comes with ap-proximately 75 built-in components that areknown as the Visual Component Library(VCL). These components are created byDelphi, and thus, they are native Object-Pas-cal compiled libraries. These VCLs enableapplications to be distributed and executed/run without any additional libraries or files.As mentioned earlier, in order to built a pro-gram or an application, a programmer has justto drop several components on a form (a ba-sic skeleton of an application or a program),

set appropriate properties of components ata design time or set properties programmati-cally at a run time, and link or glue them to-gether to respond to event handler(s). As ananalogy, to build a car, employees just haveto assemble a car frame, machine, body,wheels, and interiors, to paint, test and run.The VCLs that come with Delphi are enoughto build almost any type of application.

Data to run and validate the simulationmodel

Historical data used to run and validatethe simulation results were collected usinginvestigator-administrated questionnaires

Figure 2. Relationships between The Data Structure used to Hold Parameters and Vari-ables, and Visual Interface of the Simulation Model

CashLoansPrices

Costs

Fees

Fertilizers andField Types

Paddy HarvestRate & Capacity

Variables

Rice QuantitySent

Mill ing MachineBreakdown

Interest Rate

Rainfall

Paddy PlantationPaddy

Produc tionMiscellaneous

Data FileParameters &Var iables

Start

Read

Field?

Update

ChangeBitmap

Disablebuttons

EnableTimer

End

x

y

DisableTimer

Run

Cont rols

Edit

Outputs

Tables

Graphs

Abstract Visual

Prices

Costs

Fees

Fertilizers andField Types

Paddy HarvestRate & Capacity

Variables

Rice QuantitySent

Milling MachineBreakdown

Interest Rate

Rain Fall

Paddy Plantation

Parameters & Variables

Loans Cash

Start

Read

Field?

ChangeBitmap

Disablebuttons

X

X

EnableTimer

PaddyProduction

Update

DisableTimer

End

Miscellaneous

Controls

Edit

Run

Outputs

Tables

Graphs

Data File

Abstract Visual

Santoso

48

from twenty KUDs that were selected by us-ing a stratified random sampling. Based onpracticality, funds and time limit consider-ations, the sampled KUDs were among thoselocated in Lombok Island. Two types of datawere collected during the research, primaryand secondary data.

The primary data includes: the amount offertilizer, pesticide and paddy seed loan givenby the Government; the cash balance at thebeginning of paddy plantation season; thefirst date of paddies planted in a KUD loca-tion; the quantity and types of fertilizers,pesticides and paddies bought by a KUD;prices of fertilizer, pesticides, paddy seed,paddy, rice, and rice bran per unit; the num-ber of members; paddy field areas owned bya KUD’s members; the amount of rice loanlent by the Government; loan interest rate;lending interest rate; the number of paidworkers; the amount of salaries, generalcosts, and other costs monthly paid; Paddiesresulting from paddy fields owned by a KUD’smember; paddy to rice conversion rate; paddyto rice bran conversion rate; daily cash re-ceived and disbursed respectively from andfor businesses other than agricultural activi-ties; storage, drying floors, milling machines,paddy and rice transport capacities; the daysof fertilizer, pesticides and paddy seed loan,and rice loan liquidated; daily paddies directlybought from paddy fields by a KUD; dryingand milling costs per unit paddies; fertilizerand rice fees; frequency of milling machinebreakdowns; service and repair costs of mill-ing machines; days of drying paddies; and fre-quency of monthly morning rain.

The secondary data includes: fertilizer, pes-ticide and paddy seed loan lent by each KUDin the region; the quantity of fertilizers, pes-ticides, and paddy seeds supplied by eachKUD to its members; loan and lending in-terest rates; the quantity of paddies directlybought by each KUD from its members; andthe quantity of rice sent and sold to DOLOG.From DOLOG, data may comprise of thequantity of rice bought from KUDs and non-KUDs; the price of rice; and the days of pay-ing rice to the KUDs. Data gathered from theDepartment of Agriculture may include: theuses of each type of fertilizers and pesticidesfor wet and dry paddy fields; the age of sev-eral types of paddy varieties; paddy resultsfrom several types of paddy varieties perhectare of paddy field; paddy to rice andpaddy to rice bran conversion rates; paddyfield areas in each sampled KUD location;and places in which unsuccessful paddy sea-sons occurred, and the causes of the badpaddy season. Published or unpublishedprinted-materials from previous studies mayprovide qualitative data that are underlyingassumptions, rules and other non-numericdata.

How to validate the simulation model

Several stochastic variables are used inthe simulation model with assumptions fol-lowing certain distributions. As such, theKolmogorov-Smirnov Goodness Fit (Two-Sample) Test (D) was used to test the assump-tions underlying the random generations ofcash inflow and outflow streams, the generalcost stream, the other cost stream, and themilling machine repair cost stream. The as-

49


sumption is true (not rejected) if the calcu-lated D is less than the critical value of D(Lawand Kelton 1991). Since the final result ofthe actual system for each sampled KUD doesnot exist, the sub-models of the simulationmodel were compared against the sub-sys-tems of the actual system (Law and Kelton1991; Pidd 1993; Knepell and Arangno 1993;Paul and Balmer 1993; Kleijnen 1995a; Taberand Timpone 1996). The linear regression test(focusing on the intercept, the slope and theR square), t-test and correlation test was usedto validate those sub-models and sub-sys-tems. The sub-models and the sub-systemsthat were validated are the quantity of pad-dies harvested daily, the frequency of monthlymorning rain, and the amount of rice fees.The validations for each sampled KUD wereconducted by 25, 50 and 100 replicationswith 95 percent of confidence levels respec-tively.

The sub-models’ (modules’) results, thatcan be validated, are the quantity of paddiesharvested daily, the frequency of monthlymorning rain, and the rice fees. The test usedto validate these modules is linear regression,from which the fit between results of the sub-actual system and results of the sub-simula-tion model can be identified. The sub-simu-lation model is a complete resemblance ofthe sub- actual system under study if the in-tercept of the regression model is zero, theslope is one, and the R square is one (Cohenand Cyert 1961; Kleijnen and Groenendaal

1992; Kleijnen 1995a; Kleijnen 1995b). Thisrequirement is too idealistic to achieve, so itis suggested that the sub-simulation model isstill acceptable if there is strong evidence thatthe results of sub-simulation model and theresults of sub- actual system are positively cor-related (Kleijnen and Groenendaal 1992;Kleijnen 1995a; Kleijnen 1995b). The corre-lation tests are considered to be less strin-gent. A more rigorous requirement for deter-mining the fit between the sub-simulationmodel and the sub-actual system is if themean and variance of results of the sub-simu-lation model are equal to those of the sub-actual system (Kleijnen et al. 1998). The tstatistic (t-test) will be used to identify thesimilarity of the mean and variance of thesub- simulation model’ and sub- actual sys-tem’ results. All these tests are based on the95 percent confidence level.

Results and Discussions

In this article, only some results of thevalidation will be shown, and some of themcan be seen as follow.

Daily Cash Inflows and OutflowsTest

The summary of Kolmogorov-D and P,and the D critical table value based on sig-nificant levels of 1 percent, 5 percent and 10percent can be seen in Figure 3 and Table 1.

Santoso

50

Figure 3. The List of Line Plots of the Sorted Actual Cash Flows and the Sorted Theo-retical Cash Flows Generated by The Simulation Model for Each Sampled KUD

Sample 4

Sample 3

51


Figure 3 (Continued)

Sample 14

Sample 15

Santoso

52

Table 1.The Kolmogorov-Smirnov Goodness of Fit Test Results of Comparing The Ac-tual Distribution Against The Theoretical Distribution of Cash Inflows and Out-flows by Sampled KUD

Cash Inflows Cash Outflows Critical Value of DSample

No D P D P = 0.01 = 0.05 = 0.1

1 0.137 1.000 0.202 0.966 0.209 0.195 0.157

2 0.221 0.931 0.213 0.947 0.210 0.196 0.157

3 0.292 0.680 0.144 1.000 0.209 0.195 0.157

4 0.217 0.938 0.110 1.000 0.209 0.195 0.157

5 0.225 0.918 0.192 0.979 0.209 0.195 0.157

6 0.210 0.951 0.135 1.000 0.209 0.195 0.157

7 0.302 0.645 0.286 0.710 0.210 0.196 0.158

8 0.196 0.988 0.257 0.884 0.226 0.211 0.169

9 0.308 0.497 0.173 0.982 0.191 0.178 0.143

10 0.114 1.000 0.109 1.000 0.192 0.179 0.143

11 0.242 0.868 0.184 0.987 0.209 0.195 0.157

12 0.208 0.978 0.206 0.981 0.226 0.211 0.169

13 0.113 1.000 0.096 1.000 0.210 0.196 0.157

14 0.196 0.937 0.137 0.999 0.188 0.176 0.141

15 0.291 0.549 0.246 0.752 0.188 0.175 0.141

16 0.210 0.954 0.167 0.996 0.210 0.196 0.158

17 0.181 0.967 0.262 0.679 0.188 0.175 0.141

18 0.099 1.000 0.117 1.000 0.188 0.175 0.141

19 0.238 0.788 0.216 0.874 0.188 0.175 0.141

20 0.085 1.000 0.102 1.000 0.188 0.175 0.141

Mean 0.204 0.879 0.178 0.937 0.203 0.189 0.152

SD 0.067 0.159 0.058 0.103 0.013 0.012 0.010

53


As seen from Table 1, on average thetheoretical distribution (LogNormal) of dailycash inflows and outflows, that was gener-ated by the simulation model based on thegiven mean and standard deviation derivedfrom historical data of 20 sampled KUDs,has a similar shape to the actual distribution.Based on the criteria discussed in Section5.5.2, the theoretical distribution (LogNormal)assumption used to generate the daily cashinflows does not fit the actual distribution.This is because the calculated D (0.204) isnot smaller than the critical value of D (0.203)at the 99 percent significance level. The ac-tual distribution of cash inflows of Sample 3is more likely following a normal distributionbecause it has routine and relatively constantdaily cash inflows from its non-agriculturalactivities such building materials, and poul-try. Similarly, Sample 7 and 9 have routineand relatively constant daily cash inflows re-sulting from their handcraft businesses. Incontrast, Sample 15 and 19 have irregular,unstable and small daily cash inflows becausethey do not have substantial non-agriculturalbusinesses. Their non-agricultural businessesmainly provide salt for drying-fish processesin their location (close to dried-fish indus-tries). Also there is a large difference betweenthe mean calculated D and the mean criticalD at both 5 percent and 10 percent signifi-cant levels. Although the mean calculated Dof daily cash inflows is greater than its meancritical D value, the mean probability of thesimulation model generating daily cash in-flows following the theoretical distribution isabout 87.90 percent at 1 percent significantlevel.

On the other hand, on average the meancalculated D (0.178) of daily cash outflowsis much less than the mean critical D value(0.203) at the 99 percent confidence level,and less than the mean critical D value (0.189)

at the 95 percent confidence level. However,at the 90 percent confidence level, the meancalculated D is greater than the mean criticalD (0.152). The probability that the estimateddaily cash outflows, that are randomly gener-ated by the simulation model followingLogNormal distribution, is 93.70 percent at 1percent and 5 percent significant levels.

Also, it can be seen from Table 1 thatonly one sampled KUD (Sample 9) had theprobability (p) less than 50 percent, of a dailycash inflows stream following the LogNormaldistribution. This mean that daily cash inflowsfor this particular sampled KUD, which isgenerated by the simulation model, is morelikely not to be following the assumedLogNormal distribution. The historical datashows that the daily cash inflows of this KUDhave less variability over the season with themean of 144,630.52 and the standard devia-tion of 22,217.77. As well as being paddygrowers, farmers who are members of thisparticular KUD (Sample 9) are active in handcrafting (i.e., wood craft and hand made fab-ric/batik). Due to continuous demand forhandicrafts from tourists, farmers have con-tinuously purchased materials from the KUD.This results in less fluctuation in the KUD’scash inflows.

The Test of the Quantity ofPaddies Harvested Daily

Three important factors (the intercept,slope and R square) have to be determinedfrom the regression results to test the valid-ity of results of the simulation model’s sub-system (sub-simulation model). The mean ofthe actual quantity of paddies harvested daily,and the mean of the quantity of paddies har-vested daily, that is generated by the simula-tion model for 25, 50 and 100 replicationsfor each sampled KUD. These can be seenclearly in Figure 4 and Table 2.

Santoso

54

The outputs of the simulation modelwere analyzed on the basis of two scenarios,the proportion of paddies purchased each dayin the range of 25 percent to 100 percent andusing a fixed value of 100 percent. For bothscenarios, the simulation model was runbased on 25, 50 and 100 replications. Al-though the simulation model allowed for thenegative cash balance/availability, thesampled KUDs were not able to purchase the

entire paddies expected to be sold by farm-ers. In Scenario 1 (the range 25 to 100% ofpaddies purchased each day), the total quan-tity of paddies that could be purchased bythe KUD ranged from 9.19 percent to 62.07percent (25 replications), 9.23 percent to62.67 percent (50 replications) and 9.10 per-cent to 62.45 percent (100 replications).Meanwhile, the results of Scenario 2 (100%of paddies purchased each day) indicated that

Figure 4. The Line Plots of The Quantity of Paddies Harvested Daily of the Sub- ActualSystem and the Sub-Simulation Model

Table 2. The Results of Regression and Correlation Tests of the Quantity of PaddiesHarvested Daily of the Sub- Actual System and the Sub-Simulation Model

Replications Intercept Slope R2 Correlation Coefficient

25 -72.584 0.977 0.9996 0.9998

50 25.263 0.9773 0.9998 0.9999

100 -168.081 1.0052 0.9998 0.9999

55


8.97 percent to 97.88 percent (25 replica-tions), 9.15 percent to 97.34 percent (50 rep-lications) and 8.93 percent to 97.02 percent(100 replications) of the total quantity ofpaddies could be purchased by the KUDs. Onaverage, the total quantity of paddies pur-chased by the KUDs was greater than thatof the current practice. Based on the reportof the Department of Cooperatives and thefinding of research conducted by Ellis(1993b), KUDs were only able to purchasepaddies of about 4 percent to 10 percent ofthe total paddies harvested, which is less thanthe results of the simulation model. This studyidentified that the limitations of the paddystorage building, paddy drying floor, andpaddy milling machine capacities may con-tribute to the inability of the KUDs to pur-chase more paddies from their members. Thus,there are two methods that could be adoptedto resolve these limitations. First, the KUDscould borrow at a low interest rate providedby the Indonesian Government to fund theirnew investment in paddy storage buildings,expanding the paddy drying floor and paddymilling machines. If this approach is chosen,a careful feasibility study needs to be carriedout.

Second, the limitations may be removedby renting the facilities owned by privatepaddy wholesalers or other parties. Althoughthe second approach is cheaper, theoreticallythis approach is not preferable because thismechanism could lead to collusion betweenthe two parties, the KUDs and private paddywholesalers, to exploit farmers by buying theirproduce at the price that is below the floorprice. However, this issue is beyond this par-ticular research.

Concluding Remarks

The results of the Kolmogorov-SmirnovGoodness of Fit tests indicated that on aver-age, based on 0.01 level of significance, thecalculated D value (0.204) of daily cash in-flow streams was marginally over the criticalD value (0.203). If a strict rule is imple-mented, the assumption of the LogNormaldistribution underlying the randomly gener-ated cash inflow stream is not valid. Thiscondition resulted from the calculated D valueof one sampled KUD being very high andthe probability of the cash inflow stream fol-lowing the assumed distribution was lowerthan 0.5. After tracing back the historical data,it was found that this KUD had less varianceon its daily cash inflow stream due to routinetransactions of selling materials for hand-crafts.

Based on the Kolmogorov-SmirnovGoodness of Fit tests, the LogNormal distri-bution assumption underlying the generationof daily cash outflow, general cost, other costand paddy milling machine repair coststreams was valid as indicated by their calcu-lated D values being lower than those of thecritical D values. Therefore, in general, theassumptions employed in generating dailycash inflow and outflow, general cost, othercost and paddy milling machine repair coststream were valid.

The results of regression, correlationand t-tests indicated that the simulation-pre-dicted quantity of paddies harvested each dayresembled the real system. This was indicatedby the slope of the regression line being closeto one, and the R square and coefficient cor-relation being nearly one. Although the in-

Santoso

56

tercept of the regression line was greater thanzero, the t-test result indicated that there wasno significant difference between the simu-lation-predicted quantities of paddies pur-chased each day and the actual system. Simi-lar techniques were used to indicate that themonthly morning rain frequency and the ricefees generated by the simulation model werevalid representatives of those of the actualsystem. Thus, on the basis of the results ofthese tests, the valid sub-models of the simu-lation model may contribute to the validsimulation model as a whole.

The results of confidence interval analy-sis indicated that several KUDs required ad-ditional borrowings, and other KUDs might

need to reduce their borrowing. The shiftingfrom Scenario 1 to Scenario 2 was only sig-nificant to increase the earnings before tax(EBT) for the KUDs that were not con-strained by the limitations of their paddy stor-age building, paddy-drying floor and paddymilling machine capacities.

Although the simulation model is pow-erful enough as a decision support tool forthe management of KUDs, it should not beused by management to predict the paddyyield, but it should be used cautiously to as-sist them to determine the amount of cashrequired for purchasing paddies based on vari-ous scenarios.

References

Bassett, G. 1992. Operation Management for Service Industries: Competing in the Service Era (Vol. 261). Westport,Connecticut: Quorum Books.

Baumol, W. J. 1952. The transactions demand for cash: an inventory theoretic approach. Quarterly Journalof Economics LXVI (1):545 - 556.

Bergh, W-M. Van Den, and Hallerbach. 1991. A stochastic cash model with deterministic elements. Paperread at Modelling for Financial Decisions, 20-21 April 1989, at Catania.

Boonma, C.. 1988. Measures for the improvement of agricultural financing for small farmers in Thai-land. Paper read at Improved Agricultural Credit for Small Farms in Asia, 12-16 April, at Korea.

Calvert, C. 1995. Delphi Programming Unleashed. Indianapolis: SAMS Publishing.

Chand, S. and Th. Morton. 1982. A perfect planning horizon procedure for a deterministic cash balanceproblem. Management Science 28 (6): 652 - 669.

Chen, H-H. 1988. Measures for the improvement of agricultural financing for small farmers in Taiwan.Paper read at Improved Agricultural Credit for Small Farms in Asia, 12 – 16 April, at Korea.

Cohen, K. J., and R. M. Cyert. 1961. Computer models in dynamic economics. The Quarterly Journal ofEconomics LXXV: 112 - 127.

Constantinides, G. M. 1976. Stochastic cash management with mixed and proportional transaction costs.Management Science 22 (12): 1320 - 1331.

Demetrakakes, P., 1998. Farmer’s market. Food Processing 59 (8): 20 - 24.

Dillon, H. S. 1992. The Indonesian strategy in the field of agriculture. The Indonesian Quarterly XX (4): 438-446.

57


Fukui, J., and Y. Hirose. 1993. Poor harvest a natural disaster, but rice shortage is man-made. Tokyo BusinessToday (December): 28-32.

Golub, B., M. Holmer, R. McKendall, L. Pohlman, and S. A. Zenios. 1995. A stochastic programmingmodel for money management. European Journal of Operational Research 85: 282-296.

Greenberg, I. 1996. At Sara Lee company, Delphi 2.0 delivers a knockout punch. Infoworld 18 (12): 75.

Harijono, T. 1998. Derita petani Jabar belum Berakhir [The Farmers’ Suffering in Western Java Was NotEnded Yet]. Kompas (3 February).

Homonoff, R., and D. W. Mullins, Jr. 1975. Cash Management: An Inventory Control Limit Approach. Lexing-ton: Lexington Books.

Ida, L.. 1996. Opini: mengembangkan koperasi: Antara pengusaha besar dan intervensi pemerintah [Opinion:Developing Cooperatives: Between Big Companies and the Government Intervention]. Republika(12 July).

International Monetary Funds (IMF). 1998. Mitigating the social costs of the Asian crisis. Finance andDevelopment (September): 18 - 21.

Johnson, S. P., and K. R. Crawford. 1993. Cash management in rural America. Journal of Cash Management(Jan/Feb): 26-32.

Jones, C. 1995. Rice price stabilisation in Indonesia: An economic assessment of the changes in riskbearing. Bulletin of Indonesia Economic Studies 31 (1): 109 - 128.

Kim, Y-C. 1988. Small farm credit program and agricultural development in Korea. Paper read at Im-proved Agricultural Credit for Small Farms in Asia, at Korea.

Kleijnen, J. P. C., and W. Van Groenendaal. 1992. Simulation: A Statistical Perspective. Chichester: John Wiley& Sons.

Kleijnen, J. P. C. 1995a. Statistical validation of simulation models. European Journal of Operational Research87 (1): 21-34.

Kleijnen, J. P. C. 1995b. Verification and validation of simulation models. European Journal of OperationalResearch 82 (1): 145-162.

Knepell, P. L., and D. C. Arangno. 1993. Simulation Validation: A Confidence Assessment Methodology. LosAlamitos: IEEE Computer Society Press.

Knoeber, C. R. 1997. Explaining state bans on corporate farming. Economic Enquiry XXXV (January): 151- 166.

Krisnamurthi, B. 1996. Opini: Koperasi pedesaan dalam kondisi yang berubah [Opinion: The VillageCooperatives in the Changing Condition]. Republika (12 July).

Kroeker, C. J. 1996. The cooperative movement in Nicaragua: Empowerment and accompaniment ofseverely disadvantaged peasants. Journal of Social Issues 52 (1): 123 - 138.

Kumar, B. L. 1990. Gambhira co-operative farming society: A successful experiment in collective efforts.Indian Journal of Agricultural Economics 45 (3): 362-366.

Langdon, I. A. 1991. Role of Co-Operatives in Promoting Agricultural Production. Queensland: Gold CoastUniversity College of Graffith University.

Langdon, I. A. 1994. External equity need not compromise farmer control or co-operative principles.Paper read at The IDF Congress Melbourne, at Melbourne.

Santoso

58

Law, A. M., and W. D. Kelton. 1991. Simulation Modeling and Analysis. New York: McGraw-Hill, Inc.

Levy, A., M. Justman, and E. Hochman. 1989. The implication of financial cooperative in Israel’s semi-cooperative village. Journal of Development Economics 30: 25-46.

Madura, J. 1987. Improving cash management with simulation. Journal of Cash Management (Jan./Feb.): 47- 48.

Mensching, J., S. Garstka, and T. Morton. 1978. Protective planning-horizon procedures for a determin-istic cash balance problem. Operation Research 26 (4): 637 - 652.

Miller, M. H., and D. Orr. 1966. A model of the demand for money by firns. Quarterly Journal of EconomicsLXXX: 413 - 435.

Neave, E. H. 1970. The stochastic cash balance with mixed costs for increase and decrease. ManagementScience 16 (7): 472 - 490.

Oehler, S.. 1996. Largest U.S. AG co-op markets to and for producers. Agri Marketing 34 (6): H - M.

Pacheco, X., and S. Teixeira. 1995. Delphi Developer’s Guide. Indianapolis: SAMS Publishing.

Pambudy, N. M. 1998b. Sektor pangan, andalan yang nyaris salah urus [The Food Crop Sector, themainstay that was almost missed managing]. Kompas 21 (December).

Pambudy, N. M. 1998c. Sektor pertanian sebagai penyelamat [The agriculture sector is as a rescuer].Kompas18 (May).

Paul, R. J., and D. W. Balmer. 1993. Simulation Modelling. Lund: Chartwell-Bratt.

Pidd, M. 1993. Computer Simulation in Management Science. Chichester. John Wiley & Sons.

Pusat Penelitian dan Pengembangan Pedesaan [P3P], [The Center of Research and Rural Development].1997. Kaji Tindak Pengembangan Koperasi Mandiri Inti Dalam Wilayah Pertumbuhan Agribisnis di PropinsiNusa Tenggara Barat [The Follow-up Action of Developing the Self-financing Cooperatives within the AgribusinessGrowth Areas in the West Nusa Tenggara Province]. Mataram: Universitas Mataram.

Ramli, A. R. 1988. Measures or schemes for the improvement of agricultural financing for small farmers.Paper read at Improved Agricultural Credit for Small Farms in Asia, 12 - 16 April, at Korea.

Rasmussen, A. E. 1983. Financial Management in Co-operative Enterprises. Saskatchewan: Co-operative Collegeof Canada.

SAH., NAL., MAR., and SUP. 1999. Pembelian gabah di Sumatera kacau [The purchase of paddies inSumatera was not well managed]. Kompas 3 (March).

Santoso, B. 1993. Budget variance and working capital analyses of the provision of fertilizers and pesti-cides, and the purchase of rice by agricultural cooperatives [The KUDs]: A case study of the WestLombok regency. Master Thesis. The School of Accounting, The Faculty of Business and Manage-ment, University of South Australia, Adelaide.

Sidhu, J. S., and R. S. Sidhu. 1990. Case studies of successful and unsuccessful primary co-operativeservice society and milk producers co-operative society in Punjab. Indian Journal of AgriculturalEconomics 45 (3): 367-373.

Stone, B. K., and T. W. Miller. 1987. Daily cash forecasting with multiplicative models of cash flowpatterns. Financial Management (Winter): 45 - 65.

Suganda, H., 1998. Petani hanya kebagian “daki” [Farmers only got “grime”]. Kompas 18 (May).

Suyanto, B. 1996. HDG, KUD, dan hegemoni tengkulak [The paddy floor price, KUDs and hegemonyof paddy brokers]. Republika 5 (March).

59


Taber, C. S., and R. J. Timpone. 1996. Computational Modeling. Thousand Oaks: Sage Publications.

Thompson, R. 1986. Understanding cash flow: A system dynamics analysis. Journal of Small Business Man-agement 24 (2): 23 - 30.

Timmer, C. P. 1996. Does BULOG stabilise rice prices in Indonesia? Should it try? Bulletin of IndonesianEconomic Studies 32 (2): 45 - 74.

Timmer, C. P. 1997. Building efficiency in agricultural marketing: The long-run role of BULOG in theIndonesian food economy. Journal of International Development 9 (1): 133 - 145.

Tobin, J. 1956. The interest-elasticity of transactions demand for cash. The Review of Economics and StatisticsXXXVIII (3): 214 - 247.

World Bank. 1998a. Indonesia at a Glance. New York: The World Bank.

World Bank. 1998b. World Development Indicators 1998 CD-ROM. New York: The World Bank.

Wozniewicz, A. J., N. Shammas, and T. Campbell. 1995. Teach Yourself Delphi in 21 Days. Indianapolis:SAMS Publishing.

YAS. 1998. Krisis moneter ciptakan peluang agrobisnis [The Indonesian economic crisis created an op-portunity for agribusiness]. Kompas 2 (February).

Zaman, M. 1992. Working Capital Management Model for Public Enterprise: A Case Study of Bangladesh. MacArthur:University of Western Sydney.

developing a seasonal cash demand simulation for

Documents