A Decision Support System for a Production Plant Investment in Abroad

Mustafa Canolca, Ozcan Cavusoglu, Demet Bayraktar

Istanbul Technical University, Faculty of Management, Department of Management Engineering, Maçka, 34367, Istanbul, Turkey

canolcamc@yahoo.com, cavusogluozcan@yahoo.com, demet.bayraktar@itu.edu.tr

The purpose of this study is to propose a decision support system which will provide a comprehensive and a scientific approach for solving an investment problem of a production company. For this purpose, a detailed literature review and extensive interviews have been carried out with authorized experts. Accordingly, qualitative and quantitative decision factors have been defined for alternative countries, which have been evaluated by using AHP. Finally, the best alternative country has been selected by using goal programming taking into consideration investment, raw material cost, labor cost, profit and market share goals. The results and future work have been discussed in detail.

Key words: Investment planning and analysis, AHP, GP, Cash Flow, Production plant

1. Introduction

In today’s business environment, investment analysis has more important role for shareholders to provide a logical investment which is returning much more earning yield. Investment analysis realization is examining and assessing of economic and market trends, earning prospects, earning ratios, various other indicators and factors to determine suitable investment strategies [1]. Shareholders want to analysis many type of investment to earn money. In this article, we concentrate on to propose a decision support system which will provide a well defined and a comprehensive approach for solving investment problem of a production company which is planning investment in abroad. Foreign direct investment combines aspects of both international trade in goods and international financial flows, and is a phenomena more complex than either of these [5].

In literature, Li and others (2009) were concentrated on breakeven point analysis to evaluate financial investment alternatives. Tuzkaya and others (2008) had used Analytic Network Process (ANP) for determining production location selection. Benefits, Opportunities, Cost and Risks are determined as the main criteria by Tuzkaya and others [13]. Kologirou (2001) had utilized an expert system approach to determine agricultural area for seeding in Greece. The most suitable area is determined by proposed expert system approach which includes expert expertise and real world data [8]. To provide dispassionate results expert system could be the best way to evaluate investment alternatives. Jimenes, L.G. and Pascaul, L. B. (2007), were used net cash flow calculation to evaluate investment alternatives for different projection criteria [6]. Levary, R.R., Wan, K., (1999), had created AHP model for foreign direct investment with respect to the main criteria such as global concentration, global synergy, global strategic motivation, location and competition [9]. Badri, M.A., Mr. Davis, D., Mrs Davis, D., (2001) had used 0-1 goal programming method to determine efficient information technology for a hospital. In this study, we also use goal programming method to determine most suitable location for production investment in abroad [2].

Page 1

4/7/2023 Muzafferinho

2. Methodology

In this article, the proposed decision support system is realized in four phases as explained in the following.

Phase 1: Determining Country Selection Criteria (CSC)

In this study, country selection criteria (CSC) are determined as “Population”, “GDP”, “GDPPC”, “Sales kg”, “Sales $”, “Sales kg per capita”, “Sales $ per capita” and “Price” (Levary, R.R., Wan, K. , (1999), Tuzkaya, G., Önüt, S., Tuzkaya, U., Gülsün, B., (2008), Kahraman, C., Ruan, D., Doğan, I., (2003), expert advise) according the literature review and expert interviews as well. Then, the CSC are grouped into four main criteria as “suitability for purpose”, “obtaining difficulties”, “presentation level” and “importance level” regarding to the experts’ opinions.

Phase 2: Determining the Weights of CSC with AHP

In order to determine the weights of CSC a three-level AHP hierarchy is built as

shown in Figure 1. Eight experts including company managers and export director, sales

managers, sales representative are performed pairwised comparisons. The relative weights of

CSC are in the order of population ( 18% ), GDP ( 7% ), GDPPC ( 7% ), sales kg ( 17% ),

sales $ ( 20% ), sales kg per capita ( 11% ), sales $ per capita ( 11 % ) and price ( 8% ). .

Then, these relative weights are used for the evaluation of candidate country evaluation

process explained in Phase 3.

Figure 1: AHP Model for CSC

2

Phase 3: Candidate Country Evaluation and Selection

The production company where application is realized has performed international

business with fifty countries. These countries are taken into consideration as candidate

countries. In order to evaluate the candidate countries, the values of each CSC of candidate

countries and the weights of each CSC obtained in Phase 2 is multiplied each other. Sum of

these multiplications yields the candidates score. Among fifty countries the ten countries are

selected according the top most ten scores. The scores are C1 ( 9,61% ), C2 ( 8,91% ), C3

( 8,75% ), C4 ( 4,87% ), C5 ( 4,18% ), C6 ( 4,15% ), C7 ( 3,82% ), C8 ( 3,38% ), C9

( 3,17% ), C10 ( 3,07% ).

Phase 4: Further Evaluation for Candidate Countries by AHP

For the second elimination of the ten candidate countries determined in Phase 3, a-three level AHP hierarchy is built as shown in Figure 2.

3

Figure 2:AHP Model for Foreign Investment Alternative Selection

In the third level of AHP, under the “production cost” main criterion “raw material cost”, “depreciation”, “workers cost”, “fuel oil cost”, “energy cost”, “maintenance cost” and “packaging cost” are taken placed ([16], expert advice). Similarly, in the third level of AHP, under the “economical stability” main criteria are “inflation rate”, “political stability”,” interest rate”, “tax rate”, “credit needed”, “credit pay time”, “investment time”, “first investment cost”, “GDP” ([9],[13],[16], expert advice). Moreover, in the third level of AHP, under the “job environment” main criteria are “competitors”, “corruption”, “incentive”, and “regulation” ([11],[16], expert advice). Adding, in the third level of AHP, under the “strategic location” main criteria are “raw material availability”, “market”, “population”, “distribution alternatives”([16], expert advice). In the third level of AHP, under the “sales” main criteria are “total sales”, “price”, “price increase”, “sales increase” (expert advice). And finally in the third level of AHP, under the “human resources” main criteria are “workers characteristic”, “workers availability”, and “worker usage ratio”([9], expert advice). Also eight experts including company export director, sales managers and sales representative are performed pair wised comparisons.

In the fourth level of AHP model, all the ten candidates are placed as C1, C2, C3, C4, C5, C6, C7, C8, C9 and C10. The relative weights for each country is obtained as C1(15,06%), C2(13,99%), C3(11,83%), C4(9,59%), C5(9,55%), C6(8,65%), C7(8,47%), C8(8,47%), C9(7,83%) and C10(6,575). Finally, C1, C2, C3, C4, and C5 are selected as continuing investment candidates.

3. Conclusion

For the determining scope not only sales quantity and sales endorsement but also population, consumption kg per capita, consumption $ per capita and price also evaluated for investment criticize.

Strategic location surprisingly is more important than sales criteria than we expected from our mind.

4

Production capacity not only adequate data to calculate investment return time, also monthly sales quantity should be consider for calculating investment return time.Firm that uses our methodology for foreign direct investment analysis, may be aware of other criteria which think they are not so important for analysis such as human capital, job environment etc.

This paper prepared with only one firm expert and their expectations. In the future more than one firm’s expert may join this methodology and therefore results should be generalized.

For future research, Goal Programming Methods can be developed and implemented for five candidates investment alternative in order to determine best suitable investment alternative.

Finally, in the future expert system should be developing from this methodology.

References

1. Atan, M. ve Maden, U (2005), Bireysel Ve Kurumsal Kredibilitenin Analitik Hiyerarsi Süreci ile Çözümlenmesi, IV. İstatistik Kongresi, İstatistik Mezunları Dernegi ve Türk İstatistik Dernegi, Belek, ANTALYA2. Badri, M.A., Mr. Davis, D., Mrs Davis, D., (2001), A Comprehensive 0–1 Goal Programming Model For Project Selection, International Journal of Project Management 19, 243-2523. Bakırcı, F., Sektörel Bazda Bir Etkinlik Ölçümü: VZA ile Bir Analiz, İİB Dergisi, Vol: 20-2, pp 199-2174. Chena, Y., Liangb, L., Yangb, F., Zhuc, J., (2004), Evaluation Of Information Technology Investment: A Data Envelopment Analysis Approach, Computers & Operations Research, 1368–13795. Feenstra, R., 1998, Facts and Fallacies about Foreign Direct Investment, Dept. of

5

Economics, Univ. of California6. Jimenez, L.G., Pascaul, B., 2007. Multi criteria Cash Flow Modeling and Project Value –Multiples for Two Stage Project Valuation, International Journal of Project Management, Vol: 26, pp. 185-1947. Kadoya,S., Kuroko, T.,Namatame, T.,(2008) Contrarian Investment Strategy With Data Envelopment Analysis Concept, European Journal of Operational Research, 120-1318. Kalogirou, S., 2001. Expert Systems and GIS: an Application of Land Suitability Evaluation, Computers, Environment and Urban Systems, Vol: 22, pp 89-1129. Levary, R.R., Wan, K. , (1999) An Analytic Hierarchy Process Based Simulation Model For Entry Mode Decision Regarding Foreign Direct İnvestment, Omega 27 (6) 661– 677.10. Li, X., Deng, Z., Lou, J., 2009. Trading Strategy Design in Financial Investment hrough a Turning Points Prediction Scheme, Expert Systems with Applications 36 (2009), pp 7818-182611. Mohonty, R.P. , Deshmukh, S.G. , (1998), Advanced Technology Manufacturing Selection: A Strategic Model For Learning And Evaluation, International Journal of Production Economics 55 (3) 295–307.12. Regmi A., Deepak, M.S., Seale ,J.L., Berstein, J., 2001. Cross Country Analysis of Food Consumption Pattern,USDA13. Tuzkaya, G., Önüt, S., Tuzkaya, U., Gülsün, B., 2008, An Analitic network process approach for locating undesirable facilities: An example from Istanbul, Turkey, Journal of Environmental Management, Vol: 88, pp 970-98314. Ulucan, A., 2000.ISO 500 Şirketlerinin Etkinliklerinin Ölçülmesinde Veri Zarflama Analizi Yaklaşımı: Farklı Girdi Çıktı Bileşenleri ve Ölçeğe göre Getiri Yaklaşımları ile değerlendirmeler, Ankara Üniversitesi SBF Dergisi, Vol: 57-2, 185-20215. Verhoef, C., (2004), Quantifying the Value of IT-investments, Science of Computer Programming, 315–34216. Kahraman, C., Ruan, D., Doğan, I., 2003, Fuzzy group decision-making for facility location selection, Information Science, Vol: 157, page: 135-153

6