Upravljanje rizicima pri izgradnji kapitalnih infrastrukturnih objekata u cilju poboljšanja njihove održivosti

Abstract

Investicioni projekat u građevinarstvu se definiše kao kompleksan tehničko-tehnološki,organizacioni, pravni, ekonomski i finansijski poduhvat koji se sastoji od skupakoordinisanih i kontrolisanih aktivnosti sa jasno definisanim početkom i krajem, čiji jecilj izgradnja, rekonstrukcija, modifikacija i/ili opremanje objekta ili objekata koji supotrebni investitoru. Kompleksnost je, kao karakteristika projekta i okruženja u kome seprojekat realizuje, naročito izražena kod velikih infrastrukturnih investicionih projekata.Predmet istraživanja u ovoj disertaciji jesu kapitalni infrastrukturni projekti velikeinvesticione vrednosti, veće od pedeset miliona evra. Razmatrani su projekti izgradnjeobjekata sistema ekonomske materijalne infrastrukture, i to: objekata saobraćajne (autoputevi,železničke pruge, metro linije i aerodromi), energetske (objekti za proizvodnju iprenos električne energije i gasa) i hidrotehničke (hidrotehničke konstrukcije)infrastrukture.Ovi projekti predstavljaju motore razvoja svakog društva i države. S obzirom napotencijalne dugoročne ekonomske, društvene i ekološke efekte koje velikiinfrastrukturni projekti mogu proizvesti, proces njihovog pokretanja, planiranja irealizacije je uvek praćen značajnom pažnjom celokupnog društva.Prethodna istraživanja pokazuju da je i pored značaja koji kapitalni infrastrukturniprojekti imaju, njihova realizacija veoma često neuspešna, i to kako u pogleduneispunjenja tradicionalnih kriterijuma uspeha projekata (troškovi, vreme, kvalitet),tako i u pogledu negativih ekonomskih efekata projekata i negativnih efekata projekatana društveno i ekološko okruženje. Ova tri aspekta uticaja na okruženja projekta(ekonomski, društveni i ekološki uticaji) čine tri aspekta održivosti i primene principaodrživog razvoja na konrektnom projektu. Kao glavni razlozi za odstupanja odplaniranih performansi realizovanih kapitalnih infrastrukturnih projekata u literaturi senavode rizici koji proističu iz specifičnosti ovakvih projekata. Kapitalni infrastrukturniprojekti su, u odnosu na građevinske projekte manje investicione vrednosti, investicionozahtevniji, značajno kompleksniji, prisutna je veća neizvesnost, veći broj učesnika,dugotrajniji su, i njihovi potencijalni efekti na ekonomiju, društvo i okolinu su veći,dalekosežniji i privlače više pažnje javnosti.Prethodna istraživanja takođe otkrivaju da, za sada, ne postoji jedinstvena metodologijaza procenu održivosti infrastrukturnih objekata u ranim fazama realizacije investicionogprojekta.Konačno, oblast upravljanja rizicima prilikom realizacije infrastrukturnih projekata nijeu dovoljnoj meri obrađena u domaćoj stručnoj i naučnoj literaturi.U prvom delu istraživanja za potrebe ove disertacije sprovedeno je kvantitativnoistraživanje sa ciljem identifikacije ključnih rizika po ostvarenje troškovnih ivremenskih performansi projekata izgradnje infrastrukturnih objekata u Srbiji. Osimtoga, ispitano je i postojanje prakse upravljanja rizicima na investicionim projektima uSrbiji. Dobijeni rezultati tog dela istraživanja su pokazali da su najznačajniji rizici prirealizaciji infrastrukturnih objekata u Srbiji: Nedostatak finansijskih sredstava zarealizaciju projekta, Finansijski rizik, Politički rizik i Korupcija. Takođe, bez obzira štopostoji svest da je primena upravljanja rizicima važna i što postoji potreba za njegovomprimenom, u Srbiji se upravljanje rizicima ne primenjuje dovoljno i prisutan je manjakznanja u vezi sa upravljanjem rizicima. Međutim, među praktičarima iz oblastigrađevinarstva postoji izražena zainteresovanost da se o upravljanju rizicima nauči više.S obzirom na ovakve rezultate, u daljem istraživanju analizirani su i prikazani mogućipristupi za analizu rizika i integraciju analize ekonomske, socijalne i ekološke održivostiprojekta u fazi formiranja koncepcije kapitalnog infrastrukturnog projekta. Predloženasu i razmotrena dva pristupa – kvalitativna i kvantitativna analiza rizika, uz primenusocijalne Cost-Benefit analize (SCBA). Mogućnost i implikacije praktične primenepredloženih pristupa analizirani su na primeru dve studije slučaja kapitalnihinfrastrukturnih objekata (izgradnja postrojenja za insineraciju komunalnog čvrstogotpada i izgradnja deonice auto-puta).U disertaciji je pokazano da primena predstavljene SCBA uz monetarizaciju eksternihefekata projekata omogućava svođenje na istu meru i sveobuhvatno sagledavanjepotencijalnih ekonomskih, društvenih i ekoloških uticaja projekta kroz ceo životniciklus. Primena kvalitativne analize rizika omogućava pravovremenu identifikaciju,rangiranje i sistematičan prikaz potencijalnih pretnji po ostvarenje pojedinih ciljevaprojekta, te predlog mera za otklanjanje ili umanjenje pretnji. Stohastički pristup iMonte-Carlo analiza za kvantitativnu analizu rizika u studiji opravdanosti doprinosevećoj pouzdanosti procene finansijskih i društveno-ekonomskih rezultata projekta.Prikazanu metodologiju i pristup je moguće koristiti u budućim predinvesticionimanalizama kapitalnih infrastrukturnih objekata, naročito na domaćem tržištu i tržištuzemalja u razvoju.Cilj daljeg istraživanja bilo je ispitivanje mogućnosti za razvoj ekspertskog sistema zaprocenu rizika u ranim fazama realizacije kapitalnih infrastrukturnih projekata. Izvršenaje provera hipoteze da je na osnovu poznatih istorijskih podataka o ostvarenjuplaniranih troškova i rokova realizacije i o karakteristikama realizovaniih kapitalniihinfrastrukturniih projekata i njihovog okruženja moguće napraviti model za predviđanjeuspešnosti realizacije na novim projektima, ukoliko su karakteristike novih projekata injihovog okruženja poznate. Usvojena metodologija istraživanja podrazumevala jenajpre prikupljanje podataka sa realizovanih kapitalnih infrastrukturnih projekata,njihovu pripremu a zatim analizu primenom metoda mašinskog učenja. Mašinskoučenje je oblast kompjuterskih nauka koja se bavi kreiranjem i analizom metoda nakojima počivaju računarski programi koji uče iz iskustva.Prikupljeni su podaci o 30 saobraćajnih, 12 energetskih i 2 hidrotehnička kapitalnaprojekta (ukupno 44 projekta, svaki vrednosti preko petsto miliona evra) realizovana nateritoriji Evrope. Podaci su sistematizovani u obliku 3 binarna pokazatelja uspešnostiprojekata (prekoračenje troškova, kašnjenje u fazi građenja i kašnjenje u fazi planiranja)i 46 binarnih atributa projekata, koji opisuju izvore rizika iz 5 kategorija: učesnici naprojektu (interni i eksterni), spoljašnje okruženje projekta (pravno, društvenoekonomsko,političko), upravljanje projektom, tehnološki aspekti, razno. Metodologijaprikupljanja i pripreme podataka zasnivala se na metodologiji rada formiranoj u okvirumeđunarodne naučne COST akcije TU1003: "Megaproject – Efficient Design andDelivery of Megaprojects in the European Union". Zatim je, prema originalnoosmišljenoj metodologiji, zasnovanoj na prethodnim istraživanjima u oblastima procenerizika i primene metoda mašinskog učenja u upravljanju projektima, izvršena uporednaanaliza performansi dvanaest predloženih modela za predviđanje prekoračenjaMiljan S. Mikić, dipl. građ. inž. 8planiranih troškova izgradnje, kašnjenja u fazi građenja, kao i kašnjenja u fazi planiranjarealizacije projekata. Ispitana je mogućnost kombinovane primene statističkih metoda(Selekcija podskupa atributa zasnovana na korelaciji i Selekcija zasnovana na vrednostiinformacionog dobitka) i metoda mašinskog učenja (Metoda vektora podrške, Veštačkeneuralne mreže, K-najbližih suseda, Drvo odlučivanja, Naivni Bajesov klasifikator iLogistička regresija).Dobijeni rezultati su dokazali da je za dati skup prikupljenih podataka bilo mogućenapraviti modele za predviđanje navedenih pokazatelja uspešnosti u ranoj fazirealizacije kapitalnih infrastrukturnih projekata.Istraživanje predmetnog skupa podataka je takođe identifikikovalo podskupove odrelativno malog broja ključnih izvora rizika iz faze pre početka građenja (3-10, zavisnood problema) čije poznavanje je dovoljno da se ostvare dobijene, relativno visokeperformanse predviđanja. Za dati skup prikupljenih podataka, najznačajnijeidentifikovane kategorije rizika jesu: Društveno-ekonomsko okruženje projekta,Eksterni učesnici na projektu i Tehnološki aspekti projekta.Veoma bitna distinkcija primenjenog pristupa u odnosu na analizu korelacijepojedinačnih izvora rizika i pokazatelja performansi projekata jeste da se ovde nastojiutvrditi zajednički uticaj koji više izvora rizika istovremeno imaju na performanseprojekata.Primena predloženih modela, za rano predviđanje uspešnosti realizacije projekata,najveću korist donela bi investitoru i donosiocima odluka u ranoj fazi realizacijeprojekta, jer bi mogla da pruži bolji uvid u očekivane performanse datog projekta, kao ida upozori na izvore rizika zbog kojih bi performanse projekta potencijalno mogle bitiugrožene.Kako bi ekspertski sistem za procenu rizika prilikom pokretanja i realizacije kapitalnihinfrastrukturnih projekata bio zaokružen, neophodan je dalji rad na dopunjavanju bazepodataka.

A construction investment project is defined as a complex technical and technological,organizational, legal, economic and financial endeavour that consists of a set ofcoordinated and controlled activities with a clearly defined beginning and end, with thegoal of building, reconstructing, modifying and/or equipping a facility or facilities thatare required by the investor. Project and project environment complexity is particularlyemphasised on large infrastructure investment projects.The subject of the research in this dissertation are large infrastructure projects with aninvestment value of more than fifty million euros. The projects of planning andconstruction of hard (physical) economic infrastructure were investigated; thoseincluded ones in the traffic (highways, railways, subway lines and airports), energy(facilities for the production and transmission of electricity and gas) and hydro-technicalsector.The above listed projects represent the development engines of any society and state.Given the potential long-term economic, social and environmental effects that largeinfrastructure projects can produce, their planning and construction process is alwaysfollowed closely by the entire society.Previous research shows that, despite the importance of large infrastructure projects,their implementation is often unsuccessful, both in terms of failure to meet thetraditional project success criteria (cost, time, quality), as well as in terms of the adverseeconomic, social and environmental effects that the projects can create. Analysis ofthese three impacts (economic, social and environmental), as aspects of sustainability,allows for the incorporation of sustainable development principles within a specificproject. In the literature, risks that arrise from specific characteristics of largeinfrastructure projects are identified as the main cause of deviations from the plannedperformance of large infrastructure projects. In comparison to construction projects ofsmaller investment value, large infrastructure projects are: financially more demanding;significantly more complex; carry a greater uncertainty; include more stakeholders; areMiljan S. Mikić, dipl. građ. inž. 11longer lasting, and their potential effects on the economy, society and the environmentare greater, more far-reaching and generate more public attention.At present, there is no single accepted methodology for sustainability assessment in theearly phase (phase of conducting pre-feasiblity anf feasibility study) of an investmentproject.Additionally, the area of risk management on infrastructure projects is not sufficientlyaddressed in the domestic professional and scientific literature.In the first part of the dissertation, quantitative research was conducted in order toidentify the key risks to achieving cost and time performance of infrastructureconstruction projects in Serbia. In addition, risk management practices related toinvestment projects in Serbia were investigated. Research results showed that the mostsignificant risks associated with construction infrastructure projects in Serbia are: thelack of funds for project implementation; financial and political risks; and corruption.Also, regardless of the fact that there is awareness of the importance of riskmanagement and the need for its implementation, risk management is not implementedwell in Serbia and there is a lack of knowledge in relation to risk management.However, there is a strong interest to learn more about risk management among thepractitioners in the construction field.Considering obtained results, in further research, possible approaches for risk analysisand integration of project economic, social and environmental sustainability analysiswere addressed. Two approaches were proposed and investigated – a qualitative and aquantitative risk analysis, along with applying a Social Cost-Benefit Analysis (SCBA).The possibility and implications of the practical application of the proposed approacheswere analysed on two case studies of major infrastructure projects (Municipal solidwaste incineration plant and a Motorway section).The application of the presented SCBA with monetization of the project external effectsallows for comprehensive consideration of the potential economic, social andenvironmental impacts of the project throughout the entire life cycle of theinfrastructure facility. The application of qualitative risk analysis enables the timelyidentification, ranking and systematic overview of potential threats for the achievementof specific project objectives and the proposal of measures for the elimination orreduction of threats. The stochastic approach and Monte-Carlo Analysis for quantitativerisk analysis in the project feasibility study allows the higher reliability of assessment ofthe project financial and socio-economic results. The studied methodology andapproach can be used in future pre-investment analyses of major infrastructure facilities,especially in the domestic market and the market of developing countries.The further objective of this dissertation was to examine the possiblity for developingan expert system for risk assessment in the early phase of large infrastructure projects.The hypothesis was that based on the known historical data on the achievement ofplanned cost and deadlines and the characteristics of large infrastructure projects andtheir environment, it is possible to create a model that can predict the success or failure(regarding cost and time performance) of new projects, if the characteristics of newprojects and their environment are known. The adopted research methodology consistedof data collection from completed large infrastructure projects, data preparation andanalysis using machine learning methods. Machine learning is a field of computerscience that deals with the creation and analysis of methods that are used by computerprograms that learn from experience. In previous studies, it has been proven that certainmachine learning methods can be successfully used to predict the performance ofconstruction investment projects.Data from 30 traffic, 12 energy and 2 hydro-technical large infrastructure projects (atotal of 44 projects, each with the investment value of more than five hundred millioneuros) completed in Europe were collected. The data were transformed to the form ofthree binary project success indicators (Cost overrun, Delay in the construction phaseand a Delay in the planning phase) and 46 binary project attributes, which describe thesources of risk from five categories of project characteristics and characteristic of theproject environment: Project stakeholders (internal and external); The externalenvironment of the project (legal, socio-economic, political); Project management;Technological aspects; and Miscellaneous. The methodology of collecting andpreparing the data was based on the methodology of work of the international scientificCOST Action TU1003: "Megaproject - Efficient Design and Delivery of Megaprojectsin the European Union". Then, according to the newly proposed methodology that wasbased on previous research in the areas of risk assessment and the application ofmachine learning methods in project management, a comparative analysis of theperformance of the twelve proposed models for the prediction of the exceedance of theplanned construction costs, the delay in the construction phase, as well as the delay inthe planning phase execution of the projects was performed. The possibility of thecombined application of statistical methods (the selection of a subset of attributes basedon correlation and the selection based on the value of information gain) and machinelearning methods (Support vectors machine, Artificial neural networks, K-nearestneighbour, Decision tree, Naive Bayesian classifier, Logistic regression) was analysed.The results have shown that, for a given set of collected data, it was possible to buildmodels that predict success indicators in the early implementation stage of largeinfrastructure projects.The research also resulted with the identification of subsets of a relatively small numberof key sources of risk from the pre-construction phase of project development (3-10sources of risk, depending on the problem) whose knowledge is sufficient to yieldrelatively high performance predictions for a given set of collected data. The mostimportant identified risk categories are: the socio-economic environment of the project;the external stakeholders in the project; and the technological aspects of the project.A very important distinction between the approach applied in this dissertation and thecorrelation analysis of individual sources of risk and project performance indicators isthat in this dissertation the attempt was to determine the combined concurrent effect ofmultiple sources of risk on project performance.The greatest benefit of the application of proposed models for the early prediction ofproject success is to the investor and the decision-makers at the early stage of a project,as the models can provide a better insight into the expected performance of a givenproject, as well as to draw attention to the sources of risk that would potentiallyendanger project performance.In order to finalize the expert system for risk assessment during the planning andconstruction phases of large infrastructure projects, further research should be aimed atbroadening the database of large infrastructure projects.