Ar-ge projelerinin karmaşıklığının değerlendirilmesi: Bayes inanç ağı yaklaşımı
Assessing r&d project complexity: A bayesian belief network approach
Yazar: ZÜLFİYE DERİN
Danışman: DOÇ. DR. AYBERK SOYER
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / Endüstri Mühendisliği Ana Bilim Dalı / Mühendislik Yönetimi Bilim Dalı
Derece: Yüksek Lisans, 2024
Konu: Endüstri ve Endüstri Mühendisliği = Industrial and Industrial Engineering

Özet:
Son yıllarda gelişen teknoloji ve artan rekabet ortamı ile şirketlerin yenilikçi projeler üretmesi ve bu projeleri başarılı bir şekilde tamamlaması kritik öneme sahip olmuştur. Projenin başarısını etkileyen kritik faktörlerden biri ise belirsizliktir. Belirsizlik, çoğunlukla proje bütçesinde ve zaman planında sapmalara sebep olarak teknik ve yönetsel zorluklar yaratmaktadır. Bu koşullar altında, proje yönetiminin, karmaşıklık yönetimi ile ilişkili olduğu vurgulanmaktadır. Bu karmaşıklığın yönetilmesi için ise karmaşıklığa sebep olan faktörlerin net bir şekilde tanımlanması ve faktörler arasındaki ilişkilerin sayısal olarak hesaplanması gerekmektedir. Proje karmaşıklığı, tanımlaması ve sayısal olarak ölçülmesi zor bir kavram olarak belirtilmektedir. İncelenen akademik çalışmalarda proje karmaşıklığının birbiri ile ilişkili olan çeşitli birçok faktörden kaynaklandığı ifade edilmektedir. Daha geniş anlamda, birden fazla hedef ve çok sayıda paydaş olmak üzere birçok unsurdan oluşan ve bu unsurların bağımlı olmasından kaynaklanan yapısal belirsizlik olarak ifade edilmektedir. Literatürde, proje karmaşıklığının nitel ve nicel yöntemlerle ölçümlendiği farklı çalışmalar bulunmaktadır. Bu çalışmalarda, karmaşıklık faktörlerinin nedensel ilişkilerine odaklanmanın ve belirsizliği dikkate almanın gerekliliğinin altını çizilmektedir.

Bu çalışmanın amacı, nedensel ilişkiler üzerine kurulu pratik bir olasılıksal modelleme tekniği sunan Bayes İnanç Ağı (BBN: Bayesian Belief Network) yaklaşımını kullanarak AR-GE projelerinin karmaşıklığını değerlendirmek amacıyla yeni bir ölçüm modeli geliştirmektedir. Şirketlerin rekabet gücünün artırılmasında büyük önem taşıyan AR-GE projelerinin, küresel rekabette de belirleyici bir rol üstlenmesi sebebiyle çalışmanın kapsamını AR-GE projeleri oluşturmaktadır. AR-GE projeleri karakteristiği gereği, birçok belirsizlik içeren, teknolojik bağımlılığı yüksek olan, multidisipliner ekipler gerektiren ve uzun vadeli projelerdir. Dolayısıyla AR-GE projeleri, diğer proje türlerine göre daha karmaşık kabul edilmektedir ve esnek bir yönetim gerektirmektedir. AR-GE projelerinin karmaşıklık yönetiminde teorik ve pratik uygulamalara katkı sunmayı amaçlayan bu çalışmanın uygulaması dört bölümden oluşmaktadır: (i) karmaşıklık faktörlerinin belirlenmesi, (ii) Bayes ağı modelinin oluşturulması ve koşullu olasılık tablolarının elde edilmesi, (iii) model validasyonu, (iv) model analizleri ve çıkarımlar. 

Çalışma kapsamında öncelikle, literatür araştırması ve uzman bilgisine dayanarak 28 karmaşıklık faktörü belirlenmiş ve belirlenen faktörler makro çevre, görev çevresi, organizasyon ve proje karakteristiği olmak üzere dört grup altında kategorize edilmiştir. Karmaşıklık faktörlerinin olasılıklarının belirlenmesi için 5'li Likert ölçeğinde (1: çok az; 5: çok fazla) çevrim içi anket ile veri toplanmıştır ve veri modelde kullanılmak üzere 3'lü Likert ölçeğine (1: düşük; 2: orta; 3: yüksek) dönüştürülmüştür. İkinci aşamada toplanan veri ve uzman görüşleri ile GeNIe programında BBN yapısı oluşturulmuştur. İlk kurulan ağ yapısı, modelin ürettiği anlamsız bağlantıları çıkarmak için uzman grubu ile tekrar değerlendirilerek nihai yapı oluşturulmuştur. Sonrasında, nihai ağ yapısı üzerinden parametre öğrenmesi ile her bir faktör için koşullu olasılık tabloları elde edilmiştir. Koşullu olasılık tablolarının elde edilmesinin sebebi analiz sürecine temel oluşturmasıdır. Üçüncü aşamada, K-Katmanlı Çapraz Doğrulama yöntemi kullanılarak modelin validasyonu gerçekleştirilmiştir ve modelin doğruluğu %80 üzerinde çıkmıştır. Son olarak duyarlılık analizi, etki zinciri analizi ve senaryo analizleri gerçekleştirilmiştir.

Duyarlılık analizi sonuçlarına göre AR-GE projelerinin karmaşıklığı makro çevrede teknolojik konjonktürün belirsizlikleri, değişkenlikleri ve bağımlılıklar faktörüne; görev çevresinde müşteri talep ve beklentilerindeki belirsizlik ve değişkenlik faktörüne; proje karakteristiği kategorisinde projenin amaç ve hedeflerindeki belirsizlik faktörüne ve projenin stratejik önemi faktörüne yüksek duyarlı çıkmıştır. Koşullu olasılıklar arasındaki bağımlılık derecesini tanımlayan etki zinciri analizi sonuçlarına göre proje karmaşıklığını en çok etkileyen nedensel zincir, "Projenin amaç ve hedeflerindeki belirsizlik ve bağımlılık" → "Projenin amaç ve hedeflerinin çeşitliliği ve sayısı" → "Projenin insan kaynağı yapısı" → "Proje ekip üyeleri arasındaki etkileşim ve iş birliği" → "Proje karmaşıklığı"dır. Senaryo analizinde ise makro çevre, görev çevresi, organizasyon ve proje karakteristiği kategorisindeki faktörlerinin durumu sırasıyla "Yüksek" karmaşık olarak tanımlanmış ve sonuç olarak proje karmaşıklığı sırasıyla %52, %56, %51 ve %97 ile "Yüksek" karmaşıklık olasılığına sahip çıkmıştır.

Abstract:
In recent years, with the development of technology and an increasing competitive environment, it has become critical for companies to produce innovative projects and successfully complete these projects. A project is a structured set of outputs consisting of interrelated activities carried out within a specific time frame and budget to achieve a particular goal. Project management techniques are applied to ensure that projects are conducted within a specific systematic framework. Project management is an integrated process consisting of planning, execution, monitoring, and control phases from the initiation to the closure of the projects, following a particular methodology. The primary objective of project management is the successful completion of the project. One of the critical factors affecting the success of the project is uncertainty. Uncertainty often leads to deviations in the project budget and timeline, creating technical and managerial challenges. Therefore, uncertainty increases the complexity level of a project and significantly impacts project success. In project organizations, it is emphasized that project management is related to complexity management. To manage this complexity, it is necessary to clearly define the factors causing the complexity and quantitatively calculate the relationships between these factors.

Project complexity is indicated as a concept that is difficult to define and measure quantitatively. In the academic studies reviewed, it is stated that project complexity is caused by several interrelated factors. More broadly, it is defined as structural uncertainty arising from the interdependence and interconnectedness of many elements, including multiple objectives, multiple stakeholders, and so on. In the literature, there are different studies that measure project complexity by applying qualitative and quantitative methods. In these reviewed studies, methods such as Analytic Hierarchy Process (AHP), Fuzzy Analytic Hierarchy Process (FAHP), Fuzzy Analytic Network Process (FANP), Bayesian Belief Network (BBN), and Dynamic Bayesian Network (DBN) stand out. Fuzzy logic has been incorporated into the models developed in some of these studies to create a more realistic measurement framework. On the other hand, the BBN method is also preferred as it is a probabilistic model that allows measuring the degree of complexity under different scenarios in the presence of uncertainty. The DBN, an extended version of BBN, stands out as a model that provides time-dependent dynamics. The current literature underlines the need to focus on the causal relationships of complexity factors and to take the uncertainty into account.

This study aims to develop a new measurement model to evaluate the complexity of R&D projects using the Bayesian Belief Network (BBN) approach, which offers a practical probabilistic modeling based on causal relationships. The scope of the study is determined to consider R&D projects due to their crucial role in achieving competitive advantage and improving performance. R&D projects are generally characterized by many uncertainties, high technological dependencies, multidisciplinary teams, and their long-term nature. Therefore, R&D projects are considered more complex than other project types and require flexible management. This study, which aims to contribute to theoretical and practical applications in the complexity management of R&D projects, consists of four stages: (i) identification of complexity factors, (ii) construction of the Bayesian network model, (iii) model validation, and (iv) model analyses and conclusions.

First, based on the literature research and expert knowledge, 28 complexity factors were identified and categorized into the macro environment, task environment, organizational, and project characteristics groups.
•Complexity factors from the macro environment were determined using PESTLE analysis including political, economic, technological, legal, and environmental factors.
•Complexity factors from the task environment were defined as relationships among external stakeholders, the number and diversity of external stakeholders, customers, suppliers, and the competitive environment.
•Organizational complexity factors were defined as relationships and collaboration environment within the organization, cultural structure, financial & operational risks, and organizational structure.
•Factors in the project characteristics category included 13 factors such as uncertainties in project objectives and goals, scope and activities uncertainty, dependencies, output structure, project duration, project budget, strategic importance of the project, availability of planned resources, and human resources structure.

After defining the complexity factors, data were collected from participants with experience in R&D project management through an online survey. Participants were asked to rate (on a 5-point Likert scale, with 1 being very low level and 5 being very high level) the extent to which the 28 factors that cause project complexity apply to the most recent R&D project they were involved in/managed. Participants were also asked to evaluate the overall complexity of the most recent R&D project they considered. A total of 157 responses were received and 136 were decided to be used in the model because inconsistent responses and responses excluding R&D projects were excluded. Due to the complexity of the conditional probability table in a 28-factor network structure, which increases the complexity of the network structure and makes the model solution difficult, the questionnaire responses obtained using a 5-point Likert scale, factors with a score of 1 and 2 were classified as "low", factors with a score of 3 were classified as "moderate", and factors with a score of 4 and 5 were classified as "high". The survey results showed that the complexity level of the most recent R&D project participated in or managed by the participants was evaluated as 5% low complexity, 34% moderate complexity, and 61% high complexity. This result emphasizes the importance of this study on the complexity of R&D projects.

The Bayesian network methodology includes factors used as variables (nodes) and arrows (arcs) showing the relationships between these factors. The probability table obtained by multiplying the conditional probabilities formed according to the relationships of the variables in the network structure constitutes the quantitative feature of the network. In this stage, the 28 identified complexity factors and the project complexity factor defined as the target factor were used as variables (nodes). Using the survey data and the opinions of four experts, the Bayesian network structure was established in the GeNIe program. Here, the most important advantage of incorporating expert opinion into the model is the creation of a model based on a causal basis that is consistent with real life. The initial network structure was evaluated with the expert group to remove the meaningless connections produced by the model, and the final structure was established. Subsequently, based on the final network structure, conditional probability tables for each factor were obtained through parameter learning. The purpose of obtaining these conditional probability tables is to provide a basis for the analyses. Following the model generation, the three-level probability distribution of all factors in the network was obtained. The probability of the project complexity target factor was found as 11% low, 36% moderate, and 53% high. The supposed high complexity level of R&D projects was proven by the results obtained in the parameter learning step.

In the third stage, the validation of the Bayesian network structure was performed using the K-Fold Cross Validation method, and it was checked whether the established model reflected real data and the accuracy rate was above 80%. In the literature, models above 80% accuracy levels are considered effective. Following the model validation, sensitivity analysis, influence chain analysis, and scenario analyses were conducted as the final stage.

According to the sensitivity analysis results, the complexity of R&D projects is highly sensitive to the technological uncertainties, variabilities, and dependencies in the macro environment; the uncertainty and variability of the customer demands and expectations in the task environment; and the uncertainty in project objectives and goals and the strategic importance of the projects. Therefore, changes in the complexity levels of these factors significantly affect the probability of project complexity. According to the influence chain analysis results, which define the degree of dependence between conditional probabilities, the two most influencing causal chains on project complexity are obtained. The first one is "Uncertainty and dependencies in project objectives and goals" → "Variety and number of project objectives and goals" → "Human resource structure of the project" → "Interaction and collaboration among project team members" → "Project complexity." The second causal chain is "Financial risks of the organization" → "Structure of the planned project budget" → "Availability and management of planned project resources" → "Project complexity." In the scenario analysis, the factors in the macro environment, task environment, organization, and project characteristics categories were defined as "High", respectively, and as a result, the project complexity was found to have a "High" complexity probability with 52%, 56%, 51%, and 97%, respectively. This result shows that when the factors in the project characteristics category are high, the project complexity is 97% likely to be high. Subsequently, additional scenario analyses were conducted on the factors under the project characteristics category to obtain detailed results.