Stratejik dikey bütünleşme kararlarının modellenmesi
Modeling of strategic vertical integration decisions
Yazar: MENEKŞE GİZEM GÖRGÜN
Danışman: PROF. DR. SEÇKİN POLAT
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / Endüstri Mühendisliği Ana Bilim Dalı
Derece: Doktora, 2023
Konu: Endüstri ve Endüstri Mühendisliği = Industrial and Industrial Engineering

Özet:
İşletmelerin pazardaki rekabet üstünlüğünde faaliyet sınırları stratejik olarak büyük öneme sahiptir. İşletmeler üstünlük sağlamak için organizasyon yapıları ve içinde bulundukları çevrede uyum sağlayacak şekilde faaliyet sınırlarını belirlemek durumundadırlar. İşletmelerin faaliyetlerini kendi bünyesinde gerçekleştirmesi, dış kaynak kullanımı, ortak girişimler, stratejik işbirlikleri, firma satın almaları gibi konuların işletme performansına etkisi, uzun zamandır uygulamada ve literatürde araştırılan konulardır.
İşletmeler önemli faaliyetleri kendi bünyelerine almadıklarında rekabet üstünlüklerini kaybetme, buna karşılık önemsiz faaliyetleri firma bünyesinde gerçekleştirdiklerinde ise pazarı kaybetme riskiyle yüzleşmektedirler. İşletmelerin faaliyetlerini içeride yapma veya dışarıdan alma kararını verdikleri ve sınırlarının ne olması gerektiği üzerinde yapılan araştırmalar dikey bütünleşme stratejileri konusu kapsamındadır. Dikey bütünleşme kapsamında araştırmalar yapılmış olup sürekli gelişen pazar koşullarında çalışmalar aralıksız sürdürülmeye devam etmektedir. İşletmelerin stratejik dikey bütünleşme kararlarının modellenmesi bu tez kapsamında ele alınmıştır.
Literatürde dikey bütünleşme için karar vermeyi destekleyen farklı modeller bulunmaktadır. Bu çalışma içerisinde bu modeller metodolojilerine göre gruplandırılmakta ardından genel ve fonksiyon spesifik olarak iki ana ayrımda gözetilmekte, ve ayrıca uygulama alanları göz önünde bulundurularak sınıflandırılmaktadır.
Literatürdeki modeller incelendiğinde dikey bütünleşmenin belirsizlik yönü dahil edilmeden bu modellerin geliştirildiği saptanmıştır. Ayrıca dikey bütünleşme kararları stratejik bir nitelik taşır ve daha az sıklıkla alınır. Bu tez çalışması, literatüre yeni bir veri odaklı model sunarak, dikey bütünleşme kararlarının tüm bu özelliklerine odaklanarak literatüre katkıda bulunmayı hedeflemektedir.
Endüstri 4.0 ile birlikte BT alanındaki çalışmalar hız kazanmıştır. Strateji yönetimi ve BT alanında kararları incelemek de bu anlamda değerlidir. Bu tez çalışmasında, literatürdeki modellerden yararlanan ve sonuçları gerçek Bilişim Teknolojisi (BT) vakalarından elde edilen verilerle değerlendiren bir metodoloji önerilmektedir. BT projelerinde doğru bir şekilde yap veya satın al kararlarını tahmin eden bir model oluşturmak için çeşitli yöntemler izlenmiştir. İlk olarak, BT alanında gerçek vakalarda kullanılan yirmi bir farklı yap veya satın al kararı derlenmistir. Bu kararlar bu çalışmadan önce alınmıştır ve bu örneklerin sonuçları şu anda kullanılmaktadır. Ürünlerin (vakaların) detayları şöyledir: 4 adet web tabanlı yazılım ürünü (1 içeride yapma ve 3 satın alma kararı), 6 adet mobil uygulamalı web tabanlı yazılım (2 içeride yapma ve 4 satın alma kararı), 2 adet web hizmeti geliştirme (2 içeride yapma), 1 adet web hizmeti geliştirme ve üçüncü taraflarla entegrasyon (1 içeride yapma), 2 adet ERP yazılım ürünü (1 içeride yapma ve 1 satın alma kararı), 2 adet CRM yazılım ürünü (1 içeride yapma ve 1 satın alma kararı), 2 adet sohbet botu (1 içeride yapma ve 1 satın alma kararı), 1 adet robotik süreç otomasyonu platformu yazılım ürünü (1 içeride yapma) ve 1 adet çalışma saatleri kaydetme platformu (1 içeride yapma). Bu kararların verileri, literatürden elde edilen üç farklı bilgi temelli genel model ile değerlendirilmiştir. Bu bilgi temelli modeller arasında elde edilen en yüksek doğruluk oranı %76 olarak belirlenmiştir. Ardından literatürde sıkça rastlanan karar ağacı modellerinden de iki çalışma için analizler aynı veri seti ile yapılmış ve erişilmiş en yüksek doğru tahminleme oranı bu modellerde %71 olmuştur. Çalışmanın devamında, aynı vakalar Naïve Bayes ile üç bilgi tabanlı genel model tarafından sunulan faktörler kullanılarak analiz edilmiştir. Belirsizliği yansıtabilmek için Naïve Bayes algoritması kullanılan karar modelinde elde edilen en yüksek doğruluk oranı %67 olarak tespit edilmiştir. Analizlerin sonucunda, dikey bütünleşme kararlarını BT ile ilişkili faktörler kullanarak belirlemek için yeni bir veri temelli model önerilmiştir. Bu modelde Naïve Bayes algoritması kullanılmıştır. Veri temelli bu model, kararların %86'sını doğru bir şekilde sınıflandırmıştır.
Farklı modeller ve analiz metotları ile yürütülen bu tez çalışmasında dikey bütünleşme için belirsizlik, seyrek alınan kararlar olma ve çok boyutlu olma yönlerini ele alan; Naïve Bayes kullanan ve veriye dayalı modelin en iyi sonuçlar ile tahminleme yaptığı tespit edilmiştir.

Anahtar kelimeler: Dikey bütünleşme modelleri, al veya yap kararı, bilişim teknolojileri yönetim, Naïve Bayes.

Abstract:
The boundaries of an enterprise's activities are critical for its competitive advantage in the market. Businesses that provide superiority have to determine their activity limits in a way that will adapt to their organizational structures and the environment they are in. The effects of issues such as the in-house activities of businesses, the use of outsourcing, joint ventures, strategic collaborations, and company acquisitions on business performance are the subjects that have been researched in practice and in the literature for a long time.
Businesses face the risk of losing their competitive advantage when they do not incorporate important activities, and of losing the market when they carry out unimportant activities within the company. The decision of whether to carry out activities internally or externally and what their boundaries should be is part of the topic of vertical integration strategies. Work has been carried out within the scope of vertical integration and these efforts continue uninterrupted in constantly evolving market conditions. Vertical integration, in other words, in-house production, can have numerous advantages and disadvantages for businesses. Vertical integration can enhance coordination and communication, increase efficiency, innovation, performance, and competitive advantage, achieve economies of scale, improve quality control, accelerate innovation, enhance supply chain management, enable control in marketing activities, reduce risks, protect patents and copyrights, address sustainability issues, increase market share, and promote learning and knowledge sharing. On the other hand, vertical integration can lead to increased labor costs, resource utilization and efficiency issues, higher overall costs, increased resource dependency, reduced flexibility, loss of competitive advantage, increased inventory levels, decreased capacity utilization, management problems, diminished innovation capabilities, heightened risks, decreased focus, compromised environmental responsibility, and negative effects on company performance. Due to these reasons, vertical integration or, in other words, make or buy decision strategies, are highly significant research areas in today's industry, particularly in the IT field with technological advancements. This thesis will address the modeling of strategic vertical integration decisions of businesses.
There are different models in the literature that support decision making for vertical integration. Within the study, these models are grouped according to their methodologies, and then they are distinguished into two main categories: general and function-specific. The publications that propose a model or provide a statistical evaluation are classified within this thesis according to their methodologies as general knowledge-based models, decision tree models, optimization models, multi-criteria decision-making, and survey applications. Additionally, they are classified considering the application areas.
These models, which can be classified in different areas in the literature, ignore the uncertainty aspect of vertical integration. Vertical integration decisions, being strategic in nature, are multifaceted and occur less frequently. This thesis adds to the existing body of knowledge by introducing a novel data-oriented model that encompasses all the characteristics associated with vertical integration decisions.
Existing literature proposes prescriptive models for decision-making regarding whether to make or buy. Initially, vertical integration studies primarily focused on the industry prior to the 1980s. However, starting from the 1980s, strategies concerning make-or-buy decisions began to be explored in areas such as IT, human resources (HR), sales, and related domains. In the realm of IT, the term "make" or "build" refers to developing software in-house, while "buy" pertains to obtaining it from external sources. The inclusion of open-source software (OSS) falls within the "make" decision since internal efforts are required to adapt OSS for the product. On the other hand, in the IT context, the "buy" decision encompasses the acquisition of "Commercial off the Shelf" (COTS) software. In HR, "make" denotes preparing individuals within the organization for future tasks, whereas "buy" involves hiring individuals from the labor market. With Industry 4.0, studies in the field of IT have accelerated. Examining decisions in the fields of strategy management and IT is also valuable in this sense The efficiency of companies can be significantly impacted by make-or-buy decisions in the field of IT. Insufficient tools and approaches for determining whether to make or buy software can hinder firms' ability to leverage IT effectively in a competitive manner. Reducing software costs is a critical requirement for the majority of IT companies.
This thesis suggests an approach that incorporates existing models from the literature and assesses the outcomes using data gathered from actual Information Technology (IT) cases. Initially, twenty-one IT project cases were identified, which were successfully completed and transformed into software products. To put it differently, these decisions were made before this study, and the outcomes of these instances are presently being utilized. The details of the products (cases) are as follows: 4 web-based software products (1 make decision and 3 buy decisions), 6 web-based software with mobile application (2 make decisions and 4 buy decisions), 2 web service development (2 make decisions), 1 web service development and integration with third parties (1 make decision), 2 ERP software products (1 make decision and 1 buy decision), 2 CRM software products (1 make decision and 1 buy decision), 2 chatbots (1 make decision and 1 buy decision), 1 robotic process automation platform software product (1 make decision), and 1 working hours recording platform (1 make decision).
Several approaches were employed to develop a precise predictive model for make-or-buy decisions in IT projects within a retail company. Initially, three comprehensive models were assessed, each utilizing distinct types of information extracted from existing literature. These models aimed to predict decisions for twenty one diverse make-or-buy scenarios in the IT domain. The maximum accuracy rate achieved among these information-based models was 76%. Then, analyses were conducted using the same data set for two decision tree models, which are frequently encountered in the literature, and the highest accuracy rate reached in these models was 71%. As a continuation of the study, the same cases were analyzed using Naïve Bayes with factors presented by three information-based general models. The decision model incorporating the Naïve Bayes algorithm can effectively capture uncertainty, and the maximum achieved accuracy rate was 67%. Finally, a new data-driven model that utilizes IT-related factors for the vertical integration decision-making problem was proposed using Naïve Bayes. The data-driven model accurately classified 86% of the decisions. In comparison to other models, the data-driven vertical integration model encompasses a greater number of factors. This suggests that incorporating a larger number of factors enhances the accuracy of the model. The Naïve Bayes approach has better accuracy in predicting decisions due to the inclusion of numerous factors.
This thesis study, conducted with different models and analysis methods, evaluated the uncertainty, sparse decisions, and multidimensionality of the vertical integration problem that is lacking in the literature. The data-driven model using Naïve Bayes, which addresses these issues, provided the best results for prediction. A new model that can fill the gap in the literature for strategic make-or-buy decisions in vertical integration was proposed and compared with previous models, presenting an approach that will shed light on future studies.
Taking into account multiple factors in make-or-buy decisions introduces substantial uncertainty for companies. Approaching the issue from a managerial benefit perspective, it is recommended to use the models discussed in this study to cope with uncertainty, increase effectiveness, and objectivity by taking various factors into account. The findings of this study indicate that incorporating both internal and external factors in models leads to improved accuracy. Consequently, it is recommended for companies to take into account both internal and external factors when constructing their make-or-buy decision models.
The thesis study acknowledges certain limitations. Vertical integration decisions are not commonly encountered strategic choices within companies, resulting in a restricted number of vertical integration cases available for analysis. As a result, only twenty-one cases could be examined in this study. Consequently, Naïve Bayes was used for classification, as it could achieve consistent results with a small number of cases for the application of IT cases, and other classification algorithms could not provide the same level of accuracy with a small number of cases. Additionally, the study only covers IT cases in the retail sector. This limits the generalizability of the research findings to other sectors. Finally, the cases exclusively concentrate on the "make or buy" decisions, neglecting other potential options for vertical integration, such as joint ventures, strategic alliances, mergers, and acquisitions.
For future research, if access to a higher number of data content becomes available for validation purposes, another classification method can be explored in addition to Naïve Bayes. To generalize the research to other sectors, the models used in this study can be applied in different industries.
In future studies, it is recommended to explore not only the "make or buy" decisions but also other potential forms of vertical integration, including joint ventures, strategic alliances, mergers, and acquisitions. Furthermore, this current study exclusively centers on the "make or buy" decisions within the scope of vertical integration strategies.
Keywords: Vertical-integration models, make-or-buy decision, management of information technology, Naïve Bayes.