Hızlı tüketim ürünleri (FMCG) sektöründe endüstri 4.0 uygulamaları için dengeli performans karnesine dayalı performans değerlendirme modeli
Performance evaluation model based on balanced scorecard for industry 4.0 applications in the fast moving consumer goods sector (FMCG)
- Tez No: 951077
- Danışmanlar: PROF. DR. HÜR BERSAM SİDAL
- Tez Türü: Yüksek Lisans
- Konular: İşletme, Business Administration
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2025
- Dil: Türkçe
- Üniversite: İstanbul Teknik Üniversitesi
- Enstitü: Lisansüstü Eğitim Enstitüsü
- Ana Bilim Dalı: İşletme Ana Bilim Dalı
- Bilim Dalı: İşletme Bilim Dalı
- Sayfa Sayısı: 105
Özet
Endüstri 4.0 ve beraberinde getirdiği yenilikçi teknolojiler, şirketlerin büyümelerine, gelişimlerine ve verimliliklerini arttırmalarına yönelik kritik bir rol üstlenmektedir. Bu teknolojiler, sadece şirketlerin çalışma şekillerini değiştirmekle kalmayıp, aynı zamanda performanslarını çeşitli boyutlarda etkileyerek çağın gerekliliklerine uyum sağlamalarına olanak tanımaktadır. Performans ve verimlilik, dünya genelindeki tüm şirketler için en temel iki öğe olarak karşımıza çıkmakta ve bu iki öğenin etkin bir şekilde yönetimi şirketlerin uzun vadeli başarısı açısından kritik bir öneme sahiptir. Sanayi devrimlerinin tarihsel gelişim süreci, Endüstri 4.0'ın dayandığı temelleri anlamak için büyük önem taşımaktadır. Bu tezde, sanayi devrimlerinin dönüşüm süreçleri detaylı bir şekilde ele alınmış ve bu kapsamda Endüstri 4.0'ın şirketlere sunduğu yeni imkanlar derinlemesine incelenmiştir. Bu imkanların sağladığı dönüşüm, performans yönetimi süreçlerini daha etkinleştirmekte ve şirketlerin rekabet avantajı elde edebilmeleri için gerekli olan objektif kriterlerle performans ölçümünü mümkün hale getirmektedir. Hızlı tüketim ürünleri sektörü, kısa çevrim süresine sahip ürünlerin yoğun bir rekabet ortamında sunulması nedeniyle performans değerlendirme süreçlerine özellikle duyarlıdır. Bu bağlamda, Endüstri 4.0'ın sunduğu yenilikçi teknolojiler, sektörün ihtiyaçlarına hızlı ve etkili çözümler getirmekte, üretim süreçlerini optimize etmek, müşteri taleplerine daha hızlı yanıt verebilmek ve kaynak kullanımını daha verimli hale getirebilmek adına büyük bir önem taşımaktadır. Endüstri 4.0'ın sunduğu dijitalleşme, otomasyon, veri analitiği gibi imkanlar, şirketlerin performanslarını izlemek ve yönetmek için daha hassas ve doğru ölçüm araçları sunmaktadır. Bu nedenle, Endüstri 4.0'ın çerçevesinde bu sektörün ihtiyaçlarına yönelik bir performans ölçüm modeli sunmak bir gereklilik haline gelmiştir. Bu tezde, hızlı tüketim ürünleri sektöründeki şirketlerin Endüstri 4.0 performanslarını değerlendirebilmeleri için Denge Skor Kartı (“Balanced Scorecard”, BSC) modeli seçilmiş ve bu model temel alınarak bir performans değerlendirme modeli geliştirilmiştir. Performans değerlendirme sürecinde ilk adım olarak, BSC'nin dört ana boyutu na (Finans, Müşteri, İçsel Süreçler, Öğrenme ve Gelişme) ait alt kriterler literatür taraması yoluyla tespit edilmiştir. Daha sonra, hızlı tüketim ürünleri sektöründeki müdürlere anketler uygulanmış ve Analitik Hiyerarşi Proses (AHP) yöntemi ile bu kriterlerin önem dereceleri hesaplanmıştır. Anket sonuçları, kriter ağırlıklarının belirlenmesi ve performans değerlendirme modelinin yapılandırılmasında temel teşkil etmiştir. Son aşamada, İzmir'de yer alan bir içecek firmasında uygulamalı bir çalışma gerçekleştirilmiştir. Şirketin Endüstri 4.0 performansı, denge skor kartı boyutları temel alınarak değerlendirilmiş ve elde edilen sonuçlar kapsamlı bir şekilde yorumlanmıştır. Bu değerlendirme süreci, şirketin farklı boyutlardaki performans düzeylerini karşılaştırmaya olanak tanımış ve hangi boyutların daha yüksek performans sergilediği ile hangi boyutlarda gelişim gerektiği ortaya konulmuştur. Ayrıca, departman içi ve departmanlar arası karşılaştırmalar yapılarak şirketin genel performansına yönelik somut veriler elde edilmiştir. Bu analizler, Endüstri 4.0 uygulamalarının şirket performansına olan etkilerini anlamak ve stratejik kararlar almak açısından önemli bir zemin oluşturmuştur. Tezin bulguları, Endüstri 4.0'ın hızlı tüketim ürünleri sektöründeki şirket performansına olan etkilerini net bir şekilde ortaya koymuş ve bu kapsamda performans yönetimine ilişkin literatüre katkı sağlayan yenilikçi bir yaklaşım sunmuştur.
Özet (Çeviri)
The Industrial Revolutions are significant stages that have profoundly transformed production processes throughout human history. Each industrial revolution has brought revolutionary changes to production methods, in line with the technological advancements of the era, and these changes have not only shaped production tools but also the social structure, workforce qualifications, and management approaches. The Industrial Revolutions refer to a series of major transformation processes, and each has deeply impacted the global economy and guided the development of societies. The First Industrial Revolution began in the late 18th century and early 19th century with the invention of steam power. During this period, steam-powered machines facilitated the shift from manual labor to mass production, particularly in the textile sector, where production rapidly increased. The widespread use of railways made it easier to transport goods and contributed to economic growth. At the same time, the workforce shifted significantly from agriculture to factory-based work, leading to a transformation in the structure of labor. The Second Industrial Revolution occurred in the late 19th century and early 20th century. The widespread use of electricity increased the speed of production in factories, and internal combustion engines and automation systems revolutionized manufacturing. Henry Ford's assembly line production technique enhanced the efficiency of mass production, enabling products to be produced more quickly and at lower costs. The development of new technologies such as automobiles and airplanes revolutionized transportation, increasing mass production and leading to the expansion of industrial organizations. The Third Industrial Revolution began in the late 20th century with the integration of computers, electronic devices, and automation systems into production processes. Microchips and computer technologies made production processes more efficient. Computer-controlled machines, robots, and digital systems increased automation on production lines, reducing human intervention. Developments in communication technologies facilitated the rapid establishment of global supply chains and promoted the digitalization of the workforce worldwide. Industry 4.0 refers to the current industrial revolution, characterized by the integration of digital technologies into production processes. The combination of the Internet of Things (IoT), Artificial Intelligence (AI), big data, cyber-physical systems, and robotic technologies has made production processes smarter and more flexible. Machines communicate with each other and with humans to create a more efficient and autonomous production process. This transformation involves digitalization not only on production lines but also in business decision-making processes, supply chains, and customer relationships. Industry 4.0 enables personalized production and offers businesses opportunities to manage operations with higher efficiency and lower costs. This transformation requires adopting a new approach not only on production lines but also in corporate decision-making and performance management processes. The primary aim of this study is to develop a model that allows businesses to assess their performance in the Industry 4.0 transformation process using a holistic approach. Most existing performance evaluation systems are primarily based on financial indicators and fail to address the changing competitive conditions and the strategic needs brought by digitalization. Therefore, the study adopts the widely used Balanced Scorecard (BSC) approach as a strategic management tool; this framework is restructured in alignment with the concept of Industry 4.0. In the developed model, performance is evaluated across four main dimensions: finance, customer, internal processes, and learning and growth. Under each dimension, key performance indicators that can be influenced by Industry 4.0 applications are identified. The Balanced Scorecard, developed by Kaplan and Norton in 1992, provides a comprehensive perspective for evaluating business performance in the strategic management process. The model reveals that focusing solely on financial indicators makes it difficult for businesses to achieve their long-term strategic goals. Since financial measures reflect past performance, they are insufficient for making future strategic decisions. Therefore, the Balanced Scorecard takes into account not only financial data but also customer, internal processes, and learning and growth dimensions. The model enables the measurement and monitoring of strategic goals through these four core dimensions. The financial dimension includes criteria such as reducing total company-wide costs, effectively managing inventory, increasing profitability, and improving sales performance. The customer dimension includes criteria such as customer satisfaction, customer reliability, the ability to respond quickly to customers, and market share. The internal processes dimension involves elements such as product quality, reducing defective product rates, operational efficiency, and flexibility in production processes. The learning and growth dimension encompasses criteria such as developing a qualified workforce, improving employee motivation, and reducing employee turnover rates. This structure allows businesses to assess not only their short-term financial success but also their long-term competitiveness and digital transformation readiness. For the model to be robust and to provide guidance to decision-makers, weighting of the criteria is required. To achieve this, the Analytic Hierarchy Process (AHP) method was chosen. AHP is a numerical technique commonly used in multi-criteria decision-making problems, enabling the establishment of a hierarchical structure and determining priorities through pairwise comparisons. Initially, the criteria under each dimension were clarified, and then, through pairwise comparison surveys with experts in the field, the importance levels of the criteria were determined. The survey data were analyzed using the AHP method, and the weights of both the main dimensions and the sub-criteria were calculated. The Analytic Hierarchy Process (AHP) is a multi-criteria decision-making method used in complex decision-making problems. AHP allows decision-makers to determine the relative importance of each alternative by making pairwise comparisons between various alternatives and criteria. The process begins by defining the decision goal and identifying the criteria and sub-criteria. Pairwise comparisons are made for each criterion, and their relative weights are calculated. The same process is repeated for alternatives. The key steps in AHP involve organizing criteria and alternatives into a hierarchical structure. Decision-makers compare each criterion with others to determine their importance levels. Then, alternatives are evaluated under the defined criteria, and weighted scores are calculated. These scores create the total value for each alternative, and the most suitable alternative is determined. AHP's advantages include making the decision-making process more systematic and objective. It allows decision-makers to compare different criteria and alternatives numerically. Additionally, AHP makes complex decisions clearer and more manageable. However, when there are numerous criteria and alternatives, the pairwise comparison process can be time-consuming, and the subjective judgments of decision-makers may influence the results. To test the validity of the model, the developed performance evaluation system was applied to a real production company. During the application process, a self-assessment survey was conducted with managers from different departments within the company. Participants rated each performance criterion on a scale from 1 to 9, and these ratings were multiplied by the previously calculated weights to obtain total scores. In this way, the company's performance in each dimension, as well as its overall Industry 4.0 alignment level, were expressed in numerical terms. The results of the application revealed that the example company showed high success in customer-related criteria, such as customer satisfaction, market share, and sales performance. However, lower scores were obtained in criteria such as employee motivation, production flexibility, and employee turnover rate. This situation demonstrates that Industry 4.0 applications are not limited to technological investments and automation; they also require development in areas such as employee-focused policies, flexible organizational structures, and digital culture. The developed model clearly highlights these imbalances, enabling managers to shape their strategic decisions based on data. The most significant contribution of this study to the literature and practitioners is the development of a multi-criteria, numerical-based evaluation tool, redesigned with a perspective of Industry 4.0, that is suitable for strategic performance management. By combining the strategic perspective of the Balanced Scorecard and the analytical power of AHP, the model has made the performance evaluation process more comprehensive, transparent, and systematic. Testing the model in a production company demonstrated the practical applicability of the proposed structure, providing a strong foundation for the potential adoption of similar applications in other businesses. In future studies, it is suggested to apply this performance evaluation system in different sectors and companies of various sizes for comparative purposes. Moreover, by transferring the model to a digital platform and transforming it into a software-based decision support system, managers could be provided with faster and more user-friendly analysis capabilities. Businesses aiming to gain a competitive advantage in the transition to Industry 4.0 must not only restructure their technological infrastructure but also adapt their performance evaluation systems to this transformation. In this regard, the developed model is expected to make valuable contributions to both academic research and corporate practices.
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