Türkiye'deki illerin beyaz eşya satış verimliliklerinin veri zarflama analizi ile değerlendirilmesi
Province based efficiency assessment of white goods sales in Turkey with data envelopment analysis
- Tez No: 665854
- Danışmanlar: DOÇ. DR. ÖZGÜR KABAK
- Tez Türü: Yüksek Lisans
- Konular: Endüstri ve Endüstri Mühendisliği, Industrial and Industrial Engineering
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2021
- Dil: Türkçe
- Üniversite: İstanbul Teknik Üniversitesi
- Enstitü: Lisansüstü Eğitim Enstitüsü
- Ana Bilim Dalı: Endüstri Mühendisliği Ana Bilim Dalı
- Bilim Dalı: Mühendislik Yönetimi Bilim Dalı
- Sayfa Sayısı: 73
Özet
Dayanıklı tüketim ürünleri, Türkiye'de hane halkının tüketim harcamaları arasında önemli bir paya sahiptir. Dayanıklı tüketim ürünleri denildiğinde akla gelen en önemli ve hanelerde yaygınlığı en yüksek ürünlerin başında beyaz eşya gelmektedir. Tüketici beklentilerinin ve teknolojik gelişmelerin her geçen gün dönüşüm içinde olduğu günümüzde, beyaz eşya sektöründe faaliyet gösteren markalar yeni müşteri ve pazar payı kazanımı için kaliteli ürünler sunmanın yanı sıra satış ve servis ağlarını genişleterek rekabet etmeyi ve hizmet etkinliklerini verimli bir şekilde yönetmeyi amaçlamaktadır. Bu çalışmanın amacı, Türkiye'deki illerin beyaz eşya satış faaliyeti bazındaki verimlilik değerleri ile verimli olmayan illerin verimli olabilmesi için gerçekleştirmesi gereken potansiyel iyileştirmeleri belirlemektedir. Bu kapsamda, Türkiye'deki 81 ilde faaliyet gösteren, beyaz eşya sektöründe sektörün ülkede faaliyete başlamasından bu yana satış hizmeti sunan ve sektörde lider konumda bulunan Arçelik A.Ş. firmasının Arçelik ve Beko markalarının Türkiye'deki illerde sunduğu beyaz eşya satış hizmetlerinin verimlilik ölçümlemesi yapılmıştır. Çalışmada, etkinlik ve verimlilik ölçümünde 30 yıldan fazla bir geçmişe sahip matematiksel bir optimizasyon tekniği olan, göreceli performans değerlendirmesinde birden fazla girdi ve çıktının değerlendirmesine olanak sağlayan ve başta yöneylem araştırması literatüründe olmak üzere bir çok alanda yaygınlıkla kullanılan veri zarflama analizi uygulanmıştır. Yapılan uygulamada veri zarflama analizi temel modellerinden olan Banker, Charnes ve Cooper tarafından geliştirilen çıktı yönelimli BCC (Banker, Charnes, Cooper) modeli kullanılarak Türkiye'deki 81 ilin beyaz eşya satışları karşılaştırılmış, verimlilik değerleri ölçülmüş ve verimli olan ve verimli olmayan iller tespit edilmiştir. Verimli olmayan illerin verimli olabilmesi için gerekli potansiyel iyileştirmelerin belirlenmesinin yanı sıra, model sonuçlarına göre verimli olan illerin kendi aralarında sıralanabilmesi için Andersen ve Petersen tarafından geliştirilen süper etkinlik modeli kullanılmıştır. Modellerin çözümünde R programlama dilinin“Benchmarking”paketi kullanılarak 81 ile ait verimlilik değerleri ve sıralamaları Arçelik ve Beko markaları için ayrı ayrı ölçülmüştür. Verimli olmayan illere yönelik hesaplanan potansiyel iyileştirmelerin mevcut mağazalar tarafından karşılanıp karşılanamayacağı incelenerek iyileştirmelerin mevcut mağazalar tarafından karşılanamadığı iller için marka özelinde yeni mağaza önerileri belirlenmiştir. Bu çalışma, beyaz eşya satış potansiyelinin kullanımı konusunda verimli olmayan iller ve bu illere ait potansiyel iyileştirmelerin belirlenmesi ile yeni mağaza yatırım kararları ve mağaza iyileştirme aksiyonlarında hangi illere ve mağaza gruplarına öncelik verileceği hususunda karar vericilere fayda sağlayacaktır.
Özet (Çeviri)
In today's world, durable consumer goods reach to every single household and this category has become one of the most notable items among households' expenditures. Similarly, this category has a considerable share of consumption among households' in Turkey. Considering the whole durable consumer goods range, the most prominent sub-category that comes to mind are white goods, which also has the highest prevalence in households. In present days, where consumer expectations and technological developments are transforming day by day, brands operating in the white goods sector aim to offer best quality products to gain new customers and increase their market share, as well as to compete and manage their activities efficiently by expanding their sales and service networks. In Turkey white goods market, number of brands and products serving the market has been increased tremendously, especially in recent years. The reason of this increase is mainly based on the introduction of foreign brands to the country, which led to greater competition in the market. Established local brands that have been operating in the sector for long years forced by this situation heavily and as a result of this, the total sales shares of these established brands in the white goods sector from the domestic market have decreased in the last decade. Today, it is obvious that the rise in competition and the change in customer potential pose a threat for brands and cause inefficiency problems in the sales locations. Thus, in order to prevent relevant problems, new and more efficient ways to reach potential customers needs to be defined so that the brands can gain market share. As a first step, it is necessary to examine the customer potential and market competition in the locations where the brands are located and measure their efficiency in the basis of white goods sales. That is especially important for brands to operate efficiently in the provinces where they offer sales services, to meet the customer potential they appeal to and to position them strongly against competition. In the light of these, the objective of this study is to identify the white goods sales efficiencies of the provinces in Turkey and to determine the potential improvement opportunities that inefficient provinces should consider in order to be efficient. In this context, Arçelik A.Ş., white goods industry leader operating in 81 provinces of Turkey that has been serving in sales service sector since the initiation of white goods sector in the country, has been examined; then the white goods sales efficiencies have been measured in the provinces of Turkey for the related company' brands which are Arçelik and Beko. In this study, Data Envelopment Analysis (DEA) which is a mathematical optimization technique with a history of more than 30 years in efficiency and productivity measurement, is applied. This analysis enables the evaluation of multiple inputs and outputs in relative performance evaluation, and is widely used in many areas, especially in the operations research literature. In this analysis, output oriented BCC type data envelopment analysis which is the basic model developed by Banker, Charnes and Cooper is used. The white goods sales efficiency benchmarking analysis of 81 provinces in Turkey is conducted by output-oriented DEA approach; white goods sales data of these provinces are compared, efficiency values are measured, provinces that are efficient and inefficient are determined. 81 provinces of Turkey are used as decision making units (DMU) in the study. Ten input factors (average household income, total population in AB group of socio-economic status, total population in C group of socio-economic status, total population in DE group of socio-economic status, total number of households, population growth rate, number of workplaces, number of summerhouse, number of hotels & resorts and total number of white goods stores in provinces base) and two output factors (white goods sales (assembly) quantity and retail income in provinces base) are considered in an output oriented model. According to BCC type output-oriented model, white goods sales efficiency values for all provinces in Turkey and potential improvements for inefficient provinces have been calculated. In addition to determine the potential improvement areas that are required to transform the inefficient provinces into efficient ones, the super efficiency model developed by Andersen and Petersen is used to rank efficient provinces among themselves according to the model results. These BCC type output-oriented DEA model and super efficiency model have been applied separately for Arçelik and Beko brands. When solving the models, efficiency values and rankings of 81 provinces are measured separately for Arçelik and Beko brands by using the“Benchmarking”package of the R programming language. As a result of the applications specifically made for the brands, considering the broad distribution of stores and potential differences in the provinces; suggestions are made that the potential improvements for the inefficient provinces should primarily bring the performance of low performance stores to the reference provincial averages, and if the potential gap continues, the provinces can be efficient by opening a enough number of new stores in related province to close the gap. For Arçelik brand, as a result of the output oriented BCC model, it is concluded that 41 provinces are efficient and the average efficiency score of all provinces is 0,90. The top 5 most efficient provinces are respectively İstanbul, Osmaniye, Kırklareli, Iğdır and Isparta; according to the calculated efficiency scores, it was observed that Bolu is the last province with the lowest efficiency score. Potential improvements necessary for 40 inefficient provinces to be efficient are calculated, and it is determined whether the provincial potential gap could be compensated by achieving average sales performance of stores with low white goods sales performance compared to the reference provincial averages in the relevant provinces. According to this point of view, a suggestion is made that 27 inefficient provinces can be efficient by increasing low performed stores performances to the reference provincial averages and remaining 13 provinces can be efficient if 20 new stores are opened in total after reaching the average performance for low performing stores. When the same methodology is applied to Beko brand too, it was concluded that 35 provinces are efficient and the average efficiency score of all provinces is 0,77. The top 5 most efficient provinces are respectively Diyarbakır, Isparta, İstanbul, Bingöl and Muş; according to the calculated efficiency scores, it is observed that Kırşehir is the last province with the lowest efficiency score. With the same approach, potential improvements for 46 inefficient provinces are calculated and it is suggested that the relevant provinces can be efficient by opening total of 72 new stores in 30 provinces after potential improvements are completed for all inefficient provinces. When these efficiency scores and potential improvement volumes are compared for both brands, Arçelik reflects more efficient white goods sales services than Beko in Turkey. Beko needs much more effort to have a sales efficiency and investment to be stronger in the white goods market. On the other hand, super efficiency model results for both brands show that there is a weak correlation between efficient provincial rankings and the number of stores operating in the respective provinces. When the former result and the proposed potential improvements are evaluated together, this study shows that the performance of existing stores is more effective than the number of stores in a province being efficient in terms of white goods sales. Hence, productive resource usage and store performance improvement are the key factors for efficiency in white goods sector. This aspect of the study will provide a notable managerial support to decision makers regarding which provinces and which stores should be prioritized in actions to be taken for potent use of resources for efficiency. This study has a unique feature in terms of its suggestions that the potential improvements in the inefficient provinces are primarily made by improving low-performance stores, and by opening several new stores to close the gap in the relevant province at the point where the potential shortage continues. This unique methodology can contribute to white goods brands about store management and company investment decisions. For the future researches, a next step to study micro locational efficiency model can be to have efficiency measurements for micro locations by selecting town, neighborhood, district as decision making units rather than provinces. So that, companies can take further actions in micro location-scope. Another research topic can be store-based efficiency analysis by studying the potential consumers and sales performance of the stores in DEA.
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