Depo dijital olgunluk modelinin oluşturulması ve uygulanması
Creation and implementation of the warehouse digital maturity model
Yazar: İLKNUR YARDIMCI COŞKUN
Danışman: DOÇ. DR. MURAT BASKAK
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / Endüstri Mühendisliği Ana Bilim Dalı / Endüstri Mühendisliği Bilim Dalı
Derece: Doktora, 2023
Konu: Endüstri ve Endüstri Mühendisliği = Industrial and Industrial Engineering

Özet:
Depolar tedârik zinciri içerisinde, malzemelerin, çeşitli amaçlarla ve farklı zamanlarda kullanılmak üzere korunmak ve stoklanmak üzere istiflendiği, saklandığı ve malzeme türüne göre tasarlanabilen, çeşitli boyut ve niteliklerde olabilen, açık veya kapalı alanlardır. Bugün depolar, ürün veya malzemeleri korumaktan çok bir akış noktası hâline dönüşmüştür. Son yıllarda yaşanan pandemi ve doğal afetler gibi nedenler ile dijitalleşme hız kazanmıştır.
Günümüzde dijitalleşme yolculuğunda olan tedârik zincirlerinde, fiziksel doküman ve belgeler, yerlerini elektronik veriye bırakırken, karar verme süreçlerinde akıllı bilişsel sistemler, yazılımlar ve algoritmalar yaygın olarak kullanılmaya başlanmıştır.Dijital depolama, önde gelen çağdaş lojistik sistemleri, otomatik ve robotik teknolojilerle donatılmış dijital iletişim platformlarının bir kombinasyonudur. Dijital depoları otomatik depolardan ayıran en önemli unsur, mal veya ürünün izlenmesinin yalnızca depo içerisinde değil, tüm süreçler boyunca anlık ve gerçek zamanlı olarak yapılabilmesidir.
Tüm lojistik sektöründe olduğu gibi depolama faaliyetlerinde de dijitalleşme eğilimi hızlanmış ve birçok firma, dijital depo yönetim sistemlerini kullanmaya başlamıştır. Bununla birlikte, bir depoyu tümüyle dijital olarak nitelendirebilmek için, tüm işlemlerin otomatikleştirilmiş ve manuel işlemlerin en az düzeye indirilmiş olması gerekmektedir. Bir deponun dijital olgunluğunu ölçmek, dijital teknolojilerin ve sistemlerin depo işlemlerine ve süreçlerine ne kadar iyi bütünleştirildiğini değerlendirmeyi içerir. Bu temel etmenleri değerlendirerek, depo yöneticileri dijital altyapılarını iyileştirebilecekleri alanları ve depo işlemlerini optimize edebilecekleri alanları belirleyebilirler.
Bu çalışmada, dijital dönüşüm yolculuğunda olan depoların dijital olgunluğunun belirlenerek varolan durumlarının saptanması için depo dijital olgunluk modeli oluşturulmuş, sonrasında ise sahip olunan yatırım bütçesi ve kaynaklar ile dijitalleşme düzeyini en yüksek değere çıkararak en etkili yatırımı gerçekleştirmesine olanak sağlayan dijital yatırım karar modeli inşa edilmiştir.
Dijital depo süreçlerini belirlemek adına öncelikli olarak geleneksel depo süreçleri incelenmiş, dijital depo süreçlerinin ve süreç içi adımların belirlenmesi için Uzman Görüşü alımı gerçekleştirilmiştir. Uzman görüşü sonrası bileşenleri ve adımları belirlenen süreçlerin dijital önemlerinin belirlenebilmesi için uzmanların AHP yöntemi ile karar vermesi sağlanmıştır. Çıkan sonuçlar ile hangi depo sürecinin dijitalleşmesinin daha önemli olduğu, dijitalleşmeye katkısının daha büyük olduğu belirlenmiştir.
AHP'den elde edilen sonuçlar ile dijitallik değerlendirme ölçeği oluşturulmuş, olgunluk modeli, boyut ve düzeyleri ile tamamlanmıştır. Olgunluk modeli ile varolan durumda hangi süreçte ne durumda olduğu belirlenen depoların yatırım kararını vermelerine destek olacak bir model olan dijital yatırım karar modeli ile her bir süreç için varolan durum temel alınarak yatırım yapılması gereken süreçler saptanmış ve yatırım bütçesini kullanarak dijital skoru en yükseğe çıkaracak süreçlere yatırım yapılmasını sağlayan karar algoritması oluşturulmuş ve validasyonu gerçekleştirilmiştir.

Abstract:
Warehouses are closed or open areas in the supply chain, which can be of different sizes and features, designed according to the type of material, where materials are stacked and stored for protection and storage for various purposes and for use at different times.  Today, warehouses have become a flow point rather than protecting products or materials. In recent years, digitalization has accelerated due to reasons such as pandemics and natural disasters.
In digitalized supply chains, physical documents are replaced by electronic data, while software and intelligent cognitive systems and algorithms are widely used in decision processes. Digital warehousing is a combination of leading modern logistics systems and digital communication platforms equipped with automated and robotic technologies. The most important factor that distinguishes digital warehouses from automated warehouses is that the tracking of goods or products can be done not only within the warehouse but also throughout all processes in real time. 
As in the entire logistics sector, the digitalization trend in warehousing activities has accelerated and many companies have started to use digital warehouse management systems. However, in order to qualify a warehouse as fully digital, all processes must be automated and manual operations must be minimized. Measuring a warehouse's digital maturity involves assessing how well digital technologies and systems are integrated into warehouse operations and processes. By assessing these key factors, warehouse managers can identify areas where they can improve their digital infrastructure and optimize warehouse operations. 
In this study, a warehouse digital maturity model has been developed to determine the digital maturity of warehouses undergoing a digital transformation journey and to identify their existing conditions. Subsequently, a digital investment decision model has been constructed, which allows maximizing the level of digitalization with the available investment budget and resources, enabling the most effective investment to be realized.
In order to determine the digital warehouse processes, traditional warehouse processes were first examined, and Expert Opinion was obtained to determine the digital warehouse processes and in-process steps. In order to determine the digital importance of the processes whose components and steps were determined after the expert opinion, experts were provided to decide with the AHP method. With the results, it was determined which digitalization of the warehouse process is more important and its contribution to digitalization is greater.
With the results obtained from the AHP, a digitalization evaluation scale was created and the maturity model was completed with dimensions and levels. With the digital investment decision model, which is a model that will support the investment decision of the warehouses that are determined in which process in the current situation with the maturity model, the processes that need to be invested based on the current situation for each process have been determined and the decision algorithm that enables investment in the processes that will increase the digital score the most by using the investment budget has been created and validated.
In the literature review section, databases and years of research are specified, and publications are identified through the selection of keywords. In this section, 121 publications are presented in tabular form, including author, title, English title if the source is foreign, year, purpose, used method, and implemented application headings. At the end of the section, a gap in this field is identified.
In the warehouse management section, warehouses are classified, and each type of warehouse is explained with its characteristics. Following the warehouse types, warehouse processes are described based on the flow of operations.
In the digitalization of warehouses section, a definition of digital warehouses is provided, and their features are determined. After identifying traditional warehouse processes, digital warehouse processes and their in-process steps are determined through expert opinions. Subsequently, the digital components required for these processes to take place are identified. After determining the digital warehouse processes, the weights of these processes are determined using the AHP method.
In the section on digital warehouse performance indicators, it is essential to define indicators that measure the effectiveness of warehouse systems and evaluate the functioning of processes. The lack of defined performance indicators for digital warehouses can lead to significant errors in measurement and evaluation. To address these issues and identify key performance indicators for digital warehouses, a comprehensive literature review is conducted using content analysis, followed by expert opinions and the binary relationship matrix method. At the end of the research, a total of 31 performance indicators are identified and defined under four groups: cost, efficiency, effectiveness, reliability, and speed.
In the digital warehouse maturity model section, the model is designed to evaluate the digitalization of warehouses on a process basis. The inputs to the model are determined as warehouse processes. The evaluation scale of the digital warehouse maturity model is structured to be carried out based on the usage/application status of processes. The calculation method for the digital warehouse maturity score is determined. The maturity model found in the literature (64 studies) is listed with author, structure, maturity levels, and dimensions. A warehouse digital maturity model consisting of 9 dimensions and 5 levels is created, and maturity score ranges are calculated for each level.
In the digital investment decision model section, the purpose of the model is to determine where (which technologies) to invest within the budget limit to achieve the highest digitization. In creating this model, digital warehouse processes and their in-process steps, the digital score obtained from each process, the necessary technological components for each step, and the costs of these components are considered. The model is implemented through Linear Programming in the GAMS language, and feasible solutions are obtained by assigning hypothetical (fictional) values (costs). The solution is tested for different scenarios, and the process is completed with results such as "0 infeasible" and "0 unbounded." Ghost codes for scenarios that can be added to the model in the future based on application areas are added and left in a passive state.
The existing warehouses, as presented in the current state, need to increase their digital scores to the level required or targeted based on sector development. An important question for this increase is which equipment to invest in. To answer this question, a digital investment decision model is created to manage the investment decisions of evaluated warehouses through the digital maturity model. With this model, investment is made in technological equipment that will maximize digitization, in line with defined constraints.
The model that invests in processes that are not fully digital (at the highest level) and their in-process steps, proportionally to their contributions to digitization, achieves the highest digital score return. 
The results obtained from this study are presented under two main headings: academic and practical. Recommendations and methods for future studies are provided in a separate sub-heading.
The shadow price/opportunity cost of a unit investment in digital technologies was determined through sensitivity analysis of the digital investment decision model. The technologies that contribute the most to the digitization process with a unit investment increase (those with the highest shadow price/opportunity cost) are, in order, Warehouse Management System (WMS), Automated Guided Vehicles (Picking), OCR-Barcode-Chip-Image Reader and Processing Systems (Shipping), Robots (Picking), Sensors (Picking), Communication Infrastructure, Automated Directional Equipment, Automated Guided Vehicles (Shipping), Automatic Stretch/Shrink Wrapping Robot, Material Flow System (MFS), Robots (Placement), Vehicle and Operator Monitoring Systems, Label Reading Systems, Light Guidance Systems, Weighing and other measurement equipment, Reservation System Software, and ERP.
With this study, a warehouse manager aiming to determine digital maturity can assess the level of digital maturity by controlling processes and, using the digital investment decision model, determine which areas to invest in to address deficiencies.
The evaluations in this study are based on the assumption of a conventional shelved warehouse. Similar studies of comparable nature can be conducted for different types of warehouses.
In future studies, additional processes such as risk management can be added to the identified 9 processes. For example, in the event of a fire alarm, an automatic fire extinguishing system may be activated, or sensors may be used for damage detection after natural disasters such as earthquakes or floods. Damaged materials can be transferred to blocked storage areas and subsequently disposed of. Products with damaged packaging can be examined for quality control before being repackaged and returned to stock. These steps can be defined as in-process steps. Security, theft, and similar issues can also be addressed within this process.
The model can be enhanced by adding the company's own resources and capacity constraints (number of doors, ramps, workstation equipment, etc.) based on the identified company for implementation. Maturity level ranges can be determined by sectors and product groups. Interfaces facilitating reporting and evaluation can be developed.
The in-process steps and technologies presented in this study were evaluated independently. In future studies, relational constraints can be added to identify technologies that should not be invested in simultaneously. By adding nonlinear objective functions or constraints, a broader problem class defining the digital investment decision model with nonlinear programming can be redesigned. The solution method can be examined and compared with the existing solution.