Konteyner kapasite ve taşıma planlama politikalarının sistem dinamiği yaklaşımı ile modellemesi
The system dynamics modelling for container capacity & transportation planning policies
Yazar: MEHMET ÇAĞATAY BAHADIR
Danışman: PROF. DR. HATİCE CAMGÖZ AKDAĞ
İstanbul Teknik Üniversitesi / Fen Bilimleri Enstitüsü / İşletme Mühendisliği Ana Bilim Dalı / İşletme Mühendisliği Bilim Dalı
Derece: Doktora, 2020
Konu: Endüstri ve Endüstri Mühendisliği = Industrial and Industrial Engineering ; Ulaşım = Transportation
Dizin: Fleksible konteyner = Flexible container ; Karayolu taşımacılığı = Highway transportation ; Malzeme taşıma sistemleri = Material handling systems ; Yük taşıma = Load carrying

Özet:
Hammadde, yarı mamul ya da ürünlerin depolanması, taşınması, saklanmasında kullanılan, yeniden kullanılabilir taşıyıcı ekipmanların sürdürebilirlik açısından önemi günden güne artmaktadır. İlgili ekipmanların sağladığı kazanımlar ile birlikte, işletmeler bu ekipmanların etkin yönetimi ile ilgili birçok problem ve karar süreci ile karşı karşıyadır. Stratejik seviyeden operasyonel seviyedeki karar süreçlerine kadar doğru politikaların üretilmesi ve/veya seçilmesi işletmelere rekabet avantajı kazandıracaktır. Bu yüzden tedarik zincirinde farklı karar hiyerarşilerindeki problemler araştırma konusu olmuştur. Bu çalışmada da göbek ve ispit ağı yapısına sahip konteyner servis sağlayıcılarının, taktiksel karar hiyerarjisi içerisinde yer alan konteyner servis kapasite planlama süreci için kazanç seviyesini en fazla iyileştirecek kapasite ve taşıma politika kümelerinin belirlenmesi amaçlanmıştır. Konteyner kapasite planlama sürecinin dinamik yapısı, diğer konteyner yönetim süreçleri ile etkileşim içerisinde olması, bünyesinde birçok geri bildirim döngüsünü barındırması sebebiyle ilgili politika kümesi analizleri için üç ana faz ve altı ana aşamadan oluşan bir sistem dinamiği modelleme yaklaşımı önerisinde bulunulmuştur.
Kapasite ve taşıma planlama süreçlerini barındıran konteyner servis kapasitesi konusu, konteyner sistemi içerisinde birçok alt süreç ile etkileşim halinde olup; çalışma kapsamında konteyner sistemi kavramsal modellemesi nedensel döngü diyagramlarından yararlanılarak; filo planlama, sevkiyat planlaması, sipariş yönetimi, bakım yönetimi ve finansal yönetim süreçlerini içerecek şekilde yapılmıştır. Kavramsal modelleme aşamasından sonra vaka çalışması olarak göbek-ispit ağı dağıtım yapısının yaygın bir şekilde kullanıldığı bir sektör olan otomotiv sektöründe, ana sanayi ve tedarikçisi arasında konteyner akış süreci seçilmiş ve belirlenen amaç doğrultusunda benzetim modellemesi yapılmış ve ardından ilgili modelin gerçek hayattaki sistem davranışlarını yansıtıp yansıtmadığının belirlenmesi için model güvenilirlik testleri yapılmıştır.
Simülasyon çalışmaları yardımıyla farklı hedef kapasite oranı, sevkiyat doluluğu, değişime tepki hızı vb. parametrelerin temsil ettiği kapasite ve taşıma politika kümeleri, kazanç oranını en fazla iyileştiren politika kümesini belirlemek amacıyla tekrarlı bir şekilde geliştirilmiş ve analiz edilmiştir. Tekrarlı bir şekilde politika kümesi geliştirme faaliyetleri, çalıştırılan simülasyonlar içerisinde maksimum kazanç elde edilen politika kümesi üzerinden yürütülmüştür. Ardından karar parametrelerinin iyileştirimesi çalışmaları ile en iyi politika kümesi belirlenmiş ve politika tercihlerinin hangi koşullarda değişeceği duyarlılık analizleri ile belirlenmiştir. Bu yaklaşım sayesinde bütünsel bir bakış açısıyla ve birbiriyle etkileşim içerisinde olan konteyner sistemi süreçlerini içerecek şekilde, kapasite kullanım oranı ve müşteri hizmet seviyesi arasındaki dengeyi sağlayacak politika kümeleri geliştirilmiştir.

Abstract:
The returnable transport equipment/items which are used for storage, transportation and packaging of raw materials, semi-finished products or products increase importance day by day with the development of the concept of sustainability, and companies tend to prefer these equipments instead of expandable packaging because of their economical & environmental aspects. Along with the advantage and acquisition of containers as returnable transport equipments, companies face many issues and problems related to the effective management of the returnable transport equipments from the strategic decision level till operational decision level. Container shipping service providers aim to satisfy and transport containers and/or material requirements of customers at the right time, quality and lower cost with efficient and responsive strategy. To reach competitive business outcomes, they need to develop right policies and make right decisions for container management. Integrated capacity & transportation planning is one of the challenging issues of container management in the tactical level to optimize the conflicted goals as high resource utilization rate and service level under changing conditions. Leveling capacity against demand fluctuation is a complex and difficult problem, because container service capacity process is associated with many sub-process of container systems. While improving the performance indicator of a process, others could be affected. So, evaluating the effect of capacity and transportation policy in integrated manner rather than seperately is more accurate to find the policy set for the maximum earnings of the company. The aim of the paper is to determine the capacity and transport policy set that will maximize the earnings with holistic perspective for the container service capacity planning process. The number of available container shipment per defined period is determined by capacity and transportation policy sets for balancing market share and capacity.
A system dynamics modelling approach is proposed for the analysis and selection of the policy set containing capacity and transportation policy. Because the container service capacity planning interacts with the other container management processes, it contains many feedback loops, it has dynamic structure and non lineer relationships. The proposed methodology has six main steps. These steps are problem definition, conceptual model development, simulation modelling, model validation testing, scenarion & policy analysis and implementation steps.
According to the proposed methodology, problem statement is defined to find best capacity & transportation policy set of which parameters contains target capacity ratio, shipment planning approach, time to adjust capacity in order to maximize the earnings of the company. The problem is limited to the container service capacity planning problem of container system having hub & spoke network distribution channel. On the second stage, conceptual model is developed by causal loop diagrams. Container system is classified into six main processes covering container order and shipment management, container service capacity planning, container maintenance and srap management, warehouse capacity management and financial management processes in the conceptual modelling stage. Conceptual modelling stage shows that there are many reinforcing & balancing loops for the container sustem. Because it contains so many interlocking loops & complex structure, it is hard to predict system behavior. So the simulation model is developed to analyze system behaviour by utilizing from causal loop diagrams. This step is more case-specific than the previous steps for preparing simulation environment. The container flows between an automobile company and its suppliers, which have hub & spoke network structure is considered for the case study. The simulation development and data collection process is carried out concurrently. Then, the validity and quality of model are checked for model confidence by utilizing some proposed behaviour and structural tests introduced in literature. With the validation tests, it is aimed to evaluate whether the system in the model reflects real world cases, especially impact of the capacity & transportation planning policies on firm performance. The validation tests contain the conceptual hypotheses validation by causal loop diyagram, simulation runs in the defined period, extreme conditions test, comparison with well-known conceptualized generic models such as "bullwhip effect" and comparison of some simulation and real case variables.
The container service probiders aim to balance the tradeoff between maximization of market share and capacity utilization by leveling container service capacity against demand fluctiation. They apply some capacity and transportation planning policies based on resource utilization rate and/or customer service level. Therefore, in the policy design & analysis stage, some scenarios which include combination of the capacity & transportation policy is created and analyzed iteratively.
First, the capacity leading policy, trailing capacity policy and matching capacity policy as capacity planning policy on demand is analyzed by system dynamics modelling. Company can satisfy sudden demand increases with the leading policy where excess capacity is used or they can use full of capacity with trailing capacity policy where capacity lags demand or they endeavour to match demand capacity and demand closely over time with the matching capacity policy. The simulation shows that capacity leading policy is the policy providing the highest earnings compared to the other capacity policies.
Transportation policies have a significant impact on fleet growth and reduction decision as well as operational costs. Shipment frequency affects container cycle time and container availability in warehouse. Therefore, the effects of late shipment and early shipment policy, which supports efficiency and rapid response strategies respectively in the container shipping process, on capacity planning process and system behavior were examined. In addition to these policies, a hybrid shipping policy including the minimum number of shipments is also simulated and compared with the others. In the early shipment policy, loads are delivered without waiting for the customer to fill the delivery trucks as less truck load in order to respond to the customer quickly. On the other hand, they could wait and delivered for the full truck load until the truck is full in the late shipment policies in order not to cause transportation inefficiency. In addition to these transportation policies, depending on the terms of the agreement between the customer and service providers, a minimum shipment policy may also be applied. In this policy, the loads are delivered with a minimum number of scheduled shipments, whether full truck load is provided within the relevant time frame. These transportaion policies are simulated under capacity leading policies, which has highest earnings in the first scenario studies. The simulation studies show that late shipment policy increases sales losses due to customer service level and container investment costs due to prolonged container cycle time although full truck loading provides efficiency in the transportation,. And it provides lower earnings than other shipment policies. Early shipment is the most powerful transportation policy among them. The early shipment policy provides to increase customer satisfaction level and decrease loss of sales. Moreover, the container investment requirement decreases with the decrease in tje container cycle time.
Companies has different reaction time to change for some reasons such information delays, their perceptions or stock review period when implementing capacity planning policies. Some container service providers have high sensitivity to change, they take action faster and radically, and they have short stock review period to adjust their stock or capacity. On the other hand, the others have long stock review period and they have low sensitivity to change. These behaviours or approaches of the company could change container service capacity planning policies under the demand fluctuation. So, the parameters of time to adjust are investigated whether it makes significant difference on the earnings of the firms in the third scenario studies. The capacity leading policy has better earnings under longer adjust time to compared shorter adjust time according to the simulation results. On the other hand, matching capacity policy has better earnings under shorter adjust time. These simulation provides to claim that although capacity leading policy with the defined target capacity ratio is most powerful policy, there is opportunity to improve the resource utilization rate in the simulation in which capacity leading policy causes applied. The target capacity ratio could be reduced in a certain level to eliminate over stock for fleet capacity. Therefore, the maximum earnings is found by reducing target capacity ratio iteretively.
According to the iterative simulation approach, capacity leading policy with early shipment is the best policy set for the highest earnings. And the inefficiency transportation can be improved with a stimulating pricing strategy and collaboration, so the customer willing to order full truck containers. The total supply chain cost could be reduced with this strategy by eliminating tranportation inefficiency.
In conclusion, a methodology based on system dynamic is proposed to analyze the policy sets occuring from capacity & transportation policy combinations, which aims to provide maximum earnings in the hub-spoke network structure of container system. The methodology is applied to an automative container supply chain as a case study. As future works, the proposed methodology can be used in policy analysis in other sectors with a container supplu chain having a hub-spoke network structure, or it can be used as a new policy development tool. Within the scope of the study, container service capacity modeling was carried out by considering only the container system with hub and spoke network structure, the complex network structure is also widely preferred in the automotive industry and different industries. So the conceptual modelling may be extended to complex network structure. Although this study includes capacity and transport planning process and other interacting critical processes, the model can be expanded to cover distribution network planning processes at strategic level.