운송 정책이 공급망 효율성에 미치는 영향

### 운송 정책이 공급망 효율성에 미치는 영향: 인도 사례 연구
* 🚚 운송은 **전체 물류 비용의 40~60%**를 차지하며, 운송 효율이 **10% 개선**되면 **전체 공급망 수익성이 5% 증가**할 수 있습니다.
* 📊 2020년 글로벌 물류 시장 규모는 **9조 6천억 달러**로 2027년에는 **12조 9천억 달러**에 달할 것으로 예상되며, 전 세계 무역의 **90% 이상이 국제 배송**을 통해 이루어지고 있습니다.
* 🇮🇳 이 사례 연구는 **마디아프라데시에 공장과 전국에 유통 센터**를 보유한 스포츠웨어 공급망에 초점을 맞춘 인도에 기반을 두고 있습니다.
* 🌱 이 모델은 **CO2 배출량을 의사 결정 프레임워크에 직접 통합**하는 등 지속 가능성을 핵심 요소로 삼고 있습니다.
* 📈 공급망 효율성을 최적화하기 위해 **풀 트럭 적재와 트럭 적재량 축소 등 다양한 운송 정책**을 평가합니다.
* 🔍 이 연구는 애니로직 시뮬레이션 소프트웨어를 사용하여 **수요, 재고, CO2 배출량**과 같은 요소를 고려하여 공급망을 모델링하고 분석합니다.
* 🏭 이 모델에는 **100개 이상의 도시, 여러 공급업체 및 유통 센터**가 포함되어 있으며, 수요를 충족하면서 운송 비용을 최소화하는 데 중점을 두고 있습니다.
* 📊 이 연구는 정책 입안자와 공급망 관리자가 운송 효율성을 최적화할 수 있도록 **실질적인 권장 사항**을 제공하는 것을 목표로 합니다.

운송 정책이 공급망 효율성 및 지속가능성에 미치는 영향(CO2 배출 비용 통합 포함)은 애니로직 시뮬레이션과 ISC 방갈로르 데이터를 통해 검증되었습니다.

good afternoon, good morning, and whatever part belongs to you. So, today we are going to present one of the important topics in the supply chain, that is transportation policy. So, here I'm going to discuss the impact of different types of transportation policy in the supply chain, and we will going to discuss these topics through one of the case studies conducted in India. And, we will check the different kinds of aspects available in the any logistics to support these kinds of studies. So, today we are going to discuss this topic based on the following inputs. So, we will start the discussion on the introduction part about the supply chain. We going to discuss supply chain in terms of sport FS. Then, we will talk about the literature review and research G in supply chain. And nowadays, there is a buzzword like digital twin. So, how we collaborate with the digital twin and replicate and provide a better visualization for the supply chain. This is one of the bigger agendas nowadays because the visibility is the most important part of the supply chain. And somehow in

theoretical research their aspect is missing some of the softwares U like any logistic any logic and so these are the softwares available those provide the visualization but these all softwares are the blank box. How we were going to use those softwares effectively to visualize the supply chain that is the most important thing to visualize any particular supply chain. We require different kind of data so we have different kind of inventories like raw material inventory finished product inventory semi-finish product inventory in Warehouse as well as in the factory. So mostly most of supply chain talk about one of the part of the is Supply like a factory to customer either Factory to Distribution Center Distribution Center to the customer mostly these kind of studies based on the event cink but we have seen couple of incident in the last years like covid-19 situation where the disruptions were there so there is a need to relook our supply chain. So in short we call this hist like and ENT supply chain where we have supplier suppliers Network Factory

distribution centers and the customers. So, how this whole supplier suppliers Network towards the end of customers. We're going to collaborate each other and provide better visibility and provide a solution for those practitioners as well as for the acadm mission. So, this is one of the major research G nowadays. We will going to discuss uh this aspect then uh I'm going to cover some of the aspect about uh the discret event simulation because digital twin basically work on the three different kind of scenarios one is System Dynamics second one is discret event simulation and third one is Agent based simulation although these three kind of simulations or simulation strategies are compatible to understand or to explain any kind of scenarios but each and every techniques or each and every kind of simulation have their own limitations and Advantage one of the great advantage of discret event simulation is like we have statistical data with us and that statistical data we can simulate in what for scenarios where we can generate multiple scenarios and see what exactly uh the role of different kind of

scenarios in the actual supply chain performance. So we will going to discuss these two aspects through one of the case study. This case study is based on Indian scenario where we have one factory and it contained a number of suppliers, more than nine suppliers, to supply their products after the production. This company sends their products to the industries. Here we have different kind of, sorry, different kind of distribution centers. So different kind of distribution centers are located in different parts of India. And how different distribution centers mitigate the actual requirement of the customer, that is one of the bigger challenges for the efficient supply chain. We will discuss these kind of results through simulations and we will provide the discussion how we can use the different kind of transportation policies, different kind of, what for scenario to generate optimal solutions. Before we start this discussion, let us talk about the different kind of studies available in the present state of art that we call literature review sections. So here you can see transportation account of.

approximately 40 to 60% of the total logistic cost. According to es care 2024, the efficient Transportation can reduce inventory level by 30%. All 20 2 three paper State this is statement the global logistic Market size value at USD 9.6 trillion in 2020, and it is expected to reach 12.9 trillion by 2027. That's why we are talking about these kind of visibilities and transportation strategies because this is an exponential growing market, and if we provide a better solution strategies to CER this Market, every company can consider in the win-win situation. Another Statistics reveals that a 10% of the Improvement in transportation efficiency can lead to the 5% in overall supply chain profitability. So you can see here the role of major role of Transportation efficiency in overall surprising profitability. Over 90% of the global trade is carried out through the international shipment industry, highlighting the importance of Transportation in concerning Market. Efficient Transportation networks enable companies to assess the global market.

and increase the competitiveness in growth of growth and opportunities. So most most of the trades, most of the uh Transportation takes place through the shipments. But in case of inner transportation in one particular country, most of the supply chain relies on the road Transportation. So Road Transport, efficient Road transportation, is one of the important aspect in countries like India where internal transportation is majorly depend on the two major transportation medium. One is uh Railways and second one is roads. 79% of the uh customers consider fast and reliable delivery as a crucial factor in their online shopping and decisions. Company that Excel the transportation efficiency often achieving the higher customer satisfaction and loyalty rates. So these are the major uh, you can say, trigger points which encourage us to move for the efficient supply chain efficient Transportation strategies. But there are lot of constraints, lot of of restrictions which doesn't Avail this kind of efficient Transportation strategies. One of the uh important constraint is like availability of the product, second disruptions at supplier suppliers Network before before the

covid-19 and before uh 200 uh you can say 2019. No one talk about the supplier suppliers Network or lesser people talk about this suppli suppliers network, but nowadays it is one of the important part of supply chain. How your vendors are working, how they are providing their materials to the raw materials to the factory, and how factories can regulate the production rate and maintain the demand of the customers. So this is one of the major challenges nowadays Industries or Academia experiencing in terms of literature. So these studies evaluate how different Transportation policies that is full truck load lesser truck loads fuels regulations emission standards impact the transportation efficiency and C this particular uh case study incorporate uh different kind of loads like um in transportation we are using full truck load lesser truck load with periodic Demand with non-p periodic demand. So these are the different kind of strategies usually followed by the different supply chain managers. So this case study incorporates some of these application of full truck load and lesser truckload application.

and which have generated different kind of scenarios and compare those scenarios by introducing the carbon emission rates. So that is also important part of this case then quantifying the environmental benefit and sustainable Transportation practices such as reduction of carbon emission and lower fuel consumption. So as you all are aware about the sustainable development goals in the sustainable development goals United Nation majorly focus on the reduction of carbon emission and provide better life for the human beings under these 17 goals 1 to 17 goals most of these goals are considering or talks about the optimization of the resources. So how we will going to optimize our transportation strategies so in case of Transportation strategies there are multiple trucks available in the factories some of the truck having a lower capacity some of the truck having higher capacity so it's up to the supply chain manager situ either based on the factory or the distribution centers how they make their arrangements for the customers either full truck load or full half truck load so this is one of the important part of the supply chain to

cater, or you can say, calibrate, the carbon emission rate, because in most of the cases, wherever we require a half truckload inventory, supply chain managers usually send the full truckload, kind of situation. So, we need to understand those scenarios, and visualize those things effectively, and provide a better solution in an advanced manner, because one of the advantages of the simulation is like we can generate the scenarios before it's happen, so we can use n number of multiple things before we generate the actual scenarios. So, these are the two or three major objectives of this particular case, where we can discuss the any logistic softwares, and the role of the simulation, that is, District B simulation, distri event-based simulation for the model generation, and their explanations. So, finally, we deploy and develop the practical recommendation for the policymakers, for supply chain manager, to optimize their transportation efficiencies. So, in case of simulation-based optimization of simulation-based approach, many researchers have worked in recent years, and provide their better views. One of the important Professor, like Professor Dimitri iano Professor.

dugly do so. These are the professors who are working in this field. In the last, I've seen their papers in the last 5 to 10 years. 5 to 10 years, they are sincerely working in this particular area and provide a better visibility for the supply chain. They have found the terminology like digital twin. So, this digital twin aspect is creating a huge impact in the society because it provides visibility to the practitioners how they can manage their inventory. So, recently Sharma Kumar (2018) optimized the Transportation Route can reduce by the cost Advan roting algorithms and real-time data are critical for improving the transportation efficiency. Emission standards and fuel taxes significantly impact on the transportation cost and efficiency. Some of the transportation and emission standard has been explained by the Miller that promote the longer-term efficiency through the technological innovation. So, in recent years, integration of these Technologies improve overall supply chain sustainability. Part has been added by Smith and Jones and state that sustainable Transportation practices can reduce carbon emission by 30%.

Adoption of these electric and hybrid vehicles achieving sustainable goals around 2020. As I was talking about uh and linking this concept with the sustainable development goals. So according to CH and Professor CH 202 paper, they have described these 17 goals in different kind of situations like three B bottom lines social, economical, and environmental. So one of the important goal is like import uh goal number 17 where we talk about the Partnerships for the uh different sustainable goals and zero carbon emissions. So few of these studies are comprehensively simulate the models evaluate the combined impact of the various Transportation policy on supply chain efficiencies limited research and ing uh integrated emerging Technologies sustainable Transportation policies and their impact in Supply and performance. So there is a scarcity of cross-industrial data so this is also highlighted by researchers in recent years. So there is a need for long-term strategies assessing the sustainable impact and transportation policy in both efficiencies so to CER this uh we are basically work for the sustainable goal.

sustainable development goal 7.7 and our objective is to optimize these goals and provide a better visibility for the uh sport viers items so that is the main motive of this particular case study. Why we have consider this sport we case study because you can see here the data this data we CED by the statistica. You can see here from 2018 to 2020 there is a exponential uh growth in terms of the sportware market in India. Here you can see the sustainable AAL shares which is also going to be increased in upcoming years. Therefore we can consider this sector as a major sector where we can work and provide better visibility and visualization for the industry persons. Now how this uh sustainability the carbon emission and different kind of strategies can work for the better supply chain. So as I state that this case studies based on India so you can see here the major States and major cities in the India. So we have consider um over 100 cities in this particular case including the major Metropolitan City. Now most of the research are based on

the theoreticals, and they don't consider the emission rates. There is a time where we can include, uh, the different kind of emissions in our transportation policies, and we can simply those emission in transportation policy. So we have consider some of the aspect related to the different states and consider this data from the ISC Bangalore. You can see here the CO2 emission rate from the road transportations as I State major part of the transportation in India lead by the road transportation and Railway Transportation. You can see here the statewise, uh, vehicle emission CO2. So we have CER these, uh, two approaches. One is, uh, the visualization, second one is emission rate by including the transportation policies. We include the different kind of scenarios by generating hybrid methodology combining the case study and discrete event simulation. Our model is created and solve through the an logistic model, and this simulation model allows us to provide vtif for scenario. So what basically we do here, uh, we did here, we have considered important part of the any logistic software which provide us the calculation for the cost for the CO2.

calculation. We consider the different emission rate of the Indian States, different Indian States because Factory situated in Madhya Pradesh, and it is circulating the products all over the India. So we have to consider the different emission rates from the different states and generate the different scenarios so you can see here one of the important label of this logistic software that is path. So during the path evaluation, we can consider the number of iterations in terms of CO2 calculations. So here we can mention the number of CO2 with different number of states. So here we have collected the data and first we have generated the Green Field analysis by providing the different distribution networks. So once the distribution networks assigned, then we go with the simulation aspect including the different kind of parameters demand and supply and parameters including the carrying cost, ordering cost, inventory cost, and one of the important parts that is CO2 emission calculation including in the part section of this any logistic software. So this case study consider the inbounded and the out bounded.

Logistics where we have considered the CO2 emission cost inbounded logistic as well as the outbound Logistics, and finally we have perform the analysis for the uh Transportation policies, supply chain efficiency, and provide the sustainabilities. So uh to make this kind of schedules, we need uh to perform the data based on the latitude and longitude of the uh particular location. So we consider this kind of uh data like latitude and longitude position of the particular distribution centers, which we have we have got to GF analysis second uh the Factory location and the supplier suppliers networks. So this is the major part of this uh case where we consider the central India and Factory situated in indor and producing different types of sport wees. More than nine suppliers selling their raw materials require the portware Productions. The main suppliers provide the bamboo fibers and more than nine distribution centers in pain India from different uh, which is basically looking for the finished products and cover more than 10 cities. Here we consider the demand is periodic and proportional to the population of the cities to build the

comprehensive sustainable supply chain model. We have integrated sustainability directly into decision-making framework. So you can see here the model formulation. We have considered different decision networks. So most of the things available in the na logistic, which we have to appreciate for the developers. Like, um, we can use the full truckload calculation, half truck calculations, pending orders, and we can also provide the binary variable for the periodic check. So these are the variables we have considered in the decision variables. Now we talk about the parameters. The cost of transportation processing operating for each DC can be represented by cig, Pik, and fi respectively. Consider the CO2 emission unit supplies I2 DJ is considered by the pig. Other parameters like cost of CO2 emission, sustainable weightage factors for suppliers, I2 DC suppliers capability, total number of DCs demand point, and maximum distribution centers in the network. So our main objective is to minimize the transportation cost. So here we have considered different kinds of constants. One is suppliers and demand constants. Second, transportation mode.

constraints. As I earlier said, we will talk about the full truckload and half truckload calculation. The new thing, which you can see here, is the sustainability and the green investment. This part is pending with me. I'm working on this part, so how can we directly introduce these two aspects, that is sustainability and green investment, constraints in any logistics. Fourth and important part is additionally flow for the binary constraints. So, you can see here the results. Before we go to the result, I would like to showcase the data. So here, we have considered total six different kinds of products with their raw materials. The customers are situated in different parts of India. Factories, demand facilities, we have considered in terms of groups. Group locations, group sites, inventory locations. Path, as I said, this is the most important part of this particular case where we consider the CO2 emissions, period, and processing. So, including this kind of scenarios, we have generated VTLF for scenarios and generated multiple cases with the same data. So, one of the important parts is like

wherever we talk about the sport, we some of the inventory levels are based on the seasonal seasonality, so there would be a hike or lower kind of inventories in between the demand coming from the customers to the Distribution Center. Distribution Center to factory and Factory to the raw material. So, this is one of the important part where the carbon emission comes into the picture. Second part, how we can make the optimality in the supply chain to maintain the different scenarios in supply chain. So, we have generate a different kind of scenarios by considering different different units of CO2 emissions and generate, uh, the service level of overall supply chain. So, whenever we consider, uh, the different distribution centers, so if there is the less distribution centers, there will problem for the flexibility but we can manage multiple, uh, locations by using the similar trucks. Another, uh, advantage of, uh, minimization of the Distribution Center like, uh, we can send, uh, the number of inputs or the inventories with respect to the particular area. That is one part. Second part is if we increase the number.

of distribution centers. Ultimately, it will increase the flexibility. But again, um, there will be a chance for managing the number of vehicles. Sometimes that vehicle is going to be utilized, sometimes not. So that would be a question mark for the efficiency of the, uh, supply chain and optimal utilization of the resources. So if you can manage, uh, these scenarios, uh, comparatively and better way, we can provide the holistic view for the different kind of transportation policies and achieve the sustainable development goals. So these are the references for the model. Thank you. So one of the research questions is like how we can include the CO2 emission cost in, uh, objective functions and provide the sustainable sustainability related things. That is one of the important parts. Second question is like, uh, uh, how this, uh, what-if scenario works for the calculation of different option strategies. So as I said, full truckload, half truckload. So how it will going to evolve. So these are the two major parts of these questions. Okay. And the next question, what role do you see emerging technologies like autonomous vehicles and drone delivery playing in shaping

the future of Transportation policy and its impact on Supply chains. Drone technology is new here, especially in developing countries like India, so we require a lot of regulations. We consider a lot of regulations. So recently, Amazon started their services in terms of drone delivery. So again, there will be a time when we can consider drone delivery as one of the parts of the transportation, but yeah, it will be a promising part. Okay, yeah, interesting. It's always an interesting idea. Another question, thank you for the presentation. I have two questions. First one, how did you get the data about CO2 emissions and how did you validate it? CO2 emission data is available in ISC site. ISC Bangalore is one of the important institutes in India, so their website data is available, and I have gone through a couple of papers where the statewise CO2 emission data is available, so it's in the public domain. And the second question, how did you get the whole data for the whole system and validate it as a baseline scenario? Yeah, in simulation cases study.

validation is one of the important part, and many times people questions about the validation part. So, I have gone through a couple of cases where if we can generate the multiple scenarios and compare the results. And if the results match the one particular Baseline, so so we can say the data is come from of the valid validation situations. So, again that is problematic. Um, yes validation is important part, and still we need to work on the simulation result, especially for the validation part. Great. And there are a couple of questions in the chats. I can also just ask them for you. Uh, so hi, thanks for the presentation. I would like to know your thoughts on the construction supply chain and sustainability and which modeling technique do you recommend. Um, so for supply chain simulations, we have different kind of techniques. One of the important technique is like system Dynamic model. So, if you're looking for the behavioral thing about supply chain, how one suppliers react with each others, so these kind of Behavioral techniques we can measure through the system Dynamic model. Second, if we have holistic model.

and bigger models of supply chain, and we need to check real-time measurement. So, agent-based simulation model is one of the important models where we can integrate. So, recently we have used some of the part where we can directly link the data with any logic models where we can do the real-time forecastings. So, recently some of the pipelines have been made in terms of any logic and Python. So, if you are looking for the real-time data, agent-based demolution is best. If you have static data, discrete event simulation is best option for simulation. Okay, great. And another question, what is the run length of the simulation? Run length depends on the configuration of your system, basically. So, 3 hours for me, and again, it will depend on the number of because we have considered 100 cities. If you have multiple cities and multiple distrib centers, the run time would be moving.

Analyzing the Impact of Transportation Policies on Supply Chain Efficiency and Sustainability

anyLogistix

운송 정책이 공급망에 미치는 영향: 인도 사례 연구

운송 정책은 공급망 관리, 특히 공급업체 네트워크와 유통 센터의 맥락에서 중요한 역할을 합니다.

디지털 트윈 기술과 개별 이벤트 시뮬레이션의 사용은 공급망을 시각화하고 최적화하는 데 중요하며, 운송 및 유통에서 직면한 문제에 대한 최적의 솔루션을 생성하는 데 중점을 둡니다.

여러 공급업체와 유통 센터가 관련된 인도의 사례 연구는 고객 요구 사항을 충족하고 효율적인 공급망 흐름을 달성하는 데 있어 다양한 운송 정책의 중요성을 보여줍니다.

운송 효율성이 글로벌 물류에 미치는 영향

2020년 전 세계 물류 시장 규모는 9조 6천억 달러로 2027년에는 12조 9천억 달러로 성장할 것으로 예상되어 효율적인 운송 전략의 필요성이 강조되고 있습니다.

운송 효율성을 개선하면 전체 공급망 수익성이 5% 증가하여 업계의 성공에 중요한 역할을 할 수 있습니다.

운송 네트워크는 글로벌 무역을 촉진할 뿐만 아니라 고객 만족도에도 큰 영향을 미치며, 79%의 고객이 온라인 쇼핑을 결정할 때 빠르고 안정적인 배송을 중요하게 고려한다고 답했습니다.

스포츠웨어 산업의 지속 가능한 발전을 위한 공급망 효율성 최적화

공급망 관리자는 종종 필요한 반 트럭화물 대신 전체 트럭화물을 보내 불필요한 탄소 배출을 초래합니다.

시나리오를 시각화하고 최적의 솔루션을 제공하기 위해서는 District B 시뮬레이션과 같은 시뮬레이션 및 고급 물류 소프트웨어를 사용하는 것이 중요합니다.

연구에 따르면 새로운 기술과 지속 가능한 운송 정책을 통합하면 스포츠웨어 산업에서 탄소 배출량을 크게 줄이고 공급망의 지속 가능성을 개선할 수 있습니다.

인도 운송 물류의 CO2 배출량 감소

ISC 방갈로르의 연구는 인도 주에서 도로 및 철도 운송으로 인한 CO2 배출량을 분석합니다.

이 연구는 하이브리드 방법론과 이산 이벤트 시뮬레이션을 사용하여 다양한 배출 시나리오와 유통망을 평가합니다.

이 연구는 지속가능성을 의사결정 프레임워크에 통합함으로써 수요, 공급, CO2 배출량과 같은 요소를 고려하여 운송 비용을 최소화하고 공급망 효율성을 개선하는 것을 목표로 합니다.

인도의 지속 가능한 공급망 물류 최적화

이 연구는 인도 전역의 다양한 제품에 대한 물류에 지속 가능성 및 친환경 투자 제약을 통합하는 데 중점을 둡니다.

CO2 배출이 공급망에 미치는 영향과 지속 가능한 개발 목표를 달성하기 위한 자원의 최적 활용을 고려합니다.

이 연구는 또한 CO2 배출 비용을 객관적 기능에 통합하고 자율 주행 차량 및 드론 배송과 같은 신흥 기술이 운송 전략의 진화에 미치는 잠재적 영향에 대해서도 탐구합니다.

운송 정책이 공급망에 미치는 영향

드론 기술은 인도와 같은 국가를 중심으로 운송 분야에서 부상하고 있으며, 많은 규제가 필요하지만 미래 운송 시스템의 유망한 부분으로 여겨지고 있습니다.

CO2 배출량 데이터는 ISC 웹사이트에서 입수하여 여러 시나리오의 비교를 통해 검증했으며, 공급망 시뮬레이션에서 행동 측면과 실시간 데이터를 측정하기 위해 시스템 동적 및 에이전트 기반 시뮬레이션 모델을 권장합니다.

시뮬레이션의 실행 시간은 시스템 구성에 따라 다르며, 도시 및 물류 센터의 수에 따라 달라질 수 있음니다.