Logistics in Emerging Economies: Forecasts and Analysis for Mexico and Turkey
| dc.contributor.author | Erlandsson, Joakim | |
| dc.contributor.author | Pihl, Otto | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för teknikens ekonomi och organisation | sv |
| dc.contributor.department | Chalmers University of Technology / Department of Technology Management and Economics | en |
| dc.contributor.examiner | Andersson, Dan | |
| dc.contributor.supervisor | Parsa, Mostafa | |
| dc.date.accessioned | 2023-06-08T12:30:58Z | |
| dc.date.available | 2023-06-08T12:30:58Z | |
| dc.date.issued | 2023 | |
| dc.date.submitted | 2023 | |
| dc.description.abstract | This project focuses on examining the factors that can impact the logistics sector in emerging economy countries, specifically Mexico and Turkey. The main objective is to find variables impacting the logistics sector, and then forecast the future trends of logistics in the short-term and mid-term horizons. To accomplish this, we utilize freight transportation data obtained from the Organization for Economic Cooperation and Development (OECD) database, categorized by different modes of transportation. Additionally, we gather various factors that can influence logistics and freight transportation from the World Bank database of world development indicators. The initial step in our analysis involves data preparation and cleaning, ensuring that the information is accurate and ready for further analysis. Next, we employ principal component analysis (PCA) to reduce the dimensionality of the data. The outcomes of the PCA reveal some differences when comparing the two countries. However, we identify demographic, economic, and logistical factors as influential in predicting future increases in freight transportation for both Mexico and Turkey. To identify the factors that significantly affect logistics trends, we perform exploratory data analysis and employ a time series regression model. We utilize Exponential Smoothing (ETS) and Autoregressive Integrated Moving Average (ARIMA) methods for forecasting logistics trends in the short-term and mid-term horizons. The time series regression model, with an Adjusted R-squared value of 0.963, indicates that the variables affecting the logistics sectors of both emerging economy countries can be classified into demographic, economic, and logistical factors. Specifically, we find that the young population, GDP, and export values are statistically influential factors in shaping the future of logistics in emerging markets. In analyzing the influential factors affecting the logistics sector in Mexico, several key aspects have emerged. Manufactured exports, exports to high-income countries, and the increase in consumption among the population are identified as significant factors driving logistics development. Moreover, the presence of a young population in Mexico is crucial for the logistics growth. Another noteworthy variable is negative rural growth, indicating a shift of people from rural to urban areas. This trend is particularly important for emerging economies, often serving as an indicator of overall logistics development. Similarly, in the case of Turkey, various variables from different categories exerts a significant impact on the development of freight transportation. Demographic factors, such as age distribution and geographical positioning of the population, play a key role in logistics trend. Additionally, economic factors, including GDP, interest rate, and debt stocks, have proven influential in understanding the dynamics of freight transportation. Furthermore, logistical factors, such as merchandise imports and exports, have emerged as crucial drivers of growth in Turkey’s logistics sector. Moreover, the forecasts generated by ETS and ARIMA models demonstrate an overall upward trend in freight transportation for both Mexico and Turkey. However, variations are observed between the modes of transport and the two countries. The choice of the best-fit models differs across modes of transport. In the case of Mexico, the ARIMA model outperforms the others for all modes except when forecasting total freight. For Turkey, the ETS model is more accurate in predicting air and total freight, while ARIMA fits better for the other modes of transport. The ETS forecast for rail transportation in Turkey suggests a relatively stable trend, showing neither a significant increase nor decrease in the upcoming years. On the other hand, the ETS forecast for air freight transportation in Mexico indicates a general increasing trend for the next few years, followed by a slight decline in the final two years of the forecast, implying a potential shift in the trend. To validate the accuracy of our forecasts, we assess the residual properties and conduct statistical tests. In conclusion, this project aims to uncover the factors that impact the logistics sector in emerging economy countries, focusing on Mexico and Turkey. By employing data analysis techniques, such as PCA, exploratory data analysis, and time series regression models, we identify influential factors and generate forecasts for the short-term and mid-term horizons. The results highlight the importance of demographic, economic, and logistical factors in shaping the future trends of logistics in emerging markets. The forecasted trends indicate an overall upward trajectory in freight transportation for both countries, with variations observed across modes of transport. The reliability of our forecasts is supported by residual analysis and statistical tests. | |
| dc.identifier.coursecode | TEKX17 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12380/306135 | |
| dc.language.iso | eng | |
| dc.relation.ispartofseries | E2023:008 | |
| dc.setspec.uppsok | Technology | |
| dc.subject | Emerging Market | |
| dc.subject | Forecast | |
| dc.subject | Logistics | |
| dc.subject | Freight Transportation | |
| dc.subject | Mexico | |
| dc.subject | Turkey | |
| dc.subject | Principal Component Analysis | |
| dc.subject | Times Series Regression Model | |
| dc.subject | Exponential Smoothing | |
| dc.subject | Autoregressive Integrated Moving Average | |
| dc.title | Logistics in Emerging Economies: Forecasts and Analysis for Mexico and Turkey | |
| dc.type.degree | Examensarbete på grundnivå | sv |
| dc.type.uppsok | M | |
| local.programme | Internationell logistik 180 hp (teknologie kandidatexamen) |