On-time delivery of supplies to industry is essential because delays can disrupt production schedules. The aim of the proposed application is to predict transit times for outbound logistics thereby allowing suppliers to plan for timely mitigation of risks during shipment planning. The predictive model consists of a classifier that is trained for each specific source-destination pair using historical shipment, weather, and social media data. The model estimates the transit times for future shipments using Support Vector Machine (SVM). These estimates were validated using four case study routes of varying distances in the United States. A predictive model is trained for each route. The results show that the contribution of each input feature to the predictive ability of the model varies for each route. The mean average error (MAE) values of the model vary for each route due to the availability of testing and training historical shipment data as well as the availability of weather and social media data. In addition, it was found that the inclusion of the historical traffic data provided by INRIX™ improves the accuracy of the model. Sample INRIX™ data was available for one of the routes. One of the main limitations of the proposed approach is the availability of historical shipment data and the quality of social media data. However, if the data is available, the proposed methodology can be applied to any supplier with high volume shipments in order to develop a predictive model for outbound transit time delays over any land route.

Indiana University-Purdue University Indianapolis (IUPUI) / On-time delivery of supplies to industry is essential because delays can disrupt production schedules. The aim of the proposed application is to predict transit times for outbound logistics thereby allowing suppliers to plan for timely mitigation of risks during shipment planning. The predictive model consists of a classifier that is trained for each specific source-destination pair using historical shipment, weather, and social media data. The model estimates the transit times for future shipments using Support Vector Machine (SVM). These estimates were validated using four case study routes of varying distances in the United States. A predictive model is trained for each route. The results show that the contribution of each input feature to the predictive ability of the model varies for each route. The mean average error (MAE) values of the model vary for each route due to the availability of testing and training historical shipment data as well as the availability of weather and social media data. In addition, it was found that the inclusion of the historical traffic data provided by INRIXTM improves the accuracy of the model. Sample INRIXTM data was available for one of the routes. One of the main limitations of the proposed approach is the availability of historical shipment data and the quality of social media data. However, if the data is available, the proposed methodology can be applied to any supplier with high volume shipments in order to develop a predictive model for outbound transit time delays over any land route.

Final thesis focuses on a new trend in logistics -- environmental considerations in supply chain and innovations connected with cargo transportation. Nowadays, due to diminishing trade barriers, goods are transported across longer distances, which results into negative externalities. The main purpose of this thesis is to analyse the impact of transport of finished vehicles on the environment. The questionare was built to find out interest, measures and future visions of automotive companies in reducing the environmental impact which is a consequence of their outbound logistics.

The South African pharmaceutical market is seen as part of the so called "pharmerging" markets, together with countries like India, China and Brazil. These "pharmerging" markets are the fastest growing markets within the global pharmaceutical industry. The distribution of chronic medicine in South Africa is a growing market, as the disease burden in South Africa continues to escalate, with the incidence of chronic conditions growing at a rapid rate. The study will focus on one of South Africa’s pioneer courier medication service providers, with more than twenty years’ experience in the healthcare industry. The company will be referred to as Pharmacy X. The mission of Pharmacy X is to provide the right chronic medication, to the right patient, at the right place, at the right time. It is imperative to ensure that a patient receives his/her chronic medication on the scheduled date of delivery to ensure compliance and customer satisfaction. To achieve a competitive advantage, companies increasingly depend on their supply chain partners to minimize cost and improve business processes. The core value chain activity of outbound logistics has been outsourced by Pharmacy X to several courier companies. This study will aim to understand the importance of the outbound logistics function within the value chain of the company and the costs involved with the outsourcing of the function. The primary objective of this study was to determine the feasibility of an in-house courier operation in the Bloemfontein area versus the current outsourced courier model. In order to achieve the primary objective of the study, several secondary objectives were set and reached throughout the four chapters of this study. The study applied cost benefit analysis techniques to determine the feasibility of the Bloemfontein courier investment project. All the cost benefit analysis techniques concluded that the Bloemfontein courier investment will be a financial viable operation. The Bloemfontein courier investment will increase shareholder value over the period of the project compared to the current outsourced model. The contribution of this case study to determine the feasibility of a courier operation investment can be of value to Pharmacy X. The current projected total courier cost of Pharmacy X for the 2013 financial year amounts to more than a third of the total operational cost. The findings within the case study can lead to a greater national roll out of courier operations in order to reduce costs and increase profit margins for Pharmacy X. / MBA, North-West University, Potchefstroom Campus, 2014

The South African pharmaceutical market is seen as part of the so called "pharmerging" markets, together with countries like India, China and Brazil. These "pharmerging" markets are the fastest growing markets within the global pharmaceutical industry. The distribution of chronic medicine in South Africa is a growing market, as the disease burden in South Africa continues to escalate, with the incidence of chronic conditions growing at a rapid rate. The study will focus on one of South Africa’s pioneer courier medication service providers, with more than twenty years’ experience in the healthcare industry. The company will be referred to as Pharmacy X. The mission of Pharmacy X is to provide the right chronic medication, to the right patient, at the right place, at the right time. It is imperative to ensure that a patient receives his/her chronic medication on the scheduled date of delivery to ensure compliance and customer satisfaction. To achieve a competitive advantage, companies increasingly depend on their supply chain partners to minimize cost and improve business processes. The core value chain activity of outbound logistics has been outsourced by Pharmacy X to several courier companies. This study will aim to understand the importance of the outbound logistics function within the value chain of the company and the costs involved with the outsourcing of the function. The primary objective of this study was to determine the feasibility of an in-house courier operation in the Bloemfontein area versus the current outsourced courier model. In order to achieve the primary objective of the study, several secondary objectives were set and reached throughout the four chapters of this study. The study applied cost benefit analysis techniques to determine the feasibility of the Bloemfontein courier investment project. All the cost benefit analysis techniques concluded that the Bloemfontein courier investment will be a financial viable operation. The Bloemfontein courier investment will increase shareholder value over the period of the project compared to the current outsourced model. The contribution of this case study to determine the feasibility of a courier operation investment can be of value to Pharmacy X. The current projected total courier cost of Pharmacy X for the 2013 financial year amounts to more than a third of the total operational cost. The findings within the case study can lead to a greater national roll out of courier operations in order to reduce costs and increase profit margins for Pharmacy X. / MBA, North-West University, Potchefstroom Campus, 2014

Abstract Purpose The primary and initial aim of the research paper is to highlight the automation and digitalization in the supply chain management of textile industry. The context compared traditional and digitalized supply chain in the textile industry for digitalization. Research (Methodology/Design) The researcher adopted a qualitative approach to conduct the research on the provided topic of supply chain management. The findings and results of the research are provided based on the structured interviews conducted by the researchers. The evidence-based results will be used for porbing out the basic problems and future recommendations. Findings The finding of the research stated that a variety of challenges was being faced by the textile industry in achieving the supply chain goals and objectives utilizing traditional methods of processing. Based on the reviews presented by interview participants, the traditional model implemented in the textile industry was not strong enough and the industry faces issues and challenges. The modern models, techniques, and methods are presented in order to tackle the issues being faced by the textile industry. The research paper argues that the implementation of modern and innovative textile technology should be ensured to speed up the SCM mechanism. Value/worth The study reflected and presented useful and deep insight regarding the comparison of traditional and modern supply chain management. The digitalization that occurred in the textile industry is also highlighted in the research paper. The research also proposed a modern model and technique of supply chain that can be implemented in the textile supply chain in order to enhance the overall supply chain process. Keywords – Textile industry, Supply Chain, Digitalization, Mobility, Inbound Logistics, Outbound Logistics

Supply chain visibility has become a crucial factor for companies in times of globalization and customer satisfaction. Track-and-trace technologies are important tools in order to enhance supply chain visibility. This thesis was written in cooperation with a Swedish retailer and evaluates potential track-and-trace technologies in order to develop a solution to close their current track-and-trace gap in their outbound logistics. Currently the handover point between the retailer and the postal service provider is not clearly defined, so that shipments get lost during the transition. Therefore, a literature review about currently used track-and-trace technologies was carried out. Several technologies with a wide price and applicability range are currently used and have been analysed regarding their strength and weaknesses. A qualitative study in form of interviews was conducted within the Swedish market about how this gap could possibly be closed. Empirical findings show that the existing track-and-trace technologies do not provide a best practice solution. Especially in the field of outbound logistics, several factors and the individual process requirements of a company have to be considered in order to develop an efficient solution, so that the existing track-and-trace gap can be closed. Each company has its unique set of challenges, which have to be solved in order to successfully implement a long- lasting tracking solution. A high dependence from the postal service provider is additionally given since all process steps need to be aligned to guarantee reliability of the data afterwards. In the case of the Swedish retailer, an automatized scanning bow with a separated area for outbound parcels is expected to improve transparency of the handover and lower the total amount of lost shipments. The breakeven point would be reached within the next years, so that operational saving could soon be achieved. Due to the global outbreak of COVID-19, as well as significant problems of the retailer, the practical application could not be tested. It should therefore be part of further academic studies.

Forecasting demand is one of the processes which greatly influences the decision making within a company, and it is also one of the greatest sources of uncertainty. Inaccurate forecasts force companies to find ways to compensate for the uncertainty, often by building inventories. On the other hand, accurate forecasts help companies to achieve better customer service and lower inventory levels. Cytiva is a global life science leader who manufactures high-technology laboratory instruments at their site in Umeå. To make sure that their transportation and storage spaces are sufficient, their down-stream suppliers require information about the quantity of the final products beforehand. But as of today, the company has inconsistent outbound inventory volumes. Thus, there is a great demand for increased visibility and predictability at the Umeå site’s outbound logistics. Further, Cytiva in Umeå bases their forecasting on manually calculated estimations which is both inefficient and can create errors due to human factors. These intrinsic information inconsistencies related to the outbound logistics is prone to creating bottlenecks in their overall supply chain. The main goal of this project is to increase the accuracy of these forecasts by developing a model. The outcome will be better estimations and clearer connection between the site in Umeå and the 3PL in Rosersberg. Additionaly, a good model makes the supply chain more efficient by creating better preconditions for managing the transportation and inventory at the receiving 3PL. To make forecasts for Cytiva’s outbound inventory, we chose to focus on two of the most common families of univariate time series models, namely the ARIMA and the Exponential Smoothing family. Based on these two families we have implemented, evaluated and compared six forecasting models. Initially, the modeling was done using daily observations in order to examine whether the models could improve the company’s demand for short forecast horizons. However, except for modeling on daily observations, we also widened the time interval by merging the observations into weeks to extend the modeling perspective even further. The results showed that the use of models can noticeably improve the estimations of the inventory and transportation spaces. We conclude that, among our models, the Holt-Winters using additive seasonality is the most optimal model when the forecasts are made on a daily time frequency, while the SARIMA model performs better on the weekly data.

Background: long term volume forecasting is important for logistics service providers for planning their capacity and taking the strategic decisions. At present demand is estimated by using traditional methods of averaging techniques or with their own experiences which often contain some error. This study is focused on filling these gaps by using machine learning approaches. The sample data set is provided by the organization, which is the leading manufacturer of trucks, buses and construction equipment, the organization has customers from more than 190 markets and has production facilities in 18 countries. Objectives: This study is to investigate a suitable machine learning algorithm that can be used for forecasting demand of outbound distributed products and then evaluating the performance of the selected algorithms by experimenting to articulate the possibility of using long-term forecasting in transportation. Methods: primarily, a literature review was initiated to find a suitable machine learn- ing algorithm and then based on the results of the literature review an experiment is performed to evaluate the performance of the selected algorithms Results: Selected CNN, ANN and LSTM models are performing quite well But based on the type and amount of historical data that models were given to learn, models have a very slight difference in performance measures in terms of forecasting performance. Comparisons are made with different measures that are selected by the literature review Conclusions. This study examines the efficacy of using Convolutional Neural Networks (CNN) for performing demand forecasting of outbound distributed products at the country level. The methodology provided uses convolutions on historical loads. The output from the convolutional operation is supplied to fully connected layers together with other relevant data. The presented methodology was implemented on an organization data set of outbound distributed products per month. Results obtained from the CNN were compared to results obtained by Long Short Term Memories LSTM sequence-to-sequence (LSTM S2S) and Artificial Neural Networks (ANN) for the same dataset. Experimental results showed that the CNN outperformed LSTM while producing comparable results to the ANN. Further testing is needed to compare the performances of different deep learning architectures in outbound forecasting.

The thesis addresses the mixed model assembly line sequencing and outbound logistics planning problems in the automotive industry at the operational level. Different from the sequential decision-making procedure used in practice, the thesis proposes a scheme that integrates production sequencing and logistics planning. Mixed integer programs are established for the production sequencing, logistics planning, and integrated problems. The integrated model cannot be solved by commercial solvers in a reasonable amount of time. After studying the optimality properties of the product mode, the thesis proposes a modified integrated model. The results of numerical experiments and simulations demonstrate the benefit of the integration by comparing the modified integrated model with two sequential schemes, the Production-First-Scheme and the Logistics-First-Scheme.