Production, Operations and Supply Chain Management
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Even though the idea of nearshoring is not new, it has attracted a lot of attention lately. This strategy entails moving corporate activities, such as IT services or manufacturing, to nearby countries in order to take advantage of a variety of competitive benefits. Nearshoring is a phenomenon that is intimately related to trade wars, regional trade agreements, and changing global economic dynamics. The objective of this research is to investigate the development of nearshoring, the forces that drive it, and the effects it has on various industries, economies, and geopolitical ties. The study is specifically focused on the automotive and semiconductor sectors. The study takes a broad approach, including case studies, economic strategies, and historical patterns. It examines the growth of nearshoring in different areas, such as the USA and Europe, and analyses its effects on global trade and economic stability. In accordance to the study, nearshoring has a number of advantages, including lower costs, less risk, and easier access to trained labour. But it also brings with it difficulties like disparities in culture and regulations as well as worries about data security. Nearshoring trends have had a substantial impact on the automotive and semiconductor sectors, leading to notable changes in supply chain strategy and production processes. In response to the requirement for operational flexibility and the pressures in the global economy, nearshoring has become a popular option for these two industries. The approach emphasizes the necessity for flexible and resilient company strategies in a constantly shifting economic environment and helps to reshape the dynamics of global commerce.
The aim of this research is to investigate the unique factors that affect the
adoption of green supply chain management (GSCM) practices in Greek
companies, with a specific focus on how they align with the overall business
strategy and performance. To achieve this objective, a "bottom-up" approach
is utilized, which involves conducting case studies to assess the status of
GSCM implementation in Greece. Additionally, the research seeks to identify
the key barriers that must be overcome for successful integration of GSCM in
Greek businesses.
A primary purpose of the study is to advance the field of Green Supply Chain
Management (GSCM) research by providing new and valuable insights on
the implementation of GSCM in a context that has received relatively minor
or even no attention in previous studies. The findings of this study have
practical implications for businesses operating in Greece and beyond, as
they offer recommendations, solutions and strategies for companies seeking
to implement GSCM practices. Eventually, this research aims to contribute to
the overall development and understanding of GSCM, as well as to promote
sustainable business practices in the region.
Keywords: Green supply chain management, Greece, business strategy,
implementation, barriers, case studies, bottom-up approach
Advantest Company ("the company") is the world’s leading manufacturer of automatic test and measurement equipment used in the design and manufacturing of semiconductors. Because of the complexity of technologies and the supply chain of the semiconductor business segment, the company depends on both internal and external suppliers to manage some aspects of the supply chain of its main product, the System on Chip (“SoC”) test system. As a result, it is of ongoing interest to the company to have tools to continually strive for increased quality, reliability, capacity, and speed. Above all, these tools must enhance the relationships with the suppliers, and ensure the profitability and the company's position in the future.
The goal of this thesis is to understand the supply chain procedure at Advantest Company’s current state from the data-driven perspective by using the process mining technique. This technique is an emerging discipline, providing a comprehensive set of tools to provide fact-based insights and support process improvement. This new discipline is built on process model-driven approaches and data mining.
In this thesis, we discuss opportunities for process mining with an approach to analyse the process and how it can help the company enhance the procedure. This approach, which consists of data extraction from both third-party and internal software solutions, provides better visibility and faster disruptive event notification of the supply chain at the company.
Cost Optimization is a persistent discipline to adjust expenses and reduce costs while maximizing business value, and it has proven to be one of the most influential strategies to generate profits and stay competitive in the market. In this study, technical and strategic solutions that can lead to manufacturing cost optimization were considered. For this purpose, a test environment was created according to the DFMA methodology using the DFMA Concurrent Costing V4.1 software developed by Boothroyd Dewhurst Inc. and the production expenses of a JED029M3 aluminum bushing product from WABCO Group were analyzed.
Results revealed that these charges can be assigned to five main cost drivers Material, Machine Setup, Process, Scrap and Tools, of which Tooling was the most influential driver at lower volumes, while Material and Process remained two of the considerably substantial drivers across all ranges of volumes produced. Furthermore, among the optimization alternatives, the right choice for machine and raw material shape demonstrated to have a relative effects of less than 5% in reduction of manufacturing cost for 1,000 pieces, while outsourcing to China and transitioning toward economies of scale lowered could impact the total costs by -40% and -70%, respectively.
In today's fast-paced business environment, customers expect more than just high-quality products or services. They also demand excellent customer support that is both efficient and personalized. With the growth of businesses and the increasing complexity of products and services, providing efficient customer support has become a critical component of any successful business strategy to fulfill customers and user’s expectations. The present study investigates the need for a technical support helpdesk solution within the business unit Industrial Hydraulics of Bosch Rexroth, a global leader in drive and control technology. The primary objective of this thesis is to assess the current state of the support processes, identify areas of weakness, and leverage these insights to optimize and enhance them. The focus will be on finding a way to enhance transparency of customer data.
Based on the theory to process optimization, a current state analysis was conducted, and expert interviews were carried out to identify weaknesses and potential solutions for improving customer support. The research question "How can the Industrial Hydraulics Services of Bosch Rexroth deliver more effective and efficient customer support?" is addressed through the findings that a central platform is necessary to handle customer inquiries more efficiently, given the diverse communication channels and varying modes of operation within the business unit. The use of the existing SAP CRM ticketing system Robin is suggested as an efficient solution path, which should be optimized for future use based on the identified optimization approaches. The results underscore the importance of a unified approach to working, which is vital for the success of the business unit, resulting in increased efficiency, quality, collaboration, scalability, and customer orientation.
The sector of supply chain risk management has been expanding for several years now, with the goal to not only prepare organizations to recover after supply-chain disruptions but also mitigate risks to reduce losses.
One of the most remarkable techniques in the field is the Artificial Intelligence technology, which owing to its effectiveness and efficiency, allows humans to develop new solutions to predict or prevent a great variety of supply-chain disruptions.
This paper aims to forecast the future state of the Artificial Intelligence technology in Europe by 2035 with the use of the INKA 4 scenario manager software. A total of four areas of influence –– i.e., technological, financial, legal, and social –– were identified.
From those, 11 descriptors were created based on relevant scientific literature and were inserted in the INKA software to develop the scenarios. This process resulted in three clearly differentiated scenarios that exhibit high probabilities and positive outlook for the Artificial Intelligence technology to be widely integrated in supply chain risk management systems in Europe by 2035.
All the companies need to plan and budget for future. For planning they need sale forecasting so that accordingly they can manage their supply chain efficiently. Companies do have historical data which can be used for forecasting sale. However, the accuracy of the predictive model depends on the quality of data which is being fed to the model. Poor data quality may result in poor forecasting. Hence, there is need to work on data quality management and to formulate some generic approach for ensuring data quality. Besides, it is also required to detect abnormal sale from the past data, get the reason for those abnormal sale records and remove them from the data. Subsequently, cleaned data can be used to work on predictive modelling which will forecast sales with the most likelihood of near to accurate results. These historical data can be analyzed as a time series data by using as simple time series analysis as ARIMA or by using complicated neural network. Evaluation of these predictive models will help in making a decision of selecting a best fitted model for future forecasting. The thesis aims to work on data quality management of raw data and then analyze time series data to determine predictive model for forecasting. Besides, thesis also aims to understand how data is collected and how organization performs sales processes. This would not only facilitate in finding and bridging the gaps in the business processes but also in preparing the organization for the state-of-the-art technologies to enhance their business for future.