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The purpose of this paper is to present recent empirical results concerning offshoring and backshoring of manufacturing from and to Sweden, to increase the understanding of manufacturing relocation in an international context. In particular, extent, geographies, type of production, drivers, and benefits of moving manufacturing in both directions are investigated.

The study is based on survey data from 373 manufacturing plants. The same set of questions is used for both offshoring and backshoring between 2010 and 2015, which allows similarities and differences in decision-making and results between the two relocation directions to be identified.

There are many significant differences between offshoring and backshoring projects. Labour cost is the dominating factor in offshoring, as driver and benefit, while backshoring is related to many drivers and benefits, such as quality, lead-time, flexibility, access to skills and knowledge, access to technology, and proximity to R&D. This is also reflected in the type of production that is relocated; labour-intensive production is offshored and complex production is backshored.

Plants that have both offshored and backshored think and act differently than plants that have only offshored or backshored, which is why it is important to distinguish between these plant types in the context of manufacturing relocations.

The experience of Swedish manufacturing plants reported here can be used as a point of reference for internal manufacturing operations.

The survey design allows a unique comparison between offshoring and backshoring activity. Since Swedish firms in general have been quite active in rearranging their manufacturing footprint and have experience from movements in both directions, it is an appropriate geographical area to study in this context.

This paper proposes a heuristic, data-driven approach to the rapid performance evaluation of periodic maintenance on complex production plants. Through grouping, maintenance interval (MI)-based evaluation and performance assessment, potential nonvalue-adding maintenance elements can be identified in the current maintenance structure. The framework reduces management complexity and supports the decision-making process for further maintenance improvement.

The evaluation framework follows a prescriptive research approach. The framework is structured in three steps, which are further illustrated in the case study. The case study utilizes real-life data to verify the feasibility and effectiveness of the proposed framework.

Through a case study conducted on 9,538 pieces of equipment from eight offshore oil and gas production platforms, the results show considerable potential for maintenance performance improvement, including up to a 23% reduction in periodic maintenance hours.

The problem of performance evaluation under limited data availability has barely been addressed in the literature on the plant level. The proposed framework aims to provide a quantitative approach to reducing the structural complexity of the periodic maintenance evaluation process and can help maintenance professionals prioritize the focus on maintenance improvement among current strategies.

The proposed framework is especially suitable for initial performance assessment in systems with a complex structure, limited maintenance records and imperfect data, as it reduces management complexity and supports the decision-making process for further maintenance improvement. A similar application has not been identified in the literature.

This paper reviews and analyses renewable energy options, namely underground thermal, solar, wind and marine wave energy, in seaport cargo terminal operations.

Four renewable energy options that are deployed or tested in different ports around the world are qualitatively examined for their overall implementation potential and characteristics, and their cost and benefits. An application to the port of Singapore is discussed.

Geophysical conditions are key criteria in assessing renewable energy options. In the case of Singapore, solar power is the only suitable renewable energy option.

Being a capital-intensive establishment with high intensities of cargo operations, seaports usually involve a high level of energy consumption. The study of renewable energy options contributes to seaport sustainability.

A key recommendation is to implement a smart energy management system that enables the mixed use of renewable energy to match energy demand and supply optimally and achieve higher energy efficiency.

The use of renewable energy as an eco-friendlier energy source is underway in various ports. However, there is almost no literature that analyses and compares various renewable energy options potentially suitable for cargo terminal operations in ports. This paper narrows the knowledge gaps.

The paper aims to present an integrated foresight framework and method to support decision-makers who are confronted with today’s complex and rapidly changing world. The method aims at reducing the degree of uncertainty by addressing the inertia or duration of unfolding trends and by placing individual trends in a broader context.

The paper presents a three-layered framework and method for assessing megatrends based on their inertia or duration. It suggests that if long-term trends and key future uncertainties are studied in conjunction at a meta-level and placed in a broader multi-layered framework of trends, it can result in new insights.

The application of the proposed foresight method helps to systematically place a wide range of unrelated trends and key uncertainties in the context of a broader framework of trends, thereby improving the ability to understand the inertia, direction and mutual interaction of these trends.

The elaboration of identified trends and key uncertainties is partly case-specific and subject to interpretation. It is aimed at illustrating the potential use of the framework.

The paper presents a new approach that may, by itself or in combination with existing foresight methods, offer new means for anticipating future developments.

The use of the proposed framework has potential to provide better insight in the complexity of today’s rapid-changing world and the major transitions taking place. It aims to result in sharper foresight by reducing epistemic uncertainty for decision-makers.

The paper demonstrates how megatrends, Kondratieff waves and century-long trends can be placed in an integrated framework and analysed in conjunction.

This paper aims to offer a long-term systematic picture of the evolution of manufacturing offshoring (in terms of intensity, geography and drivers) highlighting the changes in the surrounding context and the resulting transitions points (“points in time”) that have shaped its development path.

Three statistical tools were adopted on a dataset of 644 cases. First, the authors resorted to multiple structural change tests to identify the transition points. Second, the authors explored offshoring geography by conducting a network analysis. Finally, the authors adopted gravity models to shed light on offshoring drivers.

Results highlight three offshoring phases: expansion (2002–2006), reconsideration (2007–2009) and rationalization (2010 onwards). During the first phase, characterized by economic growth, firms were mainly interested in economic savings; offshoring to low-cost countries was the prevailing location strategy. Subsequently, during the economic crisis, the number of cases declined and the main drivers became market-based factors together with the research for cost savings. Finally, in the third phase, when the economy was still stagnating and new manufacturing technologies appeared, the number of offshoring cases has further decreased, and technological- and market-based factors have become the main location drivers.

The study is the first to adopt a systematic, empirical and quantitative approach to analyze the evolution of the manufacturing offshoring considering both the phenomenon itself and the triggering changes in the surrounding context. In doing this, the authors also tested the importance of considering the point in time in offshoring strategies.