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Autonomous cars and artificial intelligence that beats humans in Jeopardy or Go are glamorous examples of the so-called Second Machine Age that involves the automation of cognitive tasks [Brynjolfsson and McAfee, 2014]. However, the larger impact in terms of increasing the efficiency of industry and the productivity of society might come from computers that improve or take over business decisions by using large amounts of available data. This impact may even exceed that of the First Machine Age, the industrial revolution that started with James Watt’s invention of an efficient steam engine in the late eighteenth century. Indeed, the prevalent phrase that calls data “the new oil” indicates the growing awareness of data’s importance. However, many companies, especially those in the manufacturing and traditional service industries, still struggle to increase productivity using the vast amounts of
data [for Economic Co-operation and Development, 2018].
One reason for this struggle is that companies stick with a traditional way of using data for decision support in operations management that is not well suited to automated decision-making. In traditional inventory and capacity management, some data – typically just historical demand data – is used to estimate a model that makes predictions about uncertain planning parameters, such as customer demand. The planner then has two tasks: to adjust the prediction with respect to additional information that was not part of the data but still might influence demand and to take the remaining uncertainty into account and determine a safety buffer based on the underage and overage costs. In the best case, the planner determines the safety buffer based on an optimization model that takes the costs and the distribution of historical forecast errors into account; however, these decisions are usually based on a planner’s experience and intuition, rather than on solid data analysis.
This two-step approach is referred to as separated estimation and optimization (SEO). With SEO, using more data and better models for making the predictions would improve only the first step, which would still improve decisions but would not automize (and, hence, revolutionize) decision-making. Using SEO is like using a stronger horse to pull the plow: one still has to walk behind.
The real potential for increasing productivity lies in moving from predictive to prescriptive approaches, that is, from the two-step SEO approach, which uses predictive models in the estimation step, to a prescriptive approach, which integrates the optimization problem with the estimation of a model that then provides a direct functional relationship between the data and the decision. Following Akcay et al. [2011], we refer to this integrated approach as joint estimation-optimization (JEO). JEO approaches prescribe decisions, so they can automate the decision-making process. Just as the steam engine replaced manual work, JEO approaches replace cognitive work.
The overarching objective of this dissertation is to analyze, develop, and evaluate new ways for how data can be used in making planning decisions in operations management to unlock the potential for increasing productivity. In doing so, the thesis comprises five self-contained research articles that forge the bridge from predictive to prescriptive approaches. While the first article focuses on how sensitive data like condition data from machinery can be used to make predictions of spare-parts demand, the remaining articles introduce, analyze, and discuss prescriptive approaches to inventory and capacity management.
All five articles consider approach that use machine learning and data in innovative ways to improve current approaches to solving inventory or capacity management problems. The articles show that, by moving from predictive to prescriptive approaches, we can improve data-driven operations management in two ways: by making decisions more accurate and by automating decision-making. Thus, this dissertation provides examples of how digitization and the Second Machine Age can change decision-making in companies to increase efficiency and productivity.
Ongoing changes in spaceflight – continuing miniaturization, declining costs of rocket launches and satellite components, and improved satellite computing and control capabilities – are advancing Satellite Formation Flying (SFF) as a research and application area. SFF enables new applications that cannot be realized (or cannot be realized at a reasonable cost) with conventional single-satellite missions. In particular, distributed Earth observation applications such as photogrammetry and tomography or distributed space telescopes require precisely placed and controlled satellites in orbit.
Several enabling technologies are required for SFF, such as inter-satellite communication, precise attitude control, and in-orbit maneuverability. However, one of the most important requirements is a reliable distributed Guidance, Navigation and Control (GNC) strategy. This work addresses the issue of distributed GNC for SFF in 3D with a focus on Continuous Low-Thrust (CLT) propulsion satellites (e.g., with electric thrusters) and concentrates on circular low Earth orbits. However, the focus of this work is not only on control theory, but control is considered as part of the system engineering process of typical small satellite missions. Thus, common sensor and actuator systems are analyzed to derive their characteristics and their impacts on formation control. This serves as the basis for the design, implementation, and evaluation of the following control approaches: First, a Model Predictive Control (MPC) method with specific adaptations to SFF and its requirements and constraints; second, a distributed robust controller that combines consensus methods for distributed system control and $H_{\infty}$ robust control; and finally, a controller that uses plant inversion for control and combines it with a reference governor to steer the controller to the target on an optimal trajectory considering several constraints. The developed controllers are validated and compared based on extensive software simulations. Realistic 3D formation flight scenarios were taken from the Networked Pico-Satellite Distributed System Control (NetSat) cubesat formation flight mission. The three compared methods show different advantages and disadvantages in the different application scenarios. The distributed robust consensus-based controller for example lacks the ability to limit the maximum thrust, so it is not suitable for satellites with CLT. But both the MPC-based approach and the plant inversionbased controller are suitable for CLT SFF applications, while showing again distinct advantages and disadvantages in different scenarios.
The scientific contribution of this work may be summarized as the creation of novel and specific control approaches for the class of CLT SFF applications, which is still lacking methods withstanding the application in real space missions, as well as the scientific evaluation and comparison of the developed methods.
Modern agriculture is the basis of human existence, a blessing, but also a curse. It provides nourishment and well-being to the ever-growing human population, yet destroys biodiversity-mediated processes that underpin productivity: ecosystem services such as water filtration, pollination and biological pest control. Ecological intensification is a promising alternative to conventional farming, and aims to sustain yield and ecosystem health by actively managing biodiversity and essential ecosystem services. Here, I investigate opportunities and obstacles for ecological intensification. My research focuses on 1) the relative importance of soil, management and landscape variables for biodiversity and wheat yield (Chapter II); 2) the influence of multi-scale landscape-level crop diversity on biological pest control in wheat (Chapter III) and 3) on overall and functional bird diversity (Chapter IV). I conclude 4) by introducing a guide that helps scientists to increase research impact by acknowledging the role of stakeholder engagement for the successful implementation of ecological intensification (Chapter V).
Ecological intensification relies on the identification of natural pathways that are able to sustain current yields. Here, we crossed an observational field study of arthropod pests and natural enemies in 28 real-life wheat systems with an orthogonal on-field insecticide-fertilizer experiment. Using path analysis, we quantified the effect of 34 factors (soil characteristics, recent and historic crop management, landscape heterogeneity) that directly or indirectly (via predator-prey interactions) contribute to winter wheat yield. Reduced soil preparation and high crop rotation diversity enhanced crop productivity independent of external agrochemical inputs. Concurrently, biological control by arthropod natural enemies could be restored by decreasing average field sizes on the landscape scale, extending crop rotations and reducing soil disturbance. Furthermore, reductions in agrochemical inputs decreased pest abundances, thereby facilitating yield quality.
Landscape-level crop diversity is a promising tool for ecological intensification. However, biodiversity enhancement via diversification measures does not always translate into agricultural benefits due to antagonistic species interactions (intraguild predation). Additionally, positive effects of crop diversity on biological control may be masked by inappropriate study scales or correlations with other landscape variables (e.g. seminatural habitat). Therefore, the multiscale and context-dependent impact of crop diversity on biodiversity and ecosystem services is ambiguous. In 18 winter wheat fields along a crop diversity gradient, insect- and bird-mediated pest control was assessed using a natural enemy exclusion experiment with cereal grain aphids. Although birds did not influence the strength of insect-mediated pest control, crop diversity (rather than seminatural habitat cover) enhanced aphid regulation by up to 33%, particularly on small spatial scales. Crop diversification, an important Greening measure in the European Common Agricultural Policy, can improve biological control, and could lower dependence on insecticides, if the functional identity of crops is taken into account. Simple measures such as ‘effective number of crop types’ help in science communication.
Although avian pest control did not respond to landscape-level crop diversity, birds may still benefit from increased crop resources in the landscape, depending on their functional grouping (feeding guild, conservation status, habitat preference, nesting behaviour). Observational studies of bird functional diversity on 14 wheat study fields showed that non-crop landscape heterogeneity rather than crop diversity played a key role in determining the richness of all birds. Insect-feeding, non-farmland and non-threatened birds increased across multiple spatial scales (up to 3000 m). Only crop-nesting farmland birds declined in heterogeneous landscapes. Thus, crop diversification may be less suitable for conserving avian diversity, but abundant species benefit from overall habitat heterogeneity. Specialist farmland birds may require more targeted management approaches.
Identifying ecological pathways that favour biodiversity and ecosystem services provides opportunities for ecological intensification that increase the likelihood of balancing conservation and productivity goals. However, change towards a more sustainable agriculture will be slow to come if research findings are not implemented on a global scale. During dissemination activities within the EU project Liberation, I gathered information on the advantages and shortcomings of ecological intensification and its implementation. Here, I introduce a guide (‘TREE’) aimed at scientists that want to increase the impact of their research. TREE emphasizes the need to engage with stakeholders throughout the planning and research process, and actively seek and promote science dissemination and knowledge implementation. This idea requires scientists to leave their comfort zone and consider socioeconomic, practical and legal aspects often ignored in classical research.
Ecological intensification is a valuable instrument for sustainable agriculture. Here, I identified new pathways that facilitate ecological intensification. Soil quality, disturbance levels and spatial or temporal crop diversification showed strong positive correlations with natural enemies, biological pest control and yield, thereby lowering the dependence on agrochemical inputs. Differences between functional groups caused opposing, scale-specific responses to landscape variables. Opposed to our predictions, birds did not disturb insect-mediated pest control in our study system, nor did avian richness relate to landscape-level crop diversity. However, dominant functional bird groups increased with non-crop landscape heterogeneity. These findings highlight the value of combining different on-field and landscape approaches to ecological intensification. Concurrently, the success of ecological intensification can be increased by involving stakeholders throughout the research process. This increases the quality of science and reduces the chance of experiencing unscalable obstacles to implementation.