Dynamic Procurement of New Products with Covariate Information: The Residual Tree Method

Problem definition: We study the practice-motivated problem of dynamically procuring a new, short life-cycle product under demand uncertainty. The firm does not know the demand for the new product but has data on similar products sold in the past, including demand histories and covariate information such as product characteristics. Academic/practical relevance: The dynamic procurement problem has long attracted academic and practitioner interest, and we solve it in an innovative data-driven way with proven theoretical guarantees. This work is also the first to leverage the power of covariate data in solving this problem. Methodology:We propose a new, combined forecasting and optimization algorithm called the Residual Tree method, and analyze its performance via epi-convergence theory and computations. Our method generalizes the classical Scenario Tree method by using covariates to link historical data on similar products to construct demand forecasts for the new product. Results: We prove, under fairly mild conditions, that the Residual Tree method is asymptotically optimal as the size of the data set grows. We also numerically validate the method for problem instances derived using data from the global fashion retailer Zara. We find that ignoring covariate information leads to systematic bias in the optimal solution, translating to a 6–15% increase in the total cost for the problem instances under study. We also find that solutions based on trees using just 2–3 branches per node, which is common in the existing literature, are inadequate, resulting in 30–66% higher total costs compared with our best solution. Managerial implications: The Residual Tree is a new and generalizable approach that uses past data on similar products to manage new product inventories. We also quantify the value of covariate information and of granular demand modeling

Architecting Networked Engineering Systems

The primary goal in this dissertation is to create a new knowledge, make a transformative influence in the design of networked engineering systems adaptable to ambitious market demands, and to accommodate the Industry 4.0 design principles based on the philosophy that design is fundamentally a decision making process. The principal motivation in this dissertation is to establish a computational framework that is suitable for the design of low-cost and high-quality networked engineering systems adaptable to ambitious market demands in the context of Industry 4.0. Dynamic and ambitious global market demands make it necessary for competitive enterprises to have low-cost manufacturing processes and high-quality products. Smart manufacturing is increasingly being adopted by companies to respond to changes in the market. These smart manufacturing systems must be adaptable to dynamic changes and respond to unexpected disturbances, and uncertainty. Accordingly, a decision-based design computational framework, Design for Dynamic Management (DFDM), is proposed as a support to flexible, operable and rapidly configurable manufacturing processes. DFDM has three critical components: adaptable and concurrent design, operability analysis and reconfiguration strategies. Adaptable and concurrent design methods offer flexibility in selection of design parameters and the concurrent design of the mechanical and control systems. Operability analysis is used to determine the functionality of the system undergoing dynamic change. Reconfiguration strategies allow multiple configurations of elements in the system. It is expected that proposed computational framework results in next generation of networked engineering systems, where tools and sensors communicate with each other via the Internet of Things (IoT), sensors data would be used to create enriched digital system models, adaptable to fast-changing market requirements, which can produce higher quality products over a longer lifetime and at a lower cost. The computational framework and models proposed in this dissertation are applicable in system design, and/or product-service system design. This dissertation is a fundamental research and a way forward is DFDM transition to the industry through decision-based design platform. Decision-based design platform is a step toward new frontiers, Cyber-Physical-Social System Design, Manufacturing, and Services, contributing to further digitization

Essays on the effective integration of risk management with operations management decisions

textIn today's marketplace, firms' exposure to business uncertainties and risks are continuously increasing as they strive to meet dynamically changing customer needs under intensifying competitive pressures. Consequently, modern supply chains are continuously evolving to effectively manage these uncertainties and the allied risks through both operational and financial hedging strategies. In practice, firms extensively use operational hedging strategies such as operational flexibility, capacity flexibility, postponement, multi-sourcing, supplier diversification, component commonality, substitutability, transshipments and holding excess stocks as operational means for risk management. On the other hand, financial hedging which involves buying and selling financial instruments, carrying large cash reserves or adopting conservative financial policies, changes the cash flow stream of the firms and may help to reduce the firms exposure to business risks and uncertainties. Overall, in this dissertation we explore how risk management can be integrated with operating decisions so as to improve the firm value creating more wealth for the shareholders. In the first essay, we focus on capacity flexibility as a means of operational hedging for risk management in an MTO production environment under demand uncertainty. We demonstrate that capacity flexibility may not only be used to hedge against the demand uncertainty, but may also be employed to effectively protect against possible suboptimal operating decisions in the future. In the second essay, we focus on operational hedging in financially constrained startup firms when making short-term production and long-term investment decisions. We provide an analytical characterization of the optimal investment and operating decisions and analyze the impact of market parameters on the operations of the firm. Our findings highlight an interesting operational hedging behavior between the process investment decisions and the short-term production commitments of the firm when they are faced with financial constraints. Our third essay focuses on the value of integrated financial risk management activities by publicly traded established firms under the risk of incurring financial distress cost. Different from the existing operations management literature, we study the risk management by a public corporation within the value framework of finance; hence our findings do not require any specific assumptions about the investors' utility functions. Moreover, we contribute to the operations management research by examining the impact of the costs of financial distress on hedging and operating plans of the firm. Overall, in this dissertation, we examine the effective integration of operational and financial risk management so as to improve the firm value creating more wealth for the shareholders.Information, Risk, and Operations Management (IROM

Multi-stage stochastic and robust optimization for closed-loop supply chain design

This dissertation focuses on formulating and solving multi-stage decision problems in uncertain environments using stochastic programming and robust optimization approaches. These approaches are applied to the design of closed-loop supply chain (CLSC) networks, which integrate both traditional flow and the reverse flow of products. The uncertainties associated with this application include forward demands, the quantity and quality of used products to be collected, and the carbon tax rate. The design decisions include long-term facility configurations as well as short-term contracts for transportation capacities by various modes that differ according to their variable costs, fixed costs, and emission rates. This dissertation consists of three papers. The first paper develops a multi-stage stochastic program for a CLSC network design problem with demands and quality of return uncertainties. The second paper focuses on robust optimization; particularly, the question of whether an adjustable robust counterpart (ARC) produces less conservative solutions than the robust counterpart (RC). Using the results of the second paper, a three-stage hybrid robust/stochastic program is proposed in the third paper, in which an ARC is formulated for a mixed integer linear programming model of the CLSC network design problem. In the first paper, a multi-stage stochastic program is proposed for the CLSC network design problem where facility locations are decided in the first stage and in subsequent stages, the capacities of transportation of different modes are contracted under uncertainty about the amounts of new and return products to transport among facilities. We explore the impact of the uncertain quality of returned products as well as uncertain demands with dependencies between periods. We investigate the stability of the solution obtained from scenario trees of varying granularity using a moment matching method for demands and distribution approximation for the quality of returns. Multi-stage solutions are evaluated in out-of-sample tests using simulated historical data and also compared with two-stage model. We observe an instance of overfitting, in which a scenario tree including more outcomes at each stage produces a dramatically different solution that has slightly higher average cost, compared to the solution from a less granular tree, when evaluated against the underlying simulated historical data. We also show that when the scenarios include demand dependencies, the solution performs better in out-of-sample simulation. In the second paper, the ARC of an uncertain linear program extends the RC by allowing some decision variables to adjust to the realizations of some uncertain parameters. The ARC may produce a less conservative solution than the RC does but cases are known in which it does not. While the literature documents some examples of cost savings provided by adjustability (particularly affine adjustability), it is not straightforward to determine in advance whether they will materialize. We establish conditions under which adjustability may lower the optimal cost with a numerical condition that can be checked in small representative instances. The provided conditions include the presence of at least two binding constraints at optimality of the RC formulation, and an adjustable variable that appears in both constraints with implicit bounds from above and below provided by different extreme values in the uncertainty set. The third paper concerns a CLSC network that is subject to uncertainty in demands for both new and returned products. The model structure also accommodates uncertainty in the carbon tax rate. The proposed model combines probabilistic scenarios for the demands and return quantities with an uncertainty set for the carbon tax rate. We constructed a three-stage hybrid robust/stochastic program in which the first stage decisions are long-term facility configurations, the second stage concerns the plan for distributing new and collecting returned products after realization of demands and returns but before realization of the carbon tax rate, and the numbers of transportation units of various modes, as the third stage decisions, are adjustable to the realization of the carbon tax level. For computational tractability, we restrict the transportation capacities to be affine functions of the carbon tax rates. By utilizing our findings in the second paper, we found conditions under which the ARC produces a less conservative solution. To solve the affinely adjustable version, Benders cuts are generated using recent duality developments for robust linear programs. Computational results show that the ability to adjust transportation mode capacities can substitute for building additional facilities as a way to respond to carbon tax uncertainty. The number of opened facilities in ARC solutions are decreased under uncertainty in demands and returns. The results confirm the reduction of total expected cost in the worst case of the carbon tax rate by increasing utilization of transportation modes with higher capacity per unit and lower emission rate

Simplexity: A Hybrid Framework for Managing System Complexity

Knowledge management, management of mission critical systems, and complexity management rely on a triangular support connection. Knowledge management provides ways of creating, corroborating, collecting, combining, storing, transferring, and sharing the know-why and know-how for reactively and proactively handling the challenges of mission critical systems. Complexity management, operating on “complexity” as an umbrella term for size, mass, diversity, ambiguity, fuzziness, randomness, risk, change, chaos, instability, and disruption, delivers support to both knowledge and systems management: on the one hand, support for dealing with the complexity of managing knowledge, i.e., furnishing criteria for a common and operationalized terminology, for dealing with mediating and moderating concepts, paradoxes, and controversial validity, and, on the other hand, support for systems managers coping with risks, lack of transparence, ambiguity, fuzziness, pooled and reciprocal interdependencies (e.g., for attaining interoperability), instability (e.g., downtime, oscillations, disruption), and even disasters and catastrophes. This support results from the evident intersection of complexity management and systems management, e.g., in the shape of complex adaptive systems, deploying slack, establishing security standards, and utilizing hybrid concepts (e.g., hybrid clouds, hybrid procedures for project management). The complexity-focused manager of mission critical systems should deploy an ambidextrous strategy of both reducing complexity, e.g., in terms of avoiding risks, and of establishing a potential to handle complexity, i.e., investing in high availability, business continuity, slack, optimal coupling, characteristics of high reliability organizations, and agile systems. This complexity-focused hybrid approach is labeled “simplexity.” It constitutes a blend of complexity reduction and complexity augmentation, relying on the generic logic of hybrids: the strengths of complexity reduction are capable of compensating the weaknesses of complexity augmentation and vice versa. The deficiencies of prevalent simplexity models signal that this blended approach requires a sophisticated architecture. In order to provide a sound base for coping with the meta-complexity of both complexity and its management, this architecture comprises interconnected components, domains, and dimensions as building blocks of simplexity as well as paradigms, patterns, and parameters for managing simplexity. The need for a balanced paradigm for complexity management, capable of overcoming not only the prevalent bias of complexity reduction but also weaknesses of prevalent concepts of simplexity, serves as the starting point of the argumentation in this chapter. To provide a practical guideline to meet this demand, an innovative model of simplexity is conceived. This model creates awareness for differentiating components, dimensions, and domains of complexity management as well as for various species of interconnectedness, such as the aligned upsizing and downsizing of capacities, the relevance of diversity management (e.g., in terms of deviations and errors), and the scope of risk management instruments. Strategies (e.g., heuristics, step-by-step procedures) and tools for managing simplexity-guided projects are outlined

Long-Term Tree Regeneration of Fragmented Agroforestry Systems Under Varying Climatic Conditions

Iberian dehesas and montados are agroforestry systems protected by the European Habitats Directive due to high levels of biological diversity associated to their savannah-like structure. Tree scattering in dehesas, montados and other agroforestry systems is, however, known to compromise tree regeneration, although recent work suggests that it may protect tree populations from climate warming by alleviating plant-plant competition. We analyze how climatic conditions, tree isolation and their interactions influence the outcomes of regeneration stages, from flower production to early seedling establishment, using data gathered during the long-term monitoring (2001–2018) of ca. 300 Holm oak Quercus ilex trees located in central Spain. Holm oak reproductive effort, predispersal seed losses, and early seedling recruitment were sensitive to climate change, especially to year-round drought. Effort and early seedling recruitment decreased, while abortion and predispersal seed predation increased, with higher drought intensity. Spring warming increases pollination effectiveness, but had no further effect on acorn crops. Forest clearing seemed to have little scope to ameliorate these negative effects, as shown by weak or no interactive effects between the spatial configuration of trees (cover or isolation) and climate variables (spring temperature or drought intensity). Forest opening aimed at decreasing adult tree mortality under climate change scenarios would then have little or no effects on tree recruitment. Landscape-scale rotations alternating shrub encroachment and thinning along periods adapted to changing climate are proposed as the main management option to preserve both oak forests and dehesas in the long term.Fil: Díaz, Mario. Consejo Superior de Investigaciones Científicas. Museo Nacional de Ciencias Naturales; EspañaFil: Sánchez Mejía, Teresa. Consejo Superior de Investigaciones Científicas. Museo Nacional de Ciencias Naturales; EspañaFil: Morán López, Teresa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentin

Program on Earth Observation Data Management Systems (EODMS), appendixes

The needs of state, regional, and local agencies involved in natural resources management in Illinois, Iowa, Minnesota, Missouri, and Wisconsin are investigated to determine the design of satellite remotely sensed derivable information products. It is concluded that an operational Earth Observation Data Management System (EODMS) will be most beneficial if it provides a full range of services - from raw data acquisition to interpretation and dissemination of final information products. Included is a cost and performance analysis of alternative processing centers, and an assessment of the impacts of policy, regulation, and government structure on implementing large scale use of remote sensing technology in this community of users

Threat Assessment for Multistage Cyber Attacks in Smart Grid Communication Networks

In smart grids, managing and controlling power operations are supported by information and communication technology (ICT) and supervisory control and data acquisition (SCADA) systems. The increasing adoption of new ICT assets in smart grids is making smart grids vulnerable to cyber threats, as well as raising numerous concerns about the adequacy of current security approaches. As a single act of penetration is often not sufficient for an attacker to achieve his/her goal, multistage cyber attacks may occur. Due to the interdependence between the power grid and the communication network, a multistage cyber attack not only affects the cyber system but impacts the physical system. This thesis investigates an application-oriented stochastic game-theoretic cyber threat assessment framework, which is strongly related to the information security risk management process as standardized in ISO/IEC 27005. The proposed cyber threat assessment framework seeks to address the specific challenges (e.g., dynamic changing attack scenarios and understanding cascading effects) when performing threat assessments for multistage cyber attacks in smart grid communication networks. The thesis looks at the stochastic and dynamic nature of multistage cyber attacks in smart grid use cases and develops a stochastic game-theoretic model to capture the interactions of the attacker and the defender in multistage attack scenarios. To provide a flexible and practical payoff formulation for the designed stochastic game-theoretic model, this thesis presents a mathematical analysis of cascading failure propagation (including both interdependency cascading failure propagation and node overloading cascading failure propagation) in smart grids. In addition, the thesis quantifies the characterizations of disruptive effects of cyber attacks on physical power grids. Furthermore, this thesis discusses, in detail, the ingredients of the developed stochastic game-theoretic model and presents the implementation steps of the investigated stochastic game-theoretic cyber threat assessment framework. An application of the proposed cyber threat assessment framework for evaluating a demonstrated multistage cyber attack scenario in smart grids is shown. The cyber threat assessment framework can be integrated into an existing risk management process, such as ISO 27000, or applied as a standalone threat assessment process in smart grid use cases