Start-up manufacturing firms: operations for survival

Start-up firms play an important role in the economy. Statistics show that a large percent of start-up firms fail after few years of establishment. Raising capital, which is crucial to success, is one of the difficulties start-up firms face. This Ph.D thesis aims to draw suggestions for start-up firm survival from mathematical models and numerical investigations. Instead of the commonly held profi t maximizing objective, this thesis assumes that a start-up firm aims to maximize its survival probability during the planning horizon. A firm fails if it runs out of capital at a solvency check. Inventory management in manufacturing start-up firms is discussed further with mathematical theories and numerical illustrations, to gain insight of the policies for start-up firms. These models consider specific inventory problems with total lost sales, partial backorders and joint inventory-advertising decisions. The models consider general cost functions and stochastic demand, with both lead time zero and one cases. The research in this thesis provides quantitative analysis on start-up firm survival, which is new to the literature. From the results, a threshold exists on the initial capital requirement to start-up firms, above which the increase of capital has little effect on survival probability. Start-up firms are often risk-averse and cautious about spending. Entering the right niche market increases their chance of survival, where the demand is more predictable, and start-ups can obtain higher backorder rates and product price. Sensitivity tests show that selling price, purchasing price and overhead cost have the most impact on survival probability. Lead time has a negative effect on start-up firms, which can be offset by increasing the order frequent. Advertising, as an investment in goodwill, can increase start-up firms' survival. The advertising strategies vary according to both goodwill and inventory levels, and the policy is more flexible in start-up firms. Externally, a slightly less frequency solvency check gives start-up firms more room for fund raising and/or operation adjustment, and can increase the survival probability. The problems are modelled using Markov decision processes, and numerical illustrations are implemented in Java

Terrorism affected regions : the impact of different supply chain risk management strategies on financial performance

Purpose: Current geo-political events, such as terrorism and climatologic adversities, have highlighted the potential risks to supply chains (SCs), and their disastrous financial impacts on supply chains. Within supply chains, risk management plays a major role in successfully managing business processes in a proactive manner and ensuring the business continuity and financial performance (FP). The purpose of this study is to explore the supply chain risks and strategies in a terrorism-affected region (TAR), and to examine supply chain risk management (SCRM) strategies and their impacts on FP, including the war on terror (WoT) and its impacts on the local logistics industry. In addition, this study investigates the knowledge gaps in the published research on terrorism-related risk in supply chains, and develops a framework of strategies and effective decision-making to enable practitioners to address terrorism-related risks for SCRM.Methodology: The study initially adopts a novel combination of triangulated methods comprising a systematic literature review, text mining, and network analysis. Additionally, risk identification, risk analysis and strategies scrutiny are conducted by using semi-structured interviews and Qualitative Content Analysis in a TAR. A model of strategies was developed from a review of existing studies and interviews. The model is empirically tested with survey data of 80 firms using fuzzy-set Qualitative Comparative Analysis (fsQCA).Findings: This study reveals a number of key themes in the field of SCRM linked with terrorism. It identifies relevant mitigation strategies and practices for effective strategic decision-making. This subsequently leads to development of a strategic framework, consisting of strategies and effective-decision making practices to address terrorism-related risks that affect SCRM. It also identifies key the knowledge gaps in the literature and explores the main contributions by disciplines (e.g., business schools, engineering, and maritime institutions) and countries.Further, it identifies the SC risks in a TAR, which consist of value streams: disruption risks, operational risks and financial risks. Among these, the emerging risks emcompass terrorist groups’ demand for protection money, smog, paedophilia and the use of containers to block protesters. To mitigate these risks, firms frequently implemented the following strategies: information sharing, SC coordination, risk sharing, SC finance, SC security and facilitation payment. Five strategies out of the six (except facilitation payment) are able to lead to FP, confirmed quantitatively as well. There are various equifinal configurations of SCRM strategies leading to FP. In addition, information sharing acts as a moderator in the relationship between SC security and FP. SC coordination has a mediating role in the relationship between information sharing and SC security capabilities and FP.Research limitations/Contribution: The sample size a limitation of the study, meaning that the findings should be generalized with caution. The most valuable implications is the identification of configurations of strategies that can help managers and policymakers in implementing those findings.Originality/value: No empirical study was found in the SCRM literature that specifically investigates the relationships between the identified strategies and FP with fsQCA, in particular in a TAR context; this study thus fills an important gap in the SCRM literature and contributes empirically

Augmented Human Machine Intelligence for Distributed Inference

With the advent of the internet of things (IoT) era and the extensive deployment of smart devices and wireless sensor networks (WSNs), interactions of humans and machine data are everywhere. In numerous applications, humans are essential parts in the decision making process, where they may either serve as information sources or act as the final decision makers. For various tasks including detection and classification of targets, detection of outliers, generation of surveillance patterns and interactions between entities, seamless integration of the human and the machine expertise is required where they simultaneously work within the same modeling environment to understand and solve problems. Efficient fusion of information from both human and sensor sources is expected to improve system performance and enhance situational awareness. Such human-machine inference networks seek to build an interactive human-machine symbiosis by merging the best of the human with the best of the machine and to achieve higher performance than either humans or machines by themselves. In this dissertation, we consider that people often have a number of biases and rely on heuristics when exposed to different kinds of uncertainties, e.g., limited information versus unreliable information. We develop novel theoretical frameworks for collaborative decision making in complex environments when the observers may include both humans and physics-based sensors. We address fundamental concerns such as uncertainties, cognitive biases in human decision making and derive human decision rules in binary decision making. We model the decision-making by generic humans working in complex networked environments that feature uncertainties, and develop new approaches and frameworks facilitating collaborative human decision making and cognitive multi-modal fusion. The first part of this dissertation exploits the behavioral economics concept Prospect Theory to study the behavior of human binary decision making under cognitive biases. Several decision making systems involving humans\u27 participation are discussed, and we show the impact of human cognitive biases on the decision making performance. We analyze how heterogeneity could affect the performance of collaborative human decision making in the presence of complex correlation relationships among the behavior of humans and design the human selection strategy at the population level. Next, we employ Prospect Theory to model the rationality of humans and accurately characterize their behaviors in answering binary questions. We design a weighted majority voting rule to solve classification problems via crowdsourcing while considering that the crowd may include some spammers. We also propose a novel sequential task ordering algorithm to improve system performance for classification in crowdsourcing composed of unreliable human workers. In the second part of the dissertation, we study the behavior of cognitive memory limited humans in binary decision making and develop efficient approaches to help memory constrained humans make better decisions. We show that the order in which information is presented to the humans impacts their decision making performance. Next, we consider the selfish behavior of humans and construct a unified incentive mechanism for IoT based inference systems while addressing the selfish concerns of the participants. We derive the optimal amount of energy that a selfish sensor involved in the signal detection task must spend in order to maximize a certain utility function, in the presence of buyers who value the result of signal detection carried out by the sensor. Finally, we design a human-machine collaboration framework that blends both machine observations and human expertise to solve binary hypothesis testing problems semi-autonomously. In networks featuring human-machine teaming/collaboration, it is critical to coordinate and synthesize the operations of the humans and machines (e.g., robots and physical sensors). Machine measurements affect human behaviors, actions, and decisions. Human behavior defines the optimal decision-making algorithm for human-machine networks. In today\u27s era of artificial intelligence, we not only aim to exploit augmented human-machine intelligence to ensure accurate decision making; but also expand intelligent systems so as to assist and improve such intelligence

Stochastic Dynamic Optimization Models for Societal Resource Allocation

We study a class of stochastic resource allocation problems that specifically deals with effective utilization of resources in the interest of social value creation. These problems are treated as a separate class of problems mainly due to the nonprofit nature of the application areas, as well as the abstract structure of social value definition. As part of our analysis of these unique characteristics in societal resource allocation, we consider two major application areas involving such decisions. The first application area deals with resource allocations for foreclosed housing acquisitions as part of the response to the foreclosure crisis in the U.S. Two stochastic dynamic models are developed and analyzed for these types of problems. In the first model, we consider strategic resource allocation decisions by community development corporations (CDCs), which aim to minimize the negative effects of foreclosures by acquiring, redeveloping and selling foreclosed properties in their service areas. We model this strategic decision process through different types of stochastic mixed-integer programming formulations, and present alternative solution approaches. We also apply the models to real-world data obtained through interactions with a CDC, and perform both policy related and computational analyses. Based on these analyses, we present some general policy insights involving tradeoffs between different societal objectives, and also discuss the efficiency of exact and heuristic solution approaches for the models.In the second model, we consider a tactical resource allocation problem, and identify socially optimal policies for CDCs in dynamically selecting foreclosed properties for acquisition as they become available over time. The analytical results based on a dynamic programming model are then implemented in a case study involving a CDC, and social return based measures defining selectivity rates at different budget levels are specified. The second application area involves dynamic portfolio management approaches for optimization of surgical team compositions in robotic surgeries. For this problem, we develop a stochastic dynamic model to identify policies for optimal team configurations, where optimality is defined based on the minimum experience level required to achieve the maximum attainable performance over all ranges of feasible experience measures. We derive individual and dependent performance values of each surgical team member by using data on operating room time and team member experience, and then use them as inputs to a stochastic programming based framework that we develop. Several insights and guidelines for dynamic staff allocation to surgical teams are then proposed based on the analytical and numerical results derived from the model