Design of experiment in production process innovation

In his famous book Design and Analysis of Experiments, Montgomery describes Design of Experiment (DOE) as a broad approach to an experiment, starting from the recognition of and statement of the problem, going through the experimental design and to the possible solution, ending to conclusion and recommendations. Specifically, DOE is known to be a powerful instrument based on statistics to design and analyze experiments. Potentiality of DOE is well known and appreciated among scholars. In some fields its potentiality is recognized and appreciated also by practitioners. That’s why there is an extensive use of Design of Experiment in improvement of industrial process quality. According to the definition given by Bisgaard, innovation is the complete process of development and eventual commercialization of new products and services, new methods of production or provision, new methods of transportation or service delivery, new business models, new markets, or new forms of organization. While the use of DOE is well spread in industrial experimentation to improve quality and robustness of processes, the advantage of using DOE for innovation is debated among scholars and among practitioners. The idea of investigating the use of DOE for production process innovation arose from this debate. Different perspectives have been investigated. The effectiveness of DOE to support and enhance the innovation of a production process is highlighted by means of a case study in which a strategy to innovate a thermoforming process for the production of a functional packaging has been developed. DOE enhanced innovation capability allowing reduction of systematic errors and distortions, full exploration of factorial space, and reduction of number of tests. DOE allowed to identify and overcome the mismatch between control factors in laboratory and in production line. Another perspective was the management of the innovation process. The positive impact on innovation process management of adoption of DOE is shown by means of a case study. DOE proved to be helpful providing proper instruments, and impacting on five dimensions typical of managerial field. Namely: decision making, integration, communication, time and cost, and knowledge management. Concerning the data analysis, some nonparametric methods of analysis have been investigated. A simulation study was used to compare some advanced univariate nonparamentric tests in a crossed factorial design. The study revealed that certain methods of analysis perform better than others depending on the data set and on the objective of the analysis. As a consequence, there does not emerge a unique approach in the design phase of the experiment, but various aspects have to be taken into account simultaneously. A thoughtful choice of the most suitable test enhances the positive impact that DOE has on the innovation of a production process. Furthermore, a novel multivariate nonparametric approach based on NonParametric Combination (NPC) applied to Synchronized Permutation (SP) tests for two-way crossed factorial design was developed. It revealed to be a good instrument for inferential statistics when assumptions of MANOVA are violated. A great advantage given by the adoption of these tests is that they well perform with small sample size. This reflects the frequent needs of practitioners in the industrial environment where there are constraints or limited resources for the experimental design. Furthermore, there is an important property of NPC of SP tests that can be exploited to increase their power: the finite sample consistency. Indeed, an increase in rejection rate can be observed under alternative hypothesis when the number of response variables increases with fixed number of observed units. Properties of this multivariate test make of it a useful instrument when using DOE to innovate a production process and some specific conditions are verified

State Dependence and Alternative Explanations for Consumer Inertia

For many consumer packaged goods products, researchers have documented a form of state dependence whereby consumers become "loyal" to products they have consumed in the past. That is, consumers behave as though there is a utility premium from continuing to purchase the same product as they have purchased in the past or, equivalently, there is a psychological cost to switching products. However, it has not been established that this form of state dependence can be identified in the presence of consumer heterogeneity of an unknown form. Most importantly, before this inertia can be given a structural interpretation and used in policy experiments such as counterfactual pricing exercises,alternative explanations which might give rise to similar consumer behavior must be ruled out. We develop a flexible model of heterogeneity which can be given a semi-parametric interpretation and rule out alternative explanations for positive state dependence such as autocorrelated choice errors, consumer search, or consumer learning.

The Measurement of Information Transmitted by a Neural Population: Promises and Challenges

All brain functions require the coordinated activity of many neurons, and therefore there is considerable interest in estimating the amount of information that the discharge of a neural population transmits to its targets. In the past, such estimates had presented a significant challenge for populations of more than a few neurons, but we have recently described a novel method for providing such estimates for populations of essentially arbitrary size. Here, we explore the influence of some important aspects of the neuronal population discharge on such estimates. In particular, we investigate the roles of mean firing rate and of the degree and nature of correlations among neurons. The results provide constraints on the applicability of our new method and should help neuroscientists determine whether such an application is appropriate for their data

Adaptive Illumination Patterns for Radar Applications

The fundamental goal of Fully Adaptive Radar (FAR) involves full exploitation of the joint, synergistic adaptivity of the radar\u27s transmitter and receiver. Little work has been done to exploit the joint space time Degrees-of-Freedom (DOF) available via an Active Electronically Steered Array (AESA) during the radar\u27s transmit illumination cycle. This research introduces Adaptive Illumination Patterns (AIP) as a means for exploiting this previously untapped transmit DOF. This research investigates ways to mitigate clutter interference effects by adapting the illumination pattern on transmit. Two types of illumination pattern adaptivity were explored, termed Space Time Illumination Patterns (STIP) and Scene Adaptive Illumination Patterns (SAIP). Using clairvoyant knowledge, STIP demonstrates the ability to remove sidelobe clutter at user specified Doppler frequencies, resulting in optimum receiver performance using a non-adaptive receive processor. Using available database knowledge, SAIP demonstrated the ability to reduce training data heterogeneity in dense target environments, thereby greatly improving the minimum discernable velocity achieved through STAP processing

INFLATION AND RELATIVE PRICE ASYMMETRY

By placing store-level price data into bivariate Structural VAR models of inflation and relative price asymmetry, this study evaluates the quantitative importance of idiosyncratic pricing shocks in short-run aggregate price change dynamics. Robustly to alternative definitions of the relative price, identification schemes dictated by two-sided (S,s) pricing theory and measures of asymmetry in the relative price distribution, idiosyncratic shocks explain about 25 to 30 percent of the forecast error variance in inflation at the 12-month horizon. While the contemporaneous correlation between inflation and relative price asymmetry is positive, idiosyncratic shocks lead to a substantial build-up in inflation only after two to five months following the initial disturbance(S,s) Pricing, Relative Price, Inflation, Structural VAR

Enhancing 3D Autonomous Navigation Through Obstacle Fields: Homogeneous Localisation and Mapping, with Obstacle-Aware Trajectory Optimisation

Small flying robots have numerous potential applications, from quadrotors for search and rescue, infrastructure inspection and package delivery to free-flying satellites for assistance activities inside a space station. To enable these applications, a key challenge is autonomous navigation in 3D, near obstacles on a power, mass and computation constrained platform. This challenge requires a robot to perform localisation, mapping, dynamics-aware trajectory planning and control. The current state-of-the-art uses separate algorithms for each component. Here, the aim is for a more homogeneous approach in the search for improved efficiencies and capabilities. First, an algorithm is described to perform Simultaneous Localisation And Mapping (SLAM) with physical, 3D map representation that can also be used to represent obstacles for trajectory planning: Non-Uniform Rational B-Spline (NURBS) surfaces. Termed NURBSLAM, this algorithm is shown to combine the typically separate tasks of localisation and obstacle mapping. Second, a trajectory optimisation algorithm is presented that produces dynamically-optimal trajectories with direct consideration of obstacles, providing a middle ground between path planners and trajectory smoothers. Called the Admissible Subspace TRajectory Optimiser (ASTRO), the algorithm can produce trajectories that are easier to track than the state-of-the-art for flight near obstacles, as shown in flight tests with quadrotors. For quadrotors to track trajectories, a critical component is the differential flatness transformation that links position and attitude controllers. Existing singularities in this transformation are analysed, solutions are proposed and are then demonstrated in flight tests. Finally, a combined system of NURBSLAM and ASTRO are brought together and tested against the state-of-the-art in a novel simulation environment to prove the concept that a single 3D representation can be used for localisation, mapping, and planning

Robust Observation and Control of Complex Networks

The problem of understanding when individual actions of interacting agents display to a coordinated collective behavior has receiving a considerable attention in many research fields. Especially in control engineering, distributed applications in cooperative environments are achieving resounding success, due to the large number of relevant applications, such as formation control, attitude synchronization tasks and cooperative applications in large-scale systems. Although those problems have been extensively studied in Literature, themost of classic approaches use to consider the unrealistic scenario in which networks always consist of identical, linear, time-invariant entities. It’s clear that this assumption strongly approximates the effective behavior of a network. In fact agents can be subjected to parameter uncertainties, unmodeled dynamics or simply characterized by proper nonlinear dynamics. Therefore, motivated by those practical problems, the present Thesis proposes various approaches for dealing with the problem of observation and control in both the framework of multi-agents and complex interconnected systems. The main contributions of this Thesis consist on the development of several algorithms based on concepts of discontinuous slidingmode control. This techniques can be employed for solving in finite-time problems of robust state estimation and consensus-based synchronization in network of heterogenous nonlinear systems subjected to unknown but bounded disturbances and sudden topological changes. Both directed and undirected topologies have been taken into account. It is worth to mention also the extension of the consensus problem to networks of agents governed by a class parabolic partial differential equation, for which, for the first time, a boundary-based robust local interaction protocol has been presented

Robust Observation and Control of Complex Networks

The problem of understanding when individual actions of interacting agents display to a coordinated collective behavior has receiving a considerable attention in many research fields. Especially in control engineering, distributed applications in cooperative environments are achieving resounding success, due to the large number of relevant applications, such as formation control, attitude synchronization tasks and cooperative applications in large-scale systems. Although those problems have been extensively studied in Literature, themost of classic approaches use to consider the unrealistic scenario in which networks always consist of identical, linear, time-invariant entities. It’s clear that this assumption strongly approximates the effective behavior of a network. In fact agents can be subjected to parameter uncertainties, unmodeled dynamics or simply characterized by proper nonlinear dynamics. Therefore, motivated by those practical problems, the present Thesis proposes various approaches for dealing with the problem of observation and control in both the framework of multi-agents and complex interconnected systems. The main contributions of this Thesis consist on the development of several algorithms based on concepts of discontinuous slidingmode control. This techniques can be employed for solving in finite-time problems of robust state estimation and consensus-based synchronization in network of heterogenous nonlinear systems subjected to unknown but bounded disturbances and sudden topological changes. Both directed and undirected topologies have been taken into account. It is worth to mention also the extension of the consensus problem to networks of agents governed by a class parabolic partial differential equation, for which, for the first time, a boundary-based robust local interaction protocol has been presented