Systematisation of Corporate Planning

The systematization of corporate planning is a subject that provokes the research interest due to the increased importance of strategic and long-term planning for the corporate development. This article analyzes some problem areas related to the differences between the essence and the procedures of long-term and strategic company planning. The aim is to highlight some views of strategic and long-term planning in companies and the distinguishing features between these categories, draw the attention of contemporary managers to pursuing and combining different methods depending on the characteristics of the company’s activity and the environment in which it operates.corporate planning; management; strategic decisions; firm economic.

Information Provision for Strategic Planning in Bulgarian SMEs

The information provision of strategic planning in small and medium sized enterprises (SMEs) is a subject that provokes the research interest due to the increased importance of strategic planning for the corporate development. This article - based on the results of a survey among 50 SMEs in Rousse region (Bulgaria) - analyzes some problem areas related to practical implementation of the concept of strategic business planning, and in particular the practice of providing information for solving strategic issues in SMEs. The aim is to highlight some typical information sources for the strategic planning in SMEs and to formulate some problem areas that need special attention and responsible action.SMEs; corporate strategic planning; information provision for strategic planning purposes.

Challenges with bearings only tracking for missile guidance systems and how to cope with them.

This paper addresses the problem of closed loop missile guidance using bearings and target angular extent information. Comparison is performed between particle filtering methods and derivative free methods. The extent information characterizes target size and we show how this can help compensate for observability problems. We demonstrate that exploiting angular extent information improves filter estimation accuracy. The performance of the filters has been studied over a testing scenario with a static target, with respect to accuracy, sensitivity to perturbations in initial conditions and in different seeker modes (active, passive and semi-active)

Video foreground detection based on symmetric alpha-stable mixture models.

Background subtraction (BS) is an efficient technique for detecting moving objects in video sequences. A simple BS process involves building a model of the background and extracting regions of the foreground (moving objects) with the assumptions that the camera remains stationary and there exist no movements in the background. These assumptions restrict the applicability of BS methods to real-time object detection in video. In this paper, we propose an extended cluster BS technique with a mixture of symmetric alpha stable (SS) distributions. An on-line self-adaptive mechanism is presented that allows automated estimation of the model parameters using the log moment method. Results over real video sequences from indoor and outdoor environments, with data from static and moving video cameras are presented. The SS mixture model is shown to improve the detection performance compared with a cluster BS method using a Gaussian mixture model and the method of Li et al. [11]

Particle approximations of the score and observed information matrix for parameter estimation in state space models with linear computational cost

Poyiadjis et al. (2011) show how particle methods can be used to estimate both the score and the observed information matrix for state space models. These methods either suffer from a computational cost that is quadratic in the number of particles, or produce estimates whose variance increases quadratically with the amount of data. This paper introduces an alternative approach for estimating these terms at a computational cost that is linear in the number of particles. The method is derived using a combination of kernel density estimation, to avoid the particle degeneracy that causes the quadratically increasing variance, and Rao-Blackwellisation. Crucially, we show the method is robust to the choice of bandwidth within the kernel density estimation, as it has good asymptotic properties regardless of this choice. Our estimates of the score and observed information matrix can be used within both online and batch procedures for estimating parameters for state space models. Empirical results show improved parameter estimates compared to existing methods at a significantly reduced computational cost. Supplementary materials including code are available.Comment: Accepted to Journal of Computational and Graphical Statistic

A Box Particle Filter for Stochastic and Set-theoretic Measurements with Association Uncertainty

This work develops a novel estimation approach for nonlinear dynamic stochastic systems by combining the sequential Monte Carlo method with interval analysis. Unlike the common pointwise measurements, the proposed solution is for problems with interval measurements with association uncertainty. The optimal theoretical solution can be formulated in the framework of random set theory as the Bernoulli filter for interval measurements. The straightforward particle filter implementation of the Bernoulli filter typically requires a huge number of particles since the posterior probability density function occupies a significant portion of the state space. In order to reduce the number of particles, without necessarily sacrificing estimation accuracy, the paper investigates an implementation based on box particles. A box particle occupies a small and controllable rectangular region of non-zero volume in the target state space. The numerical results demonstrate that the filter performs remarkably well: both target state and target presence are estimated reliably using a very small number of box particles

Parallelized Particle and Gaussian Sum Particle Filters for Large Scale Freeway Traffic Systems

Large scale traffic systems require techniques able to: 1) deal with high amounts of data and heterogenous data coming from different types of sensors, 2) provide robustness in the presence of sparse sensor data, 3) incorporate different models that can deal with various traffic regimes, 4) cope with multimodal conditional probability density functions for the states. Often centralized architectures face challenges due to high communication demands. This paper develops new estimation techniques able to cope with these problems of large traffic network systems. These are Parallelized Particle Filters (PPFs) and a Parallelized Gaussian Sum Particle Filter (PGSPF) that are suitable for on-line traffic management. We show how complex probability density functions of the high dimensional trafc state can be decomposed into functions with simpler forms and the whole estimation problem solved in an efcient way. The proposed approach is general, with limited interactions which reduces the computational time and provides high estimation accuracy. The efciency of the PPFs and PGSPFs is evaluated in terms of accuracy, complexity and communication demands and compared with the case where all processing is centralized

Strategic outsourcing directions: examples of good practice

One of the main goals of an outsourcing strategy is to transform business organizations into entities that are highly flexible and adaptive to the changing business environment. This can be achieved by concentrating on the core business, as well as by entrusting tasks, activities or even whole functions to other specialized external companies. The article aims at analyzing of good practices concerning the implementation of outsourcing as a strategic tool in industrial enterprises, bearing in mind its three main directions: the business process, the information technology, and the knowledge process outsourcing. Due to outsourcing activities, organizations can benefit from lower investment risk, improved revenue structure and lower fixed costs

Strategic management accounting in Bulgarian manufacturing SMEs

Analyzing the strategic aspects of managerial accounting in the context of reporting in industrial SMEs is a critical management issue. This paper aims to present the main results of an empirical study on this topic among some SMEs from the knitwear industry in the southern part of Bulgaria. Analytical and synthesis methods, descriptive statistics, and comparison techniques are used for this study. All the companies studied have been on the market for more than ten years and have experienced and well-trained managers and other characteristics that imply the availability of managerial reporting/accounting and the application of adapted models for strategic planning in SMEs. The analysis is intended to draw basic conclusions about the application of strategic management accounting in SMEs. In addition, the authors’ broader intention is to propose ideas for improving the strategic management reporting techniques that could be useful for management, especially in cases where strategic planning has been implemented. The authors find that the owners or managers of SMEs in Bulgaria are not aware of some features of management accounting that could be useful for their business, especially for strategic planning. It is believed that the range of services offered by external accounting organizations limits the level and thus the role of management accounting information for strategic management. Observations show that some models and techniques that could be good indicators of high risk of business failure are not applied because of the limited information base

Localisation of mobile nodes in wireless networks with correlated in time measurement noise.

Wireless sensor networks are an inherent part of decision making, object tracking and location awareness systems. This work is focused on simultaneous localisation of mobile nodes based on received signal strength indicators (RSSIs) with correlated in time measurement noises. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multi model auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localisation accuracy is demonstrated