The effects of productivity on profitability: a case study at firm level using an activity-based costing approach

Productivity and profitability are important concepts and measures describing the performance and success of a firm. We know that increase in productivity decreases the costs per unit produced and leads to better profitability. This common knowledge is not, however, enough in the modern business environment. Productivity improvement is one means among others for increasing the profitability of actions. There are many means to increase productivity. The use of these means presupposes operative decisions and these decisions presuppose informationabout the effects of these means. Productivity improvement actions are in general made at floor level with machines, cells, activities and human beings. Profitability is most meaningful at the level of the whole firm. It has been very difficult or even impossible to analyze closely enough the economical aspects of thechanges at floor level with the traditional costing systems. New ideas in accounting have only recently brought in elements which make it possible to considerthese phenomena where they actually happen. The aim of this study is to supportthe selection of objects to productivity improvement, and to develop a method to analyze the effects of the productivity change in an activity on the profitability of a firm. A framework for systemizing the economical management of productivity improvement is developed in this study. This framework is a systematical way with two stages to analyze the effects of productivity improvement actions inan activity on the profitability of a firm. At the first stage of the framework, a simple selection method which is based on the worth, possibility and the necessity of the improvement actions in each activity is presented. This method is called Urgency Analysis. In the second stage it is analyzed how much a certain change of productivity in an activity affects the profitability of a firm. A theoretical calculation model with which it is possible to analyze the effects of a productivity improvement in monetary values is presented. On the basis of this theoretical model a tool is made for the analysis at the firm level. The usefulness of this framework was empirically tested with the data of the profit center of one medium size Finnish firm which operates in metal industry. It is expressedthat the framework provides valuable information about the economical effects of productivity improvement for supporting the management in their decision making.

Carrier selection and management : A case study on Neste Oil Retail

Service provider selection has been said to be a critical factor in the formation of supply chains. Through successful selection companies can attain competitive advantage, cost savings and more flexible operations. Service provider management is the next crucial step in outsourcing process after the selection has been made. Without proper management companies cannot be sure about the level of service they have bought and they may suffer from service provider's opportunistic behavior. In worst case scenario the buyer company may end up in locked-in situation in which it is totally dependent of the service provider. This thesis studies how the case company conducts its carrier selection process along with the criteria related to it. A model for the final selection is also provided. In addition, case company's carrier management procedures are reflected against recommendations from previous researches. The research was conducted as a qualitative case study on the principal company, Neste Oil Retail. A literature review was made on outsourcing, service provider selection and service provider management. On the basis of the literature review, this thesis ended up recommending Analytic hierarchy process as the preferred model for the carrier selection. Furthermore, Agency theory was seen to be a functional framework for carrier management in this study. Empirical part of this thesis was conducted in the case company by interviewing the key persons in the selection process, making observations and going through documentations related to the subject. According to the results from the study, both carrier selection process as well as carrier management were closely in line with suggestions from literature review. Analytic hierarchy process results revealed that the case company considers service quality as the most important criteria with financial situation and price of service following behind with almost identical weights with each other. Equipment and personnel was seen as the least important selection criterion. Regarding carrier management, the study resulted in the conclusion that the company should consider engaging more in carrier development and working towards beneficial and effective relationships. Otherwise, no major changes were recommended for the case company processes.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.