Increasing value add in white collar work using lean methods

The efficient indirect office work brings competitive advantage for companies in a rapidly changing business environment. The direct work methods in factory floors have been developed already for decades, but the office work is an area where the potential to improve the value add has not been studied and utilized systematically so far. The first objective of the thesis work is to find useful method for identifying and managing value add using literature. The usefulness of the method is validated in the case company`s environment. The second objective of the work is to understand what kind of effort is required to create more efficient target setting for the white collar employees. The operative level targets should be linked more tightly to the company strategy. Lean methods are selected as a tool for the improvement, since they are widely used in all kinds of industries and they are already familiar in other functions in the case company. Based on the literature review, suitable improvement methods are selected. The core of the lean is to identify the value add of a customer and eliminate the waste. Also visual control, cross functional work team, flow office and continuous improvement are used. The methods are tested in one production line and the results and feedback indicate that methods are useful in the studied environment.

Adaptive MCMC methods with applications in environmental and geophysical models

This work presents new, efficient Markov chain Monte Carlo (MCMC) simulation methods for statistical analysis in various modelling applications. When using MCMC methods, the model is simulated repeatedly to explore the probability distribution describing the uncertainties in model parameters and predictions. In adaptive MCMC methods based on the Metropolis-Hastings algorithm, the proposal distribution needed by the algorithm learns from the target distribution as the simulation proceeds. Adaptive MCMC methods have been subject of intensive research lately, as they open a way for essentially easier use of the methodology. The lack of user-friendly computer programs has been a main obstacle for wider acceptance of the methods. This work provides two new adaptive MCMC methods: DRAM and AARJ. The DRAM method has been built especially to work in high dimensional and non-linear problems. The AARJ method is an extension to DRAM for model selection problems, where the mathematical formulation of the model is uncertain and we want simultaneously to fit several different models to the same observations. The methods were developed while keeping in mind the needs of modelling applications typical in environmental sciences. The development work has been pursued while working with several application projects. The applications presented in this work are: a winter time oxygen concentration model for Lake Tuusulanjärvi and adaptive control of the aerator; a nutrition model for Lake Pyhäjärvi and lake management planning; validation of the algorithms of the GOMOS ozone remote sensing instrument on board the Envisat satellite of European Space Agency and the study of the effects of aerosol model selection on the GOMOS algorithm.