Inventory Routing - A Strategic Management Accounting Perspective

This dissertation develops a strategic management accounting perspective of inventory routing. The thesis studies the drivers of cost efficiency gains by identifying the role of the underlying cost structure, demand, information sharing, forecasting accuracy, service levels, vehicle fleet, planning horizon and other strategic factors as well as the interaction effects among these factors with respect to performance outcomes. The task is to enhance the knowledge of the strategic situations that favor the implementation of inventory routing systems, understanding cause-and-effect relationships, linkages and gaining a holistic view of the value proposition of inventory routing. The thesis applies an exploratory case study design, which is based on normative quantitative empirical research using optimization, simulation and factor analysis. Data and results are drawn from a real world application to cash supply chains. The first research paper shows that performance gains require a common cost component and cannot be explained by simple linear or affine cost structures. Inventory management and distribution decisions become separable in the absence of a set-dependent cost structure, and neither economies of scope nor coordination problems are present in this case. The second research paper analyzes whether information sharing improves the overall forecasting accuracy. Analysis suggests that the potential for information sharing is limited to coordination of replenishments and that central information do not yield more accurate forecasts based on joint forecasting. The third research paper develops a novel formulation of the stochastic inventory routing model that accounts for minimal service levels and forecasting accuracy. The developed model allows studying the interaction of minimal service levels and forecasting accuracy with the underlying cost structure in inventory routing. Interestingly, results show that the factors minimal service level and forecasting accuracy are not statistically significant, and subsequently not relevant for the strategic decision problem to introduce inventory routing, or in other words, to effectively internalize inventory management and distribution decisions at the supplier. Consequently the main contribution of this thesis is the result that cost benefits of inventory routing are derived from the joint decision model that accounts for the underlying set-dependent cost structure rather than the level of information sharing. This result suggests that the value of information sharing of demand and inventory data is likely to be overstated in prior literature. In other words, cost benefits of inventory routing are primarily determined by the cost structure (i.e. level of fixed costs and transportation costs) rather than the level of information sharing, joint forecasting, forecasting accuracy or service levels.

Dynamic Factor Models and Forecasting Finnish Macroeconomic Variables

In recent years, thanks to developments in information technology, large-dimensional datasets have been increasingly available. Researchers now have access to thousands of economic series and the information contained in them can be used to create accurate forecasts and to test economic theories. To exploit this large amount of information, researchers and policymakers need an appropriate econometric model.Usual time series models, vector autoregression for example, cannot incorporate more than a few variables. There are two ways to solve this problem: use variable selection procedures or gather the information contained in the series to create an index model. This thesis focuses on one of the most widespread index model, the dynamic factor model (the theory behind this model, based on previous literature, is the core of the first part of this study), and its use in forecasting Finnish macroeconomic indicators (which is the focus of the second part of the thesis). In particular, I forecast economic activity indicators (e.g. GDP) and price indicators (e.g. consumer price index), from 3 large Finnish datasets. The first dataset contains a large series of aggregated data obtained from the Statistics Finland database. The second dataset is composed by economic indicators from Bank of Finland. The last dataset is formed by disaggregated data from Statistic Finland, which I call micro dataset. The forecasts are computed following a two steps procedure: in the first step I estimate a set of common factors from the original dataset. The second step consists in formulating forecasting equations including the factors extracted previously. The predictions are evaluated using relative mean squared forecast error, where the benchmark model is a univariate autoregressive model. The results are dataset-dependent. The forecasts based on factor models are very accurate for the first dataset (the Statistics Finland one), while they are considerably worse for the Bank of Finland dataset. The forecasts derived from the micro dataset are still good, but less accurate than the ones obtained in the first case. This work leads to multiple research developments. The results here obtained can be replicated for longer datasets. The non-aggregated data can be represented in an even more disaggregated form (firm level). Finally, the use of the micro data, one of the major contributions of this thesis, can be useful in the imputation of missing values and the creation of flash estimates of macroeconomic indicator (nowcasting).

The Quest for Well-being in Growth Industries 2: The Survey

This working paper reports the ongoing research conducted in the research project, The Quest for Well-being in Growth Industries: A Collaborative Study in Finland and Scotland, under the auspices of Academy of Finland research programme, The Future of Work and Well-being. The research project examines the contradictory pressures for policies and practices towards both the inhibition and the enhancement of work-related well-being that are likely in growth industries. The overall aim is to evaluate the development, implementation and use of work-related well-being policies in four selected growth industries. These – electronics, care, finance and accounting, and tourism – have been selected on the basis of EU and national forecasts, and demographic and socio-economic trends in standard and non-standard employment. In this paper we aim to review the survey that constitutes the second main phase of this research.

Actuarial report 2011: Social security schemes administered by Kela 2010-2060

Projections concerning the long-term outlook of the social security schemes administered by the Social Insurance Institution of Finland (Kela) are made regularly by the Institution’s Actuarial Section. The report at hand was compiled with the help of an aggregate model devised by the Actuarial Section. In the model, various universal factors influencing benefit trends are consolidated and the interactions between individual benefits are taken into account. The demographic forecasts underlying the report have also been made by the Actuarial Section. Estimates of income and administrative costs have been made in cooperation with the Financial Planning Section.

Categorical Meteorological Products: Evaluation and Analysis

In meteorology, observations and forecasts of a wide range of phenomena for example, snow, clouds, hail, fog, and tornados can be categorical, that is, they can only have discrete values (e.g., "snow" and "no snow"). Concentrating on satellite-based snow and cloud analyses, this thesis explores methods that have been developed for evaluation of categorical products and analyses. Different algorithms for satellite products generate different results; sometimes the differences are subtle, sometimes all too visible. In addition to differences between algorithms, the satellite products are influenced by physical processes and conditions, such as diurnal and seasonal variation in solar radiation, topography, and land use. The analysis of satellite-based snow cover analyses from NOAA, NASA, and EUMETSAT, and snow analyses for numerical weather prediction models from FMI and ECMWF was complicated by the fact that we did not have the true knowledge of snow extent, and we were forced simply to measure the agreement between different products. The Sammon mapping, a multidimensional scaling method, was then used to visualize the differences between different products. The trustworthiness of the results for cloud analyses [EUMETSAT Meteorological Products Extraction Facility cloud mask (MPEF), together with the Nowcasting Satellite Application Facility (SAFNWC) cloud masks provided by Météo-France (SAFNWC/MSG) and the Swedish Meteorological and Hydrological Institute (SAFNWC/PPS)] compared with ceilometers of the Helsinki Testbed was estimated by constructing confidence intervals (CIs). Bootstrapping, a statistical resampling method, was used to construct CIs, especially in the presence of spatial and temporal correlation. The reference data for validation are constantly in short supply. In general, the needs of a particular project drive the requirements for evaluation, for example, for the accuracy and the timeliness of the particular data and methods. In this vein, we discuss tentatively how data provided by general public, e.g., photos shared on the Internet photo-sharing service Flickr, can be used as a new source for validation. Results show that they are of reasonable quality and their use for case studies can be warmly recommended. Last, the use of cluster analysis on meteorological in-situ measurements was explored. The Autoclass algorithm was used to construct compact representations of synoptic conditions of fog at Finnish airports.

Taloustilanteen heikkenemisestä johtuvat muutokset asuntosijoittamiseen pääkaupunkiseudulla 2008

Tutkielman tavoitteena on selvittää miten taloustaantuma vaikutti asuntosijoittamisen kiinnostavuuteen ja asuntomarkkinoilla käytävään keskusteluun vuonna 2008. Tuolloin Suomen talous taantui voimakkaasti ja nopeasti yllättäen myös ennusteita laativat asiantuntijat. Ekonomistien lausunnoista puuttui yhdenmukaisuus ja tarkkuus. Ne myös saattoivat muuttua merkittävästi lyhyellä aikavälillä. Taantumassa sijoitusviestintä on varovaista ja tarkasti muotoiltua. Sijoittajat uskovat mielellään muiden sijoittajien mielipiteitä ja käsityksiä vaikkei niiden taustalla olisikaan aina todennettua faktatietoa. Asiantuntijoiden tilastoihin halutaan uskoa vaikka niitä kohtaan koetaan epäilyksiä. Toisaalta asuntosijoittamisen kannattavuuteen ja taloudelliseen tuottoon halutaan uskoa vaikka asiantuntijat voisivat todistaa toisin. Tutkimus toteutettiin kvalitatiivisena tapaustutkimuksena jota analysoitiin Greimasin aktanttimallia mukaillen. Tutkimusaineisto koostui 14 Helsingin Sanomissa julkaistuista asuntosijoittamiseen liittyvistä artikkelista sekä 13 Taloussanomien keskustelupalstalla julkaistusta mielipidekirjoituksesta. Viestien merkityksiä käytiin läpi semioottisesti määrittelemällä eri aktanteille rooleja. Tarinassa sijoittajasubjektin objektina on asunto, jonka avulla pyritään saavuttamaan mahdollisimman suuri rahallinen tuotto. Lähettäjiä ovat muun muassa tilastojen laatijat ja sijoitusneuvojat. Kaikki optimaalisen sijoituspäätöksen tekemiseen vaikuttavat aktantit käydään tarkemmin läpi tutkimuksen loppupuolella.

Monetary policy rules and estimated reaction functions for the European Central Bank

This thesis studies the interest-rate policy of the ECB by estimating monetary policy rules using real-time data and central bank forecasts. The aim of the estimations is to try to characterize a decade of common monetary policy and to look at how different models perform at this task.The estimated rules include: contemporary Taylor rules, forward-looking Taylor rules, nonlinearrules and forecast-based rules. The nonlinear models allow for the possibility of zone-like preferences and an asymmetric response to key variables. The models therefore encompass the most popular sub-group of simple models used for policy analysis as well as the more unusual non-linear approach. In addition to the empirical work, this thesis also contains a more general discussion of monetary policy rules mostly from a New Keynesian perspective. This discussion includes an overview of some notable related studies, optimal policy, policy gradualism and several other related subjects. The regression estimations are performed with either least squares or the generalized method of moments depending on the requirements of the estimations. The estimations use data from both the Euro Area Real-Time Database and the central bank forecasts published in ECB Monthly Bulletins. These data sources represent some of the best data that is available for this kind of analysis. The main results of this thesis are that forward-looking behavior appears highly prevalent, but that standard forward-looking Taylor rules offer only ambivalent results with regard to inflation. Nonlinear models are shown to work, but on the other hand do not have a strong rationale over a simpler linear formulation. However, the forecasts appear to be highly useful in characterizing policy and may offer the most accurate depiction of a predominantly forward-looking central bank. In particular the inflation response appears much stronger while the output response becomes highly forward-looking as well.

Lumettoman maan routaolojen mallintaminen ja ennustettavuus muuttuvassa ilmastossa

Raportissa on arvioitu ilmastonmuutoksen vaikutusta Suomen maaperän talviaikaiseen jäätymiseen lämpösummien perusteella. Laskelmat kuvaavat roudan paksuutta nimenomaisesti lumettomilla alueilla, esimerkiksi teillä, joilta satanut lumi aurataan pois. Luonnossa lämpöä eristävän lumipeitteen alla routaa on ohuemmin kuin tällaisilla lumettomilla alueilla. Toisaalta luonnollisessa ympäristössä paikalliset erot korostuvat johtuen mm. maalajeista ja kasvillisuudesta. Roudan paksuudet laskettiin ensin perusjakson 1971–2000 ilmasto-oloissa talviaikaisten säähavaintotietoihin pohjautuvien lämpötilojen perusteella. Sen jälkeen laskelmat toistettiin kolmelle tulevalle ajanjaksolle (2010–2039, 2040–2069 ja 2070–2099) kohottamalla lämpötiloja ilmastonmuutosmallien ennustamalla tavalla. Laskelman pohjana käytettiin 19 ilmastomallin A1B-skenaarioajojen keskimäärin simuloimaa lämpötilan muutosta. Tulosten herkkyyden arvioimiseksi joitakin laskelmia tehtiin myös tätä selvästi heikompaa ja voimakkaampaa lämpenemisarviota käyttäen. A1B-skenaarion mukaisen lämpötilan nousun toteutuessa nykyisiä mallituloksia vastaavasti routakerros ohenee sadan vuoden aikana Pohjois-Suomessa 30–40 %, suuressa osassa maan keski- ja eteläosissa 50–70 %. Jo lähivuosikymmeninä roudan ennustetaan ohentuvan 10–30 %, saaristossa enemmän. Mikäli lämpeneminen toteutuisi voimakkaimman tarkastellun vaihtoehdon mukaisesti, roudan syvyys pienenisi tätäkin enemmän. Roudan paksuuden vuosienvälistä vaihtelua ja sen muuttumista tulevaisuudessa pyrittiin myös arvioimaan. Leutoina talvina routa ohenee enemmän kuin normaaleina tai ankarina pakkastalvina. Päivittäistä sään vaihtelua simuloineen säägeneraattorin tuottamassa aineistoissa esiintyi kuitenkin liian vähän hyvin alhaisia ja hyvin korkeita lämpötiloja. Siksi näitten lämpötilatietojen pohjalta laskettu roudan paksuuskin ilmeisesti vaihtelee liian vähän vuodesta toiseen. Kelirikkotilanteita voi esiintyä myös kesken routakauden, jos useamman päivän suojasää ja samanaikainen runsas vesisade pääsevät sulattamaan maata. Tällaiset routakauden aikana sattuvat säätilat näyttävätkin yleistyvän lähivuosikymmeninä. Vuosisadan loppua kohti ne sen sijaan maan eteläosissa jälleen vähenevät, koska routakausi lyhenee oleellisesti. Tulevia vuosikymmeniä koskevien ilmastonmuutosennusteiden ohella routaa ja kelirikon esiintymistä on periaatteessa mahdollista ennustaa myös lähiaikojen sääennusteita hyödyntäen. Pitkät, viikkojen tai kuukausien mittaiset sääennusteet eivät tosin ole ainakaan vielä erityisen luotettavia, mutta myös lyhyemmistä ennusteista voisi olla hyötyä mm. tienpitoa suunniteltaessa.

Rapsista tuotetun biodieselin ja vehnästä tuotetun bioetanolin energiavirrat ja ympäristövaikutukset Euroopan unionissa

The aim of this thesis was to study the crops currently used for biofuel production from the following aspects: 1. what should be the average yield/ ha to reach an energy balance at least 0 or positive 2. what are the shares of the primary and secondary energy flows in agriculture, transport, processing and usage, and 3. overall effects of biofuel crop cultivation, transport, processing and usage. This thesis concentrated on oilseed rape biodiesel and wheat bioethanol in the European Union, comparing them with competing biofuels, such as corn and sugarcane-based ethanol, and the second generation biofuels. The study was executed by comparing Life Cycle Assessment-studies from the EU-region and by analyzing them thoroughly from the differences viewpoint. The variables were the following: energy ratio, hectare yield (l/ha), impact on greenhouse gas emissions (particularly CO2), energy consumption in crop growing and processing one hectare of a particular crop to biofuel, distribution of energy in processing and effects of the secondary energy flows, like e.g. wheat straw. Processing was found to be the most energy consuming part in the production of biofuels. So if the raw materials will remain the same, the development will happen in processing. First generation biodiesel requires esterification, which consumes approximately one third of the process energy. Around 75% of the energy consumed in manufacturing the first generation wheat-based ethanol is spent in steam and electricity generation. No breakthroughs are in sight in the agricultural sector to achieve significantly higher energy ratios. It was found out that even in ideal conditions the energy ratio of first generation wheat-based ethanol will remain slightly under 2. For oilseed rape-based biodiesel the energy ratios are better, and energy consumption per hectare is lower compared to wheat-based ethanol. But both of these are lower compared to e.g. sugarcane-based ethanol. Also the hectare yield of wheat-based ethanol is significantly lower. Biofuels are in a key position when considering the future of the world’s transport sector. Uncertainties concerning biofuels are, however, several, like the schedule of large scale introduction to consumer markets, technologies used, raw materials and their availability and - maybe the biggest - the real production capacity in relation to the fuel consumption. First generation biofuels have not been the expected answer to environmental problems. Comparisons made show that sugarcane-based ethanol is the most prominent first generation biofuel at the moment, both from energy and environment point of view. Also palmoil-based biodiesel looks promising, although it involves environmental concerns as well. From this point of view the biofuels in this study - wheat-based ethanol and oilseed rape-based biodiesel - are not very competitive options. On the other hand, crops currently used for fuel production in different countries are selected based on several factors, not only based on thier relative general superiority. It is challenging to make long-term forecasts for the biofuel sector, but it can be said that satisfying the world's current and near future traffic fuel consumption with biofuels can only be regarded impossible. This does not mean that biofuels shoud be rejected and their positive aspects ignored, but maybe this reality helps us to put them in perspective. To achieve true environmental benefits through the usage of biofuels there must first be a significant drop both in traffic volumes and overall fuel consumption. Second generation biofuels are coming, but serious questions about their availability and production capacities remain open. Therefore nothing can be taken for granted in this issue, expect the need for development.

Forecasting rain for groundnut farmers�how good is good enough

: We illustrate how climatological information about adverse weather events and meteorological forecasts (when available) can be used to decide between alternative strategies so as to maximize the long-term average returns for rainfed groundnut in semi-arid parts of Karnataka, We show that until the skill of the forecast, i.e. probability of an adverse event occurring when it is forecast, is above a certain threshold, the forecast has no impact on the optimum strategy, This threshold is determined by the loss in yield due to the adverse weather event and the cost of the mitigatory measures, For the specific case of groundnut, it is found that while for combating some pests/diseases, climatological information is adequate, for others a forecast of sufficient skill would have a significant impact on the productivity.

Exploring use of climate information for optimal design of water resources infrastructure

We conducted surveys of fire and fuels managers at local, regional, and national levels to gain insights into decision processes and information flows in wildfire management. Survey results in the form of fire managers’ decision calendars show how climate information needs vary seasonally, over space, and through the organizational network, and help determine optimal points for introducing climate information and forecasts into decision processes. We identified opportunities to use climate information in fire management, including seasonal to interannual climate forecasts at all organizational levels, to improve the targeting of fuels treatments and prescribed burns, the positioning and movement of initial attack resources, and staffing and budgeting decisions. Longer-term (5–10 years) outlooks also could be useful at the national level in setting budget and research priorities. We discuss these opportunities and examine the kinds of organizational changes that could facilitate effective use of existing climate information and climate forecast capabilities.

TURBULENT PUMPING OF MAGNETIC FLUX REDUCES SOLAR CYCLE MEMORY AND THUS IMPACTS PREDICTABILITY OF THE SUN'S ACTIVITY

Prediction of the Sun's magnetic activity is important because of its effect on space environment and climate. However, recent efforts to predict the amplitude of the solar cycle have resulted in diverging forecasts with no consensus. Yeates et al. have shown that the dynamical memory of the solar dynamo mechanism governs predictability, and this memory is different for advection- and diffusion-dominated solar convection zones. By utilizing stochastically forced, kinematic dynamo simulations, we demonstrate that the inclusion of downward turbulent pumping of magnetic flux reduces the memory of both advection- and diffusion-dominated solar dynamos to only one cycle; stronger pumping degrades this memory further. Thus, our results reconcile the diverging dynamo-model-based forecasts for the amplitude of solar cycle 24. We conclude that reliable predictions for the maximum of solar activity can be made only at the preceding minimum-allowing about five years of advance planning for space weather. For more accurate predictions, sequential data assimilation would be necessary in forecasting models to account for the Sun's short memory.

All flavours of El Nino have similar early subsurface origins

The El Nino/Southern Oscillation phenomenon, characterized by anomalous sea surface temperatures and winds in the tropical Pacific, affects climate across the globe(1). El Ninos occur every 2-7 years, whereas the El Nino/Southern Oscillation itself varies on decadal timescales in frequency and amplitude, with a different spatial pattern of surface anomalies(2) each time the tropical Pacific undergoes a regime shift. Recent work has shown that Bjerknes feedback(3,4) (coupling of the atmosphere and the ocean through changes in equatorial winds driven by changes in sea surface temperature owing to suppression of equatorial upwelling in the east Pacific) is not necessary(5) for the development of an El Nino. Thus it is unclear what remains constant through regimes and is crucial for producing the anomalies recognized as El Nino. Here we show that the subsurface process of discharging warm waters always begins in the boreal summer/autumn of the year before the event (up to 18 months before the peak) independent of regimes, identifying the discharge process as fundamental to the El Nino onset. It is therefore imperative that models capture this process accurately to further our theoretical understanding, improve forecasts and predict how the El Nino/Southern Oscillation may respond to climate change.

Ship-Mounted Real-Time Surface Observational System on board Indian Vessels for Validation and Refinement of Model Forcing Fields

A network of ship-mounted real-time Automatic Weather Stations integrated with Indian geosynchronous satellites Indian National Satellites (INSATs)] 3A and 3C, named Indian National Centre for Ocean Information Services Real-Time Automatic Weather Stations (I-RAWS), is established. The purpose of I-RAWS is to measure the surface meteorological-ocean parameters and transmit the data in real time in order to validate and refine the forcing parameters (obtained from different meteorological agencies) of the Indian Ocean Forecasting System (INDOFOS). Preliminary validation and intercomparison of analyzed products obtained from the National Centre for Medium Range Weather Forecasting and the European Centre for Medium-Range Weather Forecasts using the data collected from I-RAWS were carried out. This I-RAWS was mounted on board oceanographic research vessel Sagar Nidhi during a cruise across three oceanic regimes, namely, the tropical Indian Ocean, the extratropical Indian Ocean, and the Southern Ocean. The results obtained from such a validation and intercomparison, and its implications with special reference to the usage of atmospheric model data for forcing ocean model, are discussed in detail. It is noticed that the performance of analysis products from both atmospheric models is similar and good; however, European Centre for Medium-Range Weather Forecasts air temperature over the extratropical Indian Ocean and wind speed in the Southern Ocean are marginally better.