Demand forecasting process design and methods in medium sized enterprise

The purpose of this thesis was to study the design of demand forecasting processes and management of demand. In literature review were different processes found and forecasting methods and techniques interviewed. Also role of bullwhip effect in supply chain was identified and how to manage it with information sharing operations. In the empirical part of study is at first described current situation and challenges in case company. After that will new way to handle demand introduced with target budget creation and how information sharing with 5 products and a few customers would bring benefits to company. Also the new S&OP process created within this study and organization for it.

Low levels of sex hormone-binding globulin and hyperproinsulinemia as markers of increased pancreatic ß-cell demand in men

Low levels of sex hormone-binding globulin (SHBG) are considered to be an indirect index of hyperinsulinemia, predicting the later onset of diabetes mellitus type 2. In the insulin resistance state and in the presence of an increased pancreatic ß-cell demand (e.g. obesity) both absolute and relative increases in proinsulin secretion occur. In the present study we investigated the correlation between SHBG and pancreatic ß-cell secretion in men with different body compositions. Eighteen young men (30.0 ± 2.4 years) with normal glucose tolerance and body mass indexes (BMI) ranging from 22.6 to 43.2 kg/m2 were submitted to an oral glucose tolerance test (75 g) and baseline and 120-min blood samples were used to determine insulin, proinsulin and C-peptide by specific immunoassays. Baseline SHBG values were significantly correlated with baseline insulin (r = -0.58, P<0.05), proinsulin (r = -0.47, P<0.05), C-peptide (r = -0.55, P<0.05) and also with proinsulin at 120 min after glucose load (r = -0.58, P<0.05). Stepwise regression analysis revealed that proinsulin values at 120 min were the strongest predictor of SHBG (r = -0.58, P<0.05). When subjects were divided into obese (BMI >28 kg/m2, N = 8) and nonobese (BMI £25 kg/m2, N = 10) groups, significantly lower levels of SHBG were found in the obese subjects. The obese group had significantly higher baseline proinsulin, C-peptide and 120-min proinsulin and insulin levels. For the first time using a specific assay for insulin determination, a strong inverse correlation between insulinemia and SHBG levels was confirmed. The finding of a strong negative correlation between SHBG levels and pancreatic ß-cell secretion, mainly for the 120-min post-glucose load proinsulin levels, reinforces the concept that low SHBG levels are a suitable marker of increased pancreatic ß-cell demand.

The demand-supply balancing process in supply chain management - case mining & metals industry equipment

The aim of this thesis is to search how to match the demand and supply effectively in industrial and project-oriented business environment. The demand-supply balancing process is searched through three different phases: the demand planning and forecasting, synchronization of demand and supply and measurement of the results. The thesis contains a single case study that has been implemented in a company called Outotec. In the case study the demand is planned and forecasted with qualitative (judgmental) forecasting method. The quantitative forecasting methods are searched further to support the demand forecast and long term planning. The sales and operations planning process is used in the synchronization of the demand and supply. The demand forecast is applied in the management of a supply chain of critical unit of elemental analyzer. Different meters on operational and strategic level are proposed for the measurement of performance.

Utilization of AMI-Technology in a Multilayered Demand Response Service Environment

This thesis studied the performance of Advanced metering infrastructure systems in a challenging Demand Response environment. The aim was to find out what kind of challenges and bottlenecks could be met when utilizing AMI-systems in challenging Demand Response tasks. To find out the challenges and bottlenecks, a multilayered demand response service concept was formed. The service consists of seven different market layers which consist of Nordic electricity market and the reserve markets of Fingrid. In the simulations the AMI-systems were benchmarked against these seven market layers. It was found out, that the current generation AMI-systems were capable of delivering Demand Response on the most challenging market layers, when observed from time critical viewpoint. Additionally, it was found out, that to enable wide scale Demand Response there are three major challenges to be acknowledged. The challenges hindering the utilization of wide scale Demand Response were related to poor standardization of the systems in use, possible problems in data connectivity solutions and the current electricity market regulation model.

Accuracy improvement of a demand forecasting model

This research concerns different statistical methods that assist to increase the demand forecasting accuracy of company X’s forecasting model. Current forecasting process was analyzed in details. As a result, graphical scheme of logical algorithm was developed. Based on the analysis of the algorithm and forecasting errors, all the potential directions for model future improvements in context of its accuracy were gathered into the complete list. Three improvement directions were chosen for further practical research, on their basis, three test models were created and verified. Novelty of this work lies in the methodological approach of the original analysis of the model, which identified its critical points, as well as the uniqueness of the developed test models. Results of the study formed the basis of the grant of the Government of St. Petersburg.

Evaluating and improving demand forecast practices for B2B and B2C sectors

Demand forecasting is one of the fundamental managerial tasks. Most companies do not know their future demands, so they have to make plans based on demand forecasts. The literature offers many methods and approaches for producing forecasts. Former literature points out that even though many forecasting methods and approaches are available, selecting a suitable approach and implementing and managing it is a complex cross-functional matter. However, it’s relatively rare that researches are focused on the differences in forecasting between consumer and industrial companies. The aim of this thesis is to investigate the potential of improving demand forecasting practices for B2B and B2C sectors in the global supply chains. Business to business (B2B) sector produces products for other manufacturing companies. On the other hand, consumer (B2C) sector provides goods for individual buyers. Usually industrial sector have a lower number of customers and closer relationships with them. The research questions of this thesis are: 1) What are the main differences and similarities in demand planning between B2B and B2C sectors? 2) How the forecast performance for industrial and consumer companies can be improved? The main methodological approach in this study is design science, where the main objective is to develop tentative solutions to real-life problems. The research data has been collected from a case company. Evaluation and improving in organizing demand forecasting can be found in three interlinked areas: 1) demand planning operational environment, 2) demand forecasting techniques, 3) demand information sharing scenarios. In this research current B2B and B2C demand practices are presented with further comparison between those two sectors. It was found that B2B and B2C sectors have significant differences in demand practices. This research partly filled the theoretical gap in understanding the difference in forecasting in consumer and industrial sectors. In all these areas, examples of managerial problems are described, and approaches for mitigating these problems are outlined.

Dexamethasone-induced reactivation of bovine herpesvirus type 5 latent infection in experimentally infected rabbits results in a broader distribution of latent viral DNA in the brain

Bovine herpesvirus type 5 (BHV-5) is a major agent of meningoencephalitis in cattle and establishes latent infections mainly in sensory nerve ganglia. The distribution of latent BHV-5 DNA in the brain of rabbits prior to and after virus reactivation was studied using a nested PCR. Fifteen rabbits inoculated intranasally with BHV-5 were euthanized 60 days post-inoculation (group A, N = 8) or submitted to dexamethasone treatment (2.6 mg kg-1 day-1, im, for 5 days) and euthanized 60 days later (group B, N = 7) for tissue examination. Two groups of BHV-1-infected rabbits (C, N = 3 and D, N = 3) submitted to each treatment were used as controls. Viral DNA of group A rabbits was consistently detected in trigeminal ganglia (8/8), frequently in cerebellum (5/8), anterior cerebral cortex and pons-medulla (3/8) and occasionally in dorsolateral (2/8), ventrolateral and posterior cerebral cortices, midbrain and thalamus (1/8). Viral DNA of group B rabbits showed a broader distribution, being detected at higher frequency in ventrolateral (6/7) and posterior cerebral cortices (5/7), pons-medulla (6/7), thalamus (4/7), and midbrain (3/7). In contrast, rabbits inoculated with BHV-1 harbored viral DNA almost completely restricted to trigeminal ganglia and the distribution did not change post-reactivation. These results demonstrate that latency by BHV-5 is established in several areas of the rabbit's brain and that virus reactivation leads to a broader distribution of latent viral DNA. Spread of virus from trigeminal ganglia and other areas of the brain likely contributes to this dissemination and may contribute to the recrudescence of neurological disease frequently observed upon BHV-5 reactivation.

Addressing conflicts of interest in the research paper: a societal demand in contemporary science?

In the last decade, dialogue between science and society has found a forum in an increasing number of publications on topics such as public engagement with science and public trust in science. Concerning the latter, issues that include cases of research misconduct, accountability in research, and conflicts of interest (COIs) have shaped global discussions on the communication of science. In the publication setting, the perception that hiding COIs and/or not managing them well may affect public trust in the research record has grown among editors. We conducted a search for editorials addressing COIs between 1989 and 2011, using four major databases: Medline/PubMed, Embase, Scopus, and Web of Knowledge. We explored the content of these editorials and the relationship they established between COIs and the public trust in science. Our results demonstrate that the relationship between disclosure of COIs and public trust in science has become a major concern among editors. We, thus, argue that COIs should be discussed more openly and frequently in graduate courses in the sciences, around the globe, not only in biomedical but also in non-biomedical areas. This is a critical issue in contemporary science, as graduate students are the future voices and decision-makers of the research community. Therefore, COIs, especially in the broader context of science and society, merit closer attention from policymakers, researchers, and educators. At times of great expectations for public engagement with science, mishandling of COIs may have undesirable consequences for public engagement with science and confidence in the scientific endeavor.

Households’ willingness to engage in demand response in the Finnish retail electricity market: an empirical study

If electricity users adjusted their consumption patterns according to time-variable electricity prices or other signals about the state of the power system, generation and network assets could be used more efficiently, and matching intermittent renewable power generation with electricity demand would be facilitated. This kind of adjustment of electricity consumption, or demand response, may be based on consumers’ decisions to shift or reduce electricity use in response to time-variable electricity prices or on the remote control of consumers’ electric appliances. However, while demand response is suggested as a solution to many issues in power systems, actual experiences from demand response programs with residential customers are mainly limited to short pilots with a small number of voluntary participants, and information about what kinds of changes consumers are willing and able to make and what motivates these changes is scarce. This doctoral dissertation contributes to the knowledge about what kinds of factors impact on residential consumers’ willingness and ability to take part in demand response. Saving opportunities calculated with actual price data from the Finnish retail electricity market are compared with the occurred supplier switching to generate a first estimate about how large savings could trigger action also in the case of demand response. Residential consumers’ motives to participate in demand response are also studied by a web-based survey with 2103 responses. Further, experiences of households with electricity consumption monitoring systems are discussed to increase knowledge about consumers’ interest in getting more information on their electricity use and adjusting their behavior based on it. Impacts of information on willingness to participate in demand response programs are also approached by a survey for experts of their willingness to engage in demand response activities. Residential customers seem ready to allow remote control of electric appliances that does not require changes in their everyday routines. Based on residents’ own activity, the electricity consuming activities that are considered shiftable are very limited. In both cases, the savings in electricity costs required to allow remote control or to engage in demand response activities are relatively high. Nonmonetary incentives appeal to fewer households.

Heat Demand Forecasting Models’ Development: Use of Data Mining Tools in SQL Server Analysis Services

This thesis introduces heat demand forecasting models which are generated by using data mining algorithms. The forecast spans one full day and this forecast can be used in regulating heat consumption of buildings. For training the data mining models, two years of heat consumption data from a case building and weather measurement data from Finnish Meteorological Institute are used. The thesis utilizes Microsoft SQL Server Analysis Services data mining tools in generating the data mining models and CRISP-DM process framework to implement the research. Results show that the built models can predict heat demand at best with mean average percentage errors of 3.8% for 24-h profile and 5.9% for full day. A deployment model for integrating the generated data mining models into an existing building energy management system is also discussed.

Modelling the exothermic heat and the demand of cooling in paper and pulp industry biological wastewater treatment

This master thesis presents a study on the requisite cooling of an activated sludge process in paper and pulp industry. The energy consumption of paper and pulp industry and it’s wastewater treatment plant in particular is relatively high. It is therefore useful to understand the wastewater treatment process of such industries. The activated sludge process is a biological mechanism which degrades carbonaceous compounds that are present in waste. The modified activated sludge model constructed here aims to imitate the bio-kinetics of an activated sludge process. However, due to the complicated non-linear behavior of the biological process, modelling this system is laborious and intriguing. We attempt to find a system solution first using steady-state modelling of Activated Sludge Model number 1 (ASM1), approached by Euler’s method and an ordinary differential equation solver. Furthermore, an enthalpy study of paper and pulp industry’s vital pollutants was carried out and applied to revise the temperature shift over a period of time to formulate the operation of cooling water. This finding will lead to a forecast of the plant process execution in a cost-effective manner and management of effluent efficiency. The final stage of the thesis was achieved by optimizing the steady state of ASM1.