CONTRACTING IN THE PHARMACEUTICAL INDUSTRY: PREDICTING PAYMENTS IN STRATEGIC ALLIANCES

ABSTRACT This paper empirically analyzes how circumstances affect the creation of strategic alliances in the pharmaceutical industry, and the form these alliances take. The models introduced in this paper use the cost of capital and monitoring costs to predict the timing of the deal, which in turn allows the prediction of the deal type. The deal type is then used to predict payment types used in the deal. Deals are characterized by five payment types; upfront, royalty, milestone, equity, and research payments. Deals are also characterized by one of five deal types; co-development, license, acquisition, outsource, and asset purchase. Each of these is a response to a specific contracting problem such as cost of acquiring capital and asymmetric information. The payment types used in pharmaceutical alliances are chosen to efficiently produce monitoring or purchase assets in the face of asymmetric information and to maximize firm profits

Verentarpeen ennustaminen: Havaintoja ja toteutus

Reliable blood supply chains are critically important for modern medicine. However, blood inventories are perishable, which frames the issue as an inventory management problem with separable supply and demand components. Inventory management can be improved via multiple avenues, but reliable demand estimation is among the most powerful ones, as it helps parties involved in blood collection to scale the collection based on projected demand, thus reducing the amount of outdating units and alleviating shortages. The Finnish Red Cross Blood Service (FRCBS) is responsible for maintaining the blood supply chain in Finland. Currently, operational level (donor mobilization) estimates of demand are created weekly by using in-house expertise and planning level (budgeting) estimates by machine-generated statistical forecasts. This thesis aimed to examine the historical performance of the statistical forecasts used for budgeting and to investigate if they could be improved and expanded to monthly and weekly forecasts for different types of red blood cells. The efforts consisted of reviewing the published literature on short-term and long-term blood demand forecasting, examining the available data, establishing appropriate metrics for evaluation, and trying out better models. We find that that the mean absolute percentage error of the current forecasting methods can be improved by 22.2\% with an additional data preprocessing step and by 50.1\% by changing to a better model. The temporal resolution of forecasting was improved by changing the data source. Also, we discovered that the nature of the blood demand signal changes significantly around 2017, underlining the need to develop forecasting systems with the capability to adapt to changes. Our final implementation is built into an R Markdown file to output an easily accessible HTML for reporting. Further exploration is warranted, especially if the aim is to use forecasting operationally someday.Verihuoltoketjun luotettavuus on kriittisen tärkeä osa modernia lääketiedettä. Veri vanhenee muutamassa päivässä, mikä asettaa huoltoketjuongelman varastonhallinnan viitekehykseen erillisillä kysynnän ja tarjonnan osa-alueilla. Varastonhallintaa voi kehittää useilla eri menetelmillä, mutta kysynnän ennustaminen on menetelmistä tehokkaimpien joukossa, sillä se mahdollistaa veren keräyksen kysynnän perusteella vähentäen erääntyvien veripussien määrää ja riittämättömien varastojen riskiä. Suomen Punaisen Ristin ylläpitämä Veripalvelu vastaa verihuoltoketjun ylläpidosta Suomessa. Nykyisellään operationaalisen tason (luovuttajien kutsuminen) ennusteet tehdään viikoittaisissa kokouksissa asiantuntijoiden kokemusta hyödyntäen. Pitemmän aikavälin suunnitelmalliset (budjetointi) ennusteet tehdään laskennallisesti aikasarja-analyysillä. Tämän opinnäytetyön tavoitteena oli arvioida käytössä olevien laskennallisten ennusteiden historiallista tarkkuutta ja selvittää, voiko tarkkuutta parantaa tai ovatko ennusteet laajennettavissa viikoittaisiin ennusteisiin ja useampiin verityyppeihin. Tavoitetta edistettiin kirjallisuuskatsauksella verentarpeen lyhyen ja pitkän aikavälin ennustamiseen, saatavilla olevan datan tarkastelulla, sopivien tarkkuusmittareiden selvittämisellä ja muiden mallien testaamisella. Työn aikana selvisi, että käytössä olevia ennusteita voidaan parantaa 22,2 prosentilla lisäämällä prosessiin uusi datan esikäsittelyvaihe ja 50,1 prosentilla vaihtamalla käytettävää mallia parempaan. Ennusteen aikatarkkuutta saatiin parannettua vaihtamalla datan lähdettä. Opinnäytetyön päälöydös oli kuitenkin verentarpeen signaalin luonteen merkittävä muutos vuoden 2017 paikkeilla, mikä alleviivaa muutoksiin sopeutuvien ennustejärjestelmien tarpeellisuutta. Lopullinen järjestelmä rakennettiin R Markdown -skriptin sisälle helppolukuista HTML-raportointia varten. Tarpeen ennustamisen jatkotutkimusta tarvitaan, varsinkin jos tavoitteena on ennusteiden käyttö operationaalisesti

Application of Machine Learning to Predict Electricity Demand from Electric Vehicles in Workplace Settings

As sustainability-oriented policies begin to be implemented across the world, adapting the current electric power system (EPS) to meet the demands required by those poli- cies is key to meeting emissions targets. Part of those policies includes the continued expansion of the electrification of national transportation systems. This electrifica- tion of transportation will require the vast expansion of electric vehicle (EV) usage as well as the charging networks that will give them power. The consequent growth in anticipated energy demand must be included in infrastructure planning. As a result, forecasting the charging demand of EVs will be a vital tool to plan for the develop- ment of EPS infrastructure. The identification of the best forecasting methods is a key field of research supporting this effort. This thesis analyzed several statistical models and state-of-the-art (SoA) deep learning (DL) machine learning models to determine relevant forecasting tools for predicting EV charging loads in the context of workplace charging. Workplace charging was identified as a gap in research, where fewer attempts to model EV demand at office buildings and places of work had been recorded. The data set chosen was the NREL workplace charging data set, which included daily charging load from 2017-2020. The time series forecasting models tested include ARIMA, SARIMA, XGBoost, LightGBM, RNN, LSTM, GRU, TFT, and N-BEATS. A machine learning modelling pipeline was developed for each model. Results of modelling determined that the SoA DL models TFT and N-BEATS were the top performing models with a mean average percentage error (MAPE) score of 18.9% and 19.5%, respectively, followed by XGBoost with an MAPE of 21.1%. From a residual error analysis, it was found that TFT poorly estimated peak consumption, but was able to more consistently predict the general trends, as compared to XGBoost and N-BEATS, which performed better with extreme fluctuations, but struggled with non-extreme value

Empirical Analysis of Natural Gas Markets

Recent developments in the natural gas industry warrant new analysis of related issues. Environmental, social, and governance (ESG) investments have accelerated the shift away from coal as the dominant source of electricity. Its low environmental impact, reduced volume, and broad availability make liquefied natural gas (LNG) a popular alternative, during this time of transition between traditional fuels and newer options. In the United States, the shale gas revolution has made natural gas a game changer. In this book, we focus on empirical analyses of the natural gas market and its growing relevance worldwide

Intra-alliance performance, control rights, and today's split of tomorrow's value

Although the differential benefits reaped by individual partners are a major determinant of the performance impact of strategic alliances, previous analysis has faced methodological challenges. In response we propose a measure for relative value appropriation and an explicit theoretical framework for predicting its variation in terms of relative bargaining position. With a sample of 180 biotechnology R&D alliances, we are thus able to explain variation in value appropriation across partner types as well as individual partners of each type.Alliance performance; strategic alliances; value appropriation; bargaining position; factor markets;

Antecedents of Sales Lead Performance: Improving Conversion Yield and Cycle Time in a Business-to-Business Opportunity Pipeline

Identifying new potential customers and developing opportunities until converted to sales is a critical function of a sales organization. In most industrial business contexts, opportunities are monitored within a sales pipeline or funnel, to track the status and progress from the initial stage until the sale is completed, often using sales force automation tools, such as customer relationship management (CRM) systems to manage the process. While much is written about the adoption, usage, and failures of CRM, little empirical research exists to fully examine the levers to improve the conversion performance of sales leads, particularly in a business-to-business (B2B) industrial context. The research based view (RBV) of the firm suggests that competitive advantage is gained from a company’s distinct resources, and that in technology and other fast-paced markets, success is further determined by fast adaptation, in what is know as dynamic capability theory. This research examined certain key sales capabilities, within the high technology industrial B2B sector, to understand the impact of sales effort, sales ability and lead source, on sales lead conversion yield and cycle time. By studying the extensive CRM data base of a large semiconductor company, along with various human resource records, a quantitative study was performed to address this research, while providing useful value to sales managers seeking to improve the lead conversion performance of their organizations. Sales effort, as measured by number of sales calls made per week, and percent of time spent on selling activities was shown to modestly accelerate sales cycle times, but have no effect on the percentage of opportunities that result in wins. Sales ability, measured by annual performance ratings, prior year quota attainment and years of experience showed no effect on cycle time, nor win percentage. The most notable contribution of this research is the illumination of sales effort effects on cycle time, as previous studies of sales cycle time influences have been inconclusive. Against the backdrop of a general lengthening of industrial sales cycle times, understanding that salesperson effort can reduce the time that it takes to win an opportunity can drive meaningful improvements in salesforce efficiency and productivity

Estimating UK House Prices using Machine Learning

House price estimation is an important subject for property owners, property developers, investors and buyers. It has featured in many academic research papers and some government and commercial reports. The price of a house may vary depending on several features including geographic location, tenure, age, type, size, market, etc. Existing studies have largely focused on applying single or multiple machine learning techniques to single or groups of datasets to identify the best performing algorithms, models and/or most important predictors, but this paper proposes a cumulative layering approach to what it describes as a Multi-feature House Price Estimation (MfHPE) framework. The MfHPE is a process-oriented, data-driven and machine learning based framework that does not just identify the best performing algorithms or features that drive the accuracy of models but also exploits a cumulative multi-feature layering approach to creating machine learning models, optimising and evaluating them so as to produce tangible insights that enable the decision-making process for stakeholders within the housing ecosystem for a more realistic estimation of house prices. Fundamentally, the MfHPE framework development leverages the Design Science Research Methodology (DSRM) and HM Land Registry’s Price Paid Data is ingested as the base transactions data. 1.1 million London-based transaction records between January 2011 and December 2020 have been exploited for model design, optimisation and evaluation, while 84,051 2021 transactions have been used for model validation. With the capacity for updates to existing datasets and the introduction of new datasets and algorithms, the proposed framework has also leveraged a range of neighbourhood and macroeconomic features including the location of rail stations, supermarkets, bus stops, inflation rate, GDP, employment rate, Consumer Price Index (CPIH) and unemployment rate to explore their impact on the estimation of house prices and their influence on the behaviours of machine learning algorithms. Five machine learning algorithms have been exploited and three evaluation metrics have been used. Results show that the layered introduction of new variety of features in multiple tiers led to improved performance in 50% of models, a change in the best performing models as new variety of features are introduced, and that the choice of evaluation metrics should not just be based on technical problem types but on three components: (i) critical business objectives or project goals; (ii) variety of features; and (iii) machine learning algorithms

Generalized Additive Model Implementation for Germany Real Estate Market - Model, API, UI Development

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsHedonic pricing approach one of the most accepted methodologies for the real estate price assessment by delivering attribute-based value. It emerges from the value changing regarding object attributes conditions. In real estate market, these changes can be property renovation, material, and construction depreciation, or even expanding the plot area. The scope of the internship report is to be explained the development first prototype General Additive Model of predicting House square meter price basis on Hedonic pricing theory for a certain region of Germany. In addition to the model development, bringing it into live via Rest API and User Interface is explained in this report. Data Science Service GMBH is the owner of the project and specialized in real estate property appraisal that is derived from statistical learning models, currently only at Austria. The outcome of this project enables us to get into Germany Real Estate Market as well. The necessary data has been brought by German Market Partner, Forschung und Beratung für Wohnen, Immobilien und Umwelt GmbH (F+B), however Data Science Service GMBH (DSS) is responsible for delivering the model product from beginning to end. R Programming Drake package is used for parallel computation and to be generated maintainable adaptive data pipeline. Parameter selection based on information criteria has been done for each model in every kind of real estate property. Lastly, the statistical model is delivered by rest API to UI (Shiny Application), both are developed with R programming language