Inventory management is an important part of a good functioning logistic. Nearly all the literature on optimal inventory management uses criteria of cost minimization and profit maximization. To have a well functioning forecasting system it is important to have a balance in the inventory. But, it exist different factors that can results in uncertainties and difficulties to maintain this balance. One important factor is the customers’ demand. Over half of the stocked items are in stock to prevent irregular orders and an uncertainty demand. The customers’ demand can be categorized into four categories: Smooth, Erratic, Intermittent and Lumpy. Items with a lumpy demand i.e. the items that are both intermittent and erratic are the hardest to manage and to forecast. The reason for this is that the quantity and demand for these items varies a lot. These items may also have periods of zero demand. Because of this, it is a challenge for companies to forecast these items. It is hard to manage the random values that appear at random intervals and leaving many periods with zero demand. Due to the lumpy demand, an ongoing problem for most organization is the inaccuracy of forecasts. It is almost impossible to predict exact forecasts. It does not matter how good the forecasts are or how complex the forecast techniques are, the instability of the markets confirm that the forecasts always will be wrong and that errors therefore always will exist. Therefore, we need to accept this but still work with this issue to keep the errors as minimal and small as possible. The purpose with measuring forecast errors is to identify single random errors and systematic errors that show if the forecast systematically is too high or too low. To calculate the forecast errors and measure the forecast accuracy also helps to dimensioning how large the safety stock should be and control that the forecast errors are within acceptable error margins. The research questions answered in this master thesis are: How should one calculate forecast accuracy for stocked items with a lumpy demand? How do companies measure forecast accuracy for stocked items with a lumpy demand, which are the differences between the methods? What kind of information do one need to apply these methods? To collect data and answer the research questions, a literature study have been made to compare how different researchers and authors write about this specific topic. Two different types of case studies have also been made. Firstly, a benchmarking process was made to compare how different companies work with this issue. And secondly, a case study in form of a hypothesis test was been made to test the hypothesis based on the analysis from the literature review and the benchmarking process. The analysis of the hypothesis test finally generated a conclusion that shows that a combination of the measurements WAPE, Weighted Absolute Forecast Error, and CFE, Cumulative Forecast Error, is a solution to calculate forecast accuracy for items with a lumpy demand. The keywords that have been used to search for scientific papers are: lumpy demand, forecast accuracy, forecasting, forecast error.

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Wind power is being added to the supply mix of numerous jurisdictions, and an increasing level of uncertainties will be the new reality for many system operators. Accurately estimating these uncertainties and properly analyzing their effects will be very important to the reliable operation of the grid. A method is proposed to use historical wind speed, power, and forecast data to estimate the potential future forecast errors. The method uses the weather conditions and ramp events to improve the accuracy of the estimation. A bilevel programming technique is proposed to quantify the effects of the estimated uncertainties. It improves upon existing methods by modeling the transmission network and the re-dispatch of the generators by operators. The technique is tested with multiple systems to illustrate the feasibility of using this technique to alert system operators to potential problems during operation.

Wind power is being added to the supply mix of numerous jurisdictions, and an increasing level of uncertainties will be the new reality for many system operators. Accurately estimating these uncertainties and properly analyzing their effects will be very important to the reliable operation of the grid. A method is proposed to use historical wind speed, power, and forecast data to estimate the potential future forecast errors. The method uses the weather conditions and ramp events to improve the accuracy of the estimation. A bilevel programming technique is proposed to quantify the effects of the estimated uncertainties. It improves upon existing methods by modeling the transmission network and the re-dispatch of the generators by operators. The technique is tested with multiple systems to illustrate the feasibility of using this technique to alert system operators to potential problems during operation.

This research will contribute to the literature of managerial ability and analyst following as well as narrative disclosure in the following ways. This study is the first to investigate the association between managerial ability and external information intermediaries such as financial analysts to the best of our knowledge. Most of the earlier studies on managerial ability focus on firms’ internal information environment such as operating and financial decisions, and limited studies examine the relation between managerial ability with external perception of the information environment and narrative disclosures. We extend this literature by examining how managerial ability impacts the firm's external information environment, affecting informational intermediaries' work processes, such as financial analysts. We find that managers' higher ability leads to better performance by financial analysts regarding their forecast error, dispersion, and willingness to provide coverage on the firm. We also step further by employing more advanced and novel measures to assess managerial ability's impact on market intermediaries’ external work and perception. Able managers impact reporting informativeness, response time, and the uncertainty of the forecasts from financial analysts. Further, we examine informational channels or mediators (i.e., analyst following and readability of narrative disclosure), highlighting how managerial ability can be linked the better performance by financial analysts. We intend to show how variables like disclosure readability and analyst following mediate between managerial ability and analyst forecast properties (i.e., error and Dispersion). In the last part of the research, we answer how analysts' better performance can be a channel to help able managers increase their firms' value (i.e., analyst’s forecast error acts as the channel from the managerial ability to firm’s performance).

Long-range demand planning and capacity management play an important role for policy makers and airline managers alike. Each makes decisions regarding allocating appropriate levels of funds to align capacity with forecasted demand. Decisions today can have long lasting effects. Reducing forecast errors for long-range range demand forecasting will improve resource allocation decision making. This research paper will focus on improving long-range demand planning and forecasting errors of passenger traffic in the U.S. domestic airline industry. This paper will look to build upon current forecasting models being used for U.S. domestic airline passenger traffic with the aim of improving forecast errors published by Federal Aviation Administration (FAA). Using historical data, this study will retroactively forecast U.S. domestic passenger traffic and then compare it to actual passenger traffic, then comparing forecast errors. Forecasting methods will be tested extensively in order to identify new trends and causal factors that will enhance forecast accuracy thus increasing the likelihood of better capacity management and funding decisions.

Transportation infrastructure improvement projects are typically huge and have significant economic and environmental effects. Forecasts of demand of the facility in the form of traffic level help size the project as well as choose between several alternatives. Inaccuracy in these forecasts can thus have a great impact on the efficiency of the operational design and the benefits accrued from the project against the cost. Despite this understanding, evaluation of traffic forecast inaccuracy has been too few, especially for un-tolled roads in the United States. This study, part of a National Cooperative Highway Research Program (NCHRP) funded project, bridges this gap in knowledge by analyzing the historical inaccuracy of the traffic forecasts based on a database created as part of the project. The results show a general over-prediction of traffic with actual traffic deviating from forecast by about 17.29% on an average. The study also compares the relative accuracy of forecasts on several categorical variables. Besides enumerating the error in forecasts, this exploration presents the potential factors influencing accuracy. The results from this analysis can help create an uncertainty window around the forecast based on the explanatory variables, which can be an alternate risk analysis technique to sensitivity testing.

This research investigates the moderating effect of cultural differences between countries on the relationship between tenure and analyst accuracy. To investigate this research looks at the expected earnings per shares and the realised earnings per share from the shares included into the AEX, CAC, DAX and FTSE. The dataset consists of 466 analysts and 3.040 observations. The time period observed is 2015, 2016 and 2017. This research shows that there is no significant relationship between tenure and analyst accuracy. The results show that masculinity, individualism and long term orientation have a moderating effect on analyst accuracy. A practical implication is that managers could employ methods to change the work culture to increase analyst accuracy. An academic implication of this research is that culture should be included as a moderating factor in future analyst accuracy research.

This thesis examines the impact of earnings announcements on the stock return performance. Most literature regarding this topic is related to the US market. We follow 40 of the largest and most liquid stocks on the virtual OMX Nordic Exchange from 2010 to 2012. In this research paper, we present the theoretical framework that gives an overview of the possible research areas, and provide empirical evidence of the repercussion of the earnings announcements on stock returns. We use the event study methodology to conduct this thesis. It is a standard approach established by Fama et al. (1969). It has been used in a variety of researches for gauging the effect of new information on the market value of a security. As we expected good news and bad news to have different reactions on the stock return performances, we have split our data in good news and bad news. To differentiate good news from bad news, we measure analysts’ forecast error. It consists in subtracting the earnings per share (EPS) of the analysts’ consensus forecast from the reported EPS of the same year. The analysis is composed of three different subdivisions: the study of the abnormal return during an event window of 17 days, the cumulative abnormal return during this event window, stock price behavior from growth stocks and from value stocks. Our findings show that stock behavior gradually responds to the earnings announcement. The stock reactions that appear within pre-event window may indicate information leakage. Our results describe most average abnormal returns as statistically insignificant during the event window. Earnings information has a lower impact on the stock market. We also find that the effect of positive earnings surprise on stock price lasts longer than that of negative earnings surprise. Stocks from OMX Nordic 40 index have a stable reaction on negative earnings surprise. As a conclusion, we highlight three points. Earning interim and annual earning information disclosure were unable to influence the stock market effectively, and therefore could not fully reflect the changes on the stock price. Investors can get the abnormal returns by using this earnings information during the whole event window.

The post earnings announcement drift is a market anomaly causing a firms cumulative abnormal returns to drift in the direction of an earnings surprise. By measuring quarterly earnings surprises using two measures. The first based upon a times series prediction and the other based upon on analyst forecast errors. This study finds evidence that the drift ex-ists in Sweden and that investor’s systematically underreacts towards positive earnings sur-prises. Further this study shows that the cumulative average abnormal returns is larger for surprises caused by analyst forecast errors. While previous studies have tried to explain the drift by taking on additional risk or illiquidity in the stocks. This study provides evidence supporting that investors limitations in weighting new information causes an underreaction, hence a drift in the stock prices.