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.

<p dir="ltr">This paper examines the resilience of global food and energy supply chains against the background of recent world disruptions such as China-US trade war, novel coronavirus disease 2019 (COVID-19) pandemic, and Russia’s incursion into Ukraine. It aims at improving forecast methodologies and providing early indications of market stressors by considering three key cracks or spreads within the food and energy industries soy crush spread, crude crack spread, and cattle finish spread. The study uses Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing State Space (ETS) and Vector Error Correction Model (VECM). The profit relationships are examined in these models with regard to potential problems for supply chains in the soybean crushing industry, cattle finishing, and crude oil refining sectors. It also compares forecasting approaches like univariate (ARIMA & ETS) and multivariate (VECM). This means that it tries to gauge how accurate each one is in predicting where a given sector may be heading or where there are risks likely to happen. The situation is further complicated by on-going capacity expansions in these sectors which are expected to face more challenges due to geopolitical tensions as well as efforts to mitigate climate change internationally.The overall goal of the research is to develop forecasting methods to help industry participants, policymakers, and small producers make informed decisions amid volatility and the threat of imminent supply chain disruptions.</p>

Bachelor Thesis, Program of Master of Business and Economics, 15 hp School of Business and Economics – Linnaeus University in Växjö 2FE30E:3 Spring, 2017 Authors: Sofie Areskoug and Niklas Karlén Supervisor: Damai Nasution Examiner: Natalia Semenova Keywords: Financial Analyst, Gender, Determinants of forecast accuracy, Sweden Background: The search of finding analysts who make the best forecasts has been an ongoing process since the 1930's. Determinants that can help predict the forecast accuracy of the analysts are in the interest of both investors and brokerage houses. Newer research in this area has taken gender of the analyst into consideration. Women are widely under-represented in the analyst occupation and there is evidence that investors are apprehensive toward women in the financial sector. Purpose: The aim of this thesis is to examine determinants of forecast accuracy regarding analysts covering Swedish companies. The authors have confidence in the research to benefit investors in their decisions on the Swedish stock market. In addition, the authors aim to shed light on the unequal gender representation of female analysts. Method: This thesis has examined 519 individual scores of forecast accuracy from 284 financial analysts covering stocks on the Swedish Index OMXS30. The forecasts are from the years 2016 and 2017. This study has a quantitative strategy and the data have been tested by an OLS estimates regression. Results: The empirical evidence shows that being a female analyst have a statistically significant positive effect on forecast accuracy. Female analysts covering Swedish stocks seem to outperform their male colleagues. Furthermore, insignificant results were found for firm complexity, industry complexity, brokerage house and analyst experience.

High-quality short-term forecasts of wind speed are vital to making wind power a more reliable energy source. Gneiting et al. (2006) have introduced a model for the average wind speed two hours ahead based on both spatial and temporal information. The forecasts produced by this model are accurate, and subject to accuracy, the predictive distribution is sharp, i.e., highly concentrated around its center. However, this model is split into nonunique regimes based on the wind direction at an off-site location. This work both generalizes and improves upon this model by treating wind direction as a circular variable and including it in the model. It is robust in many experiments, such as predicting at new locations. This is compared with the more common approach of modeling wind speeds and directions in the Cartesian space and use a skew-t distribution for the errors. The quality of the predictions from all of these models can be more realistically assessed with a loss measure that depends upon the power curve relating wind speed to power output. This proposed loss measure yields more insight into the true value of each model's predictions. One method of evaluating time series forecasts, such as wind speed forecasts, is to test the null hypothesis of no difference in the accuracy of two competing sets of forecasts. Diebold and Mariano (1995) proposed a test in this setting that has been extended and widely applied. It allows the researcher to specify a wide variety of loss functions, and the forecast errors can be non-Gaussian, nonzero mean, serially correlated, and contemporaneously correlated. In this work, a similar unconditional test of forecast accuracy for spatial data is proposed. The forecast errors are no longer potentially serially correlated but spatially correlated. Simulations will illustrate the properties of this test, and an example with daily average wind speeds measured at over 100 locations in Oklahoma will demonstrate its use. This test is compared with a wavelet-based method introduced by Shen et al. (2002) in which the presence of a spatial signal at each location in the dataset is tested.

Abstract   Purpose - The purpose with this thesis is to investigate how to increase sales forecast accuracy.   Methodology – To fulfil the purpose a case study was conducted. To collect data from the case study the authors performed interviews and gathered documents. The empirical data was then analysed and compared with the theoretical framework.   Result – The result shows that inaccuracies in forecasts are not necessarily because of the forecasting technique but can be a result from an unorganized forecasting process and having an inefficient information flow. The result further shows that it is not only important to review the information flow within the company but in the supply chain as whole to improve a forecast’s accuracy. The result also shows that time series can generate more accurate sales forecasts compared to only using qualitative techniques. It is, however, necessary to use a qualitative technique when creating time series. Time series only take time and sales history into account when forecasting, expertise regarding consumer behaviour, promotion activity, and so on, is therefore needed. It is also crucial to use qualitative techniques when selecting time series technique to achieve higher sales forecast accuracy. Personal expertise and experience are needed to identify if there is enough sales history, how much the sales are fluctuating, and if there will be any seasonality in the forecast. If companies gain knowledge about the benefits from each technique the combination can improve the forecasting process and increase the accuracy of the sales forecast.   Conclusions – This thesis, with support from a case study, shows how time series and qualitative techniques can be combined to achieve higher accuracy. Companies that want to achieve higher accuracy need to know how the different techniques work and what is needed to take into account when creating a sales forecast. It is also important to have knowledge about the benefits of a well-designed forecasting process, and to do that, improving the information flow both within the company and the supply chain is a necessity.      Research limitations – Because there are several different techniques to apply when creating a sales forecast, the authors could have involved more techniques in the investigation. The thesis work could also have used multiple case study objects to increase the external validity of the thesis.

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.

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<p>In this paper, the forecast performance of an unrestricted Vector Autoregressive (VAR) model was compared against the forecast accuracy of a Vector error correction (VECM) model when computing out-of-sample forecasts for Swedish exports. The co-integrating relation used to estimate the error correction specification was based upon an economic theory for international trade suggesting that a long run equilibrium relation among the variables included in an export demand equation should exist. The results obtained provide evidence of a long run equilibrium relationship between the Swedish export volume and its main determinants. The models were estimated for manufactured goods using quarterly data for the period 1975-1999 and once estimated, the models were used to compute out-of-sample forecasts up to four-, eight- and twelve-quarters ahead for the Swedish export volume using both multi-step and one-step ahead forecast techniques. The main results suggest that the differences in forecasting ability between the two models are small, however according to the relevant evaluation criteria the unrestricted VAR model in general yields somewhat better forecast than the VECM model when forecasting Swedish exports over the chosen forecast horizons.</p>

Hurricane Katrina of 2005 was responsible for at least 81 billion dollars of property damage. In planning for such emergencies, society must decide whether to invest in the ability to evacuate more speedily or in improved forecasting technology to better predict the timing and intensity of the critical event. To address this need, we use dynamic programming and Markov processes to model the interaction between the emergency response system and the emergency forecasting system. Simulating changes in the speed of evacuation and in the accuracy of forecasting allows the determination of an optimal mix of these two investments. The model shows that the evacuation improvement and the forecast improvement give different patterns of impact to their benefit. In addition, it shows that the optimal investment decision changes by the budget and the feasible range of improvement.

The future is uncertain. We therefore make predictions and forecasts of the future in order to be able to plan and react to future events. For this purpose, financial analysts are argued to have a responsibility towards investors and the market, in helping to keep the market efficient. Given that financial analysts act in a rational way we argue that analysts should strive to maximize forecast accuracy. The purpose of this study is to investigate how accurate financial analysts’ forecasts of Swedish firms’ future values are, and what information that analysts use that significantly affect the analysts’ forecast accuracy. To investigate this we first examine whether financial analysts contribute with value to investors by comparing their forecast precision against a simple time-series model. Our findings show that financial analysts produce significantly more accurate forecasts than a time-series model in the short term. Furthermore, given that rational analysts act in their own best interest while making accurate forecasts, we argue that analysts will incorporate and use the information that is available to them for the purpose of maximizing forecast accuracy. We investigate this by testing if the analysts’ forecast accuracy is affected by; the forecast horizon, the number of analysts following a firm, the firm size, the corporate visibility, the predictability of earnings, and trading volume. We find that the forecast accuracy is better when the amount of analysts following a firm is high, the firm size is larger, the forecasted company’s corporate visibility in the news is more frequent, and the predictability of earnings is higher. The trading volume does not have a significant effect on analysts’ forecast accuracy. To conclude, we question the value of financial analysts’ forecasts for longer forecast horizons.