The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study

Leveraging analytics to produce compelling and profitable film content

Producing compelling film content profitably is a top priority to the long-term prosperity of the film industry. Advances in digital technologies, increasing availabilities of granular big data, rapid diffusion of analytic techniques, and intensified competition from user generated content and original content produced by Subscription Video on Demand (SVOD) platforms have created unparalleled needs and opportunities for film producers to leverage analytics in content production. Built upon the theories of value creation and film production, this article proposes a conceptual framework of key analytic techniques that film producers may engage throughout the production process, such as script analytics, talent analytics, and audience analytics. The article further synthesizes the state-of-the-art research on and applications of these analytics, discuss the prospect of leveraging analytics in film production, and suggest fruitful avenues for future research with important managerial implications

Estimation Of Idle Time Using Machine Learning Models For Vehicle-To-Grid (V2G) Integration And Services

As the Electric Vehicles (EVs) market continues to expand, ensuring the access to charging stations remains a significant concern. This work focuses on addressing multiple challenges related to EV charging behavior and Vehicle-to-Grid (V2G) services. Firstly, it focuses on accurate minute-ahead (20 minute \& 30 minute intervals) load forecasts for an EV charging station by using four years of historical data, from 2018-2021. This data is recorded from a university campus garage charging station. Machine Learning (ML) models such as Seasonal Auto-Regressive Integrated Moving Average (SARIMA), Random Forest (RF), and Neural Networks (NN) are employed for load forecasts in terms of Kilowatt hour (kWh) delivered from 54 charging stations. Preliminary results indicate that RF method performed better compared to other ML approaches, achieving a average Mean Absolute Error (MAE) of 7.26 on historical weekdays data. Secondly, it focuses on estimating the probability of aggregated available capacity of users for V2G connections, which could be sold back to the grid through V2G system. To achieve this, an Idle Time (IT) parameter was tracked from the time spent by the EV users at the charging station after being fully charged. ML classification methods such as Logistic Regression (LR) and Linear Support Vector Classifier (SVC) were employed to estimate the IT variable. The SVC model performed better in estimating IT variable with an accuracy of 85% over LR 81%. This work also analyzes the aggregated excess kWh available from the charging stations for V2G services, which offer benefits to both EV owners through incentives and the grid by balancing the load. ML models, including Support Vector Regressor (SVR), Gradient Boosting Regressor (GBR), Long-Short Term Memory (LSTM), and Random Forest (RF), are employed. LSTM performs better for this prediction problem with a Mean Absolute Percentage Error (MAPE) of 3.12, and RF as second best with lowest 3.59, when considering historical data on weekdays. Furthermore, this work estimated the number of users available for V2G services corresponding to 15\% and 30\% of excess kWh, by using ML classification models such as Decision Tree (DT) and K Nearest Neighbor (KNN). Among these models, DT performed better, with highest 89% and 84% accuracy respectively. This work also investigated the impact of the COVID-19 pandemic on EV users\u27 charging behavior. This study analyzes the behavior modelled as before, after, and during COVID-19, employing data visualization using K-means and hierarchical clustering methods to identify common charging pattern with connection and disconnection time of the vehicles. K-means clustering proves to be more effective in all three scenarios modeled with a high silhouette index. Furthermore, prediction of collective charging session duration is achieved using ML Models, RF and XgBoost which achieved a MAPE of 14.6% and 15.1% respectively

New product sales forecasting: the relative accuracy of statistical, judgemental and combination forecasts

This research investigates three approaches to new product sales forecasting: statistical, judgmental and the integration of these two approaches. The aim of the research is to find a simple, easy-to-use, low cost and accurate tool which can be used by managers to forecast the sales of new products. A review of the literature suggested that the Bass diffusion model was an appropriate statistical method for new product sales forecasting. For the judgmental approach, after considering different methods and constraints, such as bias, complexity, lack of accuracy, high cost and time involvement, the Delphi method was identified from the literature as a method, which has the potential to mitigate bias and produces accurate predictions at a low cost in a relatively short time. However, the literature also revealed that neither of the methods: statistical or judgmental, can be guaranteed to give the best forecasts independently, and a combination of them is the often best approach to obtaining the most accurate predictions. The study aims to compare these three approaches by applying them to actual sales data. To forecast the sales of new products, the Bass diffusion model was fitted to the sales history of similar (analogous) products that had been launched in the past and the resulting model was used to produce forecasts for the new products at the time of their launch. These forecasts were compared with forecasts produced through the Delphi method and also through a combination of statistical and judgmental methods. All results were also compared to the benchmark levels of accuracy, based on previous research and forecasts based on various combinations of the analogous products’ historic sales data. Although no statistically significant difference was found in the accuracy of forecasts, produced by the three approaches, the results were more accurate than those obtained using parameters suggested by previous researchers. The limitations of the research are discussed at the end of the thesis, together with suggestions for future research.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Decision-making under uncertainty in short-term electricity markets

In the course of the energy transition, the share of electricity generation from renewable energy sources in Germany has increased significantly in recent years and will continue to rise. Particularly fluctuating renewables like wind and solar bring more uncertainty and volatility to the electricity system. As markets determine the unit commitment in systems with self-dispatch, many changes have been made to the design of electricity markets to meet the new challenges. Thereby, a trend towards real-time can be observed. Short-term electricity markets are becoming more important and are seen as suitable for efficient resource allocation. Therefore, it is inevitable for market participants to develop strategies for trading electricity and flexibility in these segments. The research conducted in this thesis aims to enable better decisions in short-term electricity markets. To achieve this, a multitude of quantitative methods is developed and applied: (a) forecasting methods based on econometrics and machine learning, (b) methods for stochastic modeling of time series, (c) scenario generation and reduction methods, as well as (d) stochastic programming methods. Most significantly, two- and three-stage stochastic optimization problems are formulated to derive optimal trading decisions and unit commitment in the context of short-term electricity markets. The problem formulations adequately account for the sequential structure, the characteristics and the technical requirements of the different market segments, as well as the available information regarding uncertain generation volumes and prices. The thesis contains three case studies focusing on the German electricity markets. Results confirm that, based on appropriate representations of the uncertainty of market prices and renewable generation, the optimization approaches allow to derive sound trading strategies across multiple revenue streams, with which market participants can effectively balance the inevitable trade-off between expected profit and associated risk. By considering coherent risk metrics and flexibly adaptable risk attitudes, the trading strategies allow to substantially reduce risk with only moderate expected profit losses. These results are significant, as improving trading decisions that determine the allocation of resources in the electricity system plays a key role in coping with the uncertainty from renewables and hence contributes to the ultimate success of the energy transition

A top-down implementation of a dynamic management control system - as easy as it seemed? A case study of Sparebank 1 Nord Norge

Masteroppgave i bedriftsøkonomi - Nord universitet, 201

Leveraging improved seed technology, migration and climate information for building the adaptive capacity and resilience to climate risks in semi-arid regions

Droughts induced by climate change will most likely push dryland ecosystems beyond their biophysical thresholds and lead to long-term decline in agricultural productivity. Subsistence farming in developing countries where agricultural productivity is low will become less viable for many families already ravaged by food insecurity and poverty. This dissertation examines three ways of reducing vulnerability to the adverse effects of climate variability and building resilience in the farming communities residing in semiarid lands. These include the use of adaptive seed technology, migration as a livelihood diversification and adaptive strategy, and the use of climate information in farm decision-making. The second chapter evaluates the impact of improved adaptive seed technology on market participation and food security, using data from a representative sample of 1344 households selected across six agroecological zones in Kenya. The study employed two estimation procedures for impact evaluation: a control function regression using OLS and IV regression estimated by Heckman bivariate sample selection model and 2SLS regression. The study used percentile shares approach to describe distributional inequalities in improved seed adoption across households. Kenya has a well-developed seed system, through which adaptive maize seed has been introduced for various agro-ecological zones. Despite its success with improved maize breeding programs, Kenya is still grappling with food insecurity. The marketed share of household's maize produce, among adopters, was on average 12 percentage points higher than for the control group. This increased with adoption intensity, albeit at a decreasing rate. The top 20% of households accounted for 63% of the quantity and 65% of the area planted with improved maize. The bottom 40% only accounted for 6% of the quantity purchased and 5% of the area planted with improved maize. Adopting households were less vulnerable to food insecurity and stored maize for longer than non-adopters. Larger families participated less in the market and were more food insecure. Wealth and education are other key determinants of food security and market participation. The results of the study indicate a need for a strategic policy on food security in Kenya that considers the concentrated nature of the maize farming sector, to address the problem of food insecurity. Such a policy could aim at food self-sufficiency for small farms and promote commercial production by large-scale producers for national strategic reserves. There is also a need for post-harvest policies that promote safe on-farm grain storage for small and medium scale producers. The third chapter focuses on migration, because of the growing interest among scholars in understanding the relationship between migration and adaptation to climate change. Past studies have looked at climate change as a trigger for migration, but the focus has now shifted to looking at migration as an enabler of climate-change adaptation and a livelihood diversification strategy. However, those most vulnerable to climate variability are the poor who are less able to afford mobility and entry costs. This study adds to the literature by evaluating, in chapter 3, the impact of migration on household consumption expenditure, relative food expenditure share, dietary diversity, spending on agricultural inputs and adaptive capacity. The study used survey data collected from a representative sample of 653 households across three arid regions of Northern Namibia. The study employed a novel identification strategy in migration studies by combining the standard exogenous instruments and Lewbel's constructed instruments using heteroscedastic errors. The study found two-thirds of the sampled households to be migrant-sending households. Poverty and the lack of economic opportunities in the rural villages were the main push factors driving migration to towns and cities. Although tertiary education and technical training of the migrants are key determinants of remittances received by migrant-sending households, over three quarters of the migrants were unskilled and very few having tertiary level training. Migrant-sending households had lower consumption spending and higher food budget share, suggesting relative deprivation. Although consumption spending increased with number of migrants, quality of human capital had greater impact on well-being. Migration had a positive impact on household's adaptive capacity but an inverse relationship between number of migrants and adaptation suggests failure of local adaptive strategies. The study finds households with migrants to have a significantly higher spending on agricultural inputs than those without migrants, with tractor-hire services for land preparation being a major component. The effect of family labour loss is somehow, through remittances, countervailed and compensated by mechanization. In conclusion, migration can potentially play a bigger role as an adaptive and risk-mitigation strategy in the face of climate variability, but poverty, lack of post-school skills training, and low transition to tertiary-level training are key barriers. Developing markets for credit, inputs and farm output, and preparing migrants for participation in labour markets and self-employment through training can further enhance the impact of migration and build resilience to climate shocks. Due to selfreinforcing poverty traps in poor households, the study recommends targeted public programs that support higher education and technical training. Lastly, chapter 4 examined the role of climate information and early warning in decision-making among farming communities in rural Namibia. Improved climate forecasting has been heralded as an important risk management and mitigation tool in climate-sensitive economic sectors such as agriculture. However, Africa has not reaped the benefits of improved climate forecasting and empirical studies about its impact are scanty. Chapter 4 first discusses access to and utilization of climate information in farm decisionmaking, and then evaluates its impact on dietary diversity, food spending and adaptive capacity of the households using propensity score matching, with a sensitivity analysis for hidden bias. Only half of the farmers had access to climate information and most of them relied primarily on traditional knowledge to make decisions on crop and livestock production. Many of the households without access to climate information also had little knowledge of alternative adaptive strategies. The likelihood of receiving climate information increased with the number of migrants per household, household size, social networks, trust and participation in community decision-making processes, but declined with age. Although male heads were more likely to receive climate information, females headed most of the households. The main sources of information for farmers were radios and peer learning. Respondents expressed a low level of trust in information from available channels and most of them rated the information received as insufficient for decision-making. Although 95% of households owned mobile phones, only 5% received information through them, indicating untapped opportunity of using an ICT platform to share information with farmers. Households with climate information had more diversified diets and significantly higher food spending. These households also engaged in more adaptive strategies, but the scale of adoption was small. Community empowerment through enhanced access to extension services, information on alternative adaptive choices, and the development of markets, rural communication and transport infrastructure are prerequisites to access to and effective utilization of improved climate forecast information for successful adaptation

Essays in Applied Time Series Analysis

Many economic and financial issues cannot be answered without analyzing time series. For example, it can be used to find factors that are useful in predicting stock returns, or to estimate the sensitivity of losses on bank loans to business cycle fluctuations. This thesis presents three essays in applied time series analysis, using a variety of methods to answer address several important issues in economics and finance. The first essay is on the cyclicality in losses on bank loans. A unique data set allows the joint modeling of losses and macroeconomic variables, while taking into account key characteristics of the distribution of losses. The second essay is on the impact of parameter instability on the allocation of the long-term investor. The instability is estimated, and the misspecification costs are assessed. The third essay is a forecasting exercise to see whether the relationship between economic uncertainty and macroeconomic output can be exploited in real-time such that it can be used as input for policy makers

Technology and Australia's Future: New technologies and their role in Australia's security, cultural, democratic, social and economic systems

Chapter 1. Introducing technology -- Chapter 2. The shaping of technology -- Chapter 3. Prediction of future technologies -- Chapter 4. The impacts of technology -- Chapter 5. Meanings, attitudes and behaviour -- Chapter 6. Evaluation -- Chapter 7. Intervention -- Conclusion - adapt or wither.This report was commisioned by Australian Council of Learned Academies