The objective of this work is to design and implement an architectural framework for a web-based demand management system that allows an electric utility to reduce system peak load by automatically managing end-use appliances based on homeowners' preferences. The proposed framework comprises the following components: human user interface, home energy management (HEM) algorithms, web services for demand response communications, selected ZigBee and smart energy profile features for appliance interface, and security aspects for a web-based HEM system. The proposed web-based HEM system allows homeowners to be more aware about their electricity consumption by allowing visualization of their real-time and historical electricity consumption data. The HEM system enables customers to monitor and control their household appliances from anywhere with an Internet connection. It offers a user-friendly and attractive display panel for a homeowner to easily set his/her preferences and comfort settings. An algorithm to autonomously control appliance operation is incorporated in the proposed web-based HEM system, which makes it possible for residential customers to participate in demand response programs. In this work, the algorithm is demonstrated to manage power-intensive appliances in a single home, keeping the total household load within a certain limit while satisfying preset comfort settings and user preferences. Furthermore, an extended version of the algorithm is demonstrated to manage power-intensive appliances for multiple homes within a neighborhood. As one of the demand response (DR)-enabling technologies, the web services-based DR communication has been developed to enable households without smart meters or advanced metering infrastructure (AMI) to participate in a DR event via the HEM system. This implies that an electric utility can send a DR signal via a web services-enabled HEM system, and appropriate appliances can be controlled within each home based on homeowner preferences. The interoperability with other systems, such as utility systems, third-party Home Area Network (HAN) systems, etc., is also taken into account in the design of the proposed web services-based HEM system. That is, it is designed to allow interaction with authorized third-party systems by means of web services, which are collectively an interface for machine-to-machine interaction. This work also designs and implements device organization and interface for end-use appliances utilizing ZigBee Device Profile and Smart Energy Profile. Development of the Home Area Network (HAN) of appliances and the HAN Coordinator has been performed using a ZigBee network. Analyses of security risks for a web-based HEM system and their mitigation strategies have been discussed as well. / Master of Science

No description available.

In this study, the possibility that money demand of one country might be affected by macroeconomic activities of other countries is investigated. We use the seemingly unrelated regression (SUR) technique, which takes into account all covariances between residuals of country-specific money demand equations. Efficiency of estimates using the SUR technique is enhanced because it uses information contained in the contemporaneous correlation of the error terms. The hypothesis of economic interdependence is tested. A proxy for foreign influence, deviation from interest rate parity (DIRP), is tested for significance in the money demand function.

To handle uncertainties and variabilities in production demands, many manufacturing companies have adopted different strategies, such as varying quoted lead time, rejecting orders, increasing stock or inventory levels, and implementing volume flexibility. Make-to-stock (MTS) systems are designed to offer zero lead time by providing an inventory buffer for the organizations, but they are costly and involve risks such as obsolescence and wasted expenditures. The main concern of make-to-order (MTO) systems is eliminating inventories and reducing the non-value-added processes and wastes; however, these systems are based on the assumption that the manufacturing environments and customers' demand are deterministic. Research shows that in MTO systems variability and uncertainty in the demand levels causes instability in the production flow, resulting in congestion in the production flow, long lead times, and low throughput. Neither strategy is wholly satisfactory. A new alternative approach, multi-channel manufacturing (MCM) systems are designed to manage uncertainties and variabilities in demands by first focusing on customers' response time. The products are divided into different product families, each with its own manufacturing stream or sub-factory. MCM also allocates the production capacity needed in each sub-factory to produce each product family. In this research, the performance of an MCM system is studied by implementing MCM in a real case scenario from textile industry modeled via discrete event simulation. MTS and MTO systems are implemented for the same case scenario and the results are studied and compared. The variables of interest for this research are the throughput of products, the level of on-time deliveries, and the inventory level. The results conducted from the simulation experiments favor the simulated MCM system for all mentioned criteria. Further research activities, such as applying MCM to different manufacturing contexts, is highly recommended.

Due to the increasingly uncertain environments that global supply chains operate within, both due to component shortages and other types of challenges, the possibility of managing demand variability and balancing it with supply capabilities is getting more challenging. The primary way to deal with these fluctuations in demand is by building flexibility in the supply chain to meet the variations that occur and keep the customers satisfied. However, when flexibility is not enough, and the supply chain becomes increasingly strained due to geopolitical factors and customers demanding higher customizations, more efforts are required to manage the variability. This study investigates the possibility that instead of relying solely on flexibility, try to deal with the variations that arise in demand. This relates both to internal processes that increase variation but also to the variations that are caused by actual changes in demand. The study partly examines what drives internal variation and how one should work to minimize it. Moreover, concerning the external variation, demand shaping theory is applied. This is to understand how external variation can be handled by trying to steer the demand. As the theory in previous studies has primarily been applied in B2C contexts, the applicability of the theory in a B2B context is also examined. Furthermore, the study also investigates how demand and supply should be integrated to utilize the concepts in the best way possible when managing demand variability. The study has been conducted through a case study at Ericsson, where people who work within sales and supply have participated and contributed with their knowledge. The results show the importance of integration between different functions to succeed in managing demand variations and having a more significant impact on what customers buy. This means both clear communication, internally and externally, and the importance of having a unified vision and incentives that drive the company towards the same goal. In addition, the results also show that although the academic literature is mainly aimed at B2C companies, it is possible to apply the concept to B2B companies as well. / Till följd av den alltmer osäkra omvärld som globala leveranskedjor arbetar inom, med både komponentbrist och andra typer av utmaningar, blir det allt svårare att hantera den varierande efterfrågan och balansera den mot den kapacitet som företag besitter. Det primära sättet att hantera dessa fluktuationer i efterfrågan är genom att bygga in flexibilitet för att kunna bemöta de variationer som uppstår och för att behålla kunder nöjda. När flexibiliteten inte räcker till och leveranskedjan blir alltmer ansträngd, på grund av geopolitiska faktorer och kunder som kräver alltmer kundanpassade produkter, behöver man arbeta än mer med att försöka hantera variationen som uppstår. Syftet med denna studie är därför att undersöka möjligheten att, i stället för att enbart förlita sig på flexibilitet, försöka hantera de variationer som uppstår i efterfrågan. Detta relaterar både till interna processer som driver variationen men också de variationer som orsakas av faktiska förändringar i efterfrågan. Studien undersöker vad det är som driver intern variation och hur man bör arbeta för att minimera denna. I relation till den externa variationen, används bland annat demand shaping teori för att förstå hur denna typ av variation kan hanteras genom att försöka ha mer inflytande över vad kunderna väljer att köpa. Då teorin i tidigare studier enbart applicerats i B2C sammanhang, undersöks även om teorin är applicerbar i en B2B kontext. Studien undersöker också hur funktioner inom försäljning och supply bör integreras för att möjliggöra detta. Studien har genomförts som en fallstudie på Ericsson där personer som arbetar inom försäljning och supply deltagit och bidragit med sin kunskap. Resultaten visar på vikten av integration mellan olika funktioner för att lyckas hantera demand variationer och på att ha större inverkan på det kunderna köper. Detta innebär tydlig kommunikation både internt och externt, samt värdet av att ha gemensamma mål och incitament som driver företaget mot samma vision. Resultaten visar dessutom på att det finns förutsättningar för att applicera det undersökta konceptet på B2B företag trots att den akademiska litteraturen främst riktat in sig mot B2C företag.

Application of multisource feedback (MSF) increased dramatically and became widespread globally in the past two decades, but there was little conceptual work regarding self-other agreement and few empirical studies investigated self-other agreement in other cultural settings. This study developed a new conceptual framework of self-other agreement and used three samples to illustrate how national culture affected self-other agreement. These three samples included 428 participants from China, 818 participants from the US, and 871 participants from globally dispersed teams (GDTs). An EQS procedure and a polynomial regression procedure were used to examine whether the covariance matrices were equal across samples and whether the relationships between self-other agreement and performance would be different across cultures, respectively. The results indicated MSF could be applied to China and GDTs, but the pattern of relationships between self-other agreement and performance was different across samples, suggesting that the results found in the U.S. sample were the exception rather than rule. Demographics also affected self-other agreement disparately across perspectives and cultures, indicating self-concept was susceptible to cultural influences. The proposed framework only received partial support but showed great promise to guide future studies. This study contributed to the literature by: (a) developing a new framework of self-other agreement that could be used to study various contextual factors; (b) examining the relationship between self-other agreement and performance in three vastly different samples; (c) providing some important insights about consensus between raters and self-other agreement; (d) offering some practical guidelines regarding how to apply MSF to other cultures more effectively.

The depleting fossil fuel and environmental concerns have created a revolutionary movement towards the installation and utilization of Renewable Energy Sources (RES) such as wind and solar energy. The RES entails challenges, both in regards to the physical integration into a grid system and regarding management of the expected demand. The flexibility in energy demand can facilitate the alignment of the supply and demand to achieve a dynamic Demand Response (DR). The flexibility is often not explicitly available or provided by a user and has to be analyzed and extracted automatically from historical consumption data. The predictive analytics of consumption data can reveal interesting patterns and periodicities that facilitate the effective extraction and representation of flexibility. The device-level analysis captures the atomic flexibilities in energy demand and provides the largest possible solution space to generate demand/supply schedules. The presence of stochasticity and noise in the device-level consumption data and the unavailability of contextual information makes the analytics task challenging. Hence, it is essential to design predictive analytical techniques that work at an atomic data granularity and perform various analyses on the effectiveness of the proposed techniques. The Ph.D. study is sponsored by the TotalFlex Project (http://www.totalflex.dk/) and is part of the IT4BI-DC program with Aalborg University and TU Dresden as Home and Host University, respectively. The main objective of the TotalFlex project is to develop a cost-effective, market-based system that utilizes total flexibility in energy demand, and provide financial and environmental benefits to all involved parties. The flexibilities from various devices are modeled using a unified format called a flex-offer, which facilitates, e.g., aggregation and trading in the energy market. In this regards, this Ph.D. study focuses on the predictive analytics of the historical device operation behavior of consumers for an efficient and effective extraction of flexibilities in their energy demands. First, the thesis performs a comprehensive survey of state-of-the-art work in the literature. It presents a critical review and analysis of various previously proposed approaches, algorithms, and methods in the field of user behavior analysis, forecasting, and flexibility analysis. Then, the thesis details the flexibility and flex-offer concepts and formally discusses the terminologies used throughout the thesis. Second, the thesis contributes to a comprehensive analysis of energy consumption behavior at the device-level. The key motive of the analysis is to extract device operation patterns of users, the correlation between devices operations, and influence of external factors in device-level demands. A novel cost/benefit trade-off analysis of device flexibility is performed to categorize devices into various segments according to their flexibility potential. Moreover, device-specific data preprocessing steps are proposed to clean device-level raw data into a format suitable for flexibility analysis. Third, the thesis presents various prediction models that are specifically tuned for device-level energy demand prediction. Further, it contributes to the feature engineering aspect of generating additional features from a demand consumption timeseries that effectively capture device operation preferences and patterns. The demand predictions utilize the carefully crafted features and other contextual information to improve the performance of the prediction models. Further, various demand prediction models are evaluated to determine the model, forecast horizon, and data granularity best suited for the device-level flexibility analysis. Furthermore, the effect of the forecast accuracy on flexibility-based DR is evaluated to identify an error level a market can absorb maintaining profitability. Fourth, the thesis proposes a generalized process for automated generation and evaluation of flex-offers from the three types of household devices, namely Wet-devices, Electric Vehicles (EV), and Heat Pumps. The proposed process automatically predicts and estimates times and values of device-specific events representing flexibility in its operations. The predicted events are combined to generate flex-offers for the device future operations. Moreover, the actual flexibility potential of household devices is quantified for various contextual conditions and degree days. Fifth, the thesis presents user-comfort oriented prescriptive techniques to prescribe flex-offers schedules. The proposed scheduler considers the trade-off between both social and financial aspects during scheduling of flex-offers, i.e., maximizing the financial benefits in a market and at the same time minimizing the loss of user comfort. Moreover, it also provides a distance-aware error measure that quantifies the actual performance of forecast models designed for flex-offers generation and scheduling. Sixth, the thesis contributes to the comprehensive analysis of the financial viability of device-level flexibility for dynamic balancing of demand and supply. The thesis quantifies the financial benefits of flexibility and investigates the device type specific market that maximizes the potential of flexibility, both regarding DR and financial incentives. Henceforth, a financial analysis of each proposed technique, namely forecast model, flex-offer generation model, and flex-offer scheduling is performed. The key motive is to evaluate the usability of the proposed models in the device-level flexibility based DR scheme and their potential in generating a positive financial incentive to markets and customers. Seven, the thesis presents a benchmark platform for device-level demand prediction. The platform provides the research community with a centralized repository of device-level datasets, forecast models, and functionalities that facilitate comparisons, evaluations, and validation of device-level forecast models. The results of the thesis can contribute to the energy market in materializing the vision of utilizing consumption and production flexibility to obtain dynamic energy balance. The developed demand forecast and flex-offer generation models also contribute to the energy data analytics and data mining fields. The quantification of flexibility further contributes by demonstrating the feasibility and financial benefits of flexibility-based DR. The developed experimental platform provide researchers and practitioners with the resources required for device-level demand analytics and prediction.

Customer Relationship Management has been an integral part of the enterprise since two decades. Today, enterprises that focus on customer satisfaction need to manage their relationships with their customers effectively. This demand has allowed software vendors to create CRM solutions. The technology and broadband advancement allowed the CRM vendors to enhance their product portfolio by developing web-based CRM systems, in addition to their CRM on-premise solutions. These vendors adopted the business model in which CRM on-demand systems are provided via monthly-subscription fees, decreasing the total cost of ownership massively for enterprises in need of these systems. This business model is especially attractive for Small-To-Medium Enterprises who are searching for cost-efficient CRM systems. Currently, CRM on-demand market is quite saturated with more than 40 vendors providing similar solutions. Furthermore, CRM on-demand is delivered via Software-as-a-service method, which is a relatively new technology with unique benefits along with drawbacks. Thus, it’s of vital importance for managers in SMEs to make the right decision while evaluating the CRM on-demand option and systems. This research is meant to address this issue by building a proposed framework for CRM on-demand system evaluation. The inductive research uses qualitative and quantitative approaches for data collection and analysis. The evaluation criteria for CRM on-demand systems at a functional and general level were proposed. The general criteria were refined via collecting data from CRM on-demand experts and users in SMEs by structured questionnaires. Combining these criteria created the proposed framework which was applied to evaluate two major CRM on-demand systems in the market. The results indicate that CRM on-demand systems cover the basic functionalities of CRM including sales, marketing and service modules and offer enhanced functionality such as mobile CRM, social CRM and customizations. The research also revealed drawbacks of CRM on-demand systems such as disintegration with legacy applications, limited language support, limited country availability and technology maturity which needs to be addressed in the future. This research provides valuable insight for managers in SMEs when selecting CRM on-demand systems for their companies. Furthermore, the academicians interested in CRM and cloud computing could improve this initial proposed framework and adapt it further to different cases.

The depleting fossil fuel and environmental concerns have created a revolutionary movement towards the installation and utilization of Renewable Energy Sources (RES) such as wind and solar energy. The RES entails challenges, both in regards to the physical integration into a grid system and regarding management of the expected demand. The flexibility in energy demand can facilitate the alignment of the supply and demand to achieve a dynamic Demand Response (DR). The flexibility is often not explicitly available or provided by a user and has to be analyzed and extracted automatically from historical consumption data. The predictive analytics of consumption data can reveal interesting patterns and periodicities that facilitate the effective extraction and representation of flexibility. The device-level analysis captures the atomic flexibilities in energy demand and provides the largest possible solution space to generate demand/supply schedules. The presence of stochasticity and noise in the device-level consumption data and the unavailability of contextual information makes the analytics task challenging. Hence, it is essential to design predictive analytical techniques that work at an atomic data granularity and perform various analyses on the effectiveness of the proposed techniques. The Ph.D. study is sponsored by the TotalFlex Project (http://www.totalflex.dk/) and is part of the IT4BI-DC program with Aalborg University and TU Dresden as Home and Host University, respectively. The main objective of the TotalFlex project is to develop a cost-effective, market-based system that utilizes total flexibility in energy demand, and provide financial and environmental benefits to all involved parties. The flexibilities from various devices are modeled using a unified format called a flex-offer, which facilitates, e.g., aggregation and trading in the energy market. In this regards, this Ph.D. study focuses on the predictive analytics of the historical device operation behavior of consumers for an efficient and effective extraction of flexibilities in their energy demands. First, the thesis performs a comprehensive survey of state-of-the-art work in the literature. It presents a critical review and analysis of various previously proposed approaches, algorithms, and methods in the field of user behavior analysis, forecasting, and flexibility analysis. Then, the thesis details the flexibility and flex-offer concepts and formally discusses the terminologies used throughout the thesis. Second, the thesis contributes to a comprehensive analysis of energy consumption behavior at the device-level. The key motive of the analysis is to extract device operation patterns of users, the correlation between devices operations, and influence of external factors in device-level demands. A novel cost/benefit trade-off analysis of device flexibility is performed to categorize devices into various segments according to their flexibility potential. Moreover, device-specific data preprocessing steps are proposed to clean device-level raw data into a format suitable for flexibility analysis. Third, the thesis presents various prediction models that are specifically tuned for device-level energy demand prediction. Further, it contributes to the feature engineering aspect of generating additional features from a demand consumption timeseries that effectively capture device operation preferences and patterns. The demand predictions utilize the carefully crafted features and other contextual information to improve the performance of the prediction models. Further, various demand prediction models are evaluated to determine the model, forecast horizon, and data granularity best suited for the device-level flexibility analysis. Furthermore, the effect of the forecast accuracy on flexibility-based DR is evaluated to identify an error level a market can absorb maintaining profitability. Fourth, the thesis proposes a generalized process for automated generation and evaluation of flex-offers from the three types of household devices, namely Wet-devices, Electric Vehicles (EV), and Heat Pumps. The proposed process automatically predicts and estimates times and values of device-specific events representing flexibility in its operations. The predicted events are combined to generate flex-offers for the device future operations. Moreover, the actual flexibility potential of household devices is quantified for various contextual conditions and degree days. Fifth, the thesis presents user-comfort oriented prescriptive techniques to prescribe flex-offers schedules. The proposed scheduler considers the trade-off between both social and financial aspects during scheduling of flex-offers, i.e., maximizing the financial benefits in a market and at the same time minimizing the loss of user comfort. Moreover, it also provides a distance-aware error measure that quantifies the actual performance of forecast models designed for flex-offers generation and scheduling. Sixth, the thesis contributes to the comprehensive analysis of the financial viability of device-level flexibility for dynamic balancing of demand and supply. The thesis quantifies the financial benefits of flexibility and investigates the device type specific market that maximizes the potential of flexibility, both regarding DR and financial incentives. Henceforth, a financial analysis of each proposed technique, namely forecast model, flex-offer generation model, and flex-offer scheduling is performed. The key motive is to evaluate the usability of the proposed models in the device-level flexibility based DR scheme and their potential in generating a positive financial incentive to markets and customers. Seven, the thesis presents a benchmark platform for device-level demand prediction. The platform provides the research community with a centralized repository of device-level datasets, forecast models, and functionalities that facilitate comparisons, evaluations, and validation of device-level forecast models. The results of the thesis can contribute to the energy market in materializing the vision of utilizing consumption and production flexibility to obtain dynamic energy balance. The developed demand forecast and flex-offer generation models also contribute to the energy data analytics and data mining fields. The quantification of flexibility further contributes by demonstrating the feasibility and financial benefits of flexibility-based DR. The developed experimental platform provide researchers and practitioners with the resources required for device-level demand analytics and prediction.

Companies face constant change in today's dynamic business landscape, navigating shifting customer demands, globalization, and economic fluctuations. To thrive, businesses must optimize costs and meet customer needs, making supply chain management necessary. At its core lies demand management, a strategic and operational process that aligns customer needs with the capabilities of the supply chain. The ever-changing demand sets challenges for the integration between supply and demand that need to be studied. While other studies may focus on individual activities, this study treats demand management as a holistic process. Through a case study in ABB Robotics focusing on the spare parts industry, renowned for its stringent service level requirements and extensive availability. This thesis investigates demand management practices, emphasizing integration between demand and supply units, uncovering challenges across the supply chain, and exploring improvement opportunities. The study employs a single case study design with an exploratory abductive approach, using a qualitative method and interviews to gather empirical data that is later analyzed against theoretical frameworks based on prior research. Despite solid inventory planning and collaboration levels, the study identifies several challenges the company faces in meeting demand effectively, including inadequate coordination and failure to incorporate supply capabilities, divergent goals, departmental silos, lack of alignment, and systematic process deficiencies hindering informed decision-making alignment. Also, unclear allocation strategies in supply limitations and customer prioritization are present. Figure 8 in this study advocates integrating demand and supply teams equally, which is crucial for optimized cost and value. The study suggests adopting Sales and Operations Planning (S&OP) to involve stakeholders systematically. Necessary actions include understanding roles and responsibilities, setting clear goals, sharing knowledge, enhancing customer and marketing segmentation, and increasing data transparency. In conclusion, successful demand management necessitates viewing it as an integrated process involving all teams focused on reducing variables through continuous information flow.