Benefits and challenges of Enterprise Resource Planning for Pakistani SMEs

Enterprise resource planning (ERP) software is used to combine all the functions happening inside the organization with the help of one software. All the data is centralized which makes it easy to manage information for all participants. The literature on ERP is studied thoroughly the whole process of adoption till the implementation and final evaluations. But studies that focus on small and medium sized enterprises are limited in number when compared to the large scale enterprises. In case of Pakistan, research is very limited. In this thesis, the author tries to analyze the current status of SMEs usage of ERP system. The benefits obtained and challenges faced by SMEs of Pakistan are studied. Framework presented by Shang and Seddon (2000) is used to understand the benefits obtained by the SMEs in Pakistan. This is a comprehensive framework that classifies the benefits obtained by the ERP adoption, into five categories: operational benefits, managerial benefits, Strategic benefits, IT benefits, and Organizational benefits. The results show that SMEs of Pakistan are also getting many benefits after adoption of ERP. Most of the firms had implemented SAP software. Operational benefits were mentioned by all the firms. The most important benefits were report generation, quick access to critical information, better product and cost planning. Respondents also mentioned that they had reduced corruption as a result of ERP implementation. It is also an important benefit considering high corruption rate in Pakistan. Along with benefits, challenges faced by Pakistani SMEs included infrastructure problems like electricity, difficulties with integration of one module with other module, costs of adoption and lack of skilled ERP consultants. Further studies in this regard can be conducted on cloud based ERP which is fast growing all around the world.

Entwicklung eines Windleistungprognosemodells zur Verbesserung der Kraftwerkseinsatzplanung

Die Maßnahmen zur Förderung der Windenergie in Deutschland haben wichtige Anstöße zur technologischen Weiterentwicklung geliefert und die Grundlagen für den enormen Anlagenzubau geschaffen. Die installierte Windleistung hat heute eine beachtliche Größenordnung erreicht und ein weiteres Wachstum in ähnlichen Dimensionen ist auch für die nächsten Jahre zu erwarten. Die aus Wind erzeugte elektrische Leistung deckt bereits heute in einigen Netzbereichen die Netzlast zu Schwachlastzeiten. Dies zeigt, dass die Windenergie ein nicht mehr zu vernachlässigender Faktor in der elektrischen Energieversorgung geworden ist. Im Rahmen der Kraftwerkseinsatzplanung sind Betrag und Verlauf der Windleistung des folgenden Tages mittlerweile zu wichtigen und zugleich schwierig zu bestimmenden Variablen geworden. Starke Schwankungen und falsche Prognosen der Windstromeinspeisung verursachen zusätzlichen Bedarf an Regel- und Ausgleichsleistung durch die Systemführung. Das im Rahmen dieser Arbeit entwickelte Prognosemodell liefert die zu erwartenden Windleistungen an 16 repräsentativen Windparks bzw. Gruppen von Windparks für bis zu 48 Stunden im Voraus. Aufgrund von prognostizierten Wetterdaten des deutschen Wetterdienstes (DWD) werden die Leistungen der einzelnen Windparks mit Hilfe von künstlichen neuronalen Netzen (KNN) berechnet. Diese Methode hat gegenüber physikalischen Verfahren den Vorteil, dass der komplexe Zusammenhang zwischen Wettergeschehen und Windparkleistung nicht aufwendig analysiert und detailliert mathematisch beschrieben werden muss, sondern anhand von Daten aus der Vergangenheit von den KNN gelernt wird. Das Prognosemodell besteht aus zwei Modulen. Mit dem ersten wird, basierend auf den meteorologischen Vorhersagen des DWD, eine Prognose für den Folgetag erstellt. Das zweite Modul bezieht die online gemessenen Leistungsdaten der repräsentativen Windparks mit ein, um die ursprüngliche Folgetagsprognose zu verbessern und eine sehr genaue Kurzzeitprognose für die nächsten drei bis sechs Stunden zu berechnen. Mit den Ergebnissen der Prognosemodule für die repräsentativen Standorte wird dann über ein Transformationsmodell, dem so genannten Online-Modell, die Gesamteinspeisung in einem größeren Gebiet berechnet. Das Prognoseverfahren hat seine besonderen Vorzüge in der Genauigkeit, den geringen Rechenzeiten und den niedrigen Betriebskosten, da durch die Verwendung des bereits implementierten Online-Modells nur eine geringe Anzahl von Vorhersage- und Messstandorten benötigt wird. Das hier vorgestellte Prognosemodell wurde ursprünglich für die E.ON-Netz GmbH entwickelt und optimiert und ist dort seit Juli 2001 im Einsatz. Es lässt sich jedoch auch leicht an andere Gebiete anpassen. Benötigt werden dazu nur die Messdaten der Leistung ausgewählter repräsentativer Windparks sowie die dazu gehörenden Wettervorhersagen, um die KNN entsprechend zu trainieren.

Erneuerbare Energien im Verbund mit Hochleistungs-Bleiakkumulatoren zur Bereitstellung von Spitzenleistung - am Beispiel von Windkraftwerken im 20 kV-Versorgungsnetz der CEGEDEL / Luxemburg -

Die wachsende Weltbevölkerung bedingt einen höheren Energiebedarf, dies jedoch unter der Beachtung der nachhaltigen Entwicklung. Die derzeitige zentrale Versorgung mit elektrischer Energie wird durch wenige Erzeugungsanlagen auf der Basis von fossilen Primärenergieträgern und Kernenergie bestimmt, die die räumlich verteilten Verbraucher zuverlässig und wirtschaftlich über ein strukturiertes Versorgungssystem beliefert. In den Elektrizitätsversorgungsnetzen sind keine nennenswerten Speicherkapazitäten vorhanden, deshalb muss die von den Verbrauchern angeforderte Energie resp. Leistung jederzeit von den Kraftwerken gedeckt werden. Bedingt durch die Liberalisierung der Energiemärkte und die geforderte Verringerung der Energieabhängigkeit Luxemburgs, unterliegt die Versorgung einem Wandel hin zu mehr Energieeffizienz und erhöhter Nutzung der dargebotsabhängigen Energiequellen. Die Speicherung der aus der Windkraft erzeugten elektrischen Energie, wird in den Hochleistungs-Bleiakkumulatoren, errichtet im ländlichen Raum in der Nähe der Windkraftwerke, eingespeichert. Die zeitversetzte Einspeisung dieser gespeicherten elektrischen Energie in Form von veredelter elektrischer Leistung während den Lastspitzen in das 20 kV-Versorgungsnetz der CEGEDEL stellt die Innovation in der luxemburgischen Elektrizitätsversorgung dar. Die Betrachtungen beschränken sich somit auf die regionale, relativ kleinräumige Einbindung der Windkraft in die elektrische Energieversorgung des Großherzogtums Luxemburg. Die Integration der Windkraft im Regionalbereich wird in den Vordergrund der Untersuchung gerückt. Überregionale Ausgleichseffekte durch Hochspannungsleitungen der 230/400 kV-Systeme werden außer Acht gelassen. Durch die verbrauchernahe Bereitstellung von elektrischer Spitzenleistung vermindern sich ebenfalls die Übertragungskosten aus den entfernten Spitzenlastkraftwerken, der Ausbau von Kraftwerkskapazitäten kann in die Zukunft verschoben werden. Die Emission von Treibhausgasen in thermischen Kraftwerken wird zum Teil reduziert. Die Berechnungen der Wirtschaftlichkeit von Hybridanlagen, zusammengesetzt aus den Windkraftwerken und den Hochleistungs-Bleiakkumulatoren bringen weitere Informationen zum Einsatz dieser dezentralen Speichern, als Partner der nachhaltigen Energieversorgung im ländlichen Raum. Die untersuchte Einspeisung von erneuerbarer Spitzenleistung lässt sich auch in die Entwicklungsländer übertragen, welche nicht über zentrale Kraftwerkskapazitäten und Verteilungsnetze verfügen.

Energy generation and consumption in Ghana

Electricity consumption in Ghana is estimated to be increasing by 10% per annum due to the demand from the growing population. However, current sources of production (hydro and thermal facilities) generate only 66% of the current demand. Considering current trends, it is difficult to substantiate these basic facts, because of the lack of information. As a result, research into the existing sources of generating electricity, electricity consumption and prospective projects has been performed. This was achieved using three key techniques; review of literature, empirical studies and modelling. The results presented suggest that, current annual installed capacity of energy generation (i.e. 1960 MW) must be increased to 9,405.59 MW, assuming 85% plant availability. This is then capable to coop with the growing demand and it would give access to the entire population as well as support commercial and industrial activities for the growth of the economy. The prospect of performing this research is with the expectation to present an academic research agenda for further exploration into the subject area, without which the growth of the country would be stagnant.

An investigation of the impact of time of generation on carbon savings from PV systems in Great Britain

PV only generates electricity during daylight hours and primarily generates over summer. In the UK, the carbon intensity of grid electricity is higher during the daytime and over winter. This work investigates whether the grid electricity displaced by PV is high or low carbon compared to the annual mean carbon intensity using carbon factors at higher temporal resolutions (half-hourly and daily). UK policy for carbon reporting requires savings to be calculated using the annual mean carbon intensity of grid electricity. This work offers an insight into whether this technique is appropriate. Using half hourly data on the generating plant supplying the grid from November 2008 to May 2010, carbon factors for grid electricity at half-hourly and daily resolution have been derived using technology specific generation emission factors. Applying these factors to generation data from PV systems installed on schools, it is possible to assess the variation in the carbon savings from displacing grid electricity with PV generation using carbon factors with different time resolutions. The data has been analyzed for a period of 363 to 370 days and so cannot account for inter-year variations in the relationship between PV generation and carbon intensity of the electricity grid. This analysis suggests that PV displaces more carbon intensive electricity using half-hourly carbon factors than using daily factors but less compared with annual ones. A similar methodology could provide useful insights on other variable renewable and demand-side technologies and in other countries where PV performance and grid behavior are different.

The elasticity of demand is about occupancy, not prices: building European demand curves based on occupancy elasticity

The peak congestion of the European grid may create significant impacts on system costs because of the need for higher marginal cost generation, higher cost system balancing and increasing grid reinforcement investment. The use of time of use rates, incentives, real time pricing and other programmes, usually defined as Demand Side Management (DSM), could bring about significant reductions in prices, limit carbon emissions from dirty power plants, and improve the integration of renewable sources of energy. Unlike previous studies on elasticity of residential electricity demand under flat tariffs, the aim of this study is not to investigate the known relatively inelastic relationship between demand and prices. Rather, the aim is to assess how occupancy levels vary in different European countries. This reflects the reality of demand loads, which are predominantly determined by the timing of human activities (e.g. travelling to work, taking children to school) rather than prices. To this end, two types of occupancy elasticity are estimated: baseline occupancy elasticity and peak occupancy elasticity. These represent the intrinsic elasticity associated with human activities of single residential end-users in 15 European countries. This study makes use of occupancy time-series data from the Harmonised European Time Use Survey database to build European occupancy curves; identify peak occupancy periods; draw time use demand curves for video and TV watching activity; and estimate national occupancy elasticity levels of single-occupant households. Findings on occupancy elasticities provide an indication of possible DSM strategies based on occupancy levels and not prices.

The evolution of electricity demand and the role for demand side participation, in buildings and transport

This paper explores the possible evolution of UK electricity demand as we move along three potential transition pathways to a low carbon economy in 2050.The shift away from fossil fuels through the electrification of demand is discussed, particularly through the uptake of heat pumps and electric vehicles in the domestic and passenger transport sectors. Developments in the way people and institutions may use energy along each of the pathways are also considered and provide a rationale for the quantification of future annual electricity demands in various broad sectors. The paper then presents detailed modelling of hourly balancing of these demands in the context of potential low carbon generation mixes associated with the three pathways. In all cases, hourly balancing is shown to be a significant challenge. To minimise the need for conventional generation to operate with very low capacity factors, a variety of demand side participation measures are modelled and shown to provide significant benefits. Lastly, projections of greenhouse gas emissions from the UK and the imports of fossil fuels to the UK for each of the three pathways are presented.

Energy overview of Botswana: generation and consumption

Botswana has a basic need to explore its energy concept, this being its energy sources, generation and percentage of the population with access to electricity. At present, Botswana generates electricity from coal, which supplies about 29% (on average) of the country’s demand. The other 71% is imported mainly from South Africa (Eskom). Consequently, the dependence of Botswana on imports posses threats to the security of its energy supply. As a result, there is the need to understand the bases for a possible generation expansion that would substantiate existing documentation. In view of this need, this study investigates the existing energy sources as well as energy consumption and production levels in Botswana. The study would be further developed by making projections of the energy demand up until the year 2020. The key techniques that were used include; literature review, questionnaire survey and an empirical study. The results presented indicated that, current dependable operation capacity (i.e. 100MW) should be increased to 2,595 MW or more assuming 85% plant efficiency. This would then be able to meet the growing demand for energy use. In addition, the installed capacity would be able to support commercial and mining activities for the growth of the economy.

‘Nudging’ energy users: regulatory measures to address the risk of aggregate peak demand in European electricity markets

For decades regulators in the energy sector have focused on facilitating the maximisation of energy supply in order to meet demand through liberalisation and removal of market barriers. The debate on climate change has emphasised a new type of risk in the balance between energy demand and supply: excessively high energy demand brings about significantly negative environmental and economic impacts. This is because if a vast number of users is consuming electricity at the same time, energy suppliers have to activate dirty old power plants with higher greenhouse gas emissions and higher system costs. The creation of a Europe-wide electricity market requires a systematic investigation into the risk of aggregate peak demand. This paper draws on the e-Living Time-Use Survey database to assess the risk of aggregate peak residential electricity demand for European energy markets. Findings highlight in which countries and for what activities the risk of aggregate peak demand is greater. The discussion highlights which approaches energy regulators have started considering to convince users about the risks of consuming too much energy during peak times. These include ‘nudging’ approaches such as the roll-out of smart meters, incentives for shifting the timing of energy consumption, differentiated time-of-use tariffs, regulatory financial incentives and consumption data sharing at the community level.

Ontology modeling for generation of clinical pathways

Purpose: Increasing costs of health care, fuelled by demand for high quality, cost-effective healthcare has drove hospitals to streamline their patient care delivery systems. One such systematic approach is the adaptation of Clinical Pathways (CP) as a tool to increase the quality of healthcare delivery. However, most organizations still rely on are paper-based pathway guidelines or specifications, which have limitations in process management and as a result can influence patient safety outcomes. In this paper, we present a method for generating clinical pathways based on organizational semiotics by capturing knowledge from syntactic, semantic and pragmatic to social level. Design/methodology/approach: The proposed modeling approach to generation of CPs adopts organizational semiotics and enables the generation of semantically rich representation of CP knowledge. Semantic Analysis Method (SAM) is applied to explicitly represent the semantics of the concepts, their relationships and patterns of behavior in terms of an ontology chart. Norm Analysis Method (NAM) is adopted to identify and formally specify patterns of behavior and rules that govern the actions identified on the ontology chart. Information collected during semantic and norm analysis is integrated to guide the generation of CPs using best practice represented in BPMN thus enabling the automation of CP. Findings: This research confirms the necessity of taking into consideration social aspects in designing information systems and automating CP. The complexity of healthcare processes can be best tackled by analyzing stakeholders, which we treat as social agents, their goals and patterns of action within the agent network. Originality/value: The current modeling methods describe CPs from a structural aspect comprising activities, properties and interrelationships. However, these methods lack a mechanism to describe possible patterns of human behavior and the conditions under which the behavior will occur. To overcome this weakness, a semiotic approach to generation of clinical pathway is introduced. The CP generated from SAM together with norms will enrich the knowledge representation of the domain through ontology modeling, which allows the recognition of human responsibilities and obligations and more importantly, the ultimate power of decision making in exceptional circumstances.

Measurements of atmospheric electricity aloft

Measurements of the electrical characteristics of the atmosphere above the surface have been made for over 200 years, from a variety of different platforms, including kites, balloons, rockets and aircraft. From these measurements, a great deal of information about the electrical characteristics of the atmosphere has been gained, assisting our understanding of the global atmospheric electric circuit, thunderstorm electrification and lightning generation mechanisms, discovery of transient luminous events above thunderstorms, and many other electrical phenomena. This paper surveys the history of atmospheric electrical measurements aloft, from the earliest manned balloon ascents to current day observations with free balloons and aircraft. Measurements of atmospheric electrical parameters in a range of meteorological conditions are described, including clear air conditions, polluted conditions, non-thunderstorm clouds, and thunderstorm clouds, spanning a range of atmospheric conditions, from fair weather, to the most electrically active.

CASCADE: An Agent Based Framework For Modeling The Dynamics Of Smart Electricity Systems

The Complex Adaptive Systems, Cognitive Agents and Distributed Energy (CASCADE) project is developing a framework based on Agent Based Modelling (ABM). The CASCADE Framework can be used both to gain policy and industry relevant insights into the smart grid concept itself and as a platform to design and test distributed ICT solutions for smart grid based business entities. ABM is used to capture the behaviors of diff erent social, economic and technical actors, which may be defi ned at various levels of abstraction. It is applied to understanding their interactions and can be adapted to include learning processes and emergent patterns. CASCADE models ‘prosumer’ agents (i.e., producers and/or consumers of energy) and ‘aggregator’ agents (e.g., traders of energy in both wholesale and retail markets) at various scales, from large generators and Energy Service Companies down to individual people and devices. The CASCADE Framework is formed of three main subdivisions that link models of electricity supply and demand, the electricity market and power fl ow. It can also model the variability of renewable energy generation caused by the weather, which is an important issue for grid balancing and the profi tability of energy suppliers. The development of CASCADE has already yielded some interesting early fi ndings, demonstrating that it is possible for a mediating agent (aggregator) to achieve stable demandfl attening across groups of domestic households fi tted with smart energy control and communication devices, where direct wholesale price signals had previously been found to produce characteristic complex system instability. In another example, it has demonstrated how large changes in supply mix can be caused even by small changes in demand profi le. Ongoing and planned refi nements to the Framework will support investigation of demand response at various scales, the integration of the power sector with transport and heat sectors, novel technology adoption and diffusion work, evolution of new smart grid business models, and complex power grid engineering and market interactions.

A review of time use models of residential electricity demand

Residential electricity demand in most European countries accounts for a major proportion of overall electricity consumption. The timing of residential electricity demand has significant impacts on carbon emissions and system costs. This paper reviews the data and methods used in time use studies in the context of residential electricity demand modelling. It highlights key issues which are likely to become more topical for research on the timing of electricity demand following the roll-out of smart metres.

Financial implications for companies running standby generators in support of a smart grid

Integrating renewable energy into built environments requires additional attention to the balancing of supply and demand due to their intermittent nature. Demand Side Response (DSR) has the potential to make money for organisations as well as support the System Operator as the generation mix changes. There is an opportunity to increase the use of existing technologies in order to manage demand. Company-owned standby generators are a rarely used resource; their maintenance schedule often accounts for a majority of their running hours. DSR encompasses a range of technologies and organisations; Sustainability First (2012) suggest that the System Operator (SO), energy supply companies, Distribution Network Operators (DNOs), Aggregators and Customers all stand to benefit from DSR. It is therefore important to consider impact of DSR measures to each of these stakeholders. This paper assesses the financial implications of organisations using existing standby generation equipment for DSR in order to avoid peak electricity charges. It concludes that under the current GB electricity pricing structure, there are several regions where running diesel generators at peak times is financially beneficial to organisations. Issues such as fuel costs, Carbon Reduction Commitment (CRC) charges, maintenance costs and electricity prices are discussed.

Privatisation and cross-border electricity trade: from internal market to European Supergrid?

he perspective European Supergrid would consist of an integrated power system network, where electricity demands from one country could be met by generation from another country. This paper makes use of a bi-linear fixed-effects model to analyse the determinants for trading electricity across borders among 34 countries connected by the European Supergrid. The key question that this paper aims to address is the extent to which the privatisation of European electricity markets has brought about higher cross-border trade of electricity. The analysis makes use of distance, price ratios, gate closure times, size of peaks and aggregate demand as standard determinants. Controlling for other standard determinants, it is concluded that privatisation in most cases led to higher power exchange and that the benefits are more significant where privatisation measures have been in place for a longer period.