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Electricity plays an important role in the economic condition of any country. Nowadays, Pakistan is badly affected by shortage of electricity, which directly affected the economic growth of state. The purpose of this study is to propose an improved grey model DGPM(1,1,N) to forecast Pakistan's production of electricity, installed capacity and consumption.

To significantly simulate and predict accuracy, the discrete grey polynomial model DGPM(1,1,N) is improved with new information priority accumulation. The particle swarm optimization (PSO) algorithm is used for parameter optimization. The value of parameter is adjusted into improved grey model. By adjusting the parameter value in the model, the accuracy of prediction is enhanced.

The installed capacity of electricity needs more attention to improvement through implementation of effective polices, resolving major issues and funding scheme to fulfill the electricity demand of country. And improved DGPM(1,1,N) has better accuracy than original DGPM(1,1,N), DGM(1,1), nongrey models, linear regression and Holt–Winters methods.

This paper provides a practical and efficient improved grey method to predict the electricity production, consumption and installed capacity in Pakistan. This research and suggestion will help Pakistani government to formulate better policies to decrease the consumption of electricity and increase the installed capacity of electricity.

This paper not only improves the grey model with accumulation generation operator but also forecasts Pakistan's electricity production, installed capacity and consumption. It is a new idea to predict the installed capacity of electricity and the findings provide suggestions for the government to make policies.

The purpose of this paper is to develop a small data set forecasting method to improve the effectiveness when making managerial decisions.

In the grey modeling process, appropriate background values are one of the key factors in determining forecasting accuracy. In this paper, grey compensation terms are developed to make more appropriate background values to further improve the forecasting accuracy of grey models.

In the experiment, three real cases were used to validate the effectiveness of the proposed method. The experimental results show that the proposed method can improve the accuracy of grey predictions. The results further indicate that background values determined by the proposed compensation terms can improve the accuracy of grey model in the three cases.

Previous studies determine appropriate background values within the limitation of traditional grey modeling process, while this study makes new background values without the limitation. The experimental results would encourage researchers to develop more accuracy grey models without the limitation when determining background values.

This study aims to find out the dynamic and causal long-run and the short-run relationship between energy consumption (electricity usage) and energy production (electricity creation) and also find out the relationship of these two variables based on past values for the SAARC nations (Pakistan, India, Bangladesh, Sri Lanka and Nepal).

Vector auto-regressive (VAR), auto-regressive distributive Lag (ARDL) and Granger causality test have been used in this study to estimate the dynamic and causal relationship between variables.

The unit-root tests were found insignificant at a magnitude but significant at the initial difference. VAR test results were found insignificant, which means co-integration among variables exists, which was tested by ARDL approach. Results suggested that energy consumption has a short-run relationship with energy production, but it was found insignificant in the other way round. The results of this study also suggest that both variables cause each other in the long run.

This study was conducted in a limited environment as we do not have access to energy policies of SAARC countries, and also data access was limited; only five countries’ data was available. This study can help government bodies and policymakers to exchange the electricity across borders to diminish the electricity shortage in the SAARC region, as countries with abandoned resources can produce electricity at a little cost.

Penal data for this study was collected from World Development Indicators from the year 1971 to 2015.

This study aims to examine the relationship between electricity consumption per capita (ELEC) and real per capita income (Y), as the direction of causation of this relationship remains controversial in the existing literature. It also seeks to explore the relationship between energy consumption per capita (ENC) and real per capita income, over a 34‐year period (between 1975 and 2009).

The study uses Johansen cointegration technique to determine the short‐ and long‐run relationship between the variables. The authors also utilize Granger causality test to determine the causal relationship between the selected variables.

The study provides evidence of bi‐directional causality between the electricity consumption per capita and real per capita income on one hand; and energy consumption per capita and real per capita income on the other hand as the direction of causality has significant policy implications.

This study does not include all dimensions of the energy growth, but is limited to the three variables which the authors consider to be critical to economic development, including energy consumption, electricity consumption and economic growth.

The study uses a sophisticated econometric technique with additional tests of forecasting framework to examine the effect of energy demand on economic growth over a period of the next ten years, i.e. 2010‐2019, in the context of Pakistan. The impulse response describes the reaction of the system as a function of independent variable that parameterizes the dynamic behavior of the system.

The purpose of the paper is to forecast economic indicators of the Saudi economy in the context of low oil prices which have taken a toll on the Saudi oil-dependent economy between 2014 and 2017. Trades and investments have plummeted, leading to significant budget deficits. In response, the government unveiled a plan called Saudi Vision 2030 in 2016 which has triggered structural economic reforms leading to an unprecedented strategy of transition from an oil-driven economy to a modern market economy.

This paper forecasts with spectral analysis economic indicators of the Saudi economy up to 2030 to provide a clearer picture of the future economy assuming that the effects of recent reforms have not yet been traced by most of the economic indicators.

2018–2030 forecasts are all bearish except West Texas Intermediate (WTI) oil price expected to average $64.40 during the period 2019–2030. Two additional exceptions are the Saudi population that should grow to 40 million in 2030 and the swelling gross domestic product (GDP) generated by the non-oil sector resulting from bold actions of the Saudi government who is willing to become less dependent on revenues generated by the oil sector.

Government policymakers, economists and investors would have with spectral forecasts better insight and understanding of the Saudi economy dynamics at the early stage of major economic reforms implemented in the country. In 2020, the COVID-19 pandemic has brutally hurt the Saudi economy following a collapse in the global demand for oil and an oversupplied industry. The impact on the Saudi economy will depend on the optimal response brought by its government.

Saudi Vision 2030 plan has already triggered a deep transformation of the Saudi society that is reviewed in this paper.

The forecast of Saudi economic indicators is a timely topic considering the challenges facing the economy and reforms being undertaken. Applying an original forecasting technique to economic indicators adds to the originality of the paper.

In the daily energy dispatch process in a power system, accurate short-term electricity load forecasting is a very important tool used by spot market players. It is a critical requirement for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The purpose of this study is to present an improved grey Verhulst electricity load forecasting model.

To test the effectiveness of the proposed model for short-term load forecast, studies made use of Kenya’s load demand data for the period from January 2014 to June 2019.

The convectional grey Verhulst forecasting model yielded a mean absolute percentage error of 7.82 per cent, whereas the improved model yielded much better results with an error of 2.96 per cent.

In the daily energy dispatch process in a power system, accurate short-term load forecasting is a very important tool used by spot market players. It is a critical ingredient for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The fact that the model uses actual Kenya’s utility data confirms its usefulness in the practical world for both economic planning and policy matters.

In terms of generation and transmission investments, proper load forecasting will enable utilities to make economically viable decisions. It forms a critical cog of the strategic plans for power utilities and other market players to avoid a situation of heavy stranded investment that adversely impact the final electricity prices and the other extreme scenario of expensive power shortages.

This research combined the use of natural logarithm and the exponential weighted moving average to improve the forecast accuracy of the grey Verhulst forecasting model.

Operational Management, Strategic Management and Marketing Management.

The case can be taught in introductory marketing courses and management and organizational policy course and in advanced level in promotion, distribution channels, marketing research, consumer behavior and brand management courses at graduate level. Importance of market and technological research; first mover advantage and disadvantages in new market segment; importance of competition in a market; use of PESTL analysis before pursuing for any segment; types of growth strategies which could be used that is Broad base or Narrow base; how company uses marketing mix strategy; and how managers make decision in dynamic environment (contingency theory approach).

This case study relates to a real-life situation the data was collected from primary and secondary sources between 2012 and 2013. The case is of a company Pakistan Accumulators (PAL), having less than 18 years of experience, has been able to grow successfully in the dynamic environment of Pakistan. The decline of the private businesses in Pakistan due to the energy crisis has popped up a new need of power generation alternative equipment in the country. PAL, which is a privately owned company, suppliers of automotive batteries, Uninterruptable Power System (UPS) batteries, lead acid batteries and rechargeable batteries has been able to manage the growth of 20 per cent per year. In this case study, we have highlighted only one market segment of the company that is of UPS batteries, we have focused on what is the future prospect of this particular segment, its attractiveness. Also, the area of focus was the new market segments which can be targeted by the company. Basic issues of the case study: calculation of the market segment value of UPS industry; identification and solution of different challenges faced by PAL in the dynamic Pakistani market (contingency theory); recognition of different future growth prospects for PAL.

The basic objective of this case is to enhance the analytical and qualitative skills of the students by giving them the real-life perspective of a company working successfully in country like Pakistan which is facing economic and political crises. This case can also be used for understanding the problems of third-world markets and how company can pursue successfully in the long–term.

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