How important is Efficiency in any Organization? “Estimating the Efficiency Reform of Power Distribution Companies in Punjab Province, Pakistan” (LESCO, FESCO & IESCO)

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RESEARCH PAPER
How important is Efficiency in any Organization? “Estimating the Efficiency
Reform of Power Distribution Companies in Punjab Province, Pakistan”
(LESCO, FESCO & IESCO)
Nauman Mushtaq1
, Fahad Saddique2
, Dr Muhammad Jam e Kausar Ali Asghar 3
, Dr. Muhammad
Nawaz 4
, Zia-ur-Rehman5
1-2
Ph.D. Scholar, The Institute of Management Sciences, Lahore, Pakistan
3
Associate Professor, Department of Management Science, Virtual University of Pakistan. Lahore, Pakistan
4
Associate Professor, National College of Business Administration & Economics, Lahore Pakistan
5
M.Phil Scholar, The Institute of Management Sciences, Lahore, Pakistan
*Corresponding Author fahad.sadique@gmail.com
ABSTRACT
In 1998, Pakistan took the step of reforming its electrical market including Punjab Province. In the same year,
the WAPDA Act was established to maximize the whole well-being of the economy and adequately satisfy
the country's growing energy requirements. The main goals of the reforms were to obtain operational,
financial, and managerial efficiencies by decreasing electricity prices, gross margins, system faults & losses,
and infrastructure spending. It was to be accomplished by raising private sector investment in electricity
generation to utilize available generation capacity better and improve distribution utilities' efficiency.
Additionally, the reforms aimed to enhance the distribution utilities' efficiency. So these requirements are
satisfied by the fast development of the expansion of grids and the progression of technology. Current study
focused on LESCO, FESCO, and IESCO distribution network input & output characteristics relative to their
efficiencies and improving the economic and social influences on Punjab Province. The Data Envelopment
Analysis is applied. Then, the results indicate that IESCO appears to be on a rising trend, FESCO likewise
appears to be on a rising trend, and LESCO appears to be on a downward trend from the previous year into
the future years.
Keywords: LESCO, FESCO, IESCO, Reform
© 2023 The Authors, Published by (TJLSS). This is an Open Access Article under the Creative Common
Attribution Non-Commercial 4.0
INTRODUCTION
Since the 1980s, many nations have reformed their power markets to enhance performance.
A basic reform pattern for all nations includes corporatization, restructuring, establishing a
regulatory body, wholesale and retail marketplaces, and privatization (Bacon & Besant, 2001).
Furthermore, in 1998 WAPDA Act provided the framework for Punjab Pakistan's energy sector
reforms. In a highly state-regulated market with low efficiencies in production, transmission &
Traditional Journal of Law and Social Sciences (TJLSS)
January-June 2023, Vol. 02, No. 01, [44 – 59]
Published: 10th April 2023
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Traditional Journal of Law and Social Sciences (TJLSS) January-June 2023, Vol. 2, No. 1
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distribution, also including the price discrimination between various sectors of the economy,
different consuming groups, and miscellaneous geographical regions (Aziz & Ahmad, 2015),
Electric power rates below the marginal cost of production, and very huge transmission and
distribution faults & losses, the objectives of trying to introduce are increased the efficiency of
power generation, transmission, and distribution while decreasing losses, create a pricing
mechanism that reflects marginal costs and encourages energy efficiency to provide improved
investment and energy security and reduce price differentials to improve electricity usage
efficiency.
The WAPDA Act created the legal basis for unbundling the power market and determined
its policy paradigm. Pre-reform, the power market was centrally managed. After the
improvements, the vertically merged WAPDA was unbundled into divisions for generating,
transmission, and distribution utilities (DISCOs). NEPRA was created in 1998 to control the whole
power market. The DISCOs were subsequently subject to cost-of-service regulation (NEPRA,
2021), and independent power producers were authorized to generate energy in the private sector
(IPPs). Although the competition was established in energy production and distribution,
transmission remained a natural monopoly (Khan, 2014). DISCOs are publicly owned electric
utilities that sell power within their service territories. Despite these changes, the electricity market
in Punjab Pakistan is plagued by worsening financial indicators, a widening demand & supply gap,
a lackness of operational capacity, and insufficient generation capability. Including Punjab
Pakistan's electricity production is mainly fueled by fossil fuels, which reduces margins (NEPRA
2021). Generation and distribution losses may enhance the circular debt. In Punjab Pakistan, poor
administration, management, and institutional cooperation may cause the power market
inefficiency (Ullah et al., 2021). Despite new reforms, Pakistan including the Punjab energy
industry faces major problems (Malik et al., 2021). Pakistan's power market reform began
approximately 40 years ago. It is time to evaluate the reform’s success or failure in providing
continuous power for economic operations in a growing nation like Punjab Pakistan. A several
studies & researches have empirically analyzed the reforming process and the effectiveness of
advance-reform energy markets at worldwide level (Kirkpatrick et al., 2020). Moreover, this
present study now aims to check the efficiency of electric power distribution companies in Punjab,
Pakistan.
LITERATURE REVIEW
In the 1980s, structural reforms in electricity markets around the world included
unbundling state-owned utilities; establishing independent regulatory bodies; privatizing
generation and distribution companies; creating an electricity market; and granting competition
between generation, distribution, and electricity market segments (Qazi et al., 2018). The reform
process across nations demands upgrades for maximum productivity benefits (Nepal et al., 2020).
Effective electrical market changes reduce costs, increase service quality, and improve system
dependability and security (Foster et al., 2020). Reforms enhance efficiency by lowering
transmission faults and system losses (Sultana et al., 2016). Pakistan reformed its power system to
supply dependable, inexpensive energy like other nations. These reforms included the introducing
of an independent law & regulatory agency, vertical segregating of state-owned companies

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(changing the monopoly model to a single buyer model), and policy adjustments to boost market
performance (Qazi et al., 2017). After these changes, Pakistan established the WAPDA Act to
obtain the operational, financial, and managerial efficiency by reducing margins, system losses &
faults, and sectoral pricing discrepancies. The WAPDA Act aimed to stimulate new investment in
a sector hampered by overstaffing of employees, bad corruption, politically pressures, low service
quality, and inadequate billing collection (Malik et al., 2021). WAPDA was split into four
generating firms, one transmission business, and eight distribution companies (DISCOs). NEPRA
was created to foster competition in the energy sector and protect the interests of consumers,
producers, and sellers (Malik, 2021). PEPCO Management was enlisted as a private limited
company to guide, manage, and supervise the government's business & commercial operations
(Ullah et al., 2021). Several studies have analyzed Pakistan's electrical market problems and
production and distribution businesses. (Nawaz et al., 2021). Several researchers tell us that poor
reform implementation is a significant source of the electricity market's issues (Zameer et al.,
2021). This chapter uses sectoral factors to analyze the performance and efficiency of Pakistan's
electricity generating, transmission, and distribution sectors. This investigation will help
policymakers identify important concerns that hinder an efficient and sustainable power system.
METHODOLOGY
In this part, we will discuss the description of the study, which will include topics such as
the research design, population, sample size and procedure, data gathering methods, and data
analysis Strategies.
Design of Model Mathematically
The DEA-based performance model that is used in the process to monitor the effectiveness
of the collaborations is the one that proposed, 18 which emphasizes the maintenance of a unit's
classification (like example, what kinds of changes need to be made to a unit's input and output
values to maintain its classification as efficient or inefficient). This strategy involves establishing
how sensitive a unit is to changes in the values of the data being examined. Publications that have
come out are taken into consideration in this review. They cover 79 papers published between
1984 and 2004, while we cover 262 publications between 2005 and 2016. At the beginning of our
decision-making process, we looked through the standard academic using the DEA model. In light
of the findings, we carried out comprehensive forward and backward mentioned studies relative
to DEA (Cooper 2001).
Mathematically Model
Methods not based on parameters, such as programming, were used to determine the
efficiency of disco businesses. The same approach that Charnes et al. (1978-1981), who was
responsible for coining the acronym DEA, applied to multi-input and output models. Its primary
purpose is to determine effectiveness across all areas of academic research. It works by Decision
Making Units (DMUs) and it chooses most effective one from these DMUs to determine how
efficient it is. If the findings are one, it indicates that the unit is efficient, but if they are 0 or less
than 1, it indicates that the unit is inefficient. The finding of DEA is somewhere between 1 and 0
since it employs the highest ratio of weighted input and output. Most researchers agreed that it was

47
the most effective method, despite the study's limited scope. DEA was also used in the research
process that was conducted by researcher P Zhou and Kim Leng Poh in year 2008 and other Jarite
and Maria in the year (2010). As per same to Asghar and Afzal in the year (2010), "The input-
oriented DEA model is utilized to estimate technical efficiency (TE), pure technical efficiency
(PTE), and scale efficiency (SE), which are presented in the model figures.
Minimum λ0θ0
Subject to
∑ λ0j yrj ≥ yr0 (𝑟 = 1 … 𝑠) (1)
𝜃0 𝑥i0 ≥ ∑ λ0j xij
𝑛
𝑗=1
(𝑖 = 1 … 𝑛) (2)
∑ λ0j
𝑛
𝑗=1
= 1 (3)
λ0j ≥ 0 (𝑗 = 1 … 𝑛)
(1) Σ λ0j yrj ≥ yr0 is the output constraint.
(2) θ0 xi0 ≥ Σ λj x0 is the input constraint.
yrj and xio shows the output and input of the nth DMU, whereas; λ shows weight. 0 is the
DMU to be calculated, and by solving this non parametric model, we can obtain the lowest θ0
which is the vector of the efficiency score. The index j indicates DMUs for j=1,…, N. yrj is the rth
output of the jth firm for r =1,……, R. xij shows the ith input of the jth DMU for I = 1,……,n
(Mahlberg, 2000). The 3rd constraint indicates the variable return to scale (VRS) into the design
of model, and if the 3rd constraint is dropped, the frontier technology converts from VRS to CRS.
However, if (Σλ0j ≤ 1) is applied instead of the 3rd constraint, the latest model can even determine
the cauese for scale inefficiency, which may be increasing return to scale (IRS) or decreasing
return to scale (DRS)”. As used in research by by Afza, T., & Asghar, M. J. A. in the year (2012).
Index System for Evaluation the Design & Objectives of Research
The DEA model is suitable, ideal & perfect for evaluating the comparison of multi-input
and output decision-making units to acknowledge that which unit is performing better and to find
out the potential gaps to use for implementing latest & new changes.
The Population of the Research
The population used in research was comprised of all power distribution companies spread all
over Pakistan including Punjab. The detail is as under: Ex-WAPDA DISCOs
1 PESCO (KPK)
2 TESCO (Baluchistan)

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3 IESCO (Punjab)
4 GEPCO (Punjab)
5 FESCO (Punjab)
6 LESCO (Punjab)
7 MEPCO (Punjab)
8 HESCO (Sindh)
9 SEPCO (Sindh)
10 QESCO (Baluchistan)
11 KESC (Karachi, Sindh)
Sample Technique & Size
The research was quantitative; a convenience sample technique was used for this Study.
Convenience sample refers for collecting information from participants of the population who can
comfort & conveniently to provide it. The observed sample consists of the three ex-WAPDA
DISCOs: LESCO, FESCO, and IESCO from Punjab Pakistan. MEPCO & GEPCO are ignored &
skipped in this research.
Variables of the Study
This research aims to check the efficiencies of the 3 distribution companies (LESCO,
FESCO & IESCO). For this purpose, the variables used in study are the electricity purchased from
the NTDC transmission system, which was sold to various commercial consumers. Therefore, the
Study will take the distribution system as the research object.
The Input Variables:
A1: Purchased Energy Sent (GWH),
A2: Demand for Energy (MW)
The Output Variables:
B1: Energy Sale (GWH)
B2: Distribution Loss (GWH),
B3: Transmission Loss (GWH)
Data Collection
In order to result out the efficiency of the power distribution companies in Punjab (LESCO,
FESCO, and IESCO), we used secondary data. The data are collected from NEPRA reports,
internal data reports, government surveys relating to the power sector, auditor-general office audit
reports, etc. These companies are used for analysis because these data are easily accessible because

49
of urban areas, and due to a shortage of time and resources, it is difficult to collect eleven
companies’ data. Their comparison also helps to understand the efficiencies of the companies.
Descriptive statistical analysis techniques were used to analyze data. The Mean & Standard
Deviation was calculated to find out the input and output variables. The DEA model is convenient,
suitable and easy for evaluating the efficiency of multi input & output variables. All the decision
making units to know if a unit is performing better & how to find out a potential area for implement
latest reforms. The data analysis was made by a Software named DEAP 2.1 which is introduced
by a researcher named Mr.Tim Coelli to evaluate the variables for 11-year average efficiency
result.
So the component dealing with analysis is split into two parts. The first part of the analysis
focuses on historical data from 2016 to 2022. Truthful information is used for analysis for this
period. In the other part of the analysis, this covers the years 2023 through 2026; we will be using
assumed data.
Table I The Input and Output Variables (Statically Indicators):
Input Variables Output Variables
A1: Purchased Energy Sent (GWH) B1: Energy Sale (GWH)
A2: Demand of Energy (MW) B2: Distribution Loss (GWH)
B3:Transmission Loss (GWH)
Statically Descriptive Table (II): “Based upon Real Obtained Data”
SELECTED
VARIABLES
INPUT INPUT OUTPUT OUTPUT OUTPUT
A1 A2 B1 B2 B3
YEAR RESULT
2016 Mean 12790.00 2,083.00 11,252.00 1,351.00 165.00
S D 4055.00 751.00 4,138.00 863.00 29.00
2017 Mean 13973.00 2,262.00 12,350.00 1,446.00 177.00
S D 5855.00 865.00 4,930.00 935.00 27.00
2018 Mean 15078.00 2,436.00 13,360.00 1,529.00 189.00
S D 6463.00 950.00 5,484.00 988.00 28.00
2019 Mean 16307.00 2,631.00 14,494.00 1,611.00 203.00
S D 7019.00 1,025.00 5,996.00 1,035.00 29.00

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2020 Mean 17446.00 2,814.00 15,551.00 1,679.00 215.00
S D 7503-00 1,095.00 6,461.00 1,055.00 33.00
2021 Mean 18556.00 2,991.00 16,581.00 1,747.00 228.00
S D 7875.00 1,142.00 6,824.00 1,065.00 37.00
2022 Mean 19609.00 3,158.00 17,563.00 1,805.00 239.00
Statically Descriptive Table (III): “Based upon Assumed Data”
SELECTED VARIABLES INPUT INPUT OUTPUT OUTPUT OUTPUT
A1 A2 B1 B2 B3
YEAR RESULT
2023 Mean 20,775.0 3,342.0 18,658.0 1,864.0 253
S D 8,271.0 1,184.0 7,255.0 1,040.0 50.0
2024 Mean 22,020.0 3,539.0 19,826.0 1,926.0 268.0
S D 8,468.0 1,204.0 7,477.0 1,019.0 59.0
2025 Mean 23,349.0 11,750.0 21,074.0 2,058.0 2840
S D 8,652.0 12,920.0 7,695.0 957.0 71.0
2026 Mean 24,638.0 3,959.0 22,275.0 2,065.0 298.0
S D 8,871.0 1,246.0 7,957.0 948.0 80.0
Data Analysis
In accordance with the input & output variables (indicators) presented in Table I from
which is evaluated the seven years' worth of real data presented by three different electricity supply
companies in Table II and the four years' worth of estimating data presented in Table III. This
allowed us to compile a set of raw data descriptive statistics while simultaneously displaying the
DISCOs in Table IV (The Companies working for Power Distribution in Punjab, Pakistan).
Table-IV (The Power Distribution Companies of Punjab Pakistan taken in Research)
No. DMU NAME OF COMAPNY
A LESCO Called Lahore Electric Supply Company
B FESCO Called Faisalabad Electric Supply Company

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C IESCO Called Islamabad Electric Supply Company
Table V displays the All Power Annually Input-Output Indicators (Slack) for the Tenure of years
from 2016 to 2026.
Slacks Summary for Distribution Companies of Punjab (from 2016 to 2026)
SLACKS OF INPUT: SLACKS OF OUTPUT:
2016
DMU Company Name A1 A2 B1 B2 B3
A LESCO 0.00 80.12 0.00 0.00 80.83
B FESCO 0.00 55.59 0.00 0.00 0.00
C IESCO 0.00 0.00 0.000 34.096 7.350
2017
A LESCO 0.00 106.358 0.00 0.00 78.36
B FESCO 0.00 77647.17 0.00 300.78 144.99
C IESCO 0.00 0.00 0.00 0.00 0.00
2018
A LESCO 0.00 73.27 0.00 0.00 77.02
B FESCO 0.00 59.42 0.00 0.00 0.00
C IESCO 0.00 0.00 0.00 0.00 0.00
2019
A LESCO 0.00 77.54 0.00 0.00 73.71
B FESCO 0.00 57.97 0.00 13.07 0.00
C IESCO 0.00 0.00 0.00 0.00 0.00
2020
A LESCO 0.00 82.35 0.00 0.00 66.24
B FESCO 0.00 53.05 0.00 41.38 0.00
C IESCO 0.00 0.00 0.00 0.33 0.00

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This paper used genuine data surveys of disco reports and grids from 2016 to 2022 and
estimated Data from 2023 to 2026 then put in the form of input and output-oriented models
2021
A LESCO 0.00 77.99 0.00 0.00 58.97
B FESCO 0.00 47.10 0.00 64.38 0.00
C IESCO 0.00 0.26 0.00 0.14 0.00
2022
A LESCO 0.00 68.73 0.00 0.00 50.16
B FESCO 0.00 38.50 0.00 87.75 0.00
C IESCO 0.00 0.00 0.00 0.00 0.00
2023
A LESCO 0.00 55.10 0.00 0.00 40.38
B FESCO 0.00 28.46 0.00 127.73 0.00
C IESCO 0.00 0.00 0.00 0.92 0.00
2024
A LESCO 0.00 38.59 0.00 0.00 28.31
B FESCO 0.00 12.46 0.00 163.28 0.00
C IESCO 0.00 0.00 0.00 2.77 0.00
2025
A LESCO 0.00 20.32 0.00 0.00 14.56
B FESCO 0.00 0.00 0.00 0.00 0.00
C IESCO 0.00 23998.00 0.00 4.50 0.00
2026
A LESCO 0.00 0.00 0.00 0.00 0.00
B FESCO 0.00 0.00 0.00 0.00 0.00
C IESCO 0.00 0.00 0.00 0.00 0.00
MEAN MEAN
1279.95 284.52 0.52 13.63 11.26

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(Win4deap2 software version of DEAP 2.1) generated by Mr.Tim Coelli Cepa to calculate the 11
years average result for the efficiency & the input redundancy of the output deficits. It is a statically
& dynamic analysis. Initially, this paper utilized a data survey of disco's reports as an input and
output-oriented model of disco reports as an empirical assessment of each disco and the changes
to discover the reason. On the other hand, we performed dynamic analysis using the Malmquist
Model at many stages of the DEAP programmer. It includes examining each DISCO DMU
regarding the average changes in total factor supply.
RESULT & DISCUSSION
(Table VI) The Efficiency of Input & Output Variables for DISCO.s in Punjab
ELECTRIC EFFICIENCY OF DISCO'S IN PUNJAB from 2016 to 2026
2016
DMU Company Name CRSTE VRSTE SE RTS
A LESCO 0.98 0.99 0.99 drs
B FESCO 0.98 0.98 0.99 drs
C IESCO 0.98 0.99 0.99 irs
2017
A LESCO 0.99 0.99 0.99 drs
B FESCO 0.77 0.80 0.96 irs
C IESCO 0.99 1.00 0.99 irs
2018
A LESCO 0.99 0.99 0.99 drs
B FESCO 0.98 0.98 1.00 -
C IESCO 0.99 0.99 1.00 -
2019
A LESCO 0.99 0.99 0.99 drs
B FESCO 0.98 0.98 1.00 -
C IESCO 1.00 1.00 1.00 -
2020
A LESCO 0.99 0.99 0.99 drs

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B FESCO 0.99 0.99 1.00 -
C IESCO 1.00 1.00 1.00 -
2021
A LESCO 0.99 0.99 0.99 drs
B FESCO 0.99 0.99 1.00 -
C IESCO 1.00 1.00 1.00 -
2022
A LESCO 0.99 1.00 0.99 drs
B FESCO 0.99 0.99 1.00 -
C IESCO 0.99 1.00 1.00 -
2023
A LESCO 0.99 0.99 0.99 drs
B FESCO 0.99 0.99 1.00 -
C IESCO 1.00 1.00 1.00 -
2024
A LESCO 0.99 1.00 0.99 drs
B FESCO 0.99 0.99 1.00 -
C IESCO 1.00 1.00 1.00 -
2025
A LESCO 0.99 1.00 0.99 drs
B FESCO 1.00 1.00 1.00 -
C IESCO 1.00 1.00 1.00 -
2026
A LESCO 1.00 1.00 1.00 -
B FESCO 0.99 1.00 0.99 drs
C IESCO 1.00 1.00 1.00 -
MEAN 0.97 0.98 0.99

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ABBREVIATIONS CRSTE FOR Technical Efficiency FROM CRS
DEA
VRSTE FOR Technical Efficiency FROM VRS
DEA
SE FOR Scale Efficiency=CRSTE/VRSTE
RTS FOR Return to Scale(DRS IRS CRS)
DRS FOR Decreasing Return to Scale
IRS FOR Increasing Return to Scale
CRS FOR Constant Return to Scale -
About TABLE VI, when assumed that Crste represents the Technical Change (Techch)
which is the concluded result depends upon (BC 2 Model of Software) while not assuming CRS
the Vrste showed the change of Efficiency (Effch), which may be decomposed to PE Change
(Pech) and SE Change (Sech). The Scale explains the returns to scale, scale=crste / vrste. The
Vrste and Scale are the conclions depending upon (C2R Model in software). Moreover, column
last, IRS & DRS step by step indications the increased Constant (-) and decreased return to scale.
These are calcuated from ∑λ j, ∑λ j < 1, Which displays the increased returns to scale, ∑λ j = 1,
which shows the Constant returns to scale, ∑and λ j > 1, which shows the decreased returns to
scale. The Malmquist model indexing has a very good advantage. Namely, So their is no need of
consider constant return to scale or not because when evaluating, the Malmquist model used for
both Crste and Vrste calculations. The Malmquist indexes, namely Tfpch, able to decomposed into
Efficiency Changes (Effch) & Technical changes (Techch), & Efficiency changes (Effch) can be
more decomposed into Pure Efficiency Changes (Pech) & Scale Efficiency Changes (Sech). While
Effch≥1 showing about the overall Efficiency has been grown upward, Pech≥1 means Pure
Efficiency has been increased, Sech≥1 shows Scale Efficiency has been increased, Techch≥1
shows the development and progress in usage of technology, The Total Factor Productivity Tfpch
is decomposed into Effch and Techch, when Effch and Techch combined operate & cause Tfpch
increase, then the Tfpch≥1.
TABLE VI Showed the Results of Efficient and not Efficient DMUs as below year wise Real Data
Analysis:
In year 2016 DMU C was efficient with an increasing trend, and A & B were low efficient with a
decreasing trend.
In year 2017 DMU A was low efficient with decreasing trends B & C were efficient with increasing
trends.
In year 2018 DMU A is less efficient with decreasing trend, and B & C are highly efficient.
In year 2019 DMU B & C are high efficient, and A is less efficient with decreasing trend.

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In year 2020 DMU B & C are high efficient, and A is low efficient with decreasing trend.
In year 2022, DMU B & C are high efficient, and A is low efficient with decreasing trend.
Estimated Data Analysis:
In year 2023, DMU B & C are high efficient, and A is low efficient with decreasing trend.
In year 2026, DMU A & C is high efficient, and B is low efficient with decreasing trend.
From TABLE V this is compared with the all DMUs, the Efficiency of B and C are the
most effective DMUs. DMU A indicated failures of achieve more innovation efficiency due to
their respective Efficiency to scale is on a low stage and returns to scale at downward trends.
At the average stage of eleven years data, the result of Technical Efficiency is by CRS
97.10%, VRS 98.30%, and Scale Efficiency is 99.10%. By the obtained results, we can conclude
the result that every DMU needs to focus and improvement regarding the Technical Changes to
high the Total Factor Productivity.
Discussion
A Disco is Power Distribution Company which is consisting of a system of supply of electric
power to consumers or industry which is consists of a power transformer substation, a power
distribution substation and power transmission lines (TL) along with security monitoring
equipment, and other power supply equipment and facilities. This is a system that supplies
electricity to all its consumers or an industry with a power distribution substation. The grid is the
primary focal & central point of a power system and an very essential component; the flexibility
of the power system, in addition to its resilience, is an interpretation of the level of dependability
for the whole power system. Grid routine activities are carried out largely by step by step lowering
the voltages, which are then forward on to the appropriate sector of a business or consumer
population. The distribution system has the most significant influence on the supply that is made
available to all consumers. The storage of electricity is not possible. Generation, transmission,
supply to all consumers, and use are all controlled simultaneously for this purpose. Additionally,
many scholars examined this generation's topic using DEA methods, although distribution
businesses were seldom chosen as the primary study goal. A few significant areas still need to be
investigated further before fresh discoveries may be made. This study is a little attempt to
understand distribution firms' input and output efficacy from a more critical perspective.
There is a significant opportunity for management to invest sufficiently strong effort in these
promising areas. Moreover, it amounts to grasping the direction of the DISCO grid system. SERSC
is decreasing the line faults & losses in both (distribution and transmission) scale by improving
advance technology at every level of process. There is a large gap for good management to put
enough attention into explained potential areas (Nauman et al. 2020) in Power Generation Sector

57
of Pakistan and Punjab level. Now all requirements are satisfied by fast development concerning
the expansion of new grids and the progression of latest technology. Currently, this study is
focused on LESCO, FESCO, and IESCO distribution network input and output characteristics
relative to their efficiencies and improving its economic and social impacts on Punjab province of
Pakistan. The results indicate that they are almost equal to the study of (Nauman et al. 2021) in
Power Distribution Sector. IESCO appears to be on an upward trend, FESCO likewise appears to
be on an upward trend, and LESCO appears to be on a downward trend from the previous year
into the future years.
CONCLUSION
This research is trying to empirically examination of the influence of structure reforms in
the electricity market of Punjab, Pakistan on the performance and Efficiency of the DISCOs in the
post-reform energy market as well as a comparing of the performance of the various distribution
companies. In addition, the obstacles that harm the distribution firms' effectiveness in Punjab,
Pakistan were also investigated.
Reforms of the electricity market have been implemented in many developing nations for
improvement the functioning of the electricity sector for the betterment of end consumer and
provide a continuous & reliable power supply for commercial user and home economic activity.
This research contributed to the current body of literature by analyzing Punjab Pakistan's post-
reform energy market to satisfy the requirements according to their objectives.
For the study, we know that the Efficiency of power-producing firms has received
significant attention. Additionally, many scholars examined this generation's topic using DEA
methods, although distribution businesses were seldom chosen as the primary study goal. A few
significant areas still need to be investigated further before fresh discoveries may be made.
Nevertheless, the purpose of this research is to examine the input & output effectiveness for
distribution companies from a more critical perspective. It is done to adjustment to the newly
implemented reforms and most recent developments in the electricity distribution sector.
According to the provided results, the technical Efficiency is 97.10 percent according to CRS,
98.30 percent according to VRS, and 99.10 percent according to Scale Efficiency. Because there
was input redundancy, the management of companies needs to make efficient distribution system
planning, investment in management of new technology also preserves excessive amounts of
valuable resources from being waste in excess. Under the presumption of focusing its operational
processes and for economic society's coordination on research & development, the management
of companies should mage general consideration, as for the directions of redundancy and its
amounts, to grasp the direction of DISCO grid system Efficiency & improvements. It is
particularly a truth for ineffective and lower-level DMUs. There is a large amount of room for
management to invest sufficiently excellent effort into these prospective areas, particularly to
lower the rate of line losses (distribution and transmission) and improve the technology at each
level.

58
Punjab Consumption of Electricity
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