Table of Contents


Introduction


Why this Sector and Company?
For this Project we’ve taken the Construction Sector, The construction sector is a vital part of the global economy, encompassing a wide range of activities, from residential and commercial building to infrastructure development. It Facilitates the creation of essential infrastructure like highways, bridges, railways, airports, and urban centers. Improves connectivity and enhances productivity across industries. Reliance Infrastructure Limited (RInfra) is a leading Indian private sector enterprise focused on developing and operating infrastructure projects. The construction industry drives economic growth by creating jobs and enabling other sectors to thrive. RInfra is the leading partner in the Mumbai Metro Line 1 project, which connects Versova to Ghatkopar.

Topic Area 1

1.How has the organisation of our choice performed as compared to its industry average?

Ratios

Gross Profit Margin

Done by Shyam
Gross profit margin is a profitability ratio that shows the percentage of revenue remaining after deducting the cost of goods sold (COGS), indicating how efficiently a company manages its production and sales costs.

Gross Profit Margin= (Revenue-COGS/Revenue)

library(readxl)
library(ggplot2)
library(gridExtra)
library(dplyr)
library(knitr)
#------------------------------------------------------------
#Construction Industry
industry_data<-data.frame(
  company = rep(c("Reliance Infrastructure Limited","Hindustan Construction Company","Man InfraConstruction",
                  "Sadbhav Infrastructure","Welspun Enterprises"),each = 5),
  Year = rep(2020:2024, times=5),
  Revenue = c(336953, 245667, 423703, 528315, 534534, 3265.22, 2339.66, 4235.56, 4916.83, 4895.59, 
              101.41, 114.11, 233.49, 795.77, 699.22, 181.77, 192.76, 204.21, 89.41, 22.5, 
              1759.94,  1410.18,    1300.90,    2620.29,    2405.02),
  COGS = c(266135, 194548, 350736, 439391, 432598, 2635.36, 1824.55, 3419.84, 3737.65, 3587.90, 
           68.62, 81.62, 134.3, 582.19, 449.13, 79.13,  78.79,  154.3,  64.35,  21.25, 
           1400.36, 1078.55,    882.31, 1724.38,    1375.58))

#------------------------------------------------------------
#calculation of gross profit margin
industry_data$GPR<-round((industry_data$Revenue - industry_data$COGS) / (industry_data$Revenue) *100, digits = 2)
kable(industry_data, caption = "Industry Data (2020-2024)", align = "c")
Industry Data (2020-2024)
company Year Revenue COGS GPR
Reliance Infrastructure Limited 2020 336953.00 266135.00 21.02
Reliance Infrastructure Limited 2021 245667.00 194548.00 20.81
Reliance Infrastructure Limited 2022 423703.00 350736.00 17.22
Reliance Infrastructure Limited 2023 528315.00 439391.00 16.83
Reliance Infrastructure Limited 2024 534534.00 432598.00 19.07
Hindustan Construction Company 2020 3265.22 2635.36 19.29
Hindustan Construction Company 2021 2339.66 1824.55 22.02
Hindustan Construction Company 2022 4235.56 3419.84 19.26
Hindustan Construction Company 2023 4916.83 3737.65 23.98
Hindustan Construction Company 2024 4895.59 3587.90 26.71
Man InfraConstruction 2020 101.41 68.62 32.33
Man InfraConstruction 2021 114.11 81.62 28.47
Man InfraConstruction 2022 233.49 134.30 42.48
Man InfraConstruction 2023 795.77 582.19 26.84
Man InfraConstruction 2024 699.22 449.13 35.77
Sadbhav Infrastructure 2020 181.77 79.13 56.47
Sadbhav Infrastructure 2021 192.76 78.79 59.13
Sadbhav Infrastructure 2022 204.21 154.30 24.44
Sadbhav Infrastructure 2023 89.41 64.35 28.03
Sadbhav Infrastructure 2024 22.50 21.25 5.56
Welspun Enterprises 2020 1759.94 1400.36 20.43
Welspun Enterprises 2021 1410.18 1078.55 23.52
Welspun Enterprises 2022 1300.90 882.31 32.18
Welspun Enterprises 2023 2620.29 1724.38 34.19
Welspun Enterprises 2024 2405.02 1375.58 42.80 
#------------------------------------------------------------
#calculation of industry average gross profit ratio for each year

industry_avg<-industry_data %>%
  group_by(Year) %>%
  summarise(Industry_Average_GPR = mean(GPR))

kable(industry_avg, caption = "Industry Average (2020-2024)", align = "c")
Industry Average (2020-2024)
Year Industry_Average_GPR
2020 29.908
2021 30.790
2022 27.116
2023 25.974
2024 25.982 
#------------------------------------------------------------
#Reliance Infrastructure
RI_pl <- read_excel("F:/bhuj048Project/RI_PL.xlsx")
RI_pl<-as.data.frame(RI_pl)
# Clean column names (if needed)
colnames(RI_pl) <- make.names(colnames(RI_pl))
colnames(RI_pl) <- trimws(colnames(RI_pl))
#------------------------------------------------------------
# Reshaping the data to long format using reshape function
RI_pl_long <- reshape(RI_pl, 
                      varying = c("X2024", "X2023", "X2022", "X2021", "X2020"), 
                      v.names = "Amount", 
                      timevar = "Year",
                      times = c("2024","2023","2022","2021", "2020"),
                      direction = "long")
#------------------------------------------------------------
# Reset row names if necessary
rownames(RI_pl_long) <- NULL
#------------------------------------------------------------
#GROSS PROFIT RATIO= (GROSS PROFIT/REVENUE FROM OPERATIONS)*100
#GROSS PROFIT= Revenue from operations-(Purchase of stock in trade+Operating and direct expenses+Cost of power purchased+Cost of fuel+Change in inventories+Cost of materials consumed)
RI_pl_long<-data.frame(Year = c(2020, 2021, 2022, 2023, 2024),
                       RFO = c(336953, 245667, 423703, 528315, 534534),
                       Purchase_of_stock_in_trade = c(7292, 7301, 10691, 9974, 13453),
                       Operating_and_direct_expenses = c(21424, 18375, 27155, 44396, 40027),
                       Cost_of_power_purchased = c(0, 0, 0, 0, 0),
                       Cost_of_fuel = c(0, 0, 0, 0, 0),
                       Cost_of_materials_consumed = c(237342, 168262, 320852, 391508, 376418),
                       Change_in_inventories = c(77, 610, -7962, -6487, 2700))

#------------------------------------------------------------
# Calculate Gross Profit
RI_pl_long$Gross_Profit<- RI_pl_long$RFO-(RI_pl_long$Purchase_of_stock_in_trade+RI_pl_long$Operating_and_direct_expenses+
                                            RI_pl_long$Cost_of_power_purchased+RI_pl_long$Cost_of_fuel
                                          +RI_pl_long$Cost_of_materials_consumed+RI_pl_long$Change_in_inventories)
#------------------------------------------------------------
# Calculate Gross Profit Margin
RI_pl_long$Gross_Profit_Margin<- (RI_pl_long$Gross_Profit / RI_pl_long$RFO) * 100
RI_pl_long$Gross_Profit_Margin=round(RI_pl_long$Gross_Profit_Margin,digits = 2)
#------------------------------------------------------------
#view results
kable(RI_pl_long, caption = "Reliance Infrastructure Data (2020-2024)", align = "c")
Reliance Infrastructure Data (2020-2024)
Year RFO Purchase_of_stock_in_trade Operating_and_direct_expenses Cost_of_power_purchased Cost_of_fuel Cost_of_materials_consumed Change_in_inventories Gross_Profit Gross_Profit_Margin
2020 336953 7292 21424 0 0 237342 77 70818 21.02
2021 245667 7301 18375 0 0 168262 610 51119 20.81
2022 423703 10691 27155 0 0 320852 -7962 72967 17.22
2023 528315 9974 44396 0 0 391508 -6487 88924 16.83
2024 534534 13453 40027 0 0 376418 2700 101936 19.07 
#------------------------------------------------------------
#Line plot
ggplot() +
  geom_point(data = industry_avg, aes(x = Year, y = round(Industry_Average_GPR, digits = 1)), color = "blue", size = 4) +
  geom_point(data = RI_pl_long, aes(x = Year, y = round(Gross_Profit_Margin, digits = 1)), color = "blue", size = 4) +
  geom_line(data = industry_avg, aes(x = Year, y = round(Industry_Average_GPR, digits = 1), group = 1, color = "Industry Average"), linewidth = 1.5) +
  geom_line(data = RI_pl_long, aes(x = Year, y = round(Gross_Profit_Margin, digits = 1), group = 1, color = "Reliance Infrastructure"), linewidth = 1.5) +
  
  labs(
    x = "Year",
    y = "Gross Profit Margin in %",
    title = "Reliance Infrastructure vs Construction Industry",
    caption = "Done by Our Team",
    subtitle = "Gross Profit Margin (2020-2024)",
    color = "Line"  # Title for the legend
  ) +
  scale_color_manual(values = c("Industry Average" = "red", "Reliance Infrastructure" = "green")) +
  scale_y_continuous(
    limits = c(16, 32),  # Set y-axis limits
    breaks = seq(16, 32, by = 2),  # Set the breaks on the y-axis
    labels = seq(16, 32, by = 2)  # Label the y-axis with values from 16 to 32
  ) +
  theme(
    plot.title = element_text(colour = "black", size = 16, face = "bold"),  # Title font size
    plot.subtitle = element_text(colour = "blue", size = 14, face = "bold"),  # Subtitle font size
    plot.caption = element_text(size = 10),  # Caption font size
    axis.title = element_text(size = 14),  # Axis titles font size
    axis.text = element_text(size = 10),   # Axis text font size
    legend.text = element_text(size = 8,face = "bold") # Make legend text bold
  )

#------------------------------------------------------------
#Correlation 
cor(industry_avg$Year,industry_avg$Industry_Average_GPR)
## [1] -0.8875371
cor.test(industry_avg$Year, industry_avg$Industry_Average_GPR, method = "pearson")
## 
##  Pearson's product-moment correlation
## 
## data:  industry_avg$Year and industry_avg$Industry_Average_GPR
## t = -3.3365, df = 3, p-value = 0.0445
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.99257413 -0.02429391
## sample estimates:
##        cor 
## -0.8875371
cor(RI_pl_long$Year,RI_pl_long$Gross_Profit_Margin)
## [1] -0.6383508
cor.test(RI_pl_long$Year, RI_pl_long$Gross_Profit_Margin)
## 
##  Pearson's product-moment correlation
## 
## data:  RI_pl_long$Year and RI_pl_long$Gross_Profit_Margin
## t = -1.4364, df = 3, p-value = 0.2464
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.9727620  0.5584092
## sample estimates:
##        cor 
## -0.6383508
cor(industry_avg$Industry_Average_GPR,RI_pl_long$Gross_Profit_Margin)
## [1] 0.8360406
cor.test(industry_avg$Industry_Average_GPR,RI_pl_long$Gross_Profit_Margin)
## 
##  Pearson's product-moment correlation
## 
## data:  industry_avg$Industry_Average_GPR and RI_pl_long$Gross_Profit_Margin
## t = 2.6392, df = 3, p-value = 0.07771
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1761726  0.9888908
## sample estimates:
##       cor 
## 0.8360406
Interpretation

From the Visual plot, we could see that there is a clear difference in the Gross Profit Margins of Reliance Infrastructure vs Construction Industry. Moreover as the years pass by, the Gross Profit Margin seems to be declining for both.

From the Correlation test performed between the Year and the Industry Average, we may observe that there might be a significant difference in Gross Profit Margin as the years pass by, which is indicated by a negative correlation.




Fixed Asset Turnover Ratio

Done by Sundareswaran
The Fixed Asset Turnover Ratio measures how efficiently a company uses its fixed assets (like property, plant, and equipment) to generate revenue.

Fixed Asset Turnover Ratio = Sales / Average Fixed Assets

library(readxl)
library(ggplot2)
library(gridExtra)
library(dplyr)
library(knitr)
#------------------------------------------------------------
#Construction Industry
industry_data2 <- data.frame(
  company = rep(c("Reliance Infrastructure","Hindustan Construction Company",
                  "Man InfraConstruction","Welspun Enterprises","Sadbhav Infrastructure"),each = 5),
  Year = rep(2020:2024, times=5),
  Sales = c(336953, 245667, 423703, 528315, 534534, 3265.22, 2339.66, 4235.56, 
            4916.83, 4895.59,101.41, 114.11, 233.49, 795.77, 699.22, 1759.94, 1410.18,
            1300.90,2620.29, 2405.02,181.77, 192.76, 204.21, 89.41, 22.5),
  Average_Fixed_Asset_Industry =c((1158.28+1094.78)/2,(1094.78+396.14)/2,
                                  (396.14+336.36)/2,(336.36+313.77)/2,(313.77+209.60)/2,(587.15+523.88)/2,(523.88+482.03)/2,(482.03+373)/2,(373+299.9)/2,(299.99+229.63)/2,
                                  (54.17+48.28)/2,(48.28+44.69)/2,(44.69+42.65)/2,(42.65+43.53)/2,(43.53+45.92)/2,
                                  (37.45+31.57)/2,(31.57+28.56)/2,(28.56+32.50)/2,(32.50+19.82)/2,(19.82+15.91)/2,
                                  (0.42+0.40)/2,(0.40+0.41)/2,(0.41+0.50)/2,(0.50+0.43)/2,(0.43+0.29)/2))
  
#------------------------------------------------------------
#calculation of fixed asset turnover ratio
#FIXED ASSET TURNOVER RATIO=REVENUE(OR)SALES/AVERAGE FIXED ASSET
industry_data2$FATR<-industry_data2$Sales / industry_data2$Average_Fixed_Asset_Industry
industry_data2$FATR=round(industry_data2$FATR, digits = 0)
kable(industry_data2, caption = "Industry Data (2020-2024)", align = "c")
Industry Data (2020-2024)
company Year Sales Average_Fixed_Asset_Industry FATR
Reliance Infrastructure 2020 336953.00 1126.530 299
Reliance Infrastructure 2021 245667.00 745.460 330
Reliance Infrastructure 2022 423703.00 366.250 1157
Reliance Infrastructure 2023 528315.00 325.065 1625
Reliance Infrastructure 2024 534534.00 261.685 2043
Hindustan Construction Company 2020 3265.22 555.515 6
Hindustan Construction Company 2021 2339.66 502.955 5
Hindustan Construction Company 2022 4235.56 427.515 10
Hindustan Construction Company 2023 4916.83 336.450 15
Hindustan Construction Company 2024 4895.59 264.810 18
Man InfraConstruction 2020 101.41 51.225 2
Man InfraConstruction 2021 114.11 46.485 2
Man InfraConstruction 2022 233.49 43.670 5
Man InfraConstruction 2023 795.77 43.090 18
Man InfraConstruction 2024 699.22 44.725 16
Welspun Enterprises 2020 1759.94 34.510 51
Welspun Enterprises 2021 1410.18 30.065 47
Welspun Enterprises 2022 1300.90 30.530 43
Welspun Enterprises 2023 2620.29 26.160 100
Welspun Enterprises 2024 2405.02 17.865 135
Sadbhav Infrastructure 2020 181.77 0.410 443
Sadbhav Infrastructure 2021 192.76 0.405 476
Sadbhav Infrastructure 2022 204.21 0.455 449
Sadbhav Infrastructure 2023 89.41 0.465 192
Sadbhav Infrastructure 2024 22.50 0.360 62 
#------------------------------------------------------------
#calculation of industry average FATR for each year
Industry_Avg<-industry_data2 %>%
  group_by(Year) %>%
  summarise(Industry_Average_FATR = mean(FATR))
Industry_Avg$Industry_Average_FATR=round(Industry_Avg$Industry_Average_FATR)
kable(Industry_Avg, caption = "Industry Average (2020-2024)", align = "c")
Industry Average (2020-2024)
Year Industry_Average_FATR
2020 160
2021 172
2022 333
2023 390
2024 455 
#------------------------------------------------------------
#Reliance Infrastructure
RI_pl <- read_excel("F:/bhuj048Project/RI_PL.xlsx")
RI_BS <- read_excel("C:/Users/HP/Downloads/RI_BS.xlsx")
RI_BS<-as.data.frame(RI_BS)
#------------------------------------------------------------
# Clean column names (if needed)
colnames(RI_BS) <- make.names(colnames(RI_BS))
colnames(RI_BS) <- trimws(colnames(RI_BS))
#------------------------------------------------------------
# Reshaping the data to long format using reshape function
RI_BS_long <- reshape(RI_BS, 
                      varying = c("X2024", "X2023", "X2022", "X2021", "X2020"), 
                      v.names = "Amount", 
                      timevar = "Year",
                      times = c("2024","2023","2022","2021", "2020"),
                      direction = "long")
#------------------------------------------------------------
#CALCULATION OF FIXED ASSET TURNOVER RATIO
#FIXED ASSET TURNOVER RATIO=REVENUE(OR)SALES/AVERAGE FIXED ASSET
RI_pl_long<-data.frame(RFO = c(336953, 245667, 423703, 528315, 534534))
RI_BS_long<-data.frame(Year = c(2020, 2021, 2022, 2023, 2024),
                       Average_Fixed_Asset = c((1158.28+1094.78)/2,(1094.78+396.14)/2,
                                               (396.14+336.36)/2,(336.36+313.77)/2,(313.77+209.60)/2))
#------------------------------------------------------------
#Calculate Fixed Asset Turnover Ratio
RI_BS_long$Fixed_Asset_Turnover_Ratio<- RI_pl_long$RFO / RI_BS_long$Average_Fixed_Asset

# Round the values in numeric columns
RI_BS_long$Fixed_Asset_Turnover_Ratio <- round(RI_BS_long$Fixed_Asset_Turnover_Ratio)
kable(RI_BS_long, caption = "Reliance Infrastructure(2020-2024)", align = "c")
Reliance Infrastructure(2020-2024)
Year Average_Fixed_Asset Fixed_Asset_Turnover_Ratio
2020 1126.530 299
2021 745.460 330
2022 366.250 1157
2023 325.065 1625
2024 261.685 2043 
#------------------------------------------------------------
#Data Creation
Comparison_data <- data.frame(
  Year = c(2020, 2021, 2022, 2023,2024),
  Reliance_Infrastructure = (RI_BS_long$Fixed_Asset_Turnover_Ratio),
  Industry_Average = (Industry_Avg$Industry_Average_FATR))

Comparison_matrix <- as.matrix(Comparison_data[, -1])  # Exclude the Year column
rownames(Comparison_matrix) <- Comparison_data$Year    # Use Year as row names
#------------------------------------------------------------
# Bar Plot with customized y-axis breaks
bp <- barplot(
  height = t(Comparison_matrix),  # Transpose the matrix
  beside = TRUE,                  # Bars side by side
  col = c("green", "yellow"),   # Colors for companies
  main = "Reliance Infrastructure vs Construction Industry", 
  xlab = "Year",                  # X-axis label
  ylab = "FATR",                  # Y-axis label
  ylim = c(0, 2400),              # Set y-axis limits
  lwd = 2,                        # Line width for bars
  axes = FALSE)                    # Suppress default axes
#------------------------------------------------------------
# Add custom y-axis breaks
axis(2, at = seq(0, 2400, by = 400), labels = seq(0, 2400, by = 400), las = 1,lwd = 2)
#------------------------------------------------------------
# Add subtitle
mtext("FATR Comparison (2020-2024)", side = 3, line = -0.15, cex = 1, col = "red")
#------------------------------------------------------------
# Add the corresponding values inside the bars
text(
  x = as.vector(bp),                          # Bar positions (midpoints)
  y = t(Comparison_matrix) + 60,             # Place text slightly above bar values
  labels = round(t(Comparison_matrix), 0),    # Format the values (rounded to 0 decimal places)
  cex = 0.8,                                  # Adjust font size
  col = "black")                               # Color of the text
#------------------------------------------------------------
# Add legend
legend(
  x = "topleft",                             #Positioning the Legend
  inset = c(0.05, 0.05),                    #Adjusting the position slightly
  legend = colnames(Comparison_matrix),     
  fill = c("green", "yellow"))               
#------------------------------------------------------------
# Add a horizontal line at y = 0
abline(h = 0, col = "black", lwd = 2)

Interpretation

From the above Bar plot, we may observe that throughout the 5 years, the FATR of Reliance Infrastructure has an edge over the Industry Average. Moreover, as time goes on, the FATR keeps on increasing for both. On the other hand, a huge FATR of 2043:1 may also indicate that the Company is operating with a very lean Asset Base or there might have been a reduction of fixed asset due to Resale. However, it is unusual for a construction sector.




2.How has the organisation of our choice performed as compared to one of its Competitors?

For the purpose of comparison with a competitor, we have taken “Man InfraConstruction company”.


Current Ratio

Done by Murali and Bhujanganath
The current ratio is a liquidity ratio that measures a company’s ability to pay off its short-term liabilities using its short-term assets.

Current Ratio = Current Assets / Current Liabilities

# Load the package
library(readxl)
library(gridExtra)
library(ggplot2)
#----------------------------------------------------
#Man Infra Construction
MIC_BS <-read_excel("F:/bhuj048Project/MIC_BS.xlsx")
MIC_BS<-as.data.frame(MIC_BS)
# Clean column names (if needed)
colnames(MIC_BS) <- make.names(colnames(MIC_BS))
colnames(MIC_BS) <- trimws(colnames(MIC_BS))
#----------------------------------------------------
# Reshaping the data to long format using reshape function
MIC_BS_long <- reshape(MIC_BS, 
                      varying = c("X2024", "X2023", "X2022", "X2021", "X2020"), 
                      v.names = "Amount", 
                      timevar = "Year",
                      times = c("2024","2023","2022","2021", "2020"),
                      direction = "long")
#----------------------------------------------------
# Reset row names if necessary
rownames(MIC_BS_long) <- NULL
#----------------------------------------------------
#CURRENT RATIO= (CURRENT ASSETS/CURRENT LIABILITIES)
MIC_BS_long<-data.frame(Year = c(2020, 2021, 2022, 2023, 2024),
                        Current_Investment = c(0.73, 44.19, 30.33, 2.55, 103.58),
                        Inventories = c(3.90, 1.69, 3.03, 1.64, 3.42),
                        Trade_Receivables = c(32.59, 41.54, 76.40, 215.59, 61.81),
                        Cash_and_Cash_Equivalents = c(60.46, 136.47, 139.50, 166.33, 354.57),
                        Short_Term_Loans_and_Advances = c(476.33, 464.51, 556.15, 565.37, 501.74),
                        Other_Current_Assets = c(105.64, 81.54, 8.77, 36.98, 58.15),
                        Short_Term_Provisions = c(1.34, 1.19, 1.60, 2.44, 2.87),
                        Short_Term_Borrowings = c(0, 0, 0, 10.83, 8.56),
                        Trade_Payables = c(18.07, 21.11, 20.27, 106.99, 48.91),
                        Other_Current_Liabilities = c(46.27, 81.01, 77.73, 135.37, 108.75))

#----------------------------------------------------
#CALCULATE CURRENT ASSETS
MIC_BS_long$CURRENT_ASSETS<- MIC_BS_long$Current_Investment+MIC_BS_long$Inventories+MIC_BS_long$Trade_Receivables+MIC_BS_long$Cash_and_Cash_Equivalents+MIC_BS_long$Short_Term_Loans_and_Advances+MIC_BS_long$Other_Current_Asset

#CALCULATE CURRENT LIABILITIES
MIC_BS_long$CURRENT_LIABILITIES<- MIC_BS_long$Short_Term_Provisions+MIC_BS_long$Short_Term_Borrowings+MIC_BS_long$Trade_Payables+MIC_BS_long$Other_Current_Liabilities

#CALCULATE CURRENT RATIO
MIC_BS_long$CURRENT_RATIO<- (MIC_BS_long$CURRENT_ASSETS/MIC_BS_long$CURRENT_LIABILITIES)

#view results
kable(MIC_BS_long, caption = "Man InfraConstruction (2020-2024)", align = "c")
Man InfraConstruction (2020-2024)
Year Current_Investment Inventories Trade_Receivables Cash_and_Cash_Equivalents Short_Term_Loans_and_Advances Other_Current_Assets Short_Term_Provisions Short_Term_Borrowings Trade_Payables Other_Current_Liabilities CURRENT_ASSETS CURRENT_LIABILITIES CURRENT_RATIO
2020 0.73 3.90 32.59 60.46 476.33 105.64 1.34 0.00 18.07 46.27 679.65 65.68 10.347899
2021 44.19 1.69 41.54 136.47 464.51 81.54 1.19 0.00 21.11 81.01 769.94 103.31 7.452715
2022 30.33 3.03 76.40 139.50 556.15 8.77 1.60 0.00 20.27 77.73 814.18 99.60 8.174498
2023 2.55 1.64 215.59 166.33 565.37 36.98 2.44 10.83 106.99 135.37 988.46 255.63 3.866761
2024 103.58 3.42 61.81 354.57 501.74 58.15 2.87 8.56 48.91 108.75 1083.27 169.09 6.406470 
#----------------------------------------------------
#Reliance Infrastructure
#Load the readxl package
library(readxl)
library(gridExtra)
library(ggplot2)
#----------------------------------------------------
RI_CR <- read_excel("C:/Users/HP/Downloads/RI_BS.xlsx")
RI_CR<-as.data.frame(RI_CR)
# Clean column names (if needed)
colnames(RI_CR) <- make.names(colnames(RI_CR))
colnames(RI_CR) <- trimws(colnames(RI_CR))
#----------------------------------------------------
# Reshaping the data to long format using reshape function
RI_CR_long <- reshape(RI_CR, 
                      varying = c("X2024", "X2023", "X2022", "X2021", "X2020"), 
                      v.names = "Amount", 
                      timevar = "Year",
                      times = c("2024","2023","2022","2021", "2020"),
                      direction = "long")
#----------------------------------------------------
# Reset row names if necessary
rownames(RI_CR_long) <- NULL

#----------------------------------------------------
#Current RATIO= (Current Asset/Current Liabilities)

RI_CR_long<-data.frame(Year = c(2020, 2021, 2022, 2023, 2024),
                       CurrentInvestment= c(0,0,1.77,527.27,1170.00),
                       Inventories= c(3.68,3.65,3.5,3.5,0),
                       TradeReceivable= c(4106.24,2848.34,2916.09,1348.65,399.17),
                       CashAndCashEquivalents= c(252.04,129.88,158.13,584.97,182.48),
                       ShortTerm_LoansAndAdvances= c(5765.21,5740.73,5167.43,5079.58,5086.74),
                       OtherCurrentAssets= c(3762.12,3838.45,3001.92,1897.63,2017.89),
                       ShortTermBorrowings= c(741.92,448.15,3722.58,3246.81,2930.17),
                       TradePayables= c(2381.20,1705.62,1576.44,1575.33,1518.25),
                       OtherCurrentLiabilit= c(4352.89,6323.14,2752.91,3342.87,3443.04),
                       ShortTermProvision= c(47.62,20.14,0,0.02,1.34))


#----------------------------------------------------
# Calculate Current Asset
RI_CR_long$Current_Asset<- RI_CR_long$CurrentInvestment+RI_CR_long$Inventories+RI_CR_long$TradeReceivable+RI_CR_long$CashAndCashEquivalents+RI_CR_long$ShortTerm_LoansAndAdvances+RI_CR_long$OtherCurrentAssets

# Calculate Current Liabilities
RI_CR_long$Current_Liabilities<- RI_CR_long$ShortTermBorrowings+RI_CR_long$TradePayables+RI_CR_long$OtherCurrentLiabilit+RI_CR_long$ShortTermProvision


# Calculate Current Ratio
RI_CR_long$Current_Ratio<- (RI_CR_long$Current_Asset / RI_CR_long$Current_Liabilities)

#view results
kable(RI_CR_long, caption = "Reliance Infrastructure (2020-2024)", align = "c")
Reliance Infrastructure (2020-2024)
Year CurrentInvestment Inventories TradeReceivable CashAndCashEquivalents ShortTerm_LoansAndAdvances OtherCurrentAssets ShortTermBorrowings TradePayables OtherCurrentLiabilit ShortTermProvision Current_Asset Current_Liabilities Current_Ratio
2020 0.00 3.68 4106.24 252.04 5765.21 3762.12 741.92 2381.20 4352.89 47.62 13889.29 7523.63 1.846089
2021 0.00 3.65 2848.34 129.88 5740.73 3838.45 448.15 1705.62 6323.14 20.14 12561.05 8497.05 1.478284
2022 1.77 3.50 2916.09 158.13 5167.43 3001.92 3722.58 1576.44 2752.91 0.00 11248.84 8051.93 1.397037
2023 527.27 3.50 1348.65 584.97 5079.58 1897.63 3246.81 1575.33 3342.87 0.02 9441.60 8165.03 1.156346
2024 1170.00 0.00 399.17 182.48 5086.74 2017.89 2930.17 1518.25 3443.04 1.34 8856.28 7892.80 1.122071 
#----------------------------------------------------
#Data Creation
Comparison_data <- data.frame(
  Year = c(2020, 2021, 2022, 2023,2024),
  Reliance_Infrastructure = c(1.846089,1.478284,1.397036,1.156346, 1.122071),
  Man_InfraConstruction = c(10.34,7.45,8.17,3.86,6.40))
#----------------------------------------------------
# Round the values in numeric columns to 1 decimal places
Comparison_data$Reliance_Infrastructure <- round(Comparison_data$Reliance_Infrastructure, 1)
Comparison_data$Man_InfraConstruction <- round(Comparison_data$Man_InfraConstruction, 1)
#view results
kable(Comparison_data, caption = "Comparison Data (2020-2024)", align = "c")
Comparison Data (2020-2024)
Year Reliance_Infrastructure Man_InfraConstruction
2020 1.8 10.3
2021 1.5 7.4
2022 1.4 8.2
2023 1.2 3.9
2024 1.1 6.4 
Comparison_matrix <- as.matrix(Comparison_data[, -1])  # Exclude the Year column
rownames(Comparison_matrix) <- Comparison_data$Year    # Use Year as row names

#----------------------------------------------------
#Bar Plot
bp <- barplot(
  height = t(Comparison_matrix),  # Transpose the matrix
  beside = TRUE,                  # Bars side by side
  col = c("skyblue", "orange"),   # Colors for companies
  main = "Reliance Infrastructure vs Man InfraConstruction", 
  xlab = "Year",                  # X-axis label
  ylab = "Current Ratio",         # Y-axis label
  ylim = c(0, 12),lwd = 2)                 # Set y-axis limits
#----------------------------------------------------
# Add subtitle
mtext("Current Ratio Comparison (2020-2024)", side = 3, line = -0.25, cex = 1, col = "darkblue")
#----------------------------------------------------
# Add the corresponding values inside the bars
text(
  x = as.vector(bp),                          # Bar positions (midpoints)
  y = t(Comparison_matrix) + 0.3,             # Place text slightly above bar values
  labels = round(t(Comparison_matrix), 1),    # Format the values (rounded to 1 decimal places)
  cex = 0.8,                                  # Adjust font size
  col = "black")                               # Color of the text
#----------------------------------------------------
# Add legend
legend( x = "topright", 
  inset = c(0, 0.05), 
  legend = colnames(Comparison_matrix), 
  fill = c("skyblue", "orange"))
#----------------------------------------------------
# Add a horizontal line
abline(h = 0, col = "black", lwd = 2)

#----------------------------------------------------
#Paired t.test ( Reliance Infrastructure vs Man Infra Construction)
t.test(Comparison_data$Reliance_Infrastructure, Comparison_data$Man_InfraConstruction, paired = TRUE)
## 
##  Paired t-test
## 
## data:  Comparison_data$Reliance_Infrastructure and Comparison_data$Man_InfraConstruction
## t = -6.1301, df = 4, p-value = 0.003589
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  -8.485065 -3.194935
## sample estimates:
## mean difference 
##           -5.84
Interpretation

From the bar plot, we may infer that Man InfraConstruction has a high Current Ratio as compared to Reliance Infrastructure across 5 years. Moreover, we could observe that the Current Ratio of Reliance Infrastructure is declining as the years go by, although it stays above 1 which is usually fine.

From the paired t.test performed between these two, we can observe that there might a significant difference between the Current Ratio of both these companies, thereby matching with the bar plot.




Equity Ratio

Done by Hemanth and Murali
The equity ratio, also known as the proprietary ratio, measures the proportion of a company’s total assets financed by shareholder equity, indicating a company’s financial structure and leverage.

Equity Ratio = Total Equity / Total Assets

# Load the package
library(readxl)
library(gridExtra)
library(ggplot2)
#--------------------------------------
#Man Infra Construction
MIC_BS2 <-read_excel("F:/bhuj048Project/MIC_BS.xlsx")
MIC_BS2<-as.data.frame(MIC_BS)
# Clean column names (if needed)
colnames(MIC_BS2) <- make.names(colnames(MIC_BS2))
colnames(MIC_BS2) <- trimws(colnames(MIC_BS2))
#--------------------------------------
# Reshaping the data to long format using reshape function
MIC_BS_long2 <- reshape(MIC_BS, 
                       varying = c("X2024", "X2023", "X2022", "X2021", "X2020"), 
                       v.names = "Amount", 
                       timevar = "Year",
                       times = c("2024","2023","2022","2021", "2020"),
                       direction = "long")
# Reset row names if necessary
rownames(MIC_BS_long2) <- NULL
#--------------------------------------
#EQUITY RATIO= (TOTAL EQUITY/TOTAL ASSETS)
MIC_BS_long2<-data.frame(Year = c(2020, 2021, 2022, 2023, 2024),
                        Equity_Share_Capital= c(49.5,49.5,74.25,74.25,74.25),
                        Reserves_and_Surplus= c(811.24,881.15,929.77,1061.86,1189.72),  
                        Tangible_Assets= c(28.44,24.41,28.41,37.93,37.46),  
                        Intangible_Assets= c(0,3.3,3.3,0,0),    
                        Capital_Work_In_Progress= c(0.01,0.94,1.99,0,0),    
                        Intangible_Assets_Under_Development= c(0,0,0,0,0),
                        Other_Assets= c(19.83,16.04,8.95,5.61,8.46),    
                        Fixed_Assets= c(48.28,44.69,42.65,43.53,45.92), 
                        Non_Current_Investments= c(89.69,91.53,121.59,290.49,430.04),   
                        Deferred_Tax_Assets= c(4.45,4.78,3.59,3.51,2.84),   
                        Long_Term_Loans_and_Advances= c(101,116.94,117.52,0,0), 
                        Other_Non_Current_Assets= c(5.9,8.71,9.14,72.53,17.41), 
                        Current_Investments= c(0.73,44.19,30.33,2.55,103.58),   
                        Inventories= c(3.9,1.69,3.03,1.64,3.42),    
                        Trade_Receivables= c(32.59,41.54,76.4,215.59,61.81),    
                        Cash_and_Cash_Equivalents= c(60.46,136.47,139.5,166.33,354.57), 
                        Short_Term_Loans_and_Advances= c(476.33,464.51,556.15,565.37,501.74),   
                        Other_Current_Assets= c(105.64,81.54,8.77,36.98,58.15), 
                        Contingent_Liabilities= c(468.12,458.69,505.45,174.21,139.45))  

                        
#--------------------------------------
#CALCULATE TOTAL EQUITY
MIC_BS_long2$Total_Equity<- (MIC_BS_long2$Equity_Share_Capital+MIC_BS_long2$Reserves_and_Surplus)

#CALCULATE TOTAL ASSETS
MIC_BS_long2$Total_Assets<- (MIC_BS_long2$Tangible_Assets+MIC_BS_long2$Intangible_Assets+MIC_BS_long2$Capital_Work_In_Progress+
                              MIC_BS_long2$Intangible_Assets_Under_Development+MIC_BS_long2$Other_Assets+MIC_BS_long2$Fixed_Assets+
                              MIC_BS_long2$Non_Current_Investments+MIC_BS_long2$Deferred_Tax_Assets+MIC_BS_long2$Long_Term_Loans_and_Advances+
                              MIC_BS_long2$Other_Non_Current_Assets+MIC_BS_long2$Current_Investments+MIC_BS_long2$Inventories+MIC_BS_long2$Trade_Receivables+
                              MIC_BS_long2$Cash_and_Cash_Equivalents+MIC_BS_long2$Short_Term_Loans_and_Advances+MIC_BS_long2$Other_Current_Assets)


#CALCULATE EQUITY RATIO
MIC_BS_long2$EQUITY_RATIO<- (MIC_BS_long2$Total_Equity/MIC_BS_long2$Total_Assets)*100

#view results
kable(MIC_BS_long2, caption = "Man InfraConstruction (2020-2024)", align = "c")
Man InfraConstruction (2020-2024)
Year Equity_Share_Capital Reserves_and_Surplus Tangible_Assets Intangible_Assets Capital_Work_In_Progress Intangible_Assets_Under_Development Other_Assets Fixed_Assets Non_Current_Investments Deferred_Tax_Assets Long_Term_Loans_and_Advances Other_Non_Current_Assets Current_Investments Inventories Trade_Receivables Cash_and_Cash_Equivalents Short_Term_Loans_and_Advances Other_Current_Assets Contingent_Liabilities Total_Equity Total_Assets EQUITY_RATIO
2020 49.50 811.24 28.44 0.0 0.01 0 19.83 48.28 89.69 4.45 101.00 5.90 0.73 3.90 32.59 60.46 476.33 105.64 468.12 860.74 977.25 88.07777
2021 49.50 881.15 24.41 3.3 0.94 0 16.04 44.69 91.53 4.78 116.94 8.71 44.19 1.69 41.54 136.47 464.51 81.54 458.69 930.65 1081.28 86.06929
2022 74.25 929.77 28.41 3.3 1.99 0 8.95 42.65 121.59 3.59 117.52 9.14 30.33 3.03 76.40 139.50 556.15 8.77 505.45 1004.02 1151.32 87.20599
2023 74.25 1061.86 37.93 0.0 0.00 0 5.61 43.53 290.49 3.51 0.00 72.53 2.55 1.64 215.59 166.33 565.37 36.98 174.21 1136.11 1442.06 78.78382
2024 74.25 1189.72 37.46 0.0 0.00 0 8.46 45.92 430.04 2.84 0.00 17.41 103.58 3.42 61.81 354.57 501.74 58.15 139.45 1263.97 1625.40 77.76363 
#--------------------------------------
#Reliance Infrastructure
#Load the readxl package
library(readxl)
library(gridExtra)
library(ggplot2)
#--------------------------------------
RI_BS <- read_excel("C:/Users/HP/Downloads/RI_BS.xlsx")
RI_BS<-as.data.frame(RI_BS)
# Clean column names (if needed)
colnames(RI_BS) <- make.names(colnames(RI_BS))
colnames(RI_BS) <- trimws(colnames(RI_BS))
#--------------------------------------
# Reshaping the data to long format using reshape function
RI_BS_long <- reshape(RI_BS, 
                      varying = c("X2024", "X2023", "X2022", "X2021", "X2020"), 
                      v.names = "Amount", 
                      timevar = "Year",
                      times = c("2024","2023","2022","2021", "2020"),
                      direction = "long")
#--------------------------------------
# Reset row names if necessary
rownames(RI_BS_long) <- NULL

#--------------------------------------
#EQUITY RATIO= (TOTAL EQUITY/TOTAL ASSETS)

RI_BS_long<-data.frame(Year = c(2020, 2021, 2022, 2023, 2024),
                       EQUITY_SHARE_CAPITAL = c(263.03, 263.03, 263.03, 351.83, 396.17),
                       Reserve_Surplus= c(10183.98,10112.55,9877.52,7000.23,5911.10),
                       TANGIBLE_ASSETS = c(582.57, 379.57, 324.91,  302.33, 207.94),
                       INTANGIBLE_ASSETS = c(0.82, 0.04,    0.03,   0.02,   0),
                       CAPITAL_WORK_IN_PROGRESS = c(28.73,  16.53,  11.42,  11.42,  1.66),
                       INTANGIBLE_ASSETS_UNDERDEVELOPE = c(0,   0,  0,  0,  0),
                       OTHER_ASSETS = c(482.66, 0,  0,  0,  0),
                       FIXED_ASSET = c(1094.78, 396.14, 336.36, 313.77, 209.6),
                       NON_CURRENT_INVESTMENT = c(8010.34,  7655.21,    8432.81,    7666.26,    5928.73),
                       DEFERRED_TAX_ASSET_NET = c(0,    0,  0,  0,  0),
                       LONG_TERM_LOANS_AND_ADVANCES = c(13.64,  9.81,   0,  0,  0),
                       OTHER_NON_CURRENT_ASSET = c(208.78,  121.84, 21.22,  52.68,  74.03),
                       CURRENT_INVESTMENT = c(0,    0,  1.77,   527.27, 1170.00),
                       INVENTORIES = c(3.68,    3.65,   3.5,    3.5,    0),
                       TRADE_RECEIVABLES = c(4106.24,   2848.34,    2916.09,    1348.65,    399.17),
                       CASH_AND_CASHEQUIVALENTS = c(252.04, 129.88, 158.13, 584.97, 182.48),
                       SHORT_TERM_LOANS_AND_ADVANCES = c(5765.21,   5740.73,    5167.43,    5079.58,    5086.74),
                       OTHER_CURRENT_ASSETS = c(3762.12,    3838.45,    3001.92,    1897.63,    2017.890))




#--------------------------------------
# Calculate TOTAL EQUITY
RI_BS_long$Total_Equity<-(RI_BS_long$EQUITY_SHARE_CAPITAL+RI_BS_long$Reserve_Surplus)

# Calculate TOTAL ASSETS
RI_BS_long$Total_Assets<- (RI_BS_long$TANGIBLE_ASSETS+RI_BS_long$INTANGIBLE_ASSETS+RI_BS_long$CAPITAL_WORK_IN_PROGRESS+
                             RI_BS_long$INTANGIBLE_ASSETS_UNDERDEVELOPE+RI_BS_long$OTHER_ASSETS+RI_BS_long$FIXED_ASSET+
                             RI_BS_long$NON_CURRENT_INVESTMENT+RI_BS_long$DEFERRED_TAX_ASSET_NET+RI_BS_long$LONG_TERM_LOANS_AND_ADVANCES+
                             RI_BS_long$OTHER_NON_CURRENT_ASSET+RI_BS_long$CURRENT_INVESTMENT+RI_BS_long$INVENTORIES+RI_BS_long$TRADE_RECEIVABLES+
                             RI_BS_long$CASH_AND_CASHEQUIVALENTS+RI_BS_long$SHORT_TERM_LOANS_AND_ADVANCES+RI_BS_long$OTHER_CURRENT_ASSETS)


# Calculate EQUITY Ratio
RI_BS_long$EQUITY_Ratio<- (RI_BS_long$Total_Equity / RI_BS_long$Total_Assets)*100

#view results
kable(RI_BS_long, caption = "Reliance Infrastructure (2020-2024)", align = "c")
Reliance Infrastructure (2020-2024)
Year EQUITY_SHARE_CAPITAL Reserve_Surplus TANGIBLE_ASSETS INTANGIBLE_ASSETS CAPITAL_WORK_IN_PROGRESS INTANGIBLE_ASSETS_UNDERDEVELOPE OTHER_ASSETS FIXED_ASSET NON_CURRENT_INVESTMENT DEFERRED_TAX_ASSET_NET LONG_TERM_LOANS_AND_ADVANCES OTHER_NON_CURRENT_ASSET CURRENT_INVESTMENT INVENTORIES TRADE_RECEIVABLES CASH_AND_CASHEQUIVALENTS SHORT_TERM_LOANS_AND_ADVANCES OTHER_CURRENT_ASSETS Total_Equity Total_Assets EQUITY_Ratio
2020 263.03 10183.98 582.57 0.82 28.73 0 482.66 1094.78 8010.34 0 13.64 208.78 0.00 3.68 4106.24 252.04 5765.21 3762.12 10447.01 24311.61 42.97128
2021 263.03 10112.55 379.57 0.04 16.53 0 0.00 396.14 7655.21 0 9.81 121.84 0.00 3.65 2848.34 129.88 5740.73 3838.45 10375.58 21140.19 49.07988
2022 263.03 9877.52 324.91 0.03 11.42 0 0.00 336.36 8432.81 0 0.00 21.22 1.77 3.50 2916.09 158.13 5167.43 3001.92 10140.55 20375.59 49.76813
2023 351.83 7000.23 302.33 0.02 11.42 0 0.00 313.77 7666.26 0 0.00 52.68 527.27 3.50 1348.65 584.97 5079.58 1897.63 7352.06 17788.08 41.33139
2024 396.17 5911.10 207.94 0.00 1.66 0 0.00 209.60 5928.73 0 0.00 74.03 1170.00 0.00 399.17 182.48 5086.74 2017.89 6307.27 15278.24 41.28270 
#--------------------------------------
#Data Creation
Comparison_data2 <- data.frame(
  Year = c(2020, 2021, 2022, 2023,2024),
  Reliance_Infrastructure = c(42.97128,49.07988, 49.76813, 41.33139, 41.28270),
  Man_InfraConstruction = c(88.07777,  86.06929, 87.20599,78.78382, 77.76363))

# Round the values in numeric columns to 1 decimal places
Comparison_data2$Reliance_Infrastructure <- round(Comparison_data2$Reliance_Infrastructure, 1)
Comparison_data2$Man_InfraConstruction <- round(Comparison_data2$Man_InfraConstruction, 1)
kable(Comparison_data2, caption = "Comparison Data (2020-2024)", align = "c")
Comparison Data (2020-2024)
Year Reliance_Infrastructure Man_InfraConstruction
2020 43.0 88.1
2021 49.1 86.1
2022 49.8 87.2
2023 41.3 78.8
2024 41.3 77.8 
Comparison_matrix <- as.matrix(Comparison_data2[, -1])  # Exclude the Year column
rownames(Comparison_matrix) <- Comparison_data2$Year    # Use Year as row names

#--------------------------------------
ggplot() +
  geom_point(data = Comparison_data2, aes(x = Year, y = Man_InfraConstruction), color = "black", size = 4) +
  geom_point(data = Comparison_data2, aes(x = Year, y = Reliance_Infrastructure), color = "black", size = 4) +
  geom_line(data = Comparison_data2, aes(x = Year, y = Man_InfraConstruction, group = 1, color = "Man Infrastructure"), linewidth = 1.5) +
  geom_line(data = Comparison_data2, aes(x = Year, y = Reliance_Infrastructure, group = 1, color = "Reliance Infrastructure"), linewidth = 1.5) +
  labs(
    x = "Year", 
    y = "EQUITY_RATIO in %", 
    title = "Reliance Infrastructure vs Man InfraConstruction", 
    caption = "Done by Our Team", 
    subtitle = "Equity Ratio Comparison(2020 - 2024)",
    color = "Line"
  ) +
  scale_color_manual(values = c("Man Infrastructure" = "violet", "Reliance Infrastructure" = "orange")) +
  scale_y_continuous(
    limits = c(40, 90),  # Set y-axis limits
    breaks = seq(40, 90, by = 5),  # Set the breaks on the y-axis
    labels = seq(40, 90, by = 5)  # Label the y-axis with values from 40 to 90
  ) +
  theme(
    plot.title = element_text(colour = "black", size = 16, face = "bold"),  # Title font size
    plot.subtitle = element_text(colour = "blue", size = 14, face = "bold"),  # Subtitle font size
    plot.caption = element_text(size = 10),  # Caption font size
    axis.title = element_text(size = 14),  # Axis titles font size
    axis.text = element_text(size = 10),   # Axis text font size
    legend.text = element_text(size = 8,face = "bold") # Make legend text bold
  )

#----------------------------------------
#Paired t.test ( Reliance Infrastructure vs Man Infra Construction)
t.test(Comparison_data2$Reliance_Infrastructure, Comparison_data2$Man_InfraConstruction, paired = TRUE)
## 
##  Paired t-test
## 
## data:  Comparison_data2$Reliance_Infrastructure and Comparison_data2$Man_InfraConstruction
## t = -24.042, df = 4, p-value = 1.775e-05
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  -43.16913 -34.23087
## sample estimates:
## mean difference 
##           -38.7
Interpretation

From the Line plot, we can see that there is a clear difference between the % of Equity of Man InfraConstruction as compared to Reliance Infrastructure, indicating that Man InfraConstruction might be less a dependent on debt, indicating a positive sign of financial stability.

Reliance Infrastructure has a moderate financial structure, which is equally good even though not upto the level of its competitor.

From a numeric summary point of view, the paired t.test performed between these two companies indicates that there might be a significant difference in the % of equity of both these companies across 5 years using 95% Confidence Interval.


Conclusion

In this R project, we analyzed key financial ratios— Current Ratio, Equity Ratio, Gross Profit Margin and Fixed Asset Turnover Ratio to evaluate the financial health and performance of the company. The Current Ratio provided insights into the company’s short-term liquidity, indicating its ability to meet immediate liabilities. The Equity Ratio assessed financial stability by measuring the proportion of total assets financed by equity. The Profit Margin Ratio highlighted operational efficiency and profitability, while the Fixed Asset Turnover Ratio demonstrated how effectively the company utilized its fixed assets to generate revenue.

By interpreting these ratios collectively, we gained a comprehensive understanding of the company’s financial standing. The results serve as valuable indicators for stakeholders, guiding decision-making related to investment, operational improvements, and financial planning. Future research could involve benchmarking against industry standards and integrating more financial indicators for a deeper analysis.

R Markdown done by Bhujanganath
PPT done by Murali
Company Report Analysis done by Hemanth

THANK YOU