🏢 Introduction

This report analyzes International Breweries’ performance over the past three years.
It focuses on profit trends across Anglophone and Francophone territories, helping management (Mr. Stone) make strategic decisions to maximize profit and minimize loss.

📂 Data Loading and Preparation

df <- read.csv("C:\\Users\\user\\Desktop\\International-Breweries.csv", stringsAsFactors = FALSE)

# Clean columns
colnames(df) <- trimws(colnames(df))
df$PROFIT <- as.numeric(df$PROFIT)
df$YEARS <- as.numeric(df$YEARS)
df$MONTHS <- trimws(df$MONTHS)

# Define language territories
anglophone <- c("Nigeria", "Ghana", "Sierra Leone", "Liberia", "Gambia")
francophone <- c("Benin", "Togo", "Senegal", "Côte d'Ivoire", "Cameroon")

df$TERRITORY_LANG <- ifelse(df$COUNTRIES %in% anglophone, "Anglophone",
                     ifelse(df$COUNTRIES %in% francophone, "Francophone", "Unclassified"))

head(df)
##   SALES_ID SALES_REP              EMAILS      BRANDS PLANT_COST UNIT_PRICE
## 1    10101   Jardine      jard@gmail.com      trophy        150        200
## 2    10102      Gill     gillhell@uk.com   budweiser        250        500
## 3    10103   Sorvino sorvi2000@gmail.com castle lite        180        450
## 4    10104     Jones   jone.ai@yahoo.com eagle lager        170        250
## 5    10105   Andrews      andy@gmail.com        hero        150        200
## 6    10106   Jardine      jard@gmail.com   beta malt         80        150
##   QUANTITY   COST PROFIT COUNTRIES        REGION   MONTHS YEARS TERRITORY_LANG
## 1      725 145000  36250     Ghana     Southeast  January  2019     Anglophone
## 2      815 407500 203750   Nigeria          west February  2018     Anglophone
## 3      937 421650 252990      Togo    southsouth    March  2018    Francophone
## 4      765 191250  61200     Benin     northwest    April  2018    Francophone
## 5      836 167200  41800   Senegal    northeast       May  2017    Francophone
## 6      798 119700  55860     Ghana northcentral      June  2019     Anglophone

💰 Total Profit (3 Years)

total_profit_all <- sum(df$PROFIT, na.rm = TRUE)
paste("Total Profit (All Territories, 3 Years): ₦", format(total_profit_all, big.mark = ","))
## [1] "Total Profit (All Territories, 3 Years): ₦ 105,587,420"

🌍 Profit by Territory Language

profit_by_lang <- df %>%
  group_by(TERRITORY_LANG) %>%
  summarise(TOTAL_PROFIT = sum(PROFIT, na.rm = TRUE))
profit_by_lang
## # A tibble: 2 × 2
##   TERRITORY_LANG TOTAL_PROFIT
##   <chr>                 <dbl>
## 1 Anglophone         42389260
## 2 Francophone        63198160
# Visualization
ggplot(profit_by_lang, aes(x = TERRITORY_LANG, y = TOTAL_PROFIT, fill = TERRITORY_LANG)) +
  geom_bar(stat = "identity") +
  theme_minimal() +
  labs(title = "Total Profit by Territory Language (3 Years)", y = "Profit", x = "Territory")

🏆 Country with Highest Profit (2019)

top_country_2019 <- df %>%
  filter(YEARS == 2019) %>%
  group_by(COUNTRIES) %>%
  summarise(TOTAL_PROFIT = sum(PROFIT, na.rm = TRUE)) %>%
  arrange(desc(TOTAL_PROFIT)) %>%
  head(1)
top_country_2019
## # A tibble: 1 × 2
##   COUNTRIES TOTAL_PROFIT
##   <chr>            <dbl>
## 1 Ghana          7144070

📅 Year with the Highest Profit

profit_by_year <- df %>%
  group_by(YEARS) %>%
  summarise(TOTAL_PROFIT = sum(PROFIT, na.rm = TRUE)) %>%
  arrange(desc(TOTAL_PROFIT))
profit_by_year
## # A tibble: 3 × 2
##   YEARS TOTAL_PROFIT
##   <dbl>        <dbl>
## 1  2017     38503320
## 2  2018     37063850
## 3  2019     30020250

📉 Month with the Least Profit (Across 3 Years)

least_profit_month <- df %>%
  group_by(MONTHS) %>%
  summarise(TOTAL_PROFIT = sum(PROFIT, na.rm = TRUE)) %>%
  arrange(TOTAL_PROFIT) %>%
  head(1)
least_profit_month
## # A tibble: 1 × 2
##   MONTHS TOTAL_PROFIT
##   <chr>         <dbl>
## 1 April       8573830

🎄 Minimum Profit in December 2018

min_profit_dec_2018 <- df %>%
  filter(MONTHS == "December", YEARS == 2018) %>%
  summarise(MIN_PROFIT = min(PROFIT, na.rm = TRUE))
min_profit_dec_2018
##   MIN_PROFIT
## 1      38150

📆 Profit Percentage per Month (2019)

profit_month_2019 <- df %>%
  filter(YEARS == 2019) %>%
  group_by(MONTHS) %>%
  summarise(PROFIT = sum(PROFIT, na.rm = TRUE)) %>%
  mutate(PERCENT_OF_2019 = (PROFIT / sum(PROFIT)) * 100) %>%
  arrange(desc(PERCENT_OF_2019))
profit_month_2019
## # A tibble: 12 × 3
##    MONTHS     PROFIT PERCENT_OF_2019
##    <chr>       <dbl>           <dbl>
##  1 January   3263160           10.9 
##  2 August    2982800            9.94
##  3 July      2945340            9.81
##  4 April     2851470            9.50
##  5 November  2675610            8.91
##  6 June      2669080            8.89
##  7 May       2573040            8.57
##  8 March     2530620            8.43
##  9 October   2220870            7.40
## 10 December  2048780            6.82
## 11 September 1892600            6.30
## 12 February  1366880            4.55
# Visualization
ggplot(profit_month_2019, aes(x = reorder(MONTHS, -PERCENT_OF_2019), y = PERCENT_OF_2019)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  theme_minimal() +
  labs(title = "Profit Percentage by Month (2019)", x = "Month", y = "Percentage (%)") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

🍺 Brand with the Highest Profit (Senegal)

top_brand_senegal <- df %>%
  filter(trimws(tolower(COUNTRIES)) == "senegal") %>%
  group_by(BRANDS) %>%
  summarise(TOTAL_PROFIT = sum(PROFIT, na.rm = TRUE)) %>%
  arrange(desc(TOTAL_PROFIT)) %>%
  head(1)
top_brand_senegal
## # A tibble: 1 × 2
##   BRANDS      TOTAL_PROFIT
##   <chr>              <dbl>
## 1 castle lite      7012980

📊 Summary Insights

  • Total Profit: ₦105,587,420
  • Francophone Territories: ₦63,198,160
  • Anglophone Territories: ₦42,389,260
  • Top Country (2019): Ghana (₦7,144,070)
  • Best Year: 2017
  • Least Profitable Month: April
  • Minimum December 2018 Profit: ₦38,150
  • Top Brand in Senegal: castle lite

🧭 Recommendations

  1. Focus expansion in Francophone territories — they produced a higher cumulative profit over 3 years.
  2. Investigate April’s performance dip to identify recurring operational or demand-related issues.
  3. Strengthen partnerships in Ghana, which led 2019’s profits.
  4. Replicate Senegal’s success — the Castle Lite brand shows strong local demand.
  5. Analyze cost-to-profit ratios per brand in the next phase for improved efficiency.