The Jia Wu Expansion - Trend Analysis Report

Australian Household Goods Retail Sales & Southeast Asia Tourist Arrivals

0.1

0.2 Executive Summary

This report fulfills the request from Ms. Jia Wu for a data-driven analysis to support the potential expansion of Jia Wu International Co. into the Australian market for its deluxe-category high-quality household goods (targeted at five-star hotels and resorts).

The case provides: - Yearly international tourist arrivals in Southeast Asia (1991–2009) — the key driver of deluxe-category growth. - Reference to monthly Australian retail sales of household goods (Jan 1995 – Dec 2009) from the Australian Bureau of Statistics.

Important note: The actual monthly Australian sales time-series numbers are not included in the provided case PDF. Therefore the seasonal patterns and 2010 monthly forecast sections below use the only quantitative data available (tourist arrivals) as a proxy illustration. In a real-world setting, the ABS series (Catalogue 8501.0, Table 3 or similar) would be downloaded as a CSV and substituted.

The analysis shows a clear upward trend in Southeast Asian tourism, supporting the deluxe-category strategy. The same time-series techniques would be applied to the Australian monthly sales data once obtained.

0.3 1. Case Background (Excerpt from Jia Wu Expansion PDF)

“With the number of international visitor arrivals in Southeast Asia increasing steadily over the years (see Figure 1), the number of high-quality hotels and resorts has also increased accordingly. Jia Wu International has taken advantage of this opportunity.”

The company now plans to enter the Australian market (proximity + Western-culture buyers) and, if successful, expand further into Europe or the USA.

0.4 2. Data – International Tourist Arrivals in Southeast Asia (1991–2009)

```{r} # Load required packages library(ggplot2) library(dplyr) library(forecast) library(lubridate) library(knitr)

1 Hard-coded data from the case (millions of tourists)

tourist_data <- data.frame( Year = 1991:2009, Tourists_millions = c(20,22,25,27,30,32,32,31,37,39, 41,43,39,46,52,55,62,65,65) )

kable(tourist_data, caption = “International Tourist Arrivals in Southeast Asia (ASEAN Tourism Marketing Strategy, 2010)”)

2 Reproduce Figure 1 from the case + add trend

ggplot(tourist_data, aes(x = Year, y = Tourists_millions)) + geom_line(color = “steelblue”, linewidth = 1.2) + geom_point(color = “darkblue”, size = 3) + geom_smooth(method = “lm”, se = TRUE, color = “red”, linetype = “dashed”) + labs(title = “International Tourist Arrivals in Southeast Asia (1991–2009)”, subtitle = “Clear upward trend supports deluxe-category growth”, y = “Number of Tourists (millions)”, caption = “Source: ASEAN Tourism Marketing Strategy, 2010”) + theme_minimal(base_size = 14)

trend_model <- lm(Tourists_millions ~ Year, data = tourist_data) summary(trend_model)

3 Extract slope (average annual growth)

cat(“Average annual growth:”, round(coef(trend_model)[“Year”], 2), “million tourists per year”)

4 Example code you would use with real monthly data

monthly_sales <- read.csv(“abs_household_goods_monthly_1995_2009.csv”) %>% mutate(Date = ym(paste(Year, Month)))

5 Seasonal decomposition (STL)

sales_ts <- ts(monthly_sales$Sales_millions_AUD, frequency = 12) stl_decomp <- stl(sales_ts, s.window = “periodic”) plot(stl_decomp)

6 Convert to time series object

tourist_ts <- ts(tourist_data$Tourists_millions, start = 1991, frequency = 1)

7 Automatic ARIMA model

fit_arima <- auto.arima(tourist_ts) summary(fit_arima)

8 Forecast 2010–2014 (5 years ahead)

fc <- forecast(fit_arima, h = 5) plot(fc, main = “Forecast of Southeast Asia Tourist Arrivals”, ylab = “Tourists (millions)”, xlab = “Year”)

9 Point forecast for 2010

cat(“Forecasted arrivals 2010:”, round(fc$mean[1], 1), “million”)