Tools for Analytics Project - OneCom and Yfon Analysis
Anders Kjær Rosenvinge, cand.merc (IEC)
2024-11-28
1 Executive Summary
This project explores the integration of data analytics into the strategic operations of OneCom and Yfon, with a focus on sales performance and network optimization. The analysis leverages extensive datasets spanning 2021–2023 to provide insights into regional sales trends, growth patterns, and forecasting for 2024.
Key findings highlight distinct regional dynamics and company-specific growth trajectories, with OneCom exhibiting consistent upward trends across multiple regions, while Yfon shows more variable performance. Sales projections for 2024 underline significant growth potential in emerging markets, such as Africa, South America, and Asia-Pacific, driven by robust demand and market expansion opportunities. Additionally, the analysis evaluates pre-merger and post-merger production and distribution networks to identify cost-saving synergies and optimized configurations.
The outcomes of this project provide actionable insights for strategic planning, including tailored approaches to enhance market penetration in high-growth regions and address performance gaps in underperforming markets.
2 Introduction
2.1 Context
The merger of OneCom and Yfon presents a unique opportunity to integrate their operations and achieve strategic advantages in global markets. This project aims to evaluate the companies’ historical sales performance, forecast future trends, and optimize their production and distribution networks to maximize efficiency and cost-effectiveness.
2.2 Objectives
The primary objectives of this analysis are:
To assess sales trends and identify growth opportunities across regions and timeframes. To project sales for 2024 using historical data and trend-based forecasting. To optimize the production and distribution networks pre- and post-merger, identifying synergies and cost-saving opportunities.
2.3 Methodology
The project utilizes a robust data-driven approach, employing R programming for data preparation, visualization, and modeling. The datasets include:
- OneCom Sales Data: Country-level sales data from 2021 to 2023.
- Yfon Sales Data: Regional sales distributions for the same period.
- Region Classification and EU Countries List: Supporting datasets to classify countries into custom regions and identify EU membership.
Key analytical techniques include:
- Exploratory Data Analysis (EDA) to uncover patterns and trends in sales.
- Linear regression-based forecasting to predict 2024 sales performance.
- Linear programming models to optimize production and distribution networks, addressing pre-merger and post-merger configurations.
2.4 Structure
The project is structured into three parts:
- Data Preparation: Cleaning, transforming, and validating datasets to ensure consistency and accuracy.
- Sales Analysis: Evaluating sales trends, projecting future performance, and identifying growth opportunities.
- Strategic Network Redesign: Analyzing and optimizing production and distribution networks to minimize costs and improve efficiency post-merger.
2.5 Loading Required Packages
The package used to perform this report is listed below.
# Packages used for the analysis:
library(readr)
library(readxl)
library(dplyr)
library(lubridate)
library(tidyr)
library(gtsummary)
library(scales)
library(ggplot2)
library(DataExplorer)
library(knitr)
library(lpSolve)3 Part 1: Data Preparation
3.1 Data collection and importation
3.1.1 Overview of Datasets
The analysis utilizes the following datasets:
- OneCom Sales Data
- Contains sales data on OneCom from 2021 to 2023 for different countries at different dates.
- Yfon Sales Data
- Contains sales data on Yfon from 2021 to 2023 at Year-Month-Country level for different countries.
- Region Classification
- Containing information about countries and their associated regional classifications based on ISO 3166-1 and ISO 3166-2 standards.
- EU Countries List
- Contains information about countries within the European Union (EU).
3.1.2 Data Import
The analysis utilizes multiple datasets, each contributing specific
insights necessary for the study. These datasets were imported using the
read_csv() and read_excel() functions from the
readr and readxl packages in R, respectively.
This section outlines the steps taken to load the data for analysis.
OneCom Sales Data The OneCom dataset, containing monthly sales data for various countries from 2021 to 2023, was imported using the
read_csv()function. This dataset forms the basis for analyzing country-specific sales trends and their temporal changes.Yfon Sales Data by Region The Yfon dataset, representing global sales distribution across five regions (Africa, Asia, Oceania, Europe, and Americas), was stored in an Excel file. Each regional dataset was imported using the
read_excel()function, specifying the respective sheet names for regions. This segmentation enables a regional-level analysis of Yfon’s sales performance.Region Classification The region classification dataset, linking countries to their corresponding regions and subregions, was imported as a CSV file using
read_csv(). This data ensures consistency in regional groupings throughout the analysis.EU Countries List A supplementary dataset identifying which European countries are part of the European Union was also imported from an Excel file using the
read_excel()function. This dataset facilitates targeted comparisons between EU and non-EU countries.
The import process utilized efficient data handling tools, ensuring a seamless transition from raw files to analyzable datasets. Below is the R code used for importing the data:
# OneCom Sales Data
data_OneCom <- read_csv("C:/Users/Ande8/OneDrive/Skrivebord/Tools for Analytics/Project/Raw_data/sales-src_OneCom.csv")
# Yfon Sales Data by Region
data_Yfon_Africa <- read_excel("C:/Users/Ande8/OneDrive/Skrivebord/Tools for Analytics/Project/Raw_data/sales-external-src_Yfon.xlsx", sheet = "Africa")
data_Yfon_Asia <- read_excel("C:/Users/Ande8/OneDrive/Skrivebord/Tools for Analytics/Project/Raw_data/sales-external-src_Yfon.xlsx", sheet = "Asia")
data_Yfon_Oceania <- read_excel("C:/Users/Ande8/OneDrive/Skrivebord/Tools for Analytics/Project/Raw_data/sales-external-src_Yfon.xlsx", sheet = "Oceania")
data_Yfon_Europe <- read_excel("C:/Users/Ande8/OneDrive/Skrivebord/Tools for Analytics/Project/Raw_data/sales-external-src_Yfon.xlsx", sheet = "Europe")
data_Yfon_America <- read_excel("C:/Users/Ande8/OneDrive/Skrivebord/Tools for Analytics/Project/Raw_data/sales-external-src_Yfon.xlsx", sheet = "Americas")
# Region Classification
data_regions <- read_csv("C:/Users/Ande8/OneDrive/Skrivebord/Tools for Analytics/Project/Raw_data/regions-src.csv")
# EU Countries List
data_EU_regions <- read_excel("C:/Users/Ande8/OneDrive/Skrivebord/Tools for Analytics/Project/Raw_data/eu-countries-src.xlsx")3.2 Data Preparation
In this section the data preparation process, which is an essential step in ensuring the accuracy, consistency, and reliability of the datasets used in this analysis, is performed. This section outlines the transformation and integration of datasets from both OneCom and Yfon, as well as their alignment with regional classifications to enable effective comparative analysis.
The primary objectives of this section include the classification of countries into custom regions, standardization of ambiguous country names, and handling of missing or erroneous values. Additionally, the integration of OneCom and Yfon datasets into a unified structure was achieved while addressing discrepancies between the two sources. By carefully addressing these challenges, the datasets were transformed into a coherent and analyzable form, ready for subsequent modeling and interpretation.
3.2.1 Classification of Countries to Custom Regions
Regions are classified by OneCom into seven different regions:
- North America (NA)
- South America (SA)
- Europe (EU)
- Europe (Non-EU)
- Japan (J)
- Asia-Pacific (AP)
- Africa (A)
The classification of countries into the different regions is done by
merging the regions-src.csv and
eu-countries-src.xlsx datasets with the OneCom sales and
Yfon sales datasets. However, the classification of countries into the
Asia-Pacific region is not straightforward. In the regional dataset
used, countries are not explicitly categorized as part of the
Asia-Pacific region. For instance, the Asian region is divided into
sub-regions such as Southern Asia, Eastern Asia, and Western Asia.
A specific challenge arises with countries in the Western Asia sub-region. According to the United Nations Statistical Division UNESCAP, some Western Asian countries are part of the Asia-Pacific region, while others are not. For example, countries like Iraq, Israel, and Jordan are part of Western Asia but are not included in the Asia-Pacific region, whereas Armenia, Georgia, and Turkey for instance are. Therefore, classifying all Western Asian countries into the Asia-Pacific region would be incorrect and could result in overestimating the region’s sales figures.
Additionally, some minor countries from the Australian and New Zealand sub-region and the Polynesian sub-region should also not be included in the Asia-Pacific region. However, these countries show zero sales in the datasets for both OneCom and Yfon, and they are automatically excluded during the data merging process. This resolves the issue of their inclusion without manual intervention.
Western Asian Countries Excluded from the Asian-Pacific region
The following Western Asian countries are not considered part of the Asia-Pacific region:
- Bahrain
- Cyprus
- Iraq
- Israel
- Jordan
- Kuwait
- Lebanon
- Oman
- Palestine, State of
- Qatar
- Saudi Arabia
- Syrian Arab Republic
- United Arab Emirates
- Yemen
Minor Countries with Zero Sales the Asian-Pacific region
The following countries from the Australian and New Zealand or Polynesian sub-regions are excluded due to having zero sales:
- Christmas Island
- Cocos (Keeling) Islands
- Heard Island and McDonald Islands
- Norfolk Island
- Pitcairn
- Tokelau
- Wallis and Futuna
Handling Unclassified Countries
When classifying countries into OneCom’s default regional groups, some countries may remain unclassified - namely the countries from Western Asia not included in the Asia-Pacific region. To address this, two options were considered:
Delete Observations: Remove all records associated with non-classified countries. However, this would result in the loss of potentially valuable information.
Create an “Other” Group: Add an additional group, “Other,” to include all countries not classified under default regions.
Given the potential loss of information with the first approach, the second approach was chosen. The “Other” group was added to ensure no countries are excluded without consideration, thereby preserving all observations for further analysis.
3.2.2 OneCom Data Preparation
This section outlines the steps taken to clean and transform the
OneCom dataset to prepare it for analysis. These steps ensure the data
is consistent, complete, and ready for merging with other datasets.
First the Date variable was transformed into two seperate
varbiable: Year and Month. Then each observation was classified into the
regions specified section 4.1, followed by aggregation
of sales by Region-Year-Month, and finally an identifier to distinguish
OneCom data in the final merged dataset was added.
3.2.2.0.1 Step 1: Transform Dates
The Date column in the OneCom dataset was split into
separate Year and Month columns to enable
time-based analysis. The original Date column was removed
to streamline the dataset.
3.2.2.0.2 Step 2: Merging country-region with OneCom sales data
To align the OneCom sales data with regional classifications, a left
join was performed to merge the dataset with the
data-region dataset, ensuring that all sales data was
associated with the appropriate region.
3.2.2.0.3 Step 3: Aggregate Sales by Region-month
The sales data was aggregated by Region,
Year, and Month to create a panel dataset
suitable for further analysis. This step ensures that the OneCom dataset
matches the structure of the Yfon dataset for later merging.
3.2.2.0.4 Step 4: Adding “OneCom” indicator
To distinguish OneCom sales from Yfon sales in the final merged
dataset, an indicator column Company was added. This column
assigns the value “OneCom” to all rows in the OneCom dataset.
3.2.3 Yfon Data Preparation
This section details the steps taken to prepare the Yfon dataset for analysis. As the Yfon sales data is divided into regional datasets, these were first appended together, followed by alignment with custom regions, and the addition of an identifier to distinguish Yfon data in the final merged dataset.
3.2.3.0.1 Step 1: Appending regional datasets
The Yfon sales dataset was, pre-importing the data, divided by region
into several smaller datasets. To create a comprehensive dataset that
includes all regions, these regional Yfon datasets were appended
together using the bind_rows() function.
3.2.3.0.2 Step 2: Classification of Countries to Custom Regions
The classification of countries into custom regions was performed to
ensure consistency with the OneCom dataset. To achieve this, the
appended Yfon dataset was merged with the region_data
dataset. Additionally, some country names were modified to align with
the naming conventions in the region_data.
3.2.3.0.3 Step 3: Adding “Yfon” indicator
Similar to the OneCom dataset, an indicator column
Company was added to the Yfon dataset. This column assigns
the value “Yfon” to all rows, enabling differentiation between OneCom
and “Yfon” sales in the final merged dataset.
3.2.4 Mergin Prepared OneCom and Yfon Datasets
The datasets from OneCom and Yfon containing region classifications
were merged to create a unified dataset called df suitable
for analysis. A full join was performed on the columns “Year”, “Month”,
“Country”, and “Region”, ensuring that all rows from both datasets were
included, even if no match was found in the other dataset. Following the
merge, the sales data from OneCom and Yfon were consolidated into a
single column called through pivoting. Additionally, a new column,
“Company”, was introduced to indicate whether the observation belonged
to OneCom or Yfon. Finally, only the necessary columns — “Year”,
“Month”, “Country”, “Region”, “Company”, and “Sales” — were retained in
the final dataset df. This process resulted in a harmonized
dataset that combines sales data from both companies into a cohesive
structure for further analysis.
3.2.5 Data Validation
Data validation is a crucial step in the data preparation process to ensure the integrity and reliability of the dataset. This section addresses missing values, negative sales, and ambiguous country names.
3.2.5.1 Missing Values
Missing data is a common issue in datasets, and its patterns must be
understood to ensure robust analysis. The naniar package
was used to visualize missing values and their intersections in the
dataset. The gg_miss_upset() function generates an upset
plot, which highlights how missing values overlap across variables.
The intersection size, represented by the vertical bars,
shows that 97 observations have missing values in the Year,
Month, and Sales variables simultaneously.
This pattern suggests that these observations lack essential time and
sales information, which are critical for conducting meaningful
analysis. Furthermore, 97 observation shows missing values in
Year and Month indicating that some sales
values are lacking crucial identifying information.
The set size bars further indicate that the variables
Year and Month each have 194 missing entries,
and these that half of these correspond directly with the missing values
in Sales. This direct correlation reinforces the observation that the
missing data is concentrated within specific time periods, rather than
being randomly distributed.
The Country and Company variables exhibit no missing values, ensuring consistent categorization by region and company. This completeness ensures that regional and company-level analyses remain unaffected by the missing data.
Overall, the pattern of missing values is systematic and confined to rows where time and sales information is absent. Given the critical nature of these variables, it would be inappropriate to impute these values without risking bias or analytical inaccuracies. Therefore, the most robust solution is to remove these rows from the dataset. By doing so, the integrity of the remaining data is preserved, ensuring that subsequent analyses are based on complete and reliable information. Importantly, the absence of missing values in the Country and Company variables guarantees reliable categorization for regional and company-level analyses moving forward.
3.2.5.2 Negative Sales
Upon investigating the sales data further, a few observations with negative sales values were identified, as shown in the table below. Since sales data cannot logically be negative, these records were deemed erroneous and removed from the dataset.
| Year | Month | Region | Country | Company | Sales |
|---|---|---|---|---|---|
| 2023 | 10 | Africa (A) | Namibia | Yfon | -53 |
| 2022 | 9 | South America (SA) | Panama | Yfon | -101 |
| 2021 | 12 | Africa (A) | Togo | Yfon | -28 |
3.2.5.3 Standardizing Ambigoues Country Names
An investigation of the unique counts for the Country
variable revealed that some countries appeared just 36 times instead of
72, indicating that Yfon was not selling to these countries. Three
countries — Germany, USA, and the UK — stood out, as they appeared only
under OneCom, suggesting Yfon recorded no sales for them.
Given that these countries are among the world’s largest economies, this inconsistency raised concerns. The issue was traced to naming discrepancies during the merging process. To address this, the names of these countries were standardized and implemented in section 4.3, step 2, ensuring all relevant information was correctly represented in the merged dataset.
3.3 Overview of the final dataset
The final dataset comprises 10,941 observations, classified by region, country, and company. A total of 152 unique countries are included, covering data for 3 years (2021–2023) and spanning all 12 months.
The regional distribution highlights Africa (30.9%) as the largest contributor, in terms of number of observations, followed by Asia-Pacific (16%), Europe (EU and Non-EU combined, 22.3%), and South America (15%). Smaller contributions are noted for Japan (0.7%), North America (1.3%), and Other regions (13.8%).
The dataset is evenly distributed between the two companies, OneCom and Yfon, accounting for 50% of the total observations each (the minor deviation of 3 observations is due to the removal of negative sales in section 4.4.2).
For the sales variable, the mean sales value for OneCom is 15,174, while for Yfon, it is 19,859, reflecting variability across the companies. The range for OneCom’s sales spans from 20 to 1,819,030, while for Yfon, it ranges from 8 to 1,914,518.
| Variable | OneCom N = 5,4721 |
Yfon N = 5,4691 |
|---|---|---|
| Year | ||
| 2021 | 1,824 (33%) | 1,823 (33%) |
| 2022 | 1,824 (33%) | 1,823 (33%) |
| 2023 | 1,824 (33%) | 1,823 (33%) |
| Month | 1.0 ; 12.0 | 1.0 ; 12.0 |
| Region | ||
| Africa (A) | 1,692 (31%) | 1,690 (31%) |
| Asia-Pacific (AP) | 864 (16%) | 864 (16%) |
| Europe (EU) | 828 (15%) | 828 (15%) |
| Europe (Non-EU) | 396 (7.2%) | 396 (7.2%) |
| Japan (J) | 36 (0.7%) | 36 (0.7%) |
| North America (NA) | 72 (1.3%) | 72 (1.3%) |
| Other | 756 (14%) | 756 (14%) |
| South America (SA) | 828 (15%) | 827 (15%) |
| Unique Countries | ||
| 152 | 5,472 (100%) | 5,469 (100%) |
| Sales | 15,174 [20 ; 1,819,030] | 19,859 [8 ; 1,914,518] |
| 1 n (%); Min ; Max; Mean [Min ; Max] | ||
4 Part 2: Sales Analysis
4.1 Analysis of Sales
This section delves into a comprehensive analysis of sales data to uncover patterns, trends, and growth projections for OneCom and Yfon. By evaluating monthly, yearly, and regional sales figures, this analysis aims to provide insights into the companies’ market performance, seasonal fluctuations, and long-term growth trajectories. The section begins with an assessment of aggregate monthly sales, highlighting variations across the year and identifying peak performance periods. Following this, yearly sales trends are explored to illustrate the companies’ performance across different regions and reveal growth dynamics over the past three years.
To further enrich the analysis, projections for sales in 2024 are presented. These forecasts, based on historical trends and year-on-year growth rates, provide a forward-looking perspective on regional and overall market opportunities. Additionally, percentage growth figures are analyzed to offer a relative view of performance improvements across regions.
4.1.1 Monthly Aggregate Sales
| Month | Company | Aggregate Sales |
|---|---|---|
| 1 | OneCom | 13428450 |
| 2 | OneCom | 5824900 |
| 3 | OneCom | 2644570 |
| 4 | OneCom | 2766463 |
| 5 | OneCom | 3733674 |
| 6 | OneCom | 4866038 |
| 7 | OneCom | 3174125 |
| 8 | OneCom | 2947459 |
| 9 | OneCom | 2826868 |
| 10 | OneCom | 8327421 |
| 11 | OneCom | 16621757 |
| 12 | OneCom | 15872475 |
| 1 | Yfon | 6721164 |
| 2 | Yfon | 6044122 |
| 3 | Yfon | 6802412 |
| 4 | Yfon | 6875226 |
| 5 | Yfon | 9883770 |
| 6 | Yfon | 8003673 |
| 7 | Yfon | 7262075 |
| 8 | Yfon | 8743244 |
| 9 | Yfon | 9869667 |
| 10 | Yfon | 12499524 |
| 11 | Yfon | 14080653 |
| 12 | Yfon | 11822694 |
The data reveals noticeable variations in sales across months, suggesting potential seasonal trends or external factors influencing sales performance. OneCom consistently achieves higher aggregate sales than Yfon throughout the year, reflecting differences in market coverage, product performance, or other business factors. Notably, OneCom’s highest sales are recorded in November, with over 16.6 million, while Yfon’s peak sales occur in December, exceeding 14 million. Despite fluctuations, both companies maintain steady sales throughout the year, with certain months experiencing significant peaks and troughs, warranting further investigation into the underlying causes.
4.1.2 Yearly Sales by Company and Region
The plot above provides a comprehensive visualization of monthly sales numbers by year, company, and region. Both OneCom and Yfon exhibit seasonal sales patterns, with significant peaks observed in the final months of each year. This trend likely reflects increased sales activity during the holiday season, particularly in November and December. Regional contributions to sales reveal that Europe (EU and Non-EU) and North America (NA) demonstrates strong growth and pronounced seasonal trends, while other regions like Asia-Pacific (AP) and Africa (A) show relatively stable but lower sales volumes.
There are notable differences between the two companies. Yfon shows higher variability in sales across regions compared to OneCom. This is particularly evident in the Asia-Pacific and South America regions, where Yfon’s sales display larger fluctuations throughout the year. On the other hand, OneCom’s sales appear more consistent across regions, with pronounced spikes during key months. The year-on-year growth for both companies is evident, especially in 2023, where regions like North America and Europe reach their highest sales levels, indicating strong market performance and expanding operations.
This analysis highlights clear temporal and regional patterns in sales, providing valuable insights into how sales dynamics vary between companies and across regions.
4.1.3 Yearly Growth Trends by Region and Company
The plots below illustrate the yearly sales numbers for each region and company, showing trends over the years 2021, 2022, and 2023. The analysis reveals several key insights about regional performance and company growth.
Both OneCom and Yfon exhibit clear year-on-year growth in sales across most regions, indicating an overall positive trajectory. For OneCom, North America (NA) and Europe (EU) are the dominant regions, both showing significant increase over the three years. Similarly, Yfon sees consistent growth in its key markets, with Europe (Non-EU) and North America contributing the largest sales volumes.
Smaller regions, such as Africa (A) and Asia-Pacific (AP), show relatively stable sales across the years but remain less significant in terms of total contributions. However, their steady growth suggests potential for future expansion in these markets.
To better understand the growth potential of each market, the plot below illustrates the standardized sales values, providing a clearer comparison of growth potential across markets relative to one another.
The standardized plot reveals a clear pattern of yearly percentage growth across regions for both companies. For OneCom, regions such as Africa, South America, and Asia-Pacific display the most consistent upward trends, with sharp percentage growth rates, particularly in the initial years. The European Union (EU) and Non-EU Europe regions show robust performance, though their growth appears more stable compared to emerging markets. North America also exhibits steady growth but at a slower rate compared to the more dynamic regions.
For Yfon, the growth trends are “neutral”. While Africa demonstrates modest yet stable percentage increases, Asia-Pacific and Japan show more fluctuations, indicating variability in market performance. The European Union and Non-EU Europe regions experienced declines during 2022, with slight recovery in 2023, while North America maintained a relatively flat trajectory. The smaller regions such as Other and South America exhibit marginal changes, reflecting Yfon’s established presence or limited market activity in these areas.
The comparison of sales data across regions and companies highlights key trends in both absolute sales levels and percentage growth. For OneCom, regions such as Africa, South America, and Asia-Pacific demonstrate significant growth both in terms of total sales and percentage changes, reflecting strong market dynamics. In contrast, North America and Europe maintain high sales levels but exhibit relatively stable or modest growth patterns. Yfon, however, shows more subdued activity, with minor percentage growth and steady performance in key regions like Europe (Non-EU) and North America. Smaller regions such as “Other” markets and South America display consistent but less impactful growth, suggesting potential for future expansion. These findings emphasize the importance of tailored strategies to leverage growth opportunities in emerging regions while consolidating performance in established markets.
4.1.4 Expected Sales in 2024
To forecast whether total sales by region are expected to increase or decrease in 2024, a linear trend-based approach has been employed. First, the data was grouped by region and company to calculate total sales across years. Then, the year-on-year sales trend for each region and company was dtermined by calculating the difference in total sales across consecutive years. Using the latest sales data from 2023 as the baseline, projected sales for 2024 was constructed by adding the calculated trend to the 2023 sales figures. Finally, the forecasted sales was compared for 2024 to the actual sales in 2023 to calculate the expected percentage growth or decline, providing a clear indication of whether sales are likely to increase or decrease next year.
| Region | Company | Sales Trend | Sales 2023 | Forecasted Sales 2024 | Expected Growth relative to 2023 (in %) |
|---|---|---|---|---|---|
| Africa (A) | OneCom | 245789.5 | 832635 | 1078424.5 | 29.5194773 |
| Africa (A) | Yfon | -3125.5 | 990788 | 987662.5 | -0.3154560 |
| Asia-Pacific (AP) | OneCom | 292503.1 | 1120914 | 1413417.2 | 26.0950474 |
| Asia-Pacific (AP) | Yfon | 25249.5 | 2447050 | 2472299.5 | 1.0318342 |
| Europe (EU) | OneCom | 4985766.0 | 17236444 | 22222210.0 | 28.9257227 |
| Europe (EU) | Yfon | -3826.0 | 4059962 | 4056136.0 | -0.0942373 |
| Europe (Non-EU) | OneCom | 722061.5 | 2570928 | 3292989.5 | 28.0856368 |
| Europe (Non-EU) | Yfon | 117210.5 | 8321991 | 8439201.5 | 1.4084430 |
| Japan (J) | OneCom | 476033.5 | 1596880 | 2072913.5 | 29.8102237 |
| Japan (J) | Yfon | 155968.5 | 7300386 | 7456354.5 | 2.1364418 |
| North America (NA) | OneCom | 2492602.5 | 8913859 | 11406461.5 | 27.9632256 |
| North America (NA) | Yfon | 479796.5 | 12201607 | 12681403.5 | 3.9322402 |
| Other | OneCom | 69425.0 | 268460 | 337885.0 | 25.8604634 |
| Other | Yfon | 1263.0 | 542711 | 543974.0 | 0.2327205 |
| South America (SA) | OneCom | 977649.0 | 3342312 | 4319961.0 | 29.2506804 |
| South America (SA) | Yfon | 4573.0 | 1002714 | 1007287.0 | 0.4560622 |
The sales forecast for 2024 reveals distinct trends for OneCom and Yfon across different regions. OneCom demonstrates robust growth in emerging markets such as Africa (+29.52%), Asia-Pacific (+26.10%), and South America (+29.25%), indicating significant opportunities for market expansion. Established regions like North America (+27.96%) and Europe (Non-EU) (+28.08%) also show steady increases, reaffirming OneCom’s strong market position across both mature and developing markets.
Yfon, on the other hand, presents a more varied growth pattern. While it forecasts slight increases in regions like North America (+3.93%) and Europe (Non-EU) (+1.41%), it faces marginal declines in Africa (-0.31%) and Europe (EU) (-0.09%), suggesting potential challenges in these areas.
The accompanying figure further illustrates these trends, with OneCom’s projections showing consistent upward trajectories in most regions. In contrast, Yfon’s growth appears more neutral, with less pronounced changes across regions, reflecting a stable but less dynamic forecast.
Overall, OneCom’s forecasted growth underscores its strong and diversified market performance, with opportunities for further expansion, particularly in emerging markets. For Yfon, the forecast indicates the need for strategic interventions to boost growth and address performance gaps in key regions.
4.2 Reevaluating expected sales in 2024
| Region | Company | Sales Trend | Sales 2023 | Forecasted Sales 2024 | Expected Growth relative to 2023 (in %) |
|---|---|---|---|---|---|
| Asia-Pacific (AP) | OneCom | 404594.5 | 1345097 | 1749692 | 30.07921 |
The adjustment for a potential 20% stockout in the Asia-Pacific (AP) region significantly alters the sales trajectory for OneCom in this region. As shown in the red dashed line of the plot, accounting for this additional demand leads to a much steeper increase in sales for 2023 and an even more pronounced forecast for 2024. The table supports this observation, with a recalculated expected sales growth for 2024 relative to 2023 increasing to 30.08% (compared to the original 26.1%).
This suggests that, under the assumption of stockouts in 2023, the actual market demand in the AP region was significantly higher than originally recorded sales. The adjusted forecast highlights the potential for further strategic focus on this high-demand region, as satisfying this unmet demand could have a substantial impact on revenue growth.
Thus, this reevaluation emphasizes that the original conclusion in Question 2d may underestimate the sales growth potential of the AP region. Acknowledging the stockout scenario suggests that OneCom should prioritize meeting demand more effectively in the AP region to capitalize on its high growth potential.
5 Part 3: Strategic Redesign of the Network
5.1 Network Optimization Due to Potential Merger
5.1.1 Optimal Production and Distribution Network Analysis for OneCom and Yfon pre-merger
To evaluate the production and distribution networks of OneCom and Yfon prior to their merger, a strategic approach was adopted to determine the lowest achievable cost for each company. This involved collecting detailed data on plant capacities, fixed and variable production costs, market demands, transportation costs, and import duties. Using this data, linear programming models were formulated to minimize total costs while ensuring market demands were fully met and plant capacities were not exceeded. By solving the optimization problems separately for each company, the optimal allocation of production from each plant to the respective markets was identified. The results were analyzed to determine the specific contribution of each plant to market demands, highlighting key facilities and their roles in cost minimization. The following tables summarize these findings, providing insights into the operational efficiency and strategic utilization of plants for both companies.
The decision models for OneCom and Yfon were formulated to determine the lowest achievable costs for their production and distribution networks prior to the merger. The results highlight the optimal allocation of production from each plant to the respective markets, along with the total costs incurred by each company.
## OneCom Optimal Solution:
## Plant-Market Allocation:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 0e+00 4e+06 1.5e+07 0e+00 0.000000e+00 0e+00 1e+06
## [2,] 1e+07 0e+00 5.0e+06 3e+06 4.241022e-10 2e+06 0e+00
## [3,] 0e+00 0e+00 0.0e+00 0e+00 2.000000e+06 0e+00 0e+00
##
## Total Cost: 333029000
##
## Used Plants: 0 0 0##
## Yfon Optimal Solution:
## Plant-Market Allocation:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 1e+07 1e+06 4e+06 0e+00 0e+00 0e+00 1e+06
## [2,] 0e+00 0e+00 0e+00 0e+00 7e+06 3e+06 0e+00
## [3,] 2e+06 0e+00 0e+00 8e+06 0e+00 0e+00 0e+00
##
## Total Cost: 293512000
##
## Used Plants: 0 0 0| NA | SA | EU | Non-EU | Japan | AP | Africa | |
|---|---|---|---|---|---|---|---|
| Germany | - | 4 | 15 | - | - | - | 1 |
| USA | 10 | - | 5 | 3 | - | 2 | - |
| Mexico | - | - | - | - | 2 | - | - |
For OneCom, the Germany plant primarily serves the EU and SA markets, contributing a total of 19 million units to these region as well as 1 million units to the African region. The USA plant is heavily utilized, serving the NA, Non-EU, and AP markets with 20 million units. The Mexico plant focuses solely on the Japan market, supplying 2 million units. This configuration results in a total cost of $333,029,000 for OneCom’s network.
| NA | SA | EU | Non-EU | Japan | AP | Africa | |
|---|---|---|---|---|---|---|---|
| Germany | 10 | 1 | 4 | - | - | - | 1 |
| USA | - | - | - | - | 7 | 3 | - |
| China | 2 | - | - | 8 | - | - | - |
For Yfon, the Germany plant serves the NA, SA, A, and EU markets, allocating 16 million units. The USA plant primarily caters to the Japan and AP markets, supplying 10 million units. The China plant is strategically used to serve the Non-EU and Africa markets, contributing 10 million units in total. Yfon’s optimized network achieves a lower total cost of $293,512,000 compared to OneCom.
These results demonstrate that both companies have efficiently utilized their networks to minimize costs while meeting market demands. However, Yfon’s cost advantage highlights a more efficient allocation strategy, particularly in leveraging its China plant for cost-effective service to Non-EU and African markets. These findings establish a solid baseline for evaluating potential synergies and cost-saving opportunities in a post-merger scenario.
5.1.2 Optimal Production and Distribution Network Analysis for OneCom/Yfon post-merger
In this section, the optimal production and distribution network for the combined operations of OneCom and Yfon post-merger is analyzed. By merging their networks, the combined entity aims to minimize total costs while efficiently meeting market demands. Using linear programming, the optimal allocation of production from each plant to the respective markets was determined. The analysis accounts for plant capacities, fixed and variable production costs, transportation costs, and import duties, ensuring that all market demands are satisfied at the lowest achievable cost. The results highlight the contribution of each plant to specific markets and provide insights into the operational efficiency of the post-merger network.
## Merged Network Optimal Solution:
## Plant-Market Allocation:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 0.0e+00 0e+00 2e+07 0.0e+00 0e+00 0e+00 0e+00
## [2,] 0.0e+00 0e+00 0e+00 0.0e+00 0e+00 5e+06 2e+06
## [3,] 1.6e+07 0e+00 4e+06 0.0e+00 0e+00 0e+00 0e+00
## [4,] 0.0e+00 0e+00 0e+00 1.1e+07 9e+06 0e+00 0e+00
## [5,] 5.0e+06 5e+06 0e+00 0.0e+00 0e+00 0e+00 0e+00
## [6,] 1.0e+06 0e+00 0e+00 0.0e+00 0e+00 0e+00 0e+00
##
## Total Cost: 601292000
##
## Used Plants: 0 0 0 0 0 0| NA | SA | EU | Non-EU | Japan | AP | Africa | |
|---|---|---|---|---|---|---|---|
| Germany (P1) | - | - | 20 | - | - | - | - |
| Germany (P2) | - | - | - | - | 5 | 5 | 2 |
| USA (P1) | 16 | - | 4 | - | - | - | - |
| USA (P2) | - | - | - | 11 | 9 | - | - |
| Mexico | 5 | 5 | - | - | - | - | - |
| China | 1 | - | - | - | - | - | - |
The analysis of the post-merger production and distribution network reveals a highly efficient allocation of resources, achieving a total cost of $601,292,000. The merged network effectively leverages its expanded capacities across six plants to meet global market demands in a cost-effective manner.
The two plants in Germany are strategically allocated to serve the EU and AP regions. Germany Plant 1 fully caters to the EU market, supplying 20 million units, while Germany Plant 2 focuses on Japan and AP, providing 5 million units each, along with an additional 2 million units to Africa. Similarly, the two plants in the USA demonstrate high efficiency, with USA Plant 1 supplying 16 million units to the NA market and 4 million units to the EU. USA Plant 2 focuses on the Non-EU and Japan markets, contributing 11 million and 9 million units, respectively.
The Mexico plant balances its contributions between NA and SA markets, supplying 5 million units to each. Meanwhile, the China plant focuses exclusively on the NA market, contributing 1 million units. This allocation ensures that each plant operates within its capacity while minimizing transportation and production costs.
The results highlight the strategic utilization of the merged network’s resources to achieve cost efficiency and market coverage. By leveraging the combined plant capacities, the merged network optimally matches production with market demands, resulting in a highly efficient and sustainable distribution strategy.
5.1.3 Optimized Post-Merger Network Configuration with Scalable Plant Options
This section examines the optimized configuration of the production and distribution network after the merger, with flexibility in plant operations. Plants have the option to operate at full capacity, scale back to half capacity, or shut down entirely. The decision-making process accounts for minimizing total costs while satisfying market demands. The tables below illustrate the status of each plant and their contributions to meeting market demands.
## Merged Network Optimal Solution:
## Plant-Market Allocation:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 0e+00 0e+00 1.1e+07 0.0e+00 9e+06 0e+00 0e+00
## [2,] 0e+00 0e+00 0.0e+00 0.0e+00 0e+00 5e+06 2e+06
## [3,] 0e+00 0e+00 9.0e+06 1.1e+07 0e+00 0e+00 0e+00
## [4,] 0e+00 0e+00 4.0e+06 0.0e+00 0e+00 0e+00 0e+00
## [5,] 2e+06 5e+06 0.0e+00 0.0e+00 0e+00 0e+00 0e+00
## [6,] 2e+07 0e+00 0.0e+00 0.0e+00 0e+00 0e+00 0e+00
##
## Total Cost: 553785432
##
## Plant Status (Full, Scaled, Shutdown):
## [,1] [,2] [,3]
## [1,] 1 0 0
## [2,] 0 1 0
## [3,] 1 0 0
## [4,] 0 1 0
## [5,] 0 1 0
## [6,] 1 0 0Plants have the option to operate at full capacity, scale back to half capacity, or shut down entirely. The decision-making process accounts for minimizing total costs while satisfying market demands. The total cost for this configuration is $553,785,432, which reflects significant efficiency improvements compared to prior setups. The tables below illustrate the status of each plant and their contributions to meeting market demands.
| Full scale | Scaled back | Shutdown | |
|---|---|---|---|
| Germany (P1) | 1 | - | - |
| Germany (P2) | - | 1 | - |
| USA (P1) | 1 | - | - |
| USA (P2) | - | 1 | - |
| Mexico | - | 1 | - |
| China | 1 | - | - |
The results show that Germany (P1), USA (P1), and China operate at full capacity, while Germany (P2), USA (P2), and Mexico scale back their operations to half capacity. Notably, no plants are shut down entirely, reflecting the cost-effectiveness of maintaining partial operations.
| NA | SA | EU | Non-EU | Japan | AP | Africa | |
|---|---|---|---|---|---|---|---|
| Germany (P1) | - | - | 11 | - | 9 | - | - |
| Germany (P2) | - | - | - | - | 5 | 2 | - |
| USA (P1) | - | - | 9 | 11 | - | - | - |
| USA (P2) | - | - | 4 | - | - | - | - |
| Mexico | 2 | 5 | - | - | - | - | - |
| China | 20 | - | - | - | - | - | - |
The second table details the allocation of production to various markets. Germany (P1) primarily serves the EU and Japan markets, while Germany (P2) supplies Japan, AP, and Africa. USA (P1) contributes to the EU and Non-EU markets, while USA (P2) supports the Japan market. Mexico plays a significant role in the NA and SA markets, and China is the sole supplier for the NA market. This allocation ensures that market demands are met efficiently with minimal costs.
6 Conclusion
This project provides a comprehensive analysis of the sales performance and operational strategies of OneCom and Yfon, both as individual entities and as a merged organization. By examining sales trends, forecasting growth, and optimizing production and distribution networks, the findings reveal critical insights to guide future strategies.
OneCom has consistently demonstrated strong growth across key regions such as Europe, North America, and Asia-Pacific, positioning it as a leading contributor to the merged entity. Yfon, while maintaining steady performance in established markets, shows room for improvement in emerging regions. The 2024 forecasts highlight significant growth potential in markets like Africa, South America, and Asia-Pacific, where targeted investments can drive further expansion. Established regions such as North America and Europe also remain critical, sustaining positive growth trends.
The network optimization analysis showcases the cost-saving potential of a merged production and distribution system. Implementing flexible plant configurations significantly enhances operational efficiency, allowing the merged company to adapt to market fluctuations and demand shifts effectively.
To capitalize on these findings, the merged organization should prioritize investments in high-growth regions, strengthen Yfon’s presence in underperforming markets, and adopt the optimized network configurations. By aligning strategies with these insights, the merged entity can achieve operational excellence, unlock new opportunities, and maintain a competitive edge in the global market.