Discussion post

Expanding Within the Value Chain

Part III: Draft

Group 5 – Alfredo Mojena Hernandez, Allison Medina, Camila Cuesta, Jose-Daniel Hernandez, Lance Aschliman

Expanding Within the Value Chain – Group 5

Introduction and Background

In our pursuit to explore strategic growth opportunities for wholesale businesses, we chose

the topic of value chain expansion. This decision was motivated by the evolving complexities and

highly competitive nature of global markets. Value chain expansion—particularly vertical

integration—presents a compelling strategy for enhancing control over supply chains and

improving profit margins.

Our interest in this area was spurred by the work of Michael Porter, particularly his insights

on competitive advantage and value chain analysis.1 Porter's framework has provided a theoretical

backbone to our study, guiding our examination of whether a wholesale company could benefit

from expanding into the manufacturing and/or retail sectors.

Further inspiration came from existing business practices, as observed in companies like

Procter and Gamble. This company exemplifies successful vertical integration, managing both

manufacturing and wholesale distribution of diverse product lines.2 Their ability to maintain

quality control and improve logistics through internal management of production and distribution

channels encouraged us to delve deeper into this strategy and examine whether it may be an option

for the target wholesale company in question. Though it does differ from state to state in the USA

given regulatory-mandated three-tier law, the strategy is also widely popular in other areas of

manufacturing and distribution like the alcohol industry.3

The relevance of this topic is underscored by the challenges businesses face in optimizing

efficiency and profit generation in highly competitive or saturated markets. Through our project,

we aim to explore how vertical integration could serve as a lever to mitigate these challenges,

leveraging data from the provided dataset to make informed decisions about potential expansion

efforts. This approach is not only pertinent to the academic exploration of business strategies but

also vital for practical business applications, particularly for C-level executives considering

expansive corporate strategies.

Objectives and Goals

Our project aims to critically assess the feasibility and potential advantages of value chain

expansion through vertical integration for a wholesale company contemplating entry into,

specifically, the manufacturing segment. We intend to explore how such strategic expansion could

influence various aspects of the business in question, including revenue generation and overall

profit margins.

1 See https://www.britannica.com/topic/Procter-and-Gamble-Company for a comprehensive list of the various

products P&G offers where they participate in multiple areas of the value chain.

2 See https://www.britannica.com/topic/Procter-and-Gamble-Company for a comprehensive list of the various

products P&G offers where they participate in multiple areas of the value chain.

3 See https://brewerslaw.com/self-distribution-of-beer-in-florida-2019/ for a discussion of where Florida law

currently stands regarding beer self-distribution.

Expanding Within the Value Chain – Group 5

The core of our study revolves around determining whether it is financially and logistically

viable for this example wholesale company to embrace vertical integration. This involves an

analysis of the existing data to uncover the financial, logistical, and market factors that could either

facilitate or obstruct such an expansion. We seek to understand how integrating manufacturing

capabilities might impact the company's profit margins, particularly by identifying opportunities

to capture additional value currently lost in the supply chain.

Furthermore, our investigation will explore whether vertical integration could streamline

operations, reduce costs, and offer an overall more profitable model, focusing on pinpointing

existing operational inefficiencies that such an expansion could mitigate. An essential component

of our analysis is examining the impact of in-house production on product quality, especially for

products with higher return rates that might benefit from improved quality control.

By synthesizing these insights, we aim to offer a comprehensive evaluation of how

expanding the value chain could affect the company’s competitive position within the market. This

project is designed to benefit C-level executives and strategic planners by providing a nuanced

analysis of the risks and rewards associated with vertical integration. Additionally, it aims to

contribute valuable insights to academic scholars and industry professionals interested in the

dynamics of corporate strategy and supply chain management. Through this endeavor, we hope to

bridge the theoretical and practical aspects of business expansion strategies, providing actionable

recommendations that could help businesses make informed decisions to foster sustainable growth.

With respect to the given data set and wholesale company, we aim to answer the following

questions:

1) If value chain expansion into manufacturing were pursued, what group of products within

the company’s portfolio would make the most sense to manufacture?

2) If such product manufacturing is pursued, what business-related benefits would accrue in

a successful implementation?

3) If such product manufacturing is pursued, where geographically would it make most sense

to base such operations?

The Data

● What is the level of units?

In the Orders Table, each row in the data set represents a purchase order (with an associated

Order ID number). Each order also has an associated Order Date, Ship Date, Ship Mode, Customer

ID, Customer Name, Segment, Country, City, State, Postal Code, Region, Product ID, Category,

Sub-Category, Product Name, Sales (by dollar amount), Quantity (i.e., number of items

purchased), Discount (by percentage), Profit (by dollar amount), and Profit Margin (an added

percentage variable). In the Returns Table, the basic unit is also a purchase order (however, it

simply lists the Order ID for returned purchases which is how the data connects to the Orders tab).

In the People Table, the basic unit is customer names (which also connects it to the Orders tab).

Each customer has an associated age (by year) and level of education.

Expanding Within the Value Chain – Group 5

● How large is the dataset (units, variables)?

Orders Tab: 21 variables, 9994 units (one added variable)

Returns Tab: 2 variables, 296 units

People Tab: 3 variables, 795 units

● What are the horizontal and longitudinal scales of the data?

The longitudinal scale of the data is represented in the Order Date variable. Orders were

recorded between 2014 and 2017, allowing for aggregations of the various measures and

dimensions associated with the orders. In the Orders table, the measures include the variables Sales

(in dollar amount), Quantity (of items ordered), Discount (as a proportion/percentage of the sale

amount), Profit (as a dollar amount), and Profit Margin (as a percentage) which can be numerically

aggregated. The remaining variables are dimensions. The only measure in the People table is the

age of customers.

● What are the key variables of interest?

Analyzing sales data across various segments, categories, and regions, along with profit

margins and return rates, provides valuable insights into the performance of various product

categories and regions within the company's wholesale operations. By focusing on these key

variables, we can uncover areas where high sales volumes may or may not be translating into

proportional profits, indicating potential issues such as quality concerns or inefficiencies in cost

management.

Firstly, examining sales data across categories and various regions/states allows companies

to identify trends and patterns in consumer behavior and preferences. It helps in understanding

which products or services are performing well in specific markets and which ones may be

struggling to gain traction. By categorizing sales data, businesses can tailor their marketing

strategies, product offerings, and pricing strategies to better meet the needs and expectations of

different customer segments and regions.

Moreover, analyzing profit margins provides crucial insights into the cost-effectiveness of

production and sales efforts. A high volume of sales may seem promising, but if profit margins are

low, it could indicate that the cost of production or distribution is too high relative to the revenue

generated (or, as will be discussed below, there is a more strategic pairing of these items with other

high margin items). By examining profit margins across different product categories and regions,

companies can pinpoint areas where cost optimization or pricing adjustments may be necessary to

improve overall profitability.

Expanding Within the Value Chain – Group 5

Return rates also serve as a critical indicator of product quality and customer satisfaction.

High return rates suggest that customers are dissatisfied with the product for various reasons, such

as defects, poor performance, or mismatched expectations. By tracking return rates alongside sales

data, businesses can identify product categories or regions that may be experiencing quality issues

or customer dissatisfaction, allowing them to take corrective actions and potentially indicating

where inside manufacturing operations would be worthwhile.

To make it clear, the variables we will focus on in the visualizations proposed below are

Category, Sub-Category, Sales, Profit, Profit Margin, and State. The other variables mentioned

above may play a supporting role in time.

● Do you transform or create new variables?

We did add one variable to the Orders table. While Profit (in dollar amount) and Sales (in

dollar amount) are listed for each order, we wanted a more explicit view of the actual profit margins

for each order. Hence, we created a variable “Profit Margin” which calculates it for each order.

This is a measure that can be numerically aggregated and used in visualizations.

● Do you augment the data by collecting additional data?

Beyond the variable that we added, we believe that the current dataset contains the

necessary variables that are essential for beginning to explore the topic of value chain expansion

and determining the feasibility of vertical integration within the product chain of exchange. By

analyzing the existing data, our group can gain valuable insights into various aspects of the

wholesale company’s operations, including sales performance, profit margins, return rates, and

locations. These variables provide a comprehensive view of the company's current position within

the market and its potential for expansion into manufacturing operations. By leveraging the

existing data, our group has the necessary tools to come to an informed conclusion regarding if the

wholesale company will be able to enhance growth by way of value chain expansion.

Using The Data & Recognizing Limitations

The datasets at our disposal provide a crucial foundation for making informed

recommendations to C-level managers about potentially venturing into manufacturing. These

datasets will be instrumental in determining which specific products, categories, or subcategories

from the wholesale portfolio are viable candidates for such expansion based on their sales volumes

and profit margins.

Our approach involves an analysis of sales and profit margin data to identify products that

have high sales volumes but potentially struggle for profitability (i.e., they have lower profit

margins) due operational inefficiencies or supply chain issues, making them worthwhile candidates

for manufacturing. The inclusion of category and sub-category details refines our analysis,

allowing us to pinpoint potential opportunities within the product line more accurately. By

Expanding Within the Value Chain – Group 5

focusing on products with sufficient sales, we reduce the risk associated with expanding

manufacturing operations to items that may not yield sufficient market demand.

However, the datasets do present some limitations. Historical sales data and profit margins

might not fully capture future market dynamics or changes in consumer preferences, which could

affect the sustainability of the expansion. Furthermore, the internal focus of the data means we

lack insights into certain external market conditions, competitive pressures, and broader economic

factors, all of which could impact the success of new manufacturing initiatives. We also do not

have any information on current so-called “loss leader” strategies by the wholesale company. This

wholesale company may take lower margins on certain product categories or subcategories to

bolster profits in others. Regardless, successful implementation of value chain expansion would

provide benefits (see Banton’s article referenced below).

Our analysis will leverage the custom-created Profit Margin variable, which aids in

distinguishing between products that generate revenue and those that are truly profitable. This

distinction is critical as it helps identify high-margin products that could support the additional

costs associated with manufacturing. The Returns data offers another layer of insight by

highlighting products that have high return rates, which may indicate issues with quality or

customer satisfaction. This information is vital as it suggests which products might benefit from

quality improvements through in-house manufacturing. Finally, the geographical data will guide

us in selecting the optimal location for establishing a manufacturing facility. This aspect of the

data is essential for considering logistical efficiencies, potential cost savings on shipping, and

proximity to key markets.

While the dataset provides comprehensive internal data, its limitation lies in its historical

and internal focus, potentially overlooking external threats and opportunities. The absence of

external market conditions and the reliance on historical data may not fully prepare us for future

shifts in market dynamics or competitive strategies, which are crucial for the successful expansion

of manufacturing operations.

Data Story:

The first data aggregation that we examined was the number of total sales, in dollars, generated

over time by each category of product within the wholesale portfolio. The goal was to ensure that

any manufacturing operations that would be pursued would be for sufficiently revenue generating

products and categories. It would be a fool’s errand to pursue manufacturing products that only

represent a small portion of the overall sales portfolio. Such an endeavor would not be worth the

time, effort, or investment (even if overall profit margins might be improved). We can see the

breakdown of sales in the following bar chart representation of the measure Sales (in dollars) on

the vertical axis by product Category on the horizontal axis:

Expanding Within the Value Chain – Group 5

From this graphic, we took the following insights: While it is true that the Technology category

leads the company’s sales, all 3 categories play a primary role within the overall portfolio. All 3

categories represent nearly 1/3 of the company’s sales over time, indicating that all 3 are initial

candidates for manufacturing pursuits. The total sales over time for each of the categories falls

between $719,000 and $837,000.

As another safeguard for manufacturing pursuits, we also wanted to ensure that there were not

certain categories of product that should be ruled out because of historical trends in sales data. If

certain categories were high in sales historically but were experiencing significant downward sales

trends over time, then those categories should also be ruled out for manufacturing. We then

constructed a line chart portraying the annual sales for each Category of product, from years 2014

through to 2017:

Expanding Within the Value Chain – Group 5

As indicated in the line chart above, all 3 categories of products are experiencing increased total

sales (in dollars) over time; while Technology and Office Supply categories both experienced a

small dip in sales in 2015, the overall sales trend for all categories is positive. Again, none of the

3 product categories should be ruled out based on this data. Each category has healthy overall sales

and is experiencing positive overall sales trends over time.

As a result of these sales inquiries, we began to look at profit margins related to each of these

product categories. We created the following table as side-by-side comparison for overall sales of

each category and the average profit margins incurred:

Expanding Within the Value Chain – Group 5

Interestingly, we observed that while the profit margins associated with products in the Office

Supplies and Technology categories were both in the lower-to-mid teens, the average profit margin

associated with the Furniture category was much lower (at 3.88%), despite generating comparable

revenues. This was our first indication that perhaps the Furniture category would be an ideal target

for value chain expansion. If manufacturing furniture was successfully implemented, those healthy

sales numbers could be maintained all the while improving the struggling overall profit margins.

We then decided to dig deeper within the Furniture category to visualize the more fine-grained

sales data. We wanted to see whether, if furniture manufacturing is pursued, there was product

synergy in a way that would by logistically feasible for a broad-range manufacturing plant:

What we found was that there are several Sub-Categories of products within the Furniture category

that have synergies with one another for manufacturing. All the top 3 selling Sub-Categories (i.e.,

Tables, Chairs, and Bookcases) involve similar manufacturing processes and raw materials (e.g.,

the woods, metals, paints, finishes required for each of these different furniture items could be

supplied in bulk and used for their own varied furniture outputs). A more general manufacturing

plant that produces all these items would be able to capture a more general portion of the wholesale

portfolio, making the largest impact on overall profitability.

The decision to focus on manufacturing items within the furniture category is bolstered by

returns data. When we linked the Returns data sheet with the sales data, we found that there was a

non-negligible number of returns associated with the Furniture category (which was 2nd overall in

the amount of total product returns). This would seem to confirm that increased control in supply

chain and over quality control might be beneficial for the sales efforts and logistics invested in

these items:

Expanding Within the Value Chain – Group 5

Given an initial focus on manufacturing within the Furniture category, we were curious as to

whether there may be other synergies within the sales portfolio that might represent further items

to be pursued for manufacturing. Our initial intuition was to avoid the Technology category given

the famous historical difficulties in manufacturing technological products (see referenced Chen

article below). However, we thought that it would be worth producing a sales bar graph for the

Sub-Category Office Supplies to see if any of the products would fit well into the proposed

manufacturing operational structure:

Expanding Within the Value Chain – Group 5

Interestingly, the highest selling Sub-Category for the Category “Office Supplies” is “Storage.”

When we took a further look at the individual items sold within the Storage sub-category, there

were indeed products that would represent a positive synergy with the already proposed

manufacturing items (i.e., Tables, Chairs, and Bookcases within the Furniture category). For

example, the “Storage” sub-category of Office Supplies includes shelving, drawers, bins, etc., all

of which are composed of the same raw materials that would already be used in production,

potentially offering supply chain savings and logistical/investment-related efficiencies in

manufacturing. Given this information, our initial proposal would be for the wholesale company

to consider manufacturing their own items from the Tables, Chairs, and Bookshelves sub-

categories of the Furniture category and the Storage sub-category of the Office Supplies category.

With this manufacturing proposal in hand, the question then turned to where, exactly, such

manufacturing should be proposed to take place. It seemed most prudent to first look at sales data

by state to identify the hot spots for sales activity. We created a Choropleth Map for total sales by

state for the Furniture category:

Expanding Within the Value Chain – Group 5

Interestingly, the results show that furniture sales are at least relatively regionally distributed across

the United States, represented by significant sales in both eastern and western states. The states

with the highest representation of furniture sales, perhaps not surprisingly, are those with higher

population densities (i.e., California, New York, and Texas). With this in mind, we feel that it

would be prudent to suggest a more central location, Texas, as a manufacturing hub that could

capture this distribution. A manufacturing hub in Texas would be an excellent central location that

would help minimize shipping costs and logistics difficulties with the added benefit of being in a

state with no corporate income taxes (see Fritts article referenced below).

A decision for a manufacturing facility in Texas is further bolstered by aggregated profit margin

data. See the Choropleth Map constructed below, which represents the average profit margin

incurred by state in the Furniture category:

Expanding Within the Value Chain – Group 5

As it turns out, Texas is the state with the very lowest profit margin at –71.8%, indicating that sales

within Texas actually represent a significant loss for the wholesale company. Launching a

manufacturing plant in Texas, then, could provide the company with a significant overall benefit

by helping it reduce those losses by cutting third party interference in supply-side margins. A

manufacturing plant in Texas would enable the company to offer a more direct-to-consumer

approach that would benefit both the buyer and seller.

Summary and Conclusions:

The analysis above focuses on exploring strategic growth opportunities for a particular,

given wholesale business through value chain expansion, particularly vertical integration. The

findings suggest that integrating manufacturing into the wholesale company's operations,

especially within the furniture category (particularly tables, chairs, and bookshelves) and some

office supplies (particularly storage), could improve profit margins and address issues such as

quality control and logistical inefficiencies.

The study also identified Texas as an optimal location for a manufacturing hub due to its

central position, significant sales activity, and potential to optimize shipping costs. However,

limitations include a lack of external market data and reliance on historical trends, which may not

fully capture future market dynamics. Further research could explore other product categories and

regions for expansion opportunities and assess the long-term impacts of vertical integration on the

company's competitive position.

Expanding Within the Value Chain – Group 5

References:

Banton, Caroline (2021). “Loss Leader Strategy: Definition and How it Works in Retail.”

Investopedia. Accessed 4/13/2024.

https://www.investopedia.com/terms/l/lossleader.asp#:~:text=A%20loss%20leader%20strategy

%20prices,markets%20to%20gain%20market%20share.

Brewer’s Law (2019). “‘Self-Distribution’ of Beer in Florida - 2019.” Accessed 3/31/2024.

<https://brewerslaw.com/self-distribution-of-beer-in-florida-2019/>

Chen, Baizhu (2012). “The Real Reason the U.S. Doesn’t Make IPhones: We Wouldn’t Want

To.” Forbes. Accessed 4/13/2024.

https://www.forbes.com/sites/forbesleadershipforum/2012/01/25/the-real-reason-the-u-s-

doesntmake-iphones-we-wouldnt-want-to/?sh=780a5037301c

Fritts, Janelle (2022). “State Corporate Income Tax Rates and Brackets, 2022.” Tax Foundation.

Accessed 4/13/2024. https://taxfoundation.org/data/all/state/state-corporate-income-tax-rates-

brackets-2022/

Porter, Michael (1985). Competitive Advantage: Creating and Sustaining Superior Performance.

New York, NY. Simon and Schuster.

Tikkanen, Amy (2024). “Proctor and Gamble Company.” Britannica Online. Accessed

3/31/2024. <https://www.britannica.com/topic/Procter-and-Gamble-Company>

Team Contributions:

Alfredo: Introduction and Objectives and Goals

Jose-Daniel & Allison: Datasets and Limitations Camila and Lance: Data Story and Final Edits

Jose-Daniel & Allison: Summary and Conclusions