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Case Study: BioPharma Inc.
Kelli Ware
School of Business, Liberty University
June 1, 2025
Introduction
Global supply chain networks are some of the most complex and nuanced production
systems that require constant monitoring and adjusting if they are to remain successful.
Balancing cost optimization and risk management challenges companies like BioPharma must
make strategic decisions regarding plant operations, capacity adjustments, and supply chain
configurations so they can adequately manage currency rate inconsistencies, yield, and demand
uncertainty, import tariffs and global market dynamism (Chopra, 2018). This case study presents
reconfigured global production network recommendations for BioPharma Inc.’s 2013 operations
with emphasis on strategic plant operations adjustments, capacity expansion in low-cost areas,
and leveraging data and demand driven tactics to optimize agility, profitability, and strategic fit.
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Proposed BioPharma Production Network in 2013
Because of the complex nature of global supply chain networks risk in an inherent factor
to consider when making decisions. Successful risk mitigation strategies will require
manufacturers to develop processes that encompass flexibility while balancing cost optimization
endeavors to ensure risk mitigation does not negatively impact profitability (Vishnu et al., 2020).
To maximize their production network, BioPharma should place emphasis on minimizing fixed
costs associated with production line costs that have minimal yield, transportation costs and
import duties while balancing demand for their products (Chopra, 2018). To do so, plant idling in
certain areas and production reconfiguration will be required.
Proposed Network Production Configuration
•Plants to Continue Current Operations:
oBrazil: Because of its mid-range annual fixed cost ($30M), maintain production of
both product lines, less expensive variable costs for Latin America, and sales
rate of 63.6 percent for Highcal and 100 percent for Relax, this location can
primarily service Latin America with not duty fees since it’s local (Chopra, 2018)
and service Europe and Asia as a backup to the plants in India and Japan.
oIndia: Because this location has the lowest annual fixed cost ($20M), the lowest
product cost for both production lines ($18.6M for the total costs of both
products) even with the spike in Indian Rupee exchange rate in 2013 and
considerably low transportation costs, maintain production as is with both lines
servicing all locations. While its technology is outdated it is still amongst the
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most profitable locations with profits projected to grow by 10% annually for the
next half decade (Chopra, 2018).
oU.S.: Even with slightly higher fixed cost to operate coming in at $33M and
moderate fixed product costs at $19.6M total for both products, this plant had
the highest sales of both products ($35M total) in the entire network. Keeping
both product lines active will supplement other plants that will be either fully or
partially idled.
•Plants to Idle:
oMexico: High fixed costs at almost $64M and extremely low output ($6M in
sales) justifies partially idling this plant by deactivating the Relax product line
completely and product transport to Japan and Asia eliminated. This will result in
a savings of $4.8M annually in fixed costs (Relax) and $11.1M/kg in variable
material and production costs.
oJapan: Because of its low capacity (10 million kg production ability) and high
variable costs across the entire network, it costs more to operate the plant and
as such it should be fully idled. The fixed and variable cost savings can be applied
to Mexico to supplement higher production costs.
oGermany: Despite its high capacity (that can be supplemented by Mexico), this
plant has the highest fixed costs at $71M to operate and product costs at
$21.9M, as well as the highest variable costs for transportation to each location
in the network. This plant should be fully idled to save close to $100M annually.
Cost Analysis for Recommendations
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•Fixed Costs:
o Brazil: $20M + $5M (Highcal) + $5M (Relax) = $30M o India: $14M + $3M
(Highcal) + $3M (Relax) = $20M o U.S.: $23M + $5M (Highcal) + $5M (Relax) =
$33M o Mexico: $30M + $6M (Highcal) + $1.2M (Relax) = $37.2M (Cost
savings =
$4.8M) o Japan: $2.6M + $.8M (Highcal) + $.8M (Relax) = $4.2M (Cost
savings =
$16.8M) o Germany: $9M + $2.6M (Highcal) + $2.6M (Relax) = $14.2M (Cost
savings =
$56.8M) o BioPharma Total Fixed Costs: $138.6M (Cost savings
= $78.4M)
•Variable Costs (Cost per kg x Production amount) o Brazil: $8.7/kg x 11M (Highcal) +
$11.2/kg x 7M (Relax) = $174.1M o India: $8.1/kg x 10M (Highcal) + $10.1/kg x 8M
(Relax) = $161.8M o U.S.: $8.6/kg x 5M (Highcal) + $11.0/kg x 17M (Relax) = $230M
o Mexico: $8.6/kg x 12M (Highcal) + $11.1/kg x 0M (Relax) = $103.2M (Cost savings
= $199.8M)
Original Costs: $8.6/kg x 12M (Highcal) + $11.1/kg x 18 (Relax) =
$303M)
o Japan: $0/kg x 11M (Highcal) + $0/kg x 7M (Relax) = $0 (Cost savings =
$19.8M)
Original Costs: $9.9/kg x 2M (Highcal) + $12.1/kg x 0 (Relax) = $19.8M) o
Germany: $0kg x 15M (Highcal) + $0/kg x 0M (Relax) = $0 (Cost savings =
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$148.5M)
Original Costs: $9.9/kg x 15M (Highcal) + $12.2/kg x 0 (Relax) =
$148.5M)
o BioPharma Total Variable Costs: $669.1M (Cost savings = $471.3M)
The proposed network configuration saves approximately $78M annually in fixed
costs and over $470M annually in variable costs by partially idling the Mexico plant and
completely idling the Germany and Japan plants while leveraging the low cost and high
yields of Brazil and India and continuing to meet market demand. The U.S. plant supports
high domestic sales exceeding demand and the partial idling of Mexico diminishes cost
overrun which aligns with Chopra’s (2018) fundamental cost optimization tactics and the risk
minimization strategies outlined by Vishnu et al. (2020).
Proposed BioPharma Global Network Structure
With consideration of the aforementioned production network recommendations, to
maximize BioPharma’s global network structure, Landgraf should implement a strategy that
aligns with the updated production network, optimizing cost efficiency, and building supply
chain infrastructure resilience to reduce risks surrounding demand, delays and fluctuations in
prices and exchange rates (Chopra, 2018). Tripathy and Eppinger (2013) point out that global
product manufacturing organizations benefit tremendously from structuring networks in a way
that reduces coordination costs by placing centrally located hubs and idling low efficiency
plants, using design structure matrixes to identify interdependencies that support production
consolidation and diminishes coordination challenges. Landgraf should make the U.S., Brazil and
India plants the three primary production hubs as they cover three critical points across the
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span of the global network and make the Mexico plan the main Highcal supplier. Historic trends
between 2006 and 2013 reveal the depreciation of the Brazilian Real and the Indian Rupee cost
reductions continue as a result while the U.S. dollar has remained stable and costs unchanged
(Chopra, 2018).
Adding a Million Kilograms of Additional Capacity at a $3M Annual Fixed Cost
Yield loss according to Cai et al (2019) is a common theme amongst product
manufacturers in Asian regions and must be considered in the decision to increase plant
capacity. With the closure of the Japan location and the use of the India location to supply
nearing areas in addition to previous calculations, adding a million kilograms of additional
capacity to the India plant is a worthwhile investment. Low fixed and variable costs compared to
other plants along with favorable exchange rates, and high sales potential (10% increase year
over year for the next 5 years) make the plant in India the securest and most favorable option
for increased capacity. According to Chopra (2018) increasing capacity is a risk mitigation
strategy that is profitable for low-cost decentralized locations such as India. One way to reduce
yield and demand uncertainty is through revenue sharing contract between BioPharma and
India plant buyers to outline a revenue sharing ratio that distributes risks associated with
uncertainty equally (Cai et al., 2019).
Impact of Duty Fee Reductions on Recommendations
The proposed recommendations see a deeper cost reduction when duty fees that are
eliminated are encompassed in the equation. Per Chopra (2018), import tariffs account for a
30% increase in goods transported to Latin America, 3% increase to Europe, 27% increase to
Asia without Japan, 6% increase to Japan, 35% increase to Mexico and 4% increase to the U.S. In
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closing Germany and Japan locations duty fees to transport to those areas have a minimal
impact on variable costs since the import tariffs in those location are both below 10% making
the idling of those locations the best choice. Chopra (2018) endorses keeping high-yield, high
demand production in low-duty regions to reduce transportation costs reinforcing the India
plant strategy regardless of reduced profitability due to tariffs. Since higher tariff rates reduce
order quantities and decrease profits (Hu et al., 2022), keeping the plant operable in low-tariff
regions such as the United States is an intelligent strategy recommendation. The decision to
partially idle Mexico potentially presents diminished attractiveness for the import of Relax to
that region, but sales in that region in 2013 were among the lowest in the entire network at
only 3M kilograms. Hu et al. (2022) suggests the implementation of a transnational supply chain
alliance which is shown to reduce tariff costs by distributing duty related costs between the
supplier, manufacturer and buyer which lessens impacts to profits. The use of this strategy
would be useful in supplementing the transport of Relax from other regions into Mexico.
Analysis Modification to Account for Yield Differences Across Plants with One Hundred
Percent Yield Assumption
Accounting for yield differences across plants with a 100% yield assumption requires a
flexible and agile supply chain network allowing decision-makers to adjust production capacities
to manage demand dynamism while accounting for shifts in variable costs and ensuring minimal
impact to profits (Chopra, 2018). Idling the production of Relax in the Mexico plant while
keeping the plant active allows BioPharma to shift production to Mexico, if necessary, in cases of
yield variation. Moreover, the limited production at the India location along with the 1M
kilogram capacity increase also supports yield variation across the global network. Incorporating
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the use of commitment order contracts as recommended by Cai et al. (2019) would require
buyers to purchase a minimum amount of Highcal or Relax which diminishes yield uncertainty.
Offering wholesale pricing incentives could persuade buyers to commit to contracts (Cai et al.,
2019). This approach ensures cost effectiveness monitoring, helps to stabilize supply quantities
while leveraging commitment contracts to secure yield uncertainty (Cai et al., 2019).
Additional Factors Considered in BioPharma Recommendations
In refining BioPharma’s global supply chain production network a few additional critical
factors should be considered beyond cost optimization, yield variances and duty fees such as
accounting for demand uncertainty, the incorporation of data analytics and systems
interoperability. In 2013, automation and digitization are necessary components to remain
competitive in the manufacturing industry. Incorporating such factors will bring the entire
network up to date technologically, align global operations and better achieve strategic fit.
To account for demand uncertainty, Jodlbauer et al. (2023) propose the adoption of
demand driven systems over supply driven systems that shift to a make-to-order system to
prioritize buyer needs, maximize cost efficiencies and reduces forecasting inefficiencies. This
aligns with Cai et al.’s (2019) commitment contract strategy that would position BioPharma to
pre-fill orders for Highcal and Relax and mitigate disparities in production and sales at each
location. With the partial idling of Mexico, demand-driven services can begin there to execute
production reconfiguration which results in reductions in lead times and overall improved
customer service.
Using a data driven business model aligns with the on-demand service model. The use of
prescriptive analytics frames future demand forecasting by gathering data from decentralized
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data points to reduce errors that cause the bullwhip effect (Chopra, 2018 & Jodlbauer et al.,
2023). Data analytics from each of the plant locations supports necessary information sharing
between sites especially those that have adjusted operations to enhance total network
efficiency (Chopra, 2018). Implementing comprehensive data analysis will ensure real-time
tracking of inventory to support replenishment, shipments schedules, and coordination across
the spectrum of production plants (Jodlbauer et al., 2023).
Moving from demand drive systems and data drive business model integration, the next
step is interoperability which supports planning and coordinating operational flow and flexibility
between each location (Jodlbauer et al., 2023). Creating a collaborative ecosystem through
interoperable systems within the BioPharma global network will accelerate effective scalability,
increase accurate data synthesis to identify consumer behavior trends, identify material
weaknesses within the network, diminish costs and positions them to gain a competitive
advantage over manufacturers that use outdated supply chain management strategies
(Jodlbauer et al., 2023). The incorporation of a blockchain interoperability platform enables
collaboration with local suppliers which reduces costs associated with coordination, serves as a
backup in cases of diminished availability and increase network agility (Jodlbauer et al., 2023).
Conclusion
The strategic recommendations outlined for BioPharma’s cost optimization and
operational efficiency reconfigures the company’s global production network realizing a
substantial cost savings of over half million dollars in fixed and variable costs combined. By
idling the high-cost low-output plant in Germany and Japan and partially idling the plant in
Mexico, BioPharma can leverage the cost-effective operations in India, Brazil, and the lucrative
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operations in the U.S. to build a more efficient production network that align with Chopra’s
(2018) supply chain operational efficiency principles. Implementing the hub-based strategy
purported by Tripathy and Eppinger (2013) positions BioPharma to shift from a supply-based
production strategy to the demand-based framework that leans heavily on collaboration,
interoperability and data analytics to create an ecosystem that saves money, increases
efficiency, supports sustainability and scalability while garnering a competitive edge (Jodlbauer
et al.,
2023).
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References
Cai, J., Hu, X., Chen, K., Tadikamalla, P. R., & Shang, J. (2019). Supply chain coordination under
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Chopra, S. (2018). Supply Chain Management (7th ed.). Pearson Education.
https://libertyonline.vitalsource.com/books/9780134732459
Hu, X., Fu, K., Chen, Z., & Du, Z. (2022). Decision-Making of transnational supply chain
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https://doi.org/10.3390/math10050770
Jodlbauer, H., Brunner, M., Bachmann, N., Tripathi, S., & Thürer, M. (2023). Supply Chain
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