Impact of data-driven solutions on supply chain: the context of Covid-19

Businesses are increasingly adopting data-driven solutions to manage operations in a
world driven by data and technology. The increased application of data-driven solutions is
supported by the advancement of the technologies used in collecting, storing, and analyzing data
to provide decision support, predict and manage risks and increase financial and operational
efficiency. The COVID-19 pandemic that struck in 2019 has further increased the need for
businesses to leverage data-driven solutions to mitigate supply chain risks. Businesses develop
supply chains to ensure maximum returns and provide convenience to customers regardless of
their locations. This paper aims to demonstrate the impact of data-driven operations on the
supply chain during the COVID-19 era.
Coronavirus disease (Covid -19) is a respiratory illness that broke out in 2019. It is
highly contagious and has warranted several measures to be put in place (World Health
Organization, 2021). The onset of the pandemic affected businesses in many ways. This paper
analyzes how supply chains were impacted, the potential problems that came up, and how data-
driven operations could be applicable in minimizing adverse effects to businesses (Zheng et
al.,2021). A supply chain can be defined as a network of organizations that helps companies to
deliver goods and services to consumers. It encompasses the whole process of producing and
delivering products (Zheng et al.,2021). Covid 19 occassioned a shortages of supplies.
Companies could not access important products that are required in the production process.
Companies could also not reach out to the consumers and this resulted in conusmers having a
short suply od consumer products. When the pandemic first struck, many governments
announced restriction measures that were meant to reduce social interactions, such as restrictions
of movement, closure of public spaces, lockdowns, and curfews. This meant that customers

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could not move to access goods and services, which meant that businesses had to develop new
ways of getting their products and services to customers. Restrictions of movements meant that
businesses increased their storage costs. Customers were not purchasing, and thus there were no
stock movements. For businesses that deal with perishable goods, lack of movement provided an
increased risk. Businesses that have a long manufacturing process had to find a way to store their
work in progress as product demands declined (Ivanov & Das 2020).

Due to reduced demand for products and services, businesses needed to restructure their
purchasing. Depending on the nature of business, purchases can be as essential as raw materials
or other essential components of the manufacturing or processing activities. Therefore,
businesses needed to restructure their reorder levels to adjust to reduced demands. Even where
businesses continued to purchase, they were required to navigate transport and logistical
challenges. Such challenges became important due to restrictions of transport via the available
means. For businesses that make purchases in foreign countries, the option of air freight provided
significant challenges since many airports restricted any form of movement. To achieve utility in
the physical flow of goods (Mason et al., 2003) recommends that organizations should ensure
information sharing across the whole supply chain process. (Mason et al., 2003) further
recommends the global inventory visibility model, which creates supply chain efficacy to reduce
receiving cycle times, increase shipment and inventory accuracy, and decrease lead time
variability.

Technology giant IBM offers global inventory visibility and markets it as the best tool to
provide the most accurate Available-to-promise (ATP) dates. Efficiency is created when
organizations better utilize their warehousing space and streamline transport logistics to save

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time. Besides the conventional transport means, organizations can further strengthen supply
chains by the digitization of their transport models. The use of drones and unmanned aerial
vehicles (UAV) have become increasingly popular methods of improving efficiency. UAVs and
drones increase traceability and improve the supply chain experience because their design
includes distributed ledgers that are able to collect and validate inventory data in real-time;
therefore, scheduling of deliveries becomes efficient (Fernandes et al.,2019). More significantly,
the covid-19 pandemic affected the humanistic aspect of businesses. Most organizations’ supply
chains are labor-intensive and are run and operated by human beings. Even in organizations that
have a mechanized supply chain, human input is still required on many levels of the supply
chain. Businesses had to restructure staffing requirements to ensure that the risks of exposure to
individuals were drastically reduced.

The Impact of Technology

This section is an analysis of how organizations leveraged technology to ensure minimal
disruptions to their supply chains and to anticipate and reduce risks. Organizations that use data-
driven reorder levels reduce their risks of losses. Data models such as predictive statistics are
used to predict changes in customer demand levels, and this enables businesses to make more
solid decisions. (Modgil et al.,2021) studied the impact of artificial intelligence on supply chain
models. The result was that AI presents businesses with solutions that they may use to increase
resilience, reduce risks, and streamline sourcing and distribution capabilities to make them more
effective. (Modgil et al., 2021) further finds that data-driven AI helps to develop business-
specific solutions that are tailor-made to suit emerging risks and reduce the impact of disruption.

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Businesses that were still new at the time the pandemic struck were the worst affected,
according to (Helo et al., 2016). Besides the demand uncertainties that affected all businesses,
new businesses faced greater risks due to a lack of experience. For the recovery of such
businesses, (Helo et al., 2016) recommends a cloud-based virtual supply chain (virtual e-chain)
for visualizing the supply chain. However, Helo et al. (2016) recommend conditions that must be
fulfilled before a virtual e-chain can be implemented. First, organizations need to develop
modalities of collecting and sharing data with supply chain partners. This allows remote
geographical collaboration. Cloud-based solutions for data handling place organizations at
strategic points to benefit from the Internet of Things (IoT) and advanced computing, virtualized
data storage, and processing, and service-oriented technologies for data exchange (Helo et al.,
2016). Global supply chain masterminds such as Amazon have already adopted these
technologies. This is why such organizations overcame turbulent times during the pandemic and
posted the above-board results.

Organizations collect logistical data based on their transport models. Businesses can
know precisely where their delivery trucks are at any given time through technologies such as
GPS tracking. During the pandemic, this was essential because organizations could plan better
based on the activity at hand. Therefore, many businesses could fulfill their warehousing needs
through proper logistical planning. Efficient logistical planning is achieved when organizations
structure their business performance units to operate autonomously. (Sharma et al., 2020)
evaluated the usability of the Internet of Things (IoT) in supply chain management and found
that IoT in warehousing enables pharmaceutical companies to improve quality productivity,
reduce errors, and monitor storage conditions of drug products to enhance operational
effectiveness. The study by (Sharma et al., 2020) can be applied across various industries where

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product metrics such as temperature, pressure, and humidity significantly affect the product
quality. The effect of COVID-19 on product demand meant that organizations that deal in
products such as pharmaceutics where temperature monitoring was essentially needed to find
solutions to their processes to minimize wastage.
Organizations depended on data solutions to actualize contact tracing of persons to
measure the level of risks of exposure. Businesses require such information in advance in order
to plan for the replacement of staff to ensure continuity of critical activities. Data solutions
enable organizations to streamline schedules such that there is efficiency in operations.
Data-driven predictive statistics enable organizations to predict changes in demand levels
and restructure their operations to reduce wastage and cushion the organization against financial
losses and operational inefficiencies. This problem is more prevalent in organizations that are
product-based as opposed to service-based. Data analytics helped organizations to determine the
level that manufacturing plants run and build inventory that absorbed fixed costs. The challenge
with decreased demand for products is that it affects the cash flow projections of many
businesses. Pandemics affect the social-economic element of customers, such as their purchasing
power (Huber et al.,2018). Thus, businesses need data solutions that can help them better
anticipate such customer metrics early enough and allow supply chain models sufficient time to
adjust and mitigate against the loss.
Most governments allowed the continuity of essential business activities. However, the
governments also determined which activities were essential and which ones were not. Even
essential services were restricted in physical locations where the government determined had
very high risks of infection. Data-driven solutions allowed businesses to monitor levels of risks
on physical locations where logistical demands require staff to access. The decisions on this level

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required businesses to analyze the level of exposure of the staff to determine whether it was ideal
for planning logistics in those locations and which other alternatives were available. For
warehousing labor requirements, businesses also needed to evaluate the least number of staff that
can comfortably handle tasks. In some countries such as India, movement restrictions affected
many labor-intensive supply chains since most of the labor force was not able to travel to their
required workstations. In some cases, employees had to walk on foot which created time
inconveniences and reduced productivity due to exhaustion.
Data solutions could also help to predict the operations of strategic supply chain partners.
Supply chain partners can be defined as the parties within the supply chain without whom critical
activities in the supply chain cannot be achieved. Supply chain systems where partners had data-
sharing capabilities demonstrate more resilience due to their ability to leverage technology and
make accurate projections. Big Data Analytics (BDA) allows organizations to collect, process,
and extract meaningful data, which, when shared on a real-time basis, allows supply chain
partners to make strategic decisions (Belhadi et al.,2021). In the cases where supply chain
partners were unable to play their role in the process, big data analytics helped organizations
through information processing to prequalify new supplier partners and avoid disruption
(Belhadi et al.,2021).
For successful partner collaboration to be achieved, organizations need partners that share
a readiness to cooperate. Manthou et al. (2004) finds that virtual e-chain models (VeC) can be
used to specify the roles of each partner, identify key capabilities and analyze technical
coordination of tasks to determine the readiness to collaborate. In the context of the pandemic,
supply chain partners were affected differently. Therefore, businesses had to make decisions
based on the level of disruption of the supply chain partners to determine if the collaboration was

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worth continuing or if the organization had to source for new supply chain partners. This was a
key decision during the pandemic because the strength of collaboration, or the financial and
operational cost of changing supply chain partners, was the difference between firms that had
resilient supply chains and those that lacked.
There have been significant advances since the first case was reported. Many
governments have now eased most of the restrictions. However, the COVID-19 has been a lesson
to many businesses about the use of technology and specifically data-driven solutions to develop
solutions that increase the resilience of their supply chain systems. According to the research, the
businesses that used data more had reduced disruptions and better recovery than those that did
not (Paul et al.,2021). The pandemic has taught most companies the need to have robust recovery
strategies for business continuity. (Cerullo & Cerullo 2004) report that 80% of the entities that
lack recovery strategies for the supply chains do not survive after major outbreaks.
Post the pandemic, firms need to evaluate sourcing strategies to become more resilient.
Sharma et al. (2020) opine that data diversity can help organizations make real-time decisions
concerning sourcing strategies. The goal is to achieve flexibility to reduce the impact of
disruptions and provide more agility to the supply chain (Sharma et al., 2020). Additionally,
supply chain technologies require integration with market-related data. Supply chain efficiency
and utility occur when there is a synergy of organizational operations in producing products and
services for already existent market demand. Supply chain inefficiencies will always pose a
significant risk without timely and accurate market data. Integrating supply chain technologies
with demand forecasted data can help organizations better plan their actions and can help
mitigate against unprecedented occurrences in future pandemics.

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Overall, most of the technologies highlighted in this paper operate based on the quality of
data collected and fed. Therefore, firms should ensure that their data collection models run
devoid of disruptions. In the pandemic era, organizations with greater risk predictability were
less prone to vulnerability. Data solutions can also help organizations monitor internal
inefficiencies and vulnerabilities. Artificial intelligence and data modeling are essential tools that
organizations use to simulate and predict outcomes within the internal business environment. For
example, simulation can help the organization predict when an extra injection of financial
resources will help the organization. Therefore, the financial department can plan in advance to
disburse the funds to ensure business continuity.

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