Analyzing the Utilization of Data Analytics in Supply Chain Decision-Making among Small and Medium Enterprises in Lusaka, Zambia

Abstract

Data analytics has become a crucial tool for businesses, allowing them to uncover valuable insights from large datasets and enhance their operational efficiency. For Small and Medium Enterprises (SMEs), which are the foundation of many economies, effective supply chain management is essential for staying competitive and sustainable. This study investigated the utilization of data analytics in supply chain decision-making among SMEs in manufacturing, agribusiness, and retail and wholesale trade in Lusaka, Zambia. Employing a quantitative research approach, this study collected and analyzed data from a representative sample of 220 SMEs in Lusaka. The findings revealed that several intertwined factors contribute significantly to the lack of big data analytics use in supply chain decision-making among SMEs in Lusaka with many SMEs acknowledging gaps in expertise and uneven distribution of skills. Additionally, the findings suggested a significant gap in data analytics knowledge and application within the company's supply chain management. Based on these findings the study recommends that SMEs invest in data infrastructure and prioritize data analytics training to enhance the utilization of data analytics in the supply chain. In the same vein, the study recommends that policymakers develop policies such as tax incentives, grants, and subsidies that encourage the adoption of data analytics among SMEs in Lusaka.

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Analyzing the Utilization of Data Analytics in Supply Chain
Decision-Making among Small and Medium Enterprises in
Lusaka, Zambia
Leah Butala; and Bupe Getrude Mwanza
Graduate School of Business, University of Zambia, Lusaka, Zambia.
Corresponding author: leah99butala@gmail.com
DOI: https://doi.org/10.62154/ajmbr.2024.017.010528
Abstract
Data analytics has become a crucial tool for businesses, allowing them to uncover valuable
insights from large datasets and enhance their operational efficiency. For Small and Medium
Enterprises (SMEs), which are the foundation of many economies, effective supply chain
management is essential for staying competitive and sustainable. This study investigated the
utilization of data analytics in supply chain decision-making among SMEs in manufacturing,
agribusiness, and retail and wholesale trade in Lusaka, Zambia. Employing a quantitative research
approach, this study collected and analyzed data from a representative sample of 220 SMEs in
Lusaka. The findings revealed that several intertwined factors contribute significantly to the lack
of big data analytics use in supply chain decision-making among SMEs in Lusaka with many
SMEs acknowledging gaps in expertise and uneven distribution of skills. Additionally, the
findings suggested a significant gap in data analytics knowledge and application within the
company's supply chain management. Based on these findings the study recommends that SMEs
invest in data infrastructure and prioritize data analytics training to enhance the utilization of
data analytics in the supply chain. In the same vein, the study recommends that policymakers
develop policies such as tax incentives, grants, and subsidies that encourage the adoption of
data analytics among SMEs in Lusaka.
Keywords: Small and Medium Enterprises (SMEs), Supply Chain Management, Data Analytics,
Big Data Analytics, Big Data.
Introduction
The evolution of supply chain management has become a critical determinant of
organizational success. Traditional business practices often dominate organizational
culture resulting in resistance to change and innovation among employees and
management. However, with competition among businesses undergoing significant
changes, the evolution of the digital age has ushered in a new era of data-driven decision-
making, revolutionizing supply chains across different industries around the globe. Digital
transformation and restructuring of SME’s is of fundamental importance for supporting
economic growth and expanding globalization but it is also important to establish strategic
perceptions into the adoption of these modern tools (Telukdarie et al., 2022). Data
analytics, in particular, has emerged as a powerful tool that enables organizations to extract

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valuable insights from vast amounts of data and improve their operational efficiency. For
small and medium enterprises (SMEs), which form the backbone of many economies,
efficient supply chain decision-making is pivotal for competitiveness and sustainability.
Data is growing at an exponential rate nowadays, and much of it is relevant for making
decisions and policies in the following areas: Organization and customer management;
commodities and financial markets; macroeconomic growth; supply chain management;
manufacturing; and service processes (Coleman et al., 2016: Alp, 2018). With such events,
the term "Big Data" was coined to describe these enormous datasets, which traditional
computing methods are ill-equipped to manage. Therefore, Big Data Analytics has become
an essential tool for examining mountains of data in search of useful insights and trends
(Mohamed, et al., 2018). To get insight from these interconnections, businesses need to be
able to accept and integrate data from a wide variety of sources and formats (Alp, 2018).
According to Willetts, et al., (2020), Big Data is a comprehensive term that encompasses
various technologies designed to capture, store, process, and analyze intricate datasets.
These datasets are characterized by their substantial volume, velocity of high generation,
and diverse formats. With the technological advancements across the entities of Supply
Chain, data generation is also increasing at a fast rate. The data flow in the past years was
documented in terms of physical documents until the use of Information Technology (IT) in
Supply Chain. Now, majority of the information flow linked to the material flow is being
documented in the form of digital structured data. As the scope of Supply Chain is currently
worldwide, the volume of data collected from its numerous processes and the velocity at
which it is being generated can be qualified as Big Data. In addition to this, entities such as
marketing and sales are now relying on analysis of the unstructured data along with the
structured data to gain better insights into customer needs and improve upon the cost
aspects of Supply Chain processes. (Mohamed, et al., 2018).
SMEs make up a significant portion of the business environment, playing an essential role
in the economics of nations throughout. These businesses are essential to the growth of
both the domestic and international economy. In Zambia SMEs represent 97% of all
businesses in the country (FSD Zambia, 2017), 70% of gross domestic product (GDP) and
88% of employment (Invest, 2017). SMEs have an important role in society as well, since
they are often the primary employers of the most marginalized members of the labor
market (Centre International Trade, 2015). SMEs are found in several sectors in Zambia,
such as retail and wholesale trade, agribusiness, manufacturing, services, mining, tourism,
and construction. They constitute the backbone of Zambia's business landscape,
contributing significantly to its economic growth and development (Policy Monitoring
Research Centre, 2021).
However, SMEs have an extremely limited adoption of Big Data Analytics, despite the fact
that doing so might provide them with a strategic edge (Velthuijsen, et al., 2018). This was
also evident in a report by Dat (2018) which indicated that there is a gap in the literature
about the adoption of analytics and big data in SME companies. Despite this, SMEs play
crucial roles in the global economy, and SME supply chains are often cited as key

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contributors to the smooth operation of the production and distribution processes in many
sectors. In addition, supply chain management in SMEs has issues that are comparable to
those in bigger businesses (demand management, inventory management, logistics, etc.),
and these issues may be solved by using analytics (Dat, 2018). Examples of achieving
significant gains were provided by Tan and Zhan (2017), for instance, shortening the time
to market a product by more than 7 months and slashing development costs by 90%.
Moreover, in today's complicated and competitive business environment, the success of
enterprises in the SME sector relies heavily on the adoption of appropriate strategies such
as data analytics in making decisions (Kot, et al., 2018).
This study focuses on Manufacturing, Agribusiness, and Retail and Wholesale Trade sectors
which are key to Zambia's economy. There is currently just a tiny quantity of published
research on the issue of SMEs and their usage of Big Data Analytics, as noted by Bouwman
et al (2018).
Statement of the Problem
In today's data-driven business environment, companies all over the world are increasingly
relying on big data analytics (BDA) to make informed decisions and provide them a
competitive advantage (Shahid & Sheikh, 2021). BDA is becoming more popular in supply
chain management on a global level. The global BDA market was valued at USD 49.03
billion in 2022 and is projected to grow at a compound annual growth rate (CAGR) of 26.7%
from 2023 to 2030 (Grand View Research, 2023).
Despite BDA being viewed as a significant asset for decision-makers to obtain timely
insights and generate high revenue, only 5% to 10% of African companies use it for decision-
making, indicating that the continent remains behind compared to other parts of the world
(African Development Bank, 2023).
The adoption of BDA in Zambia is still in the early stages of development, primarily due to
obstacles such as restricted access to technology, a lack of digital skills, and limited
resources among SMEs (UN Global Pulse, 2012). Despite these challenges, the Ministry of
Technology and Science (2023) emphasizes Zambia's commitment to leveraging Artificial
Intelligence, Machine Learning, Internet of Things, Blockchain Technologies, Robotics, and
Big Data to transform businesses and processes. In addition, the government's
establishment of data centers to support the growing industries creates an opportunity for
SMEs to utilize the vast amount of daily data being collected. Therefore, conducting this
study will provide a deeper understanding of the benefits, address existing challenges, and
develop strategies for improved utilization of BDA among SMEs in Zambia.
A study carried out in South Africa by Seseni & Mbohwa, (2021) found that SMEs that use
big data are more profitable, productive, and innovative. In a similar study, Ogbuokiri, et al.
(2015) examined the extent to which Big Data can be harnessed for SME growth in Nigeria
and Lutfi, et al. (2022) examined the influence of technological, organizational, and
environmental factors on big data adoption in the Jordanian SMEs context. Existing
research often examines different regions, making this study unique in its targeted

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approach to Zambia's SMEs. This study fills a research gap by focusing specifically on SMEs
in Zambia and their adoption of BDA in supply chain decision-making.
Research Objectives
i. To assess the level of knowledge of data analytics by SMEs in supply chain decision-
making.
ii. To establish the utilization of data analytics by SMEs in supply chain decision-
making.
iii. To identify the challenges SMEs in Lusaka are facing in using data analytics in
supply chain decision-making.
Literature Review
This section elucidates the empirical research relevant to addressing the research
objectives.
Application of Big Data Analytics in Decision-Making
Big data analytics has become increasingly integral to decision-making processes across
various domains, offering organizations powerful insights to inform strategic, operational,
and tactical decisions. At its core, big data analytics enables the extraction, processing, and
analysis of vast volumes of structured and unstructured data to derive actionable insights.
In decision-making contexts, big data analytics facilitates evidence-based decision-making
by providing stakeholders with timely and relevant information to address complex
challenges and capitalize on opportunities (Udeh , et al., 2024). As businesses increasingly
adopt big data-driven strategies, the balance between offering personalized services and
safeguarding consumer rights is a delicate one, requiring both ethical considerations and
regulatory compliance.
One of the key ways in which big data analytics is utilized in decision-making is through
predictive analytics. By leveraging advanced statistical techniques and machine learning
algorithms, organizations can analyze historical data to forecast future trends, outcomes,
and events. For instance, in financial services, banks use predictive analytics to assess credit
risk, detect fraudulent activities, and optimize investment strategies (Rozak, et al., 2022).
Similarly, in healthcare, predictive analytics enables clinicians to anticipate patient
outcomes, identify high-risk individuals, and personalize treatment plans (Suresh, 2016).
Furthermore, big data analytics plays a crucial role in optimizing operational decision-
making processes. For example, in supply chain management, organizations use big data
analytics to enhance demand forecasting, optimize inventory management, and streamline
logistics operations. By analyzing large datasets from various sources, including sales data,
weather patterns, and social media sentiment, organizations can make data-driven
decisions to improve efficiency, reduce costs, and enhance customer satisfaction (Wamba,
et al., 2014). However, the degree to which businesses can fully capitalize on these
opportunities often hinges on their ability to manage and interpret data effectively.

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Organizations lacking in data literacy or infrastructure may struggle to realize the potential
benefits of big data, risking suboptimal decision-making and missed opportunities.
Furthermore, while big data offers cost savings and efficiency gains, it also requires
substantial upfront investments in infrastructure and talent, making it accessible
predominantly to larger organizations with the necessary resources.
Case studies provide compelling examples of the effectiveness of big data analytics in
decision making. For instance, Walmart, a retail giant, leverages big data analytics to
optimize its inventory management processes. By analyzing sales data in real-time and
integrating it with external factors such as weather forecasts and seasonal trends, Walmart
can accurately forecast demand, minimize stockouts, and optimize shelf replenishment
(Davenport, 2014). Similarly, Netflix utilizes big data analytics to personalize its content
recommendations for users based on their viewing history, preferences, and behavior
patterns. This enables Netflix to enhance user engagement, retention, and satisfaction,
ultimately driving business growth (Es, 2022). Overall, big data analytics empowers
organizations to make informed, data-driven decisions across various domains, enabling
them to gain a competitive edge, drive innovation, and achieve their strategic objectives.
Through predictive analytics, operational optimization, and personalized insights, big data
analytics revolutionizes decision-making processes, unlocking new opportunities for
organizations to thrive in an increasingly data-driven world.
While big data analytics has undeniable potential to drive innovation, efficiency, and
competitive advantage, its integration into business strategy must be approached with a
nuanced understanding of both its opportunities and challenges. Organizations must not
only focus on the technological aspects but also consider ethical, regulatory, and
operational risks to harness the true transformative power of big data analytics in the digital
age.
Supply Chain Decision-Making in SMEs
The application of big data analytics in supply chain decision-making has garnered
significant attention, particularly concerning its implications for Small and Medium-sized
Enterprises (SMEs) across different economic contexts.
Application of Big Data Analytics in Supply Chain Decision Making in SMEs from
Developed Economies Perspective
Various studies have examined the adoption and impact of big data analytics in supply chain
decision-making among SMEs in developed economies. For example, research by Karki
(2024) investigated how SMEs in the United States leverage big data analytics to enhance
supply chain visibility, optimize inventory management, and improve decision-making
processes. Their findings highlighted the significant benefits of real-time data analysis in
improving operational efficiency and responsiveness to customer demands. Similarly,
research by Bach, et al. (2020) explored the use of big data analytics in supply chain risk
management among SMEs in European countries. They found that advanced analytics

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techniques, such as predictive modeling and machine learning, enable SMEs to identify and
mitigate risks more effectively, thereby enhancing supply chain resilience and
competitiveness. Literature from developed economies suggests that SMEs are
increasingly recognizing the value of big data analytics in supply chain decision making. Key
insights include the importance of real-time data analysis, predictive capabilities, and risk
management functionalities in driving operational excellence and competitive advantage
(Lee & Mangalaraj, 2022). However, this enthusiasm for big data analytics must be
tempered with an awareness of its limitations, especially in the SME context. Many SMEs
operate under financial and technical constraints that may hinder their ability to implement
advanced analytics. The capital investment required for data infrastructure, the training of
personnel, and ongoing maintenance may be prohibitive for smaller firms, potentially
leading to a digital divide between large enterprises and SMEs.
Application of Big Data Analytics in Supply Chain Decision Making in SMEs from
Developing Economies Perspective
In contrast, research on the application of big data analytics in supply chain decision-
making among SMEs in developing economies is relatively limited but growing. Studies in
this area often highlight unique challenges and opportunities faced by SMEs operating in
less mature business environments. For instance, a study by Rahman, et al. (2017)
investigated the adoption of big data analytics among SMEs in emerging markets, including
India and China. They identified barriers such as limited access to technology infrastructure,
data privacy concerns, and skills shortages as significant challenges hindering widespread
adoption. However, they also noted that SMEs in developing economies can leverage big
data analytics to gain insights into emerging market trends, consumer behavior, and
competitive dynamics. Similarly, research by Chen & Zhang (2019) explored the role of big
data analytics in supply chain innovation among SMEs in Southeast Asia. They found that
while SMEs face resource constraints and institutional barriers, strategic partnerships and
government support initiatives can facilitate the adoption and utilization of big data
analytics for supply chain optimization and market expansion.
Literature on big data analytics adoption in supply chain decision-making among SMEs in
developing economies underscores the importance of addressing adoption challenges
infrastructure while leveraging the potential for market insights and innovation. The
application of big data analytics in supply chain decision-making for SMEs in developing
economies is undoubtedly beneficial, but not without its complexities. The optimism in the
literature regarding real-time data analysis, predictive capabilities, and risk management
functionalities must be balanced against the practical challenges of adoption for SMEs in
developing countries, including resource limitations, scalability issues, and potential risks
associated with automated decision-making. As SMEs in developing countries continue to
explore the use of big data, future research should focus on strategies that address these
challenges, ensuring that the benefits of big data analytics are accessible and sustainable
for organizations of all sizes.

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Application of Big Data Analytics in Supply Chain Decision-Making in SMEs from a
Zambian Perspective
Research specifically focusing on the application of big data analytics in supply chain
decision-making among SMEs in Zambia is limited. However, various evidence suggests a
growing interest in leveraging data-driven insights to enhance supply chain efficiency and
competitiveness. Factors influencing the adoption and utilization of big data analytics in
Zambia's SME sector include technological infrastructure constraints, data privacy and
security concerns, and skills shortages. In addition, the availability of affordable analytics
solutions tailored to the needs of SMEs and supportive government policies could play a
crucial role in driving the adoption (Chellah & Kaunda, 2021).
Moving forward, research in this area could explore case studies of Zambian SMEs that have
successfully implemented big data analytics in their supply chain operations, identifying
best practices, challenges overcome, and lessons learned. In addition, studies examining
the socio-economic impact of big data analytics adoption on SME growth, job creation, and
economic development in Zambia would provide valuable insights for policymakers and
practitioners alike. While there is growing recognition of the potential benefits of big data
analytics in supply chain decision-making among SMEs in developed and developing
economies, more research is needed to understand the unique challenges and
opportunities in different contexts, including Zambia. Such insights can inform strategies
to promote inclusive and sustainable economic growth through data-driven innovation in
SME supply chains.
Challenges of Utilizing Big Data Analytics by SMEs
Data Analytics is rapidly being utilized by large companies on a global scale to gain
competitive advantage which is well documented in the literature (Matthew Willetts et al.,
2020). The challenges faced by SMEs in the supply chain decision-making with the
integration of data analytics are mainly due to its complex characteristics of data analytics
technologies, adopting and using analytics and big data present challenges for both big
companies and SMEs. Arunachala et al. (2017) argued that general challenges in
implementing analytics include organizational challenges and technical challenges. Those
challenges also are faced by SMEs organizations, however, due to the limitations in the
different type of necessary resources, challenges in SMEs can be in larger extents. These
barriers and challenges have been categorized as human resources, strategy and
awareness, finance, IT infrastructure, and Lack of Facilitating conditions. Evidence from the
literature indicates that SMEs are underutilizing this technology for a variety of reasons, for
example lack of expertise and cost implications.
Lack of Expertise
The study done by Willetts et al., (2020) aimed at identifying barriers to the adoption of
data Analytics by SMEs to help them overcome the challenges and to exploit the benefits
of data Analytics to improve their competitive advantage which will benefit the wealth of

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the country, particularly in the aftermath of Covid-19. The study revealed that for relatively
new technology, it is unlikely that SMEs will have the skillsets required to utilize it. This
marks the first barrier to adoption and utilization of data analytics which is knowledge and
skills, regulatory barriers, and privacy issues that organizations need to be aware of to
ensure compliance, for example, data protection legislation which dictates how businesses
should store and process personal data.
IT Infrastructure
Technical barriers related to the infrastructure required to facilitate data Analytics and
hardware and software issues, including security, data storage issues, and the requirement
of a high bandwidth internet connection to support Cloud-based services, Organizational
structure, culture, top management support, and the lack of a strategy, and last but not the
least the study also revealed identified resource barriers relate to the financial constraints
an SME encounters. For instance, the cost of procuring Big Data Analytics technology, the
resources required to implement and use it, and the time an SME needs to dedicate to the
project. This theme includes the barriers-coded business cases, finance, and resources. In a
survey of 40 SMEs, they may already have knowledge but their infrastructure is inadequate
to facilitate data analytics. Alharthi, et al. (2017) state that data analytics infrastructure can
be designed using low-cost commodity hardware, however, the servers and storage
systems are connected via ethernet or fiber networks, therefore the network infrastructure
must support the high throughput and bandwidth associated with the large volumes of data
transmitted between servers.
Regulatory Issues
Regulatory issues refer to the legal, security, privacy concerns of collecting data. Lee
suggests that: ‘protecting privacy is counterproductive to both firms and customers, as big
data is a key to enhanced service quality and cost reduction. Therefore, firms and customers
need to strike a balance between the use of personal data for services and privacy concerns.
To comply with relevant legislation including the EU’s General Data Protection Regulation,
SMEs are required to have sufficient knowledge about legislation and regulation to ensure
their processes cater to these requirements. Coleman et al. state that the EU Handbook on
European data protection law contains over 200 pages and that SMEs may not be able to
afford to acquire support from lawyers to understand what the implications are for their
business. Data security will likely require a review if new Big Data technologies are
introduced into a business’, IT infrastructure. If an SME does not have sufficient security
mechanisms, confidential information could be transmitted or intercepted by unintended
parties, resulting in finance failures.
Cost of Adopting Big Data Analytics
Financial barriers highlight the investment required to adopt data analytics. SMEs are not
able to borrow large amounts of finance; therefore, they are cautious about investing in

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technologies beyond their business scope. Similarly, SMEs may not have a budget for IT or
data analytics investment. In addition to the cost factors associated with the generation,
storage, processing, analysis and interpretation of data analytics., there are also other costs
including cybersecurity and training. Lee highlights that a major issue of adopting data
analytics is investment justification, as executives may be concerned about committing to
a large investment to deliver tangible benefits which are outweighed by the tangible costs,
despite potentially large intangible benefits being delivered.
Technological Barriers
Big data technologies offer numerous opportunities and its potential is undeniable.
However, data scientists are facing different challenges when dealing with large data sets
to dig out knowledge from such mines of information (Iqbal, et al., 2018). Other barriers
include technological barriers which also refers to data scalability; data silos; infrastructure
readiness; lack of suitable software; and poor data quality. The complexity of data is the
challenge of managing data originating from a variety of sources, stored in a variety of
structured and unstructured formats which may include SMS, images, videos, audio files
and emails. Alharthi, et al., (2017) state that most organizations do not have a plan to
address this problem with many preferring to delete data rather than accommodating data
growth. Data scalability refers to the challenges of storing the large volumes of data
analytics as many organizations have to delete their data after a certain period to allow
newly created data to be stored. Arunachalam, Kumar, and Kawalek state that relational
databases offer limited data scalability and therefore suggest that technologies including
Hadoop, NoSQL, distributed file systems, parallel computing and Cloud Computing could
be implemented to accommodate data scalability. Data silos are isolated datasets without
links to other datasets, for example, data stored by individual departments of an
organization in separate information systems. Data quality measures how well a dataset
meets criteria for accuracy, completeness, validity, consistency, uniqueness, timeliness,
and fitness for purpose, and it is critical to all data governance initiatives within an
organization (IBM, 2024). Meaningful analytics require precise and high-quality data.
Inadequate or inconsistent data can result in erroneous conclusions and decisions.
Therefore, robust data integration solutions are necessary for integrating data from
multiple sources, including Enterprise Resource Planning (ERP), and Internet of Things
devices.
Strategies for Utilizing Big Data Analytics by SMEs
Small and Medium-sized Enterprises (SMEs) face various challenges in effectively utilizing
big data analytics for supply chain decision-making. However, numerous strategies and
best practices have emerged to help SMEs overcome these challenges and harness the
potential of big data analytics. This literature review explores these strategies, providing
insights into how SMEs can leverage big data analytics to enhance their supply chain
operations.

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Building Internal Data Analytics Capabilities
One of the key strategies for SMEs is to invest in building data analytics capabilities
internally. This involves hiring or training employees with the necessary skills in data
science, statistics, and programming to analyze large datasets and extract actionable
insights. By developing an in-house team of data analytics experts, SMEs can gain greater
control over their data analytics processes and tailor their analyses to meet specific business
needs. Moreover, SMEs can foster a data-driven culture within their organizations by
promoting data literacy and encouraging employees to use data-driven insights to inform
decision-making (McAfee & Brynjolfsson, 2012).
Partnerships and Collaborations
Another effective strategy for SMEs is to leverage external expertise through partnerships
or collaborations with data analytics firms or consultants. Outsourcing data analytics tasks
to specialized service providers can help SMEs overcome resource constraints and access
advanced analytics capabilities without the need for significant upfront investment in
technology and talent. Moreover, collaborating with external partners can provide SMEs
with access to domain-specific expertise and industry knowledge, enabling them to derive
greater value from their data analytics initiatives (Davenport & Harris, 2007). Furthermore,
SMEs can adopt a phased approach to big data analytics implementation, starting with pilot
projects or proof-of-concept initiatives to test the feasibility and potential benefits of big
data analytics in specific areas of their supply chain operations. By focusing on targeted use
cases or business processes, SMEs can minimize risks and demonstrate the value of big data
analytics to key stakeholders within the organization. In addition, starting small allows
SMEs to iterate and refine their analytics strategies over time, gradually expanding the
scope and scale of their analytics initiatives as they gain confidence and experience
(Bhardwaj, 2022).
Adapting Previous Business Cases
Case studies and examples of successful implementation of big data analytics strategies can
provide valuable insights and inspiration for SMEs looking to embark on their own analytics
journey. For instance, companies like Amazon and Walmart have demonstrated how
leveraging big data analytics can drive significant improvements in inventory management,
demand forecasting, and supply chain optimization. By analyzing vast amounts of
customer data in real-time, these companies can identify trends and patterns, optimize
inventory levels, and personalize product recommendations to meet customer demand
more effectively (Davenport, 2014). Moreover, SMEs can learn from the experiences of
other businesses within their industry or peer networks that have successfully implemented
big data analytics initiatives. By sharing best practices, lessons learned, and practical
insights, SMEs can avoid common pitfalls and accelerate their analytics adoption journey.
Industry associations, trade groups, and professional networks can serve as valuable

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platforms for knowledge-sharing and collaboration among SMEs interested in leveraging
big data analytics for supply chain decision-making (McAfee & Brynjolfsson, 2012).
SMEs can employ various strategies and best practices to effectively utilize big data
analytics in supply chain decision making. By investing in internal capabilities, leveraging
external expertise, adopting a phased implementation approach, and drawing insights from
successful case studies and examples, SMEs can overcome challenges and unlock the
potential of big data analytics to drive innovation, improve operational efficiency, and gain
a competitive advantage in the marketplace.
Theoretical & Conceptual Frameworks
Informed by philosophical perspectives, the study incorporates three key theoretical
perspectives: the Technology Acceptance Model, Social Penetration Theory, and Diffusion
of Innovation Theory. These theories offer valuable frameworks for understanding the
dynamics of technology adoption, human behavior, and innovation diffusion within the
context of SMEs' engagement with data analytics. The Technology Acceptance Model was
developed by Fred Davis in 1989, this theory states that the Technology acceptance model
explains the determinants of information technology end user’s behavior towards
information technology cost. The study applied this model to investigate SMEs' behaviors
towards the adoption and utilization of data analytics in supply chain decision-making. By
examining how SMEs perceive the usefulness of data analytics technologies in enhancing
their supply chain decision-making processes, we aimed to gain insights into the factors
influencing their adoption decisions and behaviors. The diffusion of innovation theory seeks
to explain how and why new ideas and practices are adopted, with timelines potentially
spread out over long periods. The theory was established in 1962 by Everett Rogers but later
edited in 2003 by Rogers. The diffusion of innovation theory was important to our study as
we examined the process by which SMEs adopt and utilize data analytics in their supply
chain and inventory management practices. Specifically, we investigated how potential
adopters perceived the advantages and disadvantages of data analytics technologies and
how these perceptions influenced their decision-making processes. The Social Penetration
Theory was developed by Irwin Altman and Dalmas Taylor in 1973, with further elaboration
in 1981, this theory explains how human exchange forms relationships with a focus on
individual influences for sharing on social media. By applying the insights from the Social
Penetration Theory, we aimed to uncover strategies for SMEs to maintain trust and
credibility with their audience, allowing for meaningful communication and feedback
exchange. These theories offer valuable frameworks for understanding the dynamics of
technology adoption, human behavior, and innovation diffusion within the context of
SMEs' engagement with data analytics.
Based on the research objectives and the reviewed pertinent literature, the conceptual
framework in Figure 1 and hypotheses were developed for this study.

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Independent Variables Dependent Variables
Figure 1.
Source: Author, 2024
The research hypotheses below were proposed as temporary solutions to the study's
problem. In this case, the null hypothesis was that there was no significant positive
relationship between the dependent and independent variables. Conversely, the
alternative hypothesis posited a significant relationship between these variables.
Ho: Knowledge and skills have no significant effect on the utilization of big data analytics
in SMEs.
Ha: Knowledge and skills significantly affect the utilization of big data analytics in SMEs.
Ho: Technological factors do not significantly impact the utilization of big data analytics in
SMEs.
Ha: Technological factors significantly impact the utilization of big data analytics in SMEs.
Ho: Organizational culture does not significantly influence the adoption and utilization of
big data analytics in SMEs.
Ha: Organizational culture significantly influences the adoption and utilization of big data
analytics in SMEs.
Ho: The availability of financial and technological resources does not have a significant
impact on the utilization of big data analytics in SMEs.
Ha: The availability of financial and technological resources significantly impacts the
utilization of big data analytics in SMEs.
Research Methodology
The research was quantitative and employed a descriptive research design. According to
Siedlecki, (2020), descriptive research designs aim to describe individuals, situations, or
phenomena by studying them as they are in nature and providing answers to questions
about what, where, when, and how. The target population for this study comprises of small
and medium-sized enterprises (SMEs) registered with Patents and Companies Registration

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Agency (PACRA) operating within the city of Lusaka, Zambia. The target population
included SME owners, managers, and IT professionals from various industry sectors such as
manufacturing, agri-business, and retail and wholesale trade. This diversity ensured that
the findings were comprehensive and applicable across different types of businesses within
the SME sector. The selection of Lusaka as the study area is based on its significance as the
capital city and a major economic hub within the country. Self-administered structured
questionnaires were utilized to capture owners, managers, and IT personnel’s views of the
study variables presented in the conceptual framework and summarized in the
operationalization. The study utilized a structured survey questionnaire that contained
elements related to the research questions on a Likert scale.
Furthermore, a stratified sampling technique was used to determine the sample size for
both formal and informal SMEs. The population of SMEs was first categorized into formal
and informal sectors based on predetermined criteria such as registration status, size, and
legal structure. The sample size 278 was determined using Cochran’s formula using
PACRA’s estimated population of 1000 SMEs in Lusaka’s CBD. However, the number of
successful respondents for this primary data comprised 220 participants categorized as 68
Manufacturing, 103 Agribusiness, and 49 Retail and Wholesale Trade. Secondary data
sources utilized included previous research reports, journals, corporate websites, and
books.
Validity & Reliability test
To ascertain that the instruments are authentic, the Split Half reliability technique was
employed. Thus, the study items in the research instruments were split into two equal
halves. Thereafter, Cronbach alpha was used to correlate the two halves using Spearman’s
correlation coefficient (rho) to determine the reliability of the study instrument before
being deployed in the field. Content validity of the instruments was ensured by constructing
them within the strict confines of the study objectives as well as reviewed literature in order
to guarantee that that they measure what they ought to measure. Validity was also ensured
through the supervisor who reviewed the instrument before going in the field and the
research committee who are both experts in the area of data analytics and its utilization.
Results and Discussion
Figure 2 provides a breakdown of the job roles within a particular organization. The most
common position is IT Personnel, with 102 individuals (43.6%) holding this role. Following
closely are Owners and Managers, with 58 (24.8%) and 66 (28.2%) individuals respectively.
A much smaller proportion of the workforce is comprised of support staff such as Cleaners,
Receptionists, and Security Guards, each accounting for less than 1% of the total
employees. In addition, there are a few individuals in roles like Students, Employees,
General Workers, and Security Guards, each representing 0.4% of the workforce.

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Figure 2: respondents Description of roles of respondents at time of data collection
Source: Field Survey, 2024
Sector of Firm
The figure below shows a breakdown of the sectors in which the businesses operate. The
most prevalent sector is Agribusiness, with 103 businesses (47.5%) engaged in this industry.
Manufacturing comes in second with 68 businesses (31.3%), followed by Retail and
Wholesale with 49 businesses (22.6%). This indicates that the majority of the businesses in
the sample are involved in agriculture-related activities, followed by manufacturing and
trade.
Valid 217
Missing 19
N %
Manufacturing 66 28.0%
Agribusiness 103 42.8%
Retail and Wholesale 48 20.3%
Total 217
Figure 3: The sector the businesses owned belong to.
Source: Field Survey, 2024
Knowledge on Data Analytics
The figure below presents the distribution of respondents based on their level of expertise
in a particular skill or domain. The levels range from "None" to "Expert," representing
increasing proficiency. The majority of respondents, 83 individuals (36.2%), fall into the
"Intermediate" category, indicating a moderate level of expertise. A significant number, 70
0
5
10
15
20
25
30
35
40
45
Percent
1. Which of the following most accurately describes your current role
in your company?

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respondents (30.6%), have a "Basic" understanding. The "Advanced" level encompasses 37
individuals (16.2%), while the "Expert" level is the least populated, with only 8 respondents
(3.5%). This suggests that while many have a foundational understanding, a smaller
proportion possess advanced or expert-level knowledge.
Valid 224
Missing 12
N %
Advanced 33 14.0%
Basic 70 29.7%
Expert 8 3.4%
Intermediate 78 33.1%
None 35 14.8%
Total 224
Figure 4: Respondents knowledge on data analytics
Source: Field Survey, 2024
Type of Data Analytics Tools Used
This figure presents the distribution of respondents based on their preferred data analysis
and visualization tools. Excel is the most popular tool, with 84 respondents (38.4%)
choosing it as their preferred tool. Tableau follows closely with 68 respondents (31.1%).
Custom in-house tools are used by 73 respondents (33.3%). SQL and Python are also
popular, with 26 (11.9%) and 24 (11%) respondents, respectively. Power BI and R are used
by 21 (9.6%) and 25 (11.4%) respondents, respectively. Statista, Java, and other tools are
used by a very small number of respondents, each accounting for less than 1% of the total.
This suggests that Excel and custom in-house tools are the most widely used tools for data
analysis and visualization among the respondents. Examples of these tools are Microsoft
Power BI, Sisense, Superset, and others.
N %
Custom In-house Tools 48 20.3%
Excel 56 23.7%
Power BI 5 2.1%
Python 3 1.3%
R 8 3.4%
SQL 7 3.0%
Tableau 44 18.6%
Total 121
Figure 5: Data analytics tools used by the SME
Source: Field Survey, 2024

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Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the
new technology of big data analytics and are in danger of being left behind (Coleman, et al.,
2016). The data highlights a significant preference for familiar and accessible tools such as
Excel and custom in-house solutions, which dominate the landscape of data analysis and
visualization tools among SMEs. While these tools offer simplicity and affordability, the
underutilization of advanced and versatile tools such as Python, SQL, Power BI, and R
indicates a missed opportunity for SMEs to enhance their analytical capabilities and
improve decision-making. Python and R, are among the most powerful tools for advanced
data analytics, yet only 11% and 11.4% of respondents, respectively, reported using them.
These programming languages offer unparalleled flexibility for performing predictive
modeling, machine learning, and statistical analysis. For instance, Python has applications
in every stage of the analytics process, including data mining, data processing, and data
visualization which would enable users to efficiently gain insights from large data sets. For
SMEs, adopting Python or R could enable the development of tailored solutions for demand
forecasting, customer segmentation, and inventory optimization. On the other hand,
Power BI and Tableau are increasingly recognized for their intuitive interfaces and robust
visualization capabilities, yet their adoption is still relatively low compared to Excel. Power
BI integrates seamlessly with Microsoft products and allows for real-time data updates and
interactive dashboards, making it particularly advantageous for SMEs seeking to monitor
performance indicators. Tableau, although slightly more popular, remains underutilized
considering its ability to handle complex datasets and create highly customized
visualizations that facilitate accurate and strategic decision-making. With only 11.9% of
respondents utilizing SQL, its potential for database management and querying is vastly
underutilized. According to Kelley (2024) SQL is a powerful tool used in data analysis for
querying and manipulating data stored in relational databases. SQL provides SMEs with the
capability to access and analyze structured data stored in relational databases efficiently.
This can improve operational decision-making by enabling deeper insights into sales trends,
customer behavior, and supply chain logistics. While the adoption of these tools may
require specialized skills or additional resources, these tools often provide unique
functionalities that can unlock new opportunities for efficiency and accuracy in supply chain
decision-making among SMEs.
Impact of Data Analytics on Decision-Making
The figure below presents the distribution of perceived benefits of data analytics in supply
chain management, as reported by 227 respondents. The most frequently cited benefit is
"Better Forecasting Accuracy", with 108 respondents (47.6%) indicating this as a significant
improvement. This suggests that data analytics is particularly valuable in improving the
precision of demand forecasts. The next most common benefit is "Improved Efficiency",
cited by 87 respondents (38.3%). This indicates that data analytics can help streamline
supply chain processes and reduce operational costs. "Reduced Costs" is also a significant
benefit, cited by 86 respondents (37.9%). This suggests that data analytics can help identify

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cost-saving opportunities and optimize resource allocation. "Enhanced Supplier
Relationships" is cited by 64 respondents (28.2%). This indicates that data analytics can
help improve communication and collaboration with suppliers. "Improved Customer
Satisfaction" is the least frequently cited benefit, with only 43 respondents (18.9%)
indicating this as a significant improvement. This suggests that while data analytics can
have a positive impact on customer satisfaction, it may not be the primary driver of this
improvement.
N %
Better Forecasting Accuracy 57 24.2%
Enhanced Supplier Relationships 14 5.9%
Improved Customer Satisfaction 6 2.5%
Improved Efficiency 36 15.3%
Reduced Costs 36 15.3%
Total 149
Figure 6: How Data analytics impacts decision-making in a company
Source: Field Survey, 2024
One of the most significant benefits of data analytics is its ability to improve the accuracy
of demand forecasts. By analyzing historical sales data, market trends, and external factors,
organizations can develop more accurate forecasts. This enables them to optimize
inventory levels, production schedules, and resource allocation, reducing the risk of
stockouts and overstock. As can be seen throughout the findings and literature, it is data
analytics that helps organizations identify cost-saving opportunities throughout the supply
chain. Using data on transportation costs, inventory levels, and supplier performance,
businesses can negotiate better deals with suppliers, optimize transportation routes, and
reduce waste. Data analytics can help organizations evaluate supplier performance,
identify potential risks, and optimize supplier selection processes. The findings also show
that data analytics is used to improve supply chain efficiency. Data analytics can be used to
optimize transportation routes, identify bottlenecks, and improve the overall efficiency of
the supply chain. Using data on transportation costs, lead times, and carrier performance,
organizations can identify opportunities for mode shift, consolidation, and route
optimization. Data analytics can help organizations evaluate supplier performance, identify
potential risks, and optimize supplier selection processes. While the results show that a
small percentage of the respondents use data analytics to improve customer satisfaction it
is important to note that data analytics can be used to predict potential quality problems
and take proactive measures to prevent them. Therefore, data analytics can help
organizations understand customer preferences, identify potential opportunities, and
improve customer satisfaction.

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Knowledge and Skills of Data Analytics
SD D N A SA
Our employees possess adequate knowledge … 3% 55% 50% 93% 10%
Our team has a deep understanding of how … 3% 35% 80% 85% 10%
We regularly apply data-driven insights … 2% 35% 78% 85% 10%
The lack of data analytics skills in our …. <1% 30% 75% 85% 10%
We invest in training programs to … <1% 30% 70% 83% 8%
Figure 7: Knowledge and skills
Source: Field Survey, 2024
The figure above presents the responses to five statements about the use of data analytics
in supply chain management within a company.
i. Our employees possess adequate knowledge of data analytics to support supply chain
decision-making.
• While the majority of respondents agree with this statement, indicating that they
believe employees had sufficient knowledge in data analytics for supply chain
decisions, a considerable number were either neutral or against the statement.
ii. Our team has a deep understanding of how data analytics can optimize supply chain
processes.
• Similar to the first statement, most respondents agree with this statement,
suggesting that the team's understanding of data analytics' potential in supply
chain optimization is competent.
iii. We regularly apply data-driven insights in our supply chain decision-making processes.
• Again, most respondents agree with this statement, indicating that data-driven
insights are frequently used in their decision-making processes.
iv. The lack of data analytics skills in our company limits our ability to make informed supply
chain decisions.
• A significant portion of respondents agree with this statement, supporting the idea
that a lack of data analytics skills hinders informed decision-making in the supply
chain.
v. We invest in training programs to improve data analytics skills for supply chain
management.
• While a fair number of respondents agree with this statement, indicating some
investment in training, the majority either disagree or are neutral, suggesting that
investment in training might not be sufficient or consistent.
Several factors contribute to the observed levels of data analytics knowledge among SMEs
in Lusaka. Understanding these factors is crucial for identifying barriers to effective data
utilization and for formulating strategies that can enhance analytical capabilities across
these organizations.

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A primary factor influencing data analytics knowledge is the limited access to quality
education and training programs focused on this area. Many SMEs struggle to find
affordable training opportunities that equip their workforce with the necessary skills, which
can significantly hinder effective data utilization (Selamat et al., 2018). The lack of
accessible training resources not only limits skill development but also prevents
organizations from staying updated with emerging trends and technologies in data
analytics. This lack of expertise is critical as it could hinder the ability of these organizations
to implement effective data-driven decision-making strategies that are essential for
optimizing supply chain operations (Chen et al., 2012). These findings underscore the
urgent need for targeted educational initiatives aimed at enhancing data literacy among
SMEs. Additionally, resource constraints significantly impede SMEs' ability to adopt data
analytics effectively. Financial limitations often restrict investments in essential analytical
tools and software (Abrol & Gupta, 2023). Many SMEs prioritize immediate operational
needs over long-term technological investments; this short-term focus can stifle innovation
and growth within these enterprises.
Data quality and accessibility issues significantly affect the effectiveness of data analytics
in SMEs (McAfee & Brynjolfsson, 2012). Poor-quality data—characterized by inaccuracies
or incomplete datasets can severely limit insights derived from analysis efforts, leading
organizations to make decisions based on flawed information. Furthermore, challenges in
accessing relevant data from various sources can impede analytical processes; many SMEs
struggle with collecting, cleaning, and integrating data effectively due to limited resources
or expertise available within their teams (Proventa International, 2023). Without reliable
and accessible data streams feeding into their analytical frameworks, even advanced
analytical tools will yield limited results; this reality further discourages SMEs from pursuing
robust data-driven strategies as they may perceive them as ineffective or unmanageable.
Challenges of Utilizing Data Analytics in Supply Chain Decision-Making
Respondents were also requested to indicate the challenges they face in the utilization of
data analytics as the figure below shows.
SD D N A SA
We face challenges in accessing … 3% 30% 60% 90% 15%
The high costs of data analytics … 2% 29% 60% 90% 17%
There is resistance within the … 2% 25% 72% 90% 15%
Lack of training in data … 1% 25% 60% 90% 15%
Our company struggles to … 1% 30% 60% 85% 15%
Figure 8: Challenges of utilizing data analytics in supply chain decision-making
Source: Field Survey, 2024

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The figure above presents the responses to five statements about the challenges faced in
implementing data analytics in supply chain management within a company. Let's break
down the responses for each statement:
i. We face challenges in accessing accurate data for supply-chain decision-making.
• The majority of respondents agree with this statement, indicating that data quality
and accessibility are significant challenges in data-driven supply chain decision-
making.
ii. The high costs of data analytics tools prevent us from fully utilizing them in supply chain
management.
• A significant portion of respondents agree with this statement, suggesting that the
cost of data analytics tools is a barrier to their wider adoption.
iii. There is resistance within the organization to adopting data-driven supply chain
practices.
• A significant number of respondents agree with this statement, indicating that
there is resistance to change and a lack of understanding about the benefits of data-
driven approaches.
iv. Lack of training in data analytics is a significant barrier to effective supply chain decision-
making.
• The majority of respondents agree with this statement, highlighting the need for
training and education in data analytics to improve decision-making capabilities.
v. Our company struggles to integrate data analytics tools with existing supply chain
processes.
• A significant portion of respondents agree with this statement, indicating that
integrating data analytics tools into existing processes is a challenge.
Despite the numerous benefits of data analytics, SMEs often encounter several challenges
in effectively utilizing data analytics in their supply chain operations. These challenges can
be attributed to several key factors such as data quality and accessibility, lack of skilled
personnel, cost constraints, and cultural and organizational barriers. Drawing from existing
literature, particularly the works of Asiri, et al. (2024) and Lutfi, et al. (2022) several insights
can be gleaned regarding the challenges of utilizing data analytics in supply chain
management.
Data quality is a fundamental aspect of effective data analytics. The presence of
inconsistent data, missing values, and errors can lead to inaccurate insights and ultimately
result in misleading conclusions that affect strategic decisions (McAfee & Brynjolfsson,
2012). SMEs often struggle to maintain high data quality due to various reasons, such as
reliance on manual data entry processes, which are prone to human error, outdated
systems that cannot handle modern data demands, and a lack of established data
governance practices that ensure consistency and accuracy across datasets. Furthermore,
accessing accurate and timely data from diverse sources can be particularly challenging for
SMEs with limited resources and inadequate IT infrastructure. These challenges necessitate

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the implementation of robust data quality management practices, including systematic
data cleaning, validation processes, and standardization efforts to ensure uniformity across
datasets. Additionally, integrating data analytics tools with existing supply chain systems
presents complexities that can impede successful implementation efforts within SMEs.
Issues such as compatibility between different software systems, problems related to data
synchronization across platforms, and inherent resistance to change from employees can
all hinder the effective deployment of these solutions (Selamat et al., 2018). SMEs can
mitigate these challenges by investing in advanced data integration tools and platforms
that can further streamline data collection processes and enhance overall data accessibility,
allowing SMEs to leverage their data more effectively for decision-making. However, the
financial implications associated with acquiring and implementing data analytics tools
represent a significant barrier for many SMEs, particularly those operating with constrained
budgets. Advanced analytics tools—such as those used for data mining, machine learning
applications, and predictive analytics software—can be prohibitively expensive and often
require specialized expertise for effective operation (Abrol & Gupta, 2023). This creates a
challenging environment where smaller firms may feel unable to compete with larger
organizations that have the resources necessary to invest in such technologies.
Organizational resistance to change is also a common challenge encountered during the
adoption of data analytics within SMEs. Employees may exhibit hesitance towards
embracing new technologies and processes due to fears of job displacement or concerns
about increased workloads associated with new systems (Udeh et al., 2024). This resistance
can significantly slow down the implementation of data-driven initiatives and hinder the
potential benefits that these technologies could bring. To effectively overcome this
resistance, it is essential for management to communicate the benefits of adopting data
analytics clearly and persuasively throughout the organization. Top management’s support
can result in the vision of its effective perceived ease of use and perceived usefulness in the
range of activities (Asiri, Al-Somali, & Maghrabi, 2024). Leaders must champion data-driven
practices by setting an example and communicating the importance of data in achieving
organizational goals. Additionally, providing employees with the necessary training
ensures they feel confident and capable of using data in their roles. Resistance often stems
from a lack of knowledge and skepticism about new technology. Suffice it to say that top-
management support can bring about technology learning and diffusion throughout the
organization, and thus plays a key role in its various adoption stages (Lutfi, et al., 2022).
Finally, a significant shortage of skilled data analysts combined with limited training
opportunities can severely restrict the effectiveness of data analytics initiatives within
SMEs. Many organizations may lack personnel with the necessary expertise required to
collect, clean, analyze, and interpret complex datasets accurately (Yoshikuni et al., 2023).
This deficiency can lead to poor-quality insights that do not adequately inform decision-
making processes, resulting in suboptimal outcomes for the business. Providing
comprehensive training programs can help employees develop the necessary skills to utilize

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data-driven insights effectively while also alleviating fears related to job security by
emphasizing how these tools can enhance rather than replace their roles.
Conclusion
This study has explored the utilization of data analytics in supply chain decision-making
within SMEs in Lusaka. Through a comprehensive literature review, empirical research, and
in-depth analysis, the study has shed light on the current state of data analytics adoption,
the challenges faced by SMEs, and the potential benefits that can be derived from its
implementation. The study revealed that several intertwined factors contribute
significantly to the lack of big data analytics use in supply chain decision-making among
SMEs in Lusaka. Firstly, while many SMEs acknowledged adequate employee knowledge
and team understanding of how data analytics can optimize supply chain processes, gaps
in expertise and uneven distribution of skills remain evident. The findings also suggested a
significant gap in data analytics knowledge and application within the company's supply
chain management. Despite some investment in training, the majority of SMEs believe that
employees lack sufficient knowledge and understanding of data analytics. This lack of
expertise limits the company's ability to effectively utilize data-driven insights in decision-
making processes, hindering the optimization of supply chain operations. Additionally, the
findings provide valuable insights into the extent of data analytics utilization among SMEs
in supply chain decision-making. While many SMEs possess foundational knowledge of
data analytics tools, the low proportion of those with advanced or expert-level proficiency
accentuates a need for skill enhancement. The widespread use of accessible tools such as
Excel and custom in-house solutions reflects a reliance on familiar platforms, yet the limited
adoption of advanced tools like Python, Power BI, and R highlights untapped potential for
leveraging sophisticated analytics capabilities. Despite this, the reported benefits—such as
better forecasting accuracy, improved efficiency, and cost reductions demonstrate the
positive impact that data analytics has on supply chain operations. Predominantly applied
in supplier selection, demand forecasting, and inventory management, data analytics is
driving optimization in key areas. Overall, while progress has been made, gaps in skills, tool
sophistication, and awareness remain, emphasizing the need for targeted training,
investment in advanced tools, and organizational commitment to enhance the strategic
utilization of data analytics in supply chain decision-making. Finally, the findings highlight
key challenges faced by SMEs in utilizing data analytics for supply chain management. A
significant number of SMEs reported difficulties in accessing accurate data, emphasizing
data quality and accessibility as major obstacles to effective decision-making. High costs of
data analytics tools also emerged as a substantial barrier, limiting their adoption and
utilization across organizations. Resistance to adopting data-driven practices within
organizations further complicates efforts, emphasizing a need for cultural shifts and greater
awareness of the benefits of analytics. Additionally, the lack of data analytics skills and
training in data analytics were identified as a critical impediment, reflecting a pressing need
for educational initiatives to build analytical competencies. Finally, the struggle to integrate

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data analytics tools with existing processes highlights operational and technological
hurdles that SMEs must overcome. However, in fostering the adoption of data analytics
among SMEs, government policies play a pivotal role in shaping an enabling environment
for technological advancement. According to (Mahardhani, 2023) public policy must handle
several key challenges, which are of utmost importance such as creating a stable
environment and ensuring legal certainty for industry players. Clear and progressive
regulations can help attract investment, encourage research and technology development,
and increase the level of trust of the parties involved. Additionally, the government can
provide financial incentives and support for companies or individuals who invest in
sustainable technology research and development. This could be in the form of tax
exemptions, research grants, or specialized funding for innovative projects. Addressing
these challenges through favorable government policies, investments in affordable data
analytics tools, comprehensive training programs, and organizational change can
significantly enhance the adoption and impact of data-driven decision-making in SMEs. By
overcoming obstacles related to data quality, technical expertise, cost, and cultural barriers,
SMEs can unlock the full potential of data analytics to improve forecasting accuracy,
optimize inventory management, streamline logistics, and strengthen supplier
relationships. As a result, SMEs can achieve greater efficiency, reduce costs, and enhance
their overall competitiveness. By embracing data analytics, SMEs can contribute to
Zambia's economic growth and social development by creating jobs, stimulating
innovation, and improving the quality of goods and services.
Recommendations
Based on the findings of this research, the following recommendations are proposed to
enhance the utilization of data analytics in supply chain management among SMEs in
Lusaka:
For SMEs:
• Invest in Data Infrastructure: Develop robust data infrastructure, including data
warehousing and data integration tools, to ensure data quality, accessibility, and
security.
• Prioritize Data Analytics Training: Invest in training programs to equip employees
with the necessary skills to collect, clean, analyze, and interpret data.
• Foster a Data-Driven Culture: Encourage a culture of data-driven decision-making
and innovation within the organization.
• Collaborate with Technology Providers: Partner with technology providers to
access affordable and user-friendly data analytics tools.
• Start Small and Scale Gradually: Begin with small-scale data analytics projects to
gain experience and build momentum.

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• Leverage External Support: Seek support from industry associations, government
agencies, and consulting firms to access expertise and resources.
For Policymakers and Regulators:
• Create a Supportive Policy Environment: Develop policies that encourage the
adoption of data analytics by SMEs, such as tax incentives, grants, and subsidies.
• Promote Data Literacy and Skills Development: Invest in education and training
programs to improve data literacy and data analytics skills among the workforce.
• Facilitate Data Sharing and Collaboration: Promote data sharing and collaboration
between SMEs, government agencies, and industry associations to improve data
quality and accessibility.
• Support the Development of Data Infrastructure: Invest in infrastructure
development, such as high-speed internet connectivity, to facilitate data-driven
decision-making.
Limitations of the Study
The study covered three sectors in Lusaka with a thorough primary data collection which
might have benefited from a broader sectorial coverage, considering variations in data
analytics utilization across different sectors. However, this was not done due to limited time
and budget as the researcher self-funded the research.
Additionally, during the research period, some key stakeholders in SMEs were unavailable
or acting in temporary roles, which posed challenges in securing accurate responses. This
limitation may have impacted the accuracy of the data collected, as some respondents were
unable to provide comprehensive insights into their organizations’ use of data analytics in
supply chain management.
Further Research
Future research should expand its scope to encompass a broader range of sectors, to
capture a more comprehensive understanding of data analytics utilization and outcomes.
Additionally, in-depth exploration of specific sectors may reveal unique challenges and
opportunities.
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