Intelligent Knowledge Management for Enhancing Sustainable Food

Systems: The Case of Sweden

Azadeh Sarkheyli

School of Information Technology, Halmstad University, Halmstad, Sweden

Keywords: AI, Intelligent Knowledge Management, Sustainable Food System, Food Supply Chain,

Knowledge-Based Theory.

Abstract: Intelligent Knowledge Management (IKM) aims to establish intelligent integration of the food system to

capture, organize, analyze, and utilize information and knowledge that promotes sustainable food production.

With the growing importance of sustainable food systems, understanding consumer behavior, customer needs,

food preferences, producer demands, and local regulations is necessary. However, integration challenges

within the Swedish food system create significant obstacles. Inappropriate Knowledge Management systems,

system complexity, dynamic environments, inability to learn from and reuse data, information overload, and

insufficient data collection and analysis contribute to these challenges. This study uses a case study approach

and literature review to collect and analyze data. The proposed solution is an IKM conceptual model based

on the knowledge-based theory of the firm, leveraging AI-powered techniques to manage and analyze large

datasets from various stakeholders in the food supply chain. This model enhances forecasting and planning

capabilities, improving decision-making processes. Future research should further develop the IKM system

to achieve the potential results outlined in this paper.

1 INTRODUCTION

The modern food supply chain is complex and poses

significant challenges, particularly regarding

sustainability. It is essential to integrate and manage

knowledge within these systems to support

sustainable food production and consumption

(Touboulic & Walker, 2015; Mensah et al., 2024).

With the global population increasing and

environmental concerns becoming more prominent,

there is an urgent need for efficient and sustainable

food systems. This urgency is particularly evident in

Sweden, where sustainability is a top national

priority, and the food sector is essential to the

economy and society.

Intelligent Knowledge Management (IKM) seeks

to tackle these challenges by establishing a

comprehensive system that captures, organizes,

analyzes, and utilizes information and knowledge

throughout the food supply chain. By making use of

advanced technologies like Artificial Intelligence

(AI) and Machine Learning (ML), IKM systems can

offer actionable insights that improve decision-

https://orcid.org/0000-0002-5390-7509

making, streamline processes, and encourage

innovation (Mena et al., 2014; Jarrahi et al., 2023).

In Sweden, incorporating sustainable practices

into the food supply chain is met with significant

barriers, including the system's complexity,

constantly changing environments, information

overload, and inadequate data management (Garnett,

2013). These challenges underscore the need for a

robust IKM system to address these issues and

promote a more sustainable food system.

This paper suggests an IKM conceptual model

based on the Knowledge-Based Theory (KBT)

tailored to Sweden's sustainable food supply chain.

The model utilizes AI-powered techniques to manage

and analyze extensive datasets from various

stakeholders, thereby enhancing forecasting and

planning capabilities and improving decision-making

processes. Through a combination of a case study and

literature reviews, this research aims to demonstrate

the potential benefits of implementing such a system

and provide a roadmap for future sustainable food

system management developments.

514

Sarkheyli, A.

Intelligent Knowledge Management for Enhancing Sustainable Food Systems: The Case of Sweden.

DOI: 10.5220/0013817600004000

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2025) - Volume 2: KEOD and KMIS, pages

514-521

ISBN: 978-989-758-769-6; ISSN: 2184-3228

The paper is structured as follows: Firstly, it

describes KBT and knowledge management for

sustainable food based on a thorough literature review

as the theoretical foundation. It then investigates the

description and analysis of sustainable food systems

in Sweden. Subsequently, it presents a goal and

conceptual models based on KBT and AI. Lastly, the

paper outlines future research and presents the

conclusion.

2 KNOWLEDGE-BASED

THEORY (KBT) OF THE FIRM

KBT posits that knowledge is the most strategically

significant resource of a firm. Unlike traditional

resources that can be easily replicated, knowledge's

unique, tacit, and complex nature provides a

sustainable competitive advantage (Grant, 1996).

This theory emphasizes the role of a firm's ability to

create, transfer, and utilize knowledge to achieve

superior performance.

According to KBT, firms exist because they are

better at integrating and coordinating knowledge than

markets (Kogut & Zander, 1992). Knowledge within

a firm is embedded in individuals and organizational

routines, processes, and cultures, making it difficult

for competitors to imitate (Nonaka & Takeuchi,

1995). This inimitability is a key source of

competitive advantage, as it allows firms to

differentiate themselves and protect their core

competencies.

The knowledge-based view also highlights the

importance of dynamic capabilities, which are the

firm's abilities to integrate, build, and reconfigure

internal and external competencies to address rapidly

changing environments (Teece et al., 1997). Firms

that continually innovate and adapt their knowledge

base are more likely to succeed in the long run.

Moreover, the theory suggests that firms should

invest in mechanisms to facilitate knowledge sharing

and creation. This includes fostering a collaborative

culture, implementing knowledge management

systems, and encouraging continuous learning and

development (Alavi & Leidner, 2001). Effective

knowledge management can improve decision-

making, innovation, and operational efficiency

(Heisig, 2024).

Critics of KBT argue that it may overemphasize

the role of knowledge and underappreciate other

critical resources, such as physical assets and

financial capital (Spender, 1996). Therefore, KBT has

been selected in this research, which has significantly

influenced strategic management literature and

practice, particularly in industries where knowledge

and innovation are paramount.

3 KNOWLEDGE MANAGEMENT

FOR SUSTAINABLE FOOD

SYSTEMS

Knowledge management is a strategic method that

systematically gathers, organizes, shares, and

analyzes knowledge within an organization (Heisig,

2024). It utilizes this knowledge to improve

performance, stimulate innovation, and accomplish

strategic objectives. Knowledge management can be

fundamental in sustainable food systems in tackling

the intricate challenges associated with sustainability,

efficiency, and resilience.

Knowledge management involves various

procedures and technologies crafted to tap into an

organization's expertise and information. These

procedures encompass knowledge generation,

storage, retrieval, and distribution. Efficient

knowledge management guarantees that valuable

information is accessible to the appropriate

individuals at the right time, facilitating informed

decision-making and continual improvement

(Nonaka & Takeuchi, 1995).

Knowledge management is essential for

integrating diverse data sources and stakeholder

viewpoints in sustainable food systems. This

integration is vital for comprehending and overseeing

food production, distribution, and consumption

dynamics. For example, knowledge management

systems can combine information from farmers,

suppliers, distributors, consumers, and regulators to

comprehensively view the food supply chain

(Garnett, 2013). Such a comprehensive perspective is

critical for pinpointing inefficiencies, minimizing

waste, and promoting sustainable practices.

Despite its potential benefits, implementing

knowledge management in sustainable food systems

presents several challenges. These include data silos,

resistance to change, absence of standardized

processes, and the necessity for cultural shifts

towards knowledge sharing (Davenport & Prusak,

1998). Overcoming these challenges necessitates a

strategic approach that involves strong leadership, a

clear knowledge management strategy, and ongoing

stakeholder training and support.

Incorporating advanced technologies like AI and

ML has significantly bolstered knowledge

management capabilities. These technologies enable

Intelligent Knowledge Management for Enhancing Sustainable Food Systems: The Case of Sweden

515

the analysis of large datasets, the identification of

patterns, and the generation of predictive insights. AI

and ML can automate the extraction of knowledge

from vast amounts of unstructured data, making it

easier to derive actionable insights (Becerra-

Fernandez & Sabherwal, 2014). For instance, AI-

powered knowledge management systems in the food

supply chain can forecast demand.

Knowledge management is a pivotal component

of sustainable food systems, offering tools and

frameworks to manage the complexity and dynamics

of the food supply chain. By leveraging advanced

technologies and fostering a culture of knowledge

sharing, knowledge management can substantially

contribute to sustainability goals. Future research and

practical applications should address implementation

challenges and further enhance knowledge

management capabilities to support the evolving

needs of sustainable food systems.

4 SUSTAINABLE FOOD

SYSTEMS IN SWEDEN

Sustainability is a top priority in Sweden's efforts to

promote environmental responsibility, economic

viability, and social well-being. In collaboration with

various stakeholders, the Swedish government has

introduced several initiatives to encourage

sustainability in the food industry. This part looks at

the main components, obstacles, and advancements in

sustainable food systems in Sweden.

Sweden has consistently prioritized sustainability,

as demonstrated by its national policies and

regulatory frameworks. The Swedish Food Strategy,

introduced in 2017, outlines the government's vision

for a sustainable food system that considers

ecological, economic, and social aspects. The strategy

emphasizes the importance of local food production,

reduced environmental impact, and improved food

security (Swedish Government, 2017).

In addition, Sweden's Environmental Objectives

system establishes specific targets for reducing

greenhouse gas emissions, enhancing biodiversity,

and minimizing the use of harmful chemicals in

agriculture. These goals are crucial for establishing a

sustainable food system that aligns with the

overarching objectives of the European Green Deal

(Naturvårdsverket, 2020).

Collaboration among various stakeholders,

including government agencies, farmers, food

producers, retailers, and consumers, is essential for

advancing sustainable food systems in Sweden.

Organizations such as the Swedish Board of

Agriculture (Jordbruksverket) and the Swedish

Environmental Protection Agency

(Naturvårdsverket) play pivotal roles in coordinating

efforts and supporting sustainable practices.

Furthermore, initiatives such as the "From Farm

to Fork" strategy underscore the significance of a

comprehensive approach involving all players in the

food supply chain. This strategy aims to establish a

more sustainable and resilient food system by

promoting sustainable farming practices, minimizing

food waste, and encouraging healthy and sustainable

diets (European Commission, 2020).

Technological innovation and research drive

sustainability in Sweden's food systems. Advances in

precision agriculture, biotechnology, and

digitalization have enabled more efficient and

sustainable farming practices. For example, using

drones and sensors for precision farming helps

optimize resource utilization, reduce environmental

impact, and enhance crop yields (Swedish University

of Agricultural Sciences, 2021).

Research institutions and universities in Sweden,

such as the Swedish University of Agricultural

Sciences (SLU), are actively involved in studying and

advocating for sustainable food production methods.

Their research contributes to developing new

technologies and practices that support sustainable

agriculture and food systems.

Despite significant progress, Sweden still faces

several challenges in establishing a sustainable food

system. These challenges include dealing with the

impact of climate change, finding the right balance

between productivity and sustainability, and ensuring

that everyone has fair access to sustainable food. The

integration challenges within Sweden's sustainable

food system have proven to be a significant obstacle.

One contributing factor to this challenge might be the

inappropriate Knowledge Management systems.

Other factors include the system's complexity and

large scale, dynamic environment, the inability to

learn from and reuse data, information overload, and

inadequate data collection and analysis. As a result, a

problem model (refer to Figure 1) has been developed

to illustrate the causes and effects of the identified

problems in Sweden's sustainable food system. These

identified problems also present opportunities for

innovation and enhancement. By continuing to invest

in research and development, fostering collaboration

among stakeholders, and implementing robust

policies, Sweden can further enhance the

sustainability of its food systems and serve as a model

for other countries.

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Figure 1: Problem model.

5 INTELLIGENT KNOWLEDGE

MANAGEMENT (IKM)

IKM represents a transformative approach to

managing the complex and dynamic information

flows within organizations and systems, particularly

in the context of sustainable food systems. IKM

integrates advanced technologies, such as AI, ML,

and big data analytics, to enhance the capabilities of

traditional knowledge management systems (Jarrahi

et al., 2023). This integration aims to improve

decision-making processes' efficiency, accuracy, and

effectiveness by leveraging vast amounts of data and

converting it into actionable insights. IKM builds on

the foundational principles of KM but significantly

extends its scope and capabilities through intelligent

technologies. The core components of IKM are

defined in Table 1.

Table 1: Key components of IKM.

KeyComponents Define

Data Collection

and Integration

IKM systems are designed to collect data

from various sources, including

databases, social media, sensors, and

transactional records. Integrating these

diverse data sources allows for a

comprehensive view of the system being

managed (Bhimani & Willcocks, 2014).

Data Analysis and

Interpretation

Advanced analytics and AI algorithms

are employed to analyze the collected

data. Machine learning techniques can

identify patterns, predict trends, and

provide insights that would be difficult or

impossible to detect using traditional

methods (Chen et al., 2012).

Table 1: Key components of IKM (cont.).

Knowledge

Creation and

Storage

The insights generated through data

analysis are transformed into knowledge

and then stored in a centralized

knowledge repository. This repository

ensures that knowledge is accessible to

all relevant stakeholders and can be

reused and updated as new data becomes

available (Nonaka & Takeuchi, 1995).

Knowledge

Sharing and

Utilization

IKM emphasizes the importance of

sharing knowledge across organizational

boundaries. Collaboration tools, digital

platforms, and communication channels

facilitate the dissemination of

knowledge, enabling stakeholders to

make informed decisions quickly and

effectively (Alavi & Leidner, 2001).

Continuous

Learning and

Adaptation

One of the key features of IKM is its

ability to learn from new data and adapt

to changing conditions continuously.

Feedback loops ensure that the system

evolves and improves over time,

enhancing its resilience and

responsiveness (Teece, 2007).

In the context of sustainable food systems, IKM

plays a crucial role in addressing the challenges

associated with food production, distribution, and

consumption. Applying IKM can lead to more

sustainable practices by optimizing resource use,

reducing waste, and improving supply chain

efficiency (refer to Table 2).

Table 2: Applications of IKM in Sustainable Food Systems.

Applications Define

Enhanced

Decision-Making

By providing real-time insights and

predictive analytics, IKM helps

stakeholders make better decisions

regarding crop selection, irrigation

scheduling, pest management, and supply

chain logistics (Kamilaris, Kartakoullis,

& Prenafeta-Boldú, 2017).

Sustainable

Practices

IKM supports adopting sustainable

practices by identifying areas where

resource use can be minimized and

environmental impacts can be reduced.

For example, AI-driven models can

optimize fertilizer application to reduce

runoff and improve soil health (Wolfert et

al., 2017).

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517

able 2: Applications of IKM in Sustainable Food Systems

(cont.).

Improved

Collaboration

The knowledge-sharing capabilities of

IKM foster collaboration among farmers,

processors, distributors, and consumers.

This collaboration leads to more

integrated and efficient food systems that

respond better to market demands and

regulatory requirements (Verdouw,

Beulens, & Trienekens, 2013).

Consumer

Engagement

IKM enables better consumer

engagement by providing transparency

and traceability throughout the food

supply chain. Consumers can access

information about their food's origin,

quality, and sustainability, which can

influence their purchasing decisions and

promote sustainable consumption

(Kshetri, 2018).

IKM represents a significant advancement in how

organizations and systems manage information and

knowledge. By integrating advanced technologies

such as AI and ML, IKM enhances decision-making,

promotes sustainable practices, and fosters

collaboration across the food system. As

sustainability and resource management challenges

continue to grow, adopting IKM will be crucial in

creating resilient and efficient systems capable of

meeting the needs of a rapidly changing world.

6 IKM FOR SUSTAINABLE

FOOD SYSTEMS

According to the problem model of the previous

section, a goal model has been created to illustrate a

comprehensive framework aimed at achieving

improved sustainable food production and

consumption (refer to Figure 2). The model

integrates various components and processes,

highlighting the interplay between data collection,

ML, AI, and knowledge management. Below is a

detailed description of each component and its role

within the model.

- Improved Sustainable Food Production and

Consumption: This is the main goal of the model,

aiming to enhance the sustainability of food

production and consumption practices.

- Intelligent Integration of a Sustainable Food

System: This component focuses on creating a

cohesive and intelligent system that integrates

various sustainable practices across the food

production and consumption chain.

- Accurate Data Collection and Analysis: Accurate

and comprehensive data collection and analysis are

critical for informed decision-making. This

component ensures that all relevant data is gathered

and analyzed effectively to support sustainable

practices.

- Utilized Scenario Analysis and Knowledge

Management Using Adaptive AI-based Decision-

Making: This process involves using scenario

analysis and adaptive AI-based decision-making

tools to manage knowledge effectively. It ensures

that decision-making processes are adaptive and

informed by the latest data and scenarios.

- Utilized ML and Predictive Modeling: ML and

predictive modeling are employed to analyze

historical data and predict future trends and

outcomes. This helps in making proactive decisions

to enhance sustainability.

- Historical Data: Historical data provides a foundation

for analysis and modeling. It is utilized to understand

past trends and inform future strategies.

- Integration of AI: AI integration is critical for

automating processes and making intelligent

decisions. It supports various aspects of the model,

including data analysis, scenario planning, and

predictive modeling.

- Standardization: Standardization ensures that

processes and data are uniform across the system,

facilitating better integration and comparison.

- Training and Awareness: Continuous training and

awareness programs are necessary to ensure that all

stakeholders are informed about sustainable

practices and how to implement them effectively.

Furthermore, interconnections in the goal model

can be explained as follows:

- Data Collection and Analysis: Accurate data

collection and analysis feed into scenario analysis

and ML/predictive modeling.

- Scenario Analysis and ML: These processes use

historical data to create models and scenarios that

inform decision-making.

- Knowledge Management and AI Integration: AI-

based decision-making tools utilize scenario

analysis and ML insights to manage knowledge

dynamically.

- Standardization and Training: These support

systems ensure the framework operates smoothly

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by maintaining consistency and enhancing

stakeholder capabilities.

Figure 2: Goal model.

Therefore, according to the problem and goal

model and KBT, a conceptual model has been

proposed (refer to Figure 3) to illustrate the

conceptual model of IKM for a sustainable food

system.

Figure 3: Conceptual model of IKM for the sustainable food

system.

The model demonstrates how IKM integrates

various sectors within the food system to enhance

sustainability. Below is a detailed description of the

key components and their interactions, as depicted in

Figure 3.

- IKM: It is the core of the model, which serves as the

central hub for the collection, analysis, and

dissemination of data, information, and knowledge

(DIK). IKM enables effective decision-making and

coordination across the entire food system.

- Production: This component includes farmers and

producers responsible for growing and supplying

raw agricultural products. IKM facilitates the flow

of DIK to and from the production sector, providing

insights and feedback (Results) that can improve

farming practices and sustainability.

- Processing and Manufacturing: This sector involves

factories, food processing plants, and

manufacturing facilities that transform raw

agricultural products into consumable goods. IKM

supports this sector by providing DIK that enhances

processing efficiency and product quality while

ensuring sustainability.

- Distribution: Distribution encompasses logistics

companies, packaging suppliers, and equipment

manufacturers responsible for moving goods from

production and processing facilities to markets.

Through IKM, distribution processes are optimized,

ensuring timely and efficient delivery of goods with

minimal environmental impact.

- Markets: This component includes supermarkets,

farmers' markets, and online platforms where

consumers can purchase food products. IKM helps

markets by providing DIK that informs supply

chain decisions, market trends, and consumer

preferences.

- Consumption: This sector comprises consumers

who purchase and consume food products. It also

includes institutions such as restaurants, cafes,

schools, and hospitals. IKM enables a better

understanding of consumption patterns and

promotes sustainable consumption practices by

disseminating relevant information and feedback.

- Food and Organic Waste: This component

addresses the management of food waste and

organic materials, involving recycling and waste

reduction efforts. IKM plays a crucial role in

managing waste by providing DIK with waste

reduction strategies, recycling processes, and the

circular economy.

Surrounding the central and primary components

are various external entities that interact with and

influence the sustainable food system:

- NGOs, Media, and Communication Channels: They

play a role in disseminating information, raising

awareness, and advocating for sustainable practices.

Intelligent Knowledge Management for Enhancing Sustainable Food Systems: The Case of Sweden

519

- International Organizations: These organizations

provide guidelines, support, and collaboration

opportunities for global sustainability efforts.

- Government Agencies: They regulate, support, and

implement policies that promote sustainability in

the food system.

- Universities and Research Institutes: These entities

contribute to the knowledge base by conducting

research and providing innovations.

- Banks and Investment Firms: Financial institutions

support sustainable practices through funding and

investments.

The bidirectional flow of DIK and Results marks

the interaction between IKM and each component.

This continuous exchange ensures that each sector

receives the necessary knowledge and feedback to

improve sustainability practices. The model

illustrates a holistic approach where all sectors and

external entities are interconnected, emphasizing the

importance of collaboration and knowledge sharing

in achieving a sustainable food system.

7 FURTHER RESEARCH

This research can present numerous avenues for

future research, particularly in Sweden. While this

study has laid the groundwork, several areas warrant

further investigation to deepen our understanding and

improve the implementation of IKM in sustainable

food systems, as described below.

Before designing the IKM system, conducting a

needs analysis based on the proposed models in this

paper is important. This should include examining the

problem, goal, and conceptual models. By doing so,

we can ensure that the system meets the requirements

of all stakeholders and explores various options. To

achieve better results, it may be beneficial to focus on

one or two counties in Sweden, such as Dalarna and

Halland, and then generalize the findings to the entire

country.

Future research should focus on longitudinal

studies that track the implementation of IKM in

Sweden's food system over extended periods. These

studies can provide valuable insights into the long-

term impacts of IKM on sustainability outcomes,

allowing researchers to identify trends, challenges,

and best practices that evolve. Longitudinal data will

critically assess the effectiveness and adaptability of

various IKM strategies to changing environmental

and economic conditions.

Comparative studies between Sweden and other

countries with similar or different food system

structures can yield significant findings. By

comparing the effectiveness of IKM in diverse

contexts, researchers can identify universal principles

and context-specific factors that influence the success

of IKM initiatives. This comparative approach can

help develop tailored strategies for cultural,

economic, and regulatory differences.

The rapid advancement of technologies such as

blockchain, the Internet of Things (IoT), and

advanced analytics offers new opportunities for

enhancing IKM. Future research should investigate

how these emerging technologies can be integrated

into IKM frameworks to improve data accuracy,

traceability, and decision-making processes. Pilot

projects and case studies exploring these technologies

can provide practical insights into their potential

benefits and limitations. Understanding the dynamics

of stakeholder engagement is necessary for

implementing IKM successfully.

Future studies should examine the roles and

perspectives of various stakeholders, including

farmers, processors, distributors, consumers, and

policymakers, in the IKM process. Research should

also explore effective collaboration models that foster

active participation and knowledge sharing among

stakeholders, leading to more inclusive and resilient

food systems. While environmental sustainability is a

primary focus, the economic and social impacts of

IKM on the food system should not be overlooked.

Future research should assess how IKM influences

the economic viability of food enterprises, job

creation, and social equity within the food system.

Understanding these impacts can help design policies

and interventions that promote holistic sustainability,

balancing ecological, economic, and social goals.

Research should focus on how policies affect the

adoption of IKM in food systems and identify ways

to improve them. Additionally, it should explore the

impact of knowledge dissemination on consumer

behavior and effective strategies to enhance

awareness of sustainable consumption.

8 CONCLUSIONS

IKM aims to integrate the food system intelligently to

promote sustainable food production. The Swedish

food system challenges include inappropriate

knowledge management systems, system complexity,

dynamic environments, and information overload.

The proposed IKM conceptual model, based on

KBT and leveraging AI-powered techniques, aims to

manage and analyze large datasets from various

stakeholders in the food supply chain. This model

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enhances forecasting, planning capabilities, and

decision-making processes, ultimately promoting

sustainable food production and consumption. The

model's holistic approach integrates intelligent

systems, AI, accurate data analysis, and continuous

learning to facilitate proactive decisions that enhance

sustainability. The model underscores the pivotal role

of IKM in integrating and optimizing various sectors

within the food system. Future research aims to

advance the understanding and application of

Intelligent Knowledge Management in enhancing

sustainable food systems. By addressing these areas,

researchers can contribute to developing adaptive and

inclusive food systems to meet the sustainability

challenges.

In conclusion, integrating IKM into the Swedish

food system has great potential for promoting

sustainability. With advanced technologies, the

proposed IKM model can facilitate informed

decision-making and efficient management across the

food supply chain.

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