Published at : 10 Jul 2024
Volume : IJtech
Vol 15, No 4 (2024)
DOI : https://doi.org/10.14716/ijtech.v15i4.6635
Taha Karasu | Department of Civil Engineering, Faculty of Technology, University of Oulu, P.O. Box 8000, FI-90014, Finland |
Zulkarnain | Department of Industrial Engineering, Faculty of Engineering, Universitas Indonesia, Kampus Baru UI, Depok 16424, Indonesia |
Pekka Leviakangas | Department of Civil Engineering, Faculty of Technology, University of Oulu, P.O. Box 8000, FI-90014, Finland |
The paper proposes a process to translate systemic grand challenges of
agricultural supply chain into company specific operationalization actions. The
objectives of this study are to elucidate the requisite stages of the process,
delineate the specific aspects addressed by each step, and present potential
methodologies employed throughout the process. The approach adopted in this
paper is inherently constructive, employing a mixed methodology that integrates
various methods and tools. Furthermore, 90 challenges of agricultural supply
chain are delineated and structured around 6 themes. The strength of evidence
of the challenges is then analyzed with a model that takes into account the
number and types of references in which the challenges are mentioned and found,
respectively. The study identified 21 challenges as “evident” and proceeded to
the next step for quality function deployment. Finally, operationalization
actions, including cooperating with other stakeholders of the supply chain,
regulators, and farmers were determined to tackle critical first mile
challenges for the case company. The paper makes a distinctive contribution by
presenting a comprehensive framework for translating systemic agricultural
supply chain challenges into actionable steps, uniquely addressing the
intersection of intricate challenges and sustainability imperatives. This novel
approach does not only advance the understanding of operationalization but also
underscores the vital role of sustainability in navigating the complexities of
modern agricultural supply chains.
Agriculture; First mile; Supply Chain; Sustainability; Quality function deployment
Climate change, biodiversity loss, deforestation,
agricultural land degradation, and declining soil quality due to compaction are
significant challenges to major production systems (Hussain et al., 2023; Karasu, Hussain, and Leviäkangas, 2023a;
FAO, 2017). These challenges
affect various stages of the agricultural supply chain (ASC), with
inefficiencies leading to the global loss of one-third of agricultural produce,
mainly at the first and last mile stages
Despite the pivotal influence of first mile activities on subsequent ASC stages, research into agricultural logistics predominantly concentrates on the last mile ASC (Karasu, Hussain, and Leviäkangas, 2023a; Lingjuan, Linhong, and Menghan, 2018). Therefore, this study, aims to determine the challenges encountered in the first mile of ASC. Dasgupta, Kanchan, and Kundu (2019) defined first mile as “essentially the leg of fulfillment cycle where products are picked up from sellers and are connected to the sortation centers to facilitate the further downstream connections to deliver the product on time to the customers.” The challenges are vast and mostly responded in an incremental manner, i.e., solving only one or few problems at a time instead of one-time systemic, or radical innovations.
The corporate sector played a crucial role in
addressing these challenges, aiming to ensure that products and services
contributed positively to the solution rather than worsening the issues
The European Union (EU) acknowledged the significant
role of the corporate sector in transitioning to a green economy, as proven by the
Green Deal initiative
The translation of the challenges into actionable
steps remained unclear due to competitive pressures which focused on meeting customer
requirements rather than addressing broader challenges. Several existing labeling
and certification systems, such as International Organization for
Standardization (ISO) standards, namely ISO 14025, ISO 1406, and ISO 14027,
often lack transparency. These systems
often assess a limited scope of parameters, raising concerns among
organizations, including the
Translating enormous challenges into
operationalization actions (OAs) poses a complex task
In recent agricultural logistics research,
technology adoption and sustainability have gained significant attention
This study addressed the following
research questions.
RQ1.
What are the first mile challenges identified in previous research and
references focusing on the agricultural supply chain, and how can these
challenges be categorized into clusters?
RQ2. How can the evidence level of first
mile challenges be evaluated, and what challenges are particularly evident?
RQ3. Which of the first mile challenges
relate the most to the selected use case, i.e., agri-machinery case company?
RQ4. How can the first mile challenges be
operationalized?
To address RQ1, this study identified and
clustered first-mile ASC challenges from existing literature, as discussed in Subsection
3.1. For RQ2, a method to rank and prioritize the evidence of challenges
was developed, as stated in Subsection 3.2. To address RQ3,
semi-structured interviews and surveys were conducted with a company specialized
in agri-machinery. The insights gathered from these interactions informed the
development of the QFD framework in Subsection 3.3. Finally, the QFD
framework was implemented in an agri-machinery company to explore RQ4, as
stated in the same subsection. This systematic process enabled the
formulation of concrete plans for agri-machinery companies to reduce first-mile
challenges effectively.
The proposed construct described
four significant steps, namely Identification (I), Structuring (S),
Prioritization, and weighing (P), including Operationalization (O),
collectively referred to as ISPO in separate sections of the research, as shown
in Figure 1. ISPO aimed to address challenges systematically using methods and
tools described in subsequent subsections. It acts as a stair-like research
process, refining systemic challenges into structured solutions and operational
actions, starting upstream rather than following traditional models
Figure
1
Overview
of the research process for transforming agricultural supply chain challenges
into operational strategies
2.1. Identification and Structuring of
Challenges (I and S)
The scientific literature
review protocol (Sauer and Seuring, 2023), supplemented by an analysis of grey literature was used to identify the
challenges. The main search terms were formulated for scanning the selected
database, Scopus. However, due to the limited use of the term first mile
logistics in preliminary research, the search focused on identifying challenges
associated with the first mile stage of ASC through the following string. The search
was limited to the last decade, ensuring the identification of up-to-date
challenges using Scopus, which was scanned by applying the following search
string.
TITLE ( (
"challenge" OR "problem" ) AND ( "supply chain"
OR "logistic" ) AND ( "agri* " ) ) AND ( PUBYEAR > 2011
AND PUBYEAR < 2023 ). Forty grey literature resources, comprising
international and national reports, were added to the 46 references screened in
Scopus. Based on the inclusion and exclusion criteria, the 86 resources was
reduced to 66 references obtained from both scientific (29) and grey literature
(37). The literature review of selected references resulted in the
identification of 90 overlapping but distinct first mile challenges of ASC.
These 90 challenges were initially grouped into 30 concepts and subsequently
clustered around 6 distinct themes. In addition, a detailed literature review
is presented in Subsection 3.1.
2.2. Prioritization and Weighing of Challenges
(P)
The need to distinguish
between evident and recessive challenges became apparent. The data obtained was
analyzed, and references were ranked based on a six-level evidence scale, which
ranged from 1 to 6
1. In how many references was the first mile challenge identified?
2. What are the evidence values of references that identified the first
mile challenge?
The following formula was used to calculate the evidence score of each challenge. In Equation 1, BEV denotes the base evidence value, representing the value of the most evident reference where a challenge is found.
where ES is the evidence
score, BEV is the base evidence value, and EV is the evidence value of other
references that a challenge is identified from (if any).
For example, assuming a challenge appeared in four references with evidence values of 2, 3, 4, and 4, then the BEV would be 2. Meanwhile, EV refers to the evidence values of other references where the challenge was identified, namely 3, 4, and 4. Equation 2 was used to calculate the evidence score of challenge X.
Subsection 3.2 further describes the analysis of evidence power for
first-mile challenges. Subsequently, the ES of the challenges is explored in
the QFD framework. The first mile challenges are ranked according to the
prioritized importance for the case company.
2.3. Operationalization of challenges (O)
Quality function deployment
(QFD) was developed as a practical tool to improve product and service quality by
focusing on customer needs and demands (Onar et al., 2016). Even though this method was
first introduced conceptually by
QFD is a practical tool that
converts consumer demands into quality characteristics, initially focused on
the context of product development
3.1. Identification and
Structuring of First Mile Challenges
Ninety first mile challenges
were identified from 66 references with varying levels of evidence. Even though
the clusters were naturally interrelated, there was enough data to categorize
each challenge distinctly. The identified clusters were economics, business,
finance, regulations, natural environment, phenomena, supply
chain management, logistics, skill set, workforce, and infrastructure
as shown in Figure 2.
The ever-increasing cost of
fuel, often caused by turbulent international politics, negatively affects
first mile actors
The agricultural industry is inherently
connected to nature, therefore, it is directly affected by changes in climate,
soil, and biodiversity (Isbister, Blackwell, and Riethmuller, 2013). Climate change and extreme
weather act as catalysts for other challenges
Regulations are intended to promote
the process development of ASC, although it often hinder the use of innovative
tools and managerial practices due to bureaucratic barriers in licensing and
registration
The efficiency of SC and the
logistics of agricultural produce is highly dependent on the location (Patidar and Agrawal, 2020). As globalization leads to
lengthier SC, efficiency becomes increasingly relevant
The Internet of Things and
blockchain are two examples of modern technologies that enhance process tracking
and support machinery and equipment management at the first-mile stage. However,
the adoption of such technologies is limited, particularly in developing
regions
Figure 2 Hierarchical clusters of
first mile challenges for agricultural logistics
Despite being a labor-intensive industry, agriculture holds immense
potential for technological advancement and automation to reduce repetitive tasks
and aid in process monitoring. However, the industry is in high need of skilled
and comprehensive professionals (Tang, Liu, and Chen, 2013). The training of stakeholders,
particularly at the first mile, is a fundamental need to equip these
individuals with the required skills for modern agricultural practices
3.2. Evidence
Power Analysis
The identified challenges were
grouped into three classes based on the evidence power. These categories are as follows
· - The evident challenges (n = 21) the evidence scores of
first mile challenges in this class were less than the value of 2.72 (M-(s/2))
and identified in at least two references, as shown in Figure 3. M: Median, s:
standard deviation
· - Median evidence challenges (n = 18) the evidence
scores of first mile challenges in this class were higher than the value of
2.72 and lower than the value of 3.88 (M+(s/2)) and were identified at least in
two references.
· - Recessive challenges (n = 51) This class has two types
of challenges. The first group included the first mile challenges with evidence
scores higher than 3.88. Meanwhile, the second group comprised challenges
identified from only one source, regardless of the evidence score.
The higher the first mile
challenges located on the diagram, the more evident the issues. The most prominent
challenge identified is too long SC and excessive circulation links. Heavy
wastage throughout the SC, extreme weather and complex and poor
road network were other identified first mile challenges from the
literature. The challenges from the regulation cluster were not classified as evident,
therefore, it was not shown in Figure 3. The only challenge classified as
evident in the skill set and workforce was aging workforce shortage
and performance of labor.
The evident challenges were presented to managers in the case company
through semi-structured interviews. For further analysis in the QFD framework,
the challenges were ranked with respect to four options, namely Crucial,
essential, not necessary, and no information. However, out of 21 evident
first mile challenges, four were identified as crucial from respective
perspectives, too long SC and excessive circulation links, high costs
in SC, fluctuant fuel costs, and nontransparent processes, as
shown in Figure 3. Additionally, seven evident first mile challenges were
identified as necessary from diverse perspectives, as shown in Figure 3.
Figure 3 Evident first mile
challenges
3.3. Demonstration
of QFD
QFD was usually applied through the House of Quality, a matrix-style chart that correlates Whats with Hows, consisting of six submatrices, as shown in Figure 4.
Figure 4 Design of house of
quality. Customer requirements (1), technical specifications (2), planning
matrix (3), interrelationship matrix (4), technical correlation matrix (5), and
technical priorities (6)
In the case of this study,
zone 1 represented evident first mile challenges, which are classified based on
respective ES, while the ES of each challenge was found in zone 3. Zone 2 consisted of
operationalization actions (OAs) aimed at reducing identified challenges. Some
identified actions were at a high level, requiring cooperation with regulatory
bodies and stakeholders, while others were related to product features.
Prospective actions were determined through workshops and meetings with a focus
working group.
In zone 3, the planning matrix
is comprised of the ES of challenges and value for importance (VFI) specific to
the case company. Furthermore, the ES model is described in Section 2.
VFI were identified through surveys and semi-structured interviews with the
case company, using a 4-level likert scale. The evident challenges were ranked as
not significant (-1), no information (0), important (3),
and crucial (5). To accurately reflect the ratings, first mile challenges
considered as not important were assigned a value of -1 on the likert scale.
Similarly, no information was assigned a neutral value quantified as 0.
The weighted prioritization
score (WPS) was located in the far-right column of zone 3 and can be developed
in varied ways depending on decision-making needs
The WPS model was developed for challenge ?, using Equation 3.
For example, the WPS for the challenge of too
long SC and excessive circulation links was calculated as follows Equation 4.
Zone
4 served as the core of the house of quality, depicting the quantified relationship
level between OAs and evident first mile challenges. The zone connects OAs with
the first mile challenges, while the respective quantified relationship levels
and symbols are shown in Table 1. The values for relationship levels were used
in zone 6 - OAs priority assessment.
Figure 5 Demonstration of House of Quality
Table 1 Relationship levels and symbols
Zone 5 showed
the mutual and reverse relationships between prospective OAs, which were
depicted by (+) and (-), respectively. This zone enabled decision makers to
understand how the implementation of an action affected others. In particular, it
allows decision makers to discern how closely ranked actions relate to each other.
Assuming an action facilitates the implementation of others (more +), then the
decision maker can prioritize it over the other.
In zone 6, the
priority of OAs was assessed by determining the relationship matrix values and
the WPS. This included summing the products of WPS for each first mile challenge
and the corresponding value in the relationship matrix for each OAs connected in
the same pathway
Where RL is the relationship level between a particular challenge and operationalization action. For example, to calculate the priority score for Decrease the cost of the vehicle:
4. Discussion
This study
sheds light on first mile challenges of ASC and their transformation into
actionable operations through an agri-machinery case. The transformation of
these overarching challenges into operationalization actions that guide further
innovative solutions were illustrated through exploration. Furthermore,
a practical application with prospective tools was demonstrated, with the
introduction of a comprehensive four-step process known as ISPO
(identification, structuring, prioritization, and operationalization).
Initially, 90 first mile ASC challenges, clustered around six themes as shown
in Figure 2, were identified, but after evidence power analysis, the number
reduced to 21. Figure 3 showed that the case company identified 11 of these as
important or crucial. Using QFD, nine challenges were prioritized with normalized
WPS exceeding 0.5. Finally, cooperating with other stakeholders of the SC,
including regulators and farmers, was considered critical in effectively
addressing prioritized first mile challenges, as shown in Figures 5 and 6.
Technological advancement and
sustainability are prominent themes in research on the agricultural supply
chain (ASC). However, significant attention has been paid to the overall supply
chain or downstream aspects rather than the upstream segment
Thicker arrows in the figure reflect a strong relationship, while
thinner arrows reflect a moderate relationship. With the help of the QFD
framework, organizations can strategically divert efforts to more effective
areas in line with prioritized solution demands
The
operationalization action plan included cooperating with regulators,
other stakeholders of the SC, and farmers as a result of the proposed
framework for the agri-machinery case company. The action of cooperating
with regulators was reported to be the most critical. The first mile
challenges with higher WPS were strategically positioned, requiring cooperation
with and support from regulatory bodies. For example, addressing challenges
related to complex and poor road network, fluctuating fuel costs
and water scarcity, as well as challenging access to fresh water
strongly depended on cooperation with these regulatory bodies
Figure 6
Critical operationalization actions and first mile challenges
The negative impacts of soil compaction are unique to the first
mile stage of SC, as opposed to several other challenges that tend to affect
other stages. Meanwhile, since the soil can easily be compacted by agriculture
machinery and equipment during logistics operations around the field
This study investigates the primary challenges faced by ASC and their
translation into actionable operations using a case study involving
agricultural machinery. It illustrates the transformation of these challenges into
practical steps through the ISPO framework: Identification, Structuring,
Prioritization, and Operationalization. In this
context, the study significance focused on the instrumental value, drawing from
constructive methodologies. The present investigation inevitably had a number
of limitations. Firstly, the Scopus search was limited to titles, potentially
excluding relevant references discussing first mile challenges solely within
the text. Secondly, grey literature searches were limited to available online
reports, possibly overlooking significant offline resources. Additionally, some
references were excluded due to limited discussion of target concepts.
Exclusion decisions included multiple research projects to ensure objectivity.
Finally, the developed method required testing across diverse ASC stakeholders,
as it was only applied in one agri-machinery use case. The proposed process
represented a construct awaiting validation through subsequent research and
practical implementation. Further research needs to be conducted, concentrating
on applying the same method to different ASC stakeholders.
The authors are grateful to Docent Professor David
Edwards for his significant support and insights in this research. The authors are
also grateful to the LEVITOI partners for funding this research through the
Business Finland (Grant ID 3509/31/2021).
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