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Archives of Business Research – Vol. 12, No. 12
Publication Date: December 25, 2024
DOI:10.14738/abr.1212.18101.
Deore, A. R., Shafighi, N., & Hajibashi, A. A. (2024). Impact of Machine Learning on Supply Chain Optimization. Archives of Business
Research, 12(12). 112-126.
Services for Science and Education – United Kingdom
Impact of Machine Learning on Supply Chain Optimization
Aakash Ramesh Deore
Najla Shafighi
Anahita Amini Hajibashi
ABSTRACT
Supply chain optimization or SCO is the progression that manages the
manufacturing industry of Germany and the rate of construction. The study aims to
and the consequence of machine learning or ML on SCO of the manufacturing
companies in Germany focusing on automobile and Internet of Things (IoT)
equipment manufacturing companies. From the study, it will be concluded that the
SCO system desires more precision in the supervision system from the employees of
the business industry.
Keywords: Artificial intelligence, Machine learning, Costs, Green supply chain
management, Command forecasting, Advancement in technology, Sustainable
development, and Manufacturing Industry
INTRODUCTION
Supply chain optimization in the manufacturing industry of Germany involves systematically
improving processes, resources, and logistics to enhance the supply chain's efficiency, cost- effectiveness, and overall performance. As per the views of Anjomshoae et al.(2022),this supply
chain optimization is a process or adjustment of supply chain management operations, and it
always makes sure that SCM can work with full efficiency. In Germany, supply chain
optimization plays a crucial role as there are advanced technologies and advanced
manufacturing sectors available. The main focus of this process is on the flow of materials,
information, and products from suppliers to manufacturers, and eventually to customers
(Aranda et al. 2019; Hemant, and Shafighi, 2023).
SCM is the optimization system in which the process of optimization of a business industry
becomes improved and the rate of the production of the business company becomes enhanced.
As per the recommendation of Abbas et al. (2020), with the help of the SCM system, the data,
information, and products related to finance became more protected. In order to track the
performance of the supplier and negotiate contracts as well as the proactive mitigation of
potential risk in SCM of manufacturing companies in Germany, the use of ML is highly
applicable. There are five kinds ofSCM that impact a business and marketindustry and these are
resources, planning, delivery of the products, making, and the return. As per the viewofAbdella
et al. (2020),SCM makes the optimization system of a business industry stronger and increases
the profit of the business organization. Therefore, the SCM is an important factor in the growth
and development of an industry. The use of the SCM in a business industry increases the
creativity in the product's optimization system, which makes the products more acceptable to
customers worldwide (Hemant, and Shafighi, 2023).
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Deore, A. R., Shafighi, N., & Hajibashi, A. A. (2024). Impact of Machine Learning on Supply Chain Optimization. Archives of Business Research, 12(12).
112-126.
URL: http://doi.org/10.14738/abr.1212.18101
AI and machine learning are set to disrupt the status quo across the automobile industry in
Germany. However, machine learning can improve the reduction ofthe emissions of vehicles. In
addition, algorithms of ML can determine various ways and reduce fuel consumption by
analyzing different datasets on driving patterns. The automobile sector has a huge amount of
data that can be utilized for proving training models of ML. Apart from this, there are several
tasks within the automotive industry that can benefit from the implementation of ML like
autonomous driving and predictive maintenance. One of the main reasons that the German
automobile industry has been rising for the past few years is the nature of home demand (Aich
et al. 2019). The supply chain changes frequently with the evolving requirements of meeting
the new demands within the maintenance of a smooth sailing flow. Lately, supply chain
optimization or SCM has been challenged by a number of factors such as inadequate inventory
planning, demand fluctuation, uncertainties in logistics, and above all the backlogs of orders.
Moreover, communication gaps also exist where there is a shortage in the supply at different
times (Pallathadka et al. 2023). In international business, sustainability implementation has
become a cause of concern along with the staff shortage in the post-Brexit era
(Commonslibrary.parliament, 2023). There has been an increase in the input prices which is
hard to deal with.
1. The main focus of the research is to determine the impact of Machine Learning in
Supply Chain Optimization in order to maintain the business in a German
manufacturing industry. Is it possible to rectify pivotal areas by applying machine
learning in SCO of German Manufacturing companies?
2. What is the existing state of adoption of machine learning in the German
manufacturing industry?
3. What are the challenges and barriers in adopting ML in SCO of the manufacturing
industries of Germany?
4. How would Sustainable development generate a substantial effect on Supply chain
Optimization?
5. Will advancement in Technologies will bring modernization to Supply chain
optimization?
6. What effect does supply chain optimization in the Manufacturing industry have on
attaining sustainable growth when machine learning and technological advancements
are combined?
LITERATURE REVIEW
Evaluating the Key Areas for Implementing ML in the SCM of Manufacturing
Companies in Germany
The role of ML in improving the efficiency of the supply chain is significant and requires the
support of data science and data computing for the growth and development of the
manufacturing industry of Germany. As per the opinion of Ivanov et al. (2019), data science
plays the vital role in the growing performance of manufacturing companies as such companies
require huge data for training of machines. According to Kamble et al. (2021), using ML can help
manufacturing companies to optimise production through getting insight of future trends and
possible changes in customer demand. Machine learning is the key component of technology
that can change the future of the economic development of Germany. The potentiality of ML to
improvethebusinessprospectofGermancompanies canbe seenthrough the automationoftasks
and the making of better decisions. Customer is the most important asset of an organisation and
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Services for Science and Education – United Kingdom
the use of ML to satisfy the demand of them can increase the value of a brand (Frazzon et al.
2019). Manufacturing companies in Germany use ML for resolving the SCM related complaints
of customers and answering FAQs through automated operation. Effective supply chain
optimization ensures the practice of raw materials to perform reliably and assemble lines of
contract for effective functioning (Cole et al. 2019).
Disadvantages of Using Machine Learning in the Supply Chain Optimization in German
Manufacturing Companies
Data is a prime component of machine learning, and the wrong input can change the course of
production negatively. Khan et al. (2020) mentioned a lack of skills in employees can hinder the
successful use of machine learning in companies which leads to loss instead of betterment. With
the use of ML in the supply chain optimization of Germany, companies need to have an idea of
the German economy and the way it can be better. However, accessing the economic
information in an unsystematic manner can bring treats unfavorable situations. The regulation
of using machine learning is strict and unable to follow it can also bring unfavourable situations
for the company leaders. Kilimci et al. (2019) mentioned machines are highly unpredictable,
and failure of machinery can happen at any time during the operation. Thus, the dependency of
manufacturing industries on the machine learning system can be highly disadvantageous.It has
been suggested that relying heavily on technology and machines can increase challenges for
logistics and SCM for manufacturing companies during cyber-attacks, system failures, or the
occurrence of glitches.
Analysis of the ML Adoption Process in SCM of Manufacturing Companies in Germany
The gradual yet effective initiation can be seen in the manufacturing sector in Germany for
managing the supply chain effectively. In this context, Ni et al. (2020) explained, ML can be
adopted in manufacturing companies, primarily to analyse the lifecycle of machines. In order to
predict the failure of machines, German companies are adopting ML. apart from this and logistic
optimization is getting help due to ML adoption. Thus, manufacturing companies in, Abbas et al.
(2020) mentioned, in-store house-related information, and the use of ML has helped many
business professionals to save money and time. The automated alarming system in each step of
anomaly can help manufacturing companies in Germany maintain safety in storehouses. The
adoption of machine has opened various paths for the German companies towards regulating
the skills and capabilities in terms of globalizing the supply chain services. As opined by Aslam
et al. (2021), off shoring the production credibility is assured by the industrialized economy
which is readily available to build the capabilities for supply of energy. Germany is known to be
a powerhouse of technology due to the devotion of the research and design project that lends
support towards sustainability. As per the critical analysis by McMaster et al. (2021), the
startup booms in Germany has attracted the technical foundations with a background of AI and
machine learning. In such a context, nearly 7% of the German economic output has centred the
technical convulsions with respect to hardware segments.
Saving costs, reducing waste production and improving quality production can be possible
through the proper visibility of market conditions. ML is able to help in getting an insight into
all the essential requirement, thus, companies in Germany is adapting ML for improving lean
production. In order to make effective decisions for the business, the leaders of German
manufacturing firms are adopting ML (Xuet al. 2022).
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Deore, A. R., Shafighi, N., & Hajibashi, A. A. (2024). Impact of Machine Learning on Supply Chain Optimization. Archives of Business Research, 12(12).
112-126.
URL: http://doi.org/10.14738/abr.1212.18101
Challenges in the Adoption of Machine Learning in Supply Chain Optimization in
Manufacturing Companies of Germany
In order to access the benefit of machine learning, the availability of sufficient data is important.
In this context, Stockheim et al. (2023) mentioned, data required to train machines and the
absence of data can prevent the process of adoption of ML for professional purposes. On the
other hand, the use of machine learning requires specialized skills, as it is a highly technical and
complex process. Gupta and Gupta (2019) mentioned, having a workforce with no necessary
expertise can bring difficulties in implementing it. Managing the supply chain with the use of
ML requires skills of handling machines and data and the lack of understanding of that skill can
hinder the adoption of ML in manufacturing companies in Germany.
The adoption of ML is undoubtedly effective, although the process is costly in terms of the
requirements of both the hardware and software. Ivanov (2021) mentioned, ML need time to
be implemented in the manufacturing companies to maintain the proper alignment for better
use. Along with time consumption, the cost of labour training and resource collection is also
high for the adoption of ML in German manufacturing companies. In contrast to these Thiems
2022) mentioned, among all external challenges, the most prominent one is the difficulty of the
model of ML in interpreting while working. The German manufacturing industry is renowned
for its focus on quality testing, improvement planning and innovation strategy, and the use of
machine learning this industry to offer help in SCM needs to have the efficacy to deal with every
aspect. However, the models of machine learning often make decisions on their own to
automate service by hindering any three of the focused areas of German companies
(Steinberget al. 2023). Thus, the challenges are highly prominent and require the support of
some effective strategies to mitigate the threats and barriers for the manufacturing companies
in Germany.
METHODOLOGY
Primary Quantitative Method has been used in the research conduction that is based on
concrete ideas assigned to the industrial domain of Germany. Further, primary quantitative
data focuses on objectiveprotocols while conducting the study that mainly includes the resilient
supply chain and security of supply scale optimization in Germany (Piao et al. 2023). Primary
data was collected through a survey of 100 respondents. These were the key manufacturing
units in Germany across crisis-hit sectors. Automobile and IOT sector in Germany have been
chosen as the targeted group to collect data. Therefore, employee 100 employees have been
chosen as sample size to gather data. In this study, there was random sampling method was
used, and the sample size was 80. During this survey it is better to shorten the view and focus
on the German Automobile and Internet of Things (IOT) equipment manufacturing sectors.
DATA ANALYSIS
The frequency analysis and pie chart analysis have been done among the candidates. The
regression analysis has been done for data collection purposes and the coefficient, ANOVA table
and model summary table have been evaluated in this research.
Correlation Analysis
Correlation Test for “Machine Learning and Supply Chain Optimization” Variables:
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Table 1: Correlation test for "Machine Learning and Supply Chain Optimization"
variables
(Source: SPSS)
The above table provides two tables of descriptive statistics and correlation values for two
variables that are related to "Supply Chain Optimization" and "Machine Learning.". In the
descriptive statistics table, the mean value for the supply chain optimization variable is 3.59.
Here, the standard deviation value for this variable is 1.01 where the number of observations
was 100. On the other hand, for the variable Machine Learning, the mean value is 3.51 which
are positive, and a higher positive value offered a higher scale of positive relationship among
variables. On the other hand, for this variable, the standard deviation value is 1.065 and this
value also signified this test.
This data provides information on the variability as well as central tendency related to mean
value giant these two variables that relate to the "Supply Chain Optimization" and "Machine
Learning" segments. In that case, the mean value suggested responses from the average number
of participants (Elshami et al. 2021). Both variables have higher mean values that suggested
higher responses from participants. On the other hand, through the help of this standard
deviation, this study offers information on the spread or of responses in the overcaution
numbers. In that case, both variables have lower values that demonstrated that less amount of
variability in the mean aspects.
In terms of Pearson correlation test refers to the test assorted with the relation where this test
measured the relationship between different two variables (Vallejos et al. 2020). The above
table also illustrated correlation coefficients between "Supply Chain Optimization" and
"Machine Learning." Here, Pearson correlation coefficients and their significance levels for a
two-tailed test have been done for these two variables for this study. Interms ofthe relationship
between supply chain optimization as well as Machine Learning, the person correlation
coefficient value is 0.720 that signified at a level of 0.01. On the other hand, if the value is
positive then there is positive relation has taken place between the two variables whereas if
there is a negative value that means -1 then it suggested a negative relationship between two
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Deore, A. R., Shafighi, N., & Hajibashi, A. A. (2024). Impact of Machine Learning on Supply Chain Optimization. Archives of Business Research, 12(12).
112-126.
URL: http://doi.org/10.14738/abr.1212.18101
different variables in this study (Kadoch et al. 2023). This correlation test indicates that a strong
positive relationship is observed between these two factors due to its positive value which is
0.720. This positive result stated that if there are increasing uses of machine learning, the
optimization in the supply chain segment. In that case, the significance level of 0.001 provides
information about this correlation that is significantly high at 0.01 level for the 2-tailed
bivariate test. This result also demonstrated that this kind of positive correlation is seen by a
lower chance (Gao et al. 2019). Therefore, the above data offers an effective positive
relationship between these two variables where the mean values for these variables are similar.
In that case, correlation results provide information that increasing uses of machine learning in
the supply chain aspects, this brought improvement in the optimization segment where an
organization gets different cost-effective supply chain practices.
Correlation Test for “Supply Chain Optimization and Advancement in Technologies”
Variable:
Table 2: Correlation test for “Supply chain optimization and Advancement in
Technologies” Variable
(Source: SPSS)
The above two tables provide results of descriptive statistics as well as correlation values for
two variables. Here, by analysing these tables, the correlation between two variables has been
discussed. In descriptive analysis, this result offers the distribution of these two variables in the
data set. Here, the mean value for the supply chain optimization variable was 3.59 which is
higher. This higher value stated that participants suggested this variable at a higher scale. On
the other hand, for this variable, the standard deviation value was 1.101 which is positive. On
the other hand, with the advancement in the technology variable, the mean value is 3.71 which
are similar to the other variable. In this context, the value for standard deviation was 1.176
among the observation number was 100. In that case, this statistical value not only offers a
tendency of the relationship between these variables but also shows the variability by the
participants against these two variables (Coccia, 2021). On the other hand, both these variables
had higher mean values which refer tothe acceptance ofthese two variables among participants
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Deore, A. R., Shafighi, N., & Hajibashi, A. A. (2024). Impact of Machine Learning on Supply Chain Optimization. Archives of Business Research, 12(12).
112-126.
URL: http://doi.org/10.14738/abr.1212.18101
helping in intelligent management, visualizations and automation purposes. Therefore, IoT
software is helping in information collection purposes and delivering proper visibility for real- time data analysis purposes.
Then again, the regression model equation is estimated as y=mx+C
Where Supply Chain Optimization is equal to 0.744 * Machine Learning +0.978 Here, Supply
Chain Optimization is denoted as y dependent variable
Machine Learning is denoted as an x-independent variable 0.744 is the slope m and 0.978 is the
constant C
Therefore, the model has indicated that there is a 0.978 unit change in y with every unit change
in x predicted.
Regression Analysis for Advancement in Technologies and Supply Chain Optimization:
Table 5: Regression Analysis for Advancement in Technologies and Supply Chain
Optimization
(Source: SPSS)
The above table is showing ANOVA table, where the F value is 240.702 and the significance
value is 0.001, which is less than 0.05. Along with this, the R square value is 0. 711 and R-value
is 0.843 which is close to 1. Therefore, modern technology is creating a positive impact on