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76
Volume 15 • Issue 4 • 2023
Engineering Management in Production and Services
Ewa Ziemba
e advent of digital transformation has signi-
cantly impacted the landscape of modern entrepre-
neurship and current business. Undoubtedly, digital
Doanh, D. C., Dufek, Z., Ejdys, J., Ginevičius, R., Korzyński, P., Mazurek, G., Paliszkiewicz, J., Wach, K., & Ziemba, E.
(2023). Generative AI in the manufacturing process: theoretical considerations. Engineering Management in Production and
Services, 15(4), 76-89. doi: 10.2478/emj-2023-0029
transformation, dened as the integration and adop-
tion of digital technologies, processes, and strategies
across various aspects of an organisation, is the pri-
mary challenge of the third decade of the twenty-rst
century, particularly in the post-pandemic era
(Głodowska et al., 2023), multidimensionally impact-
ing the manufacturing industry dened nowadays as
Volume 15 • Issue 4 • 2023
77
Engineering Management in Production and Services
Industry 4.0 (Nosalska et al., 2018). e ongoing
changes in modern business management are caused
by the use of several disruptive technologies, includ-
ing blockchain, AR, VR, social media, mobile, and IoT
(Mazurek, 2018). In recent months, it has gained
importance because of the Generative Articial Intel-
ligence (GAI) concept in various managerial aspects
and dimensions with its business and manufacturing
advantages (Korzynski et al., 2023) and dark sides
(Wach et al., 2023).
is article presents the results of a literature
review approach based on the analysis of publications.
e critical review primarily aimed to methodically
assess, analyse, and integrate the existing body of lit-
erature on applying GAI in the manufacturing process
to foster the development of innovative theoretical
frameworks and perspectives within the eld. Dier-
ent areas of manufacturing processes have been con-
sidered, such as new product design, innovation
management, human resources, quality control, pre-
dictive maintenance, forecasting and marketing strat-
egy creation and implementation. e uniqueness of
this publication lies in the review execution and litera-
ture description on the eld of retrotting and its
classication. Additionally, it involves the extraction
of the primary trends presented in the literature.
is publication answers the following questions:
What is the current development stage of theory
linked with the GAI application in the manufacturing
process? What are the most important managerial
insights concerning the application of AI to manufac-
turing processes? What AI implications can be found
in the literature on several manufacturing and product
engineering dimensions?
e article consists of four parts. e introduc-
tion includes a description of the techniques and
technologies connected to AI in manufacturing pro-
cesses. e second part presents the research methods.
e third part describes the research results, focusing
on such dimensions as new product design, innova-
tion management, human resources, quality control,
predictive maintenance, forecasting and marketing
strategy. e fourth part summarises the article.
An integrative or critical review approach was
employed to achieve the research objective. e
applied method provided a framework for under-
standing and appreciating the complexities of narra-
tive literature. Many integrative literature reviews are
designed to tackle subjects that have matured or are
in the early stages of emergence. In the case of emerg-
ing topics, the primary aim is to establish initial or
preliminary concepts and theoretical frameworks
rather than simply revisiting existing models (Snyder,
2019).
e main aim of this critical review was to sys-
tematically evaluate, critique, and synthesise the body
of literature relevant to GAI application in the manu-
facturing process. is comprehensive analysis was
conducted in a manner designed to facilitate the
emergence of novel theoretical frameworks and per-
spectives within the eld.
e integrative review method led to the
advancement of knowledge and the development of
theoretical frameworks rather than merely providing
an overview or description of previous research (Sny-
der, 2019).
e authors’ intention was to provide an inte-
grated, synthesised overview of the current state of
knowledge and research insights, existing gaps, and
future research directions in the eld of AI applica-
tion in manufacturing processes (Palmatier et al.,
2018). Alongside the normative recommendations,
this review provides summaries and suggestions that
could provide valuable managerial insights (Mazum-
dar et al., 2005) into the future application of AI to
manufacturing processes.
Authors adopted domain-based review papers,
which allowed for reviewing, synthesising, and
extending a body of literature in the substantive
domain, i.e., AI application in the manufacturing
process. e manufacturing process structure refers
to the organisation and sequence of activities involved
in the production of goods or products. It outlines the
steps and stages required to transform raw materials
or components into nished products. e specic
structure of a manufacturing process can vary widely
depending on the industry, product complexity, and
technology used. Considering the complexity and
comprehensiveness of manufacturing processes, the
conducted literature studies were focused on the fol-
lowing processes supporting the manufacturing pro-
cess: the design of new products, workforce and skill
optimisation, enhancement of quality control, pre-
dictive maintenance, demand forecasting, and mar-
keting strategy.
e application of the critical literature review
method aimed to answer the following research ques-
tions:
Volume 15 • Issue 4 • 2023
Engineering Management in Production and Services
• How can AI adaptation help manufacturers to
create products that are more ecient, eective,
and safe?
• How can AI adaptation help analyse and predict
the necessary skills required for manufacturing
processes?
• How can GAI be used to identify defects in prod-
ucts and processes?
• How can GAI be used as a proactive approach to
maintenance to predict when equipment and
machinery are likely to fail or require mainte-
nance?
• How can AI be used to analyse market trends,
consumer behaviour, and sales data to predict
demand for manufactured goods?
• How can AI be used to optimise marketing strat-
egies and improve the timing of product releases,
promotional campaigns, or sales events?
2.1. Application of Generative AI in the
design of new products
is research study investigates the GAI’s possi-
ble transformative eects within the product design
domain. GAI can be harnessed for designing new
products because it can explore vast design spaces,
generate diverse and innovative solutions, and opti-
mise designs based on predened parameters (Kwong
et al., 2016). GAI is applied to explore innovative
design possibilities and generate optimum product
designs, considering user-dened parameters (Cappa
et al., 2021). is novel GAI methodology has the
potential to transform manufacturing procedures by
facilitating the development of goods that possess
enhanced eciency, eectiveness, and safety, espe-
cially in the post-pandemic world (Villar et al., 2023).
In recent years, the emergence of GAI has pre-
sented novel opportunities for innovation across sev-
eral industries (Cappa et al., 2021), such as the
automotive industry (e.g., Tesla). Another example
could be bioengineering. In recent times, additive
manufacturing (AM) has been widely used to create
things specically designed for human use, including
orthoses, prostheses, therapeutic helmets, nger
splints, and other customised devices (Liu et al.,
2022). Product design is a domain that shows signi-
cant potential for application, where GAI might serve
as a crucial tool for exploring and optimising design
spaces (Di Vaio et al., 2020). is study focuses on the
possible advantages of utilising GAI to design novel
goods, with a particular emphasis on developing
solutions characterised by enhanced eciency, eec-
tiveness, and safety. GAI can explore new design
possibilities and generate optimised designs based on
user-dened parameters. Designers can input user-
dened parameters into GAI systems, guiding the AI
to generate designs that meet specic criteria. is
user-centric approach ensures the nal product’s
alignment with the intended goals and requirements.
is can help manufacturers to create more ecient,
eective, and safe products.
GAI can evaluate extensive datasets of preexist-
ing designs, acquiring knowledge of patterns and
correlations among dierent design components.
is allows it to investigate vast design possibilities
that may be inconvenient or time-consuming for
human designers to navigate. GAI functions by lever-
aging machine learning concepts, allowing the system
to acquire knowledge from extensive datasets and
produce novel outputs according to predetermined
criteria (Plantec et al., 2023). Within the realm of
product design, this technology allows designers and
manufacturers to swily and eectively explore
a wide range of design possibilities (Iansiti & Lakhani,
2020). GAI algorithms can develop designs that meet
or surpass specied requirements by incorporating
user-dened characteristics such as material quali-
ties, weight limitations, and performance standards
(Plantec et al., 2023). rough an iterative process,
these algorithms may generate designs that follow the
given specications (Lei et al., 2022).
In conclusion, GAI presents a groundbreaking
opportunity for revolutionising product design pro-
cesses (Hu et al., 2023). e utilisation of GAI holds
immense potential for transforming the processes
involved in product design. GAI, due to its capacity to
navigate extensive design spaces and swily produce
optimum solutions, is positioned as a pivotal facilita-
tor for innovation in the manufacturing sector (Lei et
al., 2022). e utilisation of this technology serves as
a demonstration of its capacity to generate products
with enhanced eciency, ecacy, and safety. e
ongoing progress of technology has led to the poten-
tial incorporation of GAI in the eld of product
design, which can revolutionise several industries
and facilitate the creation of innovative products that
surpass existing limitations (Wang & Wu, 2024). As
technology continues to advance, the integration of
GAI in product design holds the promise of reshap-
Volume 15 • Issue 4 • 2023
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Engineering Management in Production and Services
ing industries and driving the development of prod-
ucts that push the boundaries of what is currently
achievable.
2.2. GAI as a facilitator of the HRM
process in manufacturing
In the era of Industry 4.0, the fusion of advanced
manufacturing processes and cutting-edge digital
technologies, such as general articial intelligence,
heralds unparalleled innovations in human resource
management (HRM) (Sigov et al., 2022; Rymarczyk,
2021). Looking ahead to Industry 5.0, where human
collaboration with machines is expected to be more
harmonious and optimised (Leng et al., 2022), the
role of GAI becomes even more pivotal. Industry 5.0
focuses on the coalescence of human touch with
technological autonomy, aiming to create a balanced
ecosystem where human creativity and machine e-
ciency coexist and complement each other (Adel,
2022).
In its current form, GAI, such as ChatGPT, can
be incorporated into work settings as an element of a
custom network of applications (OpenAI, 2023).
erefore, the potential utility of GAI, exemplied by
technologies like ChatGPT, extends beyond just text
generation (Korzynski, Kozminski, & Baczynska,
2023). Its diverse functionalities can seamlessly inte-
grate with various HRM systems, working collabora-
tively to enhance and streamline numerous
HR-related processes in the manufacturing sector.
e integration of GAI with HRM systems can facili-
tate the comprehensive analysis and synchronisation
of various elements, such as workforce planning
(Koole & Li, 2023), scheduling shis (Dworski, 2023),
position description analysis (Chang & Ke, 2023) and
performance management (Budhwar et al., 2023).
In reference to workforce planning and schedul-
ing, constant manufacturing operations demand
accurate planning (Heuser, Letmathe, & Schinner,
2022), and GAI can synchronise various elements,
such as production demands, employee availability,
and skill sets, to formulate optimised shi schedules.
is application ensures that every shi is adequately
staed with individuals possessing the right skills,
enhancing the alignment with production targets
while maintaining compliance with labour regula-
tions.
Furthermore, GAI holds the key to revolutionis-
ing position description analysis and task standardi-
sation. Considering the advancements brought about
by smart factories, a precise understanding and
delineation of tasks and roles become increasingly
vital. Automated and semi-automated systems in
manufacturing lines coexist with manual processes,
highlighting the signicance of clear and well-dened
position descriptions (Cha et al., 2023). By harness-
ing GAI to analyse and standardise position descrip-
tions and tasks, organisations ensure clarity and
uniformity in role expectations and responsibilities,
synchronising manual and automated processes
eectively.
Additionally, in performance management
within the manufacturing milieu, adherence to spe-
cic production norms is pivotal. GAI may play a
fundamental role here by continually analysing
employee activities and outputs. By monitoring
adherence to production norms and standards, GAI
provides critical insights and data that enable both
managers and employees to rene and optimise per-
formance (Khang et al., 2023). is continuous over-
sight and analysis ensure that any deviations from the
established norms are rapidly identied and rectied,
contributing to the streamlined and eective func-
tioning of the manufacturing operations.
In the sphere of smart factories, the deployment
of GAI furthers the enhancement of performance
management. e centralised data hubs in smart fac-
tories, which streamline the rapid exchange of infor-
mation, are leveraged by GAI to meticulously monitor
and analyse employee performance and operations
(Haponik, 2022). is integration facilitates instanta-
neous feedback and insights, enabling immediate
corrective actions and ensuring the consistent align-
ment of operations with established production
norms and standards.
2.3. Enhance quality control process by
AI
Quality control, dened as a systematic process
involving checks, testing, verication, and response,
ensures that product features and process conditions
align with design standards and internal and external
specications (Hull, 2011). It entails examining prod-
ucts at various stages of the production process to
guarantee they meet specic criteria, such as size,
weight, colour, or other requirements (Nadira, 2023).
Despite being a critical aspect of modern manufac-
turing, it presents signicant challenges and demands
substantial time. As production enterprises expand
and the demand for higher product quality rises,
industrial processes have become increasingly intri-
cate. Consequently, the likelihood of production sys-
Volume 15 • Issue 4 • 2023
Engineering Management in Production and Services
tem failures and the associated hazards related to
product quality have escalated. When faults occur in
the production process, specic product quality
indicators can uctuate, leading to subpar quality (Xu
et al., 2024).
e rapid advancement of information technolo-
gies makes it crucial to utilise them for monitoring
and achieving stable, precise control over industrial
processes and product quality (Xu et al., 2024). To
address challenges in industrial process monitoring,
fault diagnosis, and product quality control, experts
and scholars have proposed the application of AI
(Hartung et al., 2022; Zeng et al., 2022; Xu et al.,
2024), including GAI as evidenced in recent studies
(Narasimhan, 2023; Raja, 2023; Wang et al., 2019).
e utilisation of GAI holds the potential to enhance
quality control processes by eectively detecting and
identifying defects and anomalies in various prod-
ucts. GAI can create virtual models of products, ena-
bling simulation of the manufacturing process. is
aids in the early detection and prevention of potential
defects and anomalies in actual products (Raja, 2023).
GAI can revolutionise manufacturing quality
control in several ways, such as (Raja, 2023):
• Defect identication. GAI can rapidly identify
product defects by analysing images or data from
manufacturing processes. is capability enables
manufacturers to detect defects in real-time dur-
ing the manufacturing process, ensuring that
products meet quality standards before they are
delivered to customers.
• Defect prediction. GAI can anticipate and iden-
tify potential product defects by leveraging his-
torical defect data. With this capability,
manufacturers can then pinpoint vulnerable
areas and take proactive measures to prevent
these issues.
• Automated quality control. GAI can automate
quality control tasks by analysing more data
about each production process and product,
especially in defect inspection. It enhances accu-
racy and eciency in quality control processes
and boosts worker productivity, allowing them to
concentrate on other essential tasks.
• Personalised quality control. GAI facilitates the
personalisation of quality control processes by
developing tailored inspection plans for dierent
product types. It guarantees that each product
undergoes scrutiny at a suitable level, ensuring
compliance with the necessary quality standards.
In the conventional quality control process,
humans are responsible for tasks such as understand-
ing requirements, preparing and conducting tests,
and reporting defects. However, this approach is
prone to human errors, is time-consuming, and
encounters challenges related to scalability in com-
plex systems. GAI revolutionises quality control by
automating these tasks and ensuring comprehensive
test coverage, overcoming these challenges. Using
machine learning algorithms trained on extensive
datasets and continuous learning from previous
errors, thus eliminating the need for human supervi-
sion and ensuring signicant time and resource sav-
ings, GAI (Nadira, 2023; Vaddi & Khan, 2023):
• comprehends requirements and autonomously
generates test cases;
• autonomously generates test data;
• automates test execution, minimising errors and
time consumption;
• enhances test generation and execution, leading
to quicker testing cycles, improved precision, and
elevated product quality;
• generates clear, concise, and actionable reports
aer executing the test cases and
• predicts potential issues and forecasts and likely
points to failures before they occur, enabling
proactive and real-time addressing of problems.
e overall comparison between the traditional
quality control process and the GAI-based quality
control process is presented in Table 1.
Some examples of how GAI is being used in the
manufacturing quality control process include (Raja,
2023; Srivastava, 2023; Wlodarczyk, 2023):
• Intel: GAI is used to detect imperfections in
computer chips. By analysing images of computer
chips, GAI identies defects that are too minus-
cule for human observation, signicantly
enhancing Intel’s chip quality.
• Bosch: GAI is used to forecast defects in automo-
tive components. e company uses GAI to
examine historical defect data, predicting which
parts are more likely to be faulty. is predictive
approach has signicantly reduced the number of
defective automotive parts shipped to customers.
• BMW: GAI is used to predict defects in car parts.
AI employs computer vision to analyse images or
videos of components and undergoes training to
dierentiate between defective and non-defective
car parts. Once trained, AI can inspect new car
parts in real-time, promptly identifying any
defects and detecting deviations from the stand-
ard, ensuring all required parts are without
defects and correctly mounted in their designated
places.
Volume 15 • Issue 4 • 2023
Engineering Management in Production and Services
• Siemens: GAI is used to automate wind turbine
quality control. is technology is used by Sie-
mens to customise inspection plans for various
wind turbines, streamlining the quality control
process and enhancing eciency in wind turbine
production.
• Georgia-Pacic: GAI is used to enhance the qual-
ity of paper production. AI prevents paper tear-
ing during production by predicting the optimal
speed for converting lines.
Overall, the integration of GAI into the quality
control process opens new possibilities for innovative
transformation and eciency enhancement of the
quality control process. GAI promises to reshape
manufacturing quality control, making it swier,
more eective, and exceptionally precise. Using the
analysis of production data and the application of
machine learning algorithms, it can pinpoint poten-
tial quality problems and defects.
is proactive approach empowers manufactur-
ers to address issues in real time before they escalate,
ensuring smoother production processes. Neverthe-
less, the eectiveness of AI in quality control directly
depends on the quality and diversity of data it is
trained on, as well as test algorithms. Test data and
algorithms are important in delivering accurate and
consistent results and deriving edge-case scenarios or
exceptions.
Volume 15 • Issue 4 • 2023
Engineering Management in Production and Services
2.4. Application of artificial
intelligence in the context
of predictive maintenance
in production processes
Predictive Maintenance (PdM) is a proactive
maintenance strategy that uses data, analytics, and
machine learning to predict when equipment or
machinery will likely fail, allowing for timely mainte-
nance interventions. e main goal of PdM is to
anticipate potential issues and perform maintenance
activities just before they are needed, avoiding unex-
pected breakdowns and minimising downtime. ere
is strong evidence that using AI-based solutions
improves the maintenance process in companies. AI
can be applied to various stages of PdM, encompass-
ing Data Collection, Data Preprocessing, Feature
Selection, Model Training, Model Evaluation,
Deployment, Monitoring, Alerts and Notications,
Maintenance Intervention and Feedback Loop. Arti-
cial intelligence in the context of improving mainte-
nance processes is applicable when maintenance
processes are carried out by humans (in the form of
inspections) (Shin et al., 2021) and when they are
automated.
Cost savings are among the primary benets of
PdM (Shin et al., 2021), resulting from improving
productivity (e.g., downtime reduction) (Arena et al.,
2022); reducing environmental negative impact (e.g.,
waste reduction) (Allahloh et al., 2023), improving
safety conditions (Katreddi et al., 2022) and reliability
(Achouch et al., 2022).
e literature provides numerous examples of
studies indicating the application of AI for mainte-
nance operations in various industries and sectors:
the renewable energy industry (Shin et al., 2021),
manufacturing and processing of wood products
(Rossini et al., 2021), the power generation industry
(Allahloh et al., 2023), the automotive sector (e-
issler et al., 2021; Arena et al., 2022; Katreddi et al.,
2022) and particular industrial infrastructure
(machinery) (Pandey et al., 2023).
Based on the experiment being conducted by
Bahrudin Hrnjica and Selver Soic (2020), the inte-
gration of explainable AI, embodied in a dependable
prediction model and visual representations, can
eectively assist in mitigating avoidable costs linked
to unscheduled downtime resulting from machine
errors or tool failures. is means that by having an
AI system that predicts potential issues and provides
understandable explanations and visual insights into
those predictions, businesses can make informed
decisions to address issues pre-emptively (Bahrudin
Hrnjica, Selver Soic, 2020).
To overcome the limitations of relying solely on
human inspection, Shin et al. (2021) employed
machine vision approaches to create AI-based solu-
tions (AI-assisted approach) for image-based fault
diagnoses. e authors examined the impact of AI
based on deep learning algorithm assistance on the
performance and perception of human inspectors,
considering their task prociency. e conducted
studies conrmed that implementing AI to support
inspectors signicantly improved results (specicity,
sensitivity, and time eciency), particularly when
inspectors were not experts in their eld.
e results demonstrated that AI can yield sig-
nicant benets in scenarios with limited human
resources and time-consuming expert training. e
authors have posited the hypothesis that, even in the
long-term perspective, it is improbable to fully auto-
mate the stage of reading and diagnosing images. is
limitation stems from the inherent nature of articial
intelligence algorithms, which can only recognise
faults they have thoroughly mastered based on train-
ing data. In situations involving novel issues, human
involvement remains imperative for the early detec-
tion of potentially catastrophic cases (Shin et al.,
2021).
e Digital Twin, which can deliver additional
services by leveraging physical simulation and AI
algorithms, is another example of applying AI in
maintenance process improvement. ese services
include such functions as fault diagnosis, trouble-
shooting, predicting the remaining useful life, and
facilitating maintenance activities (Rossini et al.,
2021). Application of DT solutions enabled the real-
time creation and modication of workows essential
for fault diagnosis and predictive maintenance. is
involves the dynamic addition, removal, or replace-
ment of entities to accurately represent the status of
components within the system.
Allahloh et al. (2023) showcased the viability of
markedly improving fuel eciency and anticipating
maintenance needs. Our discoveries indicate that
deploying IIoT and AI solutions opens avenues for
substantial fuel preservation, heightened perfor-
mance through predictive maintenance, and practical
strategies for industries to optimise processes and
enhance eciency in internal combustion genset
operations. e IoT platform application allows for
identifying potential issues before they become criti-
cal problems, signicantly reducing downtime and
maintenance costs (Allahloh et al., 2023).
Volume 15 • Issue 4 • 2023
Engineering Management in Production and Services
e application of articial intelligence in the
maintenance eld can predict potential system fail-
ures based on specic characteristics or system set-
tings (input variables) and may prevent future failures
and minimise downtime.
2.5. Artificial intelligence application
for demand forecasting
Forecasting demand plays a vital role in contem-
porary business operations, allowing manufacturers
to optimise their production processes, eectively
manage inventory levels, and eciently full cus-
tomer requirements (Ghosh, 2022; Tadayonrad
& Ndiaye, 2023; Viverit et al., 2023). e emergence
of articial intelligence (AI) has provided businesses
with a potent tool for examining intricate datasets
and making more precise prognoses regarding future
demand (Kumar et al., 2023). is part elucidates the
utilisation of AI for analysing market trends, con-
sumer behaviours, and sales information, ultimately
heightening the accuracy of demand forecasting and
contributing to improved decision-making within
manufacturing workows.
In the rapidly changing and dynamic landscape of
contemporary business, grasping market trends is
imperative to maintain a competitive edge (Li et al.,
2022; Mathur et al., 2023). AI algorithms can analyse
extensive quantities of information from diverse ori-
gins, including social media, online forums, news
articles, and industry reports. is enables them to
detect emerging trends and changes in consumer
preferences (Liyanage et al., 2022). AI can extract valu-
able understandings regarding customer conversa-
tions, the rising popularity of specic products, and
the inuential trends steering purchasing choices by
evaluating sentiment analysis, keyword frequency,
and topic modelling. ese insights equip manufac-
turers with the information needed to adapt their
production strategies and harmonise their oerings
with present and forthcoming market requirements
(Li et al., 2022). Specically, AI algorithms can si
through vast volumes of data sourced from social
media platforms, pinpointing nascent trends, senti-
ments, and dialogues about particular products or
sectors. Applying natural language processing (NLP)
techniques facilitates sentiment analysis, topic model-
ling, and keyword extraction, thereby facilitating an
understanding of consumer viewpoints and inclina-
tions. For example, a fashion retailer uses AI to analyse
social media conversations and identies that a par-
ticular clothing style is gaining popularity among
inuencers and consumers. is insight prompts the
retailer to adjust their production plans to meet the
anticipated demand for that style. Moreover, AI can
scan news articles, blog posts, and industry reports to
identify shis in consumer behaviour, economic
indicators, and technological advancements that
could impact market trends. AI can provide insights
into upcoming trends by analysing the frequency and
context of certain keywords and phrases. AI-powered
web scraping tools can also extract data from e-com-
merce platforms, competitor websites, and market-
places to track product prices, availability, and
customer reviews (Dwivedi et al., 2023). is data can
be analysed to detect pricing trends, product popular-
ity, and consumer feedback. An online retailer, for
instance, uses AI-driven web scraping to track com-
petitors’ pricing strategies and identies that a certain
product is consistently priced higher than similar
oerings. is insight helps the retailer adjust their
pricing strategy to remain competitive.
e utilisation of AI-driven analysis of consumer
behaviour grants manufacturers an unparalleled
comprehension of their intended audience (Sohrab-
pour et al., 2021; Yaiprasert & Hidayanto, 2023). AI
can create detailed customer proles by collecting
and interpreting data from e-commerce platforms,
loyalty programmes, and even IoT devices (Zhu et al.,
2022). Machine learning algorithms can detect pat-
terns within purchasing habits, preferences, and fac-
tors that prompt buying decisions. AI can potentially
augment the precision of demand forecasting by
identifying connections between external variables
like seasonality, economic indicators, cultural occur-
rences, and consumer purchasing trends. is, in
turn, empowers manufacturers to customise their
production and marketing approaches to synchronise
with these discernments, guaranteeing that the
appropriate products are accessible at the right
moment and in suitable quantities (Vaid et al., 2023).
AI-powered sentiment analysis can analyse customer
reviews, social media interactions, and online con-
versations to gauge consumer sentiment towards
products and brands (Hyun Baek & Kim, 2023). is
insight provides businesses with valuable feedback
and helps them address customer concerns. For
instance, restaurant chains utilise AI to analyse social
media posts and reviews. It is discovered that custom-
ers consistently praise their food quality but express
dissatisfaction with long wait times. e restaurant
management addresses this issue by optimising their
service speed, leading to improved customer satisfac-
tion.
Volume 15 • Issue 4 • 2023
Engineering Management in Production and Services
Sales data serves as a goldmine of information for
demand forecasting (Ma et al., 2016). AI-driven ana-
lytics can construct predictive models by analysing
historical sales data, considering such elements as
product life cycle, promotional endeavours, and geo-
graphical discrepancies (Abolghasemi et al., 2020).
ese models can forecast demand with remarkable
precision, aiding manufacturers in making informed
decisions about production volumes and inventory
management. Machine learning algorithms, such as
time-series analysis, regression, and neural networks,
can be trained on historical sales data to identify pat-
terns and trends, enabling accurate predictions for
future demand (Liu et al., 2023). AI can continually
learn from prior forecasting inaccuracies and modify
its models accordingly, resulting in progressively
enhanced accuracy over time. ese adaptable mod-
els aid businesses in honing their demand forecasts as
fresh sales data becomes accessible. For example, an
automobile manufacturer uses AI to forecast demand
for dierent car models. Over time, the AI system
learns that demand for SUVs is inuenced by uctu-
ating fuel prices and economic indicators. e manu-
facturer can make more accurate predictions and
optimise production plans. Consequently, incorpo-
rating AI into harnessing sales data for demand pre-
diction allows businesses to make data-driven
decisions, optimise production, and minimise the
risk of overstocking or stockouts. By analysing his-
torical sales patterns and their relationships with
external factors, AI empowers businesses to anticipate
and meet consumer demand more eectively.
While AI oers immense potential for revolu-
tionising demand forecasting, it is important to
acknowledge its benets and challenges. AI-driven
demand forecasting can lead to reduced inventory
costs, minimised stockouts, optimised production
schedules, and improved customer satisfaction
(Njomane & Telukdarie, 2022; Soori et al., 2023).
However, implementing AI systems requires substan-
tial initial investment, data infrastructure, and skilled
personnel. Additionally, the AI prediction accuracy
relies on the quality and relevance of the input data.
e dynamic nature of markets and consumer behav-
iour also challenges maintaining accurate forecasts
over extended periods. As AI continues to evolve,
demand forecasting techniques will likely become
even more sophisticated. Predictive analytics,
machine learning, and data-driven insights will drive
manufacturers to embrace AI-driven forecasting
models. Moreover, advancements in AI will facilitate
real-time analysis, enabling businesses to respond
swily to market changes and consumer behaviours.
However, ethical considerations, data privacy con-
cerns, and the need for transparent AI decision-
making processes will remain important
considerations in integrating AI into demand fore-
casting practices.
Integrating AI into demand forecasting processes
oers manufacturers a competitive edge by providing
insights into market trends, consumer behaviour, and
sales data. Manufacturers can make informed deci-
sions, optimise production, and enhance customer
satisfaction by leveraging AI algorithms to analyse
these key factors. As AI technology continues to
advance, its role in demand forecasting is poised to
become increasingly vital for businesses seeking to
thrive in a rapidly changing marketplace.
2.6. Leveraging artificial intelligence
for enhanced marketing strategies
e adoption of GAI for marketing is rapidly
growing (Kshetri et al., 2023; De Mauro, Sestino, &
Bacconi, 2022). By March 2023, 73 % of US businesses
had already incorporated GAI tools, such as chatbots,
into their marketing eorts (Dencheva, 2023).
Optimising marketing strategies in the ever-
evolving manufacturing landscape is a crucial success
facet. With the integration of GAI in manufacturing,
companies gain a powerful tool for achieving more
ecient and data-driven marketing approaches. e
application of GAI in this context goes far beyond
traditional methods, enabling manufacturers to make
smarter decisions regarding the timing of product
releases, promotional campaigns, and sales events.
One of the most remarkable aspects of utilising
AI in marketing strategy is the capacity to harness
predictive analytics. is technology allows manufac-
turers to forecast market trends and consumer behav-
iour with a high degree of accuracy. By analysing
historical data, market conditions, and consumer
preferences, GAI systems can identify potential spikes
in demand for particular products or services. Man-
agers can achieve signicant value and a competitive
edge by making eective data-based decisions (Con-
boy et al., 2020; Sivarajah et al., 2017). Furthermore,
high-performing companies tend to be more inclined
to use analytics compared to their less successful
competitors (LaValle et al., 2011). Companies have
utilised AI and machine learning to analyse historical
sales data, market trends, and external factors such as
weather conditions, which helps them predict con-
sumer demand for their products more accurately.
Volume 15 • Issue 4 • 2023
Engineering Management in Production and Services
e company’s AI-driven demand forecasting can
lead to better inventory management and optimised
marketing campaigns, ensuring products are available
when and where consumers need them. Utilising
predictive and behavioural analytics models enables
the customisation of new product oerings in
response to evolving customer requirements and the
precise targeting of marketing initiatives towards
specic audiences.
When manufacturers can foresee increased
demand accurately, they can adjust their marketing
strategies and production schedules accordingly. is
ability to predict demand patterns enables companies
to allocate resources more eectively (Tadayonrad &
Ndiaye, 2023), ensuring that products are available
when and where they are most needed. As a result,
manufacturers can maximise revenue by capitalising
on market trends and customer preferences on time.
GAI also enables manufacturers to tailor their
marketing campaigns to specic customer segments.
AI systems can identify consumer preferences and
behaviours by analysing vast datasets, allowing for
highly targeted advertising and promotional eorts.
Today, marketers can emphasise the customer and
address their immediate needs as they arise (Haleem
et al., 2022). GAI plays a crucial role in achieving
extreme personalisation of content by analysing a
potential customer’s Internet browsing history, previ-
ous purchases, and other digital traces. is approach
leads to the creation of dynamic oers, which, in
turn, can signicantly boost the conversion rate of
promotional oers (Ooi et al., 2023). is level of
personalisation can signicantly enhance the eec-
tiveness of marketing campaigns, ultimately leading
to higher interactive experiences and increased cus-
tomer satisfaction. By analysing the behaviour of
similar customers, AI can suggest products more
likely to resonate with each individual (Haleem et al.,
2022). is approach enhances the customer experi-
ence and increases cross-selling and upselling oppor-
tunities.
Moreover, GAI aids in ecient resource alloca-
tion, ensuring that marketing budgets are spent in the
most cost-eective way. Manufacturers can optimise
their marketing investments and avoid wasteful
spending by identifying which marketing channels
and strategies yield the best results. is approach
enhances a more sustainable and environmentally
friendly manufacturing process.
GAI can be set to streamline data analysis and
enhance marketing and customer service interac-
tions. Companies like Nestlé, General Mills, and AB
InBev have embraced GPT-4 to assist in deciphering
data for their business intelligence needs. Meanwhile,
Coca-Cola is leveraging ChatGPT and DALL-E 2 to
cra their marketing campaigns (Global Data, 2023).
Integrating AI in manufacturing oers a multi-
faceted approach to enhancing product eciency,
eectiveness, and safety. Manufacturers can signi-
cantly improve their overall operational outcomes by
streamlining processes, optimising resource utilisa-
tion, and implementing advanced quality control
measures. In the realm of skills analysis for manufac-
turing processes, AI adaptation plays a pivotal role.
is technology facilitates a proactive approach to
workforce development by evaluating historical data,
identifying patterns, and forecasting evolving skill
requirements, ensuring the necessary competencies
are identied and cultivated.
GAI contributes to product quality by identifying
defects in products and processes. Manufacturers can
swily address issues, improving the overall quality of
their products by analysing data patterns, anomaly
detection, and real-time insights.
In the maintenance domain, GAI emerges as a
proactive solution. is technology enables timely
interventions by predicting equipment failures or
maintenance needs through comprehensive data
analysis. is approach helps prevent downtime,
optimise operational eciency, and extend the lifes-
pan of machinery and equipment.
AI’s capabilities extend to market dynamics by
analysing trends, consumer behaviour, and sales data
to predict demand for manufactured goods. is
empowers manufacturers to align their production
with market needs, facilitating ecient inventory
management and resource allocation.
Furthermore, AI optimises marketing strategies
by leveraging data analysis. AI enhances overall mar-
keting eectiveness and customer engagement by
improving the timing of product releases, promo-
tional campaigns, and sales events. is holistic inte-
gration of AI technologies underscores their
transformative impact on various facets of the manu-
facturing industry.
AI can be leveraged to analyse and predict the
necessary skills required for manufacturing. It can
also assist in mapping current employee skills and
identifying gaps, allowing HR to proactively train or
Volume 15 • Issue 4 • 2023
Engineering Management in Production and Services
hire to meet future workforce demand. For instance,
machine learning algorithms can identify patterns in
worker skills and suggest appropriate training mod-
ules or process adjustments to increase overall manu-
facturing eciency.
AI can analyse market trends, consumer behav-
iour, and sales data to predict demand for manufac-
tured goods. Integrating GAI in manufacturing
empowers companies to revolutionise their market-
ing strategies. With predictive analytics, data-driven
insights, and highly targeted campaigns, manufactur-
ers can respond to shiing market dynamics with
precision. By maximising revenue and making more
ecient use of resources, AI is an invaluable tool for
manufacturers seeking a competitive edge in the
global marketplace, allowing companies to be exible
and adaptable to new trends and staying relevant as
consumer tastes change.
e future research directions regarding the
application of articial intelligence to enhance manu-
facturing processes are expected to be characterised
by interdisciplinary studies that integrate teams of
researchers and practitioners from technical, social,
economic, and ethical disciplines. Undoubtedly,
a long-term challenge will be the assessment of the
consequences (positive and negative) of AI applica-
tions in various areas of human life and activity.
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