2. Definition Of AI
➢AI is the simulation of Human Intelligence by machines ,
especially by computer systems , so it can automatically perform
human tasks without humans .
➢The ability of machines to imitate human intelligence.
4. AI : Ability of machine to imitate human intelligence.
ML : Algorithms to incorporate intelligence into machine by
automatically learning from data.
DL: Algorithms that mimics human brain to incorporate
intelligence into machine.
✓The term AI was first coined by Allan Turning in 1950 in his
question ’’ Can a machine think ? ’’
5. The importance of AI
1. Understand the most critical business problems that AI can Solve .
2. Identifying and prioritizing the right set of problems .
3. Developing a vision strategy and road map for the steps needed .
4. Can enter across various business lines and levels , from staff planning to
product design , maximizing performance , and product quality.
5. Can predict demand’s time and accounting for macroeconomic cycles and
weather Patterns .
6. 1- Data &
Training
Design &
Develop
Validating
& Testing
Approve
& Deploy
Monitor
&
Optimize
AI Lifecycle
8. Artificial intelligence and
relocation of production
activities :
Artificial intelligence (AI) is considered the next
strategic technological imperative as it allows firms
to manage complex tasks and to solve problems in
many industries. In the manufacturing context, the
use of AI is affecting the configuration of production
processes with possible effects on the location of
production activities.
9. Applications of AI (independent variable)
AI for big data analysis (DA)
concern the analysis of huge amounts of data to improve information
processing
AI for planning and business processes optimization (BPO)
performing key analytics and forecasting to improve predictive
maintenance, customer relationships and value-added process
AI for autonomous decision-making (ADM)
supporting or automating a company’s decision-making processes
10. In recent decades, globalization has affected all industries, as
(manufacturing) firms in advanced economies have extensively moved
abroad (offshored) their production activities, specifically to low-cost
countries, mainly in pursuit of efficiency-seeking strategies ,
Define offshoring as “the assignment of business activities to
locations outside a firm’s national borders in order to support existing
business operations”
Despite far-reaching offshoring strategies, in recent years manufacturing
firms have started to move back home some production activities
previously offshored,
This so-called backshoring, defined by Fratocchi et al. (2016, p. 100) as “the
geographic relocation of a functional, value creating operation from a
location abroad back to the domestic country of the company”,
Production relocation strategies
(dependent variable)
11. Moderator variables
-Digital competences (1st moderator)
Digital competences may be relevant for manufacturing firms in the
exploitation of new digital technologies (Sousa and Rocha, 2019) because
they could improve the effects of using a technology for specific decision-
making processes, related to the production and other business
activities, without the necessity to be linked to external
consultants/partners
12. Moderator variables
-The intended international strategies (2nd Moderator)
Being interpreted as oriented to international ambidexterity or
as concentration strategies – may have a potential effect on the
relationship between AI use and the relocation decision (offshoring
and backshoring), since they represent the strategic framework
within which firms develop their behavior location-wise .
In this respect, a firm’s strategic intention to internationalize
could be an important framework to assess more in-depth the
relationship between the use of new technology, such as AI, and
the firm’s decision about the location of production activities
(Castagnoli et al., 2021).
13. The intended international strategies
Ambidexterity strategies
Whenever the firm has to simultaneously adopt different – and
sometimes opposing – behaviours (e.g. expansion of both foreign
and domestic markets and locations) to tackle the challenges of the
competitive environment while leveraging various locations. (Lafuente
et al., 2021; Luo and Rui, 2009; Prange, 2012; Shams et al., 2021).
Concentration strategies
The firm may be interested in concentrating (locally or abroad)
business activities and investments in only a few locations with the
aim of taking up competitive advantages by specialising and
rationalising production processes as well as for risk limiting
purposes. (Lampon ´ et al., 2015)
14.
15. The effect of AI on Production relocation strategies
Both backhshoring & offshoring
Backshoring
AI in manufacturing allows to increase machine performance,
minimize maintenance costs, and optimise both productivity and
flexibility, which can drastically reduce production costs (Lee et al.,
2018; Yadav and Jayswal, 2018).
From this perspective, the use of AI could lead to backshoring of
manufacturing activities by reducing the efficiency advantages of
being located in low-cost countries (de Treville et al., 2017).
16. The effect of AI on Production relocation strategies
Both backhshoring & offshoring
Offshoring
The advantages of producing near the customer do not favor
backshoring if the customer is not located in the company’s home
country.
Offshoring is motivated not only by seeking lower costs, but also
by the need to enter new markets and be closer to customers in
foreign countries. So, for some firms, closeness to the market
works in favour of staying offshore or increasing the presence
abroad rather than backshore (Lu and Zhou, 2021).
17. Conclusion
Recent studies have shown
that research-intensive,
knowledge-based and service-
oriented manufacturing firms
tend to use AI technologies in
their domestic and foreign
plants (Kinkel et al., 2021),
so Positive effects on both
offshoring and backshoring
can be expected.
19. 1 1 / 2 0 / 2 0 2 3 A I & P R O D U C T I O N M A N A G E M E N T 19
Industry 4.0, big data, predictive analytics, and robotics are leading to a
paradigm shift on the shop floor of industrial production. However, complex,
cognitive tasks are also subject of change, due to the development of artificial
intelligence (AI). Smart assistants are finding their way into the world of
knowledge work and require cooperation with humans. Here, trust is an
essential factor that determines the success of human-AI cooperation.
Human and AI Cooperation
20. 1 1 / 2 0 / 2 0 2 3 20
▪ AI is the counterpart to robotics in manufacturing companies: while robots facilitate
physical doing of blue-collar workers, AI will support cognitive deciding of white-
collar workers (McAfee and Brynjolfsson 2017).
▪ AI is a new technology requiring new forms of interaction, existing models and
theories on trust need to be verified in light of this new technology and revised if
necessary (Detweiler and Broekens 2009; Farooq and Grudin 2016)
▪ De Visser, Pak, and Shaw (2018) point out that AI is different from previous automated
systems: autonomously (model-based) instead of automatically (rule based). Thus,
AI can behave proactively, unexpectedly, and incomprehensibly for humans
AI literature
21. 1 1 / 2 0 / 2 0 2 3 21
‘trust’ is an attribute of a successful relationship to all those
technologies experienced as a counterpart.
In information systems (IS) research on trust, the Integrative Trust Model of
Mayer, Davis, and Schoorman (1995) is widely accepted. It was originally
developed to describe interpersonal trust and therefore uses three
dimensions of the trustee: ability, benevolence, and integrity.
22. 1 1 / 2 0 / 2 0 2 3 S A M P L E F O O T E R T E X T 22
23. 1 1 / 2 0 / 2 0 2 3 S A M P L E F O O T E R T E X T 23
This research shows that the antecedents can be assigned to three groups: the
trustor, the trustee, and the context/environment.
Hoff and Bashir (2013, 2015) matched these three groups with the three layers of
trust identified by Marsh and Dibben (2005): antecedents of the category trustor
affect the dispositional trust; the antecedents of the non-human counterpart
(trustee) affect the learned trust; and those of the environment affect the
situational trust.
27. Al methods
➢Machine Learning:
This is the most crucial technology of AI, which enables machines
not only to process data but also to process unstructured knowledge.
➢ Artificial neural networks (ANN):
theory was inspired by the biological nervous system. ANN is an
advanced generation of ML algorithms.
28. Al methods
➢Machine Vision (MV):
MV refers to the technology and methods used to recognise objects,
interpret content and extract information from an image or a video
on an automated basis.
➢ Natural Language Processing (NLP):
NLP-powered systems can be used to extract information or
meaning from previous patterns in speech or text.
29. Al methods
➢ Expert System (ES):
ES is a computer program that solves problems or gives advice
based on well-deliberated calculations and unmanageable amounts
of data. these tools produce analyses and help to evaluate alternative
decision options.
➢ Speech Processing (SP):
SP refers to the using of digital signal processing techniques to
transmit speech into speech digital signals.
30. Al methods
➢ Robotics:
Robotics is an interdisciplinary branch of engineering and science
that includes mechanical engineering, electronics engineering,
information engineering, computer science and others.
➢ Evolutionary Computation (EC):
EC includes different algorithms to solve the optimisation problem.
31. The Mediating Role of Knowledge
Management Processes in the
Effective Use of Artificial
Intelligence in Manufacturing
Firms.
32. Purpose – This paper aims to provide and empirically test a conceptual model in
which artificial intelligence (AI), knowledge management processes (KMPs) and
supply chain resilience (SCR) are simultaneously considered in terms of their
reciprocal relationships and impact on manufacturing firm performance (MFP).
Design/methodology/approach – In the study, Six Hypotheses have been
developed and tested through an empirical survey administered to 120 senior
executives of Italian manufacturing firms.
Originality/value – This study demonstrates that manufacturing firms
interested in properly applying AI to ameliorate their performance and resilience
must carefully consider KMPs as a mediator mechanism.
34. 2.1 Artificial Intelligence impacts on Knowledge Management Processes, Manufacturing Firm
Performance and Supply Chain Resilience.
▪ Due to the ever-increasing amount of data and information collected by firms and fed into their
processes, AI has attracted increased interest over the last decade by scholars and practitioners (Gao et
al., 2021).
▪ AI can be briefly described as computers’ ability to perform cognitive functions, such as perceiving,
reasoning, learning and problem-solving, that are usually associated with human minds (Bawack et al.,
2021).
▪ Practically speaking, AI refers to using computers to imitate the human brain’s reasoning, learning,
planning and other thinking activities, thus solving complex problems that only human experts could
previously tackle (Lei and Wang, 2020).
▪ In particular, AI enables machines to learn, acquire, process and use knowledge to perform tasks,
revealing or unlocking knowledge that can be delivered to humans to improve decision-making
processes within organizations (Camarillo et al., 2018; Grzonka et al., 2018; Vajpayee and
Ramachandran, 2019).
(H1) AI Has a Positive Effect on KMPs
35. ▪ In other words, AI can extract new knowledge from vast quantities of data, portraying complex
mappings as a basis for human decision-making (Paschen et al., 2020).
▪ Hence, according to Bencsik (2021), There is a close mutual interaction between KM and AI:
The former makes the understanding of knowledge possible, while the latter provides the tools to
expand and use knowledge, as well as to create new knowledge in a way that was unimaginable
before (Haenlein and Kaplan, 2019; Lu et al., 2018).
▪ In this vein, as emphasized by Al Mansoori et al.(2021) in their systematic literature review,
modern organizations increasingly rely on AI mechanisms to enhance KMPs and performance
thanks to their ability to :
1) Inductively determine relationships and trends in firms’ knowledge repositories
(i.e. combining existing knowledge) to create new knowledge.
2) Help in the search for knowledge.
3) Disseminate knowledge to those who need it.
▪ Thus, AI “can help push [...] knowledge management” (Liebowitz, 2001, p. 4), making KMPs
more effective (Mittal and Kumar, 2019).
36. (H2) AI has a positive effect on MFP
▪ Furthermore, as noted by Butler et al. (2021) in their systematic literature review, AI can
improve firms’ productivity by automating data management processes and eliminating the
need for intermediaries.
▪ Hence, AI can ameliorate network communication, and in turn, this will help foster
innovation within an organization.
▪ Accordingly, Jallow et al. (2020) point out that AI adoption allows firms to gain a
competitive edge and enhance their performance by allowing better productivity, profitability
and efficiency.
▪ Explicitly referring to manufacturing firms, AI application allows for real-time decision-
making and performance improvement by enabling predictive maintenance (Chen et al.,
2021), enhanced quality control (Chiarini and Kumar, 2021) and improved safety (Pillai et
al., 2020).
37. (H3) AI has a positive effect on SCR
▪ Lastly, according to McKinsey [1], more and more companies have adopted
digitalization in general and AI in particular to mitigate the effects of disruptive events.
▪ For example, during the pandemic, numerous companies had to deploy digital
technologies to enhance their SCR and maintain satisfactory levels of operational
performance (Belhadiet al., 2021a; Mohapatra et al., 2021).
▪ In this vein, AI can provide the critical capability to devise better control mechanisms
and identify areas of disruption because it can help firms in gathering data and
processing information more efficiently and thus facilitating firms’ resource
orchestration and information processing, ameliorating the real-time coordination and
collaboration processes within their SC (Gupta et al., 2020; Modgil et al., 2021; Wamba
et al., 2020a).
38. This represents the base on which firms can build and promote SCR
(Belhadiet al., 2021b; Ruel and El Baz, 2021; Yao and Fabbe-Costes,
2018; Wamba et al., 2020b), understood as the capability to anticipate
and overcome SC disruptions (Pettit et al., 2013; Rice and Caniato,
2003; Sheffi,2005).
In this respect, AI can be considered a crucial enabler for
strengthening SCR by improving the collaboration between
contractors and suppliers, simplifying operations through higher
levels of problem-solving speed and accuracy (Ivanov and Dolgui,
2020; Modgil et al., 2021; Schniederjans et al., 2020;Wamba et al., 2021).
39. 2.2 Knowledge management processes and manufacturing firm performance
(H4) KMPs has a positive effect on MFP
❖ According to the knowledge-based view (KBV) of the firm (Grant, 1996), knowledge can be
considered the most valuable resource of a firm, the only enduring source of competitive advantage
that can improve a firm’s decision-making capacity and, consequently, its effective action (Alavi
and Leidner, 2001; Davenport and Klahr, 1998; Knight and Howes, 2012; Nonaka and Takeuchi,
1995; Paniccia, 2018).
❖ Therefore, KM is seen by academics, practitioners and policymakers as one of the most
essential strategic processes of any firm (Grant, 1996; OECD,2004).
❖ Specifically, KM “is the process of creating value from an organisation’s intangible
assets” (Liebowitz, 2004, p. 1).
❖ Consequently, increased attention has been paid to identifying KMPs critical to the
development and exploitation of the knowledge needed to create competitive advantage
(Anand et al., 2010; Linderman et al., 2010).
40. Despite the small differences that still characterise the KM literature in terms of the number and
labelling of KMPs – it is possible to state that KM encompasses five main distinct but interdependent
processes :
(1) Acquiring,
(2) Creating,
(3) Using/Applying,
(4) Archiving/Storing and Updating
(5) Sharing/Transferring (Alavi and Leidner, 2001; Heisig, 2009).
➢ These KMPs – as demonstrated by both qualitative and quantitative KM studies – must be properly
adopted by firms to improve their organizational (e.g. Choi and Lee, 2003; Khalifa et al., 2008;
Zack et al., 2009), financial (e.g.Darroch and McNaughton, 2003) and market performances (e.g.
Hussinkiet al., 2017)
➢ In the current manufacturing context, which is characterized by a paradigm shift, manufacturing
firms are increasingly focusing on managing knowledge assets instead of managing physical assets
to improve their performance (Gunasekaran and Ngai,2007).
41. ✓ Consequently, as demonstrated by Tan
and Wong (2015), manufacturing firms
are realizing the importance of KM
and adopting KMPs because they can
positively impact their performance,
bringing :
“a lot of benefits such as getting
updated information for production,
solving production problems in a
shorter time, and improving product
and process quality (p. 825) and
allowing managers to come out with a
more effective strategy to acquire the
utmost benefits for their companies.”
42. 2.3 Knowledge management processes and supply chain resilience
(H5) KMPs has a positive effect on SCR
✓ As stated before, for firms, managing the knowledge they possess, acquire, or create is crucial to being
competitive and surviving in their environment (Grant, 1996). This is particularly true in the SC context
because SCs can be viewed as cradles of knowledge, involving multiple autonomous actors with
varying backgrounds (Samuel et al., 2011).
✓ Thus, according to Desouza et al. (2003), the effective use of KMPs allows all the SC actors to better
align their objectives and interests (Liet al., 2012) and devise corrective actions before a risk event
occurs (Ellegaard, 2008; Juttner and Maklan, 2011 € ), which can ultimately affect SC performance
(Sangari et al., 2015).
✓ In particular, as demonstrated by Umar et al. (2021), the SC’s ability to properly acquire, share and use
knowledge is crucial to guaranteeing that the SC can prepare and respond to disasters, minimising its
vulnerability (Ellegaard, 2008; Kovacs and Spens, 2007; Juttner and Maklan, 2011 € ), reducing the
time required to deliver products from one actor to another (Dove, 1999) and enhancing the visibility
and alignment among SC actors (Barratt and Oke, 2007).
✓ By doing so, KMPs work to achieve and enhance SCR (Ali et al., 2021; Blackhurst et al., 2011;
Kumar and Anbanandam, 2019).
43. 2.4 Supply chain resilience and manufacturing firm performance
(H6) SCR has a positive effect on MFP
▪ SCR is an indispensable capability in times of crisis, as already demonstrated by numerous studies (e.g. El
Baz and Ruel, 2021; Nikookar and Yanadori, 2021; Ozdemir et al., 2022; Shen and Sun, 2021).
▪ Indeed, SCR concerns the ability to recover performance after having absorbed disruption effects (Hosseini
et al., 2019; Spiegler et al., 2012).
▪ In particular, SCR enables firms to minimize the negative effects of disruptions, maintain business continuity
by optimising resources (Roehrich et al., 2014) and maintain the supply to customers (Ambulkar et al.,
2016).
▪ In this vein, Li et al., 2017 have emphasized the positive financial outcomes derived from the
implementation of SCR because it allows a firm to respond more quickly and effectively to disruptions
concerning competitors, increasing the firm’s market share, goodwill and profitability.
▪ Consequently, SCR can have a direct impact on firms’ performance by ensuring consistent service and stock
availability and improving the ability to face KM processes for the effective use of AI 415 various disruption
threats (Altay et al., 2018; Ambulkar et al., 2016; Azevedo et al., 2013; Hohenstein et al., 2015; Liu and Lee,
2018; Liu et al., 2018).
